This article summarizes Professor SHAN Zhaowei's clinical experience in treating chronic atrophic gastritis based on the "three-unblocking method". It is believed that spleen and stomach qi deficiency， qi stagnation and blood stasis is the core pathogenesis of chronic atrophic gastritis， with the key lying in the dysfunction of spleen and stomach transportation and descending. Therefore， the "three-unblocking method" was proposed， namely promoting the flow of yang and and removing turbidity to restore spleen transportation， unblocking the bowels to resolve stagnation and benefit qi movement， unblocking the meridians to resolve stasis and eliminate masses. In clinic， it is often combined with microscopic syndrome differentiation via gastroscopy. For those with spleen deficiency and damp obstruction， or yang qi failing to transport， the treatment should unblock yang to transform turbidity； for those with food retention and bowel obstruction， or stagnant qi movement， the treatment should unblock bowels to resolve stagnation； for those with blood stasis and meridian obstruction， or internal masses retention， the treatment should unblock the collaterals and dissolve stasis. The prescription can choose self-formulated Ershen Sancao Decoction （二参三草汤） and combine modification， showing good clinical effectiveness.

Objective: To investigate the status of heat illness, knowledge awareness and adaptive behaviors of heat wave knowledge among food delivery riders, so as to provide a basis for optimizing heat wave response measures for food delivery riders. Methods: In November 2022, food delivery riders from a large food delivery platform in Ningbo City, Zhejiang Province were selected as survey subjects using a cluster sampling method. A self-designed electronic questionnaire was used to select demographic information, work status, lifestyle behaviors and disease history, heat illness status, knowledge awareness and adaptive behaviors of heatwave. Results: A total of 911 questionnaires were distributed, and 830 valid questionnaires were recovered, resulting in a valid response rate of 91.11%. Among the respondents, 796 (95.90%) were male, and 818 (98.55%) worked full-time. The mean age was (27.75±8.00) years. A total of 470 respondents (56.63%) had a work tenure of less than 1 year. The primary working hours were 8-<12 hours, with 504 people accounting for 60.72%. There were 108 cases of heatstroke, with an occurrence rate of 13.01%. And 286 people reported heat-related symptoms, with an occurrence rate of 34.46%. The overall awareness rate of heat wave knowledge was 73.22%, while the awareness rate of heat warning signal classification was relatively low at 9.04%. The heat wave cognition score was (5.86±1.31) points. There were statistically significant differences in heat wave cognition scores among food delivery riders of different ages, educational levels, family annual income, work tenures, and work durations (all P<0.05). Regarding positive adaptive behaviors, the number of riders paying attention to weather forecasts and actively learning about preventive measures was higher (734 people each, accounting for 88.43%). Regarding negative adaptive behaviors, the number of riders who often drank ice-cold beverages was higher (509 people, accounting for 61.33%). The heat wave adaptive behavior score was (6.88±1.77) points. There were statistically significant differences in adaptive behavior scores among riders with different educational levels, family annual income, work tenures, and smoking frequency (all P<0.05). Conclusions The occurrence rates of heatstroke and heat-related symptoms among food delivery riders are relatively high. The knowledge awareness and adaptive behaviors regarding heat wave are at a moderate level. It is suggested to strengthen health education, reinforce risk cognition of heat wave, and promote positive adaptive behaviors among food delivery riders.

