Work-related musculoskeletal disorders (WMSDs) represent a significant occupational health challenge among healthcare professionals globally, posing substantial threats to physical and mental well-being as well as work sustainability. Adopting preventive behaviors—including ergonomic postural adjustments, optimized work-rest scheduling, proper use of protective and assistive equipment, and regular physical activity—is essential for mitigating the risk of WMSDs. Guided by the social ecological model, the review synthesized current evidence on the determinants of WMSDs preventive behaviors across four levels: intrapersonal characteristics, work environment conditions, interpersonal support, and policy/institutional factors. The findings suggest that higher educational attainment, favorable health-related behavioral patterns, optimized ergonomic work environments, adoption of supportive collaborative systems, strong organizational support, as well as policy safeguards facilitate preventive behavior adoption. Conversely, limited prevention-related knowledge, low risk perception, insufficient physical activity, excessive workload, lack of appropriate protective equipment, inadequate ergonomic training, a prevailing culture of presenteeism, and inadequate policy implementation constitute significant barriers. Multi-dimensional intervention strategies targeting these determinants are warranted to enhance preventive behaviors, reduce the risk of WMSDs, and strengthen occupational health protection for healthcare professionals.

Olfactory receptors (ORs) form the largest superfamily of G protein-coupled receptors (GPCRs). Traditionally recognized for their role in the nasal olfactory epithelium, where they mediate the sense of smell, accumulating evidence has firmly established their ectopic expression in non-olfactory tissues, including the intestine, lungs, and kidneys. The intestine, as the primary site for nutrient digestion and absorption, harbors a highly complex chemical environment. To adapt to this environment, the gut employs a sophisticated network of “chemosensors” to monitor luminal contents and maintain homeostasis. Among these sensors, intestinal ORs have emerged as crucial functional components, serving as a molecular bridge that connects environmental chemical signals—such as food-derived odorants—to specific physiological responses. This discovery has significantly deepened our understanding of how dietary flavors and compounds influence intestinal physiology at the molecular level. This review systematically summarizes the expression profiles, ligand classification, and biological functions of ORs within the gastrointestinal tract. Studies indicate that intestinal ORs exhibit distinct spatial distribution patterns across different gut segments and display cell-type specificity, particularly within enterocytes and enteroendocrine cells. These receptors function as versatile sensors capable of recognizing a wide variety of ligands, including exogenous dietary components, gut microbiota metabolites such as short-chain fatty acids, and endogenous small molecules like azelaic acid. Upon activation by specific ligands, intestinal ORs trigger intracellular signaling cascades, primarily involving the AC-cAMP-PKA pathway or calcium influx channels. A major focus of this review is to elucidate the molecular mechanisms by which these receptors regulate the secretion of gut hormones. Activation of specific ORs in enteroendocrine cells has been shown to stimulate the release of hormones such as glucagon-like peptide-1 (GLP-1), peptide YY (PYY), and serotonin (5-HT), thereby modulating systemic energy metabolism, glucose homeostasis, and gastrointestinal motility. Furthermore, the review addresses the critical roles of ORs in immune regulation and pathology. Evidence suggests that specific ORs contribute to the maintenance of intestinal immune homeostasis and may offer protection against inflammation. Beyond their involvement in inflammatory responses, ORs such as Olfr78 have been shown to regulate the differentiation and function of intestinal endocrine cells. Similarly, Olfr544 has been demonstrated to alleviate intestinal inflammation by remodeling the gut microbiome and metabolome. These findings collectively suggest that specific ORs hold promise as therapeutic targets for mitigating intestinal inflammation and maintaining gut homeostasis. Additionally, the review explores the emerging role of ORs in cancer. Although OR expression is often downregulated in tumor tissues compared to normal mucosa, activation of specific ORs by certain ligands can inhibit tumor cell proliferation and migration and induce apoptosis via pathways such as MEK/ERK and p38 MAPK. Conversely, other receptors, such as OR7C1, may serve as biomarkers for cancer-initiating cells. In conclusion, intestinal ORs represent a vital component of the gut’s sensory network. The review also discusses the translational potential of these findings. By elucidating the precise pairing relationships between dietary components and specific ORs, novel therapeutic strategies could be developed. Intestinal ORs may thus emerge as promising targets for nutritional and pharmacological interventions in metabolic diseases, inflammatory bowel diseases, and malignancies.

