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.

Objective To investigatethe relationship between gastroesophageal reflux disease (GERD) symptoms and clinicopathological characteristics, p53 expression, and survival of Chinese patients with esophageal adenocarcinoma. Methods A total of 3882 patients with esophageal adenocarcinoma, 970 matched patients with esophageal squamous cell carcinoma, and 970 matched patients with gastric cardia adenocarcinoma were included to compare the incidence of GERD symptoms. Patients with esophageal adenocarcinoma were divided into two groups according to the presence and absence of GERD symptoms. The differences in clinicopathological characteristics and p53 expression between the two groups were compared by chi-square test, and the differences in survival between the two groups were compared by landmark analysis. Results The incidence of GERD symptoms increased successively in esophageal squamous cell carcinoma, esophageal adenocarcinoma, and gastric cardia adenocarcinoma. In patients with esophageal adenocarcinoma, the proportions of male, less than 65 years old, lower thoracic tumors, tumors without squamous cell carcinoma, poorly differentiation, early tumors (stage 0-I), and p53-positive tumors in the group with GERD symptoms were higher than those in the group without GERD symptoms; moreover, the long-term survival (≥15 years) was better. Conclusion GERD symptom is an important clinical sign of esophageal adenocarcinoma. It is closely related to specific clinicopathological characteristics, p53 overexpression, and good long-term prognosis.

OBJECTIVE To systematically evaluate the efficacy and safety of tislelizumab in the treatment of advanced non- small cell lung cancer （NSCLC）. METHODS Computerized searches were conducted in PubMed， Embase， the Cochrane Library， CNKI， Wanfang and other Chinese and English databases to collect randomized controlled trials （RCTs） on tislelizumab for advanced NSCLC. The search period was from the establishment of the databases to December 2024. After strictly screening the literature， extracting data and conducting quality evaluations in accordance with the inclusion and exclusion criteria， a meta-analysis was performed using RevMan 5.3 and Stata 16.0 software. RESULTS A total of 18 RCTs involving 2 337 patients were included， with 1 283 in the experimental group and 1 054 in the control group. The meta-analysis results showed that the objective response rate [RR＝1.61， 95%CI （1.48， 1.75）， P＜0.000 01]， disease control rate [RR＝1.21， 95%CI （1.13， 1.29）， P＜0.000 01]， progression free survival [HR＝0.55， 95%CI （0.45， 0.66）， P＜0.000 01]， and overall survival [HR＝0.78， 95%CI（0.62， 0.97）， P＝0.03] were significantly better in the experimental group than in the control group. There was no statistically significant difference in the incidence of adverse reactions between the two groups [RR＝1.00， 95%CI （0.73， 1.37）， P＝1.00]； among the common adverse reactions， only the incidence of liver function impairment was significantly higher in the experimental group than in the control group [RR＝1.30， 95%CI （1.10， 1.54）， P＜0.01]. CONCLUSIONS Tislelizumab in combination with chemotherapy or targeted drugs significantly improves the efficacy in patients with advanced NSCLC without increasing the risk of adverse reactions overall. However， liver function should be closely monitored during treatment.

BACKGROUND: Preclinical studies have indicated that Angong Niuhuang Pills (ANP) reduce cerebral infarct and edema volumes. This study aimed to investigate whether ANP safely reduces cerebral infarct and edema volumes in patients with moderate to severe acute ischemic stroke. METHODS: This randomized, double-blind, placebo-controlled pilot trial included patients with acute ischemic stroke with National Institutes of Health Stroke Scale (NIHSS) scores ranging from 10 to 20 in 17 centers in China between April 2021 and July 2022. Patients were allocated within 36 h after onset via block randomization to receive ANP or placebo (3 g/day for 5 days). The primary outcomes were changes in cerebral infarct and edema volumes after 14 days of treatment. The primary safety outcome was severe adverse events (SAEs) for 90 days. RESULTS: There were 57 and 60 patients finally included in the ANP and placebo groups, respectively for modified intention-to-treat analysis. The median age was 66.0 years, and the median NIHSS score at baseline was 12.0. The changes in cerebral infarct volume at day 14 were 0.3 mL and 0.4 mL in the ANP and placebo groups, respectively (median difference: -7.1 mL; interquartile range [IQR]: -18.3 to 2.3 mL, P = 0.30). The changes in cerebral edema volume of the ANP and placebo groups on day 14 were 11.4 mL and 4.0 mL, respectively ( median difference: 3.0 mL, IQR: -1.3 to 9.9 mL, P = 0.15). The rates of SAE within 90 days were similar in the ANP (3/57, 5%) and placebo (7/60, 12%) groups ( P = 0.36). Changes in serum mercury and arsenic concentrations were comparable. In patients with large artery atherosclerosis, ANP reduced the cerebral infarct volume at 14 days (median difference: -12.3 mL; IQR: -27.7 to -0.3 mL, P = 0.03). CONCLUSIONS: ANP showed a similar safety profile to placebo and non-significant tendency to reduce cerebral infarct volume in patients with moderate-to-severe stroke. Further studies are warranted to assess the efficacy of ANP in reducing cerebral infarcts and improving clinical prognosis. TRAIL REGISTRATION Clinicaltrials.gov , No. NCT04475328.
Aged ; Female ; Humans ; Male ; Middle Aged ; Double-Blind Method ; Drugs, Chinese Herbal/adverse effects* ; Ischemic Stroke/drug therapy* ; Pilot Projects ; Stroke/drug therapy* ; Treatment Outcome

