[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (1,918)

Search Parameters:
Keywords = tDCS

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
18 pages, 1654 KiB  
Article
The Role of Dendritic Cells in Adaptive Immune Response Induced by OVA/PDDA Nanoparticles
by Daniele R. Pereira, Yunys Pérez-Betancourt, Bianca C. L. F. Távora, Geraldo S. Magalhães, Ana Maria Carmona-Ribeiro and Eliana L. Faquim-Mauro
Vaccines 2025, 13(1), 76; https://doi.org/10.3390/vaccines13010076 - 16 Jan 2025
Viewed by 233
Abstract
Background/Objective: Cationic polymers were shown to assemble with negatively charged proteins yielding nanoparticles (NPs). Poly-diallyl-dimethyl-ammonium chloride (PDDA) combined with ovalbumin (OVA) yielded a stable colloidal dispersion (OVA/PDDA-NPs) eliciting significant anti-OVA immune response. Dendritic cells (DCs), as sentinels of foreign antigens, exert a [...] Read more.
Background/Objective: Cationic polymers were shown to assemble with negatively charged proteins yielding nanoparticles (NPs). Poly-diallyl-dimethyl-ammonium chloride (PDDA) combined with ovalbumin (OVA) yielded a stable colloidal dispersion (OVA/PDDA-NPs) eliciting significant anti-OVA immune response. Dendritic cells (DCs), as sentinels of foreign antigens, exert a crucial role in the antigen-specific immune response. Here, we aimed to evaluate the involvement of DCs in the immune response induced by OVA/PDDA. Methods: In vivo experiments were used to assess the ability of OVA/PDDA-NPs to induce anti-OVA antibodies by ELISA, as well as plasma cells and memory B cells using flow cytometry. Additionally, DC migration to draining lymph nodes following OVA/PDDA-NP immunization was evaluated by flow cytometry. In vitro experiments using bone marrow-derived DCs (BM-DCs) were used to analyze the binding and uptake of OVA/PDDA-NPs, DC maturation status, and their antigen-presenting capacity. Results: Our data confirmed the potent effect of OVA/PDDA-NPs inducing anti-OVA IgG1 and IgG2a antibodies with increased CD19+CD138+ plasma cells and CD19+CD38+CD27+ memory cells in immunized mice. OVA/PDDA-NPs induced DC maturation and migration to draining lymph nodes. The in vitro results showed higher binding and the uptake of OVA/PDDA-NPs by BM-DCs. In addition, the NPs were able to induce the upregulation of costimulatory and MHC-II molecules on DCs, as well as TNF-α and IL-12 production. Higher OVA-specific T cell proliferation was promoted by BM-DCs incubated with OVA/PDDA-NPs. Conclusions: The data showed the central role of DCs in the induction of antigen-specific immune response by OVA-PDDA-NPs, thus proving that these NPs are a potent adjuvant for subunit vaccine design. Full article
(This article belongs to the Special Issue Vaccines Targeting Dendritic Cells)
Show Figures

Figure 1

Figure 1
<p>Characterization of OVA/PDDA-NPs. (<b>A</b>) Hydrodynamic diameter of NPs and (<b>B</b>) protein dosage; (<b>A</b>) After OVA-NPs formation, the hydrodynamic diameter was analyzed by Dynamic Light Scattering (DLS) in ZetaPlus Analyzer. (<b>B</b>) OVA/water (100 μg/mL) or OVA/PDDA (100 μg/10 μg/mL) samples were subjected to centrifugation at 14,500 rpm/30 min. Then, the supernatants were collected, and the protein concentration was determined by the BCA method. The dotted line represents the limit of detection from the BSA standard curve. The results are expressed as the mean of the protein content of samples in quadruplicate ± SD.</p>
Full article ">Figure 2
<p>Potent effect of OVA/PDDA-NPs in induction of anti-OVA antibody production and B cell populations. Groups of BALB/c mice were immunized subcutaneously (s.c.) with 200 μL of OVA/Alum (100 μg/100 μg/mL) or OVA/PDDA (100 μg/10 μg/mL). As a control, mice received 200 μL of water. On the 21st day post-immunization, the mice received an antigenic booster, and on day 28, they were bled for antibody evaluation by ELISA. Their spleens were also collected to analyze the plasma cell (CD19<sup>+</sup>CD138<sup>+</sup>) and memory B (CD19<sup>+</sup>CD27<sup>+</sup>CD38<sup>+</sup>) cell populations by flow cytometry, as described in Materials and Methods. (<b>A</b>) Anti-OVA IgG1 (1/1280) and (<b>B</b>) anti-OVA IgG2a (1/20) antibodies in samples of individual mice/group. The results represent the optical density of the mean of the samples (n = 4) ± SD. (<b>C</b>) Number of plasma cells and (<b>D</b>) memory B cells in splenocyte suspensions of the individual mice/group (n = 4–5)/group ± SD. * <span class="html-italic">p</span> &lt; 0.01 of significance; *** <span class="html-italic">p</span> &lt; 0.001 of significance, **** <span class="html-italic">p</span> &lt; 0.0001 of significance.</p>
Full article ">Figure 3
<p>Effect of OVA/PDDA immunization in migration of CD11c<sup>+</sup> cells to draining lymph nodes and DCs maturation in vivo. Groups of BALB/c mice were immunized (s.c.) with 200 μL of OVA/Alum (100 μg/100 μg/mL) or OVA/PDDA (100 μg/10 μg/mL). As a control, mice received 200 μL of water. After 3 days of immunization, inguinal lymph nodes were obtained, and 1 × 10<sup>6</sup> cells were incubated with anti-CD11c, anti-CD80, anti-CD40, and anti-MHC II mAb labeled with fluorophores and analyzed by flow cytometry. (<b>A</b>) Number of CD11c<sup>+</sup> cells from mice groups immunized 3 days before; expression of (<b>B</b>) CD80, (<b>C</b>) CD40, and (<b>D</b>) MHC-II molecules on CD11c<sup>+</sup> cell population from mice groups immunized 3 days before. The analysis strategy is described in material and methods. The results represent the mean of the number of CD11c<sup>+</sup> cells from lymph node cell suspensions of individual mice/group (n = 4) ± SD. Mean of fluorescence intensity (MFI) of molecule expression on CD11c+ cells from individual mice/group (n = 4) ± SD. Statistical analysis was performed by one-way ANOVA with Tukey’s post-test. * <span class="html-italic">p</span> &lt; 0.01 of significance; ** <span class="html-italic">p</span> &lt; 0.05 of significance. Representative results from three independent experiments.</p>
Full article ">Figure 4
<p>Migration of CD11c<sup>+</sup> cells to draining lymph nodes induced by OVA/PDDA immunization 4 days before. Groups of BALB/c mice were immunized subcutaneously (s.c.) with 200 μL of OVA/Alum (100 μg/100 μg/mL) or OVA/PDDA (100 μg/10 μg/mL). As a control, mice received 200 μL of water. After 4 days of immunization, inguinal lymph nodes were obtained, and 1 × 10<sup>6</sup> cells were incubated with anti-CD11c, anti-CD80, anti-CD40, and anti-MHC II mAbs labeled with fluorophores and analyzed by flow cytometry. (<b>A</b>) Number of CD11c<sup>+</sup> cells from mice groups immunized 4 days before. Expression of (<b>B</b>) CD80, (<b>C</b>) CD40, and (<b>D</b>) MHC-II molecules on CD11c<sup>+</sup> cell population from mice groups immunized 4 days before. The analysis strategy is described in material and methods. The results represent the mean of the number of CD11c<sup>+</sup> cells from lymph node cell suspensions of individual mice/group (n = 4) ± SD. Mean of fluorescence intensity (MFI) of molecule expression on CD11c+ cells from individual mice/group (n = 4) ± SD. Statistical analysis was performed by one-way ANOVA with Tukey’s post-test. * <span class="html-italic">p</span> &lt; 0.01 of significance. Representative results from three independent experiments.</p>
Full article ">Figure 5
<p>Effect of OVA/PDDA-NPs on viability of BM-DCs in culture. The Presto Blue assay was performed in iBM-DCs cultures incubated with OVA/PDDA-NPs for 24 h in RPMI medium supplemented with 10% FBS or OPT-MEM. Fluorescence was determined by the means of the samples in quadruplicate ± SD. **** <span class="html-italic">p</span> &lt; 0.0001 of significance.</p>
Full article ">Figure 6
<p>Binding and uptake of OVA-FITC/PDDA and OVA-FITC by iBM-DCs. (<b>A</b>) iBM-DCs were incubated with OVA-FITC or OVA-FITC/PDDA for 30 min at 4 °C and (<b>B</b>) at 37 °C. The samples were analyzed by flow cytometry. Results are expressed as mean fluorescence intensity (MIF) of the samples in triplicate ± SD. * <span class="html-italic">p</span> &lt; 0.01 of significance; ** <span class="html-italic">p</span> &lt; 0.05 of significance; *** <span class="html-italic">p </span>&lt; 0.001 of significa and **** <span class="html-italic">p</span> &lt; 0.0001 of significance. Representative results of three independent experiments.</p>
Full article ">Figure 7
<p>Effect of OVA/PDDA-NPs in the expression of costimulatory and MHC-II on DC cultures. iBM-DCs (1 × 10<sup>6</sup>) were incubated for 18 h with OVA/LPS, OVA, and OVA/PDDA-NPs. After this, the DCs were incubated with anti-CD11c, anti-CD80, anti-CD86, anti-CD40, and anti-MHC II mAbs labeled with fluorophores and analyzed by flow cytometry. The analysis strategy is described in <a href="#app1-vaccines-13-00076" class="html-app">Supplementary Material</a>. The results represent the mean of fluorescence intensity of (<b>A</b>) CD40, (<b>B</b>) CD80, (<b>C</b>) CD86 and (<b>D</b>) MHC II expression on CD11c<sup>+</sup> cells in triplicate ± SD. * <span class="html-italic">p</span> &lt; 0.05 of significance; ** <span class="html-italic">p</span> &lt; 0.01 of significance; *** <span class="html-italic">p</span> &lt; 0.001 of significance. Representative results from three independent experiments.</p>
Full article ">Figure 8
<p>Cytokine production by DCs incubated with OVA/PDDA in vitro. iBM-DCs (1 × 10<sup>6</sup>) were incubated with OVA, OVA/LPS, or OVA/PDDA for 18 h. Then, the cell supernatants were collected to detect the cytokines using ELIS (<b>A</b>) IL-12, (<b>B</b>) TNF-alpha and (<b>C</b>) IL-6 production. The results represent the mean of the cytokine production in the samples in triplicate ± SD. * <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 and **** <span class="html-italic">p</span> &lt; 0.0001 of significance. Representative results from two independent experiments.</p>
Full article ">Figure 9
<p>DCs incubated with OVA/PDDA-NPs are able to induce proliferation of OVA-specific CD4+T cells in vitro. iDCs differentiated in vitro were incubated with OVA, OVA/LPS, and OVA/PDDA for 18 h. After this, the DCs were co-cultured with CD4<sup>+</sup> T cells purified from DO11.10 mice for 72 h. The proliferative response was evaluated using BrdU-ELISA assay. Results expressed as the mean of optical density obtained in samples in quadruplicate ± SD. ** <span class="html-italic">p</span> &lt; 0.01 and **** <span class="html-italic">p</span> &lt; 0.0001 of significance.</p>
Full article ">
14 pages, 4030 KiB  
Article
Analysis of Radio Science Data from the KaT Instrument of the 3GM Experiment During JUICE’s Early Cruise Phase
by Paolo Cappuccio, Andrea Sesta, Mauro Di Benedetto, Daniele Durante, Umberto De Filippis, Ivan di Stefano, Luciano Iess, Ruaraidh Mackenzie and Bernard Godard
Aerospace 2025, 12(1), 56; https://doi.org/10.3390/aerospace12010056 - 16 Jan 2025
Viewed by 219
Abstract
The JUpiter Icy Moon Explorer (JUICE) mission, launched on 14 April 2023, aims to explore Jupiter and its Galilean moons, with arrival in the Jovian system planned for mid-2031. One of the scientific investigations is the Geodesy and Geophysics of Jupiter and the [...] Read more.
The JUpiter Icy Moon Explorer (JUICE) mission, launched on 14 April 2023, aims to explore Jupiter and its Galilean moons, with arrival in the Jovian system planned for mid-2031. One of the scientific investigations is the Geodesy and Geophysics of Jupiter and the Galilean Moons (3GM) radio science experiment, designed to study the interior structures of Europa, Callisto, and Ganymede and the atmospheres of Jupiter and the Galilean moons. The 3GM experiment employs a Ka-band Transponder (KaT) to enable two-way coherent range and Doppler measurements used for the gravity experiment and an Ultra Stable Oscillator (USO) for one-way downlink occultation experiments. This paper analyzes KaT data collected at the ESA/ESTRACK ground station in Malargüe, Argentina, during the Near-Earth Commissioning Phase (NECP) in May 2023 and the first in-cruise payload checkout (PC01) in January 2024. The radiometric data were fitted using both NASA’s Mission Analysis, Operations, and Navigation Toolkit Environment (MONTE) and ESA’s General Orbit Determination and Optimization Toolkit (GODOT) software. The comparison of the orbital solutions showed an excellent agreement. In addition, the Doppler and range residuals allowed a preliminary assessment of the quality of the radiometric measurements. During the NECP pass, the radio link data showed a range-rate noise of 0.012 mm/s at 1000 s integration time, while the root mean square of the range residuals sampled at 1 s was 8.4 mm. During the first payload checkout, the signal power at the KaT input closely matched the value expected at Jupiter, due to a specific ground station setup. This provided early indications of the 3GM’s performance during the Jovian phase. In this test, the accuracy of range data at an integration time of 1s, particularly sensitive to the link signal-to-noise ratio, degraded to 13.6 cm, whilst the range-rate accuracy turned out to be better than 0.003 mm/s at 1000 s, thanks to the accurate tropospheric delay calibration system (TDCS) available at the Malargue station (inactive during NECP). Full article
Show Figures

