Background Public places are important areas for daily human activities. Frequent contact with public items promotes their role as vehicles for microbial spread, creating a substantial risk for the transmission of pathogenic microorganisms. Objective To understand the hygiene status and influencing factors of public items in typical public places in Shanghai from 2010 to 2024, and to provide a scientific basis for optimizing the hygiene management of public items. Methods Based on the monitoring data of public items in public places in Shanghai from 2010 to 2024, the hygiene status was evaluated in three stages: 2010–2019, 2020–2022, and 2023–2024. Chi-square test and logistic regression were used to analyze the impact of factors such as monitoring stages, public place types, and public item categories on the hygiene status. Results The public items in 13807 public places were monitored. A total of 1 022 bed sheets and 9 271 towels were tested for pH value, with qualified rates of 77.66% and 87.81%, respectively. The OR (95%CI) for the pH compliance of towels relative to bed sheets was 1.77 (1.45, 2.15). Compared with bathing places, the ORs for pH compliance of towels in hair salons and beauty salons were 2.24 (1.75, 2.87) and 2.15 (1.57, 2.95), respectively. Additionally, a total of 221 650 samples from various public items were tested for microorganisms, yielding 215554 qualified samples, with an average qualified rate of 97.25%. The qualified rates for total bacterial count, total fungal count, coliforms, and Staphylococcus aureus were 92.66%, 94.63%, 98.85%, and 99.84%, respectively. The logistic regression analysis showed that, compared to the 2016–2019 baseline, no statistically significant differences were observed in the qualified rates of total fungal count across different stages; however, significantly higher qualified rates for total bacterial count, total fungal count, coliforms, and Staphylococcus aureus were recorded during 2020–2022, with ORs (95%CI) of 1.27 (1.16, 1.38), 23.20 (8.42, 63.94), and 12.08 (1.63, 89.92), respectively. Conclusion The pH compliance ranks among the top tier nationwide for cotton textile goods in Shanghai, suggesting minimal detergent retention. Furthermore, continuous improvement in cleaning and disinfection measurements has markedly enhanced the microbial hygiene of public items in public places.

ObjectiveTo investigate the effects and underlying mechanisms of Shenqi Wenfei prescription （SQWF） on chronic obstructive pulmonary disease （COPD）. MethodsA rat model of COPD with lung Qi deficiency was established using lipopolysaccharide （LPS） combined with cigarette smoke. Forty-eight SD rats were randomly divided into a blank group， a model group， low-， medium-， and high-dose SQWF groups （2.835， 5.67， 11.34 g·kg^-1）， and a Yupingfeng group （1.35 g·kg^-1）. Drug administration began on day 29 after modeling and continued for 2 weeks. The general condition of the rats was observed， and the lung function in each group was assessed. Hematoxylin-eosin （HE） staining was used to observe pathological changes in lung tissue. The proportion of inflammatory cells in bronchoalveolar lavage fluid （BALF） was measured. Apoptosis in lung tissue was examined by terminal deoxynucleotidyl transferase-mediated dUTP nick end-labeling （TUNEL） staining. The release level of lactate dehydrogenase （LDH） in BALF was detected by a microplate assay. Reactive oxygen species （ROS） levels in lung tissue were detected using fluorescent probes. The levels of malondialdehyde （MDA）， total superoxide dismutase （SOD）， and reduced glutathione （GSH） in BALF were measured by biochemical methods. Ultrastructural changes in lung cells were observed via transmission electron microscopy. Double immunofluorescence staining was performed to detect the expression of thioredoxin-interacting protein （TXNIP） and nucleotide-binding oligomerization domain-like receptor protein 3 （NLRP3） in lung tissue. Western blot analysis was used to detect the protein expression of TXNIP， NLRP3， apoptosis-associated speck-like protein containing a CARD （ASC）， cysteinyl aspartate-specific protease-1 （Caspase-1）， Caspase-1 p20， gasdermin D （GSDMD）， GSDMD N-terminal active fragment （GSDMD-N）， interleukin-1β （IL-1β）， and IL-18 in lung tissue. Serum IL-1β and IL-18 levels were measured by ELISA. ResultsCompared with the blank group， the model group showed lassitude， fatigue， tachypnea， and audible phlegm sounds， and lung function significantly declined （P0.01）. Pulmonary emphysema and inflammatory cell infiltration were obvious. The level of inflammatory cells in BALF increased significantly （P0.05）. The number of TUNEL-positive cells increased （P0.01）. Levels of LDH， ROS， and MDA in BALF increased significantly （P0.01）， while GSH and SOD activities decreased significantly （P0.01）. Lung tissue cells showed irregular morphology， swollen mitochondria， disrupted cell membranes， and abundant vesicles， i.e.， pyroptotic bodies. Protein levels of TXNIP， NLRP3， ASC， Caspase-1， Caspase-1 p20， GSDMD， GSDMD-N， IL-1β， and IL-18 in lung tissue were significantly elevated （P0.01）， and serum IL-1β and IL-18 levels also increased significantly （P0.01）. Compared with the model group， each medication group showed alleviation of qi deficiency symptoms and improved lung function （P0.01）. Pulmonary emphysema and inflammatory cell infiltration were reduced. Inflammatory cell levels decreased （P0.05）. The number of TUNEL-positive cells decreased significantly （P0.01）. Levels of LDH， ROS， and MDA decreased significantly （P0.05）， while GSH and SOD activities significantly increased （P0.01）. Morphological and structural damage in lung tissue was improved to varying degrees. Protein levels of TXNIP， NLRP3， ASC， Caspase-1， Caspase-1 p20， GSDMD， GSDMD-N， IL-1β， and IL-18 in lung tissue significantly decreased （P0.01）， and serum IL-1β and IL-18 levels also decreased significantly （P0.05）. ConclusionSQWF can improve lung function and alleviate inflammatory responses in COPD rats. Its mechanism may be related to regulating the ROS/TXNIP/NLRP3 pathway and inhibiting pyroptosis.

