Commun Biol. 2025 Apr 2;8(1):540. doi: 10.1038/s42003-025-07944-w.

ABSTRACT

Preterm birth disrupts intestinal epithelial maturation, impairing digestive and absorptive functions. This study integrates analysis of single-cell RNA sequencing datasets, spanning fetal to adult stages, with human preterm intestinal models derived from the ileal tissue of preterm infants. We investigate the potential of extracellular vesicles (EVs) derived from human Wharton's jelly mesenchymal stem cells to promote intestinal maturation. Distinct enterocyte differentiation trajectories are identified during the transition from immature to mature stages of human intestinal development. EV treatment, particularly with the EV39 line, significantly upregulates maturation-specific gene expression related to enterocyte function. Gene set enrichment analysis reveals an enrichment of TGFβ1 signaling pathways, and proteomic analysis identifies TGFβ1 and FGF2 as key mediators of EV39's effects. These treatments enhance cell proliferation, epithelial barrier integrity, and fatty acid uptake, primarily through CFTR-dependent mechanisms-unique to human preterm models, not observed in mouse intestinal organoids. This highlights the translational potential of EV39 and CFTR activation in promoting the functional maturation of the premature human intestine.

PMID:40169914 | DOI:10.1038/s42003-025-07944-w

Basic Clin Androl. 2025 Apr 1;35(1):13. doi: 10.1186/s12610-025-00260-7.

ABSTRACT

BACKGROUND: Azoospermia, the most severe form of male infertility, is categorized into two types: non-obstructive azoospermia (NOA) and obstructive azoospermia (OA), which exhibit significant genetic heterogeneity. Azoospermia factor (AZF) deletion is a common cause of NOA, whereas congenital bilateral absence of the vas deferens (CBAVD), a severe subtype of OA, is frequently linked to cystic fibrosis transmembrane conductance regulator (CFTR) gene variants. This case report is the first to document the coexistence of a partial AZFa microdeletion and a homozygous CFTR variant in a CBAVD-affected azoospermic patient with intact spermatogenesis.

CASE PRESENTATION: A 32-year-old man presented with primary infertility and azoospermia. Clinical evaluation revealed CBAVD (normal hormone levels, low semen volume, pH 6.0, and absence of the vas deferens). Genetic analysis accidentally revealed a 384.9 kb AZFa deletion (sY84 and sY86, but not sY1064, 1182) that removed USP9Y but retained DDX3Y in the proband, his fertile brother, and his father. A homozygous CFTR variant (TG12-5T) was also detected in the proband and his brother and was inherited from heterozygous parental carriers. Microdissection testicular sperm extraction (micro-TESE) revealed intact spermatogenesis, confirmed by histology and immunofluorescence, indicating normal germ cell development.

CONCLUSION: This case expands the intricate genetic spectrum of azoospermia by illustrating the critical role of DDX3Y in the AZFa region in spermatogenesis and the variable penetrance of CFTR variant (TG12-5T) in CBAVD. These insights may refine diagnostic strategies and underscore the necessity for tailored fertility management in individuals with multifactorial genetic anomalies.

PMID:40169970 | DOI:10.1186/s12610-025-00260-7

MAbs. 2025 Dec;17(1):2483944. doi: 10.1080/19420862.2025.2483944. Epub 2025 Apr 1.

ABSTRACT

Highly concentrated antibody solutions are necessary for developing subcutaneous injections but often exhibit high viscosities, posing challenges in antibody-drug development, manufacturing, and administration. Previous computational models were only limited to a few dozen data points for training, a bottleneck for generalizability. In this study, we measured the viscosity of a panel of 229 monoclonal antibodies (mAbs) to develop predictive models for high concentration mAb screening. We developed DeepViscosity, consisting of 102 ensemble artificial neural network models to classify low-viscosity (≤20 cP) and high-viscosity (>20 cP) mAbs at 150 mg/mL, using 30 features from a sequence-based DeepSP model. Two independent test sets, comprising 16 and 38 mAbs with known experimental viscosity, were used to assess DeepViscosity's generalizability. The model exhibited an accuracy of 87.5% and 89.5% on both test sets, respectively, surpassing other predictive methods. DeepViscosity will facilitate early-stage antibody development to select low-viscosity antibodies for improved manufacturability and formulation properties, critical for subcutaneous drug delivery. The webserver-based application can be freely accessed via https://devpred.onrender.com/DeepViscosity.

