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.

ObjectiveBased on the clinical characteristics of chronic gouty arthritis （CGA） in both traditional Chinese and western medicine， this study aims to systematically evaluate the clinical concordance of existing CGA animal models， providing recommendations for establishing animal models that align with the pathological characteristics of CGA and the manifestations of traditional Chinese medicine syndromes. MethodsBy comprehensively retrieving Chinese and international databases such as China National Knowledge Infrastructure， Wanfang， VIP Chinese Science and Technology Periodical Database （VIP）， and PubMed， all relevant literature on CGA animal models was collected. Based on the guidelines， the diagnostic criteria of both traditional Chinese and western medicine were summarized and organized. The evaluation indicators for the CGA model were constructed with reference to existing evaluation modes， and the CGA animal models were analyzed to systematically evaluate the clinical concordance of existing models. ResultsThe current methods used to construct CGA animal models mainly include monosodium urate crystal induction， high-protein diet induction （poultry lack urate oxidase）， and high-fat diet combined with urate oxidase inhibitors and joint injection. Based on 11 pieces of included literature， the traditional Chinese and western medicine scoring data of each model were extracted， and the average scoring values of all models were ultimately calculated. The results show that the average clinical concordances of existing CGA animal models in both traditional Chinese and western medicine are 43.33% and 64.44%， respectively. Among them， the model with the highest clinical concordance rate is the one with a high-fat diet combined with potassium oxonate to induce hyperuricemia plus joint injection， achieving 83.33% clinical concordance in western medicine and 60% in traditional Chinese medicine. This model aligns well with the pathogenic characteristics and pathological changes of clinical CGA. ConclusionAlthough current CGA animal models can simulate some pathological characteristics of CGA， they struggle to comprehensively reflect the complex pathological processes of CGA and the characteristics of traditional Chinese medicine syndromes. Therefore， in the future， it is necessary to establish the CGA animal models that incorporate the clinical disease and syndrome characteristics of traditional Chinese and western medicine and formulate the uniform model evaluation criteria， providing more precise tools for CGA mechanism research and the development of traditional Chinese medicine.

Objective: To analyze the school tuberculosis (TB) outbreaks and preventive treatment in Hefei from 2022 to 2024, so as to provide reference for TB prevention and control in schools. Methods: Data were collected on all school based TB outbreaks occurring during 2022-2024 in Hefei, defined as ≥2 epidemiologically linked TB cases within the same school during a single semester. Statistical analyses were performed using the Chi square test. Results: Close contacts exhibited significantly higher TB incidence (2.88%) and latent mycobacterium tuberculosis infection (LTBI) rates (13.80%) in the school TB outbreaks, compared to non close contacts (0.12% and 2.63%, respectively). Among close contacts, secondary school students showed lower TB incidence (0.48%) and LTBI prevalence (3.42%) than both primary school or younger children (0.68%, 6.95%) and college students ( 0.78% , 6.50%), with statistically significant differences ( χ 2=360.91, 6.37; 791.71, 102.03, all P <0.05). The proportion of LTBI individuals recommended for preventive therapy was higher in primary school or younger groups (98.59%) than in secondary (95.25%) or college students (86.34%) ( χ 2=25.86, P <0.01). However, among those recommended, close contacts had higher uptake (85.82%) and completion rates (87.25%) of preventive therapy than non close contacts (69.63% and 70.57%); similarly, secondary school students demonstrated higher uptake (91.21%) and completion rates (86.45%) compared to primary school or younger (88.57%, 83.87%) and college students (57.28%, 64.08%) ( χ 2=30.52, 26.72; 125.17, 38.84, all P <0.01). Subsequent TB incidence among LTBI close contacts (13.30%) and among those who did not complete preventive therapy (22.73%) were significantly higher than among non close contacts (2.80%, 2.41%), respectively ( χ 2=32.19, 13.87, both P <0.05). Conclusions In school TB outbreaks, close contacts face higher LTBI prevalence and subsequent TB risk than non close contacts. College students show notably low adherence to preventive therapy. It is necessary to take targeted measures to improve the compliance of preventive measures among students.

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.

A throat swab sample from a pediatric case in Tongzhou District, Beijing was identified as enterovirus; the patient was a 1-year-and-8-month-old male sporadic case. Whole-genome sequencing revealed a viral genome length of 7 436 bp. BLAST alignment confirmed the serotype as EV-D68. Phylogenetic analysis of the whole genome indicated that this strain belongs to the B3 clade, showing closer genetic proximity to the 2018 Shanghai strain MW697453 with 99.53% whole-genome nucleotide homology. Genetic and amino acid variation analysis demonstrated that the B3 subclade to which this strain belongs exhibits a nucleotide deletion at positions 718–726, differing from deletion sites observed in other B3 clade strains. A key neuropathogenic amino acid site, T650A, was found to have undergone mutation. Recombination analysis confirmed no cross-clade recombination events in this strain. This study conducted genetic characterization of the strain's evolutionary relationship with EV-D68 strains from different regions and years in China, providing data support for formulating prevention and control measures against EV-D68 infection.

