Addressing the mental health needs of children and young people requires integrated and multidisciplinary action to reduce the risk of detrimental outcomes. Schools offer a unique environment for such provision. However, there is a critical need for school-based interventions that are feasible, co-designed, transdiagnostic, sustainable, and cost-effective beyond the conclusion of the trial, while aligning with pupil and teacher preferences. The current study reports the development phase of a new school-based transdiagnostic mental health intervention, embedded into routine classroom lessons, which aims to promote mental health and prevent the onset or escalation of mental health symptoms in young people. The study involved a total of 32 participants aged 15-63 years (Mage = 32.72, SD = 12.73), across three groups: one group of teachers (Mage = 36.80, SD = 11.29) took part in four sequential development workshops, one group of young people (Mage = 15.85, SD = 0.69) took part in one development workshop, and a new group of teachers who were not part of the initial development took part in one refinement workshop (Mage = 37.38, SD = 9.26). The study identified mental health priority areas in the classroom and brought together teachers and young people to co-develop activities that can be integrated directly into routine teaching to support and promote mental health. The intervention was deemed feasible and acceptable by both teachers and young people, subject to contextual considerations. Teachers perceived the activities to have a positive impact on young people's mental health, engagement, learning, energy, and overall enjoyment in class. The development of this intervention, with its co-production approach, has the potential to bridge the gap in intervention translation to the classroom and ultimately benefit young people's mental health.

Immunological Nonresponse (INR) in people living with HIV (PLWH) is characterized by inadequate CD4+ T-cell recovery despite effective viral suppression through antiretroviral therapy (ART). This systematic review and meta-analysis identify key clinical, demographic, and molecular predictors of INR to optimize immune recovery strategies. A comprehensive literature search was conducted in accordance with PRISMA 2020 guidelines, yielding 59 eligible studies, with a total of 19,511 individuals with INR. Study quality was assessed using the Newcastle-Ottawa Scale (NOS), and pooled effect estimates were calculated as odds ratio (OR), mean difference (MD), or standardized mean difference (SMD). Significant predictors of INR included male sex [OR 1.33 (1.12-1.58)], older age [MD 2.61 (1.42-3.81)], intravenous drug use [OR 1.67 (1.15-2.42)], hepatitis B coinfection [OR 1.30 (1.08-1.57)], hepatitis C coinfection [OR 1.85 (1.50-2.28)], tuberculosis [OR 1.77 (1.41-2.21)], advanced HIV stage [OR 2.66 (2.34-3.02)], and lower baseline and nadir CD4+ counts [SMD -3.82 (-4.94-(-2.70)), SMD -1.69 (-2.69-(-0.68))], respectively. Additionally, 57 biomarkers were identified as potential predictors of INR, implicating several immunological pathways. These findings underscore the multifactorial nature of INR and the need for personalized approaches among ART-treated individuals.

The incidence of low birthweight (LBiW) piglets, which show impaired growth compared to normal birthweight (NBiW) littermates, has increased with litter size. We previously reported that glutamine increased the bodyweight and plasma glutamine concentrations in LBiW piglets, with no effect on jejunal glutamine concentrations or glutamine transport and metabolism mRNA abundance. In this study, we investigated the effect of glutamine supplementation on proteinogenic free amino acid (FAA), protein-bound AA (PBAA) and AA-metabolite profiles, mRNA abundance, fractional protein synthesis (FPSR) and the plasma metabolome. At birth (0 days of age (d)), 24 pairs of male LBiW (1.10 kg ± 0.02) and NBiW (1.50 kg ± 0.02) littermates were selected. At 1 d, litters were standardized to 12 piglets, and piglets supplemented daily with either glutamine (1 g/kg bodyweight) or isonitrogenous amounts of alanine (1.22 g/kg bodyweight), until 5 d. Stomach content FAA, AA-metabolites, duodenal and liver tissue FAA, PBAA, AA-metabolites, biochemical indices, duodenal, colon and liver tissue FPSR, liver tissue and duodenal mucosa glutamine transport and metabolism mRNA abundance and the plasma metabolome were analysed. Stomach digesta glutamine (P < 0.001) and glutamate (LBiW, P = 0.02; NBiW, P = 0.03) concentrations were higher in glutamine than alanine piglets. Analysis by Data Integration Analysis for Biomarker Discovery using Latent Variable Approaches for Omics Studies showed glutamine, carnosine, citrulline and alanine separated the supplementation groups, whilst glutathione reductase, glutathione synthetase and tight junction protein 1 mRNA separated glutamine LBiW piglets from alanine. Duodenal RNA was lower and Protein-to-RNA ratio higher (P = 0.004) in glutamine than alanine LBiW piglets (P = 0.02). Colon FPSR was higher in NBiW than LBiW piglets supplemented with Ala. No effects on the plasma metabolome were observed. These results show that not all glutamine passed through the stomach, and that glutamine is absorbed and metabolised in the duodenum, potentially altering the intracellular pH, and appears to increase oxidative stress markers in the duodenum of LBiW piglets.

