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Th1 and Th2 cells in equine endometrosis and their interactions with endometrial fibroblasts
Abstract Mare endometrosis is a chronic degenerative condition of the endometrium, primarily characterized by fibrosis, involving interactions among fibroblasts, immune cells, and epithelial cells regulated by cytokines and growth factors. T helper (Th)1 and Th2 cells seem to play a pivotal role in fibrosis. However, their roles in equine endometrial fibrosis remain unknown. This study explores Th1 and Th2 cell distribution across different stages of endometrium histopathological Kenney and Doig categories; and evaluated their secretome effects on non-fibrotic endometrium derived fibroblast functional characteristics, extracellular matrix (ECM)-associated mRNA transcription, and transcriptomic profiles. Th1 and Th2 cells, along with cytokines (IFN-γ, IL-4, IL-13) and their receptors, were present in mare endometria at all endometrium stages. Th1 secretome influenced genes enriched in metabolism, cell cycle, and ECM-related pathways, while Th2 secretome regulated genes enriched in tissue remodeling and signaling pathways, suggesting their role in the development of fibrosis in the endometrosis progression.
Revisiting the impulse response creates an improved PID autotuner
Island recovery methods considering optimal scheduling of emergency mobile resources
Computational insights into solvent encapsulation and host–guest recognition by calix[4]arene
Correction: Generative AI for weakly supervised segmentation and downstream classification of brain tumors on MR images
Analysis of unconventional optical standing wave patterns outside a metal-substrate supported dielectric microsphere
Evaluating cell-specific gene expression using single-cell and single-nuclei RNA-sequencing data from human pancreatic islets of the same donors
Abstract Single-cell and single-nuclei RNA-sequencing (scRNA-seq and snRNA-seq) analyze cell-specific transcriptomes. However, only snRNA-seq applies to frozen biobanked samples. For human pancreatic islets, marker genes and reference-based cell type annotation methods are mainly from scRNA-seq datasets and may not be suitable for snRNA-seq. We compared human islet scRNA-seq and snRNA-seq data from the same donors (N = 4) and evaluated annotation methods by studying cell type composition and gene detection, and identified novel marker genes. We compared cell type annotations: (1) manual annotation based on identified marker genes, (2) reference-based annotation using Azimuth’s scRNA-seq pancreasref dataset, or (3) Seurat’s label transfer from the Human Pancreas Analysis Program (HPAP) scRNA-seq dataset. ScRNA-seq and snRNA-seq identified the same cell types, but predicted cell type proportions differed. Cell type proportion-differences between annotation methods were larger for snRNA-seq. Reference-based annotations generated higher cell type prediction and mapping scores for scRNA-seq than snRNA-seq. Manual annotation identified the novel snRNA-seq markers DOCK10, KIRREL3 (beta cells), STK32B (alpha cells), MECOM, AC007368.1 (acinar cells), LAMC2 and SLC28A3 (ductal cells), which improve snRNA-seq-based annotation. We confirmed ZNF385D as a snRNA-seq beta cell marker and ZNF385D silencing reduced insulin secretion. In conclusion, this study discovered novel snRNA-seq cell type marker genes in human pancreatic islets, and highlights the need for tailored snRNA-seq annotation strategies.
