Background Depressive symptoms have become a significant factor affecting the physical and mental health of the occupational population, and workers in petroleum refining enterprises face multiple stressors in their work environment. Objective To explore the impact of occupational noise exposure on depressive symptoms among workers in a petroleum refining enterprise. Methods This cross-sectional study was conducted in July 2024 using a questionnaire survey among workers of a petroleum refining enterprise in Hainan Province. Basic information of the subjects was collected. The Center for Epidemiologic Studies Depression Scale (CES-D) was used to measure depressive symptoms, the Chinese version of the Pittsburgh Sleep Quality Index (PSQI) scale was used to assess sleep quality, and the Chinese version of the Effort-Reward Imbalance (ERI) scale was used to evaluate occupational stress. Chi-square test was employed to compare the differences in reporting depressive symptoms among populations with different characteristics. Binary logistic regression models were used to analyze the impact of occupational noise exposure and other factors on depressive symptoms. Results The overall positive rate of depressive symptoms in the study population was 42.7%. The results of the multifactor analysis indicated that compared with the control group, employees in both the low-exposure and high-exposure groups had elevated odds of depressive symptoms, with OR (95%CI) of 2.244 (1.131, 4.454) and 1.970 (1.009, 3.850), respectively. This association remained robust after adjusting for potential confounders, including gender, age, work tenure, and other occupational exposures. Additionally, female [OR (95%CI)=1.483 (1.039, 2.118)], exposure to benzene, toluene, or xylene [OR (95%CI)=1.621 (1.208, 2.174)], sleep disturbance [OR (95%CI)=3.772 (2.942, 4.838)], and occupational stress [OR (95%CI)=2.018 (1.575, 2.585)] were also significantly associated with higher odds of depressive symptoms. Conclusion The positive rate of depressive symptoms is relatively high among employees in this petrochemical enterprise, and occupational noise exposure may be a risk factor for depressive symptoms.

With the accelerating trend of population aging, the number of elderly patients with lung cancer continues to rise, and the disease burden is becoming increasingly heavy. The clinical management of these patients faces severe challenges due to their decreased physiological reserve, complex comorbidities, and significant individual heterogeneity. Consequently, under traditional diagnosis and treatment models, doctors often struggle to identify the individualized risks of elderly patients in a timely and comprehensive manner, which can easily lead to decision biases such as undertreatment or overtreatment. In view of this, this study advocates for the establishment of an umbrella decision-making model specifically tailored for elderly lung cancer patients. Grounded in a multidisciplinary team (MDT) platform, this model deeply integrates oncological indicators with the comprehensive geriatric assessment (CGA) system. By holistically considering multidimensional variables including tumor burden, organ function, frailty index, cognitive status, and social support, the model establishes an operational mechanism characterized by "single entry, precise stratification, and targeted selection". Accordingly, patients can be scientifically triaged into distinct intervention tiers, such as active surveillance, minimally invasive surgery, drug therapy, radiotherapy, and best supportive care, thereby achieving real-time alignment between treatment intensity and patient fitness. This article elaborates on the construction logic and key operational procedures of this novel decision-making framework, aiming to guide clinical practice beyond the limitations of a tumor-centric perspective toward a holistic, dynamic, whole-course management strategy. This transition seeks to ensure optimal quality of life and clinical net benefit for elderly patients alongside survival prolongation.