ObjectiveTo investigate the effects and underlying mechanisms of Shenqi Wenfei prescription （SQWF） on chronic obstructive pulmonary disease （COPD）. MethodsA rat model of COPD with lung Qi deficiency was established using lipopolysaccharide （LPS） combined with cigarette smoke. Forty-eight SD rats were randomly divided into a blank group， a model group， low-， medium-， and high-dose SQWF groups （2.835， 5.67， 11.34 g·kg^-1）， and a Yupingfeng group （1.35 g·kg^-1）. Drug administration began on day 29 after modeling and continued for 2 weeks. The general condition of the rats was observed， and the lung function in each group was assessed. Hematoxylin-eosin （HE） staining was used to observe pathological changes in lung tissue. The proportion of inflammatory cells in bronchoalveolar lavage fluid （BALF） was measured. Apoptosis in lung tissue was examined by terminal deoxynucleotidyl transferase-mediated dUTP nick end-labeling （TUNEL） staining. The release level of lactate dehydrogenase （LDH） in BALF was detected by a microplate assay. Reactive oxygen species （ROS） levels in lung tissue were detected using fluorescent probes. The levels of malondialdehyde （MDA）， total superoxide dismutase （SOD）， and reduced glutathione （GSH） in BALF were measured by biochemical methods. Ultrastructural changes in lung cells were observed via transmission electron microscopy. Double immunofluorescence staining was performed to detect the expression of thioredoxin-interacting protein （TXNIP） and nucleotide-binding oligomerization domain-like receptor protein 3 （NLRP3） in lung tissue. Western blot analysis was used to detect the protein expression of TXNIP， NLRP3， apoptosis-associated speck-like protein containing a CARD （ASC）， cysteinyl aspartate-specific protease-1 （Caspase-1）， Caspase-1 p20， gasdermin D （GSDMD）， GSDMD N-terminal active fragment （GSDMD-N）， interleukin-1β （IL-1β）， and IL-18 in lung tissue. Serum IL-1β and IL-18 levels were measured by ELISA. ResultsCompared with the blank group， the model group showed lassitude， fatigue， tachypnea， and audible phlegm sounds， and lung function significantly declined （P0.01）. Pulmonary emphysema and inflammatory cell infiltration were obvious. The level of inflammatory cells in BALF increased significantly （P0.05）. The number of TUNEL-positive cells increased （P0.01）. Levels of LDH， ROS， and MDA in BALF increased significantly （P0.01）， while GSH and SOD activities decreased significantly （P0.01）. Lung tissue cells showed irregular morphology， swollen mitochondria， disrupted cell membranes， and abundant vesicles， i.e.， pyroptotic bodies. Protein levels of TXNIP， NLRP3， ASC， Caspase-1， Caspase-1 p20， GSDMD， GSDMD-N， IL-1β， and IL-18 in lung tissue were significantly elevated （P0.01）， and serum IL-1β and IL-18 levels also increased significantly （P0.01）. Compared with the model group， each medication group showed alleviation of qi deficiency symptoms and improved lung function （P0.01）. Pulmonary emphysema and inflammatory cell infiltration were reduced. Inflammatory cell levels decreased （P0.05）. The number of TUNEL-positive cells decreased significantly （P0.01）. Levels of LDH， ROS， and MDA decreased significantly （P0.05）， while GSH and SOD activities significantly increased （P0.01）. Morphological and structural damage in lung tissue was improved to varying degrees. Protein levels of TXNIP， NLRP3， ASC， Caspase-1， Caspase-1 p20， GSDMD， GSDMD-N， IL-1β， and IL-18 in lung tissue significantly decreased （P0.01）， and serum IL-1β and IL-18 levels also decreased significantly （P0.05）. ConclusionSQWF can improve lung function and alleviate inflammatory responses in COPD rats. Its mechanism may be related to regulating the ROS/TXNIP/NLRP3 pathway and inhibiting pyroptosis.