Lung cancer is one of the leading causes of cancer-related mortality worldwide, emphasizing the need for accurate, automated, and clinically interpretable diagnostic systems. Although existing deep learning (DL) models have achieved promising performance in classifying lung nodules as benign or malignant, most approaches are limited to binary classification and provide insufficient support for detailed tumor characterization and TNM staging. Furthermore, conventional convolutional neural networks (CNNs) are effective in extracting local morphological features but often fail to capture long-range spatial dependencies. In contrast, transformer-based models can learn global contextual information but may compromise fine-grained local feature representation. To address these limitations, this study proposes a hybrid ResNet-50-Vision Transformer with Radiomic Feature Fusion (ReViT-RF) framework for lung tumor classification and component-wise TNM stage prediction using thoracic computed tomography (CT) images from the publicly available Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset. The primary clinical objectives of the proposed framework are to: (i) classify pulmonary nodules as benign or malignant, and (ii) predict individual TNM components, including primary tumor extent (T), lymph node involvement (N), and metastatic status (M) for malignant cases. To improve tumor visibility and image quality, CT images are initially preprocessed using Contrast-Limited Adaptive Histogram Equalization (CLAHE) and intensity normalization. The proposed ReViT-RF architecture integrates the complementary advantages of ResNet-50 and Vision Transformer (ViT) for comprehensive feature learning. The CNN branch extracts local morphological, textural, and structural characteristics, whereas the transformer branch captures long-range spatial relationships and global contextual dependencies. In addition, handcrafted radiomic features, including shape, size, texture, and spatial descriptors, are fused with deep features to provide clinically meaningful information for accurate TNM component prediction. The dataset is divided into training, validation, and testing subsets using an 80:10:10 ratio, with data augmentation applied only to the training set to prevent information leakage. Experimental results demonstrate that the proposed framework achieves an accuracy of 97.62% for benign-malignant classification, along with high precision, recall, and F1-score values. Moreover, independent prediction of TNM components provides detailed stage-related information rather than relying on a single aggregated stage label, enabling more precise tumor assessment and clinical decision support. Furthermore, explainability analysis using Gradient-weighted Class Activation Mapping (Grad-CAM) identifies the critical tumor regions contributing to model predictions, improving interpretability and enhancing clinician confidence. Overall, by combining hybrid deep feature extraction, radiomic feature fusion, component-wise TNM stage prediction, and explainable AI analysis, the proposed ReViT-RF framework provides a reliable, comprehensive, and clinically interpretable solution for automated lung tumor analysis.

Missing data is a challenging issue in data analysis. Researchers often address the issue by simply removing incomplete cases from the analysis. However, this approach reduces the sample size and can undermine the accuracy of statistical estimates. Alternatively, imputation techniques can be used to replace missing values with plausible estimates, resulting in a more complete dataset and more reliable estimates. In this study, we propose a new imputation method, which we call Layered Stacking. The method is designed to improve the estimation of continuous missing values. To evaluate its effectiveness, comprehensive simulation studies were carried out with four distinct data generating processes using datasets generated under the missing at random mechanism, with sample sizes of 500, 1,000, 5,000 and 10,000, and missingness rates of 10, 20, 30 and 40%. The proposed method is compared with five widely used imputation techniques: k-nearest neighbors, support vector regression, decision tree, random forest and extreme gradient boosting. Evaluation metrics include mean squared imputation error (MSIE) and mean absolute percentage imputation error (MAPIE) along with standard deviation (SD) and 95% confidence interval (CI) across replications. Two real-world datasets have also been considered to assess the performance of the method. The proposed method generally achieved lower MSIE than the benchmark models, particularly for larger datasets.

Liver resection creates a clinical tension between limiting operative bleeding and maintaining organ perfusion. Low central venous pressure, fluid restriction, vascular inflow occlusion and vasoactive treatment can coincide with lower arterial pressure, yet existing reviews have centred on blood loss and transfusion. They have not resolved whether directly quantified intraoperative hypotension is associated with postoperative acute kidney injury or myocardial injury, or whether comparative haemodynamic strategies alter these outcomes. We will address both questions while keeping exposure associations distinct from management effects and kidney outcomes distinct from myocardial outcomes.