Multidrug-resistant (MDR) bacterial infections have emerged as a serious challenge of global public health crisis. The overuse and misuse of conventional antibiotics have dramatically accelerated the emergence, evolution and worldwide spread of drug-resistant bacterial strains, necessitating urgent exploration of novel antibacterial strategies. Bacteriophages serve as natural bacterial predators offering distinct advantages including high host specificity, autonomous self-replication capabilities and cost-effective large-scale production. However, wild-type phages present significant clinical limitations due to their narrow host ranges, susceptibility to rapid immune clearance and poor penetration of bacterial biofilms, which severely restrict their therapeutic applications. The convergence of synthetic biology, nanotechnology and advanced gene editing technologies has accelerated the development of engineered bacteriophage platforms, providing programmable, scalable and clinically translatable pathways to overcome these inherent biological constraints. Here, we systematically delineate four fundamental strategies for engineered bacteriophage development. Chemical modification utilizes reactive functional groups such as amino, carboxyl and thiol moieties on capsid proteins through esterification, amidation or click chemistry reactions to achieve precise drug conjugation and surface functionalization. In vivo editing encompasses ultraviolet or chemical mutagenesis for random mutation induction, homologous recombination for targeted genetic alterations, recombineering methodologies including electroporation-mediated bacteriophage recombination engineering, and CRISPR-Cas systems for precise genome editing to enable exact genetic reconstruction and host range reprogramming. In vitro synthesis leverages genome engineering platforms where intact phage genomes are transferred into yeast or host bacteria to facilitate highly efficient homologous recombination, enabling large DNA fragment assembly and cross-gene host range expansion without bacterial toxicity constraints. Directed evolution combines artificial selection through mutation library screening with rational design approaches involving chimeric receptor binding protein construction or site-specific mutagenesis, effectively balancing the discovery of unknown adaptive pathways with targeted host specificity modification. Moreover, we comprehensively discuss therapeutic applications across diverse clinical scenarios. Engineered bacteriophage effectively disrupt bacterial biofilms through sophisticated functionalized delivery platforms including nanozyme-conjugated phages, phage-liposome nanoconjugates and bio-responsive hydrogels, demonstrating significantly enhanced bactericidal efficiency compared to unmodified free phages. These bioengineered vectors attenuate bacterial virulence and resensitize pathogens to antibiotics by delivering CRISPR-Cas systems or base editors to disrupt critical virulence factors such as pili, capsule synthesis machineries and quorum sensing systems, or by inactivating antibiotic resistance determinants including beta-lactamase genes. As an intelligent nanomedicine delivery platform, engineered bacteriophage enable precise pathogen elimination an through photocatalytic reactive oxygen species generation, immunomodulatory interventions, or controlled release of antibacterial drugs. Furthermore, oral administration of engineered bacteriophage facilitates microbiota modulation, which selectively eliminate intestinal pathogens while preserve beneficial commensal microbiota, thereby restoring microbial community balance and preventing complications associated with dysbiosis. Finally, we critically analyze persistent challenges including host strain matching complexity, evolution of bacterial resistance mechanisms, pharmacokinetic optimization requirements, optimal administration route selection, large-scale production quality control standards and clinical dosing determination protocols. Through multidisciplinary integration of synthetic biology, infectious disease medicine and immunology, future translational medicine studies of bacteriophage should establish comprehensive technical platforms encompassing rapid phage screening, intelligent rational design, rigorous in vivo evaluation and standardized clinical validation processes, ultimately advancing engineered bacteriophage from laboratory innovations to clinically approved therapeutics for effectively combating MDR bacterial infections.

