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1 pages, 136 KiB  
Correction
Correction: Neve et al. Epigenetic Regulation by lncRNAs: An Overview Focused on UCA1 in Colorectal Cancer. Cancers 2018, 10, 440
by Bernadette Neve, Nicolas Jonckheere, Audrey Vincent and Isabelle Van Seuningen
Cancers 2025, 17(6), 914; https://doi.org/10.3390/cancers17060914 - 7 Mar 2025
Viewed by 65
Abstract
There was an error in the original publication [...] Full article
(This article belongs to the Special Issue Colorectal Cancers)
14 pages, 881 KiB  
Article
The Influence of Mucinous Histology on the Prognosis of Stage II and III Colorectal Cancers
by İsa Caner Aydin, Mehmet Torun, Mehmet Reşit Sönmez, Serkan Ademoğlu, Ahmet Orhan Sunar, Orhan Uzun, Selçuk Gülmez, Erdal Polat and Mustafa Duman
Medicina 2025, 61(3), 456; https://doi.org/10.3390/medicina61030456 - 6 Mar 2025
Viewed by 113
Abstract
Background and Objectives: Mucinous adenocarcinoma (MAC) and mucinous components (MCP) in colorectal cancers (CRC) have shown conflicting results regarding their prognostic impact. This study aims to evaluate survival differences between MAC, MCP, and non-mucinous adenocarcinoma (nMAC) in stage II and III CRC patients. [...] Read more.
Background and Objectives: Mucinous adenocarcinoma (MAC) and mucinous components (MCP) in colorectal cancers (CRC) have shown conflicting results regarding their prognostic impact. This study aims to evaluate survival differences between MAC, MCP, and non-mucinous adenocarcinoma (nMAC) in stage II and III CRC patients. Materials and Methods: 224 CRC patients who underwent surgery between 2013 and 2021 were analyzed retrospectively. Patients were classified as nMAC, MCP, or MAC based on the percentage of extracellular mucin. Those who received neoadjuvant therapy, had stage I or IV TNM disease, and emergency cases were excluded. Survival analysis was performed using Kaplan–Meier curves and Cox regression models. Results: MAC patients showed worse survival outcomes compared to nMAC (p = 0.025). No difference in survival was found between MCP and nMAC (p = 0.055). Multivariate analysis identified MAC (OR: 2.814; p = 0.014) and perineural invasion (PNI) (OR: 2.283; p = 0.008) as independent factors associated with worse survival. Kaplan–Meier analysis revealed MAC’s worse prognosis than nMAC (p = 0.027). Conclusions: MAC was shown to have a worse prognosis than nMAC in stage II and III CRC patients, while MCP survival rates were similar with nMAC. These findings suggest that MAC requires more careful treatment approaches, while MCP and nMAC have better survival rates. Further studies focusing on molecular and genetic profiles are needed to better understand these outcomes. Full article
(This article belongs to the Section Surgery)
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<p>Flowchart of the study design and patient data enrolment.</p>
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<p>Overall Survival Analysis of Colorectal Cancer Cases Depending on Mucinous Component Presence.</p>
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<p>Overall Survival Analysis of Colorectal Cancer Cases Depending on Mucinous Carcinoma Presence.</p>
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10 pages, 1899 KiB  
Review
Surgery for Colorectal Cancer Associated with Crohn’s Disease—Toward a Medical Treatment Strategy Based on the Differences Between Japan and Western Countries
by Yuki Sekido, Takayuki Ogino, Mitsunobu Takeda, Tsuyoshi Hata, Atsushi Hamabe, Norikatsu Miyoshi, Mamoru Uemura, Tsunekazu Mizushima, Yuichiro Doki and Hidetoshi Eguchi
Cancers 2025, 17(5), 860; https://doi.org/10.3390/cancers17050860 - 3 Mar 2025
Viewed by 141
Abstract
With advances in the treatment of Crohn’s disease (CD), the number of long-term cases is increasing, along with the incidence of CD-related cancers. Here, we discuss the clinical features, diagnosis, treatment, prognosis, and surveillance of CD-related cancers. There are regional differences in the [...] Read more.
With advances in the treatment of Crohn’s disease (CD), the number of long-term cases is increasing, along with the incidence of CD-related cancers. Here, we discuss the clinical features, diagnosis, treatment, prognosis, and surveillance of CD-related cancers. There are regional differences in the common sites and histological types of CD-related cancers, with right-sided colon cancer accounting for 40% of cases in Europe and the US, and squamous cell carcinoma being common. In Japan, rectal and anal cancers account for 80% of cases, and mucinous carcinoma is common. The prognosis of CD-associated colon cancer and sporadic colon cancer is the same; however, the prognosis of CD-associated rectal cancer is clearly worse than that of sporadic rectal cancer. Early diagnosis is important to improve the prognosis of CD-associated rectal cancer, and it is necessary to establish a surveillance method for CD-associated cancer that combines colonoscopy, anesthetic proctoscopy, and imaging, as appropriate. The basic treatment for CD-related cancer is surgical resection; however, the criteria for selecting the surgical procedure are unclear, and there is no clear evidence for multidisciplinary perioperative treatment including chemotherapy and radiotherapy. Additionally, CD-related rectal and anal cancers have a higher local recurrence rate than that of sporadic rectal cancers; therefore, thorough local control is important. Furthermore, CD-related cancers have different epidemiologies in different regions; therefore, unique diagnostic and treatment strategies must be established for each region. Full article
(This article belongs to the Special Issue Surgery for Colorectal Cancer)
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Figure 1
<p>Gross and histopathological findings of CD-associated anorectal cancer. At the time of diagnosis, bloody mucus was observed in the anal region (<b>left</b>). In the cross-section of the specimen resected after neoadjuvant chemoradiotherapy, tumor tissue containing mucus was observed (<b>center</b>). Microscopic findings showed floating carcinomas indicated by the arrowheads within the mucinous nodules (<b>right</b>).</p>
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<p>MRI T2-weighted images of a case of anorectal cancer, axial (<b>left</b>) and sagittal (<b>right</b>). A multilocular cystic lesion is seen in the rectum and anal canal.</p>
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<p>Five-year Overall Survival of CD-associated colorectal cancer and sporadic colorectal cancer patients by Stage. Modified citation from reference [<a href="#B18-cancers-17-00860" class="html-bibr">18</a>].</p>
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20 pages, 6028 KiB  
Article
Immunosuppressant-Induced Alteration of Gut Microbiota Causes Loss of Skeletal Muscle Mass: Evidence from Animal Experiments Using Mice and Observational Study on Humans
by Mitsuru Tomizawa, Shunta Hori, Tatsuo Yoneda, Fumisato Maesaka, Sayuri Onishi, Takuto Shimizu, Kenta Onishi, Yosuke Morizawa, Daisuke Gotoh, Yasushi Nakai, Makito Miyake, Kazumasa Torimoto, Nobumichi Tanaka and Kiyohide Fujimoto
J. Clin. Med. 2025, 14(5), 1628; https://doi.org/10.3390/jcm14051628 - 27 Feb 2025
Viewed by 196
Abstract
Background/Objectives: The number of older adults requiring a kidney transplant (KT) is increasing; hence, postoperative sarcopenia prevention is necessary. KT recipients require permanent oral immunosuppressants (ISs), and the gut microbiota (GM) plays a role in various systemic diseases. However, few studies have [...] Read more.