Objective To analyze the infection status and epidemic trends of soil-borne nematode infections in Yubei, Chongqing City, in 2010,2021, and 2022. Methods The local populations from four survey sites of four towns in 2010 and five sites of five towns in 2021 and 2022 were surveyed regarding their basic information using a unified form. Fecal samples of the participants were collected and tested for soil-borne nematode infections using the modified Kato-Katz thick smear method. Results In 2010, 2 049 participants were surveyed, followed by 1 000 participants in 2021 and 2022. The overall prevalence of parasitic infections declined significantly from 3.86% to 0.20%. In 2010, soil-transmitted nematode included hookworms(3.81%) and roundworms(0.29%). In 2021, the infection rates of roundworms and hookworms were 1.70% and 0.10% respectively. Notably, only Ascaris was identified in 2022(0.20%). The≥60 age group consistently exhibited the highest infection rates across all surveys, followed by the 40-59 age group. The infection rates of males in the three surveys were 3.31%,1.92%, and 0.20% respectively, and those of females were 4.37%, 1.46%, and 0.20% respectively. There was no statistically significant difference in the infection rates between males and females. Educational attainment was inversely associated with infection; in 2010, the highest prevalence was observed among those with primary education or below, whereas in 2021, illiterate or semi-literate individuals showed the highest susceptibility. The occupational distribution of infections in 2010 indicated that retirees (8.33%), farmers(4.86%), and homemakers or unemployed individuals(3.45%)were the most affected. However, in 2021 and 2022, farmers emerged as the predominant occupational group with soil-transmitted nematode infections. Conclusions The infection rate of soil-borne nematodes showed a decreasing trend in Yubei, and the infection species changed from hookworms in 2010 to Ascaris in 2022. Farmers, the elderly, and people with low education levels should continue to be the focus of preventive and control efforts.

Electroencephalography (EEG) is a non-invasive neurophysiological monitoring technique. It records the electrical activity of the cerebral cortex using electrodes placed on the scalp surface. Owing to its high safety, portability, and millisecond-level temporal resolution, EEG has been widely utilized in a variety of fields, including clinical diagnosis, brain-computer interfaces (BCIs), and cognitive neuroscience research. However, due to its microvolt-level amplitude, EEG is highly susceptible to various artifacts, including electrooculographic (EOG), electrocardiographic (ECG), electromyographic (EMG), and power line interference (PLI). These artifacts can obscure genuine neural activity and introduce spurious electrophysiological features. Consequently, they may compromise EEG signal quality, thereby reducing the reliability of downstream analyses. To address this issue, numerous EEG artifact removal methods have been developed, including both traditional denoising techniques and deep learning-based approaches. Traditional EEG denoising methods have long served as the primary solutions for artifact removal. Representative approaches include filtering, regression, and blind source separation. Although these methods have demonstrated effectiveness in specific scenarios, they suffer from several inherent limitations. Filtering assumes that artifacts and EEG signals can be separated in the frequency domain, but many artifacts, such as EOG and EMG, overlap with EEG spectra, which may lead to the loss of valuable neural information. Regression methods require high-quality artifact references to estimate and subtract contaminations, limiting their effectiveness in reference-free scenarios. Blind source separation can remove artifacts without external references, but it typically requires the number of EEG channels to exceed the number of sources, restricting its application in single- or low-channel EEG recordings. Deep learning-based EEG denoising methods address these limitations effectively. First, they learn the nonlinear mapping between contaminated and clean EEG directly from data in an end-to-end manner. This approach does not rely on assumptions about spectral separability, thereby preserving neural activity more completely. Second, the reference information is incorporated during the training phase, allowing the trained model to perform artifact removal independently without external references. Third, deep learning models can be flexibly designed to accommodate various recording setups, achieving robust denoising for both high-density and single-channel EEG. Collectively, these advantages enable deep learning-based methods to overcome the main challenges of traditional approaches, providing more accurate and reliable EEG signal recovery. The superior denoising performance of deep learning-based EEG denoising methods has attracted increasing attention in EEG artifact removal research. As a result, many deep learning-based denoising methods