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 develop the Expert Consensus on Sensitive Indicators for Assessment of the Quality of Nursing in Operating Theatre and provide a scientific and practical guidance for improving the quality of nursing in operating theatre.Methods The writing team established by the Operating Room Nursing Professional Committee of Guangdong Nursing Association conducted systematic literature retrieval and screening,and used the updated clinical Guidelines for Research and Evaluation Ⅱ in UK 2017.AGREE Ⅱ and the evidence evaluation system of the Australian JBI(Joanna Briggs Institute,JBI)Evidence-Based Health Care Center evidence level system(2016 Edition)comprehensively analyzed the evidence related to the sensitive indicators for evaluating the quality of operating room nursing and the suggestions of the writing group members.The first draft was formed based on the three-dimensional quality evaluation theoretical framework of"structure-process-result".Through the Delphi method,after two rounds of expert consultations and members'votes,the first draft was deeply revised and improved.Results Based on the three-dimensional quality evaluation theoretical framework of"structure-process-outcome"proposed by American scholar Donabedian,the expert consensus finally included five primary indicators:basic nursing quality,quality indicators of patient safety,quality indicators of hospital infection control,quality indicators of medication and safety management,and quality indicators of specialised nursing in operating theatre.The secondary indicators consisted of one structural indicator(management of commonly used instrument and equipment in operating theatre)and 17 process indicators(e.g.,infusion and blood transfusion management,body temperature management,etc.).The tertiary indicators included 26 process indicators and 11 outcome indicators(e.g.,incidence of adverse reactions of infusion during surgery,incidence of intra-operative hypothermia,etc.).Conclusion The evidence-and guideline-based Expert Consensus on Sensitive Indicators for Assessment of the Quality of Nursing in Operating Theatre based on eviclence and guidelines was established through rigorous evidence-based methods.It is operational and practical,and offers theoretical support and practical guidance for the managers of operating theatre to improve the quality of nursing.

Aim To study the therapeutic effect of al-licin on senna-induced diarrhea in mice and to explore the underlying mechanism.Methods Forty-eight C57BL/6J mice were randomly divided into six groups:control,model,loperamide positive control group(2 mg·kg-1),allicin low-dose group(6 mg·kg-1),allicin medium-dose group(12 mg·kg-1)and allicin high-dose group(18 mg·kg-1).Except for the con-trol group,the diarrhea model was induced in the other groups by intragastric administration of senna leaf ex-tract.After drug administration,several diarrhea indi-ces were measured:the rate of loose stools,diarrhea index,accumulated frequency of loose stools at differ-ent time points within 5 hours,and small intestine pro-pelling rate.Serum levels of TNF-α and IL-6 were de-tected by ELISA.Serum NO content was determined u-sing the Griess method.The activities of SOD and CAT,as well as MDA content in the ileum and colon,were measured.The pathological changes and the ex-pression of mRNA related to intestinal barrier proteins in the ileum and colon were evaluated using HE stai-ning and RT-qPCR.Results Allicin improved diar-rhea symptoms in mice induced by senna leaf.It re-duced the rate of loose stools,diarrhea index,cumula-tive number of loose stools in five hours,and the intes-tinal propulsion rate.Allicin also protected the intesti-nal mucosa,decreased serum TNF-α and IL-6 levels,and lowered MDA content in the intestines.It in-creased serum NO levels and enhanced SOD and CAT activities in the intestines.Additionally,allicin upreg-ulated the mRNA expression of AQP1,AQP4,and ZO-1 in intestinal tissues.Conclusions Allicin has a significant therapeutic effect on senna-induced diarrhea in mice.The underlying molecular mechanisms may involve anti-inflammatory and antioxidant effects,in-creased NO content,and upregulation of mRNA ex-pression of aquaporins and tight-junction proteins.

Objective To establish a stable liquid chromatography-tandem mass spectrometry(LC-MS/MS)method for detecting gamma-aminobutyric acid(GABA)in plasma,and to evaluate the value of GABA detection in the diagnosis of sleep disorders.Methods GABA was detected using a UPLC Xevo TQs system.The method was pre-validated and its performance was verified to establish a reference range for healthy individuals.The difference in plasma GABA levels between apparently healthy individuals and patients with sleep disorders was compared.Results We employed deuterated compounds as isotopic internal standards and utilized an Amide chromatographic column for separation.The mobile phase was 0.050%formic acid in water and 90%acetonitrile in water containing 0.175%formic acid and 5 mmol/L ammonium acetate with gradient elution in the column temperature of 35℃.The linear range for the detection of GABA by LC-MS/MS was 0.05-10.00 μmol/L,with a lower limit of quantification of 0.02 μmol/L,the inter-day CV<3.00%and intra assay CV<4.00%,respectively,and the recovery rate was 101.06%-109.02%.The reference ranges for plasma GABA were established by analyzing 300 healthy controls stratified by age:18-34 years(0.08-0.15 μmol/L),35-49 years(0.10-0.20 μmol/L),and≥50 years(0.12-0.23 μmol/L).Then plasma GABA was used as a biomarker for auxiliary diagnosis of sleep disorders in analyzing 221 patients and 300 healthy controls,which revealed that AUC values were 0.510(P=0.850),0.686(P=0.002),and 0.890(P<0.001)in the groups of 18-34 years,35-49 years,and≥50 years,respectively,with optimal cut-off values of 0.09,0.10 and 0.11 μmol/L.Conclusion A reliable LC-MS/MS method for detecting GABA has been established,which can detect plasma GABA levels sensitively and accurately and can be used in assisting the clinical diagnosis of sleep disorders.

