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15 pages, 3990 KiB  
Article
Long Non-Coding RNA THRIL Promotes Influenza Virus Replication by Inhibiting the Antiviral Innate Immune Response
by Mengying Chen, Jingyun Hu, Xinni Zhou, Ming Gao, Ning Li, Guihong Yang, Xiaojuan Chi and Song Wang
Viruses 2025, 17(2), 153; https://doi.org/10.3390/v17020153 - 23 Jan 2025
Viewed by 4
Abstract
Long non-coding RNAs (lncRNAs) have been recognized for their crucial roles in the replication processes of various viruses. However, the specific functions and regulatory mechanisms of many lncRNAs in influenza A virus (IAV) pathogenesis remain poorly understood. In this study, we identified lncRNA [...] Read more.
Long non-coding RNAs (lncRNAs) have been recognized for their crucial roles in the replication processes of various viruses. However, the specific functions and regulatory mechanisms of many lncRNAs in influenza A virus (IAV) pathogenesis remain poorly understood. In this study, we identified lncRNA THRIL and observed a significant reduction in its expression following IAV infection in A549 cells. The treatment of cells with the viral mimic poly (I:C), or with type I and type III interferons, resulted in a substantial decrease in THRIL expression. Furthermore, THRIL overexpression significantly enhanced IAV replication, while its silencing markedly reduced IAV replication. Additionally, IAV infection led to notable reductions in the expression levels of type I and type III interferons in cell lines overexpressing THRIL compared to control groups; conversely, cell lines with THRIL knockdown exhibited significantly higher interferon levels than control groups. Moreover, THRIL was found to inhibit the expression of several critical interferon-stimulated genes (ISGs), which are essential for an effective antiviral response. Notably, our findings demonstrated that THRIL impaired the activation of IRF3, a key transcription factor in the interferon signaling pathway, thereby suppressing host innate immunity. These results highlight THRIL’s potential as a therapeutic target for antiviral strategies. Full article
(This article belongs to the Special Issue Innate Immunity to Virus Infection 2nd Edition)
Show Figures

Figure 1

Figure 1
<p>The infection of multiple viruses downregulates the expression of THRIL. (<b>A</b>) RNA-seq analysis of A549 cells infected with the PR8 influenza virus for 12 h. The figure presents a heat map derived from the RNA-seq results. (<b>B</b>) A549 cells were infected with different multiplicities of infection of the PR8 virus for 12 h. The expression level of THRIL was detected using qRT-PCR. (<b>C</b>–<b>H</b>) A549 cells were infected with PR8 (<b>C</b>), WSN (<b>D</b>), CA04 (<b>E</b>), H9N2 (<b>F</b>), SeV (<b>G</b>), and PRV (<b>H</b>) for the indicated time period; then, the expression of THRIL was examined using qRT-PCR. Data are represented as mean ± SD; <span class="html-italic">n</span> = 3; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
Full article ">Figure 2
<p>Interferon treatment decreased the expression of THRIL. (<b>A</b>,<b>B</b>) A549 cells were stimulated with poly (I:C) for the indicated time period; then, THRIL expression was detected using RT-PCR (<b>A</b>) and qRT-PCR (<b>B</b>). (<b>C</b>–<b>F</b>) A549 cells were stimulated with IFN-β (<b>C</b>,<b>D</b>) and IL-29 (<b>E</b>,<b>F</b>) for the indicated time period; then, THRIL expression was examined using RT-PCR (<b>C</b>,<b>E</b>) and qRT-PCR (<b>D</b>,<b>F</b>). (<b>G</b>,<b>H</b>) A549 cells were treated with LPS at the indicated concentrations for 6 h. The expression of THRIL was detected using RT-PCR (<b>G</b>) and qRT-PCR (<b>H</b>). (<b>I</b>,<b>J</b>) IFNAR1 knockout (KO) and wild-type (WT) A549 cells were infected with the PR8 virus (<b>I</b>) or treated with IFN-β (<b>J</b>) for the indicated time period; then, THRIL expression was examined using RT-PCR. Data are represented as mean ± SD; <span class="html-italic">n</span> = 3; *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001. “ns” represents no significance.</p>
Full article ">Figure 3
<p>THRIL promotes the replication of influenza virus. (<b>A</b>,<b>B</b>) A549 cells stably expressing THRIL or empty vector (EV) were infected with or without PR8 virus (MOI = 1) for 24 h. After infection, the mRNA levels of viral NP were assessed using RT-PCR (<b>A</b>) and qRT-PCR (<b>B</b>). (<b>C</b>) The replication kinetics of the PR8 virus (MOI = 1) in THRIL-overexpressing A549 cells and control cells were detected using the hemagglutinin (HA) assay. (<b>D</b>) THRIL-overexpressing A549 cells and control cells were infected with the PR8 virus (MOI = 1) for 24 h. Viral titers in cell culture supernatants were examined using the plaque assay. (<b>E</b>,<b>F</b>) A549 cells stably expressing shRNA targeting THRIL (sh-THRIL) or luciferase control (sh-luc) were infected with the PR8 virus (MOI = 1) for 24 h. After infection, the mRNA levels of viral NP were assessed using RT-PCR (<b>E</b>) and qRT-PCR (<b>F</b>). (<b>G</b>) The replication kinetics of the PR8 virus (MOI = 1) in THRIL-knockdown A549 cells and control cells were detected using an HA assay. (<b>H</b>) THRIL-knockdown A549 cells and control cells were infected with the PR8 virus (MOI = 1) for 24 h. Viral titers in cell culture supernatants were examined using the plaque assay. Data are represented as mean ± SD; <span class="html-italic">n</span> = 3; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
Full article ">Figure 4
<p>THRIL negatively regulates the IAV-induced expression of interferons. (<b>A</b>–<b>D</b>) THRIL-overexpressing A549 cells and control cells were infected with the PR8 virus (MOI = 1) for 24 h. After infection, the mRNA levels of IL-28, IL-29, and IFN-β were determined using RT-PCR (<b>A</b>) and qRT-PCR (<b>B</b>–<b>D</b>). (<b>E</b>) THRIL-overexpressing A549 cells and control cells were treated as described in (<b>A</b>–<b>D</b>). IFN-β levels in the cell culture supernatants were measured using ELISA. (<b>F</b>–<b>I</b>) THRIL-knockdown A549 cells and control cells were infected with the PR8 virus (MOI = 1) for 24 h. After infection, the mRNA levels of IL-28, IL-29, and IFN-β were determined using RT-PCR (<b>F</b>) and qRT-PCR (<b>G</b>–<b>I</b>). (<b>J</b>) THRIL-knockdown A549 cells and control cells were treated as described in (<b>F</b>–<b>I</b>). IFN-β levels in the cell culture supernatants were measured using ELISA. Data are represented as mean ± SD; <span class="html-italic">n</span> = 3; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
Full article ">Figure 5
<p>THRIL inhibits the expression of several antiviral ISGs. (<b>A</b>–<b>D</b>) THRIL-overexpressing A549 cells and control cells were infected with the PR8 virus (MOI = 1) for 24 h. After infection, the mRNA levels of OAS1 (<b>A</b>), OAS2 (<b>B</b>), ISG15 (<b>C</b>), and IFITM3 (<b>D</b>) were determined using qRT-PCR. (<b>E</b>,<b>F</b>) THRIL-overexpressing A549 cells and control cells were treated as described in (<b>A</b>–<b>D</b>). The protein expression of IFITM3 in cells was detected using Western blotting (<b>E</b>). The relative levels of IFITM3 in (<b>E</b>) were quantitated using densitometry and were normalized to GAPDH levels (<b>F</b>). (<b>G</b>–<b>J</b>) THRIL-knockdown A549 cells and control cells were infected with the PR8 virus (MOI = 1) for 24 h. After infection, the mRNA levels of OAS1 (<b>G</b>), OAS2 (<b>H</b>), ISG15 (<b>I</b>), and IFITM3 (<b>J</b>) were determined using qRT-PCR. (<b>K</b>,<b>L</b>) THRIL-knockdown A549 cells and control cells were treated as described in (<b>G</b>–<b>J</b>). The protein expression of IFITM3 in cells was detected using Western blotting (<b>K</b>). The relative levels of IFITM3 in (<b>K</b>) were quantitated using densitometry and were normalized to GAPDH levels (<b>L</b>). Data are represented as mean ± SD; <span class="html-italic">n</span> = 3; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001. “ns” represents no significance.</p>
Full article ">Figure 6
<p>THRIL inhibits host innate immunity through targeting IRF3. (<b>A</b>) 293T cells were co-transfected with 500 ng IFN-β-Luc, 50 ng pRL-TK, and 500 ng empty vector (EV) or THRIL-expressing plasmids for 24 h. After that, cells were infected with SeV, and dual-luciferase activity was examined at 12 h post-infection. (<b>B</b>–<b>E</b>) 293T cells were co-transfected with 500 ng IFN-β-Luc, 50 ng pRL-TK, and 500 ng THRIL-expressing plasmid or EV, along with 300 ng RIG-I (<b>B</b>), MAVS (<b>C</b>), TBK1 (<b>D</b>), or IRF3 (<b>E</b>). Luciferase activity was detected 24 h post-transfection. (<b>F</b>–<b>I</b>) THRIL-overexpressing (<b>F</b>,<b>G</b>) or THRIL-knockdown (<b>H</b>,<b>I</b>) A549 cells and control cells were infected with the PR8 virus (MOI = 1) for 24 h. After infection, the phosphorylation levels of IRF3 were examined using Western blotting (<b>F</b>,<b>H</b>). The relative levels of p-IRF3 in (<b>F</b>,<b>H</b>) were quantitated using densitometry and were normalized to GAPDH levels (<b>G</b>,<b>I</b>). Data are represented as mean ± SD; n = 3; * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001. “ns” represents no significance.</p>
Full article ">Figure 7
<p>A schematic representation of THRIL inhibiting the host innate immune response by blocking IRF3 activation.</p>
Full article ">
15 pages, 4078 KiB  
Article
NLRC3 Attenuates Antiviral Innate Immune Response by Targeting IRF7 in Grass Carp (Ctenopharyngodon idelus)
by Lei Zhang, Haitai Chen, Xiang Zhao, Youcheng Chen, Shenpeng Li, Tiaoyi Xiao and Shuting Xiong
Int. J. Mol. Sci. 2025, 26(2), 840; https://doi.org/10.3390/ijms26020840 - 20 Jan 2025
Viewed by 273
Abstract
NLRC3 belongs to the NOD-like receptor family and is recognized as a modulator of innate immune mechanisms. In this study, we firstly report that Ctenopharyngodon idelus NLRC3 (CiNLRC3) acts as a negative regulator in the antiviral immune response. Cinlrc3 is ubiquitously [...] Read more.
