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Mesenchymal stromal cells as rescue therapy in biologic-refractory psoriasis: insights from a case series
Get PDFData availability statement:
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.Supplementary material:
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2025.1656724/full#supplementary-materialGenerative AI statement:
The author(s) declare that Generative AI was used in the creation of this manuscript. ChatGPT 4.0 was used to improve the readability of the manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.Cytokine-targeted biologics have revolutionized the management of moderate-to-severe psoriasis; however, all available therapies have failed a growing number of patients. Mesenchymal stromal cells (MSCs), with their immunomodulatory properties, offer a novel therapeutic option. Here, we report the cases of three adult female patients with long-standing, severe plaque psoriasis who were refractory to multiple biologic therapies, and were consequently treated with two intravenous infusions of allogeneic umbilical cord-derived MSCs (UC-MSCs; 1.96 – 3.00 × 106 cells/kg) 1 week (W) apart. Two patients received UC-MSCs as monotherapy; one received them alongside etanercept. Upon relapse, two patients resumed their last failed biologic at W9, while one switched to a new biologic at W24. UC-MSCs were well-tolerated and yielded variable clinical benefits. The best responder to MSCs experienced an 87% reduction in the Psoriasis Area and Severity Index (PASI 87) by W4. Two patients showed improved responses to previously failed biologics (absolute PASI of 0–2), sustained for over 2 years following reinitiation. Multi-parameter flow cytometry revealed increased frequencies of CD4+ and CD8+ skin-homing (CLA+CD103−) and skin-recirculating (CLA+CD103+) memory T cells, CD25HiCD127LoFoxP3+ regulatory T cells, and non-classical (CD14LoCD16+) monocytes, associated with clinical improvements. These findings suggest that UC-MSCs may potentially provide direct benefits for biologic-refractory psoriasis and restore responsiveness to previously ineffective biologics, possibly by resetting the immune response. Further investigation in larger cohorts is warranted.The costs of the mesenchymal stromal cells were supported by an unrestricted medical grant from Almirall. Hannah Dawe is supported by the UK Medical Research Council (MR/W006820/1) and is a King’s College London member of the MRC Doctoral Training Partnership in Biomedical Sciences. This research was partially supported by the King’s Health Partners Centre for Translational Medicine
Label-Noise-Resistant Time-Series Classification With Self-Supervised Label Correction
No full textThe reliable operation of industrial systems requires not only the prompt detection of faults but also their accurate classification into the appropriate categories. At present, numerous data-driven industrial fault detection and diagnosis models, which have been developed based on historical fault data, frequently neglect the issue of label noise. When labels are corrupted by noise, a significant degradation in the performance of industrial fault detection models can be observed. In this article, a label-noise-resistant time-series classification (LNRTSC) method based on consistency-driven label correction is proposed. First, an attention-based temporal correlation-enhanced encoder is introduced to extract low-dimensional representations of industrial time series. Then, label confidence, which is assessed based on local label consistency, is utilized to correct noisy labels during training. In addition, a two-stage self-supervised enhancement strategy is designed to guarantee the reliability of the corrected labels. Specifically, a reconstruction loss term is introduced to assist feature extraction in the warming-up stage, and a newly designed contrastive loss term is added to the loss function for the LNL training stage, which mitigates the effect of false negatives. Finally, the effectiveness of the LNRTSC method is validated on the Tennessee Eastman process and the SEU-gearbox datasets. When compared to peer methods, the LNRTSC approach demonstrates substantial improvements in fault classification performance on corrupted data.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62473103);
Royal Society of the U.K.;
Alexander von Humboldt Foundation of Germany
Network of positive affect and depression in older adults
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The data that support the findings of this study are available from the Centre for Healthy Brain Ageing (CHeBA) Research Bank following a standardized request process. Access requests can be directed to [email protected] or to the corresponding author. Data access is restricted due to participant consent terms requiring Older Australian Twins Study (OATS) investigators' review and approval of proposed secondary uses, regardless of data de-identification status.
Analysis code in R is available in the supplemental materials at the end of the manuscript. We report all data exclusions, manipulations, measures, and sample size determinations in the Method section.Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S0165032725019718?via%3Dihub#s0080 .Background:
Depression in older adults poses significant health challenges, yet the protective role of positive affect remains understudied. This research examined the complex network of positive affect and depression in older adults using advanced network analysis techniques to identify potential targets for intervention.
Methods:
Bayesian Gaussian Graphical Models and Directed Acyclic Graph modelling were used to analyse associations between ten positive affect variables and depression. Exploratory and confirmatory network analyses ensured stability and node predictability quantified variable influence. Stepwise linear regression confirmed whether specific positive affective variables identified in the networks predicted lower depression scores.