PMID:40170162 | DOI:10.1080/19420862.2025.2483944

BMC Bioinformatics. 2025 Apr 1;26(1):96. doi: 10.1186/s12859-025-06092-6.

ABSTRACT

BACKGROUND: Chromatin loops are critical for the three-dimensional organization of the genome and gene regulation. Accurate identification of chromatin loops is essential for understanding the regulatory mechanisms in disease. However, current mainstream detection methods rely primarily on single-source data, such as Hi-C, which limits these methods' ability to capture the diverse features of chromatin loop structures. In contrast, multi-source data integration and deep learning approaches, though not yet widely applied, hold significant potential.

RESULTS: In this study, we developed a method called DconnLoop to integrate Hi-C, ChIP-seq, and ATAC-seq data to predict chromatin loops. This method achieves feature extraction and fusion of multi-source data by integrating residual mechanisms, directional connectivity excitation modules, and interactive feature space decoders. Finally, we apply density estimation and density clustering to the genome-wide prediction results to identify more representative loops. The code is available from https://github.com/kuikui-C/DconnLoop .

CONCLUSIONS: The results demonstrate that DconnLoop outperforms existing methods in both precision and recall. In various experiments, including Aggregate Peak Analysis and peak enrichment comparisons, DconnLoop consistently shows advantages. Extensive ablation studies and validation across different sequencing depths further confirm DconnLoop's robustness and generalizability.

PMID:40170155 | DOI:10.1186/s12859-025-06092-6

BMC Med Inform Decis Mak. 2025 Apr 1;25(1):156. doi: 10.1186/s12911-025-02983-z.

ABSTRACT

BACKGROUND: Risk stratification of COVID-19 patients can support therapeutic decisions, planning and resource allocation in the hospital. In times of high incidence, a prognostic model based on data efficiently retrieved from one source can enable fast decision support.

METHODS: A model was developed to identify patients at risk of developing severe COVID-19 within one month based on their age, sex and imaging features extracted from the thoracic computed tomography (CT). The model was trained on publicly available data from the Study of Thoracic CT in COVID-19 (STOIC) challenge and validated on unseen data from the same study and an external, multicentric dataset. The model, trained on data acquired before any variant of concern dominated, was assessed separately on data collected at later stages of the pandemic when the delta and omicron variants were most prevalent.

RESULTS: A logistic regression based on handcrafted features was found to perform on par with a direct deep learning approach, and the former was selected for simplicity. Volumetric and intensity-based features of lesions and healthy lung parenchyma proved most predictive, in addition to patient age and sex. The model reached an area under the curve of 0.78 on the challenge test set and 0.74 on the external test set. The performance did not drop for the subset acquired at a later stage of the pandemic.

CONCLUSIONS: A logistic regression utilizing features from thoracic CT and its metadata can provide rapid decision support by estimating short-term COVID-19 severity. Its stable performance underscores its potential for real-world clinical integration. By enabling rapid risk stratification using readily available imaging data, this approach can support early clinical decision-making, optimize resource allocation, and improve patient management, particularly during surges in COVID-19 cases. Furthermore, this study provides a foundation for future research on prognostic modelling in respiratory infections.

PMID:40170034 | DOI:10.1186/s12911-025-02983-z

BMC Ophthalmol. 2025 Apr 1;25(1):162. doi: 10.1186/s12886-025-03988-y.

ABSTRACT

PURPOSE: To compare two artificial intelligence (AI) models, residual neural networks ResNet-50 and ResNet-101, for screening thyroid eye disease (TED) using frontal face photographs, and to test these models under clinical conditions.

