BACKGROUND:The local immune microenvironment plays an important regulatory role in the process of bone formation,and the immune system is intricately linked to the skeletal system.OBJECTIVE:To systematically review the promotion of bone regeneration from three aspects:immune cell regulation of microenvironment,regulation of immune response by small extracellular vesicles,and induction of immune response by bone biomaterials,and to elucidate the immune regulatory mechanisms involved in bone regeneration.METHODS:Relevant literature was retrieved from PubMed,CNKI,WanFang Database,and VIP Database,using the search terms of"osteoimmunology,immune microenvironment,small extracellular vesicles,bone regeneration,bone tissue repair,biomaterials,and tissue engineering"in English and Chinese.Repeat and irrelevant literature was screened and removed,and 92 articles that met the criteria were selected for intensive reading and review.RESULTS AND CONCLUSION:Multiple immune cells and bone cells are in the same microenvironment,and immune cells can regulate the differentiation and activity of bone cells,collectively forming an immune microenvironment that affects bone regeneration.Neutrophils can significantly reduce local inflammatory responses in the early stages of bone injury,creating a favorable microenvironment for bone regeneration.M1 macrophages can clear foreign bodies and reduce early inflammatory responses,while M2 macrophages can promote the expression of osteogenic markers and factors,playing an important role in the repair process of bone injury.B cells and T cells can directly or indirectly affect the generation and activity of osteoblasts and osteoclasts,regulate bone metabolism,and promote bone regeneration.Extracellular vesicles of small cells regulate the local immune microenvironment through paracrine secretion,promoting bone formation and angiogenesis at the site of bone injury.The metal ions,surface hydrophilicity,porosity,pore size,surface morphology,and surface roughness on the surface of biomaterials can directly regulate local immune responses,and have anti-inflammatory,angiogenic,and osteogenic effects,thereby accelerating bone regeneration.

In recent years， repetitive transcranial magnetic stimulation （rTMS） has garnered significant attention as a therapeutic approach for sleep disorders in the elderly. However， the prevailing rTMS protocols are predominantly developed based on normative neurophysiological data derived from young adults and fail to incorporate individualized parameters tailored to the brain characteristics of the elderly. To address this gap， the consensus development group synthesized the latest evidence from 2010 to 2025 and established a standardized rTMS protocol specifically for elderly patients with sleep disorders. Adhering to the Appraisal of Guidelines for Research and Evaluation II （AGREE II） framework， systematically screened randomized controlled trials （RCTs） and systematic reviews regarding rTMS in the treatment of sleep disorders across various conditions. Meanwhile， the Grading of Recommendations Assessment， Development and Evaluation （GRADE） system was employed to rigorously grade the quality of evidence and the strength of recommendations. This consensus guideline delineates precise rTMS protocols for the management of sleep disorders in the elderly， highlights the adjustment of stimulation intensity according to scalp-cortex distance recommends either MRI‑guided neuronavigation or the Beam F3/F4 heuristic approach for accurate target localization， thereby providing precise rTMS intervention protocol for sleep disorders in the elderly， aiming to enhance clinical efficacy while ensuring treatment safety. ［Funded by National Key Research and Development Program （number， 2023YFC3603200）； General Program of Shenzhen Science and Technology Innovation Commission （number， JCYJ20240813112859008， JCYJ20240813112900002）； Youth Program of Shenzhen Kangning Hospital （number， KN2023A004）； www.guidelines-registry.cn number， PREPARE-2026CN530］

Objective To investigate occupational exposure levels of lead, arsenic and cadmium in the lead smelting plant of Hunan Shui Kou Shan Nonferrous Metals Group Co. Ltd., analyze their effects on health of employees, and compare the applicability of different occupational health risk assessment methods, and to provide a basis for prevention and control of occupational exposure risks in enterprises. Methods According to systematic sampling method, 380 employees with lead, arsenic and cadmium exposure (exposure group) and 100 non-exposure employees (non-exposure group) were selected from 2022 to 2024 for on-site investigation of occupational health [concentration time-weighted average (C_TWA)] and physical examination. The risk was evaluated by qualitative assessment method, the U.S. Environmental Protection Agency (EPA) inhalation risk assessment method, and the Singapore Ministry of Manpower (MOM) semi-quantitative method. The consistency was analyzed by the Kappa test. Results C_TWA values of lead, arsenic, and cadmium in all positions were lower than the occupational exposure limit (OEL). The levels of blood lead, urine arsenic, and urine cadmium, as well as the prevalence of multiple systems in the exposure group were significantly higher than those in the non-exposure group (P<0.05). The proportions of chronic lead, arsenic, and cadmium poisoning were increasing year by year in the exposure group (P<0.05). The qualitative assessment method mainly indicated low and medium risk, while the EPA and MOM methods mainly indicated medium and high risk, with good agreement between the two methods (Kappa=0.676, P<0.05). Conclusion Although the enterprise meets the C_TWA standards, there are still occupational health risks of lead, arsenic, and cadmium. The EPA inhalation risk assessment method is more applicable.