Results:
Enthusiasm emerged as a key ancestral node with the highest predictability (R2 = 0.65), initiating cascades of positive affect. A primary pathway to depression was identified through feeling active (strength = 1.00, direction = 0.79), with an indirect pathway from feeling enthusiastic via active (strength = 0.98, direction = 0.79) to depression (strength = 1.00, direction = 0.79). Confirmatory longitudinal analysis showed that feeling active and enthusiastic consistently predicted lower depression scores (p < 0.001). The network structure remained stable across analyses.
Conclusions:
Enthusiasm was identified as a central catalyst in the positive affect network, revealing clear pathways through which positive affect may protect against depression in older adults. Enhancing enthusiastic and active emotional experiences emerged as potential effective, nonpharmacological strategies for preventing and treating depression in older adults.Funding for the Sydney Memory and Ageing Study was given by three National Health and Medical Research Council (NHMRC) Program Grants (ID No. ID350833, ID568969, and APP1093083)
Hybrid Model-Based RF Fingerprinting and Spiking Neural Networks for IoT Device Classification
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The data that support the findings of this study are available from the corresponding author upon reasonable request.Radio frequency fingerprinting identification (RFFI) leverages the unique features of communication transmitter signals to classify Internet of Things (IoT) devices, enabling individual recognition through waveform analysis. Traditional RFFI methods face challenges in extracting nonlinear features, which machine learning (ML) techniques help overcome by providing advanced wave characteristic analysis. This study introduces RFFI-SCNN, a hybrid model integrating RFFI with a spiking conventional neural network (SCNN) to enhance IoT device authentication within networks. The model operates in two phases: signal processing, where wave data are collected and preprocessed, and SCNN-based classification, where features are extracted and devices are authenticated. The proposed model's performance is evaluated against three ML-based models—1SNN, 1CNN and DCNN—based on accuracy, execution time and memory usage. Experimental results, conducted using a publicly available dataset from the Institute for the Wireless Internet of Things at Northeastern University, indicate that RFFI-SCNN achieves superior accuracy in classifying communication devices compared to 1CNN and 1SNN while also requiring less memory and shorter execution time than DCNN and 1CNN. These findings highlight the effectiveness of RFFI-SCNN in secure and efficient IoT device identification.This study was supported by Brunel University London
Polygenic predisposition to increased LDL-cholesterol concentration and Carotid Artery Intima Media Thickness in childhood and early adulthood
Get PDFConference abstract presented at the HEART UK 38th Annual Medical & Scientific conference, Warwick University, Coventry, UK, 8-10 July, 2025.Background: High polygenic risk score (PRS) for LDL-cholesterol (LDL-C), due to a burden of LDL-C-raising genetic variants, has been associated with increased concentration of LDL-C and risk of coronary heart disease (CHD) in adults. However, the effect of LDL-C PRS on LDL-C in childhood, when the cumulative lifetime effect of genetic risk is lower, remains largely unknown. Using data from the cross-sectional study of the Avon Longitudinal Study of Parents and Children (ALSPAC) we aimed to examine the LDL-C PRS association with LDL-C in children and to assess the genetic predisposition to higher carotid intima media thickness (CIMT), using LDL-C PRS and CIMT PRS.
Methods: Genotyping and cholesterol data were available for seven (n=5424), nine (n=5076), 15 (n=3484), 17 (n=3282) and 24 (n=3248) year olds. CIMT was measured at 17 (n=4633) and 24 (n=2029) years of age. Previously established LDL-C PRS (n=223 SNPs) and CIMT PRS (n=11,600 SNPs) were computed using Plink. Regression analyses were performed in R.
Results: LDL-C PRS was associated with LDL-C concentration across all age groups, with the strongest correlation at the age of seven years (p< 1.3x10-57). A significant difference in mean (SD) LDL-C between children with LDL-C PRS in 1st vs. 10th decile was observed as early as seven years of age (p<8x10-15). CIMT was measured at 17 or 24 years and was not associated with LDL-C or LDL-C PRS. However, the CIMT PRS showed association with CIMT at 17 and 24 years of age, after adjusting for covariates (gender, BMI, blood pressure and body fat). Average right/left CIMT in 17-year-olds with CIMT PRS in 1st decile was significantly lower than in those with CIMT PRS in 10th decile (p<1.25x10-5).
