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Human umbilical cord-derived mesenchymal stem cells alleviate autoimmune hepatitis by inhibiting hepatic ferroptosis

Background Autoimmune hepatitis (AIH) is a liver disease marked by immune-mediated hepatocyte damage. Current treatments have variable patient responses and considerable side effects, highlighting the need for alternative therapies. Human umbilical cord-derived mesenchymal stem cells (hUC-MSCs) have shown therapeutic potential in liver diseases, but their mechanisms in AIH remain unclear. Methods A Concanavalin A (ConA)-induced AIH-like mouse model was used to assess the therapeutic effects of hUC-MSCs. Survival, liver function-related serum marker expression, histopathology, and apoptosis were evaluated. Metabolomic profiling and ferroptosis-related markers were analyzed to uncover potential mechanisms. Results In ConA-induced AIH-like mouse model challenged with a lethal dose of ConA, hUC-MSC treatment significantly ameliorated liver tissue damage and serum liver function parameters, alleviated hepatocyte apoptosis, and improved survival. Metabolomic and ontology analyses of mouse liver tissue samples revealed that hUC-MSCs treatment altered the levels of metabolites (Glu derivatives and peptides) functionally associated with ferroptosis-related pathways. hUC-MSCs partially reversed ConA-induced malondialdehyde (MDA), oxidized glutathione (GSSG), glutamate, and Fe 2+ , while restoring reduced glutathione (GSH). Expression of COX2 was downregulated, whereas key ferroptosis suppressors, SLC7A11, GPX4, and FTH1, were upregulated following hUC-MSC treatment. Conclusions Based on the above evidence, we propose that hUC-MSCs may ameliorate ConA-induced liver injury in mice, potentially through modulation of ferroptosis-related pathways, and we support further investigation of hUC-MSCs as potential treatments for AIH. We believe that further in-depth studies are still needed to elaborate on the detailed regulatory mechanisms of MSCs on the ferroptosis pathway during the treatment of AIH.

Symptom clusters and their influencing factors among Vietnamese women after cancer treatment

Abstract This study aimed to (1) identify symptom clusters in Vietnamese women with cancer and (2) examine the factors influencing those identified clusters. A cross-sectional study was conducted in 5 hospitals across Vietnam from September to December 2023. A total of 217 valid data sets from women with cancer were included. Exploratory factor analysis was applied to identify symptom clusters, followed by structural equation modeling to confirm the underlying structure. Fatigue and appetite loss were recognized as the most common symptoms. The exploratory factor analysis showed two distinct groups of factors, occupying 54.66% of total variance: fatigue, appetite loss, pain, sleep issues, hair loss, nausea, and sexual issues (Factor 1—physical cluster) and mood issues, personal stress, depression, and anxiety (Factor 2—psychological cluster). Multiple linear regression analysis revealed that place of residence (B = 0.318; p  < 0.05) and occupation (B = 0.263; p  < 0.05) were significant predictors for the physical cluster. For the psychological cluster, physical activity (B = − 0.599, p  < 0.001) and the presence of chronic diseases (B = − 0.328, p  < 0.05) were significant influencing factors, with physical activity demonstrating a strong negative association. The physical and psychological symptom clusters underlie the multidimensional nature of symptom burden in women with cancer and highlight the need for integrative, gender-responsive care models. Culturally tailored, cluster-based interventions are required to enhance survivorship care and patients’ outcomes and quality of life.