Safety evaluation of extracellular vesicles derived from hypoxia primed mesenchymal stem cells of umbilical cord and adipose tissue
Movement of deception in motion capture
Assessing species-specific neonicotinoid toxicity using cross-species chimeric nicotinic acetylcholine receptors in a Drosophila model
Abstract Nicotinic acetylcholine receptors (nAChRs) are ligand-gated ion channels and the main mediators of synaptic neurotransmission in the insect brain. In insects, nAChRs are pivotal for sensory processing, cognition and motor control, and are the primary target of neonicotinoid insecticides. Neonicotinoids are potent neurotoxins, and pollinators such as honey bees are more sensitive and affected by extremely low sub-lethal doses. nAChR subtypes exist as homomers of α-subunits or heteromers composed of α and β subunits. The honey bee nAChRα8 subunit is orthologous to nAChRβ2 in Drosophila, raising the question of whether this α to β change makes flies less sensitive to neonicotinoids. To investigate species-specific aspects of neonicotinoid toxicity, we CRISPR-Cas9 engineered a cross-species chimeric nAChR subunit by swapping the ligand-binding domain in Drosophila of nAChRβ2 with honey bee nAChRα8. Phenotypic assessment revealed significantly impaired motor functions in climbing and flight assays when comparing flies carrying the α8/β2 chimeric channel to wild type or a β2 knock-out. Despite these motor deficits, both flies expressing the α8/β2 chimeric receptor and β2 knock-out flies showed significantly increased survival after exposure to neonicotinoids thiamethoxam and clothianidin, compared to wild type flies. Combinatorial exposure to different insecticides did not reveal differences. These findings highlight the critical role of nAChR subunit composition in motor control, and demonstrate how subtle structural modifications within a single nAChR subunit can profoundly impact motor function and pesticide response, offering new insights into the molecular mechanisms of neurotoxicity across species.
Process parameter optimization for enhanced mechanical and thermal properties of kenaf/jute hybrid composites using grey fuzzy logic
Differential responses of Cacao pathogens Colletotrichum gloeosporioides and Pestalotiopsis sp. to UVB 305 nm and UVC 275 nm
Abstract Sustainable control of microbial pathogens requires alternatives to chemical agents. However, the efficacy of physical methods like Ultraviolet-C (UVC) radiation is often inconsistent due to poorly understood, pathogen-specific resistance mechanisms. To address this, we investigated the differential responses of cacao-infecting fungi (Colletotrichum gloeosporioides and the more resistant Pestalotiopsis sp.) to UVB (305 nm) and UVC (275 nm) radiation. We developed an integrated framework using quantitative morphology, hyperspectral imaging (HSI), and machine learning to dissect the physiological underpinnings of UV sensitivity. UVC proved significantly more potent than UVB; for example, a 4-min UVC exposure achieved a similar level of inactivation on a sensitive isolate as a 30-min UVB exposure. After 30 min of UVC, the resistant Pestalotiopsis sp. maintained an 89% survival rate, whereas C. gloeosporioides isolates were almost completely inactivated (< 8% survival). HSI revealed that this resistance correlated with physiological stability, while sensitive isolates exhibited significant biochemical disruption. Machine learning models successfully classified isolates based on their UV-induced phenotypes with over 73% accuracy. This understanding enabled targeted strategies, such as synergistic treatment with sonication, which overcame the high resistance of Pestalotiopsis sp. Our work provides a mechanistic basis for optimizing physical pathogen controls by linking non-invasively measured physiological states to UV resistance.
Correction: Solvent-driven spectroscopic and quantum chemical evaluation of 2-[(trimethylsilyl) ethynyl]thiophene with molecular docking insights
Convolutional neural network based system for fully automatic FLAIR MRI segmentation in multiple sclerosis diagnosis
Abstract This study presents an automated system using Convolutional Neural Networks (CNNs) for segmenting FLAIR Magnetic Resonance Imaging (MRI) images to aid in the diagnosis of Multiple Sclerosis (MS). The dataset included 103 patients from Imam Khomeini Hospital, Tehran and an additional 10 patients from an external center. Key preprocessing steps included skull stripping, normalization, resizing, segmentation mask processing, entropy-based exclusion, and data augmentation. The nnU-Net architecture tailored for 2D slices was employed and trained using a fivefold cross-validation approach. In the slice-level classification approach, the model achieved 83% accuracy, 100% sensitivity, 75% positive predictive value (PPV), and 99% negative predictive value (NPV) on the internal test set. For the external test set, the accuracy was 76%, sensitivity 100%, PPV 68%, and NPV 100%. Voxel-level segmentation showed a Dice Similarity Coefficient (DSC) of 70% for the internal set and 75% for the external set. The CNN-based system with nnU-Net architecture demonstrated high accuracy and reliability in segmenting MS lesions, highlighting its potential for enhancing clinical decision-making.