NLRC3 belongs to the NOD-like receptor family and is recognized as a modulator of innate immune mechanisms. In this study, we firstly report that Ctenopharyngodon idelus NLRC3 (CiNLRC3) acts as a negative regulator in the antiviral immune response. Cinlrc3 is ubiquitously expressed across tested tissues, displaying particularly high expression in the intestine, spleen, gill and kidney. Notably, Cinlrc3 expression is markedly upregulated following grass carp reovirus (GCRV) infection both in vivo and in vitro. Functional assays reveal that the overexpression of CiNLRC3 hampers cellular antiviral responses, thereby facilitating viral replication. Conversely, the silencing of CiNLRC3 through siRNA transfection enhances these antiviral activities. Additionally, CiNLRC3 substantially diminishes the retinoic acid-inducible gene I (RIG-I)-like receptor (RLR)-mediated interferon (IFN) response in fish. Subsequent molecular investigations indicates that CiNLRC3 interacts with the RLR molecule node, IRF7 but not IRF3, by degrading the IRF7 protein in a proteasome-dependent manner. Furthermore, CiNLRC3 co-localizes with CiIRF7 in the cytoplasm and impedes the IRF7-induced IFN response, resulting in impairing IRF7-mediated antiviral immunity. Summarily, these findings underscore the critical inhibitory role of teleost NLRC3 in innate immunity, offering new perspectives on its regulatory functions and potential as a target for resistant breeding in fish. Full article
(This article belongs to the Section Molecular Immunology)
Show Figures

Figure 1

Figure 1
<p>CiNLRC3 sequence analysis. (<b>A</b>) Structure illustration of CiNLRC3 protein. Structure domains were indicated in a dark frame. (<b>B</b>) Multiple alignment of NLRC3 protein sequences from grass carp (OR282536.1), blunt snout bream (XM_048174542.1), zebrafish (XM_009297629.4), common carp (XM_042752008.1), human (FJ889357.1) and mouse (XM_011245902.3). The NACHT domain is marked by red underline and the LRR domains are indicated by blue underlines.</p>
Full article ">Figure 2
<p>Phylogenetic tree of NLRC3 protein homologs. The phylogenetic tree was constructed using the neighbor-joining method implemented in the MEGA 6.0 software. Bootstrap confidence values, displayed at the nodes of the tree, were calculated based on 1,000 bootstrap replications. NLRC3 homologs are listed below. Mammalian: <span class="html-italic">Homo sapiens</span> (NP_849172.2), <span class="html-italic">Pan troglodytes</span> (XP_016784787.3), <span class="html-italic">Oryctolagus cuniculus</span> (XP_051692203.1), <span class="html-italic">Mus musculus</span> (NP_001074749.1), <span class="html-italic">Castor canadensis</span> (XP_020012107.1), <span class="html-italic">Heterocephalus glaber</span> (XP_004864808.1), <span class="html-italic">Acinonyx jubatus</span> (XP_053069323.1), <span class="html-italic">Panthera tigris</span> (XM_042971676.1), <span class="html-italic">Bos taurus</span> (XP_059737649.1), <span class="html-italic">Equus caballus</span> (XP_001499317.2), <span class="html-italic">Panthera leo</span> (XM_042921978.1); Reptilian: <span class="html-italic">Varanus komodoensis</span> (XP_044289647.1), <span class="html-italic">Bodarcis raffonei</span> (XP_053220626.1), <span class="html-italic">Zootoca vivipara</span> (XP_034987245.1); Avian: <span class="html-italic">Phalacrocorax carbo</span> (XP_064318183.1), <span class="html-italic">Gallus gallus</span> (XP_015150161.3), <span class="html-italic">Haemorhous mexicanus</span> (XP_059718048.1), <span class="html-italic">Passer domesticus</span> (XP_064246427.1), <span class="html-italic">Corvus cornix cornix</span> (XP_019136980.2), <span class="html-italic">Taeniopygia guttata</span> (XP_030140601.3; Amphibians: <span class="html-italic">Xenopus tropicalis</span> (XP_017952746.1), <span class="html-italic">Bufo gargarizans</span> (XP_044160788.1), <span class="html-italic">Rana temporaria</span> (XP_040214376.1); Cartilaginous fish: <span class="html-italic">Scyliorhinus_canicula</span> (XP_038676696.1), <span class="html-italic">Heterocephalus_glaber</span> (XP_004864808.1), <span class="html-italic">Callorhinchus_milii</span> (XP_007891876.1), <span class="html-italic">Castor_canadensis</span> (XP_020012107.1), <span class="html-italic">Rhinatrema_bivittatum</span> (XP_029432650.1), <span class="html-italic">Carcharodon_carcharias</span> (XP_041062594.1), <span class="html-italic">Mobula_hypostoma</span> (XP_062915561.1), <span class="html-italic">Hypanus_sabinus</span> (XP_059834825.1), <span class="html-italic">Pristis_pectinata</span> (XP_051877340.1), <span class="html-italic">Rhincodon_typus</span> (XP_048465466.1), <span class="html-italic">Chiloscyllium_plagiosum</span> (XP_043567467.1); and Teleost: <span class="html-italic">Danio_rerio</span> (XM_009297629.4), <span class="html-italic">Ctenopharyngodon idella</span> (XP_051737605.1), <span class="html-italic">Megalobrama_amblycephala</span> (XP_048030497.1), <span class="html-italic">Carassius_gibelio</span> (XP_052451976.1), <span class="html-italic">Cyprinus_carpio</span> (XP_042607942.1), <span class="html-italic">Oryzias_latipes</span> (XP_004080575.1), <span class="html-italic">Oreochromis_niloticus</span> (XP_003438651.1), <span class="html-italic">Lates_calcarifer</span> (XP_018537323.1), <span class="html-italic">Larimichthys_crocea</span> (XP_010730059.1), <span class="html-italic">Siniperca chuatsi</span> (XM_044177955.1), <span class="html-italic">Anguilla rostrata</span> (XP_064178397.1).</p>
Full article ">Figure 3
<p><span class="html-italic">Cinlrc3</span> is induced after GCRV infection. (<b>A</b>) The distribution of <span class="html-italic">Cinlrc3</span> in the intestine, spleen, gill, kidney, heart, liver, head kidney, brain, and skin of grass carp. (<b>B</b>) The CiNLRC3 protein is in the cytoplasm. Immunofluorescence cellular localization is performed using constructed HA-CiNLRC3. The plasmid of CiNLRC3-HA is transfected in CIK cells, and the HA antibody is utilized to detect the CiNLRC3-HA fusion protein which is indicated in red fluorescence. DAPI is used for the nuclear staining. Scale bar: 20 μm. (<b>C</b>,<b>D</b>) CIK cells are challenged with GCRV-JX0901, and cell samples are collected at 0 h, 12 h, 24 h, 36 h, and 48 h. Then, the transcriptional levels of <span class="html-italic">ifn1</span> (<b>C</b>) and <span class="html-italic">Cinlrc3</span> (<b>D</b>) are detected by qPCR. (<b>E</b>–<b>P</b>) Grass carps are immersed in GCRV-Huan1307 for 30 min and the liver, spleen, kidney and gill are sampled at 0, 1, 3 and 7 dpi. <span class="html-italic">Cinlrc3</span>, <span class="html-italic">vp7</span> and <span class="html-italic">isg15</span> mRNA in the liver (<b>E</b>,<b>I</b>,<b>M</b>), spleen (<b>F</b>,<b>J</b>,<b>N</b>), kidney (<b>G</b>,<b>K</b>,<b>O</b>) and gill (<b>H</b>,<b>L</b>,<b>P</b>) are detected using qPCR. Letters with the same superscript indicate no significant difference (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 4
<p>CiNLRC3 dampens the cellular antiviral response. (<b>A</b>) CIK cells were seeded into 6-well plates and transfected with an EV or CiNLRC3 (2 μg), respectively. After transfection for 24 h, GCRV was added into the transfected wells. After 36 hpi, the cells were fixed with 4% paraformaldehyde, washed three times with PBS, and then stained with 1% crystal lavender. (<b>B</b>–<b>E</b>) Under the same transfected experiments above, samples were collected at 36 hpi. qPCR was performed to detect mRNA levels of <span class="html-italic">Cinlrc3</span> (<b>B</b>), <span class="html-italic">vp4</span> (<b>C</b>), <span class="html-italic">vp5</span> (<b>D</b>) and <span class="html-italic">ifn1</span> (<b>E</b>). (<b>F</b>,<b>J</b>) CIK cells were seeded into 6-well plates and transfected with siRNA-NC or siRNA-1/2, respectively. After transfection for 12 h, GCRV was added into the transfected wells. After 36 hpi, the cells samples were collected and qPCR was applied to detect the expression of <span class="html-italic">Cinlrc3</span> (<b>F</b>), <span class="html-italic">vp4</span> (<b>G</b>), <span class="html-italic">vp5</span> (<b>H</b>), <span class="html-italic">mx</span> (<b>I</b>) and <span class="html-italic">viperin</span> (<b>J</b>). Asterisks indicate significant differences (* <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001).</p>
Full article ">Figure 5
<p>CiNLRC3 blocks the RLR-mediated IFN response. (<b>A</b>) The GCO cells are seeded in a 24-well plate overnight and then co-transfected with an EV or CiNLRC3 (500 ng), CiIFN1pro-Luc (100 ng), and pRL-TK (10 ng). Moreover, 12 h later, poly (I; C), GCRV, SVCV is added into cells, respectively. Another 24 h later, the samples are collected after 24 h following a dual-luciferase activity assay. (<b>B</b>) The GCO cells are seeded in a 24-well plate overnight and then an EV or the CiNLRC3 (200 ng) plasmid and CiIFN1pro-Luc (100ng), PRL—TK (10 ng), are transfected into the cells. At the same time, the expressing plasmids including RIG-I, MAD5, MAVS, MITA, TBK1, IRF3, and IRF7 (200 ng) are transfected into the cells, respectively. In addition, 24 h later, the samples are collected for the dual-luciferase activity assay. Asterisks indicate significant differences (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01).</p>
Full article ">Figure 6
<p>NLRC3 interacts with IRF7 and degrades IRF7 in a proteasome-dependent manner (<b>A</b>) MITA, TBK1, IRF3 and IRF7 are NLRC3-interacting proteins. GCO cells are seeded in 10 cm<sup>2</sup> plates overnight, and then CiNLRC3 is co-transfected with EGFP-HA, MITA-HA, TBK1-HA, IRF3-HA, and IRF7-HA (5 μg each). After 36 h, the cells are collected for the Co-IP experiment. (<b>B</b>–<b>C</b>) NLRC3 degrades IRF7 but not IRF3. GCO cells are seeded in 6-well plates overnight, and then IRF3 (<b>B</b>) or IRF7-HA (<b>C</b>) (1 μg) and NLRC3-Flag (0.3, 0.5, 0.8, 1.0 μg) are co-transfected, respectively. After 36 h, cells are collected, and their bands are detected by Western blotting. (<b>D</b>) NLRC3 degrades IRF7 in a proteasome-dependent manner. GCO cells are seeded in three 6-well plates overnight, one plate co-transfected with IRF7-HA and an EV and the other two plates co-transfected with a repeat of IRF7-HA (1 μg) and NLRC3-HA (1.0 μg). Moreover, 24 h later, the indicated cells are treated with MG132 (20 μM) for 6 h. After 36 h, cells are collected, and their bands are detected by Western blotting. (<b>E</b>,<b>F</b>) IRF7 is co-located with NLRC3. 293T cells are seeded in 12-well plates overnight and transfected with Cherry-IRF7 (<b>E</b>), CiNLRC3-EGFP or EGFP and Cherry-IRF7 (1 μg each) (<b>F</b>) and fixed with 4% paraformaldehyde for 15 min after 24 h. Then, PBS is used for washing three times, DAPI is used for staining for 5 min, and photographs are taken under the microscope.</p>
Full article ">Figure 7
<p>CiNLRC3 impairs IRF7-mediated cellular antiviral response. (<b>A</b>–<b>E</b>) CIK cells are seeded in six-well plates overnight, and then EV (1 μg) or EV (0.5 μg) + CiIRF7 (0.5 μg) or CiNLRC3 (0.5 μg) + CiIRF7 (0.5 μg), respectively, and GCRV are added 24 h later. The mRNA levels of isg15, isg20 (<b>A</b>,<b>B</b>), vp5, vp6 and vp7 (<b>C</b>–<b>E</b>) are detected by qPCR after another 24 h. Letters with the same superscript indicate no significant difference (<span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">
27 pages, 6880 KiB  
Article
Deciphering the Transcriptional Metabolic Profile of Adipose-Derived Stem Cells During Osteogenic Differentiation and Epigenetic Drug Treatment
by Giulia Gerini, Alice Traversa, Fabrizio Cece, Matteo Cassandri, Paola Pontecorvi, Simona Camero, Giulia Nannini, Enrico Romano, Francesco Marampon, Mary Anna Venneri, Simona Ceccarelli, Antonio Angeloni, Amedeo Amedei, Cinzia Marchese and Francesca Megiorni
Cells 2025, 14(2), 135; https://doi.org/10.3390/cells14020135 - 17 Jan 2025
Viewed by 396
Abstract
Adipose-derived mesenchymal stem cells (ASCs) are commonly employed in clinical treatment for various diseases due to their ability to differentiate into multi-lineage and anti-inflammatory/immunomodulatory properties. Preclinical studies support their use for bone regeneration, healing, and the improvement of functional outcomes. However, a deeper [...] Read more.