The impact of Traditional Chinese Medicine utilization on life expectancy and mortality

Purpose Traditional Chinese Medicine (TCM) is extensively utilized in Asian societies and has shown potential benefits in improving survival rates of patient with specific diseases and anti-aging effects. However, its impact on life expectancy of general population remains relatively unexplored. This study aimed to investigate the impact of TCM utilization on mortality risk and life expectancy in Taiwan. Methods A nationwide longitudinal cohort study was conducted using data from the Taiwan National Health Interview Survey linked with the National Health Insurance Research Database. Cox proportional hazard models were used to calculate the risk of all-cause mortality between frequent TCM users (≥20% of outpatient visits) and non-frequent users (<20%). A rolling extrapolation algorithm was used to estimate lifetime survival functions, and inverse probability of treatment weighting was integrated to adjust for confounding variables. Results The study included 12,176 participants (1,596 frequent TCM users and 10,580 non-frequent TCM users) aged ≥55 years, with a median follow-up duration of 10.49 years. After adjustment for confounding factors, frequent TCM users had a longer life expectancy compared to non-frequent TCM users, with a difference of 1.37 years (95% CI: 0.22–3.32). Higher TCM utilization was associated with reduced mortality risk (HR: 0.89, 95% CI: 0.80–0.99). Results remained consistent across dose-response analysis and time-dependent exposure models. Conclusions This study suggests that higher TCM utilization is associated with longer life expectancy and lower mortality risk among older adults in Taiwan. Further studies are warranted to clarify potential mechanisms and to explore how TCM utilization may complement conventional healthcare in addressing the needs of aging populations.

‘They don’t have symptoms’: CAR-T therapies send autoimmune diseases into remission

Tin-based perovskite solar cells with a homogeneous buried interface

A data-driven design for sound absorption of acoustic metamaterials based on large language models

Abstract Machine learning (ML)-based data-driven approaches are extensively employed in forward and inverse acoustic metamaterial design, as evidenced by numerous research papers published in recent years. These studies require advanced ML knowledge and coding skills. Furthermore, the proposed ML models lack generalizability, being tailored to specific structures and hard to apply broadly, limiting practical applications. To address these issues, this study establishes two data-driven design strategies—agent interaction and large language model (LLM) fine-tuning—based on LLMs, eliminating the need for specialized ML knowledge. This approach provides a universal user-friendly strategy for acoustic metamaterial design. The agent interaction strategy enables ChatGPT to act as an independent agent, mapping structural parameters to sound absorption coefficients through simple text interactions, thereby facilitating both forward and inverse design. The LLM fine-tuning strategy involves retraining DeepSeek using acoustic metamaterial datasets, adjusting specific model parameters to enable performance prediction or inverse design. Results indicate that the agent interaction strategy can design acoustic metamaterials within one minute solely through dialogue and instruction. The fine-tuned LLM strategy yields design outcomes with higher accuracy compared to the conventional ML model. Additionally, the fine-tuned LLM can evolve into a specialized LLM for the metamaterial domain through continuous fine-tuning. The proposed strategies validate the application potential of LLMs in data-driven metamaterial design and provide significant guidance for advancing this field.

Decoding the distinct immune landscape and possible regulatory mechanisms of autoimmune hepatitis through integrated single-cell and bulk RNA sequencing

Objectives Autoimmune hepatitis (AIH) is a complex immune-mediated liver disorder characterized by dysregulated immune responses. This study aimed to decode the distinct immune landscape and regulatory mechanisms of AIH using integrated single-cell and bulk RNA sequencing. Methods The data of single-cell RNA-seq (scRNA-seq) and bulk RNA-seq were downloaded from GEO database. The cell clustering, differential gene expression, trajectory analysis, functional enrichment, and cell-cell communication were performed were analyzed using R software. Results Five major immune cell types were identified, with CD8 + T cells and NK cells significantly expanded in AIH. Functional enrichment showed upregulation of immune activation, inflammation, and metabolic pathways in these cells. The cell-cell communication analysis revealed robust interactions between CD8 + T and NK cells, primarily driven by the CCL5-CCR signaling axis. Integrative analysis of scRNA-seq and bulk RNA-seq identified four common DEIRGs (ITK, IL7R, CXCR4 and SORT1) and one transcription factor PRDM1. Conclusion This study identified dysregulated immune cell clusters, signaling pathways, and potential therapeutic targets in AIH.