Figure 1

Figure 1
<p>JUICE state vector comparison between MONTE and GODOT orbit determination software in terms of position (<b>top</b>) and velocity (<b>bottom</b>) with respect to the Sun as a function of the epoch. The blue, red, and green lines represent, respectively, the x, y, and z components in the ICRF reference frame.</p>
Full article ">Figure 2
<p>Range (<b>top</b>) and Doppler (<b>bottom</b>) pass-through residuals difference @ 1s for the Ka/Ka link during the near-Earth commissioning phase between MONTE and GODOT models. The left panels show the difference in the computed observables on the trajectory propagated from the a priori initial condition; the right panels show the difference in computed observables at the end of the orbit determination procedure.</p>
Full article ">Figure 3
<p>Range (<b>top</b>) and range-rate (<b>bottom</b>) residuals @ 1s for the Ka/Ka link during the near-Earth commissioning phase.</p>
Full article ">Figure 4
<p>Range residuals @1s in the X/Ka link during the near-Earth commissioning phase. The standard deviation of the residuals for the first 10 min is about 6.2 cm, while the standard deviation for the remaining part is 9.6 cm. The lower noise is due to the absence of the telemetry modulation during the first 10 min.</p>
Full article ">Figure 5
<p>Range (<b>top</b>) and range-rate (<b>bottom</b>) residuals @1s for the Ka/Ka link during the first payload checkout.</p>
Full article ">Figure 6
<p>Autocorrelation function (<b>left</b>) and power spectral density (<b>right</b>) of Doppler residuals for the Ka/Ka link during the near-Earth commissioning phase, in red, and the first payload checkout, in cyan. The NECP residuals show a significant autocorrelation at the round-trip light time, meaning that the local noise at the station is the dominant noise source.</p>
Full article ">Figure 7
<p>Overlapping Allan deviation of the relative frequency shift residuals.</p>
Full article ">
27 pages, 2509 KiB  
Review
Recent Advances in Our Understanding of Human Inflammatory Dendritic Cells in Human Immunodeficiency Virus Infection
by Freja A. Warner van Dijk, Kirstie M. Bertram, Thomas R. O’Neil, Yuchen Li, Daniel J. Buffa, Andrew N. Harman, Anthony L. Cunningham and Najla Nasr
Viruses 2025, 17(1), 105; https://doi.org/10.3390/v17010105 - 14 Jan 2025
Viewed by 279
Abstract
Anogenital inflammation is a critical risk factor for HIV acquisition. The primary preventative HIV intervention, pre-exposure prophylaxis (PrEP), is ineffective in blocking transmission in anogenital inflammation. Pre-existing sexually transmitted diseases (STIs) and anogenital microbiota dysbiosis are the leading causes of inflammation, where inflammation [...] Read more.
Anogenital inflammation is a critical risk factor for HIV acquisition. The primary preventative HIV intervention, pre-exposure prophylaxis (PrEP), is ineffective in blocking transmission in anogenital inflammation. Pre-existing sexually transmitted diseases (STIs) and anogenital microbiota dysbiosis are the leading causes of inflammation, where inflammation is extensive and often asymptomatic and undiagnosed. Dendritic cells (DCs), as potent antigen-presenting cells, are among the first to capture HIV upon its entry into the mucosa, and they subsequently transport the virus to CD4 T cells, the primary HIV target cells. This increased HIV susceptibility in inflamed tissue likely stems from a disrupted epithelial barrier integrity, phenotypic changes in resident DCs and an influx of inflammatory HIV target cells, including DCs and CD4 T cells. Gaining insight into how HIV interacts with specific inflammatory DC subsets could inform the development of new therapeutic strategies to block HIV transmission. However, little is known about the early stages of HIV capture and transmission in inflammatory environments. Here, we review the currently characterised inflammatory-tissue DCs and their interactions with HIV. Full article
(This article belongs to the Special Issue The Role of Dendritic Cells and Macrophages in HIV Infection)
Show Figures

Figure 1

Figure 1
<p>Inflammatory DC interactions with HIV in anogenital tissue. HIV enters inflamed anogenital tissue through breaches in the epithelium. Upon HIV exposure, plasmacytoid dendritic cells (pDCs) secrete IFN-I to induce an antiviral immune response and chemokines CCL3-5, which recruit CD4 T cells to infection sites and block the binding of HIV to the CCR5 co-receptor. Both Al<sup>+</sup> siglec-6<sup>+</sup> dendritic cell (ASDC) subsets are capable of polarising T cells into Th2, Th9, Th17 or Th22. CD11c<sup>+</sup> ASDCs tended to be more efficient at first-phase transfer via HIV bending to lectin receptors (MR, langerin, DC-SIGN and Siglec-1), whilst CD123<sup>+</sup> ASDCs tend to be more efficient at second-phase transfer via the entry receptors CD4/CCR5 and productive infection. DC3s can induce Th1 and Th17 polarisation. It has not yet been determined if they are capable of HIV binding and transfer; however, they express the HIV entry receptors CD4/CCR5 and Siglec-1 mRNA. Monocyte-derived dendritic cells (MDDCs) can also induce Th1 and Th17 polarisation. They can mediate first-phase transfer via Siglec-1 and likely another unidentified lectin receptor. MDDCs are also highly efficient at CD4/CCR5-mediated second-phase transfer. Epidermal CD11c<sup>+</sup> DCs can efficiently bind and transmit HIV through both first- and second-phase transfer.</p>
Full article ">Figure 2
<p>CD34<sup>+</sup> hematopoietic stem cells produce granulocyte-monocyte common progenitor (GMDP) and lymphoid progenitors. Based on IRF8 expression, GMDP gives rise to the common DC progenitors (cDP), pre-DC3 and the common monocyte progenitor (cMoP). While cDP generate DCs (pDCs, ASDCs, DC1, DC2), pre-DC3 gives rise to DC3 and cMoP gives rise to monocytes. The developmental pathway of ASDCs from pre-pDCs or directly from pre-DCs remains to be fully characterised. PDCs and ASDCs migrate from blood to inflamed tissues. DC1, DC2 and DC3 are present in healthy and inflamed tissues but become more enriched in inflamed tissues. In inflammation, CD14 monocytes migrate from blood to tissues, and they differentiate into MDM and/or MDDCs.</p>
Full article ">Figure 3
<p>Human inflammatory and steady-state mononuclear phagocyte phenotypes.</p>
Full article ">
22 pages, 5884 KiB  
Article
A Virtual Synchronous Generator Control Strategy Based on Transient Damping Compensation and Virtual Inertia Adaptation
by Yan Xia, Yang Chen, Yao Wang, Renzhao Chen, Ke Li, Jinhui Shi and Yiqiang Yang
Appl. Sci. 2025, 15(2), 728; https://doi.org/10.3390/app15020728 - 13 Jan 2025
Viewed by 272
Abstract
To mitigate the challenges posed by transient oscillations and steady-state deviations in the traditional virtual synchronous generator (TVSG) that is subjected to active power and grid frequency disturbances, a VSG control strategy based on Transient Damping Compensation and Virtual Inertia Adaptation is presented. [...] Read more.
To mitigate the challenges posed by transient oscillations and steady-state deviations in the traditional virtual synchronous generator (TVSG) that is subjected to active power and grid frequency disturbances, a VSG control strategy based on Transient Damping Compensation and Virtual Inertia Adaptation is presented. Initially, a closed-loop small-signal model for the grid-connected active power loop (APL) of the TVSG is constructed, which highlights the contradiction between the dynamic and static characteristics of TVSG output power through the analysis of root locus distribution trends. Secondly, a VSG control strategy based on Transient Damping Compensation (TDC) is proposed. The influence of APL system parameters introduced by TDC on system stability is qualitatively analyzed based on pole distribution trends and frequency response, and a comprehensive parameter design scheme is presented. In addition, based on the TDC algorithm, an improved virtual inertia adaptive strategy utilizing the Inverse Square Root Unit (ISRU) approach is designed, and the tuning range of parameters is provided. Finally, simulations and experiments verify that the proposed strategy exhibits superior active response performance and transient oscillation suppression capabilities, effectively eliminating active steady-state deviations caused by frequency disturbances in the power grid. Full article
Show Figures

