Background In 2021, chronic obstructive pulmonary disease (COPD) emerged as the forth leading cause of death in the world. However, the impact of air pollutants on COPD is still inconsistent across current studies. Objective To analyze the relationship between ambient sulfur dioxide (SO₂) exposure and hospital admissions for COPD in Lanzhou, and to examine the modified effects of SO₂ across different genders, age groups, and seasons. Methods A total of 20718 hospital admission records for COPD were collected from Lanzhou between 2016 and 2020, alongside synchronized data on SO₂ concentration and meteorological variables. A generalized additive model integrated with a distributed lag nonlinear model was used to assess the relationship and the lag effects between SO₂ exposure and COPD admissions. Potential confounders, including meteorological factors, day-of-the-week effect, and holidays, were controlled for, with a maximum lag period set at 7 d. Two-pollutant models and sensitivity analyses were conducted to ensure robustness. Results are expressed as relative risks (RR) with 95% confidence intervals (95%CI). Results During the study period, the average daily number of COPD admissions was 11.34±7.03. The average mass concentration of SO₂ was (23.72±12.35) μg·m⁻³. SO₂ exposure was positively correlated with COPD admissions; specifically, each 10 μg·m⁻³ increase in SO₂ concentration was associated with an RR of 1.440 (95%CI: 1.317, 1.573). Stratified analyses found that at a cumulative lag of 7 d, the RRs were higher among females, individuals aged ≥65 years, and during the cold season. Conversely, no significant increase in COPD admission risk related to SO₂ was observed during the warm season. For every 10 μg·m⁻³ increase in SO₂ concentration, the RR was 1.483 (95%CI: 1.311, 1.678) for females, 1.441 (95%CI: 1.311, 1.583) for those aged > 65 years， and 1.595 (95%CI: 1.313, 1.937) during the cold season. Conclusion Short-term exposure to ambient SO₂ exposure in Lanzhou is associated with an increased risk of COPD admission. The association is more pronounced among females, the elderly (aged> 65 years) and during the cold season.

ObjectiveTo explore the mechanisms of Salviae Miltiorrhizae Radix et Rhizoma in treating metabolic hypertension induced by a high-sugar and high-fat diet in rats based on network pharmacology， proteomics， and animal experiments. MethodsThirty male SD rats were randomized into the control， model， positive drug （tetrandrine， Tet， 50 mg·kg^-1·d^-1）， low-dose Salviae Miltiorrhizae Radix et Rhizoma （DS-L， 45×10⁴ mg·kg^-1·d^-1）， and high-dose Salviae Miltiorrhizae Radix et Rhizoma （DS-H， 90×10⁴ mg·kg^-1·d^-1） groups. Except for the control group， each group was fed a high-fat and high-sugar diet for 8 weeks for the modeling of metabolic hypertension. Following successful modeling， drug interventions were conducted through gavage for 4 weeks. Blood pressure and lipid indicators were monitored， and cardiac function was assessed via echocardiography. Samples from the thoracic aorta and cardiac tissue were collected for histopathological examination. Network pharmacology analysis identified key active components， potential targets， and mechanisms of Salviae Miltiorrhizae Radix et Rhizoma in treating hypertension. Proteomics technology was employed to analyze the differential proteins and major pathway targets to synergistically elucidate the antihypertensive mechanism of Salviae Miltiorrhizae Radix et Rhizoma. Ca²⁺ concentrations in the thoracic aorta and cardiac tissue were measured. The protein levels of transient receptor potential cation channel （TRPC）3， TRPC6， NADPH oxidase （NOX）2， and NOX4 were quantified via Western blotting. The levels of oxidative stress markers and inflammatory factors， including superoxide dismutase （SOD）， malondialdehyde （MDA）， tumor necrosis factor-α （TNF-α）， and interleukin-1β （IL-1β）， were determined by enzyme-linked immunosorbent assay （ELISA）. Molecular docking analysis was performed for the main components of Salviae Miltiorrhizae Radix et Rhizoma with TRPC3， TRPC6， NOX2， and NOX4. ResultsThe experimental results indicated that compared with the control group， the model group exhibited abnormally elevated blood pressure and increased lipid levels （P<0.01）. Compared with the model group， the DS-L group exhibited reduced systolic blood pressure （SBP）， diastolic blood pressure （DBP）， and mean arterial pressure （MAP） （P<0.05）， decreased serum levels of triglycerides （TG）， total cholesterol （TC）， and low-density lipoprotein cholesterol （LDL-C） （P<0.05）， and increased level of high-density lipoprotein cholesterol （HDL-C） （P<0.01）. The DS-H and Tet groups showed more significant effects （P<0.01）. Moreover， the interventions attenuated vascular wall thickening， myocardial cell injury， and collagen and lipid deposition. Network pharmacology and proteomics prediction results indicated that the core targets of Salviae Miltiorrhizae Radix et Rhizoma in treating hypertension were TRPC3， TRPC6， NOX2， and NOX4， and the core pathways included calcium signaling， cyclic guanosine monophosphate （cGMP）/cGMP-dependent protein kinase （PKG） signaling， and atherosclerosis-related pathways. The molecular mechanism experiment results indicated that compared with the control group， the model group exhibited significantly elevated tissue Ca²⁺ concentrations， exacerbated oxidative stress and inflammatory responses， and upregulated protein levels of TRPC3， TRPC6， NOX2， and NOX4 in the thoracic aorta and myocardial tissue （P<0.01）. Compared with the model group， DS-L reduced the free Ca²⁺ concentration， lowered the levels of IL-1β， TNF-α， SOD， and MDA （P<0.05）， and downregulated the protein levels of TRPC3， TRPC6， NOX2， and NOX4 in the thoracic aorta and myocardial tissue （P<0.05）. DS-H and Tet exhibited more significant effects （P<0.01）. Meanwhile， the main components of Salviae Miltiorrhizae Radix et Rhizoma had strong binding affinity with the core targets of hypertension. ConclusionSalviae Miltiorrhizae Radix et Rhizoma may ameliorate oxidative stress and mitigate inflammatory responses by regulating the TRPC3/6/NOX2/4 signaling pathway， thereby alleviating abnormal blood pressure abnormality and myocardial injury in hypertensive rats.