Background: Diabetic peripheral neuropathy (DPN) is a common chronic complication of diabetes mellitus that significantly impairs patients’ quality of life. Traditional Chinese medicine (TCM), as a major component of complementary and alternative medicine, has accumulated numerous effective formulas for the clinical management of DPN. However, systematic approaches for optimizing TCM formulas remain limited. Objective: To establish a pathway-oriented approach for TCM formula optimization and to evaluate the efficacy and mechanisms of the optimized formula in DPN. Methods: We developed 2 formula optimization algorithms that defined pathway-oriented herbal correlation (HC) and herbal contribution (HO) to screen and construct a new herbal formula, Qihongtongbi (QHTB), for treating Qi deficiency and blood stasis syndrome complicated with DPN. An animal experiment was subsequently conducted to evaluate the therapeutic effectiveness of QHTB. A total of 32 specific-pathogen-free male ob/ob mice (18–20 g) were randomly divided into 4 groups: model, Mudan Granules (MDG), QHTB low- and high-dose groups (n = 8). Ten male C57BL/6J mice (18–20 g) served as the control group. Metabolomics analysis was further employed to elucidate the biological mechanisms underlying the effects of QHTB. Results: Based on our previous study on TCM medication patterns for treating DPN, 22 candidate herbs were selected for formula optimization. The top 5 candidate herbs (total HC = 25.24 × 10 ) exhibited HC values comparable to those of MDG (total HC = 26.1 × 10 ). These herbs were Carthamus tinctorius L. (HC = 5.40 × 10 ), Astragalus mongholicus Bunge (HC = 5.23 × 10 ), Salvia miltiorrhiza Bunge (HC = 5.15 × 10 ), Commiphora myrrha (T. Nees) Engl. (HC = 5.02 × 10 ), and Glycyrrhiza glabra L. (HC = 4.44 × 10 ). HO analysis showed that the cumulative HO of these 5 herbs exceeded 50%. In vivo experiments demonstrated that, compared with the model group, QHTB significantly lowered blood glucose (P < 0.05), enhanced nerve conduction velocity (P < 0.01), improved nociceptive hypersensitivity (P < 0.01), and ameliorated sciatic nerve morphology, with efficacy comparable to MDG. Serum and fecal metabolomics further revealed that QHTB exerted multipathway regulatory effects, among which nicotinate and nicotinamide metabolism represented a key mechanism. Conclusion: In summary, this study provides a methodological reference for the systematic optimization of TCM formulas.

Objective To evaluate the characteristics, related therapeutic regimens, risk factors of cardiovascular adverse events (CVAEs) and prognosis in the current therapy of newly diagnosed multiple myeloma (NDMM) patients in China. Methods A retrospective observational study was performed on patients with NDMM treated at Zhongshan Hospital, Fudan University, from January 2013 to October 2021. The propensity score matching at a ratio of 1∶1 was employed to balance baseline confounders between the therapeutic group and the control group, and CVAEs incidences were compared between the corresponding groups. Independent risk factors for CVAEs were identified through Fine-Gray competing risk regression model. Kaplan-Meier survival curves and log-rank tests were used to assess overall survival and progression-free survival of patients. Results A total of 829 patients with NDMM were analyzed, with a median of 33.1 months of follow-up for survivors. CVAEs occurred in 65 patients (7.8%) during first-line treatment, and a total of 74 events were recorded, with 52.7% grade 3 or greater in severity. Arrhythmia (particularly atrial fibrillation), thrombotic event (TE), and heart failure were the most common types. The median time from treatment to the first occurrence of CVAEs was 26 days, while the medium occurrence time of TE was later than the other events (76 d vs 19 d, P＝0.003). The exposure to immunomodulatory drugs was related to a higher incidence of TEs, whereas other therapeutic regimens, including bortezomib, didn’t show the effect of cardiovascular toxicity. Results of multivariate analysis showed that Eastern Cooperative Oncology Group performance status ＞2, symptomatic heart diseases within three months before treatment, and estimated glomerular filtration rate≤50 mL·min⁻¹·(1.73 m²)⁻¹ were independent risk factors for CVAEs. Compared with patients without CVAE, patients who experienced CVAEs had significantly inferior progression-free survival and overall survival, and higher cardiovascular mortality (P＜0.001). Conclusions The incidence of CVAE is relatively high during first-line treatment in NDMM patients, and the prognosis of CVAE patients is poor. The risk of CVAE is significantly increased in patients with underlying cardiovascular diseases.