Population-specific polygenic risk scores for people of Han Chinese ancestry

Learned denoising with simulated and experimental low-dose CT data

Abstract Like in many other research fields, recent developments in computational imaging have focused on developing machine learning (ML) approaches to tackle its main challenges. To improve the performance of computational imaging algorithms, machine learning methods are used for image processing tasks such as noise reduction. Generally, these ML methods heavily rely on the availability of high-quality data on which they are trained. This work explores the application of ML methods, specifically convolutional neural networks (CNNs), in the context of noise reduction for computed tomography (CT) imaging. We utilize a large 2D computed tomography dataset for machine learning to carry out for the first time a comprehensive study on the differences between the observed performances of algorithms trained on simulated noisy data and on real-world experimental noisy data. The study compares the performance of two common CNN architectures, U-Net and MSD-Net, that are trained and evaluated on both simulated and experimental noisy data. The results show that while sinogram denoising performed better with simulated noisy data if evaluated in the sinogram domain, the performance did not carry over to the reconstruction domain where training on experimental noisy data shows a higher performance in denoising experimental noisy data. Training the algorithms with an optimization in the reconstruction domain mapping directly from sinogram to reconstruction significantly improved model performance, emphasizing the importance of matching raw measurement data to high-quality CT reconstructions. The study furthermore suggests the need for more sophisticated noise simulation approaches to bridge the gap between simulated and real-world data in CT image denoising applications and gives insights into the challenges and opportunities in leveraging simulated data for machine learning in computational imaging.

Correction: Application of PSO-integrated K-means algorithm in resident digital portrait classification

Maternal urinary metabolomic signatures preceding spontaneous preterm birth: A pilot study

Prognostic value of laboratory markers and clinical scores for mortality in intensive care unit patients with sepsis

Introduction Sepsis is a life-threatening condition, especially for patients in the intensive care unit (ICU), where early identification of the prognosis is critical. This study aimed to evaluate the prognostic value of inflammatory markers, clinical scores, and specific laboratory findings for predicting ICU and in-hospital mortality in sepsis patients. Methods A retrospective cohort study was conducted on adult patients with sepsis who were admitted to the ICU of a university hospital between September 2019 and December 2022. To minimize selection bias, all eligible patients during the study period were consecutively included. Data were extracted from electronic medical records and included demographics, clinical characteristics, inflammatory markers, and clinical scores such as the Charlson Comorbidity Index (CCI), Clinical Frailty Scale (CFS), Eastern Cooperative Oncology Group (ECOG) performance status, Simplified Acute Physiology Score 3 (SAPS 3), and Sequential Organ Failure Assessment (SOFA). The primary outcomes were ICU and in-hospital mortality. Univariate and multivariate Cox regression analyses were performed to identify predictors of mortality. Results A total of 213 ICU patients with sepsis were included in the study. The patients were 62.0% male with a mean age of 73.1 ± 12.6 years. The ICU and in-hospital mortality rates were 29.6% and 36.6%, respectively. Non-survivors had higher clinical severity scores and poorer nutritional and perfusion profiles than survivors. Multivariate analysis revealed that elevated lactate levels (a marker of tissue hypoperfusion) and higher SAPS 3 scores were independently associated with ICU mortality. For in-hospital mortality, lower albumin levels and higher SAPS 3 scores were significant predictors. Conclusions High lactate and SAPS 3 scores were independent predictors of mortality, while higher albumin levels showed a potential protective effect. Early identification of these factors may aid in the management of sepsis.