Adipose-derived mesenchymal stem cells (ASCs) are commonly employed in clinical treatment for various diseases due to their ability to differentiate into multi-lineage and anti-inflammatory/immunomodulatory properties. Preclinical studies support their use for bone regeneration, healing, and the improvement of functional outcomes. However, a deeper understanding of the molecular mechanisms underlying ASC biology is crucial to identifying key regulatory pathways that influence differentiation and enhance regenerative potential. In this study, we employed the NanoString nCounter technology, an advanced multiplexed digital counting method of RNA molecules, to comprehensively characterize differentially expressed transcripts involved in metabolic pathways at distinct time points in osteogenically differentiating ASCs treated with or without the pan-DNMT inhibitor RG108. In silico annotation and gene ontology analysis highlighted the activation of ethanol oxidation, ROS regulation, retinoic acid metabolism, and steroid hormone metabolism, as well as in the metabolism of lipids, amino acids, and nucleotides, and pinpointed potential new osteogenic drivers like AOX1 and ADH1A. RG108-treated cells, in addition to the upregulation of the osteogenesis-related markers RUNX2 and ALPL, showed statistically significant alterations in genes implicated in transcriptional control (MYCN, MYB, TP63, and IRF1), ethanol oxidation (ADH1C, ADH4, ADH6, and ADH7), and glucose metabolism (SLC2A3). These findings highlight the complex interplay of the metabolic, structural, and signaling pathways that orchestrate osteogenic differentiation. Furthermore, this study underscores the potential of epigenetic drugs like RG108 to enhance ASC properties, paving the way for more effective and personalized cell-based therapies for bone regeneration. Full article
(This article belongs to the Special Issue New Insights into Adipose-Derived Stem Cells (ADSCs))
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<p>RG108 treatment in ASC morphology and proliferation. (<b>A</b>) Phase-contrast microphotographs showing ASCs treated with RG108 or DMSO (as mocked control) for 72 h in culturing medium. Scale bars represent 400 μm. Representative images of three (n = 3) independent experiments. (<b>B</b>) Proliferation of ASCs treated with RG108 or DMSO for 72 h using Trypan blue exclusion dye. Bars are median values of three (n = 3) independent experiments.</p>
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<p>RG108 positively affects ASC osteogenic differentiation. (<b>A</b>) Phase-contrast microphotographs showing ASCs treated with RG108 or DMSO (as mocked control) at days 0, 7, 14, and 21 after osteogenic induction. Scale bars: 200 μm. (<b>B</b>) Phase-contrast microphotographs depicting mineralization (intense red clusters) in ASCs treated with RG108 or DMSO and stained with Alizarin Red at 14- and 21-day time points of osteogenic induction. Scale bars: 400 μm. (<b>C</b>) mRNA expression of osteogenic markers RUNX2, ALPL, and COL1A1 was evaluated by qRT-PCR. Data were normalized to GAPDH mRNA expression. Bars represent means ± SD of three (n = 3) independent experiments, each performed in triplicate. * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 vs. T0 control; <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 vs. T7 DMSO; <sup><span>$</span></sup> <span class="html-italic">p</span> &lt; 0.05 vs. T21 DMSO. (<b>D</b>) Western blot analysis of ALPL protein expression in ASCs induced toward osteogenesis with or without RG108 treatment. Hsp90 was used as internal control. Densitometric analysis of ALPL expression is shown as relative expression compared to the T0 control sample.</p>
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<p>Transcriptional changes during osteogenesis differentiation. (<b>A</b>) Correlation analysis of log2 fold changes at reported time points (T7, T14, and T21) compared to T0. Red dots indicate upregulated genes, while blue dots represent downregulated genes. Dashed lines indicate log2 fold change thresholds, set at +1 and −1. (<b>B</b>) Cluster heatmap of upregulated and downregulated genes at distinct time points (T0, T7, T14, and T21) identified in (<b>A</b>). Expression values are reported as Z-score. (<b>C</b>) River plot of metabolic gene set enrichment analysis (REACTOME v2024.1) of upregulated genes. The gene ratio of upregulated genes to total genes in the different pathways is reported. Significance is reported as −log<sub>10</sub> (FDR) and indicated with color gradient.</p>
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<p>River plots of upregulated genes (<b>A</b>) and downregulated (<b>B</b>) genes related to non-metabolic processes during ASC osteogenic differentiation.</p>
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<p>NanoString result validation by qRT-PCR. (<b>A</b>) qRT-PCR results of selected upregulated and (<b>B</b>) downregulated genes across ASC osteogenic differentiation compared to the T0 control sample. Transcript levels were normalized to GAPDH mRNA expression. Bars represent means ± SD of three (n = 3) independent experiments, each performed in triplicate. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001 vs. T0 control sample.</p>
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<p>Transcriptional changes during osteogenesis differentiation of ASCs treated with RG108. (<b>A</b>) Correlation analysis of log2 fold changes in ASCs treated or not with RG108 at reported time points (T7, T21) compared to T0. Red dots indicate upregulated genes, blue dots indicate downregulated genes. Dotted lines indicate log2 fold change thresholds set at +1 and −1. (<b>B</b>) Cluster heatmap of upregulated and downregulated genes after RG108 treatment at distinct time points (T0, T7, and T21) identified in (<b>A</b>). Expression values are reported as Z-score. (<b>C</b>) Graph bars of enriched pathways for RG108 T7-upregulated genes calculated with Metascape tool. X axis shows significance reported as −log10 <span class="html-italic">p</span> value (<span class="html-italic">p</span>). (<b>D</b>) Graph bars of enriched pathways for RG108 T21-upregulated genes calculated with Metascape tool. X axis shows significance reported as −log10 <span class="html-italic">p</span> value (<span class="html-italic">p</span>).</p>
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<p>DNA methylation profiles of genes deregulated by RG108 treatment during ASC osteogenesis. (<b>A</b>) DNA methylation array data from four different subcutaneous adipose tissues showing methylation status of genes involved in ethanol oxidation (ADH1C, ADH4, ADH6, and ADH7) regulated by RG108. (<b>B</b>) DNA methylation status of transcription factors (MYCN, MYB, TP63, and IRF1) whose expression is upregulated by RG108 treatment. (<b>C</b>) DNA methylation profile of HLA-DQA1. (<b>D</b>) DNA methylation status of SLC2A3. Data are reported as methylation scores in a range from 0 to 1, where 1 indicates a fully methylated site while 0 indicates a fully demethylated site.</p>
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15 pages, 2581 KiB  
Article
Characterization of the First Marine Pestivirus, Phocoena Pestivirus (PhoPeV)
by Lars Söder, Denise Meyer, Olaf Isken, Norbert Tautz, Matthias König, Alexander Postel and Paul Becher
Viruses 2025, 17(1), 107; https://doi.org/10.3390/v17010107 - 14 Jan 2025
Viewed by 429
Abstract
The first marine pestivirus, Phocoena pestivirus (PhoPeV), isolated from harbor porpoise, has been recently described. To further characterize this unique pestivirus, its host cell tropism and growth kinetics were determined in different cell lines. In addition, the interaction of PhoPeV with innate immunity [...] Read more.
The first marine pestivirus, Phocoena pestivirus (PhoPeV), isolated from harbor porpoise, has been recently described. To further characterize this unique pestivirus, its host cell tropism and growth kinetics were determined in different cell lines. In addition, the interaction of PhoPeV with innate immunity in porcine epithelial cells and the role of selected cellular factors involved in the viral entry and RNA replication of PhoPeV were investigated in comparison to closely and distantly related pestiviruses. While Bungowannah pestivirus (BuPV), a unique porcine pestivirus closely related to PhoPeV, exhibits a broad cell tropism, PhoPeV only infects cells from pigs, cattle, sheep, and cats, as has been described for classical swine fever virus (CSFV). Viral titers correlate with the amount of intracellular PhoPeV-specific RNA detected in the tested cell lines. PhoPeV replicates most efficiently in the porcine kidney cell line SK6. Pestiviruses generally counteract the cellular innate immune response by degradation of interferon regulatory factor 3 (IRF3) mediated by the viral N-terminal protease (Npro). No degradation of IRF3 and an increased expression of the type 1 interferon-stimulated antiviral protein Mx1 was observed in porcine cells infected with PhoPeV whose genome lacks the Npro encoding region. Infection of a CD46-deficient porcine cell line suggested that CD46, which is implicated in the viral entry of several pestiviruses, is not a major factor for the viral entry of PhoPeV. Moreover, the results of this study confirmed that the cellular factor DNAJC14 plays a crucial role in viral RNA replication of non-cytopathic pestiviruses, including PhoPeV. Full article
(This article belongs to the Section Animal Viruses)
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<p>Infection of different mammalian cell lines with PhoPeV, BuPV, and CSFV Alfort-T. Swine kidney-6 (SK6), Madin–Darby bovine kidney (MDBK), sheep fetal thymus (SFTR), Crandel–Rees feline kidney (CRFK), and seal kidney cells (SEK-2B) were infected with PhoPeV, BuPV, and CSFV Alfort-T at an MOI of 0.5. Viral antigens were detected by immunofluorescence analysis at 72 hpi. Cell nuclei were stained with DAPI (blue). All mock controls (bottom) tested negative.</p>
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<p>Viral growth kinetics and viral RNA synthesis in porcine, bovine, ovine, and feline cell lines infected with PhoPeV. Porcine SK6, PK-15, and 38A<sub>1</sub>D cells, bovine MDBK cells, ovine SFTR cells, and feline CRFK cells were infected with PhoPeV at an MOI of 0.1. Supernatants and cell lysates of infected cells were collected over a period of 96 hpi or 168 hpi (SK6). (<b>A</b>) Virus titers were determined as 50% tissue culture infectious doses (TCID<sub>50</sub>) per mL. (<b>B</b>) After extraction of total cellular RNA from the collected cell lysates, the viral RNA copy numbers were determined by RT-qPCR and expressed as log 10 copies per 50 ng of RNA. Each time point was evaluated in triplicates. Mean values and standard deviations were calculated by GraphPad Prism software version 9.0.0.</p>
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<p>Expression of IRF3 and the Mx1 protein in porcine cells at 24 h (<b>A</b>, left) and 48 h (<b>B</b>, right) after infection with PhoPeV, BuPV, and CSFV Alfort-T. Porcine PK-15 cells were infected with PhoPeV, BuPV, and CSFV Alfort-T at an MOI of 0.5. Cell lysates were collected at 24 hpi and 48 hpi, and protein levels were quantified. For each lysate, 50 µg of total protein were used for immunoblot analysis, as witnessed by comparable beta-actin content in each lane (bottom panels). As a negative control, PK-15 cells were inoculated with cell culture medium (Mock). Degradation of IRF3 was detected after infection with BuPV and CSFV Alfort-T (<b>A</b>,<b>B</b>). PhoPeV-infected cells show no degradation of IRF3 (<b>A</b>,<b>B</b>) and induction of Mx1 expression (<b>B</b>).</p>
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<p>Relevance of the cellular protein DNAJC14 for viral RNA replication of PhoPeV. Cells were infected with PhoPeV, BuPV, and CSFV Alfort-T using an MOI of 1 and with APPV at an MOI of 0.1. Viral antigen was detected by immunofluorescence at 72 hpi. Virus infection of all selected viruses, in the presence of DNAJC14, was detected in the swine kidney 6 wild-type cell line (SK6 WT) and in the DNAJC14-KO rescue cells (SK6 DNAJC14-KO GST-Jiv90-WT). Similar to BuPV and CSFV, no replication of PhoPeV was detected in the DNAJC14 knock out cell line SK6 DNAJC14-KO. Only APPV infection could be detected in this knockout cell line. Viral antigens were stained with Cy3-conjugated secondary antibodies (red), and cell nuclei were stained with DAPI (blue; lower left corner). All mock controls tested negative.</p>
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23 pages, 7031 KiB  
Article
Fluorescence Lifetime Endoscopy with a Nanosecond Time-Gated CAPS Camera with IRF-Free Deep Learning Method
by Pooria Iranian, Thomas Lapauw, Thomas Van den Dries, Sevada Sahakian, Joris Wuts, Valéry Ann Jacobs, Jef Vandemeulebroucke, Maarten Kuijk and Hans Ingelberts
Sensors 2025, 25(2), 450; https://doi.org/10.3390/s25020450 - 14 Jan 2025
Viewed by 417
Abstract
Fluorescence imaging has been widely used in fields like (pre)clinical imaging and other domains. With advancements in imaging technology and new fluorescent labels, fluorescence lifetime imaging is gradually gaining recognition. Our research department is developing the tauCAMTM, based on the [...] Read more.