have been developed and successfully applied in neural engineering areas. However, a systematic review of the techniques and applications in this field is still lacking. To address this gap, this paper reviews recent advances in deep learning-based EEG denoising from four perspectives: technical principle, benchmark dataset, denoising model, and evaluation method. Representative applications in neural signal analysis and BCI decoding are also summarized. Furthermore, the advantage, existing challenge, and future research direction of deep learning-based EEG denoising are discussed. This review aims to provide valuable theoretical insights and technical guidance for researchers. It is also expected to promote further advances and broader applications of deep learning-based EEG denoising techniques.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

ObjectiveThis study aims to explore the mechanism of action of Huangqi Guizhi Wuwutang in treating rheumatoid arthritis （RA）-associated sarcopenia by regulating autophagy and improving skeletal muscle homeostasis based on network pharmacology，bioinformatics，machine learning，and animal experiments. MethodsActive ingredients and targets of Huangqi Guizhi Wuwutang were screened using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform （TCMSP），PubChem，and SwissTargetPrediction databases. RA-related datasets were retrieved from the GEO database，and differential genes were screened. Sarcopenia-related targets were searched through GeneCards and the Comparative Toxicology Database （CTD），and autophagy-related gene sets were downloaded from the Human Autophagy Database （HADb）. Their intersection was analyzed to identify autophagy-related therapeutic targets，followed by enrichment analysis. A protein-protein interaction （PPI） network was constructed using the STRING database，and key targets were selected using multiple methods. Machine learning was applied to predict models based on the expression profiles of intersecting targets，and nomogram models were constructed based on key targets. Molecular docking of the top four active ingredients with key targets was performed using AutoDockVina. A collagen-induced arthritis （CIA） rat model was established using bovine type Ⅱ collagen，with SD rats divided into groups including a blank group，a model group，and low-，medium-，and high-dose groups of Huangqi Guizhi Wuwutang （2.44，4.88，and 9.76 g·kg^-1） and administered for five consecutive weeks. Joint scores and gastrocnemius muscle mass were recorded and analyzed after modeling. Hematoxylin and eosin （HE） staining and Masson's staining were used to observe pathological changes in muscle tissue. Immunofluorescence staining was applied to observe the protein expression levels of myosin heavy chain （MYHC） and insulin-like growth factor-1 （IGF-1） in skeletal muscle. Western blot was used to detect the protein expression levels of autophagy-related proteins ATG5，Beclin1，LC3B，muscle-specific proteins （MuRF1），MaFbx，and MYHC. Real-time quantitative reverse transcription PCR （Real-time PCR） was performed to measure the mRNA expression levels of ATG5，Beclin1，LC3B，MuRF1，MaFbx，and MYHC in muscle tissue. ResultsNetwork pharmacology revealed that Huangqi Guizhi Wuwutang shared 25 common targets with autophagy genes related to RA-associated sarcopenia. The PPI network and machine learning identified six key targets，which were primarily involved in autophagy and inflammatory pathways. Animal experiments showed that compared to the blank group，the model group had significantly higher joint scores （P<0.01） and lower gastrocnemius muscle index （P<0.01）. HE staining indicated a significant reduction in the cross-sectional area of gastrocnemius muscle fibers，with notable inflammatory cell infiltration and muscle atrophy in the model group. Masson's staining revealed obvious collagen fiber proliferation and deposition，with significant muscle fibrosis in the model group. The protein and mRNA expression levels of ATG5，Beclin1，LC3B，MuRF1，and MaFbx were significantly increased （P<0.01），while the protein expression of MYHC and IGF1 was significantly downregulated （P<0.01）. Compared with the model group，the high-dose group of Huangqi Guizhi Wuwutang showed significantly reduced protein and mRNA expression levels of ATG5，Beclin1，LC3B，MuRF1，and MaFbx （P<0.01） and increased protein expression levels of MYHC and IGF1 （P<0.01）. The cross-sectional area of muscle fibers increased，and the muscle cell morphology approached normal. Moreover，pathological abnormalities in the gastrocnemius muscle were significantly improved，with reduced collagen fiber proliferation （P<0.01）. ConclusionHuangqi Guizhi Wuwutang can mediate autophagy by regulating the expression of ATG5，Beclin1，LC3B，and IGF1，thereby reducing skeletal muscle catabolism and improving skeletal muscle homeostasis，which contributes to the treatment of RA-associated sarcopenia. The findings provide insight into the mechanisms underlying the effects of Huangqi Guizhi Wuwutang in the treatment of RA-related sarcopenia and offer a reference for its enhanced clinical application.