Impact of macrolide therapy on clinical outcomes in hospitalized pneumonia patients: a retrospective study from Lebanese hospitals

Effects of manufacturing modality, primer, and adhesive polymerization on the shear bond strength of customized lingual brackets to glazed zirconia: An in vitro study

Introduction Bonding fixed appliances to zirconia restorations is challenging, yet adult orthodontics increasingly involves ceramic crowns and patient-driven esthetic choices such as lingual appliances. Customized lingual brackets may improve fit and reduce adhesive thickness, but evidence on their bonding to zirconia is limited. Materials and methods This in vitro study evaluated the shear bond strength of customized lingual brackets bonded to glazed zirconia after airborne-particle abrasion. Bracket manufacturing was either three-dimensionally (3D) printed cobalt-chromium or cast nickel-chromium. Primers were a universal adhesive (Single Bond Universal, 3M) or a primer containing 10-methacryloyloxydecyl dihydrogen phosphate Z-Prime Plus (Bisco), and adhesives were a light-cure orthodontic composite or a dual-cure resin cement. One hundred twenty-eight specimens (n = 16 per group) were tested. Shear bond strength was analyzed with three-way ANOVA, followed by post-hoc Tukey tests. Adhesive Remnant Index (ARI) scores were evaluated with ordinal regression. Significance was set at α = 0.05. Results Manufacturing modality significantly affected bond strength, with additively manufactured cobalt-chromium exceeding cast nickel-chromium (P = 0.049). The primer category and polymerization mode showed no significant main effects (P > 0.20) and no significant interactions. Group means clustered 9–10 MPa, and all combinations met the clinically accepted threshold. Additively manufactured brackets exhibited lower ARI scores than cast brackets (P < 0.001), indicating failures closer to the zirconia–adhesive interface. The fabrication×primer term was significant for ARI (P = 0.017). Conclusions On glazed, sandblasted zirconia, shear bond strength of customized lingual brackets showed a borderline main effect of fabrication method, whereas primer type and adhesive polymerization mode were not statistically significant. Failures were predominantly located at or near the zirconia–adhesive interface. Within this in vitro model, base manufacturing may warrant attention, whereas primer and curing mode may be selected for handling and workflow considerations, with clinical relevance yet to be established.

War, diet, and PTSD in Ukrainian youth

Using triangulation to evaluate findings from random-intercept cross-lagged panel models: An application with data on curiosity and creativity

In a recent study, researchers found cross-lagged effects between curiosity and creativity in an analysis with the random-intercept cross-lagged panel model (RI-CLPM) and concluded that curiosity and creativity mutually reinforce each other. However, it is known that the RI-CLPM can give biased results. Here, we used triangulation and analyzed the same data ( N  = 400) with additional models, including a latent change score model (LCSM) and multilevel regression analyses of person-mean centered scores. Only results from the original RI-CLPM were consistent with the conclusion of mutually reinforcing effects between curiosity and creativity while results from the other models contradicted this conclusion. Moreover, the model of spurious longitudinal associations (MoSLA) suggested that data might have been generated without any direct effects between curiosity and creativity. An aggregation of available evidence made us conclude that longitudinal associations between curiosity and creativity in the present data probably were spurious, possibly due to confounding by a trait common to curiosity and creativity and common auto-correlated state factors with effects on curiosity and creativity measured at the same occasion. The present study, and the available analytic script, can be used as a model/tutorial by researchers wishing to scrutinize results from the RI-CLPM.

Fair human-centric image dataset for ethical AI benchmarking

Abstract Computer vision is central to many artificial intelligence (AI) applications, from autonomous vehicles to consumer devices. However, the data behind such technical innovations are often collected with insufficient consideration of ethical concerns 1–3 . This has led to a reliance on datasets that lack diversity, perpetuate biases and are collected without the consent of data rights holders. These datasets compromise the fairness and accuracy of AI models and disenfranchise stakeholders 4–8 . Although awareness of the problems of bias in computer vision technologies, particularly facial recognition, has become widespread 9 , the field lacks publicly available, consensually collected datasets for evaluating bias for most tasks 3,10,11 . In response, we introduce the Fair Human-Centric Image Benchmark (FHIBE, pronounced ‘Feebee’), a publicly available human image dataset implementing best practices for consent, privacy, compensation, safety, diversity and utility. FHIBE can be used responsibly as a fairness evaluation dataset for many human-centric computer vision tasks, including pose estimation, person segmentation, face detection and verification, and visual question answering. By leveraging comprehensive annotations capturing demographic and physical attributes, environmental factors, instrument and pixel-level annotations, FHIBE can identify a wide variety of biases. The annotations also enable more nuanced and granular bias diagnoses, enabling practitioners to better understand sources of bias and mitigate potential downstream harms. FHIBE therefore represents an important step forward towards trustworthy AI, raising the bar for fairness benchmarks and providing a road map for responsible data curation in AI.