Background Diarrhea disease is a common intestinal infectious disease, and its incidence is affected by meteorological conditions. A better understanding of its epidemiological patterns and influencing factors, together with the construction of reliable prediction models, is of great significance for precise public health prevention and control. Objective To clarify the epidemic characteristics of adult diarrhea disease in Shanghai, analyze the associations of meteorological factors and a web search index with adult diarrhea disease, and develop and compare forecasting models to support precise regional prevention and control. Methods Weekly surveillance data of adult diarrhea disease cases from the Shanghai Comprehensive Surveillance Information System for Diarrhea Diseases, together with concurrent meteorological observation data and web search index (Baidu index) data from 2014 to 2019, were collected. A distributed lag non-linear model (DLNM) was adopted to analyze the associations of multiple meteorological factors and the web search index with the number of diarrhea disease cases. By integrating meteorological factors and web search index data, three types of forecasting models were developed, including autoregressive integrated moving average (ARIMA), Random Forest, and extreme gradient boosting (Xgboost), and their predictive performances were evaluated. Results Adult diarrhea disease in Shanghai exhibited seasonal variation, with an major incidence peak in summer and winter peaks in some years. The number of cases declined annually after 2015. Mean temperature was significantly associated with the risk of diarrhea disease, and both low and high temperature exposures were associated with increased risks. The highest risk was observed at 32.8°C (RR=2.04, 95%CI: 1.62, 2.55), while the strongest effect of low temperature was observed at 0.9 °C (RR=1.53, 95%CI: 1.25, 1.88). When relative humidity exceeded 69%, the risk of diarrhea disease increased with relative humidity, reaching a peak at 81% (RR=1.20, 95%CI: 1.07, 1.35). When weekly cumulative precipitation exceeded 16 mm, the risk also increased with increasing precipitation, reaching a maximum at 105 mm (RR=1.23, 95%CI: 1.07, 1.42). The web search index was positively associated with the risk of diarrhea disease. Model prediction indicated that both the Random Forest model and the Xgboost model adequately captured the overall trend in diarrhea disease cases, with R² values generally exceeding 0.7. Notably, the Xgboost model demonstrated greater accuracy in capturing peak intensities. Conclusion Meteorological factors are associated with adult diarrhea disease in Shanghai. The web search index may serve as an auxiliary indicator for diarrhea forecasting. Machine learning models, with advantages in integrating multisource data, may provide effective predictive tools for the prevention and control of diarrhea disease.

The Expert Consensus on Clinical Application of Qinbaohong Zhike Oral Liquid in Treatment of Acute Bronchitis and Acute Attack of Chronic Bronchitis （GS/CACM 337-2023） was released by the China Association of Chinese Medicine on December 13^th， 2023. This expert consensus was developed by experts in methodology， pharmacy， and Chinese medicine in strict accordance with the development requirements of the China Association of Chinese Medicine （CACM） and based on the latest medical evidence and the clinical medication experience of well-known experts in the fields of respiratory medicine （pulmonary diseases） and pediatrics. This expert consensus defines the application of Qinbaohong Zhike oral liquid in the treatment of cough and excessive sputum caused by phlegm-heat obstructing lung， acute bronchitis， and acute attack of chronic bronchitis from the aspects of applicable populations， efficacy evaluation， usage， dosage， drug combination， and safety. It is expected to guide the rational drug use in medical and health institutions， give full play to the unique value of Qinbaohong Zhike oral liquid， and vigorously promote the inheritance and innovation of Chinese patent medicines.