Fluorescence imaging has been widely used in fields like (pre)clinical imaging and other domains. With advancements in imaging technology and new fluorescent labels, fluorescence lifetime imaging is gradually gaining recognition. Our research department is developing the tauCAMTM, based on the Current-Assisted Photonic Sampler, to achieve real-time fluorescence lifetime imaging in the NIR (700–900 nm) region. Incorporating fluorescence lifetime into endoscopy could further improve the differentiation of malignant and benign cells based on their distinct lifetimes. In this work, the capabilities of an endoscopic lifetime imaging system are demonstrated using a rigid endoscope involving various phantoms and an IRF-free deep learning-based method with only 6-time points. The results show that this application’s fluorescence lifetime image has better lifetime uniformity and precision with 6-time points than the conventional methods. Full article
(This article belongs to the Section Optical Sensors)
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<p>Schematic diagram of the FLT endoscopy imaging.</p>
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<p>(<b>a</b>) Intensity pattern of the endoscope illumination through the FoV at WD of 3 cm, and (<b>b</b>) the 1D pattern through the mentioned red line in the diagonal direction, which has a Gaussian distribution. (<b>c</b>) The orange square marks the location of strong illumination, with its corresponding IRF showing a clear signal. (<b>d</b>) The green square indicates a region where the endoscope attenuates the light intensity, corresponding to a noise-dominated IRF.</p>
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<p>Resolution analysis of the FLT endoscopy system based on resolution test target USAF 1951.</p>
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<p>Topology of FLTCNN to analyze mono-exponential fluorescence decays. The details of hyperparameters in each layer in parenthesis represent the number of filters, and the kernel size, respectively. The input is an image stack of (128,128,6). The architecture of SimiResBlock, and DownSampleBlock (consists of 4, 2D convolutional layers with decrementing filter sizes) are shown with a dashed box. The BN and the ReLU are added after convolutional layers.</p>
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<p>Synthetic training data generation flow for mono-exponential fluorescence signal model.</p>
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<p>(<b>a</b>) MAE graph of training/validation vs. epochs. (<b>b</b>) The MAE of predicted results of the testing datasets. (<b>c</b>) The mean value of MAE for a lifetime is under different conditions. The SNR takes the value between 20 to 1000 for A = 10, 20, 50, and 100. The blue area denotes the lifetime range of training data. (<b>d</b>) t-SNE visualization was obtained via the last activation map before the down-sampling block, where each point represented a TPSF voxel and was assigned a randomized lifetime value.</p>
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<p>FLT image of a uniform ICG-equivalent phantom predicted by (<b>a</b>) Lmfit (Levenberg–Marquardt), (<b>b</b>) FLTCNN in the macroscopic wide-field regime. (<b>c</b>) Shows the normalized fluorescence intensity of the phantom. (<b>d</b>) FLT histogram of the ICG uniform phantom processed with Lmfit and FLTCNN.</p>
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<p>(<b>a</b>,<b>b</b>) FLT and intensity images, and (<b>c</b>) FLT histogram of the ICG uniform phantom captured by FLT endoscopy system and processed with FLTCNN algorithm.</p>
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<p>(<b>a</b>,<b>b</b>) FLT and intensity images of the uniform ICG phantom under an angle.</p>
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<p>(<b>a</b>) Lmfit analysis in the macroscopic regime, (<b>b</b>) Lmfit analysis of 6-time points in the endoscopy regime, (<b>c</b>) FLTCNN analysis in the endoscopy regime, and (<b>d</b>) fluorescence intensity image of the concentration ICG phantom, Quel Imaging. (<b>e</b>–<b>g</b>) histogram of each well related to each approach to predict lifetime.</p>
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<p>FLT images of ICG-equivalent phantoms (distortion, coin, and vessel, Quel Imaging) analyzed with (<b>a</b>–<b>c</b>) Lmfit full decay, (<b>d</b>–<b>f</b>) Lmfit 6-data point, (<b>g</b>–<b>i</b>) FLTCNN, and (<b>j</b>–<b>l</b>) fluorescence intensity.</p>
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<p>The lifetime distribution of the ICG distortion, coin, and vessel phantoms calculated by (<b>a</b>) Lmfit analysis in the macroscopic regime, (<b>b</b>) Lmfit analysis of 6-time points in the endoscopy regime, and (<b>c</b>) FLTCNN analysis in the endoscopy regime.</p>
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<p>(<b>a</b>) Lmfit analysis in the macroscopic regime, (<b>b</b>) Lmfit analysis of 6-time points in the endoscopy regime, (<b>c</b>) FLTCNN analysis in the endoscopy regime, and (<b>d</b>) fluorescence intensity image of QUEL mixed phantoms containing ICG in ST01/LU02 and OTL38 in ST01/LU02. (<b>e</b>–<b>g</b>) histogram of each well related to each approach to predict lifetime.</p>
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26 pages, 1014 KiB  
Article
Integrated Risk Framework (IRF)—Interconnection of the Ishikawa Diagram with the Enhanced HACCP System in Risk Assessment for the Sustainable Food Industry
by Mirel Glevitzky, Ioana Glevitzky, Paul Mucea-Ștef, Maria Popa, Gabriela-Alina Dumitrel and Mihaela Laura Vică
Sustainability 2025, 17(2), 536; https://doi.org/10.3390/su17020536 - 12 Jan 2025
Viewed by 511
Abstract
This paper presents a new risk assessment methodology called the Integrated Risk Framework (IRF) through the application of Ishikawa diagrams combined with the enhanced Hazard Analysis and Critical Control Point (HACCP) system. This risk investigation technique aims to ensure a significantly higher level [...] Read more.
This paper presents a new risk assessment methodology called the Integrated Risk Framework (IRF) through the application of Ishikawa diagrams combined with the enhanced Hazard Analysis and Critical Control Point (HACCP) system. This risk investigation technique aims to ensure a significantly higher level of quality, safety, and sustainability in food products by using improved classical methods with strong intercorrelation capabilities. The methodology proposes expanding the typology of basic physical, chemical, and biological risks outlined by the ISO 22000 Food Safety Management System standard, adding other auxiliary risks such as allergens, fraud/sabotage, Kosher/Halal compliance, Rapid Alert System for Food and Feed notification, or additional specific risks such as irradiation, radioactivity, genetically modified organisms, polycyclic aromatic hydrocarbons, African swine fever, peste of small ruminants, etc. depending on the specific technological process or ingredients. Simultaneously, it identifies causes for each operation in the technological flow based on the 5M diagram: Man, Method, Material, Machine, and Environment. For each identified risk and cause, its impact was determined according to its severity and likelihood of occurrence. The final effect is defined as the risk class, calculated as the arithmetic mean of the impact derived at each process stage based on the identified risks and causes. Within the study, the methodology was applied to the spring water bottling process. This provided a new perspective on analyzing the risk factors during the bottling operations by concurrently using Ishikawa diagrams and HACCP principles throughout the product’s technological flow. The results of the study can form new methodologies aimed at enhancing sustainable food safety management strategy. In risk assessment using these two tools, the possibility of cumulative or synergistic effects is considered, resulting in better control of all factors that may affect the manufacturing process. This new perspective on studying the dynamics of risk factor analysis through the simultaneous use of the fishbone diagram and the classical HACCP system can be extrapolated and applied to any manufacturing process in the food industry and beyond. Full article
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<p>Risk analysis steps for IRF.</p>
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<p>Flow diagram of bottled natural spring water manufacturing process: (<b>a</b>) sparkling water and (<b>b</b>) still water.</p>
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<p>Ishikawa diagram—recommendations to determine the risk-generating causes of manufacturing stages.</p>
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<p>Identifying the factors that may produce the risks related to the water bottling process.</p>
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20 pages, 3942 KiB  
Article
Twelve-Month Outcomes and Optical Coherence Tomography (OCT) Biomarkers After Intravitreal Dexamethasone Implantation in Pseudophakic Eyes with Post-Vitrectomy Cystoid Macular Edema (CME)—Refractory to Medical Therapy
by Francesco Pignatelli, Alfredo Niro, Giuseppe Addabbo, Pasquale Viggiano, Giacomo Boscia, Maria Oliva Grassi, Francesco Boscia, Cristiana Iaculli, Giulia Maria Emilia Clima, Antonio Barone and Ermete Giancipoli
Diagnostics 2025, 15(2), 147; https://doi.org/10.3390/diagnostics15020147 - 10 Jan 2025
Viewed by 402
Abstract
Background: In this study, we evaluated the incidence of cystoid macular edema (CME) after pars plana vitrectomy (PPV) for different retinal pathologies and assessed the role of optical coherence tomography (OCT) biomarkers in guiding treatment decisions in post-surgical CME patients who were [...] Read more.