Objective To explore the prevalence of depressive symptoms in postoperative patients with ovarian cancer and to analyze its influencing factors from multiple dimensions, including clinical characteristics, psychological factors, and laboratory indicators. Methods A cross-sectional study was conducted, which enrolled 235 postoperative patients with ovarian cancer. Depressive status was assessed using the patient health questionnaire, and the demographic, pathological, and medical record data of the patients were collected using the generalized anxiety disorder scale, Pittsburgh sleep quality index, European organization for research and treatment of cancer quality of life questionnaire core 30, and ECOG performance status score. Peripheral blood tumor marker (CA125), routine blood test, lymphocyte subsets, and serum cytokine levels were measured. Univariate and multivariate binary logistic regression analysis were used for statistical analysis. Results The prevalence of depression in postoperative patients with ovarian cancer was 39.15% (92/235). Univariate analysis showed that ECOG score ≥ 2 points, pain, anxiety, poor sleep quality, low quality of life, low life satisfaction, tumor recurrence, six or more cycles of chemotherapy, as well as higher levels of CA125, NLR, and NAR, and lower hemoglobin levels were significantly associated with depression (all P<0.05). Multivariate binary Logistic regression analysis showed that anxiety (OR=1.975, 95%CI: 1.231-3.170), sleep efficiency (OR=4.181, 95%CI: 1.211-14.43), sleep latency (OR=34.806, 95%CI: 4.258-284.542), ECOG performance status score, cognitive function (OR=0.918, 95%CI: 0.868-0.97), and life satisfaction were independent risk factors for depression (all P<0.05). Laboratory indicators were not independent influencing factors in the multivariate Logistic regression model. Conclusion Depression in postoperative patients with ovarian cancer is influenced by physiological, psychological, and social factors. Clinical management should focus on patients with anxiety, sleep disorders, poor physical condition, and low life satisfaction, and a comprehensive prevention and treatment strategy centered on psychological intervention and taking into account symptom management and social support should be implemented.

ObjectiveTo investigate the epidemiological and clinical characteristics of human bocavirus (HBoV) in hospitalized children with acute lower respiratory tract infection (ALRTI) at a single-center children’s hospital in Shanghai, thereby providing evidence for the diagnosis, treatment, and prevention of HBoV infection. MethodsA retrospective study was conducted on 19 537 hospitalized children with ALRTI at Shanghai Children’s Hospital from January 2021 to December 2023. Multiplex polymerase chain reaction (PCR) combined with capillary electrophoresis was used to detect HBoV and 12 other common respiratory viruses /atypical pathogens. The positive detection rate, demographic characteristics (sex, age), temporal distribution (year, season) of HBoV, as well as the clinical characteristics of severe and non-severe pneumonia were analyzed. ResultsThe overall HBoV-positive rate was 2.57% (503/19 537), with 59.44% (299/503) being single infections and 40.56% (204/503) being co-infections. The positive detection rate was significantly higher in boys than that in girls (2.78% vs 2.33%, χ²=3.88, P=0.049). The highest infection rate was observed in toddlers, followed by infants (χ²=379.57, P<0.001). The positive rate peaked in 2021 and reached its lowest point in 2023 (χ²=45.49, P<0.001), with epidemics mainly prevalent in summer and autumn. The main clinical symptoms were cough (90.06%, 453/503), fever (75.94%, 382/503), and wheezing (39.96%, 201/503). Children with severe pneumonia showed a higher incidence of wheezing compared with the non-severe group (P<0.001), while underlying diseases and co-infections had no significant association with disease severity (P>0.05). ConclusionHBoV was an important pathogen of ALRTI in children, predominantly affecting infants and toddlers, with higher susceptibility in boys and seasonal peaks in autumn and summer. The main clinical manifestations included cough, fever, and wheezing, with wheezing being more prevalent in children with severe pneumonia.

OBJECTIVE To explore a synergistic medication strategy integrating stage-specific modifications of Bushen huoxue decoction with hormone replacement therapy （HRT） for premature ovarian insufficiency （POI）. METHODS The connotation of Academician Tong Xiaolin’s “state-target differentiation and treatment” theory and the disease stage characteristics of POI were summarized. The latent stage， subclinical stage， clinical stage and exhaustion stage of POI were classified into corresponding core pathogenesis “states” and key objective indicator “targets”. Taking Bushen huoxue decoction as the basic prescription， its staged modification scheme and its sequential combination with HRT were analyzed. RESULTS & CONCLUSIONS The four stages of POI correspond to four pathogenic states respectively： kidney yin deficiency， kidney deficiency and blood stasis， heart-kidney disharmony， and deficiency of both yin and yang. Follicle-stimulating hormone （FSH）， anti-Müllerian hormone （AMH）， antral follicle count （AFC） and Kupperman score were taken as the evaluation targets of staging. Combined with the “state-target” characteristics of each stage， a medication principle based on Bushen huoxue decoction with syndrome differentiation modification and stage-adjusted administration was established： traditional Chinese medicine dominates in the latent stage， a sequential collaborative regimen is adopted in the subclinical stage， HRT is dominant supplemented by traditional Chinese medicine in the clinical stage， and integrated traditional Chinese and Western medicine is applied to consolidate the root cause in the exhaustion stage.