The use of wastewater analysis, or wastewater-based epidemiology, to assess and monitor the situation of drug abuse is now widely used at home and abroad. However, there is currently a lack of effective evaluation methods and effective ways of comparison, supervision and standardization, which is not conducive to the analysis and comparisons of data in different countries and regions. Quality control techniques can control the laboratory's analytical errors, safeguard the consistency and comparability of identification conclusions, and promote the further improvement of the level and capacity of urban drug governance, thus playing significant roles. This paper provides an overview of sample collection, sample preservation and transportation, laboratory analysis, back-calculation of drug use and external laboratory quality control in the process of wastewater analysis, with a view to exploring more comprehensive scientific and objective methods and approaches suitable for examining and evaluating qualitative and quantitative analysis of drugs in wastewater among laboratories.

ObjectiveTo investigate the relationship between mitochondrial DNA copy number (mtDNAcn) and cognitive dysfunction, and its mediating role between type 2 diabetes mellitus (T2DM) and cognitive dysfunction. MethodsA case-control study was conducted from May 2019 to April 2021 at the Shanghai Yangpu District Central Hospital, China. A total of 193 subjects were recruited and divided into two groups based on the Montreal Cognitive Assessment (MoCA): normal control (NC) group (n=95) and cognitive impairment group (n=98). The prevalence of T2DM was determined on the basis of medical history, while mtDNAcn in peripheral blood samples was quantified using realtime fluorescent quantitative polymerase chain reaction. ResultsUnivariate analyses revealed that the mean mtDNAcn in the cognitive impairment group was 0.76±0.37, significantly lower than that in the NC group (1.06±0.45) (P<0.05). Logistic regression analyses showed that higher mtDNAcn was associated with a reduced risk of cognitive impairment (OR=0.315, 95%CI: 0.125‒0.795). Additionaly, a statistically significant positive correlation was observed between mtDNAcn and the total MoCA score (r=0.381, P<0.01). Morever, T2DM history (OR=2.741, 95%CI: 1.002‒7.497) and elevated glycosylated hemoglobin (HbA1c) levels (OR=1.796, 95%CI: 1.190‒2.711) were identified as risk factors for cognitive impairment. Mediation analyses indicated that mtDNAcn served as a mediator between T2DM/HbA1c and the risk of cognitive impairment, with proportions of mediating effect of 9.04% and 9.18%, respectively. ConclusionPatients with mild and moderate cognitive impairment have significantly lower mtDNAcn than those with normal cognitive function. Reduced mtDNAcn is an influencing factor for cognitive dysfunction and may play a mediating role in the association between T2DM and mild to moderate cognitive impairment.