ObjectiveTo establish a high-performance liquid chromatography（HPLC） for the quantitative analysis of multiple components in Gegen Qinlian tablets， and to comprehensively evaluate the quality of samples from different manufacturers by integrating chemical pattern recognition and technique for order preference by similarity to ideal solution（TOPSIS）， in order to provide a reference basis for quality evaluation and control of Gegen Qinlian tablets. MethodsHPLC was employed to determine the contents of 10 components in 28 batches of Gegen Qinlian tablets collected from 6 manufacturers， and taking the detection results as variables， SIMCA 14.1 and SPSS 26.0 were employed for cluster analysis（CA）， principal component analysis（PCA）， and orthogonal partial least squares-discriminant analysis（OPLS-DA） to identify key components affecting the quality. Then， TOPSIS analysis was employed to rank the quality of Gegen Qinlian tablets from the 6 manufacturers and establish a comprehensive quality evaluation method. ResultsA quantitative method for Gegen Qinlian tablets was established. After methodological validation， the method was found to be stable and reliable， and could be used for the quantitative analysis of this preparation. The contents of 3′-hydroxy puerarin， puerarin， 3′-methoxy puerarin， daidzein， coptisine hydrochloride， epiberberine， jatrorrhizine hydrochloride， berberine hydrochloride， palmatine hydrochloride and baicalin in 28 batches of samples were 3.58-7.35， 24.88-42.32， 4.20-9.36， 4.33-7.60， 2.52-6.44， 0.93-4.10， 0.58-3.05， 10.68-22.92， 0.82-4.82， 11.73-60.16 mg·g^-1， respectively. Among them， puerarin， berberine hydrochloride and baicalin all met the limit requirements for this preparation specified in the 2025 edition of the Pharmacopoeia of the People's Republic of China. CA and PCA clustered the 28 batches of samples into 5 categories， PCA extracted 2 principal components with a cumulative variance contribution rate of 90.588%， and OPLS-DA screened out 4 differential markers with variable importance in the projection（VIP） values>1.0， namely baicalin， 3′-hydroxy puerarin， coptisine hydrochloride and palmatine hydrochloride， which might be the main components affecting the quality of Gegen Qinlian tablets. TOPSIS analysis showed that the comprehensive score of each evaluation index（C_i） values of different manufacturers were different. Among them， the C_i of manufacturer B was ranked higher， indicating potentially superior quality， while the C_i of manufacturer A was ranked lower， suggesting potentially inferior quality. ConclusionThis study establishes a quantitative method for Gegen Qinlian tablets， and the content uniformity of the same manufacturer is good， while there are differences in the contents of active components among different manufacturers. Through the chemical pattern recognition analysis， it is found that the content differences of Gegen Qinlian tablets may be related to baicalin， 3′-hydroxy puerarin， coptisine hydrochloride and palmatine hydrochloride.

With the aging of the global population， osteoporosis （OP） is becoming a major public health concern worldwide. Currently， the commonly used anti-osteoporosis drugs in clinical practice have limited application due to many side effects. Therefore， developing more effective and safer strategies for the prevention and treatment of OP has become a research focus in this field. In recent years， the clinical efficacy and advantages of traditional Chinese medicine （TCM） in treating OP have been gradually recognized. With the deepening pharmacological research on TCM for OP prevention and treatment， it is found that the active ingredients of Scutellaria baicalensis can promote bone formation or inhibit bone resorption by regulating signaling pathways， including Wnt/β-catenin， osteoprotegerin （OB）/receptor activator of nuclear factor-κB ligand （RANKL）/RANK （OPG/RANKL/RANK）， and bone morphogenetic protein 2 （BMP-2）/Smad， mitogen-activated protein kinase （MAPK）， and mammalian target of rapamycin （mTOR）. However， existing research on active ingredients of S. baicalensis for OP treatment is scattered， making it difficult for scholars to gain a systematic understanding of its research and application. This review summarized the literature on the active ingredients of S. baicalensis in OP treatment worldwide， clarified their mechanisms of action， and explored some issues， providing references for the integration of TCM in OP prevention and treatment.

With the aging of the global population， osteoporosis （OP） is becoming a major public health concern worldwide. Currently， the commonly used anti-osteoporosis drugs in clinical practice have limited application due to many side effects. Therefore， developing more effective and safer strategies for the prevention and treatment of OP has become a research focus in this field. In recent years， the clinical efficacy and advantages of traditional Chinese medicine （TCM） in treating OP have been gradually recognized. With the deepening pharmacological research on TCM for OP prevention and treatment， it is found that the active ingredients of Scutellaria baicalensis can promote bone formation or inhibit bone resorption by regulating signaling pathways， including Wnt/β-catenin， osteoprotegerin （OB）/receptor activator of nuclear factor-κB ligand （RANKL）/RANK （OPG/RANKL/RANK）， and bone morphogenetic protein 2 （BMP-2）/Smad， mitogen-activated protein kinase （MAPK）， and mammalian target of rapamycin （mTOR）. However， existing research on active ingredients of S. baicalensis for OP treatment is scattered， making it difficult for scholars to gain a systematic understanding of its research and application. This review summarized the literature on the active ingredients of S. baicalensis in OP treatment worldwide， clarified their mechanisms of action， and explored some issues， providing references for the integration of TCM in OP prevention and treatment.

