Background The prevalence of low back pain among workers using vibrating tools in the automobile manufacturing industry is high; however, research on associated risk factors remains limited. Objective To investigate the association between use of vibrating tools and low back pain among automobile manufacturing workers, and to estimate related population attributable risk percentage (PAR%). Methods A cross-sectional survey was conducted using the Chinese Musculoskeletal Disorders Questionnaire among workers with over one year of service in an automobile manufacturing enterprise. Workers were categorized into an exposed group (vibrating tool users) and a control group (non-users) based on their use of vibrating tools during work. The variables showing statistically significant differences between the two groups were selected as covariates for matching by 1∶2 nearest-neighbor propensity score. Chi-square test was used to compare the prevalence of low back pain between the matched exposed and control groups. Log-binomial regression model was employed to calculate the prevalence ratio (PR), and the PAR% of low back pain attributable to vibrating tool use was subsequently determined. Results A total of 6973 valid questionnaires were collected, comprising 5676 males and 1297 females, with an overall low back pain prevalence of 40.3% (2809/6973). The exposed group included 1255 workers (1134 males, 121 females), with a mean age of (44.2 ± 8.7) years and a mean tenure in the current position of (23.5 ± 9.9) years; the matching control group consisted of 2510 workers (2279 males, 231 females), with a mean age of (44.7 ± 8.6) years and a mean tenure in the current position of (24.0 ± 10.0) years. The prevalence of low back pain was 76.7% (963/1255) in the exposed group, significantly higher than the 29.9% (750/2510) in the control group (P<0.001). The log-binomial regression analysis showed a PR of 2.57 (95%CI: 2.28, 2.90) for the exposed group, with a PAR% of 22.03%. Conclusion The use of vibrating tools among automobile manufacturing workers is positively associated with reporting low back pain. Implementing effective measures to mitigate vibration hazards is crucial for reducing the burden of low back pain in this population.

Critically ill patients often experience significant skeletal muscle wasting due to prolonged bed rest, metabolic disorders, inflammatory responses and malnutrition, which affects the patient's mobility and may also lead to increased mortality. Timely and accurate assessment of muscle status is important for optimizing treatment strategies and improving patient prognosis. There are various limitations in the current methods of assessing muscle mass, and muscle ultrasound, as a noninvasive, convenient, low-cost and suitable technique for bedside monitoring, has received increasing attention for its application in muscle assessment of critically ill patients. However, there are still a number of challenges in its practical application, such as the lack of uniform standards for the measurement method, the high dependence on the operation, and the reproducibility of the data that needs to be optimized, and so on. The aim of this article is to systematize the research progress of muscle ultrasound in muscle assessment of critically ill patients, and to discuss the advantages and limitations of its clinical application, in order to provide a scientific basis for future research and clinical practice.
Humans ; Critical Illness ; Ultrasonography ; Muscle, Skeletal/diagnostic imaging*