Background/Objectives: The number of older adults requiring a kidney transplant (KT) is increasing; hence, postoperative sarcopenia prevention is necessary. KT recipients require permanent oral immunosuppressants (ISs), and the gut microbiota (GM) plays a role in various systemic diseases. However, few studies have evaluated post-kidney transplantation frailty and the associations among ISs, GM, and muscle mass alterations. Therefore, we investigated the effects of ISs on GM and skeletal muscle mass in mice and human KT recipients. Methods: Mice were treated with six different ISs, and their skeletal muscle mass, GM diversity, and colonic mucosal function were assessed. Human KT recipients and donors were monitored before and after surgery for 1 year, and GM diversity was evaluated before and 1 month after surgery. Results: The abundance of Akkermansia, crypt depth, and mucin 2 expression were lower in tacrolimus- and prednisolone-treated mice. The psoas muscle volume changes at 1 month and 1 year after surgery were lower in KT recipients than in donors. Furthermore, the beta diversity was significantly different between the operative groups (p = 0.001), and the KT group showed the lowest Shannon index. Conclusions: The findings of this study indicate potential links among ISs, GM, and muscle mass decline. Further investigation is required to improve therapeutic strategies and patient outcomes. Full article
(This article belongs to the Special Issue Sustaining Success Through Innovation in Kidney Transplantation)
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Graphical abstract

Graphical abstract
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<p>(<b>a</b>) A schematic illustration of the mouse experiment workflow. The mice were randomly divided into seven groups (n = 8/group), and treatments were administered through gastric gavage once daily for 28 consecutive days. The mice were subjected to computed tomography (CT) imaging on days 0, 14, and 28. The mice were euthanized on the day after the final CT imaging, and blood, psoas muscle, rectum, and fecal samples were harvested. Feces were collected from three mice/group. (<b>b</b>) The workflow of the observational study on human kidney transplant (KT) recipients and donors. In total, 20 consecutive living donor kidney transplantations were performed at our institute, and 19 recipients and 19 donors who consented to this study were enrolled. Patients who could not comply with preoperative CT or bioelectrical impedance analysis (BIA) were excluded (one recipient and three donors). Finally, the data of 18 recipients and 16 donors were included in the analyses. Post-surgery, the patients were subjected to CT imaging at 1, 6, and 12 months and BIA at 1, 3, 6, and 12 months. Missing data at any point were allowed, and only the obtained data were analyzed. Fecal samples were collected only from consenting patients. The number of data analyzed at each point is indicated to the right of the arrow.</p>
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<p>(<b>a</b>) Representative computed tomography (CT) images at the L5 pedicle level after being treated with various immunosuppressants. (<b>b</b>) Change rate of psoas muscle cross-sectional area in each group at the L3, L4, and L5 pedicle levels. (<b>c</b>) Representative images of hematoxylin and eosin (HE)-stained myocytes from each group following treatment with various immunosuppressants. Scale bar = 100 μm. (<b>d</b>) Comparison of myocyte cross-sectional length of each group after treatment with various immunosuppressants.</p>
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<p>Differences in the gut microbiota (GM) composition and metabolic function between the poor muscle development (PMD) and the muscle development (MD) groups. (<b>a</b>) The weighted unique fraction (UniFrac) principal coordinate analysis (PCoA) plot of two groups. The GM showed significant differences. (<b>b</b>) The microbial taxonomic composition with the relative abundance of the two groups. The red font indicates bacterial groups that showed significant differences between the two groups. (<b>c</b>) Abundant taxa were identified using linear discriminant analysis effect size (LEfSe) analysis in each group. The abundance of beneficial <span class="html-italic">Akkermansia</span> bacteria was high in the MD group. (<b>d</b>) Volcano plots of differences in the GM function between the two groups based on Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2 (PICRUSt2) analysis. Fatty acid biosynthesis, glucose and glucose-1-phosphate degradation, and menaquinone biosynthesis were predicted to decrease via the MetaCyc pathway in the PMD group. Based on Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologs, fatty acyl-acyl carrier protein thioesterase B (FATB), which is involved in the biosynthesis of medium-chain triglycerides, was predicted to decrease in the PMD group. The modules and kos with declined function (<span class="html-italic">q</span>-value &lt; 0.001 and log2 fold change &lt; –2.0) are indicated with blue lines on the KEGG metabolic pathway.</p>
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<p>Difference in colonic mucosal barrier between poor muscle development (PMD) and muscle development (MD) groups. (<b>a</b>) Representative images of pathological specimens stained with Alcian blue and immunofluorescence of mucin-2 (MUC2). Scale bar = 100 μm. (<b>b</b>) Comparison of crypt depth in each group. (<b>c</b>) Comparison of MUC2 expression in each group.</p>
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<p>Change rate of body composition parameters measured using computed tomography (CT) images and bioelectrical impedance analysis (BIA) in recipients and donors for 1 year. (<b>a</b>) Representative CT images of recipients analyzed using SYNAPSE VINCENT. (<b>b</b>) Comparison of psoas muscle volume and psoas muscle (PM) area change rate at the L3 level between recipients and donors. (<b>c</b>) Comparison of skeletal muscle index (SMI) and bone mineral content change rate between recipients and donors based on BIA. (<b>d</b>) Comparison of psoas muscle volume and psoas muscle area change rate at the L3 level between recipients treated with tacrolimus (TAC) and cyclosporine A (CyA).</p>
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<p>Hypothesis of mechanism underlying skeletal muscle mass loss mediated by gut microbiota (GM). The solid line indicates the mechanism suggested by this study, and the dotted lines indicate the mechanisms suggested by previous studies.</p>
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19 pages, 1097 KiB  
Review
The Role of TIM-3 in Glioblastoma Progression
by Farah Ahmady, Amit Sharma, Adrian A. Achuthan, George Kannourakis and Rodney B. Luwor
Cells 2025, 14(5), 346; https://doi.org/10.3390/cells14050346 - 27 Feb 2025
Viewed by 213
Abstract
Several immunoregulatory or immune checkpoint receptors including T cell immunoglobulin and mucin domain 3 (TIM-3) have been implicated in glioblastoma progression. Rigorous investigation over the last decade has elucidated TIM-3 as a key player in inhibiting immune cell activation and several key associated [...] Read more.
Several immunoregulatory or immune checkpoint receptors including T cell immunoglobulin and mucin domain 3 (TIM-3) have been implicated in glioblastoma progression. Rigorous investigation over the last decade has elucidated TIM-3 as a key player in inhibiting immune cell activation and several key associated molecules have been identified both upstream and downstream that mediate immune cell dysfunction mechanistically. However, despite several reviews being published on other immune checkpoint molecules such as PD-1 and CTLA-4 in the glioblastoma setting, no such extensive review exists that specifically focuses on the role of TIM-3 in glioblastoma progression and immunosuppression. Here, we critically summarize the current literature regarding TIM-3 expression as a prognostic marker for glioblastoma, its expression profile on immune cells in glioblastoma patients and the exploration of anti-TIM-3 agents in glioblastoma pre-clinical models for potential clinical application. Full article
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<p>Cells which express TIM-3 in the glioblastoma tumor micro-environment. A range of immune cell subsets, including innate (i.e., NK cells, monocytes, and macrophages) and adaptive (B cells, T cells, and Tregs) cells, have been reported to express varying levels of TIM-3 on their surface in either/both the peripheral blood of glioblastoma patients and at the tumor site. Brain cells, such as microglia and glioblastoma tumor cells, also express varying levels of TIM-3 on their surface. Created in BioRender.</p>
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<p>T cell exhaustion in a TIM-3-dependant manner. Mechanistic pathway leading to T cell exhaustion. Tumor cells and/or antigen presenting cells (APC) interact with the T cells with signal 1 consisting of antigen being presented by tumor and/or APC on their major histocompatibility complex to the T cell receptor (TCR) of the T cell. This interaction is a co-stimulatory response. Signal 2 consists of either cell surface TIM-3 ligands (i.e., phosphatidylserine (PtdSer) and carcinoembryonic antigen cell adhesion molecule 1 (CEACAM1) or soluble TIM-3 ligands (i.e., galectin-9 (Gal-9) and high mobility group protein B1 (HMGB1) binding to the TIM-3 receptor, initiating a signaling cascade. Binding of any of these ligands to the TIM-3 receptor causes the transcription factor BAT3 to be released from the cytoplasmic tail of the TIM-3 complex, leading to enhanced binding of Fyn instead, and leading to the reduction in the BAT3/Lck association. This process results in the downregulation of T cells and ultimately leads to T cell exhaustion. Created in BioRender.</p>
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<p>Therapy combinations and survival outcomes for glioblastoma patients. Monotherapy and/or dual therapy combinations (consisting of either anti-TIM-3, anti-PD-1, or stereotactic radiosurgery) has either no effect or small sub-optimal increases in survival in mice bearing glioblastoma tumors. Triple combination therapy consisting of anti-TIM-3, anti-PD-1, and stereotactic radiosurgery improves survival which is dependent on the presence of functional T cells (CD4+ and CD8+ T cells). Created in BioRender.</p>
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19 pages, 6563 KiB  
Article
Immunomodulatory Effects of a Standardized Botanical Mixture Comprising Angelica gigas Roots and Pueraria lobata Flowers Through the TLR2/6 Pathway in RAW 264.7 Macrophages and Cyclophosphamide-Induced Immunosuppression Mice
by Seo-Yun Jang, Hyeon-A Song, Min-Ji Park, Kyung-Sook Chung, Jong Kil Lee, Eun Yeong Jang, Eun Mi Sun, Min Cheol Pyo and Kyung-Tae Lee
Pharmaceuticals 2025, 18(3), 336; https://doi.org/10.3390/ph18030336 - 27 Feb 2025
Viewed by 369
Abstract
Background: As the population ages, enhancing immune function is crucial to mitigating age-related physiological decline. Since immunostimulant drugs are known to have potential side effects, medicinal plants emerge as promising candidates offering a safer alternative. To leverage the advantages of medicinal plants [...] Read more.