Conclusions: LDL-C PRS influences LDL-C concentration as early as seven years of age. CIMT PRS correlates with measures of CIMT at ages 17 and 24 years. Polygenic scores testing in childhood might provide the opportunity to assess future CHD risk before the emergence of clinical risk factors of CHD later in life
Red Pill Leadership Behaviours and Discourse Ethics
Get PDFRed Pill ideology, an online ecosystem that frames men as victims of feminist progress, has moved well beyond fringe forums to shape leadership norms in corporate and political arenas. Scholars have charted its spread across the manosphere, yet we know little about how these narratives crystallise into day-to-day leadership behaviours that undermine workplace ethics and equity. This study conceptualises Red Pill leadership behaviours as a distinctive, discourse-driven form of toxic leadership and examines how they distort organisational decision-making. Grounded in Habermasian discourse ethics and extended with Fraser’s critique of power asymmetries, we investigate how Red Pill leaders subvert open deliberation and justify exclusion. Employing critical netnography and thematic analysis, we analyse a multi-source dataset comprising 66 keynote speeches and high-profile interviews, 227 social media artefacts posted by 34 executives, 23 corporate case files, 20 investigative media articles, and 13 podcast episodes, produced between 2018 and 2024. Our findings identify three interlocking behaviour clusters: (1) exploitative influence and manipulation; (2) control, supremacy, and suppression of dissent; and (3) dehumanisation with harmful outcomes that normalise male supremacist grievance, delegitimise diversity initiatives, and marginalise opposing voices. By theorising these behaviours and mapping their communicative tactics, we show how Red Pill leadership manufactures legitimacy, monetises grievance, and embeds misogyny in workplace culture. We conclude by outlining multilevel policy and organisational interventions that promote ethical deliberation, critical reflexivity, and inclusive governance
Optimal subsampling proportional subdistribution hazards regression with rare events in big data
Get PDFThe data set is provided by Surveillance Research Program, National Cancer Institute SEER*Stat software (seer.cancer.gov/seerstat) version 8.3.9.1.2000 Mathematics Subject Classification:
Primary 62N01; Secondary 62P10.The proportional subdistribution hazards (PSH) model has been widely employed for analyzing competing risks data which have mutually exclusive events with multiple causes and commonly occur in clinical research. With the rapid development of healthcare industry, massively sized survival data sets are becoming increasingly prevalent and classical PSH models are computationally intensive with large data sets. In this article, we propose the optimal subsampling estimators and two-step algorithm for the Fine-Gray model. Asymptotic properties of the proposed estimators are established and an extensive simulation study is conducted to demonstrate the efficiency of the estimators. Our proposed methodology is then illustrated with the large dataset from the SEER (Surveillance, Epidemiology, and End Results) database.National Natural Science Funds of China (Grant No. 12101015); Scientific Research Foundation of North China University of Technology (No. 110051360002); Fundamental Research Funds for Beijing Universities, NCUT (No.110052971921/007); National Natural Science Foundation of China (No.11861042); China Statistical Research Project (No. 2020LZ25)
Drivers to adopt agroforestry and sustainable land-use innovations: A review and framework for policy
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No data was used for the research described in the article.What influences individuals' decisions to adopt sustainable land-use practices? The drivers of such complex decisions are manyfold. We develop a conceptual framework of the predictors that are external (contextual), related to the innovation, and internal or intrinsic to individuals. This framework can guide the design and evaluation of policies to encourage such decisions and subsequent behaviour. The conceptual framework is based on a literature review that includes empirical qualitative and quantitative analyses, mainly focused on agroforestry and its subtype, silvopasture. We inventoried 207 adoption drivers (predictors) used across the studies reviewed. We grouped these predictors into key concepts along these categories: farm and household characteristics, social environment and institutions, individual objective and subjective factors, and variables related to the land-use practice (knowledge, technical feasibility and economically rational motives). The concepts in the framework incorporate and enhance those proposed in earlier reviews of adoption of a range of sustainable land-use practices (soil conservation, organic farming, conservation agriculture, ecological farming practices, etc.). The framework is also interdisciplinary and comprehensive by including behavioural, socioeconomic and biophysical factors. It is applicable to a range of sustainable farming innovations. It can be used to evaluate policy ex-ante, by assessing what place-based conditions or barriers may need to be addressed through tailored policy instruments, as well as to inform the selection of explanatory variables in ex-post evaluations
Implicit bias in referrals to relational psychological therapies: review and recommendations for mental health services
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The dataset supporting this study is publicly available on Brunel University's Figshare repository. It can be accessed at the following link: https://doi.org/10.17633/rd.brunel.27332307.v2.Introduction: Timely and appropriate psychological treatment is an essential element required to address the growing burden of mental health issues, which has significant implications for individuals, society, and healthcare systems. However, research indicates that implicit biases among mental health professionals may influence referral decisions, potentially leading to disparities in access to relational psychological therapies. This study investigates bias in referral practices within mental health services, identifying key themes in referral procedures and proposing recommendations to mitigate bias and promote equitable access.