Background Ozone (O₃) is a major air pollutant. The existing monitoring system has uneven distribution of sites, insufficient coverage in underdeveloped areas, and low temporal resolution, making it difficult to obtain hourly data. This limits the dynamic identification of pollution and the formulation of prevention and control strategies. Objective To construct an hourly O₃ concentration estimation model based on ensemble machine learning, aiming to improve the accuracy of pollution exposure assessment and explore O₃ health impacts. Methods This study integrated land use regression modeling with modern machine learning techniques, employing random forest and XGBoost algorithms to construct base models, and stacking integration using non-negative least squares. The ensemble model was trained and validated across China using high-resolution, multi-source geographic data (e.g., meteorologicaldata, population density, land cover types, and aerosol optical thickness). It was tested in Taiyuan City, combined with a distributed lag non-linear model to analyze the association between O₃ and emergency admissions. Results The constructed ensemble model performed well in predicting O₃ concentration, with a higher coefficient of determination (R²) and a lower root-mean-square deviation (RMSE) compared to the single models. The R² improved from 0.90 to 0.92, and the RMSE decreased from 11.41 to 10.62, enhancing both prediction accuracy and generalization ability. In the application to Taiyuan City, the model successfully imputed the hourly-level data for the entire year. The distributed lag non-linear model analysis revealed that the relative risk (RR) values for the 6th to 8th days following O₃ exposure were 1.14 (95%CI: 1.01, 1.29), 1.16 (95%CI: 1.02, 1.31), and 1.14 (95%CI: 1.01, 1.29), respectively, which were significantly higher than 1, indicating a significant lagged association (lagged 6-8 d) between O₃ and the number of emergency room visits. Conclusion A high-precision, hourly-level O₃ concentration estimation model is successfully constructed by combining the land use regression model with an ensemble machine learning approach to provide a scientific basis for environmental policy formulation and public health intervention. The application of the model verifies its generalization ability and practical application value, which can provide a new technical framework for subsequent environmental health research.

To compare the performance differences between the multidisciplinary team (MDT) model and the conventional diagnostic and treatment model for lung cancer, and to explore a high-quality development pathway for optimizing lung cancer diagnostic and treatment resources.

Infection is an important trigger for the initiation of pancreatic islet autoimmunity in type 1 diabetes mellitus （T1DM）. From the perspective of latent pathogen and dryness disorder， this study analyzes the core pathogenesis and dynamic evolution of T1DM in the subclinical and clinical stages. Combined with the T1DM "constraint， heat， deficiency， damage" state and target differentiation and treatment theory， the course of infection-induced T1DM is divided into three stages， including latent dryness， dry-heat and collateral damage. The "latent dryness" stage corresponds to the subclinical phase of T1DM， while the "dry-heat" stage corresponds to the clinical phase， and the "collateral damage" stage corresponds to the phase in which chronic complications develop. Treatment principles include supplementing deficiency and dispelling pathogen during the "latent dryness" stage， clearing heat and moistening dryness in the "dry-heat" stage， and dissolving stasis and unblocking collaterals in the "collateral damage" stage. Furthermore， syndrome-targeted and target-directed therapeutic modifications were made according to T1DM-related autoimmune activity， metabolic comorbidities， and vascular comorbidities， providing a reference for clinical management of T1DM.

BACKGROUND:The local immune microenvironment plays an important regulatory role in the process of bone formation,and the immune system is intricately linked to the skeletal system.OBJECTIVE:To systematically review the promotion of bone regeneration from three aspects:immune cell regulation of microenvironment,regulation of immune response by small extracellular vesicles,and induction of immune response by bone biomaterials,and to elucidate the immune regulatory mechanisms involved in bone regeneration.METHODS:Relevant literature was retrieved from PubMed,CNKI,WanFang Database,and VIP Database,using the search terms of"osteoimmunology,immune microenvironment,small extracellular vesicles,bone regeneration,bone tissue repair,biomaterials,and tissue engineering"in English and Chinese.Repeat and irrelevant literature was screened and removed,and 92 articles that met the criteria were selected for intensive reading and review.RESULTS AND CONCLUSION:Multiple immune cells and bone cells are in the same microenvironment,and immune cells can regulate the differentiation and activity of bone cells,collectively forming an immune microenvironment that affects bone regeneration.Neutrophils can significantly reduce local inflammatory responses in the early stages of bone injury,creating a favorable microenvironment for bone regeneration.M1 macrophages can clear foreign bodies and reduce early inflammatory responses,while M2 macrophages can promote the expression of osteogenic markers and factors,playing an important role in the repair process of bone injury.B cells and T cells can directly or indirectly affect the generation and activity of osteoblasts and osteoclasts,regulate bone metabolism,and promote bone regeneration.Extracellular vesicles of small cells regulate the local immune microenvironment through paracrine secretion,promoting bone formation and angiogenesis at the site of bone injury.The metal ions,surface hydrophilicity,porosity,pore size,surface morphology,and surface roughness on the surface of biomaterials can directly regulate local immune responses,and have anti-inflammatory,angiogenic,and osteogenic effects,thereby accelerating bone regeneration.