Background: As the population ages, enhancing immune function is crucial to mitigating age-related physiological decline. Since immunostimulant drugs are known to have potential side effects, medicinal plants emerge as promising candidates offering a safer alternative. To leverage the advantages of medicinal plants with fewer side effects and develop a potent immune-enhancing agent, we investigated the efficacy of a novel immunomodulatory candidate derived from the combination of Angelica gigas and Pueraria lobata (CHL). Methods: In vitro, CHL was treated in RAW 264.7 macrophages at various time points, and the experiments conducted in the study were performed using ELISA, Western blot, and RT-qPCR analysis. In vivo, C57BL/6 mice were administrated CHL for 16 days (p.o.) and CTX on the three days (i.p.), and experiments were conducted with ELISA, western blot, RT-qPCR analysis, H&E staining, flow cytometry, gut microbiome, and correlation analysis. Results: In vitro, CHL has upregulated NO and cytokines expression, substantially enhancing the NF-κB and MAPK activation. Furthermore, CHL promoted the TAK1, TRAF6, and MyD88 via TLR2/6 signaling. In vivo, the CHL improved the reduced body weight and immune organs’ indices and recovered various cytokines expression, NK cell cytotoxicity activity, and immune cell population. CHL also improved the histological structure and tight junction markers, mucin-2, and TLR2/6 in the intestines of CTX-induced mice. Conclusions: Overall, CHL demonstrated immunostimulatory potential by enhancing immune responses and restoring immune function, suggesting its promise as a safe and effective immune-enhancing agent. Full article
(This article belongs to the Special Issue The Role of Phytochemicals in Aging and Aging-Related Diseases)
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<p>Effects of CHL on immune mediator production and expression in RAW 264.7 macrophages. (<b>A</b>,<b>B</b>) Cells were stimulated with <span class="html-italic">A. gigas</span> (100 μg/mL), <span class="html-italic">P. lobata</span> (100 μg/mL), CHL (50, 100, or 200 μg/mL), or LPS (5 ng/mL) for 24 h. (<b>C</b>) Cells were pretreated with polymyxin B (0.1 μg/mL) and then stimulated with CHL (200 μg/mL) or LPS (5 ng/mL) for 24 h. (<b>D</b>–<b>K</b>) Cells were stimulated with CHL (50, 100, or 200 μg/mL) or LPS (5 ng/mL). LPS was used as a positive control and β-actin was used as an internal control. Data are presented as mean ± SEM of three independent experiments. ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 vs. CON; ### <span class="html-italic">p</span> &lt; 0.001 vs. LPS-treated cells.</p>
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<p>Effects of CHL on the TLR2/6 signaling pathway in RAW 264.7 macrophages. (<b>A</b>–<b>I</b>) Cells were stimulated with CHL (50, 100, or 200 μg/mL) or LPS (5 ng/mL) for 15–30 min or 6 h. LPS was used as a positive control and β-actin was used as an internal control. Data are presented as mean ± SEM of three independent experiments. * <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. CON.</p>
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<p>Effects of CHL on body weight, immune organ indices, and cytokine expression in CTX-treated mice. (<b>A</b>) Body weights and indices of (<b>B</b>) spleen and (<b>C</b>) MLN were measured at the end of the animal experiments. Data are presented as mean ± SEM (<span class="html-italic">n</span> = 23–24). (<b>D</b>–<b>H</b>) Cytokine production and (<b>G</b>–<b>M</b>) mRNA expression of IL-12, IFN-γ, TNF-α, IL-4, and IL-6. Data are presented as mean ± SEM (n = 7). # <span class="html-italic">p</span> &lt; 0.05 vs. CON group; * <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. CTX group.</p>
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<p>Effects of CHL on natural killer (NK) cell activity and characterization of innate immune cell population in CTX-treated mice. (<b>A</b>) NK cell activity of CHL, cell ratio between splenocytes and YAC-1 = 1:5, 1:10, or 1:20. Population of (<b>B</b>) CD3<sup>−</sup>/NK1.1<sup>+</sup> NK cells, (<b>C</b>) CD11b<sup>+</sup>/MHC II<sup>+</sup> dendritic cells, (<b>D</b>) CD11b<sup>+</sup>/Ly6C<sup>+</sup> monocytes, (<b>E</b>) CD11b<sup>+</sup>/Ly6C<sup>+</sup>/F4/80<sup>+</sup> macrophages, and (<b>F</b>) CD11b<sup>+</sup>/Ly6G<sup>+</sup> neutrophils. Data are presented as mean ± SEM (<span class="html-italic">n</span> = 6–7). # <span class="html-italic">p</span> &lt; 0.05 vs. CON group; * <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. CTX group.</p>
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<p>Effects of CHL on the characterization of the adaptive immune cell population in CTX-treated mice. Populations of (<b>A</b>) CD3<sup>+</sup> T cells, (<b>B</b>) CD3<sup>+</sup>/CD4<sup>+</sup> T helper cells, (<b>C</b>) CD3<sup>+</sup>/CD4<sup>+</sup>/IFN-γ<sup>+</sup> Th1 cells, (<b>D</b>) CD3<sup>+</sup>/CD4<sup>+</sup>/IL-4<sup>+</sup> Th2 cells, (<b>E</b>) CD3<sup>+</sup>/CD4<sup>+</sup>/IL-17<sup>+</sup> Th17 cells, and (<b>F</b>) CD3<sup>+</sup>/CD4<sup>+</sup>/CD25<sup>+</sup>/FoxP3<sup>+</sup> regulatory T cells. Data are presented as mean ± SEM (<span class="html-italic">n</span> = 6–7). # <span class="html-italic">p</span> &lt; 0.05 vs. CON group; * <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. CTX group.</p>
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<p>Effects of CHL on histological changes in the intestine and the expression of tight junction markers and mucin 2 in CTX-treated mice. Histological changes in the (<b>A</b>) small intestine and (<b>B</b>) colon. (<b>C</b>–<b>L</b>) Protein and mRNA expression of tight junction-related markers (ZO-1, occludin, and claudin-1) and MUC2 in the small and large intestines. Data are presented as mean ± SEM (<span class="html-italic">n</span> = 6–7). # <span class="html-italic">p</span> &lt; 0.05 vs. CON group; * <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. CTX group.</p>
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<p>Effects of CHL on gut microbiome composition in CTX-treated mice. Analysis of microbial diversity: (<b>A</b>) Principal coordinate analysis (PCoA) plots; (<b>B</b>) Chao1 index; (<b>C</b>) Simpson index between each group. (<b>D</b>–<b>G</b>) The relative ratio of <span class="html-italic">Firmicutes</span>, <span class="html-italic">Deferribacteres</span>, <span class="html-italic">Bacteroidetes</span>, and <span class="html-italic">Proteobacteria.</span> (<b>H</b>,<b>I</b>) <span class="html-italic">GPR41</span> and <span class="html-italic">GPR43</span> mRNA expression in the colon. Correlation analysis between gut microbiota and (<b>J</b>) immune response and (<b>K</b>) intestinal immunity. Data are presented as mean ± SEM (<span class="html-italic">n</span> = 6–7). # <span class="html-italic">p</span> &lt; 0.05 vs. CON group; * <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. CTX group.</p>
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<p>Effects of CHL on TLR2/6 signaling pathway in CTX-treated mice. (<b>A</b>) TLR2 and (<b>B</b>) TLR6 mRNA expressions of the small intestine. Data are presented as the means ± SEM (<span class="html-italic">n</span> = 7). # <span class="html-italic">p</span> &lt; 0.05 vs. CON group; * <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. CTX group. Correlation analysis between gut microbiota and (<b>C</b>) TLR2/6. Regression analysis between TLR2/6 and (<b>D</b>,<b>E</b>) Firmicutes; (<b>F</b>,<b>G</b>) Deferribacteres; (<b>H</b>,<b>I</b>) Bacteroidetes; and (<b>J</b>,<b>K</b>) Proteobacteria. R means correlation coefficient value and P means <span class="html-italic">p</span> value.</p>
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<p>Representative HPLC chromatograms of CHL and standards (nodakenin and tectoridin). HPLC chromatograms of (<b>A</b>) nodakenin standard and CHL at 330 nm and (<b>B</b>) tectoridin standard and CHL at 260 nm.</p>
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19 pages, 2022 KiB  
Article
Prognostic Differences Between Early-Onset and Late-Onset Colorectal Cancer
by Vlad Alexandru Ionescu, Gina Gheorghe, Ioana-Alexandra Baban, Alexandru Barbu, Teodor Florin Georgescu, Loredana-Crista Tiuca, Ninel Antonie Iacobus and Camelia Cristina Diaconu
Medicina 2025, 61(3), 390; https://doi.org/10.3390/medicina61030390 - 24 Feb 2025
Viewed by 200
Abstract
Background and Objectives: Early-onset colorectal cancer (EO-CRC) has become a significant public health concern due to its alarming rise in incidence and the poor prognosis associated with this disease. The aim of our study was to identify epidemiological, clinical, and paraclinical characteristics [...] Read more.
