OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

Since Li Dongyuan formally proposed the concept of "wind medicinals" （Feng Yao），their clinical application has been highly valued by physicians throughout history. However，influenced by the evolution of the term and connotation of "wind medicinals" in modern times，its conceptual understanding，leading to a decline in clinical utilization. Since the new century，Professor Wang Mingjie has integrated LIU Wanxu's sweat pore （Xuanfu） theory into the reinterpretation of wind medicinals，proposing the "Xuanfu-dredging wind medicinal theory"， which has gained widespread recognition in academic circles，revitalizing their clinical application. This study traces the origin of the Xuan-dredging wind medicinals theory and reviews their current functions and clinical applications，finding that the theoretical framework is preliminarily established. Characterized by their pungent and dispersing properties，wind medicines act by opening the Xuanfu throughout the body，exerting therapeutic effects such as dispelling pathogens，resolving stagnation，and enhancing treatments like blood-activation，spleen-fortification，and heat-clearing. They are widely used，showing advantages in treating systemic diseases including ophthalmic and cardiovascular/cerebrovascular disorders. Modern pharmacological research indicates preliminary consensus on hypotheses of cerebral，intestinal，hepatic，and renal Xuanfu. studies on formulas （e.g.，Qufeng Tongqiao Fang），single herbs （e.g.，Mahuang and Gegen），and active constituents （e.g.，tetramethylpyrazine） provide evidence that wind medicines improve key mechanisms like blood-brain barrier function and cerebral microcirculation （material basis of cerebral Xuanfu），supporting their use in brain disorders （e.g.，cerebral ischemia，depression）. Despite clinical and pharmacological support，the clinical application system for wind medicines remains incomplete. Future efforts should focus on high-quality clinical research and mechanistic studies to establish personalized application systems，enhance Xuanfu opening practices，and ensure the effectiveness and safety of wind medicines.

Intra-abdominal Hernia with Bowel Obstruction Triggering Multi-organ Failure: The Role of Preoperative Enteritis This report details the clinical course of a 41-year-old male who developed fulminant multiple organ dysfunction syndrome following emergency surgery for a strangulated intra-abdominal hernia. Preoperative manifestations, including diarrhea and marked leukocytosis, pointed towards an underlying infectious enteritis. We posited that surgical release of the obstructed, ischemic bowel segment precipitated a massive systemic influx of endotoxins and inflammatory mediators, triggering septic shock and rapid sequential organ failure. This case underscored the critical need to suspect concomitant gastrointestinal infection in patients presenting with mechanical bowel obstruction accompanied by infectious signs. Aggressive perioperative sepsis management encompassing early empiric antimicrobial therapy, vigilant hemodynamic monitoring, and preparedness for advanced organ support is essential to mitigate this severe complication.

Objective In order to understand the current resistance status of Aedes albopictus(Ae. albopictus) adult population to commonly used insecticides and the resistance dynamics development in Zhejiang Province and provide a theoretical basis for the scientific and rational use of hygienic insecticides. Methods Resistance determination was carried out using the diagnostic dose method(GB/T 26347-2010)recommended by WHO in the adult mosquito contact cylinder method to monitor the resistance of Ae. albopictus field populations in 11 cities and districts in Zhejiang Province in 2024. Results The monitoring result showed that adult Ae. albopictus in 11 areas were resistant or suspected resistant populations to both cis-cypermethrin and permethrin; suspected and resistant populations to the organophosphate insecticide malathion were as high as 81.82%; while suspected resistance to the carbamate insecticide residual carbofuran accounted for 45.45%, and the rest of the monitoring sites were sensitive to it. Conclusions The result suggest that the generalized resistance of Ae. albopictus populations to pyrethroids and organophosphorus insecticides in the field in Zhejiang Province needs to be highly emphasized. In future mosquito vector and mosquito-borne disease control, insecticides should be selected based on the resistance monitoring information, while following scientific guidelines for insecticide use.