Figure 1

Figure 1
<p>TVSG main circuit topology and its simplified structure.</p>
Full article ">Figure 2
<p>TVSG control block diagram.</p>
Full article ">Figure 3
<p>Small-signal model of the APL in the TVSG.</p>
Full article ">Figure 4
<p>The trend of pole distribution of the APL system in the TVSG.</p>
Full article ">Figure 5
<p>Small-signal model of the APL in the TDC-VSG.</p>
Full article ">Figure 6
<p>The trend of pole distribution of the APL system in the TDC-VSG.</p>
Full article ">Figure 7
<p>The unit step response characteristic curve of the TDC-VSG.</p>
Full article ">Figure 8
<p>The selectable regions of <span class="html-italic">J</span><sub>ω</sub> and <span class="html-italic">D</span><sub>T</sub> under phase angle constraints.</p>
Full article ">Figure 9
<p>ISRU(<span class="html-italic">x</span>) function and its derivative trend.</p>
Full article ">Figure 10
<p>Feasible region for <span class="html-italic">J</span><sub>ω</sub> and <span class="html-italic">f</span><sub>cp</sub>.</p>
Full article ">Figure 11
<p>The improved output characteristics of the TDC-VSG under different control coefficients.</p>
Full article ">Figure 12
<p>Simulation results of the TVSG with different damping coefficients <span class="html-italic">D</span><sub>ω</sub>.</p>
Full article ">Figure 13
<p>Results of the TDC-VSG control strategy with compensation coefficient <span class="html-italic">K</span><sub>j</sub>.</p>
Full article ">Figure 14
<p>Results of the TDC-VSG control strategy with different transient damping coefficients <span class="html-italic">D</span><sub>T</sub>.</p>
Full article ">Figure 15
<p>Results of different VSG control strategies.</p>
Full article ">Figure 16
<p>Results of output voltage and current under different control strategies.</p>
Full article ">Figure 17
<p>Simulation results of the TDC-VSG control strategy under different power inductance values.</p>
Full article ">Figure 18
<p>VSG grid-connected experimental platform based on the HIL platform.</p>
Full article ">Figure 19
<p>Experimental results under different test conditions: (<b>a</b>) Test Condition 1; (<b>b</b>) Test Condition 2.</p>
Full article ">
15 pages, 7711 KiB  
Article
Neo-BCV: A Novel Bacterial Liquid Complex Vaccine for Enhancing Dendritic Cell-Mediated Immune Responses Against Lung Cancer
by Zilong Zhu, Zhuze Chu, Fei Fei, Chenxi Wu, Zhengyue Fei, Yuxia Sun, Yun Chen and Peihua Lu
Vaccines 2025, 13(1), 64; https://doi.org/10.3390/vaccines13010064 - 13 Jan 2025
Viewed by 446
Abstract
Background: In the past decade, immunotherapy has become a major choice for the treatment of lung cancer, yet its therapeutic efficacy is still relatively limited due to the various immune escape mechanisms of tumors. Based on this, we introduce Neo-BCV, a novel bacterial [...] Read more.
Background: In the past decade, immunotherapy has become a major choice for the treatment of lung cancer, yet its therapeutic efficacy is still relatively limited due to the various immune escape mechanisms of tumors. Based on this, we introduce Neo-BCV, a novel bacterial composite vaccine designed to enhance immune responses against lung cancer. Methods: We investigated the immune enhancing effect of Neo-BCV through in vivo and in vitro experiments, including flow cytometry, RNA-seq, and Western blot. Results: We have demonstrated that Neo-BCV can promote Dendritic cells (DCs) maturation and induce DCs differentiation into pro-inflammatory subgroups, significantly enhancing cytotoxic T lymphocyte (CTL)-mediated anti-tumor responses. Transcriptome sequencing revealed that Neo-BCV exerts its effects by specifically inhibiting the JAK2-STAT3 signaling pathway, a crucial regulator of cancer progression, metabolism, and inflammation. Moreover, Neo-BCV significantly improved the immune microenvironment in both tumor and spleen tissues without inducing notable toxic effects in major organs. Conclusions: These findings highlight Neo-BCV’s potential as a safe and effective therapeutic strategy, offering a novel avenue for clinical translation in lung cancer immunotherapy. Full article
Show Figures

Figure 1

Figure 1
<p>The effect of Neo-BCV on BMDC maturation in vitro. (<b>A</b>) Morphology of BMDCs under light microscope. (<b>B</b>) Flow cytometry was used to detect the expression levels of CD86 and MHC-II on the surface of DCs, along with a semi-quantitative statistical chart. ** <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 2
<p>Neo-BCV vaccine induces tumor-specific immune responses in mice. (<b>A</b>) Neo-BCV experimental treatment protocol; (<b>B</b>) Images of mouse tumor tissues after treatment; (<b>C</b>) Tumor growth curves in mice; (<b>D</b>) Tumor tissue weights in mice after treatment. **** <span class="html-italic">p</span> &lt; 0.0001.</p>
Full article ">Figure 3
<p>Immune response induced by Neo-BCV in the tumor. (<b>A</b>) Flow cytometry analysis of CD8<sup>+</sup> T and CD4<sup>+</sup> T lymphocyte expression in tumor tissues and semi-quantitative statistical charts. (<b>B</b>) Flow cytometry analysis of cDC1, CD103<sup>+</sup> DC, and CD83<sup>+</sup> DC expression in tumor tissues and semi-quantitative statistical charts. (<b>C</b>) Flow cytometry analysis of cytotoxic T lymphocyte perforin and granzyme B expression in tumor tissues and semi-quantitative statistical charts. n = 6, data are presented as mean ± standard deviation ( <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi mathvariant="normal">x</mi> </mrow> <mo>¯</mo> </mover> </mrow> </semantics></math> ± s). Inter-group comparisons were analyzed using Student’s <span class="html-italic">t</span>-test. * <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; ns indicates no significant difference.</p>
Full article ">Figure 4
<p>Immune markers induced by Neo-BCV in the spleen. (<b>A</b>) Flow cytometry analysis of DC expression in spleen tissues and semi-quantitative statistical charts. (<b>B</b>,<b>C</b>) Expression levels of CCR7, CD44, CD69, CD83, CD86, and Ki-67 in the spleen of the PBS and Neo-BCV-treated groups, along with semi-quantitative statistical charts. n = 6; staining intensity was used as the analysis metric, and statistical comparisons were made using the average OD value of the positive regions. Data are presented as mean ± standard deviation ( <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi mathvariant="normal">x</mi> </mrow> <mo>¯</mo> </mover> </mrow> </semantics></math> ± s). Inter-group comparisons were analyzed using Student’s <span class="html-italic">t</span>-test; ** <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 indicates no significant difference.</p>
Full article ">Figure 5
<p>Tumor tissue transcriptomics.(<b>A</b>,<b>B</b>) Differential Gene Expression. (<b>A</b>) Volcano plot of differential genes, where the x-axis represents the fold change in gene or transcript expression between the two samples, and the y-axis represents the statistical significance of the differential expression, indicated by the <span class="html-italic">p</span>-value. (<b>B</b>) Clustering analysis of differential genes, with the color in the figure representing the expression level of that gene in this group of samples; red indicates high expression, while blue indicates low expression. (<b>C</b>,<b>D</b>) Enrichment Analysis of Differential Genes. (<b>C</b>) GO enrichment analysis, where each node represents a GO term, and the color intensity indicates the enrichment level; darker colors represent higher enrichment levels, with each node displaying the name of the GO term and the <span class="html-italic">p</span>-value from the enrichment analysis. (<b>D</b>) KEGG enrichment analysis, where the x-axis represents the enrichment factor and the y-axis represents the functional pathways enriched in the KEGG pathway. (<b>E</b>,<b>F</b>) Representative Western blot images and semi-quantitative statistical charts of the phosphorylation and total protein levels of STAT3 and JAK2 in tumor tissues. **** <span class="html-italic">p</span> &lt; 0.0001. The original Western blot figures can be found in <a href="#app1-vaccines-13-00064" class="html-app">Supplementary File S1</a>.</p>
Full article ">Figure 6
<p>Safety considerations of Neo-BCV. (<b>A</b>) Body weight change curves of mice in different treatment groups. (<b>B</b>) H&amp;E staining of major organs (lung, liver, kidney, and heart) after two weeks of different treatments (scale bar = 100 μm). ns indicates no significant difference.</p>
Full article ">Figure 7
<p>Summary diagram. Neo-BCV mediates CTL anti-tumor immune responses by activating /dendritic cells (DCs). Simultaneously, it exerts anti-tumor effects through the inhibition of the JAK2-STAT3 signaling pathway.</p>
Full article ">
25 pages, 696 KiB  
Review
The Potential of Transcranial Direct Current Stimulation (tDCS) in Improving Quality of Life in Patients with Multiple Sclerosis: A Review and Discussion of Mechanisms of Action
by James Chmiel, Donata Kurpas and Marta Stępień-Słodkowska
J. Clin. Med. 2025, 14(2), 373; https://doi.org/10.3390/jcm14020373 - 9 Jan 2025
Viewed by 453
Abstract
Background/Objectives: Multiple sclerosis (MS) is the most prevalent incurable nontraumatic neurological disability in young individuals. It causes numerous symptoms, including tingling, fatigue, muscle spasms, cognitive deficits, and neuropsychiatric disorders. This disease significantly worsens quality of life (QoL), and this dimension of general [...] Read more.
Background/Objectives: Multiple sclerosis (MS) is the most prevalent incurable nontraumatic neurological disability in young individuals. It causes numerous symptoms, including tingling, fatigue, muscle spasms, cognitive deficits, and neuropsychiatric disorders. This disease significantly worsens quality of life (QoL), and this dimension of general functioning provides valuable information about the effectiveness of treatment and well-being. There are psychological interventions that can improve QoL, but their number is limited. Therefore, searching for new methods that are as effective and safe as possible is ongoing. Methods: This review examines the potential effectiveness of transcranial direct current stimulation (tDCS) in improving the quality of life in patients with MS. Searches were conducted in the PubMed/Medline, Research Gate, and Cochrane databases. Results: The search yielded seven studies in which QoL was a primary or secondary outcome. Stimulation protocols displayed heterogeneity, especially concerning the choice of the stimulation site. Four studies demonstrated the effectiveness of tDCS in improving QoL, all of which (two) used anodal stimulation of the left DLPFC. Stimulation of the motor cortex has produced mixed results. The potential mechanisms of action of tDCS in improving QoL in MS are explained. These include improved synaptic plasticity, increased cerebral blood flow, salience network engagement through tDCS, and reduction of beta-amyloid deposition. The limitations are also detailed, and recommendations for future research are made. Conclusions: While the evidence is limited, tDCS has shown potential to improve QoL in MS patients in some studies. Prefrontal stimulation appears promising, and further research is recommended to explore this approach. Full article
(This article belongs to the Special Issue Multiple Sclerosis: Diagnosis, Treatment and Clinical Management)
Show Figures

Figure 1

Figure 1
<p>Flow chart depicting the different phases of the systematic review.</p>
Full article ">
18 pages, 8046 KiB  
Article
Molecular Mechanism of VSV-Vectored ASFV Vaccine Activating Immune Response in DCs
by Yunyun Ma, Junjun Shao, Wei Liu, Shandian Gao, Guangqing Zhou, Xuefeng Qi and Huiyun Chang
Vet. Sci. 2025, 12(1), 36; https://doi.org/10.3390/vetsci12010036 - 9 Jan 2025
Viewed by 456
Abstract
The vesicular stomatitis virus (VSV)-vectored African swine fever virus (ASFV) vaccine can induce efficient immune response, but the potential mechanism remains unsolved. In order to investigate the efficacy of recombinant viruses (VSV-p35, VSV-p72)-mediated dendritic cells (DCs) maturation and the mechanism of inducing T-cell [...] Read more.
The vesicular stomatitis virus (VSV)-vectored African swine fever virus (ASFV) vaccine can induce efficient immune response, but the potential mechanism remains unsolved. In order to investigate the efficacy of recombinant viruses (VSV-p35, VSV-p72)-mediated dendritic cells (DCs) maturation and the mechanism of inducing T-cell immune response, the functional effects of recombinant viruses on DC activation and target antigens presentation were explored in this study. The results showed that surface-marked molecules (CD80, CD86, CD40, and MHC-II) and secreted cytokines (IL-4, TNF-α, IFN-γ) were highly expressed in the recombinant virus-infected DCs. In addition, the co-culture results of recombinant virus-treated DCs with naive T cells showed that the Th1- and Th17-type responses were effectively activated. Taken together, the study indicated that the VSV-vectored ASFV vaccine activated the maturation of DCs and the Th1- and Th17-type immune response, which provided a theoretical basis for the development of novel ASF vaccines. Full article
Show Figures