Background: In this study, we evaluated the incidence of cystoid macular edema (CME) after pars plana vitrectomy (PPV) for different retinal pathologies and assessed the role of optical coherence tomography (OCT) biomarkers in guiding treatment decisions in post-surgical CME patients who were refractory to medical therapy over a follow-up period of 12 months. Methods: Medical records of consecutive pseudophakic patients, who underwent PPV for different retinal pathologies, were retrospectively evaluated in this single-center, uncontrolled study. The incidence of post-PPV CME was assessed. Eyes with post-PPV CME in the first 2 months after surgery, with available clinical and OCT data for 12 months after surgery, were included in the evaluation. The mean best-corrected visual acuity (BCVA; logMAR), mean central macular thickness (CMT; μm) change, and response to different treatments [medical therapy and intravitreal dexamethasone (DEX) implant] were evaluated 1, 3, 6, 9, and 12 months after PPV. The impact of OCT biomarkers on the exposure to DEX implants was assessed. Adverse events, potentially related to the treatment, were investigated as well. Results: Of the 346 pseudophakic patients (352 eyes) who participated in this study, 54 (54 eyes) developed CME within the first 2 months after PPV (incidence of 15.3%). Among them, 48 patients were deemed eligible for the 12-month analysis. Preoperative mean BCVA (1.44 ± 0.99 logMAR) significantly improved to 0.32 ± 0.37 logMAR after 12 months (p < 0.001). The mean baseline CMT of 347 (±123.5) μm significantly decreased to 290 μm (±80.4; p = 0.003) by the end of the follow-up. Twenty-five eyes (52%) required one or more DEX implants for CME, due to being refractory to topical therapy. Significant correlations were found between the mean CMT values at various time points. Additionally, patients who required DEX implants at months 3 and 9 were more likely to present intraretinal fluid (IRF), disorganization of inner retinal layers (DRIL), disorganization of outer retinal layers (DROL), and hyper-reflective foci (HRF) at 1-month OCT. Five patients experienced a slight increase in intraocular pressure (IOP), which was successfully managed with topical medication. Conclusions: Topical therapy alone can be a valuable option for post-PPV CME in approximately 50% of patients. Significant visual recovery and macular thickness reduction at 12 months demonstrated that DEX implants can be a safe and effective second-line treatment for pseudophakic patients with post-PPV CME and who are refractory to medical therapy. Early post-surgical OCT biomarkers may indicate a more severe CME that might benefit from the steroid implant. Full article
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<p>Early (<b>A</b>) and late (<b>B</b>) frames of a fluorescein angiogram, along with macular spectral domain optical coherence tomography (SD-OCT) scans (<b>C</b>,<b>D</b>), of a patient who developed cystoid macular edema (CME) one month after small-gauge pars plana vitrectomy (PPV) for rhegmatogenous retinal detachment repair. Extensive macular leakage was observed during the late phase of the angiogram, with a diffuse and irregular macular hyperfluorescence (<b>B</b>). Late-phase optic disk hyperfluorescence was also evident (<b>B</b>). The vertical (<b>C</b>) and horizontal (<b>D</b>) SD-OCT B-scans crossing the foveal center showed significant central macular thickening, the presence of intraretinal hyporeflective cysts, and a subfoveal hyporeflective cuff of fluid. Subsequent vertical (<b>E</b>) and horizontal (<b>F</b>) SD-OCT B-scans demonstrated a significant reduction in central macular thickness, with complete resolution of intraretinal and subretinal fluid, 3 months after intravitreal dexamethasone administration.</p>
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<p>Spectral domain optical coherence tomography (SD-OCT) at month 1, revealed a macular edema with large intraretinal cysts (blue arrows), subretinal fluid at the fovea (star), hyper-reflective foci (HRF, orange arrowheads), and disorganization of inner retinal layers, (DRIL, red arrowhead).</p>
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<p>Observation and therapy flow chart. PPV, pars plana vitrectomy; CME, cystoid macular edema; OCT, optical coherence tomography; FFA, fundus fluorescein angiography; DEX, dexamethasone; NSAID, nonsteroidal anti-inflammatory drug; CMT, central macular thickness.</p>
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<p>Best-corrected visual acuity (BCVA) changes during follow-up. * <span class="html-italic">p</span>-value, Wilcoxon test (BCVA baseline as reference value).</p>
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<p>Central macular thickness (CMT) changes during follow-up, starting from 1 month. * <span class="html-italic">p</span>-value, Wilcoxon test (CMT 1 month as reference value).</p>
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<p>Post-vitrectomy cystoid macular edema (CME) after surgery for rhegmatogenous retinal detachment in the right eye of a 65-year-old lady. (<b>A</b>) Early- and (<b>B</b>) late-phase fluorescein angiography, performed one month after surgery, showed the classical perifoveal petaloid staining pattern and late leakage of the optic disc. (<b>C</b>) Optical coherence tomography (OCT) scan taken at month 1 revealed the presence of intraretinal cysts (blue arrows) and subretinal fluid (yellow star). (<b>D</b>) At month 3, OCT revealed a complete resolution of intraretinal and subretinal fluid after topical therapy. Best-corrected visual acuity improved from 20/200 to 20/30; central macular thickness decreased from 366 µm to 205 μm.</p>
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18 pages, 475 KiB  
Article
Frequency-Domain Characterization of Finite Sample Linear Systems with Uniform Window Inputs
by Qihou Zhou
Signals 2025, 6(1), 1; https://doi.org/10.3390/signals6010001 - 10 Jan 2025
Viewed by 381
Abstract
We discuss determining a finite sample linear time-invariant (FS-LTI) system’s impulse response function, h[n], in the frequency domain when the input testing function is a uniform window function with a width of L and the output is limited to [...] Read more.
We discuss determining a finite sample linear time-invariant (FS-LTI) system’s impulse response function, h[n], in the frequency domain when the input testing function is a uniform window function with a width of L and the output is limited to a finite number of effective samples, M. Assuming that the samples beyond M are all zeros, the corresponding infinite sample LTI (IS-LTI) system is a marginally stable system. The ratio of the discrete Fourier transforms (DFT) of the output to input of such an FS-LTI system, H0[k], cannot be directly used to find h[n] via inverse DFT (IDFT). Nevertheless, H0[k] contains sufficient information to determine the system’s impulse response function (IRF). In the frequency-domain approach, we zero-pad the output array to a length of N. We present methods to recover h[n] from H0[k] for two scenarios: (1) Nmax(L,M+1) and N is a coprime of L, and (2) NL+M+1. The marginal stable system discussed here is an artifact due to the zero-value assumption on unavailable samples. The IRF obtained applies to any LTI system up to the number of effective data samples, M. In demonstrating the equivalence of H0[k] and h[n], we derive two interesting DFT pairs. These DFT pairs can be used to find trigonometric sums that are otherwise difficult to prove. The frequency-domain approach makes mitigating the effects of interferences and random noise easier. In an example application in radar remote sensing, we show that the frequency-domain processing method can be used to obtain finer details than the range resolution provided by the radar system’s transmitter. Full article
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<p>Comparison of direct IDFT of <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mn>1</mn> </msub> <mrow> <mo>[</mo> <mi>k</mi> <mo>]</mo> </mrow> </mrow> </semantics></math> (blue stems) and <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mn>1</mn> </msub> <mrow> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </mrow> </semantics></math> (red circles) for <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>21</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>i</mi> <mi>L</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>.</p>
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<p>Example of <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>h</mi> <mn>1</mn> </msub> <mrow> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> <mo>,</mo> <msub> <mi>h</mi> <mn>2</mn> </msub> <mrow> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>h</mi> <mn>3</mn> </msub> <mrow> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>15</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math>.</p>
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<p>Example of <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>h</mi> <mn>1</mn> </msub> <mrow> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> <mo>,</mo> <msub> <mi>h</mi> <mn>2</mn> </msub> <mrow> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>h</mi> <mn>3</mn> </msub> <mrow> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </mrow> </semantics></math> for a complex output with <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>23</mn> </mrow> </semantics></math>. The upper and lower plots are for the real and imaginary parts, respectively.</p>
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<p>Comparison of direct IDFT of <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mn>2</mn> </msub> <mrow> <mo>[</mo> <mi>k</mi> <mo>]</mo> </mrow> </mrow> </semantics></math> (blue stems) and <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mn>2</mn> </msub> <mrow> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </mrow> </semantics></math> (red circles) for <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>12</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>i</mi> <mi>L</mi> </msub> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>.</p>
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<p>Comparison of <math display="inline"><semantics> <mrow> <msub> <mi>g</mi> <mn>1</mn> </msub> <mrow> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>g</mi> <mn>2</mn> </msub> <mrow> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>g</mi> <mn>3</mn> </msub> <mrow> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>g</mi> <mn>4</mn> </msub> <mrow> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>8</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>18</mn> </mrow> </semantics></math>. The LTI system has a finite IRF.</p>
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<p>Comparison of <math display="inline"><semantics> <mrow> <msub> <mi>g</mi> <mn>1</mn> </msub> <mrow> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>g</mi> <mn>2</mn> </msub> <mrow> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>g</mi> <mn>3</mn> </msub> <mrow> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>g</mi> <mn>4</mn> </msub> <mrow> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>12</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>32</mn> </mrow> </semantics></math>. The LTI system has an infinite IRF.</p>
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<p>Example of frequency filtering in the presence of noise without averaging. The black line (<math display="inline"><semantics> <msub> <mi>q</mi> <mn>0</mn> </msub> </semantics></math>) represents the ideal input signal without noise. The recovered signal without filtering (<span class="html-italic">h</span>) is shown as black stems. Symbols corresponding to <math display="inline"><semantics> <msub> <mi>h</mi> <mrow> <mn>1</mn> <mi>f</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>h</mi> <mrow> <mn>2</mn> <mi>f</mi> </mrow> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mi>h</mi> <mrow> <mn>3</mn> <mi>f</mi> </mrow> </msub> </semantics></math> are filtered results of <math display="inline"><semantics> <mrow> <msub> <mi>h</mi> <mn>1</mn> </msub> <mrow> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>h</mi> <mn>2</mn> </msub> <mrow> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>h</mi> <mn>3</mn> </msub> <mrow> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </mrow> </semantics></math>, respectively. The green line is the received signal (<math display="inline"><semantics> <mrow> <mi>y</mi> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </semantics></math>) divided by 8.</p>
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<p>Example of frequency filtering in the presence of noise with 1000 averages. The ideal input signal without noise is represented by the blue asterisks (<math display="inline"><semantics> <msub> <mi>q</mi> <mn>0</mn> </msub> </semantics></math>). The black squares and red circles are the recovered target signals averaged over 1000 independent realizations without and with filtering, respectively, The black and red lines represent the standard deviations multiplied by 10 for the recovered signals without and with filtering, respectively.</p>
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13 pages, 312 KiB  
Article
Market Reaction to Earnings Announcements Under Different Volatility Regimes
by Yusuf Joseph Ugras and Mark A. Ritter
J. Risk Financial Manag. 2025, 18(1), 19; https://doi.org/10.3390/jrfm18010019 - 5 Jan 2025
Viewed by 743
Abstract
This study investigates the occurrence and persistence of abnormal stock returns surrounding corporate earnings announcements, particularly emphasizing how varying frequencies of financial reporting influence market behavior. Specifically, this research examines the effects of the timing and frequency of disclosures on market reactions and [...] Read more.
This study investigates the occurrence and persistence of abnormal stock returns surrounding corporate earnings announcements, particularly emphasizing how varying frequencies of financial reporting influence market behavior. Specifically, this research examines the effects of the timing and frequency of disclosures on market reactions and stock price volatility during critical earnings announcement periods. By analyzing firms within the Dow Jones Industrial Average (DJIA) from 2014 to 2024, this study evaluates the interplay between financial reporting schedules and market responses to stock prices. Furthermore, it considers the impact of peer firms’ reporting practices on the assimilation of firm-specific information into stock prices. Using econometric models, including Vector Auto Regression (VAR), Impulse Response Functions (IRFs), and Self-Exciting Threshold Autoregressive (SETAR) models, causal relationships between reporting frequency, stock price volatility, and abnormal return patterns across different volatility regimes are identified. The findings highlight that quarterly reporting practices intensify market responses and contribute to significant variations in stock price behavior in high-volatility periods. These insights provide a deeper understanding of the role of financial disclosure practices and forward-looking guidance in shaping market efficiency. This study contributes to ongoing discussions about balancing the transparency benefits of frequent reporting with its potential to amplify market volatility and sector-specific risks, offering valuable implications for policymakers, investors, and corporate managers. Full article
(This article belongs to the Special Issue Advances in Accounting & Auditing Research)
22 pages, 2251 KiB  
Article
Evaluating Anesthesia Guidance for Rescue Analgesia in Awake Patients Undergoing Carotid Endarterectomy with Cervical Plexus Blocks: Preliminary Findings from a Randomized Controlled Trial
by Michał Jan Stasiowski, Nikola Zmarzły and Beniamin Oskar Grabarek
J. Clin. Med. 2025, 14(1), 120; https://doi.org/10.3390/jcm14010120 - 28 Dec 2024
Viewed by 446
Abstract
Background/Objectives: Eversion carotid endarterectomy (CEA) in awake patients is performed using cervical plexus blocks (CPBs) with or without carotid artery sheath infiltration (CASI) under ultrasound guidance. Although adequacy of anesthesia (AoA) guidance monitors nociception/antinociception balance, its impact on intraoperative analgesia quality and perioperative [...] Read more.
