Background: DeepFlu predicts symptomatic influenza onset from pre-exposure gene expression, but the contribution of individual genes remains unclear.Methods: We apply SHapley Additive exPlanations (SHAP), an explainable AI (XAI) method, to evaluate gene-level contributions in DeepFlu models trained on H1N1 and H3N2 influenza gene expression data (GSE52428 and GSE73072 datasets).Results: Five key genes—HLA-DQA1, HLA-DQB1, XIST, RPS4Y1, and KDM5D—were shared across influenza subtypes. These genes are associated with immune response and sex-linked pathways, with elevated XIST and reduced RPS4Y1/KDM5D expression suggesting higher susceptibility in females. Conclusion: Integrating SHAP with DeepFlu enhances the interpretability of influenza risk predictions, offering actionable biological insights for early intervention.
This paper investigates diffusion models for data augmentation in fetal ultrasound image classification, addressing the critical challenge of limited medical image datasets for rare conditions. We fine-tuned Stable Diffusion with Low-Rank Adaptation (LoRA) to generate synthetic ultrasound images and systematically evaluated their impact on classification performance across multiple experimental conditions. Our results demonstrate that augmenting training data with synthetic images significantly improves classification accuracy and effectively addresses class imbalance issues. Notably, models trained with a combination of real and synthetic data showed enhanced generalization capabilities and improved performance on minority classes. These findings establish diffusion-based augmentation as a promising approach to overcome data scarcity constraints in medical imaging applications, with potential implications for clinical decision support systems.
Stroke (Cerebrovascular Accident, CVA) is one of the diseases with the greatest impact on human health, particularly due to the neurological dysfunctions that often follow, such as upper or lower limb paralysis, foot drop, muscle weakness, and cognitive impairment, which significantly affect patients' daily lives. Existing research indicates that the first three months after a stroke is the "golden period" for treatment, during which rehabilitation training can greatly improve a patient's quality of life and promote neuroplasticity. However, some stroke patients are unable to engage in active rehabilitation training due to physical limitations and can only rely on passive rehabilitation methods. To address this issue, this study proposes a closed-loop brain-controlled exoskeleton system based on brain-computer interface (BCI) technology to enhance rehabilitation in post-stroke patients. This study designed a brain-controlled exoskeleton system consisting of open-loop control and closed-loop feedback. In the open-loop control stage, patients control the exoskeleton's standing, walking, and sitting commands through electroencephalogram (EEG) signals; in the closed-loop feedback stage, the rehabilitation effect is evaluated through brainwave analysis and clinical evaluation. Experimental results demonstrate that the system can effectively assist patients in regaining motor control during training and elicit significant neurophysiological responses in the preparation and execution stages of movement. Additionally, clinical evaluation results showed that patients made notable progress in muscle strength and functional recovery. Although the study sample size was small (n=3) and the treatment period was short, the study highlights the potential application of brain-controlled exoskeleton systems in post-stroke rehabilitation. Future studies should expand the sample size and include long-term follow-up to further assess its clinical feasibility and long-term effects.
This research investigates the complex biochemical mechanisms underlying aging by analyzing primary human fibroblasts using a longitudinal multi-omics dataset. This dataset includes cytology, DNA methylation and epigenetic clocks, bioenergetics, and cytokine profiling. Key findings indicate that mitochondrial efficiency declines with age, while glycolysis becomes more prevalent to compensate for energy demands. Epigenetic clocks, such as Hannum and PhenoAge, showed strong correlations with biological age (rho > 0.650, p < 1e-6), validating the experimental setup and confirming that the cultured fibroblasts were aging appropriately. Fibroblasts with SURF1 mutations exhibited accelerated aging, marked by bioenergetic deficits, increased cell volume, and reduced proliferative capacity, underscoring the pivotal role of mitochondrial dysfunction in cellular senescence. Novel insights were gained from analyzing cytokines like IL-18 and PCSK9, some of which were linked to age-related diseases such as Alzheimer's and cardiovascular disorders. Experimental treatments revealed distinct effects on cellular aging. Dexamethasone reduced inflammation but also increased DNA methylation, induced metabolic inefficiencies, and shortened cellular lifespan. By uncovering connections between mitochondrial dysfunction, epigenetic biomarkers, and immune dysregulation, this research identifies potential therapeutic targets for age-related diseases.
Rapid identification of antibiotic-resistant infections is crucial, as antimicrobial resistance is a global health crisis. Yet, conventional antibiotic susceptibility tests (AST) often require days to yield results. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has emerged as a rapid, cost-effective tool for bacterial identification and shows promise for resistance profiling by detecting spectral biomarkers. In this study, we harness MALDI-TOF MS with machine learning and deep learning to predict ciprofloxacin resistance across four Gram-negative bacteria, Escherichia coli, Klebsiella pneumoniae, Acinetobacter baumannii, and Acinetobacter nosocomialis, using a cross-species "basket-wise" approach. We extracted features from mass spectra using kernel density estimation-based peak detection and m/z binning, then trained a random forest (RF) classifier and a convolutional neural network (CNN) to distinguish ciprofloxacin-resistant and susceptible isolates. To interpret the models, we employed dual feature importance analyses: gradient-weighted class activation mapping (Grad-CAM) for the CNN to highlight critical m/z regions and an ensemble RF-based method to identify significant peak features. The CNN achieved higher overall accuracy than the RF, especially in three of four species, while the ensemble RF approach identified interpretable sets of around 20 important m/z peaks per organism. Several informative peaks overlapped between species, indicating some common resistance-associated spectral signatures. However, no single universal marker was found across all species. These findings demonstrate an AI-enhanced MALDI-TOF MS framework for rapid AMR detection, yielding accurate predictions and interpretable spectral markers. The approach highlights clinical potential to guide effective therapy and bolster antimicrobial stewardship, particularly for underrepresented pathogens such as A. nosocomialis.