ObjectiveThe rapid advancement of bioanalytical technologies has heightened the demand for high-throughput, label-free, and real-time cellular analysis. Electrochemical impedance spectroscopy (EIS) operating in the GHz frequency range (GHz-EIS) has emerged as a promising tool for characterizing cell suspensions due to its ability to rapidly and non-invasively capture the dielectric properties of cells and their microenvironment. Although GHz-EIS enables rapid and label-free detection of cell suspensions, significant challenges remain in interpreting GHz impedance data for complex samples, limiting the broader application of this technique in cellular research. To address these challenges, this study presents a novel method that integrates GHz-EIS with deep learning algorithms, aiming to improve the precision of cell suspension concentration identification and quantification. This method provides a more efficient and accurate solution for the analysis of GHz impedance data. MethodsThe proposed method comprises two key components: dielectric property dataset construction and backpropagation (BP) neural network modeling. Yeast cell suspensions at varying concentrations were prepared and separately introduced into a coaxial sensor for impedance measurement. The dielectric properties of these suspensions were extracted using a GHz-EIS dielectric property extraction method applied to the measured impedance data. A dielectric properties dataset incorporating concentration labels was subsequently established and divided into training and testing subsets. A BP neural network model employing specific activation functions (ReLU and Leaky ReLU) was then designed. The model was trained and tested using the constructed dataset, and optimal model parameters were obtained through this process. This BP neural network enables automated extraction and analytical processing of dielectric properties, facilitating precise recognition of cell suspension concentrations through data-driven training. ResultsThrough comparative analysis with conventional centrifugal methods, the recognized concentration values of cell suspensions showed high consistency, with relative errors consistently below 5%. Notably, high-concentration samples exhibited even smaller deviations, further validating the precision and reliability of the proposed methodology. To benchmark the recognition performance against different algorithms, two typical approaches—support vector machines (SVM) and K-nearest neighbor (KNN)—were selected for comparison. The proposed method demonstrated superior performance in quantifying cell concentrations. Specifically, the BP neural network achieved a mean absolute percentage error (MAPE) of 2.06% and an R² value of 0.997 across the entire concentration range, demonstrating both high predictive accuracy and excellent model fit. ConclusionThis study demonstrates that the proposed method enables accurate and rapid determination of unknown sample concentrations. By combining GHz-EIS with BP neural network algorithms, efficient identification of cell concentrations is achieved, laying the foundation for the development of a convenient online cell analysis platform and showing significant application prospects. Compared to typical recognition approaches, the proposed method exhibits superior capabilities in recognizing cell suspension concentrations. Furthermore, this methodology not only accelerates research in cell biology and precision medicine but also paves the way for future EIS biosensors capable of intelligent, adaptive analysis in dynamic biological research.

Intestinal fibrosis is a major complication of inflammatory bowel disease (IBD), leading to a high incidence of surgical interventions and significant disability. Despite its clinical relevance, no targeted pharmacological therapies are currently available. This review aims to explore the underlying mechanisms driving intestinal fibrosis and address unresolved scientific questions, offering insights into potential future therapeutic strategies. We conducted a literature review using data from PubMed up to October 2024, focusing on studies related to IBD and fibrosis. Intestinal fibrosis results from a complex network involving stromal cells, immune cells, epithelial cells, and the gut microbiota. Chronic inflammation, driven by factors such as dysbiosis, epithelial injury, and immune activation, leads to the production of cytokines like interleukin (IL)-1β, IL-17, and transforming growth factor (TGF)-β. These mediators activate various stromal cell populations, including fibroblasts, pericytes, and smooth muscle cells. The activated stromal cells secrete excessive extracellular matrix components, thereby promoting fibrosis. Additionally, stromal cells influence the immune microenvironment through cytokine production. Future research would focus on elucidating the temporal and spatial relationships between immune cell-driven inflammation and stromal cell-mediated fibrosis. Additionally, investigations are needed to clarify the differentiation origins of excessive extracellular matrix-producing cells, particularly fibroblast activation protein (FAP) + fibroblasts, in the context of intestinal fibrosis. In conclusion, aberrant stromal cell activation, triggered by upstream immune signals, is a key mechanism underlying intestinal fibrosis. Further investigations into immune-stromal cell interactions and stromal cell activation are essential for the development of therapeutic strategies to prevent, alleviate, and potentially reverse fibrosis.
Humans ; Fibrosis/metabolism* ; Inflammatory Bowel Diseases/pathology* ; Animals ; Transforming Growth Factor beta/metabolism* ; Intestines/pathology*