Background/Objectives: Eversion carotid endarterectomy (CEA) in awake patients is performed using cervical plexus blocks (CPBs) with or without carotid artery sheath infiltration (CASI) under ultrasound guidance. Although adequacy of anesthesia (AoA) guidance monitors nociception/antinociception balance, its impact on intraoperative analgesia quality and perioperative outcomes in awake CEA remains unexplored. Existing literature lacks evidence on whether AoA-guided anesthesia enhances clinical outcomes over standard techniques. This study aimed to assess the role of AoA guidance in improving intraoperative analgesia and perioperative outcomes in patients undergoing CEA with CPBs alone or with CASI compared to standard practice. Methods: A randomized controlled trial included 184 patients divided into three groups: CPBs with intravenous rescue fentanyl (IRF) and lidocaine (LID) guided by hemodynamic observation (C group), AoA-guided IRF and LID (AoA group), and AoA-guided IRF, LID, and CASI (AoA-CASI group). Primary outcomes included perioperative adverse events, and secondary outcomes assessed rescue medication demand and hemodynamic stability. Results: Analysis of 172 patients revealed no significant differences between groups in perioperative adverse events or hemodynamic parameters (p > 0.05). However, the AoA-CASI group demonstrated significantly reduced IRF and LID usage compared to the C and AoA groups (p < 0.001). No significant advantage was observed between the AoA and C groups regarding adverse events (p = 0.1). Conclusions: AoA-guided anesthesia with or without CASI does not significantly reduce perioperative adverse events or improve hemodynamic stability in awake CEA. Clinical implications suggest that focusing on surgical technique optimization may yield greater benefits in reducing adverse events compared to advanced anesthetic monitoring. Further studies are warranted to explore alternative approaches to enhance clinical outcomes. Full article
(This article belongs to the Special Issue Current Clinical Management of Regional Analgesia and Anesthesia)
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<p>Randomization graph. C, classic technique; AoA, Adequacy of Anesthesia; CASI, carotid artery sheath infiltration.</p>
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<p>Hemodynamic stability during eversion CEA in the study groups. The dashed red line indicates a MAP of 65 mmHg, which is the threshold below which intraoperative hypotension occurs. C, classic technique; AoA, Adequacy of Anesthesia; CASI, carotid artery sheath infiltration; HR, heart rate; SAP, systolic blood pressure; MAP, mean arterial pressure; DAP, diastolic arterial pressure.</p>
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<p>Incidence of neurological complications according to group allocation. C, classic technique; AoA, Adequacy of Anesthesia; CASI, carotid artery sheath infiltration.</p>
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<p>Number of patients requiring rescue interventions in the study groups. C, classic technique; AoA, Adequacy of Anesthesia; CASI, carotid artery sheath infiltration.</p>
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<p>Cumulative dose of rescue interventions in the study groups. C, classic technique; AoA, Adequacy of Anesthesia; CASI, carotid artery sheath infiltration; IRF, intravenous rescue fentanyl; LID, lidocaine.</p>
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24 pages, 9504 KiB  
Article
Gegen Qinlian Decoction Attenuates Colitis-Associated Colorectal Cancer via Suppressing TLR4 Signaling Pathway Based on Network Pharmacology and In Vivo/In Vitro Experimental Validation
by Yaoyao Xu, Qiaoyan Cai, Chunyu Zhao, Weixiang Zhang, Xinting Xu, Haowei Lin, Yuxing Lin, Daxin Chen, Shan Lin, Peizhi Jia, Meiling Wang, Ling Zhang and Wei Lin
Pharmaceuticals 2025, 18(1), 12; https://doi.org/10.3390/ph18010012 - 25 Dec 2024
Viewed by 665
Abstract
Background: Gegen Qinlian Decoction (GQD), is used for intestinal disorders like ulcerative colitis, irritable bowel syndrome, and colorectal cancer. But the precise mechanisms underlying its anti-inflammatory and anti-tumor effects are not fully elucidated. Methods: Use network pharmacology to identify targets and [...] Read more.
Background: Gegen Qinlian Decoction (GQD), is used for intestinal disorders like ulcerative colitis, irritable bowel syndrome, and colorectal cancer. But the precise mechanisms underlying its anti-inflammatory and anti-tumor effects are not fully elucidated. Methods: Use network pharmacology to identify targets and pathways of GQD. In vivo (azoxymethane/dextran sodium sulfate (AOM/DSS)-induced colitis-associated colorectal cancer (CAC) mouse model) and in vitro (lipopolysaccharide (LPS)-stimulated RAW264.7 macrophages) experiments were conducted to explore GQD’s anti-inflammatory and anti-tumor effects. We monitored mouse body weight and disease activity index (DAI), and evaluated colon cancer tissues using hematoxylin and eosin staining. Expression of Ki67 and F4/80 was determined by immunohistochemistry analysis. The protein levels of TLR4 signaling pathway were assessed by western blotting analysis. Enzyme-linked immunosorbent assay measured IL-1β, IL-6, and TNF-α levels. Immunofluorescence (IF) staining visualized NF-κB and IRF3 translocation. Results: There were 18, 9, 24 and 77 active ingredients in the four herbs of GQD, respectively, targeting 435, 156, 485 and 691 genes. Through data platform analysis, it was concluded that there were 1104 target genes of GQD and 2022 target genes of CAC. Moreover, there were 99 intersecting genes between GQD and CAC. The core targets of GQD contained NFKB1, IL1B, IL6, TLR4, and TNF, and GQD reduced inflammation by inhibiting the TLR4 signaling pathway. In vivo experiment, GQD increased mouse body weight, lowered DAI scores, while also alleviating histopathological changes in the colon and decreasing the expressions of Ki67 and F4/80 in the AOM/DSS-induced mice. GQD reduced IL-1β, IL-6, and TNF-α levels in the serum and downregulated TLR4, MyD88, and phosphorylation of IκBα, P65, and IRF3 in the colon tissue from AOM/DSS-induced mice. In vitro, GQD suppressed pro-inflammatory cytokines and TLR4 signaling pathway in the LPS-induced RAW264.7 cells, and combined with TAK242, it further reduced the phosphorylation of IκBα, P65. Conclusions: GQD mitigated CAC by inhibiting the TLR4 signaling pathway, offering a potential therapeutic approach for CAC management. Full article
(This article belongs to the Section Pharmacology)
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<p>Quality control of GQD using UPLC-MS. (<b>A</b>) The positive and negative ion chromatogram of GQD. (<b>B</b>) The peaks of reference substances. 1. Puerarin; 2. Liquiritin; 3. Puerarin 6″-O-xyloside; 4. Berberine; 5. Baicalin; 6. Glycyrrhizic acid. GQD: Gegen Qinlian Decoction.</p>
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<p>Active ingredients and target genes of GQD. Searching in the Batman-TCM database revealed compounds and target gene information for <span class="html-italic">Pueraria lobata</span> (Willd.) Ohwi, <span class="html-italic">Scutellaria baicalensis</span> Georgi, <span class="html-italic">Coptis chinensis</span> Franch, and <span class="html-italic">Glycyrrhiza uralensis</span> Fisch, with specific numbers listed. GQD: Gegen Qinlian Decoction.</p>
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<p>Identifying the intersecting genes between GQD and CAC as well as creating the compound-intersecting genes-disease association map. (<b>A</b>) Venny diagram of intersecting genes of GQD and CAC; Analysis of intersecting genes via protein-protein interaction network. (<b>B</b>) Development of the compound-targets network and compound-targets-pathway network from the 4 herbs of GQD. GQD: Gegen Qinlian Decoction; CAC: Colitis-Associated Colorectal Cancer.</p>
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<p>KEGG and GO pathway enrichment analyses. (<b>A</b>) Analyzing the pathway enrichment of the intersecting genes between GQD and CAC. (<b>B</b>) GO analysis of biological processes, cellular components, and molecular functions associated with the therapeutic effects of GQD for CAC. GQD: Gegen Qinlian Decoction; CAC: Colitis-Associated Colorectal Cancer; KEGG: Kyoto Encyclopedia of Genes and Genomes; GO: Gene Ontology.</p>
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<p>GQD attenuated the symptoms in AOM/DSS-induced CAC mice. (<b>A</b>) Sketch of the animal experimental design. (<b>B</b>) Body weight of three groups. n = 6. (<b>C</b>) The DAI score of three groups. n = 6. (<b>D</b>,<b>E</b>) The macroscopic pathology, as well as the length and weight of the mouse colon in three groups. The red arrow indicated the location of the tumor. n = 3. (<b>F</b>) The pathological morphology of the colon with different multiples via HE staining in three groups. n = 4. * <span class="html-italic">p</span> &lt; 0.05 vs. Control group; <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 vs. AOM/DSS group. GQD: Gegen Qinlian Decoction; CAC: Colitis-Associated Colorectal Cancer; AOM/DSS: Azoxymethane/Dextran Sodium Sulfate; DAI: Disease Activity Index; HE: Hematoxylin and Eosin.</p>
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<p>GQD reduced the tumor incidence and improved the survival rate in AOM/DSS-induced CAC mice. (<b>A</b>) Comparison of colonic macroscopic morphology and number of tumors in three groups. The red arrow indicated the location of the tumor. n = 3. (<b>B</b>) The IHC staining results of Ki67 in three groups. The red arrow represented the characteristics of Ki67 positive expression. n = 4. (<b>C</b>) The survival rate of the three groups. n = 6. * <span class="html-italic">p</span> &lt; 0.05 vs. Control group; <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 vs. AOM/DSS group. GQD: Gegen Qinlian Decoction; CAC: Colitis-Associated Colorectal Cancer; AOM/DSS: Azoxymethane/Dextran Sodium Sulfate; IHC: Immunohistochemistry.</p>
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<p>GQD inhibited inflammation by downregulating the TLR4-related signaling pathways in AOM/DSS-induced CAC mice. (<b>A</b>) Serum inflammatory factors in three groups. n = 4. (<b>B</b>) The protein expression of TLR4, Myd88, p-IκBα, IκBα, p-P65, P65, p-IRF3, and IRF3 in three groups. n = 3. (<b>C</b>) The IHC staining results of F4/80 in three groups. The red arrow represented the characteristics of F4/80 positive expression. n = 4. * <span class="html-italic">p</span> &lt; 0.05 vs. Control group; <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 vs. AOM/DSS group. GQD: Gegen Qinlian Decoction; CAC: Colitis-Associated Colorectal Cancer; AOM/DSS: Azoxymethane/Dextran Sodium Sulfate; IHC: Immunohistochemistry.</p>
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<p>GQD inhibited the inflammatory cytokine secretion in LPS-induced RAW264.7 cells. (<b>A</b>) The impact of GQD on the viability of RAW264.7 cells via the CCK8 assay. n = 6. (<b>B</b>,<b>C</b>) The levels of IL-1β, IL-6, and TNF-α in each group. n = 3. * <span class="html-italic">p</span> &lt; 0.05 vs. Control group; <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 vs. LPS group; <sup>&amp;</sup> <span class="html-italic">p</span> &lt; 0.05 vs. LPS + 25 μg/mL GQD group; <sup><span>$</span></sup> <span class="html-italic">p</span> &lt; 0.05 vs. LPS + 50 μg/mL GQD group. GQD: Gegen Qinlian Decoction; LPS: Lipopolysaccharide.</p>
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<p>GQD inhibited TLR4-ralated signaling pathway in LPS-induced RAW264.7 cells. The protein expression of TLR4, Myd88, TRIF, p-IκBα, IκBα, p-P65, P65, p-IRF3, IFR3 in each group. n = 3. * <span class="html-italic">p</span> &lt; 0.05 vs. Control group; <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 vs. LPS group. GQD: Gegen Qinlian Decoction; LPS: Lipopolysaccharide.</p>
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<p>GQD inhibited nuclear translocation of NF-κB, IRF3 in LPS-induced RAW264.7 cells. (<b>A</b>) The expression and nuclear translocation of NF-κB protein in each group by IF staining. n = 4. (<b>B</b>) The expression and nuclear translocation of IRF3 protein in each group by IF staining. n = 4. GQD: Gegen Qinlian Decoction; LPS: Lipopolysaccharide; IF: Immunofluorescence.</p>
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<p>Synergistic effect of GQD and TAK242 on the inhibiting TLR4-related signaling pathways. The protein expression of p-IκBα, IκBα, p-P65, P65, p-IRF3, IFR3 in each group. n = 3. * <span class="html-italic">p</span> &lt; 0.05 vs. Control group; <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 vs. LPS group; <sup>&amp;</sup> <span class="html-italic">p</span> &lt; 0.05 vs. LPS + GQD. GQD: Gegen Qinlian Decoction; LPS: Lipopolysaccharide.</p>
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36 pages, 11803 KiB  
Article
Interplay of Transcriptomic Regulation, Microbiota, and Signaling Pathways in Lung and Gut Inflammation-Induced Tumorigenesis
by Beatriz Andrea Otálora-Otálora, César Payán-Gómez, Juan Javier López-Rivera, Natalia Belén Pedroza-Aconcha, Sally Lorena Arboleda-Mojica, Claudia Aristizábal-Guzmán, Mario Arturo Isaza-Ruget and Carlos Arturo Álvarez-Moreno
Cells 2025, 14(1), 1; https://doi.org/10.3390/cells14010001 - 24 Dec 2024
Viewed by 754
Abstract
Inflammation can positively and negatively affect tumorigenesis based on the duration, scope, and sequence of related events through the regulation of signaling pathways. A transcriptomic analysis of five pulmonary arterial hypertension, twelve Crohn’s disease, and twelve ulcerative colitis high throughput sequencing datasets using [...] Read more.
