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.

This study aimed to comprehensively examine the association of gallstones, cholecystectomy, and cancer risk. Multivariable logistic regressions were performed to estimate the observational associations of gallstones and cholecystectomy with cancer risk, using data from a nationwide cohort involving 239 799 participants. General and gender-specific two-sample Mendelian randomization (MR) analysis was further conducted to assess the causalities of the observed associations. Observationally, a history of gallstones without cholecystectomy was associated with a high risk of stomach cancer (adjusted odds ratio (aOR)=2.54, 95% confidence interval (CI) 1.50-4.28), liver and bile duct cancer (aOR=2.46, 95% CI 1.17-5.16), kidney cancer (aOR=2.04, 95% CI 1.05-3.94), and bladder cancer (aOR=2.23, 95% CI 1.01-5.13) in the general population, as well as cervical cancer (aOR=1.69, 95% CI 1.12-2.56) in women. Moreover, cholecystectomy was associated with high odds of stomach cancer (aOR=2.41, 95% CI 1.29-4.49), colorectal cancer (aOR=1.83, 95% CI 1.18-2.85), and cancer of liver and bile duct (aOR=2.58, 95% CI 1.11-6.02). MR analysis only supported the causal effect of gallstones on stomach, liver and bile duct, kidney, and bladder cancer. This study added evidence to the causal effect of gallstones on stomach, liver and bile duct, kidney, and bladder cancer, highlighting the importance of cancer screening in individuals with gallstones.
Humans ; Mendelian Randomization Analysis ; Gallstones/complications* ; Female ; Male ; Cholecystectomy/statistics & numerical data* ; Middle Aged ; Risk Factors ; Aged ; Adult ; Neoplasms/etiology* ; Stomach Neoplasms/epidemiology*

OBJECTIVES: To study the effects of early respiratory training combined with swallowing function training on physical development and neurodevelopment at a corrected gestational age of 6 months in infants with bronchopulmonary dysplasia (BPD). METHODS: A total of 69 BPD infants who could not be fed completely orally were prospectively selected from the Department of Neonatology of Hunan Children's Hospital between January 2018 and January 2021. Based on a random number table, the infants were divided into a conventional group (35 cases) and a training group (34 cases) (with 8 cases lost to follow-up; the final follow-up included 31 cases in the training group and 30 cases in the conventional group). Both groups received routine clinical treatment and care, while the training group additionally received respiratory and swallowing function training until the infants could independently feed orally. The weight, length, Gesell Developmental Schedule (GDS) results, readmission rate, and multiple readmission rate (two or more admissions) were compared between the two groups at a corrected age of 6 months. RESULTS: At corrected gestational age of 6 months, the training group had higher weight, length, and GDS scores in personal-social, language, gross motor, fine motor, and adaptive development compared to the conventional group (P<0.05). The readmission rate and multiple readmission rate were lower in the training group compared to the conventional group (P<0.05). CONCLUSIONS Early respiratory training combined with swallowing function training for BPD infants in a neonatal intensive care unit setting helps improve physical and neurological development and reduces the readmission rate.
Humans ; Bronchopulmonary Dysplasia/physiopathology* ; Prospective Studies ; Male ; Female ; Infant ; Deglutition/physiology* ; Gestational Age ; Infant, Newborn ; Breathing Exercises ; Child Development

Osteoporosis is a prevalent skeletal condition characterized by reduced bone mass and strength, leading to increased fragility. Buqi-Tongluo (BQTL) decoction, a traditional Chinese medicine (TCM) prescription, has yet to be fully evaluated for its potential in treating bone diseases such as osteoporosis. To investigate the mechanism by which BQTL decoction inhibits osteoclast differentiation in vitro and validate these findings through in vivo experiments. We employed MTS assays to assess the potential proliferative or toxic effects of BQTL on bone marrow macrophages (BMMs) at various concentrations. TRAcP experiments were conducted to examine BQTL's impact on osteoclast differentiation. RT-PCR and Western blot analyses were utilized to evaluate the relative expression levels of osteoclast-specific genes and proteins under BQTL stimulation. Finally, in vivo experiments were performed using an osteoporosis model to further validate the in vitro findings. This study revealed that BQTL suppressed receptor activator of NF-κB ligand (RANKL)-induced osteoclastogenesis and osteoclast resorption activity in vitro in a dose-dependent manner without observable cytotoxicity. The inhibitory effects of BQTL on osteoclast formation and function were attributed to the downregulation of NFATc1 and c-fos activity, primarily through attenuation of the MAPK, NF-κB, and Calcineurin signaling pathways. BQTL's inhibitory capacity was further examined in vivo using an ovariectomized (OVX) rat model, demonstrating a strong protective effect against bone loss. BQTL may serve as an effective therapeutic TCM for the treatment of postmenopausal osteoporosis and the alleviation of bone loss induced by estrogen deficiency and related conditions.
Animals ; NFATC Transcription Factors/genetics* ; Drugs, Chinese Herbal/pharmacology* ; Ovariectomy ; Osteoclasts/metabolism* ; Female ; Osteogenesis/drug effects* ; Rats, Sprague-Dawley ; Rats ; NF-kappa B/genetics* ; Osteoporosis/genetics* ; Signal Transduction/drug effects* ; Bone Resorption/genetics* ; Cell Differentiation/drug effects* ; Humans ; RANK Ligand/metabolism* ; Mitogen-Activated Protein Kinases/genetics* ; Transcription Factors

A highly efficient oxidase-mimetic silver phosphate/nickel hydroxystannate(Ag3PO4/NiSn(OH)6)composite was synthesized via a precipitation method using nickel hydroxystannate(NiSn(OH)6)as the support.The abundant hydroxyl groups(—OH)on NiSn(OH)6 not only provided nucleation sites for Ag3PO4 nanoparticles but also improved their dispersion and overall material stability.Based on oxidase-like activity of Ag3PO4/NiSn(OH)6 and inhibitory effect of sodium dodecylbenzenesulfonate(SDBS)on this catalytic activity,a novel colorimetric sensing method for SDBS detection was developed.Under optimized experimental conditions,the method exhibited a linear range of 3.69-42.7 μmol/L,with a detection limit of 0.135 μmol/L(S/N=3).The regression equation was ΔA652=0.01125C(μmol/L)+0.1498,with a correlation coefficient(R2)of 0.992.Practical application in dishwashing liquid analysis achieved satisfactory recoveries of 96.9%-106.4%,demonstrating the method's reliability for real sample detection.