METHODS: A total of 1601 face photographs were obtained. These photographs were preprocessed by cropping to a region centered around the eyes. For the deep learning process, photographs from 643 TED patients and 643 healthy individuals were used for training the ResNet models. Additionally, 81 photographs of TED patients and 74 of normal subjects were used as the validation dataset. Finally, 80 TED cases and 80 healthy subjects comprised the test dataset. For application tests under clinical conditions, data from 25 TED patients and 25 healthy individuals were utilized to evaluate the non-inferiority of the AI models, with general ophthalmologists and fellowships as the control group.

RESULTS: In the test set verification of the ResNet-50 AI model, the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity were 0.94, 0.88, 0.64, and 0.92, respectively. For the ResNet-101 AI model, these metrics were 0.93, 0.84, 0.76, and 0.92, respectively. In the application tests under clinical conditions, to evaluate the non-inferiority of the ResNet-50 AI model, the AUC, accuracy, sensitivity, and specificity were 0.82, 0.82, 0.88, and 0.76, respectively. For the ResNet-101 AI model, these metrics were 0.91, 0.84, 0.92, and 0.76, respectively, with no statistically significant differences between the two models for any of the metrics (all p-values > 0.05).

CONCLUSIONS: Face image-based TED screening using ResNet-50 and ResNet-101 AI models shows acceptable accuracy, sensitivity, and specificity for distinguishing TED from healthy subjects.

PMID:40169995 | DOI:10.1186/s12886-025-03988-y

BMC Oral Health. 2025 Apr 1;25(1):465. doi: 10.1186/s12903-025-05827-4.

ABSTRACT

BACKGROUND: The aim was to fully automate molar teeth developmental staging and to comprehensively analyze a wide range of deep learning models' performances for molar tooth germ detection on panoramic radiographs.

METHODS: The dataset consisted of 210 panoramic radiographies. The data were obtained from patients aged between 5 and 25 years. The stages of development of molar teeth were divided into 4 classes such as M1, M2, M3 and M4. 9 different convolutional neural network models, which were Cascade R-CNN, YOLOv3, Hybrid Task Cascade(HTC), DetectorRS, SSD, EfficientNet, NAS-FPN, Deformable DETR and Probabilistic Anchor Assignment(PAA), were used for automatic detection of these classes. Performances were evaluated by mAP for detection localization performance and confusion matrices, giving metrics of accuracy, precision, recall and F1-scores for classification part.

RESULTS: Localization performance of the models varied between 0.70 and 0.86 while average accuracy for all classes was between 0.71 and 0.82. The Deformable DETR model provided the best performance with mAP, accuracy, recall and F1-score as 0.86, 0.82, 0.86 and 0.86 respectively.

CONCLUSIONS: Molar teeth were automatically detected and categorized by modern artificial intelligence techniques. Findings demonstrated that detection and classification ability of deep learning models were promising for molar teeth development staging. Automated systems have a potential to alleviate the burden and assist dentists.

TRIAL REGISTRATION: This is retrospectively registered with the number 2023-1216 by the university ethical committee.

PMID:40169944 | DOI:10.1186/s12903-025-05827-4

Sci Rep. 2025 Apr 1;15(1):11170. doi: 10.1038/s41598-025-95752-x.

ABSTRACT

Counting the number of people in a room is crucial for optimizing smart buildings, enhancing energy efficiency, and ensuring security while preserving privacy. This study introduces a novel radar-based occupancy estimation method leveraging a 24-GHz Continuous Wave (CW) radar system integrated with time-frequency mapping techniques using Continuous Wavelet Transform (CWT) and power spectrum analysis. Unlike previous studies that rely on WiFi or PIR-based sensors, this approach provides a robust alternative without privacy concerns. The time-frequency scalograms generated from radar echoes were used to train deep-learning models, including DarkNet19, MobileNetV2, and ResNet18. Experiments conducted with sedentary occupants over 4 hours and 40 minutes resulted in 1680 image samples. The proposed approach achieved high accuracy, with DarkNet19 performing the best, reaching 92.7% on CWT images and 92.3% on power spectrum images. Additionally, experiments in a walking environment with another continuous 1 hour of data achieved 86.5% accuracy, demonstrating the method's effectiveness beyond static scenarios. These results confirm that CW radar with deep learning can enable non-intrusive, privacy-preserving occupancy estimation for smart building applications.