Background and Objectives: Early-onset colorectal cancer (EO-CRC) has become a significant public health concern due to its alarming rise in incidence and the poor prognosis associated with this disease. The aim of our study was to identify epidemiological, clinical, and paraclinical characteristics that could explain the more aggressive evolution of EO-CRC compared to late-onset colorectal cancer (LO-CRC). Materials and Methods: We conducted a retrospective study over a two-year period, including 204 patients diagnosed with colorectal cancer (CRC). The patients were divided into two subgroups: those with EO-CRC and those with LO-CRC. Statistical analysis was performed using IBM SPSS Statistics, Version 29.0. Results: EO-CRC was identified in 11.3% of the patients included in the study. Compared to LO-CRC patients, EO-CRC patients exhibited a tendency for more distal tumor localization and a stenotic endoscopic appearance (43.5% vs. 29.3%). Regarding histopathological diagnosis, EO-CRC patients demonstrated a higher proportion of the mucinous histologic subtype (34.8% vs. 14.4%) and a significantly greater percentage of poorly differentiated tumors (39.1% vs. 14.5%; p = 0.010). Immunohistochemical results, available for a limited number of patients, revealed higher CDX2 positivity in LO-CRC patients (p = 0.012) and higher HER2 positivity in EO-CRC patients (p = 0.002). Smoking (p = 0.006) and hypertension (p = 0.002) were more prevalent in EO-CRC patients than in LO-CRC patients. Conclusions: Patients with EO-CRC exhibit distinct histopathological and molecular characteristics compared to those with LO-CRC, which may contribute to their poorer prognoses. The higher prevalence of the mucinous histological subtype, poor tumor differentiation, increased HER2 expression, and reduced CDX2 expression suggest potential molecular pathways driving the aggressive nature of EO-CRC. These findings highlight the need for tailored screening strategies and personalized therapeutic approaches in younger CRC patients. Future studies should further investigate the underlying mechanisms and potential biomarkers that could guide early diagnoses and targeted treatments. Full article
(This article belongs to the Section Gastroenterology & Hepatology)
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<p>Age group distribution of patients with EO-CRC.</p>
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<p>Sex distribution of patients included in the study. This figure illustrates the distribution of patients according to gender, comparing EO-CRC and LO-CRC cases. Data are expressed as percentages (95% CI—95% confidence intervals).</p>
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<p>Tumor localization of patients included in the study. This figure illustrates the anatomical distribution of colorectal tumors in patients, comparing early-onset colorectal cancer (EO-CRC) and late-onset colorectal cancer (LO-CRC).</p>
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<p>Distribution of patients by tumor stages, presenting 95% confidence intervals (CIs). This figure illustrates the distribution of colorectal cancer stages among patients, comparing EO-CRC and LO-CRC.</p>
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<p>Distribution of patients by tumor differentiation grade. This figure illustrates the distribution of colorectal cancer cases based on tumor differentiation grade, comparing EO-CRC and LO-CRC. Tumor differentiation was categorized as well differentiated (G1), moderately differentiated (G2), and poorly differentiated (G3). Data are presented as percentages, with statistical analysis performed using chi-square test, with a significance threshold set at <span class="html-italic">p</span> &lt; 0.05.</p>
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14 pages, 1681 KiB  
Case Report
Obstructive Jaundice Induced by Hilar Mucinous Cystic Neoplasm of the Liver: A Rare Case Report and Literature Review
by Pengcheng Wei, Shengmin Zheng, Chen Lo, Yongjing Luo, Liyi Qiao, Jie Gao, Jiye Zhu, Yi Wang and Zhao Li
Curr. Oncol. 2025, 32(3), 126; https://doi.org/10.3390/curroncol32030126 - 23 Feb 2025
Viewed by 321
Abstract
Mucinous cystic neoplasm of the liver (MCN-L) is a rare benign tumor accounting for less than 5% of all liver cysts, with MCN-L in the hilar region being exceptionally uncommon and often misdiagnosed due to its complex presentation. A 48-year-old woman presented with [...] Read more.
Mucinous cystic neoplasm of the liver (MCN-L) is a rare benign tumor accounting for less than 5% of all liver cysts, with MCN-L in the hilar region being exceptionally uncommon and often misdiagnosed due to its complex presentation. A 48-year-old woman presented with obstructive jaundice following initial laparoscopic drainage of hepatic cysts, where pathology initially indicated benign cystic lesions. Months later, imaging revealed an enlarged cystic lesion in the left liver lobe with intrahepatic bile duct dilation. Further evaluations, including ultrasound, enhanced CT, and MRI, confirmed a large cystic lesion compressing the intrahepatic bile ducts. After a multidisciplinary discussion, hepatic cyst puncture and drainage were performed, temporarily alleviating jaundice. However, she returned with yellowish-brown drainage fluid and worsening jaundice, prompting cyst wall resection. Postoperative pathology confirmed MCN-L. Three months later, jaundice subsided, and a hepatic resection of segment 4 was performed, with pathology confirming low-grade MCN-L. At a 12-month follow-up, the patient showed no abnormalities. This case highlights the diagnostic and therapeutic challenges of MCN-L in the hilar region, as it can easily be mistaken for other liver cystic lesions on imaging. Pathologic examination is essential for definitive diagnosis, and early radical surgical resection is critical to improve prognosis and reduce the risk of malignancy and recurrence. Full article
(This article belongs to the Section Gastrointestinal Oncology)
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<p>Imaging findings of the patient. (<b>A</b>,<b>B</b>) Abdominal ultrasound shows a large, anechoic area in the liver (primarily in segments S4/5), measuring approximately 11.2 × 9.6 cm, with poor internal sound transmission and no detectable color flow signals. The left and part of the right intrahepatic bile ducts are dilated, with a maximum width of approximately 0.5 cm. (<b>C</b>,<b>D</b>) Contrast-enhanced CT reveals a large cystic density lesion in the liver, measuring approximately 10.8 × 9.5 cm. The unenhanced CT value is about 10 HU, and no enhancement is observed after contrast administration. (<b>E</b>–<b>H</b>) Contrast-enhanced MRI demonstrates a large cystic lesion with a water-like signal in the liver, measuring approximately 10.8 × 9.5 cm, with clear boundaries and slightly lobulated edges. The lesion compresses the confluence of the left and right intrahepatic bile ducts, causing significant intrahepatic bile duct dilatation, while the extrahepatic bile ducts remain unaffected.</p>
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<p>Surgical procedures and postoperative pathological findings. (<b>A</b>,<b>B</b>) In the initial surgery, a large cystic lesion is observed in liver segments S4/5. The cyst wall is dissected along its margin with the liver. (<b>C</b>) Intraoperative cholangioscopy during the initial surgery to rule out the possibility of intraductal lesions. (<b>D</b>) In the second surgery, segment 4 of the liver is resected, and the residual surface is ablated using an argon plasma coagulator. (<b>E</b>) Gross specimen from the second surgery showing the resected liver segment. (<b>F</b>) HE staining of the cyst wall resection specimen from the initial surgery (magnification ×40). (<b>G</b>) HE staining of the cyst wall resection specimen from the initial surgery (magnification ×100), showing tissue edema and focal lymphocytic infiltration. (<b>H</b>) HE staining of the liver resection specimen from the second surgery (magnification ×40), revealing fibrous cystic wall-like tissue partially lined by mucinous columnar epithelium. Hemorrhage, cholesterol crystal deposition, and multinucleated foreign body giant cells are observed, consistent with a low-grade mucinous cystic neoplasm. The surrounding liver parenchyma exhibits mild steatosis.</p>
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<p>Timeline of clinical events and total bilirubin (TBIL) and carbohydrate antigen 19-9 (CA19-9) levels. The normal upper reference limits are 21 µmol/L for TBIL and 34 U/mL for CA19-9.</p>
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24 pages, 3013 KiB  
Article
Machine Learning-Driven Radiomics Analysis for Distinguishing Mucinous and Non-Mucinous Pancreatic Cystic Lesions: A Multicentric Study
by Neus Torra-Ferrer, Maria Montserrat Duh, Queralt Grau-Ortega, Daniel Cañadas-Gómez, Juan Moreno-Vedia, Meritxell Riera-Marín, Melanie Aliaga-Lavrijsen, Mateu Serra-Prat, Javier García López, Miguel Ángel González-Ballester, Maria Teresa Fernández-Planas and Júlia Rodríguez-Comas
J. Imaging 2025, 11(3), 68; https://doi.org/10.3390/jimaging11030068 - 20 Feb 2025
Viewed by 334
Abstract
The increasing use of high-resolution cross-sectional imaging has significantly enhanced the detection of pancreatic cystic lesions (PCLs), including pseudocysts and neoplastic entities such as IPMN, MCN, and SCN. However, accurate categorization of PCLs remains a challenge. This study aims to improve PCL evaluation [...] Read more.