Figure 1

Figure 1
<p>Phenotypic alterations in infected BMDCs. (<b>A</b>–<b>D</b>) Bar graph shows the percentages of the surface maturation markers CD40, CD80, CD86, and MHC-II in BMDCs from the different groups after being treated with recombinant viruses (MOI = 0.1) at 24 h, respectively. The results are shown as the mean ± SEM from three replicates per group. ** <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: not significant.</p>
Full article ">Figure 2
<p>The cytokine secretion in infected BMDCs. (<b>A</b>–<b>F</b>) The secretion levels of IL-4, IL-10, IL-8, IL-12p70, IFN-γ, and TNF-α in BMDCs treated with recombinant viruses (MOI = 0.1) for 12 h, 24 h, and 48 h, respectively. The results are shown as the mean ± SEM from three replicates per 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, **** <span class="html-italic">p</span> &lt; 0.0001, ns: not significant.</p>
Full article ">Figure 3
<p>TLR expression in BMDCs infected with recombinant viruses. (<b>A</b>–<b>D</b>) The mRNA expression levels of TLR3, TLR7, TLR8, and TLR9 in BMDCs infected with recombinant viruses (MOI = 0.1) for 12 h, 24 h, and 48 h, respectively. The results are shown as the mean ± SEM from three replicates per 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, **** <span class="html-italic">p</span> &lt; 0.0001, ns: not significant.</p>
Full article ">Figure 4
<p>The mean fluorescence intensity of Dextran-FITC uptake by BMDCs and the migration ability of infected BMDCs. (<b>A</b>) The results are shown as the mean ± SD from three replicates per group. (<b>B</b>) The schematic diagram of the migration of infected BMDCs in transwell. (<b>C</b>) The proportion of migratory BMDCs after being stimulated with recombinant viruses (MOI = 0.1) for 24 h. The results are shown as the mean ± SEM from three replicates per 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, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
Full article ">Figure 5
<p>The apoptosis and cell viability of BMDCs. (<b>A</b>–<b>C</b>) The apoptosis rates of BMDCs after being stimulated with recombinant viruses (MOI = 0.1) for 12 h, 24 h, and 48 h, respectively. (<b>D</b>) Bar graph showing statistical analysis of apoptosis rates in treated BMDCs. (<b>E</b>) Cell viability of BMDCs after being stimulated with recombinant viruses (MOI = 0.1) for 12 h, 24 h, and 48 h, respectively. The results are shown as the mean ± SEM from three replicates per group. * <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">Figure 6
<p>The antigen expression of the matured BMDCs. (<b>A</b>) The fluorescence expression of ASFV p72 or p30 protein in BMDCs after being stimulated with recombinant viruses (MOI = 0.1) for 24 h. (<b>B</b>) The relative mRNA expression of p72/p30 gene in BMDCs after being stimulated with recombinant viruses (MOI = 0.1) for 24 h. The results are shown as the mean ± SEM from three replicates per group. ** <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 7
<p>The proliferation and activation ability of T cells. (<b>A</b>) The proliferation of T cells in co-cultured cells was measured by mixed lymphocyte reaction assay when BMDCs were infected with recombinant viruses (MOI = 0.1) for 12 h, 24 h, and 48 h. (<b>B</b>) The percentage of CD40L in co-cultured cells was analyzed by flow cytometry when BMDCs were infected with recombinant viruses (MOI = 0.1) for 12 h, 24 h, and 48 h. The results are shown as the mean ± SEM from three replicates per 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, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
Full article ">Figure 8
<p>The percentage of CD3+CD4+T or CD3+CD8+T cells in co-cultured cells. (<b>A</b>,<b>B</b>) The percentage of CD4+ or CD8+ cells of CD3+T cells in co-cultured cells was measured when BMDCs were infected with recombinant viruses (MOI = 0.1) for 12 h, 24 h, and 48 h, respectively. (<b>C</b>) The ratio of CD4+T to CD8+T cells. The results are shown as the mean ± SEM from three replicates per group. ** <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 9
<p>The polarization of CD4+T lymphocyte subsets. (<b>A</b>–<b>D</b>) Dot plots present individual values of the triplicates per treatment. Statistical differences among individual groups at a time and per individual group over the time points were analyzed using a two-way ANOVA with Bonferroni’s multiple comparison statistical test. * <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 ">
17 pages, 2844 KiB  
Article
Developing an Effective Therapeutic HPV Vaccine to Eradicate Large Tumors by Genetically Fusing Xcl1 and Incorporating IL-9 as Molecular Adjuvants
by Zhongjie Sun, Zhongyan Wu and Xuncheng Su
Vaccines 2025, 13(1), 49; https://doi.org/10.3390/vaccines13010049 - 9 Jan 2025
Viewed by 422
Abstract
Background: Human papillomavirus (HPV) is a prevalent infection affecting both men and women, leading to various cytological lesions. Therapeutic vaccines mount a HPV-specific CD8+ cytotoxic T lymphocyte response, thus clearing HPV-infected cells. However, no therapeutic vaccines targeting HPV are currently approved for clinical [...] Read more.
Background: Human papillomavirus (HPV) is a prevalent infection affecting both men and women, leading to various cytological lesions. Therapeutic vaccines mount a HPV-specific CD8+ cytotoxic T lymphocyte response, thus clearing HPV-infected cells. However, no therapeutic vaccines targeting HPV are currently approved for clinical treatment due to limited efficacy. Our goal is to develop a vaccine that can effectively eliminate tumors caused by HPV. Methods: We genetically fused the chemokine XCL1 with the E6 and E7 proteins of HPV16 to target cDC1 and enhance the vaccine-induced cytotoxic T cell response, ultimately developing a DNA vaccine. Additionally, we screened various interleukins and identified IL-9 as an effective molecular adjuvant for our DNA vaccine. Results: The fusion of Xcl1 significantly improved the quantity and quality of the specific CD8+ T cells. The fusion of Xcl1 also increased immune cell infiltration into the tumor microenvironment. The inclusion of IL-9 significantly elevated the vaccine-induced specific T cell response and enhanced anti-tumor efficacy. IL-9 promotes the formation of central memory T cells. Conclusions: the fusion of Xcl1 and the use of IL-9 as a molecular adjuvant represent promising strategies for vaccine development. Full article
(This article belongs to the Section DNA and mRNA Vaccines)
Show Figures

Figure 1

Figure 1
<p>Construction and analysis of HPV16 therapeutic DNA vaccine fused with chemokine Xcl1. (<b>A</b>) The structural prediction diagram of the E6E7 protein and the Xcl1-E6E7 fusion protein. In the diagram, dark blue indicates Xcl1, red represents the linker, bright blue denotes E6, and light purple signifies E7. (<b>B</b>) Following the transfection of HEK293T cells for 24 h, Western blot analysis was conducted to assess the expression levels of the E6E7 and Xcl1-E6E7 DNA vaccines, alongside the protein levels of P53 and Rb. (<b>C</b>) Flow cytometry was employed to determine the proportion of CD11c+CD8+ DC cells bound to E6E7 and Xcl1-E6E7 proteins. The left panel presents a representative flow cytometry chart, while the right panel displays the corresponding statistical analysis (N = 3, mean ± SEM).</p>
Full article ">Figure 2
<p>Efficacy analysis of E6E7 and Xcl1-E6E7 plasmid DNA vaccines. (<b>A</b>) E7-Specific CD8+ T cell response. Peripheral blood samples were collected on days 9, 14, 21, and 28 following the administration of 5 µg and 25 µg doses of E6E7 and Xcl1-E6E7 vaccines. The E7-specific CD8+ T cell responses were dynamically analyzed (N = 5, mean ± SEM). (<b>B</b>) The DNA vaccine injection frequency patterns. (<b>C</b>) The graph illustrates the dynamics of E7-specific T cell levels over time under various injection frequency regimens (N = 5, mean ± SEM). (<b>D</b>,<b>E</b>) After 14 days later administering 25 µg (<b>D</b>) and 5 µg (<b>E</b>) doses of the E6E7 and Xcl1-E6E7 vaccines, tumor cells were inoculated to assess tumor growth, with growth curves plotted accordingly (N = 5, mean ± SEM). (<b>F</b>) Tumor growth post inoculation and vaccination. Four days following tumor cell inoculation, 25 µg doses of the E6E7 and Xcl1-E6E7 vaccines were administered. Tumor sizes were measured and growth curves were plotted. A second tumor inoculation was conducted in mice where tumors completely regressed (N = 5, mean ± SEM). (<b>G</b>) Fourteen days post vaccination in figure (<b>F</b>), flow cytometry was used to assess E7-specific CD8+ T cell levels in the peripheral blood of mice from each group (N = 5, mean ± SEM).</p>
Full article ">Figure 3
<p>Analysis of immune cell subpopulations within the tumor microenvironment. (<b>A</b>) Flow cytometry analysis of the proportion of E7-specific CD8+ T cells in tumors from mice treated with E6E7 or Xcl1-E6E7 vaccines. (<b>B</b>) Detection of the proportion of Granzyme B (GranB)-positive cells within the E7-specific CD8+ T cell population in each treatment group. (<b>C</b>–<b>E</b>) Flow cytometry analysis of CD45+ total white blood cells and the proportions of CD8+ and CD4+ T cells within the tumor microenvironment of mice treated with E6E7 or Xcl1-E6E7 vaccines. (<b>F</b>) Flow cytometry analysis of the proportion of regulatory T cells (Tregs) within the CD4+ T cell population. (<b>G</b>) Flow cytometry analysis of the proportion of natural killer (NK) cells relative to total white blood cells in the tumor microenvironment of mice in each group. (N = 5, mean ± SEM) for each figure.</p>
Full article ">Figure 4
<p>IL-9 enhances the therapeutic effect of the Xcl1-E6E7 plasmid DNA vaccine. (<b>A</b>–<b>C</b>) Mice with established subcutaneous tumors, averaging 30 mm<sup>3</sup> in volume, formed by 1 × 10<sup>5</sup> TC-1 cells per mouse, were treated with either E6E7 or Xcl1-E6E7 plasmid DNA. Tumor growth curves were plotted for individual mice across the empty vector group (<b>A</b>), E6E7 group (<b>B</b>), and Xcl1-E6E7 group (<b>C</b>). (<b>D</b>) Xcl1-E6E7 DNA vaccine was administered 4 or 7 days after TC-1 tumor cell inoculation. The levels of E7-specific CD8+ T cells in peripheral blood were dynamically monitored and plotted as a curve. (<b>E</b>) Fourteen days post immunization with the Xcl1-E6E7 plasmid DNA vaccine combined with plasmid DNA expressing IL-7, IL-9, IL-21, or IL-33, E7-specific CD8+ T cell levels were assessed in peripheral blood. (<b>F</b>,<b>G</b>) Fourteen days after immunization with plasmid DNA expressing IL-9 and IL-33 in combination with GPC3 plasmid DNA vaccines, ELISPOT analysis of mouse spleen cells was conducted (<b>G</b>), F showing representative IFN-γ spots. (<b>H</b>,<b>I</b>) The MPEC proportion of E7-specific CD8+ T cells in the spleen of Xcl1-E6E7 with and without IL-9-immunized mice was analyzed after 28 days, along with the expression of the central memory marker CD62L in MPECs. (<b>J</b>) The plots show the tumor growth curve of Xcl1-E6E7 with and without IL-9-treated mice (N = 5, mean ± SEM).</p>
Full article ">Figure 5
<p>Efficacy analysis of Xcl1-E6E7 + mIL-9 combined with immune checkpoint inhibitors. (<b>A</b>) Flow cytometry analysis of the proportions of CD4+ and CD8+ T cells in peripheral blood, conducted one week after the administration of anti-CD4 and anti-CD8 antibodies. (<b>B</b>) Fourteen days post immunization with Xcl1-E6E7 + mIL-9, mice were inoculated with 5 × 10<sup>5</sup> TC-1 tumor cells to observe tumor growth. Anti-CD4 and anti-CD8 antibodies were administered intraperitoneally starting one week after vaccination, with dosing twice a week (N = 5, mean ± SEM). (<b>C</b>) Mice with TC-1 xenograft tumors were treated with 10, 25, and 100 µg doses of the Xcl1-E6E7+mIL-9 vaccine. Tumor volumes in each group were measured regularly, and growth curves were plotted (N = 7, mean ± SEM). (<b>D</b>,<b>E</b>) Mice were subcutaneously inoculated with 5 × 10<sup>5</sup> TC-1 tumor cells to form tumors, then randomly assigned to treatment groups. Treatments included anti-OX40, anti-4-1BB, anti-PD-1, anti-PD-L1, and anti-CTLA-4, either alone (<b>D</b>) or in combination with Xcl1-E6E7 + mIL-9 plasmid DNA (<b>E</b>). Tumor growth was monitored, and growth curves were plotted (N = 5, mean ± SEM). (<b>F</b>) Dynamic monitoring of E7-specific CD8+ T cell levels in the peripheral blood of mice immunized with XCL1-E6E7 + mIL-9 plasmid DNA, either alone or in combination with anti-CTLA-4 antibody (N = 5, mean ± SEM).</p>
Full article ">
14 pages, 24309 KiB  
Article
The Influence of Terfenol-D Content on the Structure and Properties of Multiferroic Composites Obtained Based on PZT-Type Material and Terfenol-D
by Dariusz Bochenek, Artur Chrobak, Grzegorz Dercz, Przemysław Niemiec, Dagmara Brzezińska and Piotr Czaja
Materials 2025, 18(2), 235; https://doi.org/10.3390/ma18020235 - 8 Jan 2025
Viewed by 289
Abstract
In this work, three composite materials based on Terfenol-D and PZT-type material were obtained with a classic sintering method using a combination of 0–3 phases, where the ferroelectric phase was doped PZT material (P) and the magnetic phase was Terfenol-D (T). The percentage [...] Read more.
In this work, three composite materials based on Terfenol-D and PZT-type material were obtained with a classic sintering method using a combination of 0–3 phases, where the ferroelectric phase was doped PZT material (P) and the magnetic phase was Terfenol-D (T). The percentage of P and T components in the composites was variable, i.e., 90% P/10% T (P90-T10), 70% P/30% T (P70-T30), and 50% P/50% T (P50-T50). Structural, microstructure, dielectric, and magnetic properties and DC electric conductivity of multiferroic composites were investigated. Chemical composition analyses and X-ray studies showed a decomposition of the composite compositions, forming additional phases, most of which contained rare earth elements and Fe. Microstructural SEM-BE (backscattering) images distinguished areas of bright intensity with a dominant ferroelectric phase and dark areas with a dominant magnetic element dominance. Despite the composition decomposition, the composite materials retained good dielectric and magnetic properties at room temperature. The highest stability of dielectric parameters was maintained by the P90-T10 composition with high values of permittivity ε = 570 at room temperature RT (εm = 7300 at the phase transition temperature Tm) and the lowest dielectric tangent loss (tanδ of 0.32 and 1.94 for RT and Tm, respectively). Increasing the Terfenol-D share in the composite causes a significant increase in dielectric tangent loss and electrical conductivity, a decrease in permittivity, and an increase in the degree of phase transition blurring. The magnetic properties for all P-T composite compositions at RT were preserved and were 0.31 emu/g, 1.60 emu/g, and 4.56 emu/g for P90-T10, P70-T30, P50-T50, respectively. For the M-H hysteresis loop at room temperature, the maximum magnetization increased from 1.17 emu/g for (P90-T10) to 15.18 emu/g for (P50-T50), while the coercive field decreased from 271.8 mT for P90-T10 to 9.7 mT for P50-T50. It is also interesting to maintain the high saturation of the M-H magnetic hysteresis loop in the composite with the lowest Terfenol-D content (P90-T10). The magnetic properties for all P-T composite compositions at room temperature were preserved and were 0.31 emu/g, 1.60 emu/g, and 4.56 emu/g for P90-T10, P70-T30, and P50-T50, respectively. For the M-H hysteresis loop at RT, the maximum magnetization increased from 1.17 emu/g for (P90-T10) to 15.18 emu/g for (P50-T50), while the coercive field decreased from 0.272 T for P90-T10 to 0.001 T for P50-T50. It is also interesting to maintain the high saturation of the M-H magnetic hysteresis loop in the composite with the lowest Terfenol-D content (P90-T10). Due to the tendency to combine with oxygen and the high electric conductivity of Terfenol-D, limiting its amount in the composite composition is appropriate. At 10% of Terfenol-D, the composite has good dielectric properties, and the magnetic parameters remain satisfactory. Full article
Show Figures

