Objective To investigate the prevalence of work-related musculoskeletal disorders (WMSDs) in passenger drivers and its influencing factors. Methods A total of 951 passenger drivers in Guangdong Province were selected as the research subjects using the judgmental sampling method. A Musculoskeletal Injury Questionnaire was employed to assess the prevalence of WMSDs in the past year. Results The prevalence of WMSDs in passenger drivers was 41.11%. The result of multivariable logistic regression analysis showed that married drivers had a higher risk of WMSDs than single drivers (P<0.05). The lower the frequency of physical exercise, the longer the driving time per week, the longer the continuous driving time, the more restricted the driving working space, the poorer the foot comfort during driving, and the more affected the normal meal, the higher the risk of WMSDs (all P<0.05). The risk of WMSDs in drivers with sleep time ≤ 8.0 h/d was higher than that in drivers with sleep time > 8.0 h/d (P<0.01), and the risk of WMSDs in drivers with the same posture for a long time on the shoulder was higher than that in drivers without this poor working posture (P<0.01). Conclusion WMSDs were prevalent among passenger drivers, which was associated with demographic and adverse ergonomic factors. Intervention on lifestyle and adverse ergonomic factors could further reduce the risk of WMSDs of passenger drivers.

The current clinical treatment of bladder cancer (BCa) is mainly surgical treatment，supplemented by postoperative chemotherapy and immunotherapy.However，due to the lack of specificity，targeting and other reasons，the therapeutic effect is not satisfactory.In recent years，it has been found that metal nanoparticles (MNPs) prepared by gold，silver，and so on，as bladder infusion drugs or drug carriers，can not only accurately target BCa cells，but also have high stability and drug release rate，thereby reducing the side-effects of chemotherapy drugs.Based on domestic and foreign studies，this paper reviews the progress of MNPs in the treatment of BCa，including gold，silver，copper and other MNPs，and prospects the trend of bladder perfusion combined with nanomedical drugs.

ObjectiveTobacco-related diseases remain one of the leading preventable public health challenges worldwide and are among the primary causes of premature death. In recent years, accumulating evidence has supported the classification of nicotine addiction as a chronic brain disease, profoundly affecting both brain structure and function. Despite the urgency, effective diagnostic methods for smoking addiction remain lacking, posing significant challenges for early intervention and treatment. To address this issue and gain deeper insights into the neural mechanisms underlying nicotine dependence, this study proposes a novel graph neural network framework, termed TI-GNN. This model leverages functional magnetic resonance imaging (fMRI) data to identify complex and subtle abnormalities in brain connectivity patterns associated with smoking addiction. MethodsThe study utilizes fMRI data to construct functional connectivity matrices that represent interaction patterns among brain regions. These matrices are interpreted as graphs, where brain regions are nodes and the strength of functional connectivity between them serves as edges. The proposed TI-GNN model integrates a Transformer module to effectively capture global interactions across the entire brain network, enabling a comprehensive understanding of high-level connectivity patterns. Additionally, a spatial attention mechanism is employed to selectively focus on informative inter-regional connections while filtering out irrelevant or noisy features. This design enhances the model’s ability to learn meaningful neural representations crucial for classification tasks. A key innovation of TI-GNN lies in its built-in causal interpretation module, which aims to infer directional and potentially causal relationships among brain regions. This not only improves predictive performance but also enhances model interpretability—an essential attribute for clinical applications. The identification of causal links provides valuable insights into the neuropathological basis of addiction and contributes to the development of biologically plausible and trustworthy diagnostic tools. ResultsExperimental results demonstrate that the TI-GNN model achieves superior classification performance on the smoking addiction dataset, outperforming several state-of-the-art baseline models. Specifically, TI-GNN attains an accuracy of 0.91, an F1-score of 0.91, and a Matthews correlation coefficient (MCC) of 0.83, indicating strong robustness and reliability. Beyond performance metrics, TI-GNN identifies critical abnormal connectivity patterns in several brain regions implicated in addiction. Notably, it highlights dysregulations in the amygdala and the anterior cingulate cortex, consistent with prior clinical and neuroimaging findings. These regions are well known for their roles in emotional regulation, reward processing, and impulse control—functions that are frequently disrupted in nicotine dependence. ConclusionThe TI-GNN framework offers a powerful and interpretable tool for the objective diagnosis of smoking addiction. By integrating advanced graph learning techniques with causal inference capabilities, the model not only achieves high diagnostic accuracy but also elucidates the neurobiological underpinnings of addiction. The identification of specific abnormal brain networks and their causal interactions deepens our understanding of addiction pathophysiology and lays the groundwork for developing targeted intervention strategies and personalized treatment approaches in the future.

