Wearable devices are widely utilized in the field of health monitoring. Given the real-time requirements of wearable devices for dynamic tracking of muscle fatigue, and addressing the issues of prolonged computation time and failure to extract fatigue-related modal information when applying the variational mode decomposition (VMD) algorithm to surface electromyography (sEMG) signals, this paper proposes the fast VMD (FVMD) algorithm. The objective was to rapidly decompose fatigue-related modal information and extract a complete fatigue curve to alert users. The proposed algorithm is an engineering acceleration of the VMD algorithm. FVMD extends the original signal and applies the Fourier transform to convert the time-domain variational problem into the frequency domain. The optimization problem was constrained to the positive frequency range to simplify calculations while leveraging the unilateral spectrum characteristics to streamline optimization and focus on narrowband modes. The alternating direction method of multipliers framework was employed to decompose the problem into subproblems solvable in closed form, with modal updates inspired by Wiener filtering. The Lagrange multipliers were iteratively updated, and convergence criteria were established to ensure stability. The time-domain signal was reconstructed via the inverse Fourier transform. According to the experimental results, the proposed algorithm exhibits a substantial improvement in processing time compared to the original algorithm and other enhanced algorithms. Compared with other VMD variant algorithms using the same experimental data, the fast recursive VMD (FRVMD) algorithm takes 9.93 s. In contrast, the FVMD method can complete the same task in a shorter time. An evaluation metric was used to select the muscle fatigue modal component most correlated with the original signal and extract the muscle fatigue curve. The FVMD algorithm enhances computational efficiency, overcoming the computational limitations of wearable devices, and provides reliable technical support for real-time muscle fatigue quantification and early warning in scenarios such as sports rehabilitation and occupational health.

Comprehensive biochemical analysis is an essential part of clinical diagnostics, pharmaceutical development, and biomedical research. The gold-standard technique for such analysis is spectrophotometry, which has demonstrated excellent reliability, quantitative accuracy, and broad biomolecular sensitivity. Commercial micro-volume spectrophotometers, which are instrumental to applications with limited sample volumes, are often restricted to well-equipped laboratories due to the high cost and portability limitations especially in resource-limited settings. To address this gap, we propose a proof-of-concept, 3D-printed micro-volume spectrophotometer that integrates precision optics, microfluidic sample handling (8–15 µL), combined with spectral reconstruction and machine learning-based concentration estimation algorithm in a compact and affordable format. Preliminary validation of the proposed system based on bovine serum albumin (BSA) protein quantification via Biuret assay demonstrates strong agreement with commercial spectrophotometers (R2 > 0.91). With a production cost of approximately $36 and rapid measurement, the architecture provides a framework that can be extended to other absorbance-based biochemical assays ensuring accessible spectrophotometric systems for point-of-care and resource-limited applications, overcoming traditional limits of cost, sample volume, and portability.

N6,2′-O-dimethyladenosine (m6Am) is a crucial RNA modification that plays a pivotal role in regulating gene expression and maintaining RNA stability. Given its dynamic involvement in various biological processes and diseases, accurately identifying m6Am is essential for understanding cellular mechanisms and pathogenesis. Furthermore, detecting m6Am modifications is key to deciphering regulatory pathways and elucidating disease mechanisms. In this study, we propose Deep-m6Am, a deep learning–based model for precisely identifying m6Am sites in RNA sequences. The proposed framework employs a comprehensive feature extraction process, i.e., integrating pseudo single nucleotide composition (PseSNC), pseudo dinucleotide composition (PseDNC), and pseudo trinucleotide composition (PseTNC) to capture complex sequence patterns. To enhance computational efficiency and eliminate noisy or redundant features, a supervised SHAP (SHapley Additive exPlanations) algorithm is utilized, ensuring the selection of the most informative features. Finally, a multilayer deep neural network (DNN) is used as a classification algorithm for identifying m6Am sites. The performance of the proposed model was evaluated in comparison with traditional machine learning (ML) algorithms and existing models. Experimental results demonstrate that Deep-m6Am outperforms previous approaches by 6.67% and traditional ML algorithms by 7.39%.These findings highlight Deep-m6Am as a promising tool for advancing drug discovery and improving the diagnosis of diseases associated with m6Am modifications.