BACKGROUND: The profile and clinical significance of the oral microbiome in patients with non-obstructive hypertrophic cardiomyopathy (noHCM) and obstructive hypertrophic cardiomyopathy (oHCM) remain unexplored. The objective of this study was to evaluate the difference of oral microbiome between noHCM and oHCM patients. METHODS: This cross-sectional study enrolled 18 noHCM patients and 26 oHCM patients from Fuwai Hospital, Chinese Academy of Medical Sciences between 2020 and 2021. Clinical and periodontal evaluations were conducted, and subgingival plaque samples were collected. Metagenomic sequencing and subsequent microbial composition and functional analyses were performed. RESULTS: Compared to oHCM patients, those with noHCM had higher systolic blood pressure (138.1 ± 18.8 mmHg vs . 124.2 ± 13.8 mmHg, P = 0.007), a larger body circumference (neck circumference: 39.2 ± 4.0 cm vs . 35.1 ± 3.7 cm, P = 0.001; waist circumference: 99.7 ± 10.5 cm vs . 92.2 ± 10.8 cm, P = 0.027; hip circumference: 102.5 ± 5.6 cm vs . 97.5 ± 9.1 cm, P = 0.030), a greater left ventricular end-diastolic diameter (46.6 ± 4.9 mm vs . 43.1 ± 4.9 mm, P = 0.026), and a lower left ventricular ejection fraction (64.1 ± 5.7 % vs . 68.5 ± 7.8%, P = 0.048). While overall biodiversity and general microbial composition were similar between the noHCM and oHCM groups, ten taxa displayed significant differences at the genus and species levels, with Porphyromonas gingivalis showing the highest abundance and greater enrichment in noHCM (relative abundance: 7.79535 vs . 4.87697, P = 0.043). Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis identified ten distinct pathways, with pathways related to energy and amino acid metabolism being enriched in oHCM patients, and those associated with genetic information processing less abundant in the oHCM group. Metabolic potential analysis revealed ten significantly altered metabolites primarily associated with amino sugar and nucleotide sugar metabolism, porphyrin metabolism, pentose and glucuronate interconversion, and lysine degradation. CONCLUSIONS The higher abundance of Porphyromonas gingivalis , which is known to impact cardiovascular health, in noHCM patients may partially account for clinical differences between the groups. Pathway enrichment and metabolic potential analyses suggest microbial functional shifts between noHCM and oHCM patients, potentially reflecting inherent metabolic changes in HCM.
Humans ; Cardiomyopathy, Hypertrophic/microbiology* ; Female ; Male ; Microbiota/genetics* ; Middle Aged ; Cross-Sectional Studies ; Adult ; Mouth/microbiology* ; Aged

The chemical constituents from leaves of Cyclocarya paliurus were isolated and purified by chromatography on silica gel, C_(18) reverse-phase silica gel, and Sephadex LH-20 gel, as well as semi-preparative high-performance liquid chromatography. Six compounds were identified by UV, IR, NMR, MS, calculated ECD, and comparison with literature data as cyclopaloside D(1), boscialin(2),(5R,6S)-6-hydroxy-6-[(E)-3-hydroxybut-1-enyl]-1,1,5-trimethylcyclohexanone(3), 3S,5R-dihydroxy-6R,7-megastigmadien-9-one(4), 3S,5R-dihydroxy-6S,7-megastigmadien-9-one(5), and gingerglycolipid A(6), respectively. Among them, compound 1 was identified as a new tetralone glycoside, and compounds 2-6 were isolated from leaves of C. paliurus for the first time. Furthermore, compound 1 exhibited strong antioxidant activity, with the IC_(50) of(454.20±31.81)μmol·L~(-1) and(881.82±42.31)μmol·L~(-1) in scavenging DPPH and ABTS free radicals, respectively.
Plant Leaves/chemistry* ; Glycosides/isolation & purification* ; Juglandaceae/chemistry* ; Tetralones/isolation & purification* ; Drugs, Chinese Herbal/isolation & purification*

Atractylodes lancea is a perennial herbaceous plant of Asteraceae, with rhizomes for medical use. However, A. lancea plants from different habitats have great variability, and the germplasm resources of A. lancea are unclear and mixed during production. Therefore, it is urgent to protect new varieties of A. lancea. The distinctness, uniformity, and stability(DUS) testing of new plant varieties is the foundation of plant variety protection, and the DUS testing guidelines are the technical basis for variety approval agencies to conduct DUS testing. In this study, the phenotypic traits of 94 germplasm accessions of A. lancea were investigated considering the breeding and variety characteristics of A. lancea in China. The traits were classified and described, and 24 traits were preliminarily determined, including 20 basic traits that must be tested and four traits selected to be tested. The 20 basic traits included 3 quality traits, 5 false quality traits, and 12 quantitative traits, corresponding to 1 plant traits, 2 stem traits, 8 leaf traits, 6 flower traits, and 3 seed traits. The measurement ranges and coefficients of variation of eight quantitative traits were determined, on the basis of which the grading criteria and codes of the traits were determined and assigned. The guidelines has guiding significance for the trait evaluation, utilization, and breeding of new varieties of A. lancea.
Atractylodes/growth & development* ; China ; Phenotype ; Guidelines as Topic ; Plant Breeding