BACKGROUND:Neutrophil extracellular traps and activated platelets are involved in the invasion,metastasis,growth,and angiogenesis of lung cancer,and are closely related to the development and prognosis of lung cancer.OBJECTIVE:To review the mechanism of neutrophil extracellular traps and activated platelets in lung cancer and their application in diagnosis,prognosis,and treatment of lung cancer.METHODS:"Platelet activation,lung neoplasms,extracellular traps,treatment"for English search terms and"lung cancer,neutrophil-extracellular traps,platelet activation,P-selectin,treatment"for Chinese search terms were searched in PubMed and CNKI databases.After reading the title and abstract of the literature,according to the inclusion and exclusion criteria,63 articles with high relevance were finally included.RESULTS AND CONCLUSION:(1)Formation of neutrophil extracellular traps and platelet activation were induced by lung tumor.(2)Neutrophil extracellular traps and activated platelets jointly promote the proliferation,growth and metastasis of lung cancer.(3)Neutrophil extracellular traps can be used as a novel biomarker for the diagnosis,prognosis and progression of lung cancer.(4)Targeting neutrophil extracellular traps and activating platelets can be used as potential therapies for lung cancer.

ObjectiveTo provide a reference for the rational clinical use of cold Chinese medicines by sorting and analyzing their properties， flavors， meridian tropism， primary therapeutic indications， methods of administration， dosages， and precautions as recorded in the 2020 edition of Pharmacopoeia of the People's Republic of China （Chinese Pharmacopoeia）. MethodsCold Chinese medicines for internal and external use included in the 2020 edition of Chinese Pharmacopoeia were entered one by one， and their efficacy， properties， flavors， meridian tropism， methods of administration， dosages， and usage precautions were statistically classified and summarized to guide clinical medication use. ResultsA total of 259 cold Chinese medicines for internal use were included and categorized into 18 efficacy groups， mainly comprising heat-clearing drugs， water-excreting and dampness-draining drugs， and phlegm-resolving， cough- and asthma-relieving drugs. Their predominant flavors were bitter， sweet， and pungent， and they primarily entered the liver， lung， and stomach meridians. The main methods of administration included decocting first， grinding into powder for oral use， or preparing into pills or powders， with most dosages ranging from 9 to 15 g. A total of 83 cold Chinese medicines for external use were included， involving 16 efficacy categories. Their main flavors were bitter， sweet， and pungent， primarily entering the liver， lung， and large intestine meridians. The main external application methods were grinding into powder for topical use or preparing decoctions for fumigation and washing， with most dosages ranging from 9 to 15 g. Whether for internal or external use， cold Chinese medicines should be used with caution or contraindicated in pregnant women. ConclusionThe cold Chinese medicines included in the 2020 edition of the Chinese Pharmacopoeia are mainly suitable for patients with carbuncles， swellings， and coughs. However， in clinical practice， it is necessary to strictly follow the principles of syndrome differentiation and treatment， pay attention to administration methods and dosages， and use cold medicines rationally and effectively to improve clinical efficacy.

BACKGROUND:Neutrophil extracellular traps and activated platelets are involved in the invasion,metastasis,growth,and angiogenesis of lung cancer,and are closely related to the development and prognosis of lung cancer.OBJECTIVE:To review the mechanism of neutrophil extracellular traps and activated platelets in lung cancer and their application in diagnosis,prognosis,and treatment of lung cancer.METHODS:"Platelet activation,lung neoplasms,extracellular traps,treatment"for English search terms and"lung cancer,neutrophil-extracellular traps,platelet activation,P-selectin,treatment"for Chinese search terms were searched in PubMed and CNKI databases.After reading the title and abstract of the literature,according to the inclusion and exclusion criteria,63 articles with high relevance were finally included.RESULTS AND CONCLUSION:(1)Formation of neutrophil extracellular traps and platelet activation were induced by lung tumor.(2)Neutrophil extracellular traps and activated platelets jointly promote the proliferation,growth and metastasis of lung cancer.(3)Neutrophil extracellular traps can be used as a novel biomarker for the diagnosis,prognosis and progression of lung cancer.(4)Targeting neutrophil extracellular traps and activating platelets can be used as potential therapies for lung cancer.

Hepatic alveolar echinococcosis is a highly invasive zoonotic parasitic disease with poor prognosis. Surgical intervention serves as the pivotal approach to achieve radical cure and improve the prognosis of hepatic alveolar echinococcosis patients. In recent years, with the popularization of the concept of precision surgery and the development of the multidisciplinary diagnosis and treatment model, the surgical treatment strategies for hepatic alveolar echinococcosis have been continuously enriched, and the selection of surgical procedures has become increasingly diversified. Although key surgical techniques such as radical hepatectomy, autologous liver transplantation and allogeneic liver transplantation have achieved remarkable progress in clinical application, many insurmountable challenges still remain. Therefore, by sorting out the latest evidence-based advances in the field of surgical treatment for hepatic alveolar echinococcosis, this article focuses on discussing the application status and bottlenecks of radical hepatectomy, autologous liver transplantation and allogeneic liver transplantation in hepatic alveolar echinococcosis, aiming to provide a reference for the clinical treatment of hepatic alveolar echinococcosis.

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.