Neoadjuvant therapy has become the standard treatment for locally advanced resectable esophageal cancer, significantly improving long-term survival compared to surgery alone. Neoadjuvant therapy has evolved to include various strategies, such as concurrent chemoradiotherapy, chemotherapy, immunotherapy, or targeted combination therapy. This enriches clinical treatment options and provides a more personalized and scientific treatment approach for patients. This article aims to comprehensively summarize current academic research hot topics, review the rationale and evaluation measures of neoadjuvant therapy, discuss challenges in restaging methods after neoadjuvant therapy, and identify the advantages and disadvantages of various neoadjuvant therapeutic strategies.

Objective: To explore the relationship of physical exercise behavior with mobile phone addiction and mental health of college students, so as to provide evidence for interventions to improve mobile phone addiction and mental health of college students. Methods: From October 8 to December 20 in 2024, 896 college students from 4 colleges in Beijing were selected using a combination of convenience sampling and stratified random cluster sampling method. Physical Activity Rating Scale (PARS-3), Mobile Phone Addiction Tendency Scale (MPATS), Adolescence Mental Health Diathesis Questionnaire (AMHDQ) and Symptom Checklist 90 (SCL-90) were used. The correlation of mobile phone addiction and mental health on physical exercise behavior of college students were analyzed by multivariable Logistic regression and linear regression. Results: Among the surveyed college students, 504 (56.25%) students had low exercise, 262 (29.24%) had moderate exercise, 130 (14.51%) had high exercise, and 392 (43.75%) had sufficient exercise. The total score of PARS-3 was 18.00 ( 15.00 , 33.00) points. Logistic regression analysis showed that the total score of MPATS ( OR=1.022, 95%CI =1.008-1.036), the total score of SCL-90 ( OR=1.010, 95%CI = 1.005 -1.015), the total AMHDQ score ( OR=0.995, 95%CI =0.992-0.998) were significantly associated with insufficient exercise among college students ( P <0.05). Linear regression analysis showed that the scores of MPATS, AMHDQS and SCL-90 were significantly correlated with physical exercise behavior ( B=-0.20, 0.04, -0.07, P <0.05). Conclusion The physical exercise behavior of college students is related to mobile phone addiction and mental health.

OBJECTIVE To compare the efficacy， safety， and cost-effectiveness of two different formulations of short-acting recombinant human growth hormone （rhGH） in the treatment of patients with short stature. METHODS Data from patients with short stature treated with short-acting rhGH at the Leshan People’s Hospital from August 2016 to June 2023 were collected. Patients were divided into powder formulation group and aqueous formulation group based on the rhGH formulation used. The changes in growth-related efficacy indicators and the occurrence of adverse drug reactions were compared between two groups after 12 months of treatment； cost-effectiveness analysis and sensitivity analysis were used to compare the cost per unit of effect achieved； subgroup analysis was performed by dividing the patients into growth hormone deficiency （GHD） subgroup and idiopathic short stature （ISS） subgroup based on clinical diagnosis. RESULTS After 12 months of treatment， the height and the levels of insulin-like growth factor-1 and insulin-like growth factor binding protein-3 in serum in aqueous formulation group and powder formulation group were significantly increased compared to before treatment （P＜0.001）， but there was no statistically significant difference in the changes of the above indicators between the two groups（P＞0.05）. The analysis results of GHD and ISS subgroups were consistent with the overall population. In the overall population， the cost-effectiveness ratio of powder formulation group （2 582 yuan/cm） was significantly better than that of aqueous formulation group （6 729 yuan/cm）， with a statistically significant difference （P＜0.001）， and the result was consistent in the GHD and ISS subgroups as well as in the sensitivity analysis. No serious adverse drug reactions occurred in either powder formulation or aqueous formulation group， and there was no statistically significant difference in the incidence of various adverse reactions between two groups （P＞0.05）. CONCLUSIONS Short-acting rhGH powder and aqueous formulations have equivalent efficacy and safety， but the powder formulation has greater economic advantages.