Figure 1

Figure 1
<p>FESEM microstructure images of P-T composites (cross-fracture of the samples) made in SB mode (<b>a</b>–<b>c</b>) and BE mode (<b>d</b>–<b>f</b>): (<b>a</b>,<b>d</b>) P90-T10, (<b>b</b>,<b>e</b>) P70-T30, and (<b>c</b>,<b>f</b>) P50-T50.</p>
Full article ">Figure 2
<p>Surface EDS analysis of composite materials P-T: (<b>a</b>) P90-T10, (<b>b</b>) P70-T30, and (<b>c</b>) P50-T50.</p>
Full article ">Figure 3
<p>Point EDS analysis for composite P50-T50.</p>
Full article ">Figure 4
<p>Linear EDS analysis of composite materials P-T: (<b>a</b>) P90-T10, (<b>b</b>) P70-T30, and (<b>c</b>) P50-T50.</p>
Full article ">Figure 5
<p>XRD patterns of the P-T composites at room temperature.</p>
Full article ">Figure 6
<p>Permittivity vs. temperature of P-T composites: (<b>a</b>) P90-T10, (<b>b</b>) P70-T30, and (<b>c</b>) P50-T50.</p>
Full article ">Figure 7
<p>Dielectric loss factor (tan<span class="html-italic">δ</span>) vs. temperature of P-T composites: (<b>a</b>) P90-T10, (<b>b</b>) P70-T30, and (<b>c</b>) P50-T50.</p>
Full article ">Figure 8
<p>Permittivity (<b>a</b>) and dielectric loss factor (tan<span class="html-italic">δ</span>) (<b>b</b>) vs. temperature of P-T composites for 1 kHz.</p>
Full article ">Figure 9
<p>The ln<span class="html-italic">σ</span><sub>DC</sub>(1000/<span class="html-italic">T</span>) relationship for P-T composites.</p>
Full article ">Figure 10
<p>Temperature dependencies of magnetization (<b>a</b>) and magnetic hysteresis loops at −262 °C (<b>b</b>) and at RT (<b>c</b>) for P-T multiferroic ceramic composites.</p>
Full article ">
18 pages, 2224 KiB  
Article
HIFU-CCL19/21 Axis Enhances Dendritic Cell Vaccine Efficacy in the Tumor Microenvironment
by Bum-Seo Baek, Hyunmi Park, Ji-Woong Choi, Eun-Young Lee and Seung-Yong Seong
Pharmaceutics 2025, 17(1), 65; https://doi.org/10.3390/pharmaceutics17010065 - 6 Jan 2025
Viewed by 441
Abstract
Background/Objectives: Effectively targeting treatment-resistant tumor cells, particularly cancer stem cells (CSCs) involved in tumor recurrence, remains a major challenge in immunotherapy. This study examines the potential of combining mechanical high-intensity focused ultrasound (M-HIFU) with dendritic cell (DC) vaccines to enhance immune responses against [...] Read more.
Background/Objectives: Effectively targeting treatment-resistant tumor cells, particularly cancer stem cells (CSCs) involved in tumor recurrence, remains a major challenge in immunotherapy. This study examines the potential of combining mechanical high-intensity focused ultrasound (M-HIFU) with dendritic cell (DC) vaccines to enhance immune responses against OLFM4-expressing tumors, a CSC marker linked to immune evasion and tumor growth. Methods: M-HIFU was applied to induce immunogenic cell death by mechanically disrupting tumor cells, releasing tumor-associated antigens and creating an immunostimulatory environment. DC vaccines loaded with OLFM4 were then administered to boost the immune response within this primed environment. Results: The combination of M-HIFU and DC vaccine significantly inhibited tumor growth and metastasis, with enhanced T-cell activation and increased recruitment of immune cells due to elevated chemokines CCL19 and CCL21. This synergy promoted immune memory, reducing the likelihood of recurrence. Conclusions: M-HIFU effectively promotes the migration of DC vaccines through CCL19/21, presenting a promising approach for cancer treatment. Further studies are recommended to optimize this combination for clinical applications, with potential to improve patient outcomes in challenging cancer types. Full article
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>OLFM4-specific immune responses induced by mechanical HIFU compared to thermal HIFU in tumor-bearing Mice. (<b>a</b>) Schematic illustrating the timeline of thermal and mechanical HIFU (▼) treatments following inoculation with B16F10-Luc2-OLFM4 tumor cells on day 0. (<b>b</b>) Tumor growth monitoring: B6 mice (n = 5/group) were injected with 2 × 10<sup>6</sup> B16F10-Luc-OLFM4 tumor cells in the right flank 7 days before the first immunization. Tumor size was measured every three days for 22 days. Data are presented as mean ± standard error for 5 mice. (<b>c</b>) Luminescence from the tumors was measured using IVIS (<b>left</b>), and total flux was quantified (<b>right</b>). (<b>d</b>) Effector cells were collected 9 days after the final immunization and incubated with CFSE-labeled target cells at an effector-to-target (E:T) ratio of 40:1 for 16 h. The percentage of specific lysis was calculated using the following formula: Specific Lysis (%) = [1 − (% CFSE<sup>high</sup>/% CFSE<sup>low</sup>)] × 100. Data from three independent experiments are shown as mean ± standard error. (<b>e</b>) Lymphocytes were collected from inguinal lymph nodes 9 days after the last immunization, stained with CFSE, and co-cultured with PBS or OLFM4 for two days. Lymphocyte proliferation was assessed by CFSE dilution. Representative data from three independent experiments are shown. (<b>f</b>) The number of IFN-γ-producing splenocytes was compared by ELISPOT after stimulation with PBS or OLFM4 for two days. * Statistically significant at <span class="html-italic">p</span> &lt; 0.05, according to Student’s <span class="html-italic">t</span>-test.</p>
Full article ">Figure 2
<p>Mechanical HIFU combined with DC vaccine therapy for enhanced tumor suppression in OLFM4-expressing tumor Models. (<b>a</b>) Schematic illustrating the timeline of M-HIFU and DCs[SC] treatments (▼) following inoculation with B16F10-Luc2-OLFM4 tumor cells on day 0. (<b>b</b>) B6 mice (n = 5/group) were injected with 2 × 10<sup>6</sup> B16F10-Luc-OLFM4 tumor cells into the right flank 7 days before the first immunization. Tumor size was measured every three days for 22 days. Data are shown as the mean ± standard error for 5 mice. (<b>c</b>) Luminescence from the tumors was measured using IVIS, and the total flux was quantified. (<b>d</b>) Effector cells were collected 9 days after the final immunization and incubated with CFSE-labeled target cells at an effector-to-target (E:T) ratio of 40:1 for 16 h. Specific lysis was calculated using the following formula: Specific Lysis (%) = [1 − (% CFSE<sup>high</sup>/% CFSE<sup>low</sup>)] × 100. Data from three independent experiments are presented as mean ± standard error. (<b>e</b>) Lymphocytes were collected from inguinal lymph nodes 9 days after the last immunization, stained with CFSE, and co-cultured with PBS or OLFM4 for two days. Lymphocyte proliferation was assessed by measuring the CFSE dilution. Representative data from three independent experiments are shown. (<b>f</b>) The number of IFN-γ-producing splenocytes was compared using ELISPOT after stimulation with PBS or OLFM4 for two days. * Statistically significant at <span class="html-italic">p</span> &lt; 0.05, according to Student’s <span class="html-italic">t</span>-test.</p>
Full article ">Figure 3
<p>M-HIFU and DC vaccine administration effects on DC migration and tumor microenvironment (TME). (<b>a</b>) Schematic representation of the experimental design. CD45.1<sup>+</sup> DCs were injected either intratumorally (I.T.) or subcutaneously (S.C.) after M-HIFU treatment (▼). The migration of DCs to the lymph nodes and chemokine expression in the tumor microenvironment (TME) were evaluated. (<b>b</b>) Chemokine expression (CCL19, CCL21) at the tumor site was measured using quantitative RT-PCR. M-HIFU significantly increased the expression of both CCL19 and CCL21 compared to the control, suggesting alterations in the TME. The experiments were conducted in duplicate, with a total of n = 16 per group (<span class="html-italic">p</span> &lt; 0.01). (<b>c</b>) Quantification of migrated DCs in the lymph nodes after treatment. M-HIFU significantly enhanced DC migration compared to the control (n = 6 per group, <span class="html-italic">p</span> &lt; 0.05). Migrated DC percentage = (percentage of CD45.1<sup>+</sup> × Total lymph node cell number)/Total number of injected cells × 100 (<b>d</b>) CCR7 expression levels in the migrated DCs were assessed using flow cytometry. No significant differences in CCR7 expression were observed between the treatment and control groups (n.s., not significant). * Statistically significant at <span class="html-italic">p</span> &lt; 0.05, according to Student’s <span class="html-italic">t</span>-test.</p>
Full article ">Figure 4
<p>Comparative efficacy of subcutaneous vs. intratumoral DC vaccination in conjunction with mechanical HIFU in tumor models. (<b>a</b>) B16F10 cells were injected both subcutaneously (S.C.) and intravenously to induce cancer in both the subcutaneous region and the lungs, establishing a model of lung metastasis along with S.C. tumors. M-HIFU was applied to the S.C. tumor site, and dendritic cells (DCs) were administered either subcutaneously at the base of the tail or intratumorally (▼). (<b>b</b>) After the mice were sacrificed, lung paraffin blocks were prepared, and histological sections were stained with H&amp;E. (<b>c</b>) The percentage of the tumor area relative to the total lung area was quantified using ImageJ software V2 by measuring the cancerous regions and the overall lung area from the histological sections. (<b>d</b>) Additionally, the number of visible tumor nodules in the lungs was manually counted. (<b>e</b>) Effector cells were harvested 9 days after the final immunization and incubated with CFSE-labeled target cells at an effector-to-target (E:T) ratio of 40:1 for 16 h. Specific lysis was calculated using the formula Specific Lysis (%) = [1 − (% CFSE<sup>high</sup>/% CFSE<sup>low</sup>)] × 100. Data from three independent experiments are presented as mean ± standard error. (<b>f</b>) Lymphocytes were collected from inguinal lymph nodes 9 days after the final immunization, stained with CFSE, and co-cultured with PBS or OLFM4 for two days. Lymphocyte proliferation was assessed by measuring CFSE dilution. Data from three independent experiments are presented. (<b>g</b>) The number of IFN-γ-producing splenocytes was compared using ELISPOT after two days of stimulation with OLFM4. * Statistically significant at <span class="html-italic">p</span> &lt; 0.05, according to Student’s <span class="html-italic">t</span>-test.</p>
Full article ">
19 pages, 6220 KiB  
Article
Synergistic Synbiotic-Containing Lactiplantibacillus plantarum and Fructo-Oligosaccharide Alleviate the Allergenicity of Mice Induced by Soy Protein
by Jing Bai, Qian Zeng, Wen Den, Liheng Huang, Zhihua Wu, Xin Li, Ping Tong, Hongbing Chen and Anshu Yang
Foods 2025, 14(1), 109; https://doi.org/10.3390/foods14010109 - 2 Jan 2025
Viewed by 659
Abstract
Prebiotics and probiotics have key roles in the intervention and treatment of food allergies. This study assesses the effect of Lactiplantibacillus plantarum synergistic fructo-oligosaccharide (Lp–FOS) intervention using an allergic mouse model induced by soy protein. The results showed that Lp synergistic FOS significantly [...] Read more.
Prebiotics and probiotics have key roles in the intervention and treatment of food allergies. This study assesses the effect of Lactiplantibacillus plantarum synergistic fructo-oligosaccharide (Lp–FOS) intervention using an allergic mouse model induced by soy protein. The results showed that Lp synergistic FOS significantly decreased clinical allergy scores, inhibited specific antibodies (IgE, IgG, and IgG1), IL-4, IL-6, and IL-17A levels, and increased IFN-γ and IL-10 levels. Meanwhile, flow cytometry showed that Lp–FOS intervention inhibited the percentage of dendritic cell (DC) subsets in splenocytes and increased the Th1/Th2 and Treg/Th17 ratios. Furthermore, Lp–FOS intervention upregulated the mRNA levels of T-bet and Foxp3 and downregulated the mRNA levels of GATA3. Finally, non-targeted metabolomic analysis showed that Lp–FOS improved serum metabolic disorders caused by food allergies through regulating glycine, serine, and threonine metabolism, butanoate metabolism, glyoxylate and dicarboxylate metabolism, the biosynthesis of cofactors, and glycerophospholipid metabolism. These data showed that the combination formulation Lp–FOS could be a promising adjuvant treatment for food allergies. Full article
Show Figures