Shift work, as a form of non-conventional work schedule, significantly increases the risk of vascular aging and related diseases due to circadian rhythm disruption, sleep disturbances, and unhealthy lifestyle behaviors. This review systematically summarized the mechanisms and epidemiological evidence linking shift work to vascular aging. Available studies indicated that shift work disrupts the rhythmic expression of core circadian clock genes (e.g., BMAL1, CLOCK, PER, CRY), leading to dysregulated glucose and lipid metabolism, enhanced oxidative stress, chronic inflammation, and altered diurnal blood pressure patterns, thereby directly impairing endothelial function and accelerating arterial stiffness. Sleep deprivation and fragmentation further reduce nitric oxide bioavailability and promote endothelin-1 secretion, exacerbating vascular constriction and inflammatory responses. Existing epidemiological data show that shift workers exhibit higher prevalence of hypertension, metabolic syndrome, and atherosclerosis compared with non-shift workers, accompanied by abnormal vascular function indicators such as pulse wave velocity and carotid intima-media thickness. Additionally, unhealthy dietary habits, psychological stress, and sedentary behavior in shift workers synergistically contribute to vascular aging. This review aims to provide a theoretical basis for the pathological link between shift work and vascular aging and to inform public health policy formulation and occupational health management.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

Objective To investigate the effects and underlying mechanisms of a spray prepared from exosomes derived from M2 macrophages induced by interleukin-4 （IL-4） and tantalum particles （Ta） on the healing of pressure ulcers. Methods Bone marrow-derived macrophages were polarized into M2 macrophages using IL-4 or Ta, and exosomes （Exo-IL-4/Exo-Ta） were extracted. The regulatory effects of Exo-IL-4/Exo-Ta on M1 macrophage phenotypes and fibroblast matrix secretion were evaluated in vitro. Proteomic analysis was conducted to explore the biological processes and regulatory networks associated with Exo-Ta. A rat pressure ulcer model was used to assess the effects of Exo-IL-4/Exo-Ta spray on wound healing rate, inflammatory cell infiltration, and collagen deposition. Results In vitro, Exo-IL-4/Exo-Ta induced the polarization of M1 macrophages to M2 macrophages, reduced the secretion of pro-inflammatory factors, and promoted the expression of anti-inflammatory substances. Additionally, Exo-IL-4/Exo-Ta enhanced the production of collagen and fibronectin in fibroblasts. Proteomic analysis revealed that Exo-Ta primarily participated in biological processes such as energy metabolism and macromolecule biosynthesis. In vivo, Exo-IL-4/Exo-Ta spray accelerated wound healing, reduced inflammatory infiltration, and improved tissue remodeling in the rat pressure ulcer model. Conclusion Exosome sprays derived from M2 macrophages could accelerate pressure ulcer healing by modulating inflammation and promoting tissue regeneration, which demonstrated excellent clinical application potential.

Objective To study the spatial distribution of the incidence of pulmonary tuberculosis in Hubei Province and its influencing factors, so as to improve the theoretical basis for scientific development of tuberculosis prevention and control measures in the future. Methods The data of reported incidence of tuberculosis and related influencing factors in various counties and districts of Hubei Province in 2020 were collected. Global Moran's I index, hotspot analysis and geographically weighted regression (GWR) model analysis were used to calculate the spatial autocorrelation of the incidence of tuberculosis, and to analyze the influencing factors affecting the incidence rate of tuberculosis. Results There were obvious regional differences in the space distribution of the incidence rate of tuberculosis. Hot spot analysis showed positive spatial correlation and obvious clustering. The GWR model (AICc=784.251) in this study had higher AICc value compared to the ordinary least squares regression (OLS) model (AICc=804.2585). The GWR model showed that the increase in the proportion of the population aged 65 and above and the proportion of the ethnic minority population had a significant promoting effect on the increase of the incidence rate of tuberculosis, and there was significant spatial heterogeneity. The effect of PM_2.5 concentration on the incidence rate of pulmonary tuberculosis varied in different regions, and the degree of effect was also different. Conclusion The proportion of people aged 65 and above and the proportion of ethnic minorities may significantly influence the incidence of pulmonary tuberculosis. The effect of PM_2.5 concentration varies in different regions, so targeted measures should be formulated according to the situation in different regions.

Objective: To investigate the association between occupational stress and health-related quality of life (HRQoL) among grassroots medical personnels, so as to provide the reference for improving their physical and mental well-being. Methods: From March to May 2024, medical staff from nine street community health service centers in Liangxi District, Wuxi City, Jiangsu Province were selected by a convenient sampling method. Basic information was collected through questionnaire surveys. Occupational stress was assessed using the Core Occupational Stress Measurement Scale. Health utility value and the Visual Analog Scale (EQ-VAS) score were investigated using the European Quality of Life Five-Dimension Five-Level (EQ-5D-5L) questionnaire. The association between occupational stress and HRQoL was analyzed using multiple linear regression model. Results: A total of 909 individuals were surveyed, including 141 males (15.51%) and 768 females (84.49%). The average occupational stress score was (24.41±9.45) points. The average health utility value was (0.96±0.07), with 372 individuals (40.92%) experiencing health problems in at least one dimension. The primarily problems were pain/discomfort and anxiety/depression, with 249 (27.39%) and 265 (29.15%) individuals, respectively. The average EQ-VAS score was (86.62±13.84) points. Multiple linear regression analysis revealed that occupation (nurses, β'=0.101; pharmacists, β'=0.101; medical technicians, β'=0.090), professional title (intermediate, β'=-0.079; associate senior, β'=-0.081; senior, β'=-0.101), and occupational stress (β'=-0.288) were influencing factors for health utility value. Occupation (nurses, β'=0.087; pharmacists, β'=0.146; medical technicians, β'=0.073) and occupational stress (β'=-0.226) were influencing factors for EQ-VAS score. After adjusting for variables such as gender, age, educational level, occupation, professional title, work experience, and monthly income, grassroots medical personnels with higher occupational stress score had lower health utility values (β'=-0.296) and EQ-VAS score (β'=-0.237). Conclusions Occupational stress can reduce the HRQoL among grassroots medical personnels. It is recommended to regularly assess occupational stress levels and intervene early to address their psychological and physical health issues.