Currently, the drug delivery system (DDS) based on nanomaterials has become a hot interdisciplinary research topic. One of the core issues is drug loading and controlled release, in which the key lever is carriers. Vaterite, as an inorganic porous nano-material, is one metastable structure of calcium carbonate, full of micro or nano porous. Recently, vaterite has attracted more and more attention, due to its significant advantages, such as rich resources, easy preparations, low cost, simple loading procedures, good biocompatibility and many other good points. Vaterite, gained from suitable preparation strategies, can not only possess the good drug carrying performance, like high loading capacity and stable loading efficiency, but also improve the drug release ability, showing the better drug delivery effects, such as targeting release, pH sensitive release, photothermal controlled release, magnetic assistant release, optothermal controlled release. At the same time, the vaterite carriers, with good safety itself, can protect proteins, enzymes, or other drugs from degradation or inactivation, help imaging or visualization with loading fluorescent drugs in vitro and in vivo, and play synergistic effects with other therapy approaches, like photodynamic therapy, sonodynamic therapy, and thermochemotherapy. Latterly, some renewed reports in drug loading and controlled release have led to their widespread applications in diverse fields, from cell level to clinical studies. This review introduces the basic characteristics of vaterite and briefly summarizes its research history, followed by synthesis strategies. We subsequently highlight recent developments in drug loading and controlled release, with an emphasis on the advantages, quantity capacity, and comparations. Furthermore, new opportunities for using vaterite in cell level and animal level are detailed. Finally, the possible problems and development trends are discussed.

With the widespread use of antibiotics, drug-resistant bacterial infections have become a significant threat to human health. Finding new antibacterial strategies that can effectively control drug-resistant bacterial infections has become an urgent task. Unlike small molecule drugs that target bacterial proteins, antisense oligonucleotide (ASO) can target genes related to bacterial resistance, pathogenesis, growth, reproduction and biofilm formation. By regulating the expression of these genes, ASO can inhibit or kill bacteria, providing a novel approach for the development of antibacterial drugs. To overcome the challenge of delivering antisense oligonucleotide into bacterial cells, various drug delivery systems have been applied in this field, including cell-penetrating peptides, lipid nanoparticles and inorganic nanoparticles, which have injected new momentum into the development of antisense oligonucleotide in the antibacterial realm. This review summarizes the current development of small nucleic acid drugs, the antibacterial mechanisms, targets, sequences and delivery vectors of antisense oligonucleotide, providing a reference for the research and development of antisense oligonucleotide in the treatment of bacterial infections.

OBJECTIVE: To investigate the effects of Danmu Extract Syrup (DMS) on lipopolysaccharide (LPS)-induced acute lung injury (ALI) in mice and explore the mechanism. METHODS: Seventy-two male Balb/C mice were randomly divided into 6 groups according to a random number table (n=12), including control (normal saline), LPS (5 mg/kg), LPS+DMS 2.5 mL/kg, LPS+DMS 5 mL/kg, LPS+DMS 10 mL/kg, and LPS+Dexamethasone (DXM, 5 mg/kg) groups. After pretreatment with DMS and DXM, the ALI mice model was induced by LPS, and the bronchoalveolar lavage fluid (BALF) were collected to determine protein concentration, cell counts and inflammatory cytokines. The lung tissues of mice were stained with hematoxylin-eosin, and the wet/dry weight ratio (W/D) of lung tissue was calculated. The levels of tumor necrosis factor-α (TNF-α), interleukin (IL)-6 and IL-1 β in BALF of mice were detected by enzyme linked immunosorbent assay. The expression levels of Claudin-5, vascular endothelial (VE)-cadherin, vascular endothelial growth factor (VEGF), phospho-protein kinase B (p-Akt) and Akt were detected by Western blot analysis. RESULTS: DMS pre-treatment significantly ameliorated lung histopathological changes. Compared with the LPS group, the W/D ratio and protein contents in BALF were obviously reduced after DMS pretreatment (P<0.05 or P<0.01). The number of cells in BALF and myeloperoxidase (MPO) activity decreased significantly after DMS pretreatment (P<0.05 or P<0.01). DMS pre-treatment decreased the levels of TNF-α, IL-6 and IL-1 β (P<0.01). Meanwhile, DMS activated the phosphoinositide 3-kinase/protein kinase B (PI3K/Akt) pathway and reversed the expressions of Claudin-5, VE-cadherin and VEGF (P<0.01). CONCLUSIONS DMS attenuated LPS-induced ALI in mice through repairing endothelial barrier. It might be a potential therapeutic drug for LPS-induced lung injury.
Mice ; Male ; Animals ; Proto-Oncogene Proteins c-akt/metabolism* ; Lipopolysaccharides ; Phosphatidylinositol 3-Kinases/metabolism* ; Interleukin-1beta/metabolism* ; Vascular Endothelial Growth Factor A/metabolism* ; Tumor Necrosis Factor-alpha/metabolism* ; Claudin-5/metabolism* ; Acute Lung Injury/chemically induced* ; Lung/pathology* ; Interleukin-6/metabolism* ; Drugs, Chinese Herbal