Objective: To explore the efficacy of focal radiofrequency ablation (RFA) in the treatment of low-to-intermediate risk localized prostate cancer and its impact on postoperative urinary control and sexual function recovery，in order to explore the feasibility of minimally invasive methods for the treatment of localized prostate cancer. Methods: Clinical data of 28 patients with low-to-intermediate risk localized prostate cancer who underwent RFA in Nanjing Drum Tower Hospital，Affiliated Hospital of Medical School during Jun.2017 and Feb.2021 were retrospectively analyzed.The 5-year failure-free survival (FFS) rate，surgery related complications，postoperative urinary control and sexual function were collected.The differences between the survival curves of patients in the low-risk and intermediate-risk subgroups were assessed with log-rank test and Breslow test. Results: All surgeries were successfully completed under local anesthesia.During the median follow-up of 43 (40-49) months，the 5-year FFS rate predicted by Kaplan-Meier method was 78.57%; 25 patients (89.29%) did not experience surgery-related complications; 27 patients (96.43%) were able to control urination; 1 patient developed new-onset sexual dysfunction.There was no significant difference in the survival curves between patients in the low-risk and intermediate-risk groups (P>0.05). Conclusion: RFA for patients with low-to-intermediate risk localized prostate cancer has good clinical efficacy，little impact on urinary control and sexual function recovery，and few postoperative complications，which can be used as one of the treatment options for these patients.

Objective: To understand the growth and development levels of four physical measurements in children aged 3-6 years in China, so as to provide a reference for child nutrition improvement and health promotion. Methods: A stratified random sampling method was used to collect physical measurement data from 120 kindergartens 25 842 children aged 3-6 years across 24 provinces and cities in seven natural geographical regions of North China, Northeast China, East China, Central China, South China, Southwest China and Northwest China from 2020 to 2023. The development levels of head circumference, chest circumference, waist circumference, and hip circumference were evaluated using a grading method. The analysis of gender and age differences was conducted using Mann-Whitney U- test and Kruskal-Wallis test, and the comparison of abnormal detection rates for different genders was conducted using Chi square test. Results: The distribution range of children aged 3-6 was 51.74(50.08, 53.33) cm in terms of head circumference, 55.73(52.09, 59.04) cm in terms of chest circumference, 53.04(48.92, 56.40) cm in terms of waist circumference, and 59.36(56.30, 62.32) cm in terms of hip circumference. The detection rate of abnormal head circumference in boys and girls aged 3-6 years old was relatively high (19.71%-42.02%), and the detection rate of abnormal physical circumference development levels in boys was higher than that in girls of all ages ( χ 2=5.63-83.35, P <0.05). The detection rate of abnormal hip circumference (4.89%-6.53%) and chest circumference (4.51%-6.38%) in boys and girls aged 3-6 was relatively low, and there was no statistically significant difference in the abnormal rate between different ages and genders ( χ 2=0.00-1.61, 0.00-3.71, P >0.05). The detection rate of abnormal waist circumference in boys and girls aged 3-6 was relatively high (13.70%-42.45%), and the detection rate of abnormal waist circumference in girls was higher than that in boys aged 4-6 groups ( χ 2=10.49-58.18, P < 0.05). Conclusions The overall physical development of children aged 3-6 years in China is improving, but the abnormal detection rates for head circumference and waist circumference are relatively high. Child healthcare should focus on preventing and treating abdominal obesity, with differentiated health intervention strategies based on different age groups and genders.