The increasing use of high-resolution cross-sectional imaging has significantly enhanced the detection of pancreatic cystic lesions (PCLs), including pseudocysts and neoplastic entities such as IPMN, MCN, and SCN. However, accurate categorization of PCLs remains a challenge. This study aims to improve PCL evaluation by developing and validating a radiomics-based software tool leveraging machine learning (ML) for lesion classification. The model categorizes PCLs into mucinous and non-mucinous types using a custom dataset of 261 CT examinations, with 156 images for training and 105 for external validation. Three experienced radiologists manually delineated the images, extracting 38 radiological and 214 radiomic features using the Pyradiomics module in Python 3.13.2. Feature selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression, followed by classification with an Adaptive Boosting (AdaBoost) model trained on the optimized feature set. The proposed model achieved an accuracy of 89.3% in the internal validation cohort and demonstrated robust performance in the external validation cohort, with 90.2% sensitivity, 80% specificity, and 88.2% overall accuracy. Comparative analysis with existing radiomics-based studies showed that the proposed model either outperforms or performs on par with the current state-of-the-art methods, particularly in external validation scenarios. These findings highlight the potential of radiomics-driven machine learning approaches in enhancing PCL diagnosis across diverse patient populations. Full article
(This article belongs to the Section Medical Imaging)
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<p>Processing pipeline. Illustration of the original study (<b>left</b> image), output of the soft-tissue normalization and manual segmentation of the pancreas and lesions, red and blue respectively (middle image) and image feature extraction of the segmented lesion (<b>right</b> image).</p>
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<p>Methodology for pancreatic cyst classification algorithm based on radiological and radiomics features. (<b>A</b>) Comprehensive workflow of the methodology employed for developing a classification algorithm based on radiological and radiomics features. The process begins by defining inputs, which include CT images with corresponding pancreas and cyst segmentations, along with diagnoses indicating whether the lesion is mucinous (1) or non-mucinous (0). Pre-processing is applied to these images to reduce unnecessary computational loads. Image processing techniques are then utilized on lesion segmentations to extract the initial subset of features. Subsequently, a feature analysis procedure is executed to identify the most critical features, forming the foundation for constructing the final diagnosis model. The final diagnosis model is designed for subsequent generalization to unseen datasets. (<b>B</b>) Organization of the dataset during the feature selection step, encompassing three sub-steps: LASSO selection of the most important features, construction of the final model using the selected features, and evaluation on the external testing set.</p>
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<p>Imaging features of PCLs detected by CT. (<b>A</b>) Patient with a 76 mm serous cystic neoplasm (SCN) in the head of the pancreas. Enhancing septations, peripheral calcifications and central scar with calcification. (<b>B</b>) Well-defined 122 mm pseudocyst in the tail of the pancreas with denser areas, which may indicate the presence of blood or hemorrhage within the cyst. (<b>C</b>) Intraductal Papillary Mucinous Neoplasm (IPMN) measuring 33 mm in the pancreatic body, displaying communication with the main pancreatic duct, which is dilated (7 mm). (<b>D</b>) Illustration of a mucinous cystic neoplasm located in the body-tail of the pancreas. The lesion measures 49 mm and exhibits septae along with thick walls, indicative of its characteristic features. Pancreatic cystic lesions are depicted in yellow, while the pancreas is highlighted in green.</p>
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<p>Distribution and Characteristics of Mucinous and Non-Mucinous Lesions Across Cohorts. (<b>a</b>) Proportions of mucinous and non-mucinous lesions within the training, internal validation, and external test sets, demonstrating consistency across datasets. (<b>b</b>) Age distribution of patients with mucinous and non-mucinous lesions, showing a significantly higher median age for the mucinous group (<span class="html-italic">p</span> &lt; 0.001). (<b>c</b>) Venn diagram illustrating the overlap of patient samples across the three cohorts, highlighting dataset composition and shared cases.</p>
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<p>Internal and external validation results. (<b>A</b>) Internal and external validation cohorts’ accuracy evolution with respect to the threshold to filter parameters depending on their LASSO coefficients. A threshold of 0.8 is selected as optimal as maximizing internal validation cohort accuracy, keeping a 60% of the total number of features. (<b>B</b>) Comparative analysis of the model performance metrics on the test set, with results categorized into internal and external datasets. The evaluation encompasses key performance indicators, including Accuracy, Sensitivity, Specificity, Precision, F1 Score, and Area Under the Curve (AUC).</p>
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<p>Comparative performance of radiomics models for PCL classification. Studies integrating radiomics with radiological features including the proposed model demonstrate balanced sensitivity and specificity [<a href="#B22-jimaging-11-00068" class="html-bibr">22</a>,<a href="#B26-jimaging-11-00068" class="html-bibr">26</a>,<a href="#B27-jimaging-11-00068" class="html-bibr">27</a>,<a href="#B28-jimaging-11-00068" class="html-bibr">28</a>,<a href="#B29-jimaging-11-00068" class="html-bibr">29</a>,<a href="#B30-jimaging-11-00068" class="html-bibr">30</a>,<a href="#B31-jimaging-11-00068" class="html-bibr">31</a>,<a href="#B32-jimaging-11-00068" class="html-bibr">32</a>,<a href="#B33-jimaging-11-00068" class="html-bibr">33</a>,<a href="#B34-jimaging-11-00068" class="html-bibr">34</a>].</p>
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<p>Validation Methods in Radiomics Studies: Internal vs. External Cohorts [<a href="#B22-jimaging-11-00068" class="html-bibr">22</a>,<a href="#B26-jimaging-11-00068" class="html-bibr">26</a>,<a href="#B27-jimaging-11-00068" class="html-bibr">27</a>,<a href="#B28-jimaging-11-00068" class="html-bibr">28</a>,<a href="#B29-jimaging-11-00068" class="html-bibr">29</a>,<a href="#B30-jimaging-11-00068" class="html-bibr">30</a>,<a href="#B31-jimaging-11-00068" class="html-bibr">31</a>,<a href="#B32-jimaging-11-00068" class="html-bibr">32</a>,<a href="#B33-jimaging-11-00068" class="html-bibr">33</a>,<a href="#B34-jimaging-11-00068" class="html-bibr">34</a>].</p>
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13 pages, 1465 KiB  
Article
Correlation of GNAS Mutational Status with Oncologic Outcomes in Patients with Resected Intraductal Papillary Mucinous Neoplasms
by Julia Evans, Kylee Shivok, Hui Hsuan Chen, Eliyahu Gorgov, Wilbur B. Bowne, Aditi Jain, Harish Lavu, Charles J. Yeo and Avinoam Nevler
Cancers 2025, 17(4), 705; https://doi.org/10.3390/cancers17040705 - 19 Feb 2025
Viewed by 344
Abstract
Background: Intraductal papillary mucinous neoplasms (IPMNs) are pre-malignant pancreatic lesions that may progress to invasive pancreatic ductal adenocarcinoma (PDAC). IPMN-associated invasive carcinoma (iIPMN) has been associated with more favorable survival outcomes compared to non-iIPMN-derived PDAC. Here, we aim to investigate the genetic landscape [...] Read more.
Background: Intraductal papillary mucinous neoplasms (IPMNs) are pre-malignant pancreatic lesions that may progress to invasive pancreatic ductal adenocarcinoma (PDAC). IPMN-associated invasive carcinoma (iIPMN) has been associated with more favorable survival outcomes compared to non-iIPMN-derived PDAC. Here, we aim to investigate the genetic landscape of IPMNs to assess their relevance to oncologic outcomes. Methods: This retrospective study used a large single-institution prospectively maintained database. Patients who underwent curative-intent pancreatic resection between 2016 and 2022 with histologically confirmed diagnosis of IPMN were included. Demographic, pathologic, molecular, and oncologic outcome data were recorded. Kaplan–Meier survival analyses were performed. PDAC data from public genetic databases were used for mutational correlation analysis. p-value ≤ 0.05 was considered as significant. Results: A total of thirty-nine patients with resected IPMN with complete clinical and sequencing data were identified and included in the final cohort. The male-to-female distribution was 21:18, and the mean age was 70.1 ± 9.1 years. GNAS mutations occurred in 23.1% of patients, and 89.7% of patients had iIPMN. In iIPMN patients, GNAS mutation was strongly associated with improved disease-free survival: all GNAS-mutant patients survived to follow-up with significantly fewer recurrences than in GNAS wild-type (WT) patients (p = 0.013). Mutated GNAS closely co-occurred with wild-type KRAS (p < 0.001), and further analysis of large genomic PDAC datasets validated this finding (OR 3.47, p < 0.0001). Conclusions: Our study suggests prognostic value of mutational status in malignant resected IPMNs. WT GNAS, mutant P53, and mutant KRAS each correlate with recurrence and decreased survival. Further studies are required to validate these preliminary observations. Full article
(This article belongs to the Special Issue Surgical Oncology for Hepato-Pancreato-Biliary Cancer)
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<p>Kaplan–Meier survival analysis comparing patients with GNAS mutant invasive IPMNs and GNAS wild-type invasive IPMNs for (<b>A</b>) recurrence-free survival (<span class="html-italic">p</span> = 0.013) and (<b>B</b>) overall survival (<span class="html-italic">p</span> = 0.025) in patients after complete resection. IPMN—intraductal papillary mucinous neoplasm.</p>
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<p>Kaplan–Meier survival analysis comparing all patients (invasive and non-invasive IPMNs) with GNAS mutant IPMNs and GNAS wild-type IPMNs for (<b>A</b>) recurrence-free survival (<span class="html-italic">p</span> = 0.003) and (<b>B</b>) overall survival (<span class="html-italic">p</span> = 0.004) in patients after complete resection.</p>
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<p>Kaplan–Meier survival analysis of patients with invasive IPMNs comparing patients with P53 mutant and P53 wild-type tumors, showing (<b>A</b>) recurrence-free survival (<span class="html-italic">p</span> = 0.005) and (<b>B</b>) overall survival (<span class="html-italic">p</span> = 0.018) in patients after complete resection.</p>
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<p>Kaplan–Meier survival analysis of patients with invasive IPMNs comparing patients with KRAS mutant and KRAS wild-type tumors, showing (<b>A</b>) recurrence-free survival (<span class="html-italic">p</span> = 0.011) and (<b>B</b>) overall survival (<span class="html-italic">p</span> = 0.054) in patients after complete resection.</p>
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19 pages, 2419 KiB  
Article
Promiscuity in Polyphenol–Protein Interactions—Monitoring Protein Conformational Change upon Polyphenol–Protein Binding by Nano-Differential Fluorimetry (Nano-DSF)
by Dorothea Schmidt, Amelie Wohlers and Nikolai Kuhnert
Molecules 2025, 30(4), 965; https://doi.org/10.3390/molecules30040965 - 19 Feb 2025
Viewed by 225
Abstract
In this article, we introduce nano-differential fluorimetry (nano-DSF) as an analytical technique that is suitable for investigating polyphenol–protein interactions in solution. Nano-DSF monitors conformational changes in proteins induced by external agents upon interaction at the molecular level. We demonstrate the suitability of this [...] Read more.