Duodenum-preserving pancreatic head resection， also known as Beger surgery， has a high incidence rate of bile duct injury after surgery， while the treatment modality for bile duct injury depends on the severity of the injury， and endoscopic therapy is often challenging in case of severe bile duct injury. Recently a patient with biliary fistula after Beger surgery was admitted to Affiliated Hangzhou First People’s Hospital， Westlake University， and successful diagnosis and treatment were achieved through oral choledochoscopy-assisted percutaneous-endoscopic rendezvous technique.

Background Light gradient boosting machine (LightGBM) has become a popular choice in prediction models due to its high efficiency and speed. However, the "black box" issues in machine learning models lead to poor model interpretability. At present, few studies have evaluated the severity of occupational injuries from the perspective of LightGBM model and model interpretability. Objective To evaluate the application value of LightGBM models and model interpretability methods in occupational injury prediction. Methods The Mine Safety and Health Administration (MSHA) occupational injury data set of mining industry workers from 1983 to 2022 was used. Injury severity (death/fatal occupational injury and permanent/partial disability) was used as the outcome variable, and the predictor variables included the month of occurrence, age, sex, time of accident, time since beginning of shift, accident time interval from shift start, total experience, total mining experience, experience at this mine, cause of injury, accident type, activity of injury, source of injury, body part of injury, work environment type, product category, and nature of injury. Feature sets were screened using least absolute shrinkage and selection operator (Lasso) regression. A LightGBM model was then employed to predict occupational injury, with area under curve (AUC) of the model serving as the primary evaluation metric; an AUC closer to 1 indicates better predictive performance of the model. The interpretability of the model was evaluated using Shapley additive explanations (SHAP). Results Through Lasso regression, 7 key influencing factors were identified, including accident time interval from shift start, experience at this mine, cause of injury, accident type, body part of injury, nature of injury, and work environment type. A LightGBM model, constructed based on feature selection via Lasso regression, demonstrated good predictive performance with an AUC value of 0.9941 (95%CI: 0.9917, 0.9966), accuracy of 0.9743, specificity of 0.9781, and sensitivity of 0.9640. The predicted probability of fatal occupational injuries showed high consistency with the actual probability of fatal occupational injuries. In the occupational injury prediction model, the importance of each indicator was analyzed through its SHAP value, and it was found that the body part of injury and the nature of injury were the two main features that affected the prediction results of the model, and the impacts of other features were relatively small. The distribution of SHAP values across body part of injury was broad, with significant impacts on the model's prediction of fatal risk, particularly for injuries to the head and neck, as well as multi-part injuries. The nature of injury also exerted influences on the model in different directions, with suffocation/drowning, crushing, and multi-part injuries having a greater impact on the risk of fatal occupational injuries. Conclusion LightGBM model is capable of efficiently processing large-scale data and providing high-precision prediction results. Research on model interpretability aids in more accurately exploring and analyzing various key risk factors for fatal occupational injuries among mining workers, and further reveals the complex interactions among these factors. This, in turn, enables better preventive intervention and protection measures, as well as optimal resource allocation for labor workers.