ObjectiveGastric hemorrhage is one of the most common and life-threatening emergencies of the upper digestive tract. Early identification and continuous monitoring are essential for reducing rebleeding rates and mortality, particularly within the critical early hours after onset. Although endoscopy and radiological imaging can accurately localize bleeding sites, these approaches are invasive, resource-intensive, and unsuitable for continuous bedside monitoring. Electrical impedance tomography (EIT), as a noninvasive and radiation-free functional imaging technique, offers real-time visualization of conductivity distribution and has the potential for detecting intragastric bleeding based on the electrical contrast between blood and surrounding gastric tissues. In this study, a three-dimensional gastric EIT (3D-gEIT) framework is proposed to achieve noninvasive, real-time, and dynamic monitoring of gastric hemorrhage, with emphasis on spatial localization and quantitative volume assessment. MethodsA three-dimensional upper-abdominal simulation model incorporating the stomach, gastric wall, gastric contents, and surrounding tissues was established. Three electrode configurations, namely the dual layer ring, the four layer staggered ring, and the opposed dual plane array, were designed and systematically compared to evaluate their influence on depth sensitivity and spatial resolution. Based on the Tikhonov-Noser hybrid regularization scheme, a region-clustering constraint was introduced to develop the TK-Noser-RCC algorithm. This approach aggregates spatially adjacent elements with similar conductivity variations, thereby enhancing structural continuity and suppressing isolated noise artifacts. To validate the proposed framework, an upper-abdominal physical phantom was constructed using agar to simulate background tissue conductivity. Hemispherical high-conductivity inclusions with volumes ranging from 10 ml to 50 ml were attached to the inner gastric wall to mimic localized bleeding under different gastric filling states. Boundary voltages were acquired under a 120 kHz excitation current and reconstructed using the TK-Noser-RCC algorithm. Furthermore, an in vivo animal experiment was performed using a porcine model with adult-scale abdominal dimensions. A total of 100 ml of autologous blood was injected incrementally into the stomach to simulate progressive gastric hemorrhage, and time-difference EIT reconstruction was conducted at each injection stage to assess the dynamic system response under physiological conditions. ResultsSimulation results demonstrated that the opposed dual-plane electrode array achieved superior depth sensitivity distribution and spatial resolution. For a 40 ml hemorrhage model, the average ICC and SSIM improved by 55.9% and 38.8% compared with the dual-layer ring configuration, and by 64.0% and 39.5% compared with the four-layer staggered configuration. The proposed region-clustering constraint significantly enhanced reconstruction stability. Under added Gaussian noise of 40 dB and 30 dB, ICC values remained approximately 0.85, indicating effective artifact suppression and preservation of boundary integrity. In physical phantom experiments, reconstructed hemorrhage volumes increased approximately linearly with the preset hemispherical volumes, and the reconstructed high-conductivity regions closely matched the actual bleeding locations. Both empty-stomach and full-stomach conditions were evaluated, demonstrating that the opposed dual-plane configuration maintained stable imaging performance across varying gastric contents. In the animal experiment, reconstructed low-impedance regions expanded progressively with increasing injected blood volume. The spatial localization of the hemorrhage remained stable throughout the procedure, and no significant artifacts were observed. Quantitative analysis showed that reconstructed volume and average conductivity variation exhibited an approximately linear growth trend with injected blood volume, confirming the sensitivity of the system to dynamic intragastric conductivity changes. ConclusionThe proposed 3D-gEIT framework enables quantitative reconstruction of gastric hemorrhage volume and spatial distribution with improved depth sensitivity, structural continuity, and noise robustness compared with conventional EIT approaches. By integrating optimized electrode configuration and a region-clustering-constrained reconstruction algorithm, the system provides stable dynamic monitoring under both controlled phantom conditions and in vivo physiological environments. This method offers a noninvasive, real-time, and low-cost imaging strategy for early diagnosis, postoperative monitoring, and bedside surveillance of gastric bleeding.

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 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.