Inflammation can positively and negatively affect tumorigenesis based on the duration, scope, and sequence of related events through the regulation of signaling pathways. A transcriptomic analysis of five pulmonary arterial hypertension, twelve Crohn’s disease, and twelve ulcerative colitis high throughput sequencing datasets using R language specialized libraries and gene enrichment analyses identified a regulatory network in each inflammatory disease. IRF9 and LINC01089 in pulmonary arterial hypertension are related to the regulation of signaling pathways like MAPK, NOTCH, human papillomavirus, and hepatitis c infection. ZNF91 and TP53TG1 in Crohn’s disease are related to the regulation of PPAR, MAPK, and metabolic signaling pathways. ZNF91, VDR, DLEU1, SATB2-AS1, and TP53TG1 in ulcerative colitis are related to the regulation of PPAR, AMPK, and metabolic signaling pathways. The activation of the transcriptomic network and signaling pathways might be related to the interaction of the characteristic microbiota of the inflammatory disease, with the lung and gut cell receptors present in membrane rafts and complexes. The transcriptomic analysis highlights the impact of several coding and non-coding RNAs, suggesting their relationship with the unlocking of cell phenotypic plasticity for the acquisition of the hallmarks of cancer during lung and gut cell adaptation to inflammatory phenotypes. Full article
(This article belongs to the Topic Inflammatory Tumor Immune Microenvironment)
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<p>Venn diagram with the transcriptomic metafirm in common and unique to each type of inflammatory disease. Created with BioRender.com.</p>
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<p>Coding (lilium and grey) and non-coding (purple) transcriptional regulatory network (cncTRN) of key upregulated transcription factors (TFs) and lncRNA in pulmonary arterial hypertension (PAH). Created with Cytoscape.</p>
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<p>Coding (lilium and grey) and non-coding (purple) transcriptional regulatory network (cncTRN) of key upregulated transcription factors in CD. Created with Cytoscape.</p>
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<p>Coding (lilium and grey) and non-coding (purple) transcriptional regulatory network (cncTRN) of key upregulated transcription factors in ulcerative colitis. Created with Cytoscape.</p>
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<p>Microbiome interaction with membrane receptor of PAH-related cells activating signaling pathways involved in transcriptional regulation during lung inflammation. In red are the upregulated genes and TFs; in black are the key upregulated TFs. Created with BioRender.com.</p>
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<p>Microbiome interaction with membrane receptor of CD-related cells, activating signaling pathways involved in transcriptional regulation during gut inflammation. In red are the upregulated genes and TFs; in black are the key upregulated TFs. Created with BioRender.com.</p>
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<p>Microbiome interaction with membrane receptor of UC-related cells, activating signaling pathways involved in transcriptional regulation during gut inflammation. In red are the upregulated genes and TFs; in black are the key upregulated TFs. Created with BioRender.com.</p>
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17 pages, 8649 KiB  
Article
LPS Disrupts Endometrial Receptivity by Inhibiting STAT1 Phosphorylation in Sheep
by Xing Fan, Jinzi Wei, Yu Guo, Juan Ma, Meiyu Qi, He Huang, Peng Zheng, Wenjie Jiang and Yuchang Yao
Int. J. Mol. Sci. 2024, 25(24), 13673; https://doi.org/10.3390/ijms252413673 - 21 Dec 2024
Viewed by 599
Abstract
Uterine infections reduce ruminant reproductive efficiency. Reproductive dysfunction caused by infusion of Gram-negative bacteria is characterized by the failure of embryo implantation and reduced conception rates. Lipopolysaccharide (LPS), a major component of the outer membrane of Gram-negative bacteria, is highly abortogenic. In this [...] Read more.
Uterine infections reduce ruminant reproductive efficiency. Reproductive dysfunction caused by infusion of Gram-negative bacteria is characterized by the failure of embryo implantation and reduced conception rates. Lipopolysaccharide (LPS), a major component of the outer membrane of Gram-negative bacteria, is highly abortogenic. In this study, the effects of LPS infusion on the endometrial receptivity of sheep were studied during three critical periods of embryo implantation. The results showed that LPS infusion on d12, d16, and d20 of pregnancy in vivo interfered with the expression of prostaglandins (PGs) and affected the expression of adhesion-related factors (ITGB1/3/5, SPP1), key implantation genes (HOXA10, HOXA11 and LIF), and progestational elongation genes (ISG15, RSAD2 and CXCL10) during embryo implantation. In addition, after LPS infusion on d12, d16, and d20, the phosphorylation level of STAT1 significantly decreased and the protein expression level of IRF9 significantly increased on d12, suggesting that LPS infusion in sheep impairs endometrial receptivity through the JAK2/STAT1 pathway. Sheep endometrial epithelial cells were treated with 17 β-estrogen, progesterone, and/or interferon-tau in vitro to mimic the receptivity of the endometrium during early pregnancy for validation. LPS and the p-STAT1 inhibitor fludarabine were both added to the model, which resulted in reduced p-STAT1 protein expression, significant inhibition of PGE2/PGF2α, and significant suppression of the expression of key embryo implantation genes. Collectively, these results indicate that LPS infusion in sheep on d12, d16, and d20 impairs endometrial receptivity through the JAK2/STAT1 pathway, which is responsible for LPS-associated pregnancy failure. Full article
(This article belongs to the Section Molecular Biology)
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<p>Effect of LPS on prostaglandin expression in sheep endometrium. (<b>A</b>) The secretion of PGE2 and PGF2α in endometrial tissue was measured on d12, d16, and d20 of pregnancy using an ELISA kit. (<b>B</b>) The secretion of PGE2 and PGF2α in endometrial tissue was measured on d12, d16, and d20 of pregnancy using an ELISA kit. (<b>C</b>) The ratio of PGE2 and PGF2α in endometrial tissue on d12, d16, and d20 of pregnancy. (<b>D</b>) The rate-limiting enzymes <span class="html-italic">PTGS1</span>, <span class="html-italic">PTGS2</span> (<b>E</b>), <span class="html-italic">PTGES</span> (<b>F</b>), and <span class="html-italic">PGFS</span> (<b>G</b>) of synthesized PGs in endometrial tissue on d12, d16, and d20 of pregnancy were measured by real-time quantitative PCR. All data are presented as the mean ± SEM, <span class="html-italic">n</span> ≥ 3; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Effect of LPS on endometrial receptivity genes in sheep. (<b>A</b>) The pro-conceptus elongation gene <span class="html-italic">ISG15</span>, <span class="html-italic">RSAD2</span> (<b>B</b>), and <span class="html-italic">CXCL10</span> (<b>C</b>) on d12, d16, and d20 of pregnancy in endometrial tissue were measured by real-time quantitative PCR. (<b>D</b>) The adhesion molecules <span class="html-italic">ITGB1</span>, <span class="html-italic">ITGB3</span> (<b>E</b>), <span class="html-italic">ITGB5</span> (<b>F</b>), <span class="html-italic">SPP1</span> (<b>G</b>), and <span class="html-italic">MUC1</span> (<b>H</b>) on d12 of pregnancy in endometrial tissue were measured by real-time quantitative PCR. (<b>I</b>) The endometrial receptivity markers <span class="html-italic">HOXA10</span>, <span class="html-italic">HOXA11</span> (<b>J</b>), and <span class="html-italic">LIF</span> (<b>K</b>) on d12 of pregnancy in endometrial tissue were measured by real-time quantitative PCR. All data are presented as the mean ± SEM, <span class="html-italic">n</span> ≥ 3; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>LPS affected JAK2/STAT1 pathways. (<b>A</b>) The protein level of p-JAK2, T-JAK2, p-STAT1, T-STAT1, and IRF9 on d12, d16, and d20 of pregnancy in sheep endometrial tissue. (<b>B</b>) p-JAK2/β-actin, T-JAK2/β-actin (<b>C</b>), and p-JAK2/T-JAK2 (<b>D</b>) ratio on d12, d16, and d20 of pregnancy in sheep endometrial tissue. (<b>E</b>) p-STAT1/β-actin, T-STAT1/β-actin (<b>F</b>), and p-STAT1/T-STAT1 (<b>G</b>) ratio on d12, d16, and d20 of pregnancy in sheep endometrial tissue. (<b>H</b>) The IRF9/β-actin ratio on d12, d16, and d20 of pregnancy in sheep endometrial tissue. All data are presented as the mean ± SEM, <span class="html-italic">n</span> ≥ 3; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Establishment of a receptive sheep endometrial epithelial cell model for sheep. (<b>A</b>) Confocal microscopy was used to observe the morphology of sEECs. Red: Cy3-labeled cytokeratin 18 protein; blue, DAPI-labeled nuclei; scale bar: 20 µm. (<b>B</b>) Expression of ISG15 was measured under different concentrations in sEECs. (<b>C</b>–<b>E</b>) The endometrial receptivity-related genes <span class="html-italic">ISG15</span>, <span class="html-italic">RSAD2</span>, <span class="html-italic">CXCL10</span>, <span class="html-italic">HOXA10</span>, <span class="html-italic">HOXA11</span>, <span class="html-italic">LIF</span>, <span class="html-italic">ESR1</span>, <span class="html-italic">ESR2</span>, and <span class="html-italic">PGR</span> in sEECs were measured by real-time quantitative PCR. GAPDH (sheep) was used as the reference gene in all samples. sEECs: sheep endometrial epithelial cells. All data are presented as the mean ± SEM, <span class="html-italic">n</span> ≥ 3; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Effect of LPS or fludarabine treatment on the expression of endometrial receptivity-related genes under hormone treatment. (<b>A</b>) The protein level of p-STAT1 and T-STAT1 in sEECs. (<b>B</b>) The secretion of PGE2 and PGF2α in sEECs. (<b>C</b>–<b>E</b>) The pro-conceptus elongation genes <span class="html-italic">ISG15</span>, <span class="html-italic">RSAD2</span>, <span class="html-italic">CXCL10</span>, adhesion molecules <span class="html-italic">ITGB1/3/5</span>, <span class="html-italic">MUC1</span>, <span class="html-italic">SPP1</span>, and receptivity markers <span class="html-italic">HOXA10</span>, <span class="html-italic">HOXA11</span>, <span class="html-italic">LIF</span> mRNA expression levels in sEECs. GAPDH (sheep) was used as the reference gene in all samples. (<b>F</b>) Confocal microscope images of SPP1 expression in four treatment groups. Red: Cy3-labeled SPP1 protein; blue, DAPI-labeled nuclei; scale bar: 20 µm. All data are presented as the mean ± SEM, <span class="html-italic">n</span> ≥ 3; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Schematic characterization of the cellular mechanism of LPS infusion effects on endometrial receptivity in sheep during early pregnancy. LPS blocked the effect of IFN-τ in the three stages of sheep embryo implantation and impaired the endometrial receptivity, which is characterized by interfering with the secretion of prostaglandins, hindering the elongation of the conceptus, and reducing the adhesion of the embryo by inhibiting the phosphorylation of STAT1.</p>
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12 pages, 5258 KiB  
Article
Brolucizumab for the Treatment of Diabetic Macular Edema: An Optical Coherence Tomography-Based Analysis
by Marco R. Pastore, Serena Milan, Stefano Gouigoux, Olimpia Colombo, Silvia Rinaldi, Gabriella Cirigliano and Daniele Tognetto
Diagnostics 2024, 14(24), 2858; https://doi.org/10.3390/diagnostics14242858 - 19 Dec 2024
Viewed by 487
Abstract
Objectives: The objectives of this study were to evaluate the structural and functional outcomes after the loading phase with brolucizumab in switched patients with diabetic macular edema (DME) and to identify potential predictive biomarkers of treatment response. Methods: A total of [...] Read more.