Objective The aim of the study is to analyze the incidence patterns and epidemiological characteristics of parasitic and vector-borne diseases in the Minhang and Jinshan districts of Shanghai, and provide evidence-based information for optimizing prevention and control measures. Methods We collected information from epidemic reports and epidemiological investigation forms for cases of parasitic and vector-borne diseases from 2005 to 2024 in Shanghai's Minhang and Jinshan districts. Descriptive epidemiological method were adopted to conduct a comparative analysis of incidence trends, epidemic characteristics, and infection sources. Results A total of 229 cases in 7 categories were reported between 2005 and 2024, with an average annual incidence rate of 0.36/100 000. The average annual incidence rate in Minhang District was significantly higher than that in Jinshan District. The most prevalent diseases were malaria and dengue fever, followed by Japanese encephalitis. Monthly case reports were available, predominantly from June to October, with a peak in July-August. A statistically significant difference was observed between the two districts in case distribution during June-October (P＜0.001), whereas no significant difference was found for July-August (P=0.331). The majority of cases were young men (111/229,48.47%), aged 20-39 years. Migrant workers/farmers constituted the largest occupational group (55/229, 24.02%). Registered residents of other provinces and cities (147/229,64.19%), and the main source of infection was overseas importation (112/229,48.91%). There were no statistically significant differences in gender and household-registration composition between the two districts (P＞0.05). Significant differences existed in age, occupation, and source of infection (P＜0.05). The imported diseases were malaria and dengue fever, with Africa and Southeast Asia being the primary source locations. Malaria was the predominant disease imported from other provinces and cities, with cases having been imported from abroad since 2012. Local infections were mainly Japanese encephalitis, followed by malaria (with the last local case reported in 2010). Cases between 2005 and 2009 were predominantly imported from other provinces and cities. The proportion of cases imported from abroad increased sharply after 2010, particularly in Minhang District. Local infections sometimes arise in Jinshan District, with Japanese encephalitis serving as the primary type. Conclusions Incidence rates of parasitic and vector-borne diseases in Minhang and Jinshan districts have generally remained low. To further reduce this disease burden, it will be necessary to strengthen joint measures for prevention and control, eliminate the environmental risk factors of vector organisms, enhance monitoring and precise control of the environment and inbound personnel to prevent the occurrence and transmission of parasitic and vector-borne diseases.

BACKGROUND: The purpose of this study was to evaluate the safety and efficacy of subsequent radiotherapy (RT) following first-line treatment with durvalumab plus chemotherapy in patients with extensive-stage small cell lung cancer (ES-SCLC). METHODS: A total of 122 patients with ES-SCLC from three hospitals during July 2019 to December 2021 were retrospectively analyzed. Inverse probability of treatment weighting (IPTW) analysis was performed to address potential confounding factors. The primary focus of our evaluation was to assess the impact of RT on progression-free survival (PFS) and overall survival (OS). RESULTS: After IPTW analysis, 49 patients received durvalumab plus platinum-etoposide (EP) chemotherapy followed by RT (Durva + EP + RT) and 72 patients received immunochemotherapy (Durva + EP). The median OS was 17.2 months vs . 12.3 months (hazard ratio [HR]: 0.38, 95% confidence interval [CI]: 0.17-0.85, P = 0.020), and the median PFS was 8.9 months vs . 5.9 months (HR: 0.56, 95% CI: 0.32-0.97, P = 0.030) in Durva + EP + RT and Durva + EP groups, respectively. Thoracic radiation therapy (TRT) resulted in longer OS (17.2 months vs . 14.7 months) and PFS (9.1 months vs . 7.2 months) compared to RT directed to other metastatic sites. Among patients with oligo-metastasis, RT also showed significant benefits, with a median OS of 17.4 months vs . 13.7 months and median PFS of 9.8 months vs . 5.9 months compared to no RT. Continuous durvalumab treatment beyond progression (TBP) prolonged OS compared to patients without TBP, in both the Durva + EP + RT (NA vs . 15.8 months, HR: 0.48, 95% CI: 0.14-1.63, P = 0.238) and Durva + EP groups (12.3 months vs . 4.3 months, HR: 0.29, 95% CI: 0.10-0.81, P = 0.018). Grade 3 or 4 adverse events occurred in 13 (26.5%) and 13 (18.1%) patients, respectively, in the two groups; pneumonitis was mostly low-grade. CONCLUSION Addition of RT after first-line immunochemotherapy significantly improved survival outcomes with manageable toxicity in ES-SCLC.
Humans ; Small Cell Lung Carcinoma/therapy* ; Retrospective Studies ; Male ; Female ; Middle Aged ; Lung Neoplasms/therapy* ; Aged ; Antibodies, Monoclonal/therapeutic use* ; Adult ; Immunotherapy/methods* ; Aged, 80 and over