OBJECTIVE: To observe the effects of Tiaoshu Anshen (regulating the hinge and calming the mind) acupuncture on sleep quality and serum levels of 5-hydroxytryptamine (5-HT) and dopamine (DA) in patients with chronic insomnia. METHODS: A total of 58 patients with chronic insomnia were randomly divided into an acupuncture group and a medication group, 29 cases in each group. Tiaoshu Anshen acupuncture was applied at Baihui (GV20) and bilateral Shenmen (HT7), Sanyinjiao (SP6), Benshen (GB13) in the acupuncture group, once a day, 1-day interval was taken after 6 consecutive days of treatment. Estazolam tablet was given orally before bed in the medication group, 1 mg each time. The 4-week treatment was required in both groups. Before and after treatment, the sleep quality was assessed by Pittsburgh sleep quality index (PSQI) and polysomnography (PSG), the serum levels of 5-HT and DA were detected by ELISA. RESULTS: After treatment, the item scores and total scores of PSQI were decreased compared with those before treatment in the two groups (P<0.05); in the acupuncture group, the scores of sleep quality, sleep latency, sleep time, sleep efficiency, sleep disorders and total score of PSQI were lower than those in the medication group (P<0.05). After treatment, the total sleep time (TST) was prolonged (P<0.05), the sleep latency (SL) and wake after sleep onset (WASO) were shortened (P<0.05), the sleep efficiency (SE%), percentage of non-rapid eye movement stage 3 (N3%), percentage of rapid eye movement stage (REM%) and serum levels of 5-HT were increased (P<0.05) compared with those before treatment; the percentage of non-rapid eye movement stage 1 (N1%), percentage of non-rapid eye movement stage 2 (N2%) and serum levels of DA were decreased (P<0.05) compared with those before treatment in the two groups. After treatment, in the acupuncture group, TST was longer, while SL and WASO were shorter than those in the medication group (P<0.05), SE%, N3%, REM% and serum level of 5-HT were higher, while N1%, N2% and serum level of DA were lower than those in the medication group (P<0.05). CONCLUSION Tiaoshu Anshen acupuncture may improve the sleep quality by regulating the serum neurotransmitter levels i.e. 5-HT and DA in patients with chronic insomnia.
Humans ; Sleep Initiation and Maintenance Disorders/physiopathology* ; Male ; Acupuncture Therapy ; Female ; Middle Aged ; Adult ; Serotonin/blood* ; Sleep Quality ; Acupuncture Points ; Dopamine/blood* ; Aged ; Neurotransmitter Agents/blood* ; Young Adult

In this paper, near-infrared spectroscopy(NIRS) was employed to analyze 129 batches of commercial products of Reduning Injection. The batch reporting rate was estimated according to the report of Reduning Injection in the direct adverse drug reaction(ADR) reporting system of the drug marketing authorization holder of the Center for Drug Reevaluation of the National Medical Products Administration(National Center for ADR Monitoring) from August 2021 to August 2022. According to the batch reporting rate, the samples of Reduning Injection were classified into those with potential risks and those being safe. No processing, random oversampling(ROS), random undersampling(RUS), and synthetic minority over-sampling technique(SMOTE) were then employed to balance the unbalanced data. After the samples were classified according to appropriate sampling methods, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA), uninformative variables elimination(UVE), and genetic algorithm(GA) were respectively adopted to screen the features of spectral data. Then, support vector machine(SVM), logistic regression(LR), k-nearest neighbors(KNN), naive bayes(NB), random forest(RF), and artificial neural network(ANN) were adopted to establish the risk prediction models. The effects of the four feature extraction methods on the accuracy of the models were compared. The optimal method was selected, and bayesian optimization was performned to optimize the model parameters to improve the accuracy and robustness of model prediction. To explore the correlations between potential risks of clinical use and quality test data, TreeNet was employed to identify potential quality parameters affecting the clinical safety of Reduning Injection. The results showed that the models established with the SVM, LR, KNN, NB, RF, and ANN algorithms had the F1 scores of 0.85, 0.85, 0.86, 0.80, 0.88, and 0.85 and the accuracy of 88%, 88%, 88%, 85%, 91%, and 88%, respectively, and the prediction time was less than 5 s. The results indicated that the established models were accurate and efficient. Therefore, near infrared spectroscopy combined with machine learning algorithms can quickly predict the potential risks of clinical use of Reduning Injection in batches. Three key quality parameters that may affect clinical safety were identified by TreeNet, which provided a scientific basis for improving the safety standards of Reduning Injection.
Spectroscopy, Near-Infrared/methods* ; Drugs, Chinese Herbal/administration & dosage* ; Machine Learning ; Algorithms ; Humans ; Quality Control