PMID:40169921 | DOI:10.1038/s41598-025-95752-x

Sci Rep. 2025 Apr 1;15(1):11076. doi: 10.1038/s41598-025-95672-w.

ABSTRACT

Anti-money laundering has been an issue in our society from the beginning of time. It simply refers to certain regulations and laws set by the government to uncover illegal money, which is passed as legal income. Now, with the emergence of cryptocurrency, it ensures pseudonymity for users. Cryptocurrency is a type of currency that is not authorized by the government and does not exist physically but only on paper. This provides a better platform for criminals for their illicit transactions. New algorithms have been proposed to detect illicit transactions. Machine learning and deep learning algorithms give us hope in identifying these anomalies in transactions. We have selected the Elliptic Bitcoin Dataset. This data set is a graph data set generated from an anonymous blockchain. Each transaction is mapped to real entities with two categories: licit and illicit. Some of them are not labeled. We have run different algorithms for predicting illicit transactions like Logistic Regression, Long Short Term Memory, Support Vector Machine, Random Forest, and a variation of Graph Neural Networks, which is called Graph Convolution Network (GCN). GCN is of special interest in our case. Different evaluation parameters such as accuracy, ROC and F1 score are analyzed for different models. Our experimental results show that the proposed GCN model gives the accuracy [Formula: see text], the AUC 0.9444 and the RMSE 0.1123, which concludes that our GCN is better than the existing models, in particular with the model proposed in Weber et al. (Anti-money laundering in bitcoin: experimenting with graph convolutional networks for financial forensics, 2019. http://arxiv.org/abs/1908.02591 ).

PMID:40169862 | DOI:10.1038/s41598-025-95672-w

Sci Rep. 2025 Apr 1;15(1):11058. doi: 10.1038/s41598-025-95381-4.

ABSTRACT

The fast development of social media platforms has led to an unprecedented growth of daily short text content. Removing valued patterns and insights from this vast amount of textual data requires advanced methods to provide information while preserving its essential components successfully. A text summarization system takes more than one document as input and tries to give a fluent and concise summary of the most significant information in the input. Recent solutions for condensing and reading text are ineffective and time-consuming, provided plenty of information is available online. Concerning this challenge, automated text summarization methods have developed as a convincing choice, achieving important significance in their growth. It was separated into two kinds according to the abstraction methods utilized: abstractive summarization (AS) and extractive summarization (ES). Furthermore, automatic text summarization has many applications and spheres of impact. This manuscript proposes an Adaptive Search Mechanism Based Hierarchical Learning Networks for Social Media Data Summarization and Classification Model (ASMHLN-SMDSCM) technique. The ASMHLN-SMDSCM approach aims to present a novel approach for text summarization on social media using advanced deep learning models. To accomplish that, the proposed ASMHLN-SMDSCM model performs text pre-processing, which contains dissimilar levels employed to handle unprocessed data. The BERT model is used for the feature extraction process. Furthermore, the moth search algorithm (MSA)-based hyperparameter selection process is performed to optimize the feature extraction results of the BERT model. Finally, the classification uses the TabNet and convolutional neural network (TabNet + CNN) model. The efficiency of the ASMHLN-SMDSCM method is validated by comprehensive studies using the FIFA and FARMER datasets. The experimental validation of the ASMHLN-SMDSCM method illustrated a superior accuracy value of 98.87% and 98.55% over recent techniques.

PMID:40169845 | DOI:10.1038/s41598-025-95381-4

Sci Rep. 2025 Apr 1;15(1):11053. doi: 10.1038/s41598-025-93536-x.