Background Diseases severely affect the efficiency of workers. Comorbidity refers to the coexistence of two or more chronic diseases or health problems in the same individual. Previous studies have primarily focused on occupational injuries caused by environmental exposures, while the analysis of the epidemiological characteristics of self-reported diseases and occupational injuries among manufacturing workers has been insufficient. Objective To analyze the distribution of self-reported diseases and occupational injuries among manufacturing workers, the strength of correlation between different diseases, and common disease combinations, and to preliminarily explore the relationship between self-reported diseases and occupational injuries. Methods A cross-sectional survey was conducted to investigate the occupational injuries of 2382 workers in 2 manufacturing enterprises over the past year, and 2355 questionnaires were valid after cleaning. The questionnaire included three parts: basic information, occupational injury occurrence, and disease prevalence. The self-reported diseases diagnosed by physicians included musculoskeletal diseases, cardiovascular diseases, respiratory diseases, mental health problems, neurological and sensory organ diseases, digestive organ diseases, reproductive and urinary organ diseases, skin diseases, tumors, endocrine and metabolic system diseases, blood diseases and birth defects, a total of 11 types. Log-binomial model was used to calculate the prevalence ratio (PR) value of the association between different diseases, and UpSet diagram was used to present the disease combination pattern. Results In total, 40.2% (946/2355) of the study participants reported at least one disease, while 8.6% (203/2355), 5.2% (122/2355), and 5.9% (140/2355) reported having two, three, and four or more diseases, respectively. A higher prevalence was observed for musculoskeletal diseases, neurological and sensory organ diseases, digestive organ diseases, and skin diseases, which accounted for 18.0% (423/2355), 13.8% (324/2355), 9.3% (218/2355), and 9.1% (214/2355), respectively. Workers with musculoskeletal disorders, cardiovascular diseases, or neurological and sensory organ diseases reported significantly higher incidence of occupational injuries compared with workers without the corresponding diseases (P<0.001, P=0.041, and P=0.006, respectively). Musculoskeletal diseases were strongly associated with comorbidities of neurological and sensory organ diseases (PR values were 1.86 and 1.98, respectively), and the other patters were musculoskeletal diseases and digestive organ diseases (PR and OR values were 1.57 and 2.35, respectively), and musculoskeletal diseases and cardiovascular diseases (PR and OR values were 1.46 and 2.22, respectively). The largest binomial comorbidity group involved musculoskeletal diseases and neurological and sensory organ diseases, accounting for 5.4% (25/465) of the comorbid workers. Conclusion Among the self-reported diseases of manufacturing workers, musculoskeletal diseases, neurological and sensory organ diseases, digestive organ diseases, and skin diseases are more common. Musculoskeletal diseases and neurological and sensory organ diseases are the most common in all comorbidities. Musculoskeletal diseases, cardiovascular diseases, and neurological and sensory organ diseases are associated with occupational injuries. In the prevention and control of occupational injuries, joint prevention strategies targeting multiple diseases should be strengthened.

BACKGROUND:The development of artificial intelligence in the medical field is rapidly advancing,with increasing research on its applications in the field of bone trauma.Through bibliometric analysis,this paper analyzed the research hotspots of artificial intelligence in the field of bone trauma in recent years,and predicted the future research trend. OBJECTIVE:To summarize the development history,research status,hot spots,and future development trends of artificial intelligence technology in the field of bone trauma to provide new insights for future research. METHODS:This study selected relevant literature from the Web of Science core database,covering the period from the inception to August 2023,and retrieved 420 articles related to the application of artificial intelligence,machine learning,and deep learning in the field of bone trauma.After manual screening,202 articles related to this article were exported,and Citespace software was used for visual analysis of cooperation of countries,institutions,cited journals,citation analysis,keyword co-occurrence,and other aspects. RESULTS AND CONCLUSION:(1)The overall number of publications from the 202 selected articles showed an upward trend,indicating significant research potential for future studies.The country with the highest centrality and the highest publication volume was the United States.The University of California(USA)was the most prolific research institution.(2)The top five most commonly used keywords in bone trauma research using artificial intelligence were deep learning,artificial intelligence,bone density,machine learning,and diagnosis.The keyword with the highest centrality was bone density,and the keyword with the highest frequency was deep learning.(3)The top 10 most cited reference papers provided comprehensive insights into the feasibility of applying artificial intelligence techniques to the diagnosis of bone trauma from various perspectives.Among them,eight papers focused on bone and joint injuries and deep convolutional neural networks.One paper discussed the use of deep learning in detecting osteoporosis in CT scans to prevent fragility fractures,while another paper explored the correlation between the application of artificial intelligence in identifying changes in skin texture and the recognition of bone characteristics.(4)In the future,the research hotspots of artificial intelligence will mainly focus on the specific study of fractures caused by bone and joint trauma and osteoporosis.The research trend mainly focuses on improving the performance of artificial intelligence algorithms,using new artificial intelligence technologies to accurately classify and quickly and efficiently diagnose bone injuries,especially for the diagnosis of complex and hidden fractures.By establishing finite element analysis models,more standardized evaluations of bone injuries can be achieved.