Objective: To analyze the phylogenetic characteristics of VP1 gene of Coxsackievirus A10 (CVA10) isolates from 2004 to 2023, and to understand the genetic evolution and epidemic trends of CVA10, so as to provide references for the prevention and control of hand, foot, and mouth disease. Methods: The full-length sequences of the VP1 region of CVA10 isolates were retrieved from the BV-BRC database before December 15, 2024. Gene typing, sequence analysis, evolutionary analysis, and amino acid mutation site analysis were conducted using bioinformatics software. Results: A total of 1 253 CVA10 isolates VP1 region nucleotide full-length sequences from 2004 to 2023 were included, with 9 strains from 2004 to 2008, 338 strains from 2009 to 2012, and 906 strains from 2013 to 2023. China had the highest number of CVA10 isolates, with 1 143 strains accounting for 91.22%, and the predominant genotype was C3. Compared to the prototype strain, the nucleotide sequence homology of the VP1 region of CVA10 isolates ranged from 74.94% to 77.63%, while the amino acid sequence homology ranged from 88.59% to 93.62%. The third codon position preferred cytosine and thymine. The top three most abundant amino acids were threonine, alanine, and valine. The average relative synonymous codon usage of 30 amino acid codon groups was greater than 1. The average amino acid substitution entropy value was 0.04, with four amino acid mutation-prone sites identified, and the mutation-prone rate was 1.35%. Conclusions From 2004 to 2023, the majority of CVA10 isolates were primarily sourced from China, with genotype C3 being the predominant circulating strain in China. The nucleotide homology between the CVA10 isolates and the prototype strain was relatively low, and mutation-prone sites were identified, indicating that enhanced monitoring of viral variation is necessary.

Objective:To evaluate the predictive efficacy of different comorbidity indices for hospitalization due to acute exacerbations in chronic obstructive pulmonary disease (COPD) patients with comorbidities (CO-COPD).Methods:This retrospective cohort study included 259 stable COPD patients with comorbidities from Beijing Tongren Hospital, Capital Medical University, between October 2021 and September 2023, all with ≥1-year follow-up. Patients were categorized into hospitalized ( n=75) and non-hospitalized ( n=184) groups based on acute exacerbation events. Clinical characteristics, comorbidities, and comorbidity indices, including Charlson Comorbidity Index (CCI), COPD-specific Comorbidity Test (COTE), and comorbidities in chronic obstructive lung disease index (COMCOLD) were compared between two goups. Risk facors of hospitalization due to acute exacerbations were analyzed by Cox regression. Modified indices were developed by incorporating additional respiratory comorbidities (asthma, bronchiectasis, lung cancer) weighted by hazard ratios (HRs) from Cox reguression. The predictive performance of different comorbidity indices for hospitalization was assessed by receiver operating characteristic (ROC) curves. Results:Hospitalized patients exhibited lower BMI, FEV 1% predicted, and FEV 1/FVC (all P<0.05), alongside higher modified British Medical Research Coucil (mMRC) scores and COPD assessment test (CAT) scores, eosinophil counts, and Global Initiative for Chronic Obstructive Lung Disease, (GOLD)severity ( t=3.73, Z=-3.43, Z=-2.43, Z=-11.10, Z=-11.32, Z=-1.80, χ2=17.62, all P<0.05); and also higher use rates of inhaled corticosteroid (ICS) and systemic oral corticosteroid (OCS) ( χ2=5.48, 7.15, all P<0.05). The comorbidities of asthma, bronchiectasis, lung cancer, hypertension, coronary atherosclerotic heart disease, anxiety and depression in hospitalized group were significantly higher ( χ2=22.49, 18.30, 15.63, 5.10, 4.68, 7.46, 5.16, all P<0.05), along with the increased CCI and COTE index ( P<0.05). Comorbid asthma, bronchiectasis, and lung cancer were independent risk factors for hospitalization ( HR=1.841, 2.924, and 2.076, respectively; all P<0.05). Original CCI and COTE showed moderate predictive value ( AUC=0.609 and 0.655), while modified CCI, COTE, and COMCOLD demonstrated improved performance ( AUC=0.730, 0.760, and 0.713, respectively). At optimal cutoffs (modified CCI>3.5, COTE>4.5, COMCOLD>6.5), sensitivities were 61.3%, 76.0%, and 58.7%, with specificities of 70.1%, 61.4%, and 72.3%. Age-stratified analysis revealed enhanced predictive utility of modified indices across age groups. Conclusions:CCI, COTE, and COMCOLD provide modest predictive value for hospitalization in CO-COPD. Modified indices incorporating respiratory comorbidities significantly improve risk stratification, offering clinical utility for identifying high-risk patients in primary care settings.