In this article, we introduce nano-differential fluorimetry (nano-DSF) as an analytical technique that is suitable for investigating polyphenol–protein interactions in solution. Nano-DSF monitors conformational changes in proteins induced by external agents upon interaction at the molecular level. We demonstrate the suitability of this technique to qualitatively monitor an interaction between selected dietary polyphenols and selected proteins including BSA, ovalbumin, amylase, pepsin, trypsin, mucin and ACE-1. Protein conformational changes induced by dietary polyphenols can be investigated. As a major advantage, measurements are carried out at a high dilution, avoiding the precipitation of polyphenol–protein complexes, allowing the rapid and efficient acquisition of quantitative and qualitative binding data. From this concentration, quantitative binding data could be obtained from the fluorescence response curve in line with published values for the association constants. We demonstrate that qualitative interactions can also be established for real food extracts such as cocoa, tea or coffee containing mixtures of dietary polyphenols. Most importantly, we demonstrate that polyphenols of very different structural classes interact with the same protein target. Conversely, multiple protein targets show an affinity to a series of structurally diverse polyphenols, therefore suggesting a dual level of promiscuity with respect to the protein target and polyphenol structure. Full article
(This article belongs to the Special Issue Bioactive Phenolic and Polyphenolic Compounds, Volume III)
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<p>Chemical structures of relevant dietary polyphenols studied.</p>
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<p>Chemical structures of relevant dietary polyphenols studied.</p>
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<p>Nano-DSF curves showing change in F<sub>350</sub>/F<sub>330</sub> ratio upon heating of pepsin in the absence and presence of varied concentrations of EGCG (decimals indicate weight ratio of pepsin to EGCG). (<b>a</b>) Direct measurement of F<sub>350</sub>/F<sub>330</sub> ratio versus temperature; (<b>b</b>) first derivative of F<sub>350</sub>/F<sub>330</sub> ratio versus temperature. The curves indicate a change in unfolding processes in the presence of EGCG as a consequence of EGCS binding to the protein. The AlphaFold structure of pepsin is shown in the diagram.</p>
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<p>F<sub>350</sub>/F<sub>330</sub> ratio concentration curves of four selected polyphenols (5-caffeoyl quinic acid (CQA), epi-gallocatechin gallate (EGCG), caftaric acid (CA) and epi-catechin (EC) against five selected proteins at constant protein concentration and varied polyphenol concentration (error bars small; experiments carried out in triplicate).</p>
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<p>F<sub>350</sub>/F<sub>330</sub> ratio concentration curves of four selected proteins (including ovalbumin (OVA), bovine serum albumin (BSA)) against six selected polyphenols (5-caffeoyl quinic acid (CQA), epi-gallocatechin gallate (EGCG), caftaric acid (CA), epi-catechin (EC), quercetin-3-glucoside (Q3G) and malvidin-3-glucoside (M3G)) at constant protein concentration and varied polyphenol concentration (experiments in triplicate).</p>
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<p>F<sub>350</sub>/F<sub>330</sub> ratio concentration curves of two selected proteins against five selected dietary aqueous extracts at constant protein concentration and varied extract concentration. Extract concentration given as gallic acid equivalents (GAE) determined by FRAP assay (experiments carried out in triplicate).</p>
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<p>Schematic diagram of polyphenols interacting with proteins (AlphaFold protein structures of human lipase (<b>A</b>), pepsin (<b>B</b>); amylase (<b>C</b>) and ACE-1 (<b>D</b>)) obtained from Uniprot as AlphaFold models (<a href="http://www.uniprot.com" target="_blank">www.uniprot.com</a> (accessed on 1 April 2024)).</p>
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22 pages, 2878 KiB  
Article
Protective Role and Enhanced Intracellular Uptake of Curcumin in Retinal Cells Using Self-Emulsifying Drug Delivery Systems (SNEDDS)
by Elide Zingale, Sebastiano Masuzzo, Tatu Lajunen, Mika Reinisalo, Jarkko Rautio, Valeria Consoli, Agata Grazia D’Amico, Luca Vanella and Rosario Pignatello
Pharmaceuticals 2025, 18(2), 265; https://doi.org/10.3390/ph18020265 - 17 Feb 2025
Viewed by 407
Abstract
Background: Sirtuin-1 (SIRT1), a histone deacetylase enzyme expressed in ocular tissues with intracellular localization, plays a critical protective role against various degenerative ocular diseases. The link between reduced SIRT1 levels and diabetic retinopathy (DR) has prompted the exploration of natural therapeutic compounds that [...] Read more.
Background: Sirtuin-1 (SIRT1), a histone deacetylase enzyme expressed in ocular tissues with intracellular localization, plays a critical protective role against various degenerative ocular diseases. The link between reduced SIRT1 levels and diabetic retinopathy (DR) has prompted the exploration of natural therapeutic compounds that act as SIRT1 agonists. Curcumin (CUR), which has been shown to upregulate SIRT1 expression, is one such promising compound. However, effective delivery of CUR to the deeper ocular tissues, particularly the retina, remains a challenge due to its poor solubility and limited ocular penetration following topical administration. Within this context, the development of self-nanoemulsifying drug delivery systems (SNEDDS) for CUR topical ocular delivery represents a novel approach. Methods: In accordance with our prior research, optimized SNEDDS loaded with CUR were developed and characterized post-reconstitution with simulated tear fluid (STF) at a 1:10 ratio, showing suitable physicochemical and technological parameters for ocular delivery. Results: An entrapment efficiency (EE%) of approximately 99% and an absence of drug precipitation were noticed upon resuspension with STF. CUR-SNEDDS resulted in a better stability and release profile than free CUR under simulated ocular conditions. In vitro analysis of mucoadhesive properties revealed that CUR-SNEDDS, modified with a cationic lipid, demonstrated enhanced interactions with mucin, indicating the potential for improved ocular retention. Cytotoxicity tests demonstrated that CUR-SNEDDS did not affect the viability of human corneal epithelial (HCE) cells up to concentrations of 3 μM and displayed superior antioxidant activity compared to free CUR in an oxidative stress model using retinal pigment epithelial (ARPE-19) cells exposed to hydroquinone (HQ). Cell uptake studies confirmed an enhanced accumulation of CUR within the retinal cells following exposure to CUR-SNEDDS compared to neat CUR. CUR-SNEDDS, at lower concentrations, were found to effectively induce SIRT1 expression. Conclusions: The cytocompatibility, antioxidant properties, and enhanced cellular uptake suggest that these developed systems hold promise as formulations for the delivery of CUR to the retina. Full article
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<p>Solubility (mg/mL) of CUR in different vehicles (oils and surfactants).</p>
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<p>(<b>A</b>) Macroscopic visualization of AC and AC after reconstitution 1:10 in STF and (<b>B</b>) microscopic morphological analysis of AC after reconstitution (1:100,000 with PBS) by Zeta view analysis.</p>
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<p>Mucoadhesion strength of A+ in contact with mucin dispersion in terms of (<b>A</b>) absorbance and (<b>B</b>) zeta potential. Each bar represents the mean value ±SD; <span class="html-italic">n</span> = 3. Statistical analysis was performed by 2-way ANOVA (**** <span class="html-italic">p</span> &lt; 0.0001 vs. A+ at different time points).</p>
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<p>In vitro CUR release from CUR-SNEDDS (AC) compared to free CUR investigated for 48 h.</p>
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<p>Stability investigation of native CUR(C) in PBS and CURC-loaded SNEDDS (AC) at different conditions of exposition: 4 °C, 25 °C light and dark, and 40 °C. (Statistical analysis was made with Tukey’s multiple comparisons test **** <span class="html-italic">p</span> &lt; 0.0001 vs. C at different exposition conditions).</p>
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<p>Evaluation of cytotoxicity of CUR-SNEDDS loaded with different concentrations of CUR (0.1–5 μM), respectively, on the (<b>A</b>) HCE and (<b>B</b>) ARPE-19 cell lines (**** <span class="html-italic">p</span> &lt; 0.0001 vs. control, *** <span class="html-italic">p</span> &lt; 0.0005 vs. control).</p>
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<p>The (<b>A</b>) internalization and uptake of CUR (central column): 0.1 µM; 0.5 µM; 1 µM; 2 µM and CUR-loaded SNEDDS (right column): AC 0.1 µM; AC 0.5 µM; AC 1 µM; AC 2 µM into ARPE-19. White arrows point out CUR nanocarriers poutside cells. (<b>B</b>) Quantitative evaluation of recovered CUR in medium and not internalized after uptake test (**** <span class="html-italic">p</span> &lt; 0.0001 vs. C). (<b>C</b>) Assessment of SIRT1 protein expression levels following AC treatment for 24 h at selected concentrations of 0.1 and 0.5 µM (** <span class="html-italic">p</span> &lt; 0.005 vs. CTRL).</p>
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<p>Evaluation of HQ (600 μM) effect on ARPE-19 cell viability and recovery with co-treatment of HQ and SNEDDS loaded with different concentrations of CUR (0.1–2 μM) (**** <span class="html-italic">p</span> &lt; 0.0001 vs. HQ; *** <span class="html-italic">p</span> &lt; 0.0005 vs. HQ).</p>
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13 pages, 4368 KiB  
Article
Applications of Multiplex Immunohistochemistry in Evaluating Spatiotemporal Heterogeneity of T Cells
by Mercedes Machuca-Ostos, Tim de Martines, Kanako Yoshimura, Junichi Mitsuda, Sumiyo Saburi, Alisa Kimura, Hiroki Morimoto, Koichi Yoshizawa, Nana Sakurai, Nanako Murakami, Kayo Kitamoto, Makoto Yasuda, Yoichiro Sugiyama, Hiroshi Ogi, Saya Shibata, Aya Miyagawa-Hayashino, Eiichi Konishi, Kyoko Itoh, Takahiro Tsujikawa and Shigeru Hirano
Immuno 2025, 5(1), 7; https://doi.org/10.3390/immuno5010007 - 17 Feb 2025
Viewed by 262
Abstract
T cell phenotypes and kinetics are emerging as crucial factors associated with immunotherapeutic responses in a wide range of solid cancer types. However, challenges remain in understanding the spatial and temporal profiles of T cells with differential phenotypes due to difficulties in single-cell [...] Read more.