ABSTRACT

This paper presents an enhanced ensemble classification framework for Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) under diverse operational conditions, including Standard Operating Conditions (SOC) and Extended Operating Conditions (EOC). The proposed method integrates the strengths of Residual Neural Networks (ResNet) replacing Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and template matching, leveraging majority voting to combine their complementary capabilities. The ensemble framework achieves improved robustness and classification accuracy across varied scenarios. The methodology employs ResNet, a deep learning architecture known for its superior feature extraction and classification capabilities, replacing AlexNet to address limitations in generalization and consistency. ResNet demonstrated better performance with average accuracies of 92.67% under SOC and 88.9% under EOC, showing consistent results across all six target classes, as compared to the CNN-based ensemble approach with average accuracies of 90.30% under SOC and 87.22% under EOC. The SVM is employed for its robustness in handling overfitting and classifying features extracted from 16 region properties. Template matching is included for its resilience in challenging conditions where deep learning techniques may underperform. Experimental validation using the MSTAR dataset, a standard benchmark for SAR ATR, highlights the effectiveness of this ensemble approach. The results confirm significant improvements in classification accuracy and robustness over individual classifiers, demonstrating the practical applicability of the ensemble approach to real-world SAR ATR challenges. This research advances SAR ATR by addressing critical challenges, including noise, occlusion, and variations in viewing angles while achieving high classification performance under diverse conditions. The integration of ResNet further enhances the framework's adaptability and reliability.

PMID:40169814 | DOI:10.1038/s41598-025-93536-x

Sci Rep. 2025 Apr 1;15(1):11127. doi: 10.1038/s41598-025-93059-5.

ABSTRACT

Bladder (BL) cancer is the 10th most common cancer worldwide, ranking 9th in males and 13th in females in the United States, respectively. BL cancer is a quick-growing tumor of all cancer forms. Given a malignant tumor's high malignancy, rapid metastasis prediction and accurate treatment are critical. The most significant drivers of the intricate genesis of cancer are complex genetics, including deoxyribonucleic acid (DNA) insertions and deletions, abnormal structure, copy number variations (CNVs), and single nucleotide variations (SNVs). The proposed method enhances the identification of driver genes at the individual patient level by employing attention mechanisms to extract features of both coding and non-coding genes and predict BL cancer based on the personalized driver gene (PDG) detection. The embedded vectors are propagated through the three dense blocks for the binary classification of PDGs. The novel constructure of graph neural network (GNN) with attention mechanism, called Multi Stacked-Layered GAT (MSL-GAT) leverages graph attention mechanisms (GAT) to identify and predict critical driver genes associated with BL cancer progression. In order to pick out and extract essential features from both coding and non-coding genes, including long non-coding RNAs (lncRNAs), which are known to be crucial to the advancement of BL cancer. The approach analyzes key genetic changes (such as SNVs, CNVs, and structural abnormalities) that lead to tumorigenesis and metastasis by concentrating on personalized driver genes (PDGs). The discovery of genes crucial for the survival and proliferation of cancer cells is made possible by the model's precise classification of PDGs. MSL-GAT draws attention to certain lncRNAs and other non-coding elements that control carcinogenic pathways by utilizing the attention mechanism. Tumor development, metastasis, and medication resistance are all facilitated by these lncRNAs, which are frequently overexpressed or dysregulated in BL cancer. In order to reduce the survival of cancer cells, the model's predictions can direct specific treatment approaches, such as RNA interference (RNAi), to mute or suppress the expression of these important genes. MSL-GAT is followed by three dense blocks that spread the embedded vectors to categorize PDGs, making it possible to determine which genes are more likely to cause BL cancer in a certain patient. The model facilitates the identification of new treatment targets by offering a thorough understanding of the molecular landscape of BL cancer through the integration of multi-omics data, encompassing as genomic, transcriptomic, and epigenomic metadata. We compared the novel approach with classical machine learning methods and other deep learning-based methods on benchmark TCGA-BLCA, and the leave-one-out experimental results showed that MSL-GAT achieved better performance than competitive methods. This approach achieves accuracy with 97.72% and improves specificity and sensitivity. It can potentially aid physicians during early prediction of BL cancer.

PMID:40169776 | DOI:10.1038/s41598-025-93059-5

Hereditas. 2025 Apr 1;162(1):50. doi: 10.1186/s41065-025-00401-y.

ABSTRACT

PURPOSE: Ubiquitination plays a crucial role in various diseases. This study aims to explore the potential ubiquitination related genes in IPF.