This study aimed to develop a one-step loop-mediated isothermal amplification combined with a lateral flow dipstick (LAMP-LFD) DNA biosensor for the detection of α0-thalassemia (SEA deletion). Genomic DNA was obtained from 48 α0-thalassemia (SEA deletion)-positive and 48 (SEA deletion)-negative (normal) Thai subjects. DNA was analyzed by LAMP assay at 61°C for 60 min using specific primers for the α0-thalassemia (SEA deletion) type. Following amplification, the products were visually detected using an LFD, with results available within 5-10 min. The one-step LAMP-LFD DNA biosensor could detect DNA carrying the α0-thalassemia (SEA deletion) type at levels as low as 200 pg, with 100% sensitivity and 100% specificity. Furthermore, no cross-reactivity was observed with DNA carrying other α0-thalassemia deletion types (THAI deletion). The LAMP-LFD DNA biosensor is useful for identifying carriers of α0-thalassemia (SEA deletion) (the most common deletion type found in Thailand and across Southeast Asia). This novel one-step LAMP-LFD kit will be beneficial for medical laboratory technologists performing genetic carrier screening tests as it is simple to perform and requires minimal equipment and time. Furthermore, this method supports clinicians in providing genetic counseling and implementing preventive strategies against severe thalassemia, a significant public health concern in Thailand.

Diabetic foot ulcers (DFUs) are serious complications of diabetes associated with delayed healing, infection, and limb amputation. Modelling DFU progression remains challenging because publicly available image datasets generally contain independent single-timepoint images rather than longitudinal follow-up sequences. This study proposes a texture-aware conditional diffusion framework for generating severity-conditioned, progression-like synthetic DFU images from a single RGB input. Instead of claiming patient-specific temporal prediction, the framework models plausible population-level visual transitions across ordinal pseudo-stages derived from clinically relevant severity cues. The full model achieved an SSIM of 0.851 ± 0.020, PSNR of 24.76 ± 1.12 dB, and FID of 44.33 ± 5.02, with higher observed SSIM and PSNR and lower FID values than the evaluated GAN and VAE baselines across structural fidelity and distributional realism. The GAN baseline achieved SSIM 0.782 ± 0.031, PSNR 21.98 ± 1.56 dB, and FID 59.13 ± 6.59, while the VAE baseline achieved SSIM 0.738 ± 0.038, PSNR 19.63 ± 1.83 dB, and FID 69.77 ± 7.44. The framework also obtained a Texture Consistency Score of 0.79 ± 0.04 and a Granulation Tissue Fidelity Score of 0.76 ± 0.05, indicating preservation of fine-grained texture and granulation-related appearance patterns. Ablation results confirmed the contribution of texture conditioning and cross-attention to image fidelity. Advanced severity-state analysis produced SSIM values of 0.674, 0.804, and 0.726 across three pseudo-stages, supporting severity-aligned structural consistency. Since true longitudinal future-state images were unavailable, these metrics indicate synthetic image fidelity and distributional realism, not longitudinal progression accuracy. Longitudinal DFU cohorts and larger clinician-centred validation remain necessary before clinical decision-support use or temporal progression claims can be justified. To assess generalization beyond the primary 493-image dataset, external validation was additionally performed on an independent DFU dataset under a zero-shot setting, demonstrating stable cross-dataset performance with only modest degradation in image fidelity and texture consistency. Therefore, the proposed framework should be interpreted as modelling cross-sectional severity-conditioned appearance interpolation across independent cases, rather than true patient-specific future progression prediction.

Knee osteoarthritis (KOA) is a whole-joint disease characterized by inflammation, immune dysregulation, and metabolic imbalance. To define lipid metabolism-related transcriptional heterogeneity within the infrapatellar fat pad (IPFP)/synovium compartment, we integrated publicly available single-cell transcriptomic data from IPFP/synovium-derived joint cells with bulk synovial transcriptomic datasets from patients with KOA and controls. Lipid metabolism-related scores were derived using multiple single-cell scoring algorithms, and candidate genes were identified through score-associated analyses, differential expression, protein-protein interaction analysis, and five machine-learning approaches, followed by external bulk validation and single-cell characterization. Myeloid cells showed the most consistent KOA-associated increases in lipid metabolism-related transcriptional scores. Fifty composite-score-associated genes were identified and were enriched in lipid metabolic, lysosomal, and PPAR-related pathways. Cross-screening prioritized PLIN2, LTA4H, and PPT1, with LTA4H showing the strongest association with lipid metabolism-related transcriptional scores. LTA4H was predominantly enriched in myeloid cells, particularly monocytes and inflammatory macrophages, and was increased in KOA myeloid cells and high-score cells. Cell-cell communication, myeloid subclustering, and pseudotime analyses linked LTA4H-positive myeloid cells to inflammatory signaling and the transition from monocytes toward inflammatory macrophages. In silico knockout analysis further implicated LTA4H in regulatory programs related to leukocyte migration, chemotaxis, phagocytosis, and cytokine receptor binding. In a rat KOA model, LTA4H protein expression was increased in knee synovial tissues and spatially colocalized with CD68-positive macrophages. These findings define a myeloid-cell-centered context for LTA4H in KOA and link its expression to lipid metabolism-related transcriptional features and inflammatory myeloid-cell states.