Figure 1

Figure 1
<p>Intervention effect of Lp and FOS on mice allergic to soybeans. (<b>A</b>) Schematic diagram of mice with soy allergies and their preventive model. (<b>B</b>) Clinical score of acute hypersensitivity. (<b>C</b>) Body temperature. (<b>D</b>) Weight gain. * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
Full article ">Figure 2
<p>Serum-specific antibody levels in mice. (<b>A</b>) IgE. (<b>B</b>) IgG. (<b>C</b>) IgG1. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
Full article ">Figure 3
<p>The levels of cytokines in mice. (<b>A</b>) IFN-γ. (<b>B</b>) IL-10. (<b>C</b>) IL-4. (<b>D</b>). IL-17A. (<b>E</b>) IL-6. * <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, and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
Full article ">Figure 4
<p>Flow cytometric analysis for DCs in the spleen. (<b>A</b>) Gating strategies of DC subsets in splenocytes. (<b>B</b>) Percentages of CD11c+CD103+ DCs. (<b>C</b>) Percentages of CD11c+CD40+ DCs. (<b>D</b>) Percentages of CD11c+CD86+ DCs. * <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, and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
Full article ">Figure 5
<p>Flow cytometric analysis for T cell subsets in spleens. (<b>A</b>) Gating strategies of T cell subsets in splenocytes. (<b>B</b>) Percentage of T-bet+. (<b>C</b>) Percentages of GATA3+. (<b>D</b>) Percentage of RORγt+. (<b>E</b>) Percentage of Foxp3+. (<b>F</b>) Th1/Th2 ratio. (<b>G</b>) Treg/Th17 ratio. * <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, and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
Full article ">Figure 6
<p>Effects of Lp–FOS intervention on the mRNA expression of transcription factors in the intestine. (<b>A</b>) T-bet. (<b>B</b>) GATA3. (<b>C</b>) Foxp3. (<b>D</b>) RORγt. * <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, and **** <span class="html-italic">p</span> &lt; 0.0001. The mRNA expression levels were normalized to GAPDH housekeeping gene expression.</p>
Full article ">Figure 7
<p>Metabolic analysis of mouse serum. (<b>A</b>) PLS-DA analysis. (<b>B</b>) Permutation tests using 200 random permutations in the PLS-DA model. (<b>C</b>,<b>D</b>) Volcano plot of soy protein vs control and Lp–FOS vs soy protein. (<b>E</b>,<b>F</b>) KEGG pathway enrichment analysis between soy protein vs control and Lp–FOS vs. soy protein. (<b>G</b>) Statistical map of the top 20 most significant pathways among all detected metabolites. (<b>H</b>) Heatmap of correlation coefficients between the potential metabolic biomarkers in serum and allergic indicators. Asterisks denote significant differences (* 0.01 &lt; <span class="html-italic">p</span> ≤ 0.05, ** 0.001 &lt; <span class="html-italic">p</span> ≤ 0.01, *** <span class="html-italic">p</span> ≤ 0.001).</p>
Full article ">
14 pages, 2405 KiB  
Article
The Inhibitory Effects of Alpha 1 Antitrypsin on Endosomal TLR Signaling Pathways
by Ahmed S. Elshikha, Georges Abboud, Rigena Avdiaj, Laurence Morel and Sihong Song
Biomolecules 2025, 15(1), 43; https://doi.org/10.3390/biom15010043 - 1 Jan 2025
Viewed by 462
Abstract
Endosomal toll-like receptors (TLRs) TLR7, TLR8, and TLR9 play an important role in systemic lupus erythematosus (SLE) pathogenesis. The proteolytic processing of these receptors in the endolysosome is required for signaling in response to DNA and single-stranded RNA, respectively. Targeting this proteolytic processing [...] Read more.
Endosomal toll-like receptors (TLRs) TLR7, TLR8, and TLR9 play an important role in systemic lupus erythematosus (SLE) pathogenesis. The proteolytic processing of these receptors in the endolysosome is required for signaling in response to DNA and single-stranded RNA, respectively. Targeting this proteolytic processing may represent a novel strategy to inhibit TLR-mediated pathogenesis. Human alpha 1 antitrypsin (hAAT) is a protease inhibitor with anti-inflammatory and immunoregulatory properties. However, the effect of hAAT on endosomal TLRs remains elusive. In this study, we first tested the effect of hAAT on TLR9 signaling in dendritic cells (DCs). We showed that hAAT inhibited TLR9-mediated DC activation and cytokine production. Human AAT also lowered the expressions of interferon signature genes. Western blot analysis showed that hAAT reduced the expression of the active form (cleaved) of TLR9 in DCs, indicating a novel mechanism of hAAT function in the immune system. We next tested the effect of hAAT on TLR7/8 signaling. Similar to the effect on TLR9 signaling, hAAT also inhibited R848 (TLR7 and 8 agonist)-induced DC activation and functions and lowered the expressions of interferon signature genes. Our in vivo studies using hAAT transgenic mice also showed that hAAT attenuated R848-induced pathogenesis. Specifically, hAAT completely blocked the R848 induction of germinal center T cells (GC T), B cells (GC B), and plasma cells (GC PCs), as well as T follicular T helper cells (TFH), which are all critical in lupus development. These data demonstrated that hAAT inhibited TLR7/8 and TLR9 signaling pathways, which are critical for lupus development. These findings not only advanced the current knowledge of hAAT biology, but also implied an insight into the clinical application of hAAT. Full article
(This article belongs to the Special Issue Roles of Alpha-1 Antitrypsin in Human Health and Disease Models)
Show Figures