<p>Physical exercise contributes to good health and affects gut microbiota. We investigated the gut microbiota features of students majoring in sports. Fecal samples were collected from the sports majors (SS group, 50 students, 11 males, and 39 females, 18.7 ± 0.8 years old) and non-sports majors (NC group, 50 students, 23 males, and 27 females, 19.0 ± 1.0 years old) for high-throughput sequencing analysis. Their clinical data were statistically analyzed. The results showed that the gut microbiota compositions are similar in evolution and development, and significant differences were observed in the proportion of rare species, such as <italic>Fusobacterium</italic>, <italic>Mitsuokella</italic>, <italic>Acidaminococcus</italic>, and <italic>Butyricicoccus</italic>. Compared with the NC group, the SS group exhibited a more diverse gut microbiota, with a high abundance of <italic>Alistipes</italic>, <italic>Faecalibacterium</italic>, <italic>Roseburia</italic>, <italic>Prevotella</italic>, etc., at the genus level, as well as <italic>Faecalibacterium prausnitzii</italic>, <italic>Bifidobacterium adolescentis</italic>, <italic>Bacteroides uniformis</italic>, and <italic>Bacteroides plebeius</italic> at the species level. The research shows that long-term physical exercise or training can enhance gut microbiota diversity and boost beneficial bacteria, which are conducive to intestinal environmental conditions.</p>

<p>Deep brain stimulation (DBS) pacemakers are sophisticated medical devices that deliver electrical signals to targeted areas of the brain via implanted electrodes, effectively regulating abnormal brain activity and relieving symptoms of treatment-resistant neurological disorders. However, proximity to other electromagnetic equipment may introduce additional noise, which can be disruptive to individuals. To mitigate this issue, we propose a novel polymer-based nanocomposite for pacemakers for signal denoising. This research focused on the development and analysis of nanocomposites comprising polypropylene (PP) combined with montmorillonite nanoclay and graphene nanosheets (GNs). The nanocomposites were created by blending them through melting, using varying ratios of clay to GNs, with a total loading of 4 wt.%. This study focused on enhancing the signal-to-noise ratio for brain pacemakers by using nanocomposites. It investigated the noise reduction properties of PP nanocomposites, specifically in the outlet gate of the pacemaker. This research aimed to find the ideal ratio of clay to GNs in the PP matrix. X-ray diffraction (XRD) and differential scanning calorimetry (DSC) were conducted to analyze the crystalline structure and filler dispersion, as well as thermal behavior and filler–matrix interactions in the material. Scanning electron microscopy was employed to observe the dispersion of the nanofillers in the PP, and sound tube testing was conducted to evaluate the noise levels of the composites. The findings indicated that a porous structure of the nanocomposite with dispersed microspheres within the PP matrix and a long pathway facilitated increased dissipation of acoustic waves, making it suitable for denoising in brain pacemakers. Furthermore, the nanocomposite containing 2.75 wt.% of nanoclay and 1.25 wt.% of graphene components within the polypropylene matrix demonstrated a favorable signal-to-noise ratio compared to other evaluated nanocomposites.</p>

<abstract> <p>Marine algae are a valuable source of sulfated polysaccharides presenting varied structural characteristics and biological activities. Regarding sulfated polysaccharides extracted from marine microalgae, molecular characteristics and bioactivity have yet to be fully explored, especially in diatoms. Sulfated polysaccharides from marine diatoms have great potential to produce numerous health benefits and lead to new biomedical materials. Nevertheless, these potential applications are based on the polysaccharide molecular characteristics, which define their functional properties. Therefore, a detailed understanding of sulfated polysaccharides from marine diatoms may represent the starting point for a broad development of innovative applications, especially in the biomedical area. In this context, the present manuscript aims to review marine diatom sulfated polysaccharides' molecular characteristics and biological activity, looking for a more profound knowledge of these macromolecules and their potential applications.</p> </abstract>