OBJECTIVE: To observe the effect of electroacupuncture (EA) pretreatment of "biaoben acupoint combination" on cardiomyocyte mitochondrial fission in the rats with myocardial ischemia-reperfusion injury (MIRI) and explore its mechanism. METHODS: Fifty male SD rats were randomly divided into a sham-operation group, a model group, an EA pretreatment group, an EA pretreatment + Compound C group and an EA pretreatment+ML385 group, 10 rats in each group. In the EA pretreatment, the EA pretreatment + Compound C group and the EA pretreatment+ML385 group, EA was delivered at bilateral "Neiguan" (PC6), "Zusanli" (ST36) and "Guanyuan" (CV4) for 20 min, with continuous wave and 2 Hz of frequency, 1 mA of current, once daily for consecutive 7 days. On day 8, in the EA pretreatment + Compound C group and the EA pretreatment+ML385 group, 30 min before model preparation, the intraperitoneal injection with Compound C (0.3 mg/kg) and ML385 (30 mg/kg) was administered respectively. Except in the sham-operation group, the ligation of the left anterior descending coronary artery was performed to prepare MIRI rat model in the rest groups. In the sham-operation group, the thread was not ligated. After modeling, the content of reactive oxygen species (ROS) in the ischemic area was measured by flow cytometry, superoxide dismutase (SOD) was detected using xanthine oxidase method, and malondialdelyde (MDA) was detected using thiobarbituric acid (TBA) chromatometry. The morphology of myocardial tissue in the ischemic area was observed with HE staining, and the mitochondria ultrastructure of cardiomyocytes observed under transmission electron microscopy. Using immunofluorescence analysis, the positive expression of mitochondrial fission factor (MFF), mitochondrial fission 1 protein antibody (Fis1) and dynamin-related protein 1 (Drp1) was detected; and with immunohistochemical method used, the protein expression of adenosine monophosphate-activated protein kinase (AMPK), nuclear factor E2-associated factor2 (Nrf2) and Drp1 in the ischemic area was detected. RESULTS: Compared with the sham-operation group, the content of ROS and MDA in the myocardial tissue of the ischemic area, and the positive expression of MFF, Fis1 and Drp1 increased in the model group (P<0.01); the content of SOD and the protein expression of AMRK and Nrf2 decreased (P<0.01), and the protein expression of Drp1 elevated (P<0.01). Compared with the model group, the content of ROS and MDA in the myocardial tissue of the ischemic area, and the positive expression of MFF, Fis1 and Drp1 were dropped in the EA pretreatment group (P<0.01); the content of SOD and the protein expression of AMRK and Nrf2 rose (P<0.01), and the protein expression of Drp1 declined (P<0.01); and in the EA pretreatment+Compound C group and the EA pretreatment+ML385 group, the positive expression of MFF, Fis1 and Drp1, and the protein expression of Drp1 were all reduced (P<0.01). When compared with the EA pretreatment + Compound C group and the EA pretreatment+ML385 group, the content of ROS and MDA in the myocardial tissue of the ischemic area, and the positive expression of MFF, Fis1 and Drp1 were dropped in the EA pretreatment group (P<0.01); the content of SOD and the protein expression of AMRK and Nrf2 rose (P<0.01, P<0.05), and the protein expression of Drp1 decreased (P<0.05). In comparison with the model group, the EA pretreatment+Compound C group and the EA pretreatment+ML385 group, the cardiac muscle fiber rupture, cell swelling and mitochondrial disorders were obviously alleviated in the EA pretreatment group. The morphological changes were similar among the model group, the EA pretreatment+Compound C group and the EA pretreatment+ML385 group. CONCLUSION Electroacupuncture pretreatment of "biaoben acupoint combination" attenuates myocardial injury in MIRI rats, probably through promoting the phosphorylation of AMPK and Nrf2, inhibiting the excessive mitochondrial fission induced by Drp1, and reducing mitochondrial dysfunction caused by mitochondrial fragmentation and vacuolation.
Animals ; Electroacupuncture ; Male ; Rats, Sprague-Dawley ; Myocardial Reperfusion Injury/physiopathology* ; Myocytes, Cardiac/cytology* ; Rats ; Acupuncture Points ; Mitochondrial Dynamics ; Humans ; Reactive Oxygen Species/metabolism* ; NF-E2-Related Factor 2/genetics* ; Superoxide Dismutase/metabolism*

Neurological diseases are a major health concern, and brain injury is a typical pathological process in various neurological disorders. Different biomarkers in the blood or the cerebrospinal fluid are associated with specific physiological and pathological processes. They are vital in identifying, diagnosing, and treating brain injuries. In this review, we described biomarkers for neuronal cell body injury (neuron-specific enolase, ubiquitin C-terminal hydrolase-L1, αII-spectrin), axonal injury (neurofilament proteins, tau), astrocyte injury (S100β, glial fibrillary acidic protein), demyelination (myelin basic protein), autoantibodies, and other emerging biomarkers (extracellular vesicles, microRNAs). We aimed to summarize the applications of these biomarkers and their related interests and limits in the diagnosis and prognosis for neurological diseases, including traumatic brain injury, status epilepticus, stroke, Alzheimer's disease, and infection. In addition, a reasonable outlook for brain injury biomarkers as ideal detection tools for neurological diseases is presented.
Humans ; Biomarkers/cerebrospinal fluid* ; Nervous System Diseases/diagnosis* ; Brain Injuries/metabolism* ; Phosphopyruvate Hydratase/cerebrospinal fluid* ; Glial Fibrillary Acidic Protein/blood* ; S100 Calcium Binding Protein beta Subunit/blood* ; tau Proteins/cerebrospinal fluid* ; Ubiquitin Thiolesterase/blood* ; Myelin Basic Protein/cerebrospinal fluid* ; Neurofilament Proteins/blood* ; MicroRNAs/blood* ; Brain Injuries, Traumatic/metabolism*