Figure 1

Figure 1
<p><b>The effect of hAAT on CpG-induced DC activation and functions</b>. Conventional DCs were induced from BM cells from B6 mice. During the induction, cells were treated with or without 0.1, 0.5, 1, or 2 mg/mL hAAT. After the induction, cells were stimulated with CpG (0, 1, 5, and 10 µg/mL) for 6 h and subjected to flow cytometry analyses to detect DC activation using antibodies recognizing CD80, CD86, and I-A/I-E (<b>A</b>). Cytokines (IFN-I, IL-12, and chemokine CXCL10) in culture media were detected (<b>B</b>). n = 3 (cells from 3 mice) for all groups. *, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01; and ***, <span class="html-italic">p</span> &lt; 0.001 by one-way ANOVA. #, <span class="html-italic">p</span> &lt; 0.05 by <span class="html-italic">t</span> test.</p>
Full article ">Figure 2
<p><b>The effect of hAAT on CpG-induced gene expression.</b> (<b>A</b>). IFN-I signature genes. (<b>B</b>). Cytokine genes. DCs treated with or without 1 mg/mL hAAT were stimulated with 0 or 1 μg/mL CpG for 6 h. The cells were harvested and gene expressions were assessed by RT-qPCR using primers specific to the genes of IRF-7, Mx-1, and ISG-15, and IL-12p40 or TNF-α. n = 6 mice for all groups. *, <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; and ****, <span class="html-italic">p</span> &lt; 0.0001 by <span class="html-italic">t</span> test.</p>
Full article ">Figure 3
<p><b>The effect of hAAT on TLR9, p-NF-ĸB, and p-IĸBα.</b> DCs were treated with hAAT (1 mg/mL) and stimulated with or without 5 µg/mL CpG for 15 min. The cells were lysed and subjected to Western blot analyses using antibodies recognizing TLR9 (<b>A</b>), p-NF-kB (<b>B</b>), or p-IkBα (<b>C</b>). B-actin levels were used as loading controls; (-) and Unstimulated indicate untreated cells (with neither AAT nor CpG treatment). FL-TLR9, full-length TLR9; C-TLR9, cleaved TLR9; RE, relative to. n = 3 (cells from 3 mice) for all groups. *, <span class="html-italic">p</span> &lt; 0.05 and **, <span class="html-italic">p</span> &lt; 0.01 by one-way ANOVA. Western blot original images can be found in <a href="#app1-biomolecules-15-00043" class="html-app">Figure S2</a>.</p>
Full article ">Figure 4
<p><b>The effect of hAAT on R848-induced DC activation and functions.</b> During the cDC induction, cells were treated with 0, 1, or 2 mg/mL hAAT. After the induction, cells were stimulated with R848 (0, 0.5, 1, or 3 µg/mL) for 6 h and subjected to flow cytometry analyses to detect DC activation using antibodies recognizing CD86 (<b>A</b>) and I-A/I-E (<b>B</b>). IL-12 (<b>C</b>) and INF-I (<b>D</b>) in the cell culture media were detected. n = 3 (cells from 3 mice) for all groups. *, <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; and ****, <span class="html-italic">p</span> &lt; 0.0001 by one-way ANOVA.</p>
Full article ">Figure 5
<p><b>The effect of hAAT on R848-induced gene expression.</b> Human-AAT-treated DCs were simulated with 0 or 1 μg/mL R848 for 1, 4, or 6 h. The cells were harvested and gene expressions were assessed by RT-qPCR using primers specific to genes of IRF-7, Mx-1, and ISG-15 or TNF-α. n = 3 to 8. ***, <span class="html-italic">p</span> &lt; 0.001; and ****, <span class="html-italic">p</span> &lt; 0.0001 by <span class="html-italic">t</span> test.</p>
Full article ">Figure 6
<p><b>Germinal center T cells (A), B cells (B), and plasma cells (C) in hAAT-tg mice are resistant to R848 treatment</b>. hAAT-tg, C57BL/6 (B6), and B6.TC (TC) mice were treated with 100 µL R848 in acetone 3 times (2-day interval). Control mice were treated with 100 µL acetone. n = 4 for all R848 treated groups. For untreated groups, n = 5 for hAAT-tg, n = 2 for B6, and n = 2 for TC. ns, <span class="html-italic">p</span> &gt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001; and ****, <span class="html-italic">p</span> &lt; 0.0001 by one-way ANOVA.</p>
Full article ">Figure 7
<p><b>The effect of hAAT on T cell populations.</b> (<b>A</b>). Central memory T cells (Tcm). (<b>B</b>). Treg related cells. (<b>C</b>). T follicular helper cells (T<sub>FH</sub>) and T follicular regulatory cells (T<sub>FR</sub>). n = 4 (cells from 4 mice) for all R848 treated groups. For untreated groups, n = 5 mice for hAAT-tg, n = 2 mice for B6, and n = 2 mice for TC. ns, <span class="html-italic">p</span> &gt; 0.05; *, <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; and ****, <span class="html-italic">p</span> &lt; 0.0001 by one-way ANOVA.</p>
Full article ">
19 pages, 4268 KiB  
Article
Effects of Cordyceps cicadae Polysaccharide on Gut Microbiota, the Intestinal Mucosal Barrier, and Inflammation in Diabetic Mice
by Lijia Sun, Huaibo Yuan, Huiqing Ma and Yani Wang
Metabolites 2025, 15(1), 8; https://doi.org/10.3390/metabo15010008 - 1 Jan 2025
Viewed by 554
Abstract
Background: Polysaccharides produced by the edible fungus Cordyceps cicadae can regulate blood sugar levels and may represent a suitable candidate for the treatment of diabetes and its complications. However, there is limited information available about the mechanism of how C. cicadae polysaccharide (CCP) [...] Read more.
Background: Polysaccharides produced by the edible fungus Cordyceps cicadae can regulate blood sugar levels and may represent a suitable candidate for the treatment of diabetes and its complications. However, there is limited information available about the mechanism of how C. cicadae polysaccharide (CCP) might improve diabetic conditions. Methods: This study investigated its effects on the intestinal microbiota, intestinal mucosal barrier, and inflammation in mice with type 2 diabetes mellitus (T2DM) induced by streptozotocin, and its potential mechanisms. Results: Compared with the DC (diabetes model control group), CCPH oral treatment significantly increased the number of beneficial bifidobacteria, bifidobacteria, and lactobacilli (p < 0.01), restored the diversity of intestinal microorganisms in diabetic mice, and the proportions of Firmicutes and Bacteroidetes (34.36%/54.65%) were significantly lower than those of the DC (52.15%/32.09%). Moreover, CCPH significantly reduced the content of endotoxin (lipopolysaccharide, LPS) and D-lactic acid(D-LA) (p < 0.05), the activities of antioxidant enzymes and total antioxidant capacity were significantly increased (p < 0.01), and the content of proinflammatory cytokines TNF-α, IL-6, and IL-1β were reduced by 42.05%, 51.28%, and 52.79%, respectively, compared with the DC. The TLR4/NF-κB signaling pathway, as a therapeutic target for diabetic intestinal diseases, plays a role in regulating the inflammatory response and protecting the intestinal barrier function. Molecular mechanism studies showed that oral treatment with CCPH down-regulated the expression of NF-κB, TLR-4, and TNF-α genes by 18.66%, 21.58%, and 34.87%, respectively, while up-regulating the expression of ZO-1 and occludin genes by 32.70% and 25.11%, respectively. CCPH regulates the expression of short-chain fatty acid levels, increases microbial diversity, and ameliorates mouse colon lesions by inhibiting the TLR4/NF-κB signaling pathway. Conclusions: In conclusion, it is demonstrated that in this murine model, the treatment of diabetes with C. cicadae polysaccharide can effectively regulate intestinal microbiota imbalance, protect intestinal mucosal barrier function, and reduce inflammation in vivo, suggesting this natural product can provide a suitable strategy for the treatment of T2D-induced gut dysbiosis and intestinal health. Full article
(This article belongs to the Section Endocrinology and Clinical Metabolic Research)
Show Figures

Figure 1

Figure 1
<p>Quantification by cultivation of beneficial bacteria in the intestines of diabetic mice under various treatments (n = 10 per group), with Bifidobacterium (<b>A</b>), Lactobacillus (<b>B</b>), and Bacteroides (<b>C</b>). Data are presented as mean ± standard deviation (SD) values. Statistical significance is indicated as A (<span class="html-italic">p</span> &lt; 0.01) and a (<span class="html-italic">p</span> &lt; 0.05) vs. the normal control and as B (<span class="html-italic">p</span> &lt; 0.01) and b (<span class="html-italic">p</span> &lt; 0.05) vs. the diabetic control.</p>
Full article ">Figure 2
<p>Intestinal mucosal barrier indicators of the diabetic mice (n = 10 per group). (<b>A</b>) LPS serum levels and (<b>B</b>) lactic acid content. The compared statistical significance is given as a: <span class="html-italic">p</span> &lt; 0.05, A: <span class="html-italic">p</span> &lt; 0.01; compared with the DC group as B: <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">Figure 3
<p>Levels of intestinal antioxidant capacity (<span class="html-italic">n</span> = 10 per group). (<b>A</b>) CAT level, (<b>B</b>) SOD level, (<b>C</b>) T-AOC level. Compared with the NC group, a: <span class="html-italic">p</span> &lt; 0.05, A: <span class="html-italic">p</span> &lt; 0.01; compared with the DC group, B: <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">Figure 4
<p>Content of proinflammatory cytokines (<b>A</b>–<b>C</b>) in diabetic mice (n = 10 per group). Compared with the NC group, A: <span class="html-italic">p</span> &lt; 0.01; compared with the DC group, B: <span class="html-italic">p</span> &lt; 0.01.</p>
Full article ">Figure 5
<p>Venn diagram of OTU distribution.</p>
Full article ">Figure 6
<p>Distribution of microbial communities at the phylum level. (<b>A</b>–<b>D</b>) microbial species composition from the NC group, DC group, PC group and CCPH group.</p>
Full article ">Figure 7
<p>PCA analysis.</p>
Full article ">Figure 8
<p>HE (<b>left</b>) and AB-PAS (<b>right</b>) stained mouse colon sections. (<b>A</b>–<b>D</b>) Colon from the NC group, DC group, PC group, and CCPH group, respectively.</p>
Full article ">Figure 9
<p>mRNA expression levels of NF-kB, TLR-4, TNF-α, ZO-1, and occludin in the colon.</p>
Full article ">
20 pages, 3795 KiB  
Article
Exploring Machine Learning Classification of Movement Phases in Hemiparetic Stroke Patients: A Controlled EEG-tDCS Study
by Rishishankar E. Suresh, M S Zobaer, Matthew J. Triano, Brian F. Saway, Parneet Grewal and Nathan C. Rowland
Brain Sci. 2025, 15(1), 28; https://doi.org/10.3390/brainsci15010028 - 29 Dec 2024
Viewed by 593
Abstract
Background/Objectives: Noninvasive brain stimulation (NIBS) can boost motor recovery after a stroke. Certain movement phases are more responsive to NIBS, so a system that auto-detects these phases would optimize stimulation timing. This study assessed the effectiveness of various machine learning models in identifying [...] Read more.
Background/Objectives: Noninvasive brain stimulation (NIBS) can boost motor recovery after a stroke. Certain movement phases are more responsive to NIBS, so a system that auto-detects these phases would optimize stimulation timing. This study assessed the effectiveness of various machine learning models in identifying movement phases in hemiparetic individuals undergoing simultaneous NIBS and EEG recordings. We hypothesized that transcranial direct current stimulation (tDCS), a form of NIBS, would enhance EEG signals related to movement phases and improve classification accuracy compared to sham stimulation. Methods: EEG data from 10 chronic stroke patients and 11 healthy controls were recorded before, during, and after tDCS. Eight machine learning algorithms and five ensemble methods were used to classify two movement phases (hold posture and reaching) during each of these periods. Data preprocessing included z-score normalization and frequency band power binning. Results: In chronic stroke participants who received active tDCS, the classification accuracy for hold vs. reach phases increased from pre-stimulation to the late intra-stimulation period (72.2% to 75.2%, p < 0.0001). Late active tDCS surpassed late sham tDCS classification (75.2% vs. 71.5%, p < 0.0001). Linear discriminant analysis was the most accurate (74.6%) algorithm with the shortest training time (0.9 s). Among ensemble methods, low gamma frequency (30–50 Hz) achieved the highest accuracy (74.5%), although this result did not achieve statistical significance for actively stimulated chronic stroke participants. Conclusions: Machine learning algorithms showed enhanced movement phase classification during active tDCS in chronic stroke participants. These results suggest their feasibility for real-time movement detection in neurorehabilitation, including brain–computer interfaces for stroke recovery. Full article
(This article belongs to the Special Issue The Application of EEG in Neurorehabilitation)
Show Figures