METHODS: The gene microarray dataset GSE24206 was obtained from GEO database. Subsequently, through differential expression analysis and molecular signatures database, we obtained 1734 differentially expressed genes and 742 ubiquitination related genes. Through the venn diagram analysis, we obtained 53 differentially expressed ubiquitination related genes. Then, gene-ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, protein-protein interactions (PPI) and gene set enrichment analysis (GSEA) were applied for the differentially expressed ubiquitination related genes. Finally, the expression of CDC20 and ITCH in IPF patients and cells were validated by qPCR and western blot assay.

RESULTS: A total of 53 differentially expressed ubiquitination related genes (36 up-regulated genes and 17 down-regulated genes) were identified between 17 IPF patients and 6 healthy controls. GO and KEGG enrichment analysis of ubiquitination related genes mainly involved in regulation of protein ubiquitination, regulation of post-translational protein modification and ubiquitin mediated proteolysis. The PPI results demonstrated that these ubiquitination related genes interacted with each other. The GSEA analysis results for some of the hub genes mainly involved epithelial mesenchymal transition, inflammatory response, hypoxia, and apoptosis. The experiment expression level of CDC20 and ITCH in IPF patients and IPF cells were consistent with the bioinformatics analysis results.

CONCLUSION: We identified 53 potential ubiquitination related genes of IPF through bioinformatics analysis. CDC20 and ITCH and other ubiquitination related genes may influence the development of IPF through epithelial mesenchymal transition and inflammatory response. Our research findings provide insights into the mechanisms of fibrosis and may provide evidence for potential therapeutic targets for fibrosis.

PMID:40170095 | DOI:10.1186/s41065-025-00401-y

Epigenetics Chromatin. 2025 Apr 1;18(1):17. doi: 10.1186/s13072-025-00580-y.

ABSTRACT

BACKGROUND: DNA methylation plays a crucial role in gene regulation and epigenetic inheritance across diverse organisms. Daphnia magna, a model organism in ecological and evolutionary research, has been widely used to study environmental responses, pharmaceutical toxicity, and developmental plasticity. However, its DNA methylation landscape and age-related epigenetic changes remain incompletely understood.

RESULTS: In this study, we characterized DNA methyltransferases (DNMTs) and mapped DNA methylation across the D. magna genome using whole-genome bisulfite sequencing. Our analysis identified three DNMTs: a highly expressed but nonfunctional de novo methyltransferase (DNMT3.1), alongside lowly expressed yet functional de novo methyltransferase (DNMT3.2) and maintenance methyltransferase (DNMT1). D. magna exhibits overall low DNA methylation, targeting primarily CpG dinucleotides. Methylation is sparse at promoters but elevated in the first exons downstream of transcription start sites, with these exons showing hypermethylation relative to adjacent introns. To examine age-associated DNA methylation changes, we analyzed D. magna individuals across multiple life stages. Our results showed no significant global differences in DNA methylation levels between young, mature, and old individuals, nor any age-related clustering in dimensionality reduction analyses. Attempts to construct an epigenetic clock using machine learning models did not yield accurate age predictions, likely due to the overall low DNA methylation levels and lack of robust age-associated methylation changes.

CONCLUSIONS: This study provides a comprehensive characterization of D. magna's DNA methylation landscape and DNMT enzymes, highlighting a distinct pattern of exon-biased CpG methylation. Contrary to prior studies, we found no strong evidence supporting age-associated epigenetic changes, suggesting that DNA methylation may have a limited role in aging in D. magna. These findings enhance our understanding of invertebrate epigenetics and emphasize the need for further research into the interplay between DNA methylation, environmental factors, and gene regulation in D. magna.

PMID:40170124 | DOI:10.1186/s13072-025-00580-y

Pediatr Rheumatol Online J. 2025 Apr 1;23(1):36. doi: 10.1186/s12969-025-01084-5.

ABSTRACT

BACKGROUND: Systemic lupus erythematosus (SLE) is a multi-systemic autoimmune disease that causes inflammation of the serosa (serositis). This retrospective study aimed to evaluate the clinical characteristics of serositis in childhood-onset SLE (cSLE) and analyze its association with long-term outcomes.