ObjectiveTo investigate the mechanism by which Wendantang regulates the silent information regulator 3 （SIRT3）/peroxisome proliferator-activated receptor-gamma coactivator-1α （PGC-1α） pathway to influence energy metabolism and thereby prevent and treat myocardial ischemia （MI） in a rat model of hyperlipidemia （HL）. MethodsThirty SD rats were randomly assigned into five groups： control， model， low-dose （3.702 g·kg^-1·d^-1） Wendantang， high-dose （7.404 g·kg^-1·d^-1） Wendantang， and positive control （trimetazidine， 0.006 g·kg^-1·d^-1）， with six rats in each group. The control group was fed normally， while the other groups were fed with a high-fat diet for six weeks for the modeling of HL. Subsequently， the drug intervention groups were administrated with corresponding drugs by gavage， and the control and model groups received an equivalent volume of normal saline for 14 days. One hour after the last gavage， the other groups except the control group were injected intraperitoneally with posterior pituitary hormone （30 U·kg^-1） to induce MI. Electrocardiography （ECG） was employed to detect changes in the electrocardiogram. Hematoxylin-eosin staining was performed to observe cardiac pathological changes. Enzyme-linked immunosorbent assay was employed to measure the serum levels of cardiac troponin I（cTnI）， myoglobin （MYO）， and creatine kinase-MB （CK-MB）. Colorimetry was used to determine the levels of total cholesterol （TC） and triglycerides （TG） in the serum and ATP， malondialdehyde （MDA）， and superoxide dismutase （SOD） in the myocardial tissue. Western blot was employed to determine the protein levels of SIRT3， PGC-1α， adenosine monophosphate-activated protein kinase （AMPK）， and phosphorylated AMPK （p-AMPK） in the myocardial tissue. Real-time PCR was employed to measure the mRNA levels of SIRT3， PGC-1α， and AMPKα in the myocardial tissue. ResultsCompared with the control group， the model group showed significant J-point deviation and elevation in the ECG image， increased heart rate， disarrangement of myocardial fibers with unclear boundaries， elevated levels of CK-MB， cTnI， MYO， TC， and TG （P<0.05， P<0.01）， declined levels of SOD and ATP （P<0.01）， down-regulated mRNA levels of SIRT3， PGC-1α， and AMPK （P<0.05）， and down-regulated protein levels of SIRT3， PGC-1α， and p-AMPK （P<0.05）. Compared with the model group， the low-dose and high-dose Wendantang groups and the trimetazidine group showed inhibited J-point deviation and elevation in the ECG image， slowed heart rate， reduced inflammatory cell infiltration， alleviated disarrangement of myocardial fibers， declined levels of CK-MB， cTnI， MYO， TC， and TG （P<0.05， P<0.01）， elevated level of SOD （P<0.01）， up-regulated mRNA levels of SIRT3， PGC-1α， and AMPK （P<0.05， P<0.01） and up-regulated protein levels of SIRT3， PGC-1α， and p-AMPK （P<0.05， P<0.01）. ConclusionWendantang can effectively intervene in HL-associated MI in rats by reducing oxidative stress in myocardial cells， alleviating lipid metabolism disorders， and improving myocardial energy metabolism via the SIRT3/PGC-1α signaling pathway.