Aim To predict the mechanism of Fufang Congrong Yizhi Capsules (FCYC) in the treatment of mild cognitive impairment (MCI) by network pharmacology method, and further validate it in combination with cellular experiments. Methods TCMSP, Gene-Cards, OMIM and TTD databases, Chinese Pharmacopoeia and related literature were used to screen the active ingredients of FCYC and the targets of MCI treatment. The TCM-compound-target-disease network and PPI of intersection targets were constructed, and the GO and KEGG analysis were performed by the Ehamb bioinformation platform. GO and KEGG analysis were performed through Yihanbo biological information platform. Cell model of MCI was established by PC-12 injury induced by Aβ

Idiosyncratic drug-induced liver injury (IDILI) has long posed a challenging and pivotal concern in pharmaceutical research. The complex composition of traditional Chinese medicine (TCM) has introduced a bottleneck in current research, hindering the elucidation of the component basis associated with IDILI in TCM. Using Epimedii Folium (EF) and Psoraleae Fructus (PF) as illustrative examples, this study endeavors to establish an in vitro evaluation model, providing a high-throughput and preliminary assessment method for screening components related to TCM-induced IDILI. A TNF-α-mediated HepG2 susceptible model was first established in this study, with the focus on the index components present in EF and PF. The release of lactate dehydrogenase (LDH) in the cell supernatant served as the detection index. A concentration-toxicity response curve was constructed, and the hepatotoxic components of EF and PF were identified utilizing the synergistic toxicity index. The LDH results unveiled the hepatotoxic effects of bavachin, backuchiol, isobavachin, neobavaisoflavone, psoralidin, isobavachalcone, icarisid I, and icarisid II on both normal and susceptible cells, categorizing these 8 components as both direct hepatotoxicity components and idiosyncratic hepatotoxicity components. Bavachin and neobavaisoflavone exhibited no hepatotoxicity on normal cells but demonstrated significant effects on susceptible cells, designating them as potential idiosyncratic susceptible hepatotoxicity components. The study further delineated that 10 EF components and 3 PF components were direct immune-promoting hepatotoxicity components. Additionally, 14 idiosyncratic immune-promoting hepatotoxicity components were identified, encompassing 10 EF components and 4 PF components, with neobavaisoflavone, bavachinin, and isobavachin being potential idiosyncratic susceptible immune-promoting hepatotoxicity components. Synergistic toxicity index results indicated that 13 idiosyncratic immune-promoting hepatotoxicity components (except anhydroicaritin) combined with bavachin demonstrated synergistic hepatotoxicity on susceptible cells. Notably, 3 idiosyncratic susceptible immune-promoting hepatotoxicity components combined with bavachin exhibited synergistic hepatotoxicity, with neobavaisoflavone displaying the highest synergistic toxicity index and bavachinin the lowest. In summary, this methodology successfully screens hepatotoxic and immune-promoting hepatotoxic components in EF and PF, distinguishing the types of components inducing hepatotoxicity, evaluating the hepatotoxicity degree of each component, and elucidating the synergistic relationships among them. Importantly, these findings align with the characteristics of IDILI. The method provides an effective model tool for the fundamental research of TCM-related IDILI components.

