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.

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 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.

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.

BACKGROUND:The occurrence of neuronal axonal injury can result in neurological dysfunction,and the facilitation of axonal elongation is anticipated to play a pivotal role in the treatment of diseases affecting the nervous system.OBJECTIVE:To investigate whether ectomesenchymal stem cells-derived extracellular vesicles can promote neuronal axonal elongation.METHODS:(1)Ectomesenchymal stem cells were obtained from nasal mucosa using the tissue adherence method,and the specific markers of were identified through immunofluorescence.Ectomesenchymal stem cells-derived extracellular vesicles were acquired via ultracentrifugation and identified.(2)Ectomesenchymal stem cells-derived extracellular vesicles(0,0.5,1.0,1.5 mg/mL)were incubated with PC12 cells for 72 hours.The cytotoxicity and proliferation of ectomesenchymal stem cells-derived extracellular vesicles on PC12 cells were assessed using the CCK-8 assay.(3)Ectomesenchymal stem cells-derived extracellular vesicles(1.0 mg/mL)were incubated with PC12 cells or neurons for 72 hours.The changes in axon length were observed using microscopic analysis.The expression levels of axon-related markers β3-tubulin(early stage),growth associated protein 43(middle stage),and neurofilament 200(mature stage)were analyzed through real-time fluorescence quantitative PCR and Western blotting.These investigations aimed to explore the potential of ectomesenchymal stem cells-derived extracellular vesicles in promoting neurite elongation within PC12 cells or neurons.RESULTS AND CONCLUSION:(1)The majority of the acquired ectomesenchymal stem cells exhibited a spindle-shaped morphology,while a minority displayed irregular shapes,and demonstrated high expression levels of mesenchymal stem cell-specific markers Nestin,CD44,and Vimentin.The obtained ectomesenchymal stem cells-derived extracellular vesicles fulfilled the biological criteria for extracellular vesicles.(2)Within the detected protein concentration range of 0.5 to 1.5 mg/mL,the proliferation of PC12 cells was promoted by ectomesenchymal stem cells-derived extracellular vesicles,and this effect was further enhanced with increasing concentrations.(3)Ectomesenchymal stem cells-derived extracellular vesicles increased the length of axons in PC12 cells and neurons and the expression of axon-related markers β3-tubulin,growth associated protein 43,and neurofilament 200.Above findings suggest that ectomesenchymal stem cells-derived extracellular vesicles have the potential to enhance neuronal axonal elongation.

Myxedema coma is a rare condition, typically arising from long-standing, untreated hypothyroidism and triggered by factors such as infection, hypothermia, or severe illness. This report details a successfully treated case of myxedema coma with cardiac attest, accompanied by a literature review, to enhance clinical awareness and improve the diagnosis and management of this critical condition.