Objectives: The objectives of this study were to evaluate the structural and functional outcomes after the loading phase with brolucizumab in switched patients with diabetic macular edema (DME) and to identify potential predictive biomarkers of treatment response. Methods: A total of 28 eyes with DME, switched to brolucizumab, were retrospectively reviewed. Main outcomes during the follow-up period, up to 6 weeks after the fifth injection, included changes in best-corrected visual acuity (BCVA), central subfield thickness (CST), macular volume, subfoveal choroidal thickness, intraretinal and subretinal fluid (IRF and SRF), cyst dimension including maximal horizontal cyst diameter (MHCD), maximal vertical cyst diameter (MVCD), width-to-height ratio (WHR), foveal avascular zone (FAZ) dimension, and vessel density (VD). Results: At the last follow-up, BCVA was significantly improved (p = 0.003). Significant reduction of CST was demonstrated after each injection time point (p < 0.05), and a dry macula was detected in 64.3% of patients at the last follow-up. The WHR was 1.23 ± 0.46, and a negative correlation to final CST (p < 0.0001) was found. In FAZ and VD analysis, no significant variation was detected. At the last disease activity assessment, the treatment regimen was q12 in 64% of patients. Conclusions: Brolucizumab leads to anatomical and functional improvements in switched eyes affected by DME. WHR may represent a predictive biomarker of treatment response. Full article
(This article belongs to the Special Issue Optical Coherence Tomography in Diagnosis of Ophthalmology Disease)
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<p>Cyst dimension measurement.</p>
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<p>Illustration of the user interface of the Heidelberg Spectralis II OCT-A with the parameters used in this study.</p>
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<p>SVP and DVP FAZ measurements.</p>
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<p>DCP and SVP vessel density after ImageJ processing.</p>
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<p>Early diabetic macular edema.</p>
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<p>Advanced diabetic macular edema.</p>
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<p>Severe diabetic macular edema.</p>
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<p>Variations of mean central subfield thickness, mean macular volume, and mean subfoveal choroidal thickness during the intravitreal treatment. Variations of mean central subfield thickness (<b>A</b>), mean macular volume (<b>B</b>), and mean subfoveal choroidal thickness (<b>C</b>) during the intravitreal treatment. Data were recorded 6 weeks after each injection. IVB = intravitreal injection of brolucizumab.</p>
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<p>Patients reaching dry macula during treatment with brolucizumab.</p>
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16 pages, 1428 KiB  
Article
A Definition of a Heywood Case in Item Response Theory Based on Fisher Information
by Jay Verkuilen and Peter J. Johnson
Entropy 2024, 26(12), 1096; https://doi.org/10.3390/e26121096 - 14 Dec 2024
Viewed by 532
Abstract
Heywood cases and other improper solutions occur frequently in latent variable models, e.g., factor analysis, item response theory, latent class analysis, multilevel models, or structural equation models, all of which are models with response variables taken from an exponential family. They have important [...] Read more.
Heywood cases and other improper solutions occur frequently in latent variable models, e.g., factor analysis, item response theory, latent class analysis, multilevel models, or structural equation models, all of which are models with response variables taken from an exponential family. They have important consequences for scoring with the latent variable model and are indicative of issues in a model, such as poor identification or model misspecification. In the context of the 2PL and 3PL models in IRT, they are more frequently known as Guttman items and are identified by having a discrimination parameter that is deemed excessively large. Other IRT models, such as the newer asymmetric item response theory (AsymIRT) or polytomous IRT models often have parameters that are not easy to interpret directly, so scanning parameter estimates are not necessarily indicative of the presence of problematic values. The graphical examination of the IRF can be useful but is necessarily subjective and highly dependent on choices of graphical defaults. We propose using the derivatives of the IRF, item Fisher information functions, and our proposed Item Fraction of Total Information (IFTI) decomposition metric to bypass the parameters, allowing for the more concrete and consistent identification of Heywood cases. We illustrate the approach by using empirical examples by using AsymIRT and nominal response models. Full article
(This article belongs to the Special Issue Applications of Fisher Information in Sciences II)
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<p>(<b>a</b>) IRFs and (<b>b</b>) IIFs for a typical, suspect, and problematic item. Note the IIFs are put on a <math display="inline"><semantics> <msqrt> <mrow> <mi>I</mi> <mo>(</mo> <mi>θ</mi> <mo>)</mo> </mrow> </msqrt> </semantics></math> scale for ease of visualization.</p>
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<p>(<b>a</b>) Fitted <math display="inline"><semantics> <mrow> <mi>π</mi> <mo>(</mo> <mi>θ</mi> <mo>)</mo> </mrow> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mo>∂</mo> <mi>θ</mi> </msub> <mi>π</mi> <mrow> <mo>(</mo> <mi>θ</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> for Example 1. The mental rotation items are colored; item MR3 is orange, item MR4 is green, item MR6 is cyan, and item MR8 is red.</p>
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<p>(<b>a</b>) Test information function and (<b>b</b>) fitted <math display="inline"><semantics> <mrow> <mi>I</mi> <mo>(</mo> <mi>θ</mi> <mo>)</mo> </mrow> </semantics></math> for Example 1. The mental rotation items are colored; item MR3 is orange, item MR4 is green, item MR6 is cyan, and item MR8 is red. Note that the overall trend in the item information plots is reflected in the test information function.</p>
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<p>(<b>a</b>) Fitted <math display="inline"><semantics> <mrow> <mi>I</mi> <mi>F</mi> <mi>T</mi> <mi>I</mi> </mrow> </semantics></math> for the total 16 items and (<b>b</b>) fitted <math display="inline"><semantics> <mrow> <mi>I</mi> <mi>F</mi> <mi>T</mi> <mi>I</mi> </mrow> </semantics></math> for Example 1 with the two most severe Heywood case items removed. The mental rotation items are colored; item MR3 is orange, item MR4 is green, item MR6 is cyan, and item MR8 is red.</p>
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<p>Box plots of (<b>a</b>) EAP predicted scores (<math display="inline"><semantics> <mover accent="true"> <mi>θ</mi> <mo>^</mo> </mover> </semantics></math>) and (<b>b</b>) standard errors (<math display="inline"><semantics> <mrow> <mi>SE</mi> <mo>(</mo> <mover accent="true"> <mi>θ</mi> <mo>^</mo> </mover> <mo>)</mo> </mrow> </semantics></math>), shown over the proportion correct. Note that while most boxes are fairly modest, for high proportions correct, the boxes are unexpectedly wide, indicating the instability induced by the mental rotation items.</p>
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<p>(<b>a</b>) Fitted <math display="inline"><semantics> <mrow> <mi>I</mi> <mi>F</mi> <mi>T</mi> <mi>I</mi> </mrow> </semantics></math> and (<b>b</b>) box plots of EAP predicted scores for Example 1, with a loose N(0,1) prior set on the asymmetry parameter of the RH model. The mental rotation items are colored; item MR3 is orange, item MR4 is green, item MR6 is cyan, and item MR8 is red.</p>
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<p>(<b>a</b>) Fitted <math display="inline"><semantics> <mrow> <mi>I</mi> <mi>F</mi> <mi>T</mi> <mi>I</mi> </mrow> </semantics></math> and (<b>b</b>) box plots of EAP predicted scores for Example 1, with a strict N(0,0.25) prior set on the asymmetry parameter of the RH model. The mental rotation items are colored; item MR3 is orange, item MR4 is green, item MR6 is cyan, and item MR8 is red.</p>
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<p>(<b>a</b>) Fitted <math display="inline"><semantics> <mrow> <mi>I</mi> <mo>(</mo> <mi>θ</mi> <mo>)</mo> </mrow> </semantics></math> and (<b>b</b>) fitted <math display="inline"><semantics> <mrow> <mi>I</mi> <mi>F</mi> <mi>T</mi> <mi>I</mi> <mo>(</mo> <mi>θ</mi> <mo>)</mo> </mrow> </semantics></math> for Example 2. Item 11 is bolded.</p>
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<p>(<b>a</b>) Fitted <math display="inline"><semantics> <mrow> <mi>I</mi> <mi>F</mi> <mi>T</mi> <mi>I</mi> <mo>(</mo> <mi>θ</mi> <mo>)</mo> </mrow> </semantics></math> without item 11 and (<b>b</b>) fitted <math display="inline"><semantics> <mrow> <mi>I</mi> <mi>F</mi> <mi>T</mi> <mi>I</mi> <mo>(</mo> <mi>θ</mi> <mo>)</mo> </mrow> </semantics></math> without items 17–32 for Example 2. Items 31 and 11 are bold in <b>a</b> and <b>b</b>, respectively.</p>
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