T cell phenotypes and kinetics are emerging as crucial factors associated with immunotherapeutic responses in a wide range of solid cancer types. However, challenges remain in understanding the spatial and temporal profiles of T cells with differential phenotypes due to difficulties in single-cell analysis with preserved tissue structures. Here, we provide an optimized 12-marker multiplex immunohistochemical (IHC) panel and single-cell-based quantitative assessment to identify the spatial distributions of T cell phenotypes in formalin-fixed paraffin-embedded sections. This panel revealed differential T cell populations with spatial localizations in human tonsil tissue, where the percentages of CD8+ T cell-expressing programmed death receptor-1 (PD-1), T cell immunoglobulin and mucin domain 3 (TIM3), and other T cell phenotypic markers vary by tonsillar tissue components such as follicles, parenchyma, and epithelium. A specimen from salivary gland adenocarcinoma during hyper-progression, followed by anti-PD-1 treatment, exhibited the exclusion of CD8+ T cells from the intratumoral regions. Although the vast majority of peritumoral CD8+ T cells exhibited proliferative effector T cell phenotypes with PD-1TIM3Ki67+CD45RA+, intratumoral CD8+ T cells showed exhausted phenotypes with PD-1+TIM3 and increased Eomes expression, which might be related to poor therapeutic response in this case. To verify these findings in the context of temporal changes, we analyzed six longitudinal samples from a single patient with maxillary sinus cancer, observing increased T cell exhaustion along with metastasis and progression. Together, highly multiplexed IHC can be applied to analyze the spatiotemporal phenotypes of T cells, potentially contributing to the understanding of the mechanisms of resistance to immunotherapy. Full article
(This article belongs to the Special Issue Next-Generation Cancer Immunotherapy)
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<p>A 12-marker multiplex immunohistochemical (IHC) panel visualizes T cell functional markers with preserved tissue structures. (<b>A</b>) FFPE tonsil tissue sections were subjected to immunodetection with a 13-plex panel to reveal the complexity of the functional status of T cells. Pseudo-colored merged composite images are shown. Scale bar = 200 μm. (<b>B</b>) Selected markers with nuclei (hematoxylin) are shown in support of <a href="#immuno-05-00007-f001" class="html-fig">Figure 1</a>A. Scale bar = 200 μm. (<b>C</b>) A hematoxylin-stained image was used for automated cell segmentation, and the results served as templates for quantifying pixel intensities, area-shape measurements, and cell locations by single-cell analysis. These data were used for density plots similar to flow cytometry. The x and y axes are shown on a logarithmic scale.</p>
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<p>Quantification of T cell functional markers in different regions of the palatine tonsil tissue. (<b>A</b>) Epithelium, parenchyma, and follicle areas in a tonsil tissue were comparatively analyzed using a 13-marker multiplex IHC panel. (<b>B</b>) Location gates were created for the three regions on image cytometry, a technique to analyze cell populations in specific tissue locations. The cellular composition within each region was analyzed. Cells were classified according to the color coding shown on the right. (<b>C</b>–<b>E</b>) Radar charts showing the positivity rates of Eomes, T-bet (T-box transcription factor), TCF1 (T cell factor 1), CD45RA, CD103, TOX (thymocyte selection-associated high mobility group box), Ki67, TIM3 (T cell immunoglobulin and mucin-domain containing-3), and PD-1 (programmed cell death protein 1) among CD8<sup>+</sup> T cells (CD3<sup>+</sup>CD8<sup>+</sup>) in the epithelium (<b>C</b>), parenchyma (<b>D</b>), and follicle (<b>E</b>) regions. The results presented are based on single measurements for each sample.</p>
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<p>Spatial and phenotypic analysis of T cell heterogeneity in immunotherapy-resistant parotid gland carcinoma. (<b>A</b>) A parotid carcinoma specimen with immunotherapy resistance was analyzed by multiplex IHC and image cytometry and divided into intratumoral and peritumoral areas. Scale bar = 200 μm. (<b>B</b>) The cell densities of the immune cell lineages are shown, comparing the intratumoral and peritumoral regions. (<b>C</b>,<b>D</b>) Radar charts showing the positive percentages of markers on CD3<sup>+</sup>CD8<sup>+</sup> T cells in the peritumoral (<b>C</b>) and intratumoral regions (<b>D</b>). The results presented are based on single measurements for each sample.</p>
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<p>Longitudinal changes in immune cell composition and T cell functional status in a case of maxillary sinus cancer. (<b>A</b>) Longitudinal changes in long axis diameters of target tumors are shown with anatomical sites and sequential treatment regimens. (<b>B</b>) The relative abundance of CD8<sup>+</sup> T cells (CD8 T), regulatory T cells (T<sub>REG</sub>), helper T cells, B cells, natural killer cells (NK), mast cells, CD66b<sup>+</sup> granulocytes (Gr), tumor-associated macrophages (TAM), and dendritic cells (DC) is shown, expressed as percentages of the total number of CD45<sup>+</sup> cells within the tumor. (<b>C</b>) Expression levels of PD-L1 on tumor cells and CD45<sup>+</sup> immune cells. Data are shown as PD-L1-positive percentages in each cell type. (<b>D</b>) The relative abundance of the functional subtypes including resident memory T cells (Trm), effector T cells (Teff), memory T cells (Tmem), progenitor exhausted T cells (Tex-prog), and terminally exhausted T cells (Tex-term) is shown as percentages of total CD8<sup>+</sup> T cells. Data are normalized to the values at the baseline timepoint. (<b>E</b>) Radar charts showing the positive percentages of markers on CD3<sup>+</sup>CD8<sup>+</sup> T cells for timepoints #1–6, stratified by PD-1 and TIM3 expression. The results presented are based on single measurements for each sample. The data points for each time point in (<b>A</b>–<b>C</b>) are connected by lines to illustrate the trend.</p>
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18 pages, 518 KiB  
Article
Cystic Fluid Total Proteins, Low-Density Lipoprotein Cholesterol, Lipid Metabolites, and Lymphocytes: Worrisome Biomarkers for Intraductal Papillary Mucinous Neoplasms
by Fahimeh Jafarnezhad-Ansariha, Nicole Contran, Chiara Cristofori, Manuela Simonato, Veronica Davanzo, Stefania Moz, Paola Galozzi, Paola Fogar, Evelyn Nordi, Andrea Padoan, Ada Aita, Matteo Fassan, Alberto Fantin, Anna Sartori, Cosimo Sperti, Alessio Correani, Virgilio Carnielli, Paola Cogo and Daniela Basso
Cancers 2025, 17(4), 643; https://doi.org/10.3390/cancers17040643 - 14 Feb 2025
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Abstract
Objectives: Pancreatic cystic neoplasms (PCNs), particularly intraductal papillary mucinous neoplasms (IPMNs), present a challenge for their potential malignancy. Despite promising biomarkers like CEA, amylase, and glucose, our study investigates whether metabolic indices in blood and cystic fluids (CFs), in addition to lymphocyte subsets [...] Read more.
