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.

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.

Houpo Qiwutang originated from the Synopsis of the Golden Chamber， and it consists of seven medicines： Magnoliae Officinalis Cortex， Rhei Radix et Rhizoma， Aurantii Fructus Immaturus， Cinnamomi Ramulus， Zingiberis Rhizoma Recens， Glycyrrhizae Radix et Rhizoma， and Jujubae Fructus. It is a basic formula for the treatment of abdominal fullness. Through the bibliometric method， the historical history， drug base， preparation and dosage， decoction method， and ancient and modern applications of Houpu Qiwu Tang were analyzed by means of textual research. The research finds that Houpu Qiwu Tang has been passed down through the generations in an orderly manner with fewer changes. The drug base of this formula is basically clear， and the base of Magnoliae Officinalis Cortex， Rhei Radix et Rhizoma， Cinnamomi Ramulus， Zingiberis Rhizoma Recens， and Jujubae Fructus is consistent with the 2020 edition of Chinese Pharmacopoeia. The mainstream base of Aurantii Fructus Immaturus is the dried young fruit of Citrus aurantium of Rutaceae family， and the historical mainstream base of Glycyrrhizae Radix et Rhizoma is the dried root of Glycyrrhiza uralensis of Leguminosae family. The modern dosage of this formula is 110.40 g of Magnoliae Officinalis Cortex， 41.40 g of Rhei Radix et Rhizoma， 69 g of Aurantii Fructus Immaturus， 27.60 g of Cinnamomi Ramulus， 69 g of Zingiberis Rhizoma Recens， 41.40 g of Glycyrrhizae Radix et Rhizoma， and 30 g of Jujubae Fructus. In addition， the decoction method is to add 2 000 mL of water with the above seven flavors of the medicine， boil it to 800 mL， and then take 160 mL in a warm state each time. The amount of the medicine taken for each time is 22.08 g of Magnoliae Officinalis Cortex， 8.28 g of Rhei Radix et Rhizoma， 13.80 g of Aurantii Fructus Immaturus， 5.52 g of Cinnamomi Ramulus， 13.80 g of Zingiberis Rhizoma Recens， 8.28 g of Glycyrrhizae Radix et Rhizoma， and 6 g of Jujubae Fructus. The modern application of this formula involves the digestive system， respiratory system， and urinary system. It is more advantageous in digestive system diseases such as early postoperative inflammatory bowel obstruction， functional dyspepsia， gastric pain， functional abdominal distension， and gastric reflux esophagitis. By comprehensively examining the key information of Houpu Qiwu Tang， this paper aims to provide literature support for the development and clinical application of this formula.

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.

Purpose: There is still insufficient evidence for predicting stroke risk in patients with mild carotid atherosclerotic stenosis. This study aimed to explore the association between carotid intraplaque neovascularization (IPN) in mild stenosis and ischemic stroke, using contrast-enhanced ultrasound (CEUS) imaging. Methods: This retrospective observational study included 369 patients from July 2021 to March 2022. These patients were categorized as symptomatic or asymptomatic based on their recent history of ipsilateral ischemic stroke. Initial parameters of carotid plaques, such as IPN grading and contrast enhancement index, were assessed using B-mode ultrasonography and CEUS. The follow-up period lasted 12 months or until a newly-developed ischemic stroke occurred. Logistic regression models and Cox proportional-hazards regression models were employed to explore the associations between ultrasonic parameters and the incidence of recent and future ischemic strokes. Results: In patients with mild stenosis, both increasing age and grade 2 carotid IPN were significant predictors of recent primary ischemic stroke. Furthermore, grade 2 carotid IPN independently predicted future ischemic strokes in both symptomatic and asymptomatic patients. Conclusion This study demonstrated that carotid IPN as detected by CEUS imaging holds potential as a useful non-invasive biomarker for predicting recent and future ischemic strokes in patients with mild carotid stenosis.

ObjectiveTo investigate the prevalence rate of chronic non-communicable diseases （NCDs） and the association with quality of life in middle-aged and elderly patients with HIV/AIDS. MethodsThis cross-sectional study surveyed 432 patients with HIV/AIDS （aged≥45 years） in the Infectious Disease Center in Guangzhou Eighth People’s Hospital of Guangzhou Medical University， and 366 participants were included in the analysis after quality control. A questionnaire and the EuroQol 5-Dimensional 3-level version （EQ-5D-3L） were used to investigate NCDs and quality of life and Tobit regression model was used to estimate the association between chronic diseases and quality of life. ResultsAmong the 366 participants， 29（7.9%） had cardiovascular disease， 45（12.3%） had hypertension， 122（33.3%） had hyperglycemia， 151（41.3%）had hyperlipidemia，7（1.9%） had cancer， 17 （4.6%） had chronic kidney disease， 38 （10.4%） had chronic liver disease， 21（5.7%） had musculoskeletal disorders， and 253（69.1%） suffered from at least one type of chronic diseases. The median （lower and upper quartiles） of EQ-5D utility index was 1.000（0.964~1.000）. Multivariate Tobit regression results of the total population showed that cancer ［b_a=-0.08，95%CI （-0.15，-0.01），P=0.036］， chronic kidney disease ［b_a=-0.07， 95%CI （-0.12，-0.02），P=0.006］， musculoskeletal disease ［b_a=-0.09， 95%CI （-0.13， -0.05），P<0.001］， and ≥3 types of chronic diseases［b_a=-0.05， 95%CI（-0.08，-0.01），P=0.013］ were negatively correlated with EQ-5D utility index. The stratified analysis results of different CD4+T cell levels showed that hypertension ［b_a=-0.07， 95%CI （-0.12， -0.02）， P=0.007］， chronic kidney disease ［b_a=-0.10，95%CI （-0.18，-0.03）， P=0.006］， musculoskeletal disease ［b_a=-0.15， 95%CI （-0.22，-0.07）， P<0.001］ and ≥3 types of chronic diseases ［b_a=-0.09， 95%CI （-0.09， -0.01）， P<0.001］ were negatively correlated with EQ-5D utility index in the group with CD4≤500 （cells/μL）， whereas cancer［b_a=-0.11， 95%CI （-0.20，-0.01）， P=0.031］ was negatively correlated with EQ-5D utility index in the group with CD4＞500（cells/μL）. ConclusionsThe prevalence rate of chronic non-communicable diseases in middle-aged and elderly patients with HIV/AIDS is relatively high. The classification of NCDs such as cancer or chronic kidney disease or other chronic diseases and the numbers of NCDs categories are negatively correlated with quality of life. However，this association varies among patients with HIV/AIDS of different CD4+T cell levels. It is suggested that we should try to prevent and identify NCDs at an early stage， strengthen linkages and integration of health services for AIDS and chronic NCDs， and jointly manage and control AIDS with chronic diseases to improve the quality of life among people living with HIV/AIDS.