ObjectiveTraditional Chinese medicine (TCM) constitutes a valuable cultural heritage and an important source of antitumor compounds. Poria (Poria cocos (Schw.) Wolf), the dried sclerotium of a polyporaceae fungus, was first documented in Shennong’s Classic of Materia Medica and has been used therapeutically and dietarily in China for millennia. Traditionally recognized for its diuretic, spleen-tonifying, and sedative properties, modern pharmacological studies confirm that Poria exhibits antioxidant, anti-inflammatory, antibacterial, and antitumor activities. Pachymic acid (PA; a triterpenoid with the chemical structure 3β-acetyloxy-16α-hydroxy-lanosta-8,24(31)-dien-21-oic acid), isolated from Poria, is a principal bioactive constituent. Emerging evidence indicates PA exerts antitumor effects through multiple mechanisms, though these remain incompletely characterized. Neuroblastoma (NB), a highly malignant pediatric extracranial solid tumor accounting for 15% of childhood cancer deaths, urgently requires safer therapeutics due to the limitations of current treatments. Although PA shows multi-mechanistic antitumor potential, its efficacy against NB remains uncharacterized. This study systematically investigated the potential molecular targets and mechanisms underlying the anti-NB effects of PA by integrating network pharmacology-based target prediction with experimental validation of multi-target interactions through molecular docking, dynamic simulations, and in vitro assays, aimed to establish a novel perspective on PA’s antitumor activity and explore its potential clinical implications for NB treatment by integrating computational predictions with biological assays. MethodsThis study employed network pharmacology to identify potential targets of PA in NB, followed by validation using molecular docking, molecular dynamics (MD) simulations, MM/PBSA free energy analysis, RT-qPCR and Western blot experiments. Network pharmacology analysis included target screening via TCMSP, GeneCards, DisGeNET, SwissTargetPrediction, SuperPred, and PharmMapper. Subsequently, potential targets were predicted by intersecting the results from these databases via Venn analysis. Following target prediction, topological analysis was performed to identify key targets using Cytoscape software. Molecular docking was conducted using AutoDock Vina, with the binding pocket defined based on crystal structures. MD simulations were performed for 100 ns using GROMACS, and RMSD, RMSF, SASA, and hydrogen bonding dynamics were analyzed. MM/PBSA calculations were carried out to estimate the binding free energy of each protein-ligand complex. In vitro validation included RT-qPCR and Western blot, with GAPDH used as an internal control. ResultsThe CCK-8 assay demonstrated a concentration-dependent inhibitory effect of PA on NB cell viability. GO analysis suggested that the anti-NB activity of PA might involve cellular response to chemical stress, vesicle lumen, and protein tyrosine kinase activity. KEGG pathway enrichment analysis suggested that the anti-NB activity of PA might involve the PI3K/AKT, MAPK, and Ras signaling pathways. Molecular docking and MD simulations revealed stable binding interactions between PA and the core target proteins AKT1, EGFR, SRC, and HSP90AA1. RT-qPCR and Western blot analyses further confirmed that PA treatment significantly decreased the mRNA and protein expression of AKT1, EGFR, and SRC while increasing the HSP90AA1 mRNA and protein levels. ConclusionIt was suggested that PA may exert its anti-NB effects by inhibiting AKT1, EGFR, and SRC expression, potentially modulating the PI3K/AKT signaling pathway. These findings provide crucial evidence supporting PA’s development as a therapeutic candidate for NB.

As early mobilization of patients in the intensive care unit(ICU)is increasingly recognized as a key measure for improving patient prognosis and reducing complications,related research is increasing.However,conventional early mobilization methods are often limited by factors such as patient condition,nursing resources,and environmental factors,making it difficult to achieve optimal results.In recent years,virtual reality(VR)technology,owing to its unique immersion and interactive features,has emerged as a focus of research in the field of ICU patient rehabilitation.This review summarizes the advances in the application of VR technology in the early mobilization of ICU patients,focusing on its advantages over traditional methods,specific implementation approaches,clinical outcomes,and challenges encountered during application.By synthesizing existing evidence,this review aims to provide valuable references for clinical practice and to explore future directions for VR technology in ICU rehabilitation,with the goal of promoting its widespread application in the early mobilization of critically ill patients.