Process analytical technology(PAT) is a key means for digital transformation and upgrading of the traditional Chinese medicine(TCM) manufacturing process, serving as an important guarantee for consistent and controllable TCM product quality. Near-infrared(NIR) spectroscopy has become the core technology for measuring the TCM manufacturing process. By incorporating NIR spectroscopy into PAT and starting from the construction of a smart platform for the TCM manufacturing process, this paper systematically described the development history and innovative application of the combination of NIR spectroscopy with chemometrics in measuring the TCM manufacturing process by the research team over the past two decades. Additionally, it explored the application of a validation method based on accuracy profile(AP) in the practice of NIR spectroscopy. Furthermore, the software development progress driven by NIR spectroscopy supported by modeling technology was analyzed, and the prospect of integrating NIR spectroscopy in smart factory control platforms was exemplified with the construction practices of related platforms. By integrating with the smart platform, NIR spectroscopy could improve production efficiency and guarantee product quality. Finally, the prospect of the smart platform application in measuring the TCM manufacturing process was projected. It is believed that the software development for NIR spectroscopy and the smart manufacturing platform will provide strong technical support for TCM digitalization and industrialization.
Spectroscopy, Near-Infrared/methods* ; Drugs, Chinese Herbal/analysis* ; Software ; Medicine, Chinese Traditional ; Quality Control

Knee osteoarthritis(KOA)is a chronic degenerative joint disease,which poses a major challenge particularly among the elderly population due to its high incidence and high disability.Imaging examination has been used commonly to diagnose KOA.However,it faces imitations in predicting disease progression due to the lack of prior information and constraints in manpower and time.With the rapid evolution of big data and computational technologies,artificial intelligence(AI)is progressively integrating into various healthcare domains.Therefore,the integration of artificial intelligence(AI)into healthcare holds promise for revolutionizing KOA diagnosis and treatment.AI-assisted diagnostic models have demonstrated the potential to automate diagnosis,classify disease severity,and predict disease progression with improved efficiency and accuracy.In addition,these models provide personalized diagnosis and treatment options,as well as accurate disease progression risk assessment.Despite these promising outcomes,challenges such as high costs associated with data annotation and limitations in model generalization capabilities persist.This paper reviews recent advancements in AI applications and summarizes the potential value of utilizing AI applications for KOA.To further enhance the utilization of AI in KOA management to overcome current limitations,future efforts should focus on standardizing clinical sample databases,optimizing AI algorithms,and enhancing external verification sets.

Objective To investigate the status of population dynamics and distribution changes of Aedes aegypti in Guangdong Province.Methods Continuous monitoring was conducted from May 2018 to July 2024 in Wushi Town and Qishui Town,Leizhou City,Zhanjiang City,Guangdong Province.Additionally,a survey of the distribution of Ae.aegypti along the Leizhou Peninsula coast was carried out.Results The density of Ae.aegypti in Zhanjiang showed a gradual decline from 2018 to 2024.The last detection of adult Ae.aegypti in Wushi Town was in September 2021,and the last larva was found in October 2023.No Ae.aegypti was detected in Qishui Town during surveys from 2021 to 2024.A survey of 18 coastal villages in the Leizhou Peninsula revealed no detections of Ae.aegypti.Conclusions This study provides a basis for understanding the distribution and population density fluctuations of Ae.aegypti,assessing its invasion risk,and scientifically conducting relevant prevention and control efforts.

Objective: To construct a 5G unmanned aerial vehicle (UAV) airport platform for blood transportation and explore its feasibility and advantages within the blood emergency support system. Methods: Based on 5G high-speed network transmission technology, a UAV management system was designed to achieve a closed-loop management of the entire transportation process, including blood distribution, route information, flight status, emergency dispatch, hospital reception, real-time temperature monitoring, and video surveillance. Integrated with an open UAV airport, the first "5G UAV Blood Transportation Intelligent Airport Platform" was established. Results: At present, the platform has settled in 2 sets of UAV systems, established 17 routes, and carried out regular UAV blood transportation services for 15 hospitals. From January 1, 2024 to June 30, 2025, a total of 12 134 sorties were completed, with a total transported blood weight of 7 692.38 kg, including 25 500 units of red blood cells, 3 824.5 units of platelets, 1 350 370 mL of plasma, and 10 810 units of cryoprecipitate. Compared to land transportation, UAV delivery saved an average of 46.8 minutes during rush hours (maximum: 89.3 minutes) and an average of 32.3 minutes during non-rush hours (maximum: 59.1 minutes). In terms of the quality of UAV blood transportation, the temperature of suspended red blood cells was between 4 and 8℃, that of platelets was between 20 and 24℃, and that of plasma was below 0℃. No damage has occurred so far. Conclusion: The UAV blood transportation platform can stably provide blood delivery services during both routine and emergency conditions, ensuring timely blood delivery and stable blood quality.