METHODS: We retrospectively reviewed the medical records of patients with cSLE diagnosed at a medical center in Taiwan, analyzing data collected from January 2002 to December 2022. We analyzed the clinical features of patients with serositis as pleuritis and/or pericarditis with at least a small effusion (> 0.5 cm in depth) on sonography or chest radiography. Cox proportional hazards regression was used to calculate the hazard ratios (HR) and 95% confidence intervals (CI) for the association between serositis and all-cause mortality.

RESULTS: 185 patients with cSLE were enrolled, of whom 38 (20.54%) had serositis. Patients with serositis had a younger age at SLE diagnosis, a higher SLE Disease Activity Index 2000 score at serositis diagnosis, and an increased prevalence of lupus nephritis, central nervous system manifestations, end-stage renal disease (ESRD), a higher Systemic Lupus International Collaborating Clinics (SLICC)/American College of Rheumatology (ACR) damage index score, and a higher mortality than that of patients without serositis. Multivariate Cox regression analysis showed that both serositis (hazard ratio [HR]: 5.585, confidence interval [CI]: 1.853-17.80) and ESRD (HR: 13.956; CI: 3.822-50.964) were associated with mortality risk. Kaplan-Meier survival curve analysis revealed that patients with both serositis and ESRD had the poorest 20-year survival rate. Patients with late-onset serositis (occurring 1 year after SLE diagnosis) had higher mortality rates than those with early-onset serositis.

CONCLUSION: Children with lupus serositis had higher disease activity, a higher prevalence of comorbidities, and mortality. Patients with both serositis, especially late-onset serositis, and ESRD had an increased risk of poor long-term survival.

PMID:40170018 | DOI:10.1186/s12969-025-01084-5

Oncogene. 2025 Apr 1. doi: 10.1038/s41388-025-03363-7. Online ahead of print.

NO ABSTRACT

PMID:40169919 | DOI:10.1038/s41388-025-03363-7

Nat Med. 2025 Apr 1. doi: 10.1038/s41591-025-03532-x. Online ahead of print.

ABSTRACT

Disease progression is a substantial challenge in patients with non-Hodgkin lymphoma (NHL) undergoing chimeric antigen receptor T cell (CAR-T) therapy. Here we present InflaMix (INFLAmmation MIXture Model), an unsupervised quantitative model integrating 14 pre-CAR-T infusion laboratory and cytokine measures capturing inflammation and end-organ function. Developed using a cohort of 149 patients with NHL, InflaMix revealed an inflammatory signature associated with a high risk of CAR-T treatment failure, including increased hazard of death or relapse (hazard ratio, 2.98; 95% confidence interval, 1.60-4.91; P < 0.001). Three independent cohorts comprising 688 patients with NHL from diverse treatment centers were used to validate our approach. InflaMix consistently and reproducibly identified patients with a higher likelihood of disease relapse and mortality, and it provided supplementary predictive value beyond established prognostic markers, including tumor burden. Moreover, InflaMix exhibited robust performance in cases with missing data, maintaining accuracy when considering only six readily available laboratory measures. These findings show that InflaMix is a valuable tool for point-of-care clinical decision-making in patients with NHL undergoing CAR-T therapy.

PMID:40169864 | DOI:10.1038/s41591-025-03532-x

Mol Psychiatry. 2025 Apr 2. doi: 10.1038/s41380-025-02977-3. Online ahead of print.