Figure 1

Figure 1
<p>(<b>A</b>) A total of 10 participants with chronic stroke and 11 healthy controls were randomized into active and sham stimulation groups. (<b>B</b>) The participants were fitted with EEG electrodes and HD-tDCS delivering anodal stimulation to the ipsilesional side (contralateral to the motor deficit); for healthy participants, laterality was randomized. (<b>C</b>) During the stimulation phase, the participants performed a VR motor task involving reaches toward a virtual blue sphere target (3 cm radius) placed 0.3–0.5 m away. The task had three steps: hold, prep (Cue), and move. An example VR scene is shown. (<b>D</b>) Sham participants received 30 s of ramp-up current, then no stimulation, while active stimulation included 30 s ramp-up followed by 20 min of stimulation. Twelve reaches were performed at each time period. EEG was recorded at pre, 5 min (intra5), 15 min (intra15), and post-stimulation (post). (<b>E</b>) The raw EEG signal was recorded from all channels. After normalization, the power spectral density was calculated and binned across frequency bands to complete feature extraction. (<b>F</b>) Thirteen ML models were trained using 70% of the pre-stim data and then tested on 30% of the pre-stim data and 100% of the data from other time periods. This was performed to simulate ex vivo training of an onboard BCI.</p>
Full article ">Figure 2
<p>(<b>A</b>) Classification of disease state (healthy versus chronic stroke) using all frequencies from 1 to 50 Hz and all electrodes. Although most algorithms detected differences prior to stimulation, LDA was not affected. (<b>B</b>) Mean accuracies. (<b>C</b>) To account for baseline differences at the pre-stimulation time periods, we normalized the accuracies to the pre-stimulation accuracy for each group. Asterisks (*) indicate a significant difference between active and sham groups. We observed a significantly increased classification accuracy for the active stim group at the intra5 and post-stimulation time periods, with accuracies converging at intra15. Values in parentheses represent the number of algorithms per group at each time period.</p>
Full article ">Figure 3
<p>(<b>A</b>) Hold versus reach movement classification using all frequencies of 1–50 Hz and all electrodes. In the CS active group, the classification accuracy increased after stimulation and peaked at intra15 (*, asterisk). Furthermore, the accuracy at this time period was significantly higher than in the CS sham group at the same time (†, cross). (<b>B</b>) Movement classification was higher at intra15 than pre in the CS active group. (<b>C</b>) Movement classification was higher for intra15 CS active than intra15 CS sham. Values in parentheses represent the number of algorithms per group at each time period. (<b>D</b>) Mean accuracies. Values in parentheses represent the number of algorithms per group at each time period. (<b>E</b>) We observed a tradeoff between the training time and accuracy, as LDA produced the highest accuracy with a short training time compared to the other models with the exception of DT, which obtained a slightly higher mean accuracy but required the longest training time.</p>
Full article ">Figure 4
<p>(<b>A</b>) Hold versus reach movement classification using all frequencies of 1–50 Hz and all electrodes by the frequency band used (delta through gamma) in CS active participants only. Gamma PSD alone produced the highest classification accuracy for most models, although this was not statistically significant. (<b>B</b>) Mean accuracies. (<b>C</b>) When comparing the classification accuracy over time by band, we observe a broadband increase in the accuracy, seen in all bands at intra15. We also observe some differences in the band response, highlighted here using colored asterisks corresponding to each band. Values in parentheses represent the number of algorithms per individual frequency band at each time period. (<b>D</b>) When aggregating algorithms by method, we observed that ensemble methods (such as global voting, or hard voting) resolved frequency bands more so than other models, although this was not significant. Dimensionality reduction (LDA) and regression (LR) methods outperformed the others. Values in parentheses represent the number of algorithms per method at each time period.</p>
Full article ">Figure 5
<p>(<b>A</b>) Hold versus reach movement classification using C3 or C4 electrodes and all frequencies of 1–50 Hz. Number of asterisks (*) represents significance level between contra- and ipsi-lesional accuracy. Here, we investigated the effect of recording electrode lesion laterality. We observed that contralesional classification accuracy significantly decreases during stimulation compared to ipsilesional accuracy in CS active participants; this is not seen in any of the other groups. Values in parentheses represent the number of algorithms per group at each time period. (<b>B</b>) Mean accuracies.</p>
Full article ">
21 pages, 5423 KiB  
Article
Virtual Inertia Methods for Supporting Frequency Stabilisation in Autonomous AC/DC Microgrids
by Faysal Hardan, Rosemary Norman and Pietro Tricoli
Electronics 2025, 14(1), 91; https://doi.org/10.3390/electronics14010091 - 28 Dec 2024
Viewed by 618
Abstract
Isolated microgrids have long been considered alternative power system entities that can integrate various types of distributed energy sources such as diesel and renewable power generators including energy storage. Renewable energy sources, such as wind and solar PV, introduce low inertia and high [...] Read more.
Isolated microgrids have long been considered alternative power system entities that can integrate various types of distributed energy sources such as diesel and renewable power generators including energy storage. Renewable energy sources, such as wind and solar PV, introduce low inertia and high intermittency to the microgrid. For this reason, coordinated control and frequency stabilisation are crucial for maintaining higher service levels in the microgrid. This paper reports on the design and development of two proposed methods for virtual inertia provision, namely model-based and filter-based methods, which support the frequency stability of AC/DC microgrids. The inertial power produced by these methods was implemented through power-controlled voltage source converters, associated with a Li-ion battery energy storage system. To derive and develop the functions for the virtual inertia providers using these methods, a new electromechanical power-speed model was developed to represent the interaction between the microgrid AC/DC-sides and its generators. Small-signal analysis using the linearised form of this model was carried out, in addition to deriving the law for the model-based virtual inertia method. Detailed physical-system simulation and tests were performed, and performance analysis of the resulting generator speed-responses using the proposed methods illustrated their merits compared with other methods, namely the standard df/dt and frequency-event techniques. Full article
Show Figures

Figure 1

Figure 1
<p>The proposed structure of the studied AC/DC microgrid.</p>
Full article ">Figure 2
<p>(<b>a</b>) Structure of the utilised VSC, including its network interface equivalent circuit. (<b>b</b>) Representation of the VSCs as paralleled voltage sources. (<b>c</b>) Equivalent voltage source of the VSCs.</p>
Full article ">Figure 2 Cont.
<p>(<b>a</b>) Structure of the utilised VSC, including its network interface equivalent circuit. (<b>b</b>) Representation of the VSCs as paralleled voltage sources. (<b>c</b>) Equivalent voltage source of the VSCs.</p>
Full article ">Figure 3
<p>(<b>a</b>) Frequency responses of the VSC active power deviation <math display="inline"><semantics> <mrow> <mo>∆</mo> <mi>P</mi> </mrow> </semantics></math> using <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>g</mi> </mrow> <mrow> <mn>11</mn> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>s</mi> </mrow> </mfenced> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>g</mi> </mrow> <mrow> <mn>12</mn> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>s</mi> </mrow> </mfenced> </mrow> </semantics></math>, (<b>b</b>) the corresponding step responses with unit steps of inputs <math display="inline"><semantics> <mrow> <mo>∆</mo> <mi>D</mi> <msub> <mrow> <mi>v</mi> </mrow> <mrow> <mi>d</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>∆</mo> <mi>D</mi> <msub> <mrow> <mi>v</mi> </mrow> <mrow> <mi>q</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
Full article ">Figure 4
<p>(<b>a</b>) Frequency responses of generator speed via <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>g</mi> </mrow> <mrow> <mn>31</mn> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>s</mi> </mrow> </mfenced> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>g</mi> </mrow> <mrow> <mn>32</mn> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>s</mi> </mrow> </mfenced> <mo>,</mo> <mtext> </mtext> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">n</mi> <mi mathvariant="normal">d</mi> </mrow> </semantics></math> <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>g</mi> </mrow> <mrow> <mn>33</mn> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>s</mi> </mrow> </mfenced> </mrow> </semantics></math>, (<b>b</b>) the corresponding step responses of the speed with unit steps of inputs <math display="inline"><semantics> <mrow> <mo>∆</mo> <mi>D</mi> <msub> <mrow> <mi>v</mi> </mrow> <mrow> <mi>d</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>∆</mo> <mi>D</mi> <msub> <mrow> <mi>v</mi> </mrow> <mrow> <mi>q</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
Full article ">Figure 5
<p>Simplified block diagram of the proposed model-based VI method and its switching mechanism.</p>
Full article ">Figure 6
<p>Proposed structure of the filter-based VI method for generating a VI power demand.</p>
Full article ">Figure 7
<p>Predicted speed responses to <math display="inline"><semantics> <mrow> <mo>∆</mo> <msub> <mrow> <mi>P</mi> </mrow> <mrow> <mi>e</mi> </mrow> </msub> <mo>=</mo> <mo>±</mo> <mn>0.1</mn> <mtext> </mtext> <mi mathvariant="normal">M</mi> <mi mathvariant="normal">W</mi> </mrow> </semantics></math> AC-load step at various values of the filter time constant.</p>
Full article ">Figure 8
<p>Block diagram of the control system of the diesel engine generator considered for testing the VI methods.</p>
Full article ">Figure 9
<p>Block diagram of the standard external VI method, including frequency dead-band as an event detector, in addition to the switching function <span class="html-italic">SW</span>.</p>
Full article ">Figure 10
<p>(<b>a</b>) Microgrid three-phase voltages and their magnitude during VI provision using the standard <math display="inline"><semantics> <mrow> <mi>d</mi> <mi>f</mi> <mo>/</mo> <mi>d</mi> <mi>t</mi> </mrow> </semantics></math> method, (<b>b</b>) corresponding VSCs’ current waveforms, and (<b>c</b>) the same VSCs’ current waveforms decaying over a longer timespan after a 0.1 pu change in the microgrid load.</p>
Full article ">Figure 11
<p>(<b>a</b>) Speed responses and their VSC power obtained for all VI methods (filter method at <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>f</mi> </mrow> </msub> <mo>=</mo> <mn>0.2</mn> <mi>s</mi> </mrow> </semantics></math>). (<b>b</b>) Expanded sections of graphs in (<b>a</b>).</p>
Full article ">Figure 12
<p>(<b>a</b>) Speed responses and their VSC power when using the filter-based VI method at various values of time-constant <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math>. (<b>b</b>) Expanded sections of graphs in (<b>a</b>).</p>
Full article ">
Back to TopTop