Objectives: Pancreatic cystic neoplasms (PCNs), particularly intraductal papillary mucinous neoplasms (IPMNs), present a challenge for their potential malignancy. Despite promising biomarkers like CEA, amylase, and glucose, our study investigates whether metabolic indices in blood and cystic fluids (CFs), in addition to lymphocyte subsets and hematopoietic stem/progenitor cells (HSPCs), can effectively differentiate between high- and low-risk PCNs. Materials and Methods: A total of 26 patients (11 males, mean age 69.5 ± 9 years) undergoing Endoscopic Ultrasound-guided Fine Needle Aspiration were consecutively enrolled. Analyses included blood, serum, and CF, assessing glucose, CEA, cholesterol (total, HDL, and LDL), and total proteins. Flow cytometry examined immunophenotyping in peripheral blood and cystic fluids. Mass spectrometry was used for the metabolomic analysis of CF. Sensitivity, specificity, and ROC analyses evaluated discriminatory power. Results: A total of 25 out of 26 patients had IPMN. Patients were categorized as low or high risk based on multidisciplinary evaluation of clinical, radiological, and endoscopic data. High-risk patients showed lower CF total proteins and LDL cholesterol (p = 0.005 and p = 0.031), with a marked reduction in CF lymphocytes (p = 0.005). HSCPs were absent in CF. In blood, high-risk patients showed increased non-MHC-restricted cytotoxic T cells (p = 0.019). The metabolomic analysis revealed significantly reduced middle and long-chain acyl carnitines (AcCa) and tryptophan metabolites in high-risk patients. ROC curves indicated comparable discriminant abilities for CF lymphocytes (AUC 0.868), CF total proteins (AUC 0.859), and CF LDL cholesterol (AUC 0.795). The highest performance was achieved by the AcCa 14:2 and 16:0 (AUC: 0.9221 and 0.8857, respectively). Conclusions: CF levels of glucose, CEA, LDL cholesterol, and total proteins together with lymphocyte counts are easy translational biomarkers that may support risk stratification of PCNs in IPMN patients and might be endorsed by metabolomic analysis. Further studies are required for potential clinical integration. Full article
(This article belongs to the Special Issue Multimodal Treatment for Pancreatic Cancer)
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<p>Cystic fluid biochemical parameters in high- and low-risk IPMN patients. <span class="html-italic">p</span> &lt; 0.05 was considered significant. LR, low-risk; HR, high-risk.</p>
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22 pages, 12777 KiB  
Article
Effect of Food Matrix on Regulation of Intestinal Barrier and Microbiota Homeostasis by Polysaccharides Sulfated Carrageenan
by Xuke Shang, Juanjuan Guo and Peilin Chen
Foods 2025, 14(4), 635; https://doi.org/10.3390/foods14040635 - 14 Feb 2025
Viewed by 405
Abstract
Carrageenan (CGN) has side effects on the intestinal barrier. Damage to the intestinal barrier is associated with exposure to sulfate groups. Food matrix has significant influence on the exposure quantity of sulfate groups and conformation in κ-CGN, but the corresponding side effects are [...] Read more.
Carrageenan (CGN) has side effects on the intestinal barrier. Damage to the intestinal barrier is associated with exposure to sulfate groups. Food matrix has significant influence on the exposure quantity of sulfate groups and conformation in κ-CGN, but the corresponding side effects are not reported specifically. This study aimed to explore the regulatory effect of κ-CGN dissolved in aqueous (κ-CGN) and in 3% casein (κ-carrageenan-casein, κ-CC) on the intestinal barrier and microbiota homeostasis. Research has shown that both κ-CGN and κ-CC can induce different extents of intestinal barrier damage through disrupting microbiota homeostasis. Importantly, κ-CGN in casein with lower sulfate groups content was found to repair the intestinal barrier injury induced by an equivalent dose of κ-CGN aqueous through increasing the abundance of Oscillibacter and decreasing Weissella. These alleviating effects were reflected in lower levels of tumor necrosis factor (TNF)-α and C-reaction protein (CRP), higher levels of interleukin (IL)-10, raised secretion of mucus and goblet cells, and improved expression of epithelial cell compact proteins zonula occluden (ZO)-1 and mucin protein 2 (MUC2). This study states that κ-CGN in casein has a positive regulatory effect on the intestinal barrier damage compared to in aqueous solution, which can provide guidance for processing and utilization of CGN. Full article
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<p>The conformational characterizations of the κ-CGN and κ-CC. (<b>A</b>) The basic unit structures of κ-CGN. (<b>B</b>) Physical diagram and conformational characterizations of the κ-CGN in the simulated intestinal phase. (<b>C</b>) Physical diagram and conformational characterizations of the κ-CC in the simulated intestinal phase. (<b>D</b>) Confocal laser scanning microscopy images. The arrows indicate the microscopic morphological features of the sample observed under laser confocal microscopy. κ-CGN appears green while casein is red/orange.</p>
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<p>Effect of κ-CGN and κ-CC on colitis in mice (n = 8 for each group). (<b>A</b>) Body weight change from week 1 to 8. (<b>B</b>) Body weight change at week 8. (<b>C</b>) Fecal condition. (<b>D</b>) DAI scores change from week 1 to 8. (<b>E</b>) DAI scores change at week 8. (<b>F</b>) Spleen changes. (<b>G</b>) Spleen organ index at week 8. (<b>H</b>) Colon length change at week 8. (<b>I</b>) Colon condition. The arrows indicate the length of the colon, and the circles indicate the occurrence of congestion. Independent samples <span class="html-italic">t</span>-tests were used for a single comparison of differences between groups and multiple comparisons were performed using the Turkey post hoc test after a significant one-way ANOVA (<span class="html-italic">p</span> &lt; 0.05). Uppercase letters represent differences within the κ-CGN groups, lowercase letters represent differences within the κ-CC groups. “*” represents differences between the κ-CGN and κ-CC groups (<span class="html-italic">p</span> &lt; 0.05) and “**” represents differences between the κ-CGN and κ-CC groups (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Effects of κ-CGN and κ-CC on inflammatory cytokines and the intestinal barrier (n = 8 for each group). (<b>A</b>–<b>C</b>) Serum inflammatory cytokines levels of TNF-α, CRP, and IL-10. (<b>D</b>) Images of HE staining. The dotted line indicates the surface of the irregular crypt and arrows indicate infiltration of inflammatory cells. (<b>E</b>) HAI scores. (<b>F</b>) Quantification of mucus secretion. (<b>G</b>) Images of AB-PAS staining. Circles and arrows indicate acidic mucus staining. Independent samples <span class="html-italic">t</span>-tests were used for a single comparison of differences between groups and multiple comparisons were performed using the Turkey post hoc test after a significant one-way ANOVA (<span class="html-italic">p</span> &lt; 0.05). Uppercase letters represent differences within the κ-CGN groups, lowercase letters represent differences within the κ-CC groups. “*” represents differences between the κ-CGN and κ-CC groups (<span class="html-italic">p</span> &lt; 0.05) and “**” represents differences between the κ-CGN and κ-CC groups (<span class="html-italic">p</span> &lt; 0.01). scale bar = 50 μm.</p>
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<p>Effect of κ-CC and κ-CGN on the expression of ZO-1 and MUC2 in mice (n = 8 for each group). (<b>A</b>,<b>B</b>) The mRNA levels of ZO-1 and MUC2. (<b>C</b>,<b>D</b>) Western bolt results and the protein expression of ZO-1. (<b>E</b>) The protein expression of MUC2. (<b>F</b>) Immunohistochemistry staining. The arrows indicated MUC2 staining. Independent samples <span class="html-italic">t</span>-tests were used for a single comparison of differences between groups and multiple comparisons were performed using the Turkey post hoc test after a significant one-way ANOVA (<span class="html-italic">p</span> &lt; 0.05). Uppercase letters represent differences within the κ-CGN groups, lowercase letters represent differences within the κ-CC groups. “*” represents differences between the κ-CGN and κ-CC groups (<span class="html-italic">p</span> &lt; 0.05) and “**” represents differences between the κ-CGN and κ-CC groups (<span class="html-italic">p</span> &lt; 0.01). scale bar = 50 μm.</p>
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<p>Effect of κ-CGN and κ-CC on the gut microbiota at genus (n = 8 for each group). (<b>A</b>) Alpha diversity. (<b>B</b>) Beta diversity. (<b>C</b>) Stacked column plot of microbial genus relative abundance. (<b>D</b>) Heatmap analysis of relative abundance of top 50 genera. (<b>E</b>) The Kruskal–Wallis test results for comparison of microbial abundance among six groups. (<b>F</b>) Relative abundance of differential bacteria in κ-CGN and κ-CC groups, which were calculated by a Wilcoxon rank sum test, * <span class="html-italic">p</span> &lt; 0.05, and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>LEfSe analysis of gut microbiota and Spearman’s analysis between the microbiota and biochemical indexes. (<b>A</b>) Taxonomic cladogram obtained from LEfSe analysis among six groups. Different colors indicate the enrichment of the biomarker taxa. The circle from inside to outside means the rank from kingdom to genus, and the circle size represents the taxa abundance in the community. (<b>B</b>) Circle bar of LDA scores from LEfSe analysis at genus (LDA &gt; 3). (<b>C</b>) Correlation analysis of characteristic microbiota and biochemical indexes in the κ-CGN group. (<b>D</b>) Correlation analysis of characteristic microbiota and biochemical indexes in the κ-CC group. The color scale represents the strength of correlation, ranging from 0.5 (strong positive correlation) to − 0.5 (strong negative correlation). * <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 diagram of κ-CGN solution and κ-CC causing microbiota changes in mice. (The arrows indicate upward and downward changes in microbiota or physiological indicators.)</p>
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