ABSTRACT

Altered levels of human plasma metabolites have been implicated in the etiology of bipolar disorder (BD). However, the causality between metabolites and the disease was not well described. We performed a bidirectional metabolome-wide Mendelian randomization (MR) analysis to evaluate the potential causal relationships between 871 plasma metabolites and BD. We used DrugBank and ChEMBL to evaluate whether related metabolites are potential therapeutic targets. Finally, Bayesian colocalization analysis was performed to identify shared genomic loci BD and identified metabolites. Our MR results showed that six metabolites were significantly associated with a reduced risk of BD, including arachidonate (20:4n6) (OR: 0.90, 95% CI: 0.84-0.95) and sphingomyelin (d18:2/24:1, d18:1/24:2) (OR: 0.92, 95% CI: 0.87-0.96), while five metabolites were significantly associated with an increased risk of BD, including 1-palmitoyl-2-linoleoyl-GPE (16:0/18:2) (OR: 1.09, 95% CI: 1.05-1.13). However, our reverse MR analysis showed that BD was not associated with the levels of any metabolite. Additionally, the leave-one-out analysis revealed SNPs within chromosome 11 loci harboring MYRF, FADS1, and FADS2 as ones with the potential to influence partial causal effects. Druggability evaluation showed that 10 of the BD-related metabolites, such as sphingomyelin and cytidine, have been targeted by pharmacologic intervention. Colocalization analysis highlighted one colocalized region (chromosome 11q12) shared by 11 metabolites and BD and pointed to some genes as possible players, including FADS1, FADS2, FADS3, and SYT7. Our study supported a causal role of plasma metabolites in the susceptibility to BD, and the identified metabolites may provide a new avenue for the prevention and treatment of BD.

PMID:40169804 | DOI:10.1038/s41380-025-02977-3

Br J Cancer. 2025 Apr 1. doi: 10.1038/s41416-025-02987-6. Online ahead of print.

ABSTRACT

BACKGROUND: Individualized treatment decisions for multiple myeloma (MM) patients require accurate risk stratification that accounts for patient-specific consequences of cytogenetic abnormalities on disease progression.

METHODS: Previously, SYstems Genetic Network AnaLysis (SYGNAL) of multi-omics tumor profiles from 881 MM patients generated a mmSYGNAL network of transcriptional programs underlying disease progression across MM subtypes. Here, through machine learning on activity profiles of mmSYGNAL programs we have generated a unified framework of cytogenetic subtype-specific models for individualized risk classifications and prediction of treatment response.

RESULTS: Testing on 1,367 patients across five independent cohorts demonstrated that the framework of mmSYGNAL risk models significantly outperformed cytogenetics, International Staging System, and multi-gene biomarker panels in predicting PFS at primary diagnosis, pre- and post-transplant and even after multiple relapses, making it useful for individualized risk assessment throughout the disease trajectory. Further, treatment response predictions were significantly concordant with efficacy of 67 drugs in killing myeloma cells from eight relapsed refractory patients. The model also provided new insights into matching MM patients to drugs used in standard of care, at relapse, and in clinical trials.

CONCLUSION: Activities of transcriptional programs offer significantly better prognostic and predictive assessments of treatments across different stages of MM in an individual patient.

PMID:40169765 | DOI:10.1038/s41416-025-02987-6

Sci Rep. 2025 Apr 1;15(1):11090. doi: 10.1038/s41598-025-86745-x.

ABSTRACT

The Mayombe and Batéké Plateau ecozones of the Democratic Republic of Congo (DRC) are experiencing differentiated deforestation and forest degradation, together with a trend toward homogenization of their agricultural diversity. These may undermine efforts to sustainably reverse household food, nutrition, and livelihood insecurity. In this context, this study seeks to assess the importance of yam in the role of agrobiodiversity among populations in the two contrasting ecozones. A sample of 351 households was surveyed. A dataset of about 202 testimonies from six focus groups and observations in 86 peasant agroforestry fields was also analyzed using descriptive statistics, correlation and regression, and calculations of different indices of crop importance. Overall, plant, animal, and fish species represent respectively 60.9%, 26.7% and 12.4% of genetic resources. About 50 of 72 species of these resources are found in both study areas. Regarding the overall use of species, the five top-ranked species that were utilized as food included Manihot esculenta, followed by Arachis hypogaea, Zea mays, Dioscorea alata, and Musa acuminata. Living in the Mayombe ecozone increases the household's preference for growing yams by up to 5.7 times. Population density was correlated with agricultural diversity. Villages with a high population density showed greater crop diversity than those with a low population density. In short, yam remains an important but under-represented crop, the contribution of which could be increased to secure sustainable livelihoods through biodiversity-rich peasant agroforestry systems.

PMID:40169681 | DOI:10.1038/s41598-025-86745-x