Correction: Trends in access to primary care among Canadian older adults before, during, and following the COVID-19 public health emergency

Low level of plasma DNase is associated with worse clinical outcome in testicular germ cell tumor patients and exogeneous DNase I improves cisplatin treatment efficacy

Background Germ cell tumor (GCT) patients with unfavourable response to first-line therapy still lack reliable diagnostic and effective treatment Detailed correlation of total extracellular DNA (ecDNA), other DNA species and endogenous DNase levels in GCT patients’ plasma and translational utility remains under- investigated. Study aim and methods We determined DNase plasma levels, ecDNA of different subcellular origin and neutrophil extracellular trap (NETs)-associated markers. Next, we determined the associations of these parameters with a level of the DNA damage, immune inflammatory index, specific immune cell subpopulations in a cohort of the 117 GCT patients and 19 matched healthy donors (HDs). Moreover, we investigated how exogenous DNase affects antitumor effect of cisplatin in GCT model of cisplatin-resistant embryonal carcinoma NTERA-2 CisR. Results Our data demonstrate that high level of ecDNA and low level of DNase in GCT patients’ plasma is associated with significantly worse progression-free survival and overall survival. The level of the plasma ecDNA was five times higher in the GCT patients compared to the HDs. The patients with higher total ecDNA and ncDNA, but not mtDNA, had inferior PFS and OS compared to the patients with lower ecDNA (all p < 0.05). There was an inverse correlation between plasma DNase and ecDNA levels, and between plasma DNase level and clinical outcome. Importantly, combined treatment with cisplatin and human recombinant DNase I delayed growth of the NTERA-2 CisR xenografts and prolonged animal survival. Importantly, Pulmozyme significantly reduced intratumoral microvascular density in our preclinical model. Conclusion Our data confirm the association between low plasma DNase activity and worse overall survival for the first time in GCT patients. This study further validated the prognostic value of total ecDNA in GCT patients. More importantly, our preclinical data substantiated beneficial effect of Pulmozyme combination with cisplatin treatment to improve the therapeutic outcome in refractory disease.

Lymphoid gene expression supports neuroprotective microglia function

Abstract Microglia, the innate immune cells of the brain, play a defining role in the progression of Alzheimer’s disease (AD) 1 . The microglial response to amyloid plaques in AD can range from neuroprotective to neurotoxic 2 . Here we show that the protective function of microglia is governed by the transcription factor PU.1, which becomes downregulated following microglial contact with plaques. Lowering PU.1 expression in microglia reduces the severity of amyloid disease pathology in mice and is linked to the expression of immunoregulatory lymphoid receptor proteins, particularly CD28, a surface receptor that is critical for T cell activation 3,4 . Microglia-specific deficiency in CD28, which is expressed by a small subset of plaque-associated PU.1 low microglia, promotes a broad inflammatory microglial state that is associated with increased amyloid plaque load. Our findings indicate that PU.1 low CD28-expressing microglia may operate as suppressive microglia that mitigate the progression of AD by reducing the severity of neuroinflammation. This role of CD28 and potentially other lymphoid co-stimulatory and co-inhibitory receptor proteins in governing microglial responses in AD points to possible immunotherapy approaches for treating the disease by promoting protective microglial functions.