The functional realization of proteins and other biological macromolecules depends on conformational dynamics and allosteric regulation, and elucidating their molecular mechanisms is an important foundation for understanding life processes. Molecular dynamics simulations are a powerful tool for investigating conformational evolution at the atomic level. However, traditional methods are limited by simulation timescales and high free-energy barriers, making it difficult to effectively capture rare conformations and their transition pathways. As a result, the development of enhanced sampling techniques has become key to overcoming this bottleneck. As a classical enhanced sampling technique, metadynamics suffers from several shortcomings, including strong dependence on collective variables and significant errors caused by bias potential accumulation. This article reviews three major improvement strategies. The first combines stochastic resetting with metadynamics, using trajectory-resetting mechanisms to improve sampling efficiency while avoiding the difficulty of optimizing collective variables. The second, SinkMeta, employs a “sinking” bias effect to enable efficient exploration of specific regions and paths. The third, OPES-based hybrid methods, improve the stability of free-energy estimation by optimizing the target distribution or the way the bias is constructed. These methods provide new ideas for characterizing free-energy landscapes and studying conformational transitions in complex biological systems, while also promoting the continued development of enhanced sampling methodologies.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

The mental health management of laboratory animals is a critical factor in ensuring the reliability of scientific research data. However, due to the subtle nature of mental state alterations and the limitations of current assessment methods, the mental health of laboratory animals is often overlooked by researchers. Therefore, there is an urgent need to explore a new, objective, simple, and practical method for evaluating mental health. Stress, as a primary factor inducing alterations in the mental state of animals, can influence experimental results across multiple research fields through the neuroendocrine-immune network. This paper first elucidates the necessity of mental health management in laboratory animals from three perspectives: factors contributing to stress, neural mechanisms of stress, and research areas affected by stress. It highlights that excluding animals with abnormal mental states before experiments can enhance the efficiency and reproducibility of biomedical studies. Second, this paper briefly summarizes existing methods for assessing the health of laboratory animals, pointing out that approaches such as behavioral tests and metabolomics have many limitations in evaluating stress responses. Existing methods struggle to meet the demand for simple, objective, and non-invasive assessment methods in animal management. Therefore, this paper focuses on hair, a biological sample with the advantages of cumulative information and non-invasive collection, and systematically describes the theoretical basis and technological advances in using hair characteristics as indicators of stress responses from three perspectives: hair traits, the levels of substances in hair, and artificial intelligence (AI). It proposes and supports an innovative technical pathway that combines hair traits with AI-based image analysis, with the aim of offering an improved solution for non-invasive and objective stress assessment, while providing theoretical support for improving the health management system of laboratory animals.

Objective: To investigate the effects of high intensity interval training (HIIT) on mental health and sleep quality in female college students with normal weight obesity, so as to provide a reference for promoting their physical and mental health. Methods: From September to November 2024, 42 female college students who met the criteria for normal weight obesity were recruited from Anqing Medical College and randomly assigned to an HIIT group ( n =21) or a control group ( n =21) using a random number table. The HIIT group completed an 8 week intervention (3 sessions/week) at 75%-90% of maximum heart rate (HR max ), whereas the control group maintained their usual lifestyle. Before and after the intervention, body composition was measured, and anxiety, depressive symptoms, and sleep quality were assessed using the Generalized Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), and Pittsburgh Sleep Quality Index (PSQI), respectively. Statistical analysis included t-test, two way mixed analysis of variance, simple effect analysis, Wilcoxon signed rank test，Mann-Whitney U test, Pearson product moment correlation analysis, and Fisher s Z-transformation test. Results: Before the intervention, there were no statistically significant differences between the HIIT group and the control group in body composition indicators (body weight, body mass index, body fat percentage, visceral fat level, visceral fat area, and waist circumference), psychological indicators (anxiety symptoms and depressive symptoms), and sleep quality problems among female college students (all P >0.05). After the intervention, the above indicators in the HIIT group[（58.53±1.05）kg，（21.93±0.36）kg/m 2，（28.15±0.48）%，（7.31±0.43），（ 78.54± 4.28）cm 2，（75.91± 1.26 ）cm，2(1,3) scores,2(1,4) score,（6.15±1.58）score] all decreased ( F/Z =3.95，12.76，23.65，43.97，19.06，22.04，-3.35， -4.01 ，20.52，all P <0.05), and were all lower than those in the control group ( F/U =4.45， 6.82 ， 52.34 ，28.46，34.25，13.49， 344.0 ，327.0，31.62，all P <0.05).There were no statistically significant differences in the above indicators in the control group before and after the intervention (all P >0.05).Before the intervention, the total score of sleep quality problems in the HIIT group of female college students was positively correlated with the depression symptom score ( r =0.54, P < 0.05 ), while the correlation in the control group was not statistically significant ( r =-0.10, P >0.05). After the intervention, the correlation was strengthened in the HIIT group ( r =0.81, P <0.05), and the strength of the association was greater than that in the control group ( Z =3.50, P <0.05). Conclusion HIIT may improve sleep quality and mental health in female college students with normal weight obesity.