Objective To summarize the epidemiological and clinical features of infectious diseases of the central nervous system(CNS)by a single-center analysis.Methods A retrospective analysis was conducted on the data of 1247 cases of CNS infectious diseases diagnosed and treated in the First Medical Center of PLA General Hospital from 2001 to 2020.Results The data for this group of CNS infectious diseases by disease type in descending order of number of cases were viruses 743(59.6％),Mycobacterium tuberculosis 249(20.0％),other bacteria 150(12.0％),fungi 68(5.5％),parasites 18(1.4％),Treponema pallidum 18(1.4％)and rickettsia 1(0.1％).The number of cases increased by 177 cases(33.1％)in the latter 10 years compared to the previous 10 years(P<0.05).No significant difference in seasonal distribution pattern of data between disease types(P>0.05).Male to female ratio is 1.87︰1,mostly under 60 years of age.Viruses are more likely to infect students,most often at university/college level and above,farmers are overrepresented among bacteria and Mycobacterium tuberculosis,and more infections of Treponema pallidum in workers.CNS infectious diseases are characterized by fever,headache and signs of meningeal irritation,with the adductor nerve being the more commonly involved cranial nerve.Matagenomic next-generation sequencing improves clinical diagnostic capabilities.The median hospital days for CNS infectious diseases are 18.00(11.00,27.00)and median hospital costs are ￥29,500(￥16,000,￥59,200).The mortality rate from CNS infectious diseases is 1.6％.Conclusions The incidence of CNS infectious diseases is increasing last ten years,with complex clinical presentation,severe symptoms and poor prognosis.Early and accurate diagnosis and standardized clinical treatment can significantly reduce the morbidity and mortality rate and ease the burden of disease.

Objective To analyze the pathogenic characteristics and drug sensitivity of candidaemia,and construct a short-term mortality risk prediction scoring model.Methods The clinical data of patients with candidaemia admitted to the 909 Hospital of Joint Logistics Support Force from January 2011 to December 2020 were retrospectively analyzed,and the composition of pathogen composition,drug sensitivity test results and incidence of hospitalized patients were analyzed.324 cases of candidaemia were randomly divided into modeling group(190 cases)and validation group(134 cases),and the risk factors were screened by binary logistic regression.According to the odds ratio(OR)score,the 30 day mortality risk prediction scoring model was constructed,and the predictive performance of the model was verified both in modeling and validation groups.Results 356 strains of Candida including 126 strains of C.albicans(35.39%),79 strains of C.tropicalis(22.19%),74 strains of C.parapsilosis(20.79%),48 strains of C.glabrata(13.48%),14 strains of C.guilliermondii(3.93%),8 strains of C.krusei(2.25%),and 7 strains of other Candida(1.97%)were detected in 336 patients with candidemia.The incidence of candidaemia among hospitalized patients increased from 0.20 ‰ in 2011 to 0.48 ‰ in 2020.The resistance rate of candida to amphotericin B was significantly lower than that of fluconazole,voriconazole and itraconazole(P＜0.05).Among the 324 cases included in the model,95 patients died in 30 days after diagnosis,and the mortality rate was 29.32%.The proportion of males,fever,and parenteral nutrition in modeling group was significantly higher than that in validation group(P＜0.05),while the proportion of chronic lung disease and surgical history within one month were lower than those in validation group(P＜0.05).Logistic regression analysis showed that chronic renal failure,mechanical ventilation,severe neutropenia,failure to receive anti-fungal treatment within 72 hours,and APACHE Ⅱ≥20 were risk factors for short-term death of candidaemia,the OR values were 3.179,1.970,2.979,2.080,and 2.399,and the risk scores were 6,4,6,4,and 5,respectively.The area under the curve(AUC)of the risk scoring model for modeling group was 0.792(95%CI 0.721-0.862),and the result of Hosmer-Lemeshow(H-L)test was P=0.305;The AUC of validation group was 0.796(95%CI 0.735-0.898),and the H-L test result was P=0.329.A risk score≤8 indicated a low risk group for short-term mortality,a score of 9-15 indicated a medium risk group,and a score≥16 indicated a high risk group.Conclusions The incidence of candidemia in hospitalized patients is increasing and the mortality is high.The risk prediction score model can effectively predict the short-term prognosis and facilitate the early identification of the prognosis.