<abstract><sec> <title>Background</title> <p>Gestational diabetes mellitus (GDM), characterized by glucose intolerance during pregnancy, poses substantial health risks for both mothers and infants due to the interplay of insulin resistance and β-cell dysfunction. Molecular biomarkers, including SNPs, microRNAs (miRNAs), and proteins, have been linked to GDM development during pregnancy. Notably, miRNA-mediated regulation of gene expression holds pivotal roles in metabolic disorders. This study aims to identify diagnostic biomarkers for GDM and establish a diagnostic model.</p> </sec><sec> <title>Methods</title> <p>Firstly, gene expression data from GDM samples (N = 9) and normal samples (N = 9) were sourced from the Gene Expression Omnibus (GEO) database. Subsequently, the limma package was employed to discern differentially expressed genes (DEGs), with subsequent functional and enrichment analyses executed using the clusterProfiler package. A comprehensive exploration of genes significantly correlated with GDM was undertaken via weighted gene co-expression network analysis (WGCNA). The construction of a protein-protein interaction (PPI) network was facilitated by STRING, while visualization of hub genes was achieved through Cytoscape. Moreover, the miRNA-mRNA network was established using StarBase. Concurrently, immune infiltration significantly correlated with hub genes was identified.</p> </sec><sec> <title>Results</title> <p>In this study, 209 DEGs between normal and GDM samples were identified, and these genes were associated with collagen containing extracellular matrix heparin binding and axon guidance, etc. Then, 18 modules were identified by WGCNA and the brown module including 212 genes had a significantly negative correlation with GDM (r = −0.66, P = 0.003). Additionally, five low gene expressions (CXCL12, MEF2C, MMP2, SOX17 and THBS2) and two high gene expressions (BMP4 and SFRP5) were identified as GDM related hub genes. Moreover, hub genes regulated by alternations of miRNAs were established and three hub genes (CXCL12, MEF2C and THBS2) were negatively correlated with activated Natural Killer (NK) cells while two hub genes (BMP4 and SFRP5) were positively correlated with activated NK cells.</p> </sec><sec> <title>Conclusions</title> <p>This study offers novel hub genes that could contribute to the diagnostic approach for GDM, potentially shedding light on the intricate mechanisms underpinning GDM's developmental pathways.</p> </sec></abstract>

<abstract> <p>Type 2 diabetes (T2D) is a major global health problem often caused by the inability of pancreatic islets to compensate for the high insulin demand due to apoptosis. However, the complex mechanisms underlying the activation of apoptosis and its counter process, anti-apoptosis, during T2D remain unclear. In this study, we employed bioinformatics and systems biology approaches to understand the anti-apoptosis-associated gene expression and the biological network in the pancreatic islets of T2D mice. First, gene expression data from four peripheral tissues (islets, liver, muscle and adipose) were used to identify differentially expressed genes (DEGs) in T2D compared to non-T2D mouse strains. Our comparative analysis revealed that <italic>Gm2036</italic> is upregulated across all four tissues in T2D and is functionally associated with increased cytosolic Ca<sup>2+</sup> levels, which may alter the signal transduction pathways controlling metabolic processes. Next, our study focused on islets and performed functional enrichment analysis, which revealed that upregulated genes are significantly associated with sucrose and fructose metabolic processes, as well as negative regulation of neuron apoptosis. Using the Ingenuity Pathway Analysis (IPA) tool of QIAGEN, gene regulatory networks and their biological effects were analyzed, which revealed that glucose is associated with the underlying change in gene expression in the islets of T2D; and an activated gene regulatory network—containing upregulated <italic>CCK, ATF3, JUNB, NR4A1, GAST</italic> and downregulated <italic>DPP4</italic>—is possibly inhibiting apoptosis of islets and β-cells in T2D. Our computational-based study has identified a putative regulatory network that may facilitate the survival of pancreatic islets in T2D; however, further validation in a larger sample size is needed. Our results provide valuable insights into the underlying mechanisms of T2D and may offer potential targets for developing more efficacious treatments.</p> </abstract>

<abstract> <p>The primary objective of the research article is to describe the functionality of wrist watch which acts as a digital stethoscope to measure health constraints such as heart pulse through blood pressure. The second objective is the detection of blockages of an artery due to fatty, cholesterol-deposited, over a period when the blood is passing through due to stress and exercise, etc. Pressure and velocity are two inputs with their respective results of contrast and expansion of veins at the outputs. The major parameters in detecting the laminar flow are pressure and velocity. These parameters of a vein are analyzed by integrating valves in the vein. The movement of blood laminar flow in the vein is captured by a MEMS-based piezoelectric sensor by its functionality. The proposed design performance accuracy is estimated by modeling of vein's laminar flow when blood is circulating. The coagulation effect of the vein is used to measure heart stroke by placing MEMS along with the stent, as MEMS are tiny in size. The functionality of a digital Stethoscope works on the piezoelectric effect that generates an electrical signal when pressure is applied from the vein. The accuracy, functionality, and performance of the design can be analyzed by COMSOL multi-physics. Applications of MEMS include detection, prevention, and alert during the second heart stroke, and also used in the bionic eye and automotive electronics.</p> </abstract>