Methods: A systematic review of literature published between 2002 and 2022 was conducted, focusing on biases, referral practices, and relational psychological therapies. The search strategy involved full-text screening of studies meeting inclusion criteria, specifically those examining professional and organizational implicit bias in mental health referrals. Thematic synthesis was employed to analyze and categorize bias within these domains, providing a structured framework for understanding its impact on referral decision making processes.
Results: The search yielded 2,964 relevant papers, of which 77 underwent full-text screening. Ultimately, eight studies met the inclusion criteria and were incorporated into the review. The analysis revealed that bias development mechanisms in referral decisions occurred across five key domains: resource allocation, organizational procedures, clinical roles, decision-making, and referral preferences. These domains highlight organizational and practitioner-level factors contributing to disparities in access to psychological therapies.
Discussion: Findings suggest that implicit biases within referral processes can limit equitable access to psychological therapies, particularly relational therapies that emphasize therapeutic alliance and patient-centered care. This study provides recommendations to address these biases, including standardized referral guidelines, enhanced professional training on implicit bias, and improved oversight mechanisms within mental health services.The author(s) declare financial support was received for the research, authorship, and/or publication of this article. CNWL NHS Foundation Trust provided a grant to Brunel University of London
Complement Factor H and Properdin act as soluble pattern recognition receptors for SARS-CoV-2 and differentially modulate Infection
No full textData availability statement:
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.Supplementary material:
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2025.1620229/full#supplementary-materialIntroduction: An unbalanced immune response and excessive inflammation are the major hallmarks of severe SARS-CoV-2 infection, which can result in multiorgan failure and death. The dysregulation of the complement system has been shown in various studies as a crucial factor in the immunopathology of SARS-CoV-2 infection. Complement alternative pathway has been linked to the excessive inflammation in severe SARS-CoV-2 infection in which decreased levels of factor H (FH) and elevated levels of properdin (FP) were observed. The current study investigated the potential immune protective roles of FP and FH against SARS-CoV-2 infection.
Methods: The interactions between FH and FP and the SARS-CoV-2 spike (S) and its receptor binding domain (RBD) were evaluated using direct ELISA. The cell binding and luciferase-based viral entry assays utilising S protein expressing lentiviral pseudotypes were used to evaluate the possible modulatory effects of FH, FP, and recombinant thrombospondin repeats 4 and 5 (TSR4 + 5) on SARS-CoV-2 cell entry. Using RT-qPCR, we also assessed the immunomodulatory roles of FH and FP in the cytokine response induced by SARS-CoV-2 pseudotypes.
Results: FH and FP were found to bind to both the RBD and SARS-CoV-2 S proteins. The treatment of FP or TSR4 + 5 enhanced cell binding and entry of SARS-CoV-2 pseudotypes that was administered in A549 cells expressing human ACE2 and TMPRSS2 (A549-hACE2+TMPRSS2 cells). FP increases the affinity between host ACE2 and SARS-CoV-2, according to in silico work. In A549-hACE2+TMPRSS2 cells, the effect of FP on viral cell entry and binding was counteracted by anti-FP antibody treatment. On the other hand, SARS-CoV-2 lentiviral pseudotypes’ cell entry and binding were decreased by FH treatment. The A549-hACE2+TMPRSS2 cells that were challenged with SARS-CoV-2 alphaviral pseudotypes (expressing spike, envelope, nucleocapsid, and membrane proteins) pre-treated with FP or TSR4+5 showed an upregulation of pro-inflammatory cytokine transcripts, including NF-κB and IL-1β, IL-8, IL-6, TNF-α, IFN-α, and RANTES. Contrary to this, the expression of these pro-inflammatory cytokines was downregulated by FH treatment. FH treatment decreased S protein-mediated NF-κB activation, but FP treatment enhanced it in A549-hACE2+TMPRSS2 cells.
Discussion: These results imply that FH may function as a SARS-CoV-2 cell entry and binding inhibitor, reducing the inflammatory response linked to infection independently of complement activation. FP could aid cell viral entry and binding and aggravate hyperinflammation that might contribute to the severity of the infection.The author(s) declare financial support was received for the research and/or publication of this article. This study was supported by an UAEU UPAR grant (#12F061 to UK, PP). MMN and NT are funded by the Wellcome Trust; CK and SI-T acknowledge the Department of Biotechnology, India (grant BT/PR40165/BTIS/137/12/2021) for their research support. BA-R is supported by a grant via the ASPIRE Precision Medicine Research Institute Abudhabi (ASPIREPMRIAD) award grant #VRI-20-10
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