OBJECTIVE: As an age-related neurodegenerative disease, the prevalence of mild cognitive impairment (MCI) increases with age. Within the framework of traditional Chinese medicine, spleen-kidney deficiency syndrome (SKDS) is recognized as the most frequent MCI subtype. Due to the covert and gradual onset of MCI, in community settings it poses a significant challenge for patients and their families to discern between typical aging and pathological changes. There exists an urgent need to devise a preliminary diagnostic tool designed for community-residing older adults with MCI attributed to SKDS (MCI-SKDS). METHODS: This investigation enrolled 312 elderly individuals diagnosed with MCI, who were randomly distributed into training and test datasets at a 3:1 ratio. Five machine learning methods, including logistic regression (LR), decision tree (DT), naive Bayes (NB), support vector machine (SVM), and gradient boosting (GB), were used to build a diagnostic prediction model for MCI-SKDS. Accuracy, sensitivity, specificity, precision, F1 score, and area under the curve were used to evaluate model performance. Furthermore, the clinical applicability of the model was evaluated through decision curve analysis (DCA). RESULTS: The accuracy, precision, specificity and F1 score of the DT model performed best in the training set (test set), with scores of 0.904 (0.845), 0.875 (0.795), 0.973 (0.875) and 0.973 (0.875). The sensitivity of the training set (test set) of the SVM model performed best among the five models with a score of 0.865 (0.821). The area under the curve of all five models was greater than 0.9 for the training dataset and greater than 0.8 for the test dataset. The DCA of all models showed good clinical application value. The study identified ten indicators that were significant predictors of MCI-SKDS. CONCLUSION The risk prediction index derived from machine learning for the MCI-SKDS prediction model is simple and practical; the model demonstrates good predictive value and clinical applicability, and the DT model had the best performance. Please cite this article as: Ai YT, Zhou S, Wang M, Zheng TY, Hu H, Wang YC, Li YC, Wang XT, Zhou PJ. Development of a machine learning-based risk prediction model for mild cognitive impairment with spleen-kidney deficiency syndrome in the elderly. J Integr Med. 2025; 23(4): 390-397.
Humans ; Cognitive Dysfunction/diagnosis* ; Aged ; Male ; Female ; Machine Learning ; Spleen ; Aged, 80 and over ; Kidney ; Medicine, Chinese Traditional

The structure of intestinal tissue is complex. In vitro simulation of intestinal structure and function is important for studying intestinal development and diseases. Recently, organoids have been successfully constructed and they have come to play an important role in biomedical research. Organoids are miniaturized three-dimensional (3D) organs, derived from stem cells, which mimic the structure, cell types, and physiological functions of an organ, making them robust models for biomedical research. Intestinal organoids are 3D micro-organs derived from intestinal stem cells or pluripotent stem cells that can successfully simulate the complex structure and function of the intestine, thereby providing a valuable platform for intestinal development and disease research. In this article, we review the latest progress in the construction and application of intestinal organoids.
Organoids/cytology* ; Intestines/physiology* ; Humans ; Animals ; Pluripotent Stem Cells

OBJECTIVE: Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health. Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment, including high dimensionality, correlated exposure, and subtle individual effects. METHODS: We proposed a novel statistical approach, the generalized functional linear model (GFLM), to analyze the health effects of exposure mixtures. GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation. The robustness and efficiency was evaluated under various scenarios through extensive simulation studies. RESULTS: We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey (NHANES). In the first application, we examined the effects of 37 nutrients on BMI (2011-2016 cycles). The GFLM identified a significant mixture effect, with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI, respectively. For the second application, we investigated the association between four pre- and perfluoroalkyl substances (PFAS) and gout risk (2007-2018 cycles). Unlike traditional methods, the GFLM indicated no significant association, demonstrating its robustness to multicollinearity. CONCLUSION GFLM framework is a powerful tool for mixture exposure analysis, offering improved handling of correlated exposures and interpretable results. It demonstrates robust performance across various scenarios and real-world applications, advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.
Humans ; Environmental Exposure/analysis* ; Linear Models ; Nutrition Surveys ; Environmental Pollutants ; Body Mass Index