Interpretable deep learning for personalized energy expenditure prediction using ECG and acceleration signals in incremental exercise
Abstract Energy expenditure (EE) assessment is crucial in both sports science and health management. However, current EE prediction models often overlook individual differences and lack dynamic correlation analysis between multi-modal data and EE. Building upon previous research, this study proposes a personalized dynamic-static feature fusion framework, which integrates two types of information to improve energy expenditure (EE) prediction during incremental load exercise: dynamic signals (physiological signals recorded continuously during exercise, such as tri-axial acceleration and electrocardiography [ECG]) and static physiological metrics (stable individual traits measured at rest, such as BMI, body-fat percentage, resting heart rate, and resting oxygen uptake [VO2]). These two feature sets were combined through a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) neural network architecture. CNN layers extract local temporal patterns from dynamic signals, and LSTM layers model temporal dependencies over longer intervals. The model prediction performance was evaluated using root mean square error (RMSE), coefficient of determination (R²), mean absolute error (MAE) and Bland-Altman plots, and the results show that the CNN + LSTM model significantly outperforms both the traditional autoregressive (AR) linear model and the LSTM model that uses only a single modality (acceleration or ECG). Analysis of feature values and SHAP values revealed that accelerometer features played a dominant role in EE prediction during moderate-to-high intensity exercise. As exercise intensity increased, the contribution of ECG features gradually increased, with ECG features dominating during high-intensity exercise, demonstrating the complementary effect and dual contribution of these two types of features in EE prediction at different exercise intensities. This study demonstrates that personalized dynamic-static feature fusion can effectively predict EE during incremental exercise tests and analyzes the dynamic changes in the contribution of different features across different intensity ranges, providing a theoretical basis and methodological reference for related research.
Impact of metabolic and bariatric surgery on the paediatric & adolescent metabolome: A systematic review and meta-analysis
Abstract Metabolic bariatric surgery (MBS) is an effective treatment for paediatric obesity, yet the mechanisms underlying weight loss remain unclear. This systematic review and meta-analysis evaluated the short- and long-term effects of MBS on the paediatric metabolome to provide insights into metabolic pathways contributing to surgical outcomes. This prospectively registered systematic review (PROSPERO ID: CRD42024607784) adhered to PRISMA guidelines. Meta-analysis was undertaken on pre-defined post-operative weight and metabolic parameters in paediatric patients (aged 5- 19 years) following MBS. Outcomes were reported as weighted or standardised mean Difference with 95 percent confidence intervals from random effects modelling. Quality scoring and quantitative assessment of bias were performed. Results from 12 studies (451 patients, mean age 16.9 years) across five countries were included. The median follow-up was 12 months. Patients underwent Roux-en-Y gastric bypass (RYGB, n = 275) or laparoscopic sleeve gastrectomy (LSG, n = 140). Most studies used serum and urine assays; two included tissue biosamples. MBS was associated with significant long-term weight reduction, with a mean BMI decrease of -14.4 kg/m2 (95% CI: -17.5 to -11.3) and %TWL of 25% (95% CI: 18.6 to 32.2). Metabolic improvements included reduced cholesterol (-10 mg/dL), LDL (-14.6 mg/dL), triglycerides (-33.3 mg/dL), and increased HDL (+ 8.0 mg/dL). Significant enhancements were noted in glycaemic, pancreatic and insulin regulation, evidenced by decreased HOMA-IR (-4.1) and C-peptide (-1.8 ng/mL). Liver function parameters, ALT (-14.4 U/L), AST (-5.4 U/L), and GGT (-9.6 U/L) and inflammatory cytokines, IL-6 (-12.2 pg/mL) and TNF-α (-54 pg/mL) significantly declined following surgery. These findings demonstrate a distinct metabolic signature of MBS in adolescents, leading to substantial weight loss and improvements in cardiovascular, glycaemic, and liver health, alongside reduced systemic inflammation. These results underscore the efficacy of MBS as a therapeutic intervention for adolescents living with severe obesity, demonstrating a profound impact on the paediatric metabolome.