Objective:To explore the effects of Ziyu Ointment on granulation tissue,TGF-β and downstream Smad protein in the process of wound healing in rats by constructing a rat model of back trauma.Methods:A rat model of back trauma was constructed,and 100 SD male rats were separated into healthy group,back trauma group,Ziyu Ointment treatment group,petrolatum treatment group,Ziyu Ointment+TGF-β1 activation group,with 20 per group.Wound formation surgery was performed,and wound healing of rats in each group was recorded at 5th,10th,and 15th day after operation;the levels of IL-6,IL-1β and TNF-α in the serum of rats in each group were detected;the granulation tissue formed by wound healing at 15th day was selected for HE and Masson staining,the proportion of blood vessel area and collagen area of granulation tissue of rats in each group were analyzed and calculated;the mRNA expression levels of TGF-β1 and Smad3 in granulation tissues of rats in each group were detected by fluorescence quantitative PCR;the expression levels of TGF-β1,Smad3 and p-Smad3 in granulation tissues of rats in each group were detected by Western blot.Results:The healthy group rats had complete epidermal tissue;the levels of serum IL-6,IL-1β,TNF-α,the expression of TGF-β1 mRNA and protein in the wound granulation tissue,and the phosphorylation of Smad3 in the back trauma group were obviously higher than the healthy group(P<0.05);compared with back trauma group,the wound healing rate,proportion of collagen area,and propor-tion of blood vessel area in Ziyu Ointment treatment group and petrolatum treatment group were obviously increased,the serum IL-6,IL-1β,TNF-α contents,TGF-β1 mRNA and protein expression,and Smad3 phosphorylation were obviously decreased(P<0.05);compared with Ziyu Ointment treatment group,the petrolatum treatment group,Ziyu Ointment+TGF-β1 activation group had obviously lower wound healing rate,collagen area percentage,and blood vessel area percentage,and obviously lower serum IL-6,IL-1β,TNF-α contents,TGF-β1 mRNA and protein expression and Smad3 phosphorylation(P<0.05).Conclusion:Ziyu Ointment can inhibit the expression of related genes and proteins by regulating the TGF-β/Smad signal pathway,thereby alleviating inflammation and promoting wound healing.

Inflammatory bowel disease(IBD)is a chronic and recurrent intestinal disease,and has become a major global health issue.Individuals with IBD face an elevated risk of developing colorectal cancer(CRC),and recent studies have indicated that mitochondrial dysfunction plays a pivotal role in the pathogenesis of both IBD and CRC.This review covers the pathogenesis of IBD and CRC,focusing on mitochondrial dysfunction,and explores pharmacological targets and strategies for addressing both conditions by modulating mitochondrial function.Additionally,recent advancements in the phar-macological modulation of mitochondrial dysfunction for treating IBD and CRC,encompassing mitochondrial damage,release of mitochondrial DNA(mtDNA),and impairment of mitophagy,are thoroughly summarized.The review also provides a systematic overview of natural compounds(such as flavonoids,alkaloids,and diterpenoids),Chinese medicines,and intestinal microbiota,which can alleviate IBD and attenuate the progression of CRC by modulating mitochondrial function.In the future,it will be imperative to develop more practical methodologies for real-time monitoring and accurate detection of mitochondrial function,which will greatly aid scientists in identifying more effective agents for treating IBD and CRC through modulation of mitochondrial function.

Objective To investigate the current status of family resilience of children with cancer and analyze its influencing factors,to provide a basis for medical staff to formulate intervention plans.Methods Using a convenient sampling method,children with cancer who were hospitalized in 2 tertiary hospitals in Henan Province from January to April 2024 were selected for the survey.A general information questionnaire,family resilience assessment scale,quality of life family version,ZBI caregiver burden interview,and social support rating scale were used to understand the current status of family resilience of children with cancer and to explore the related influencing factors by univariate analysis and multiple stepwise linear regression analysis.Results A total of 280 questionnaires were distributed and 265 valid questionnaires were recovered,with a valid questionnaire recovery rate of 94.64％.The total score of family resilience for primary caregivers of children with cancer was(185.63±30.66).The multiple stepwise linear regression analysis results showed that the children's self-care ability,caregiver's work status,family care burden,and social support level were the influencing factors for family resilience of children with cancer(P＜0.05),and the explanatory variance was 51.3％.Conclusion The family resilience of children with cancer is at a medium level.The worse the children's self-care ability and the heavier the family care burden,the worse the family resilience;the caregiver's work status and good social support are helpful for the family resilience of children with cancer.Healthcare workers should develop intervention programs to address these factors to enhance the family resilience of children with cancer.

OBJECTIVE To construct the drug utilization evaluation criteria for Dezocine injection,so as to provide reference for the rational drug use in medical institutions.METHODS On the basis of evidence methodology,relevant guidelines/expert consensus,systematic reviews/meta-analysis were consulted;the evaluation criteria framework for Dezocine injection was established after screening evidence.Delphi method was employed,whereby 28 clinicians and clinical pharmacists from secondary and above-level medical institutions across eight provinces,including Tianjin,Beijing and Shandong,were selected to participate in two rounds of questionnaire surveys.The final indicators were determined based on the experts'enthusiasm coefficient,authority coefficient,and degree of coordination.RESULTS The effective recovery rate of questionnaire was 100%in the first round and 92.86%in the second round;expert authority coefficient was 0.82 in the first round and 0.81 in the second round;the coordination degree of experts in the first round was 0.29,and in the second round was 0.31(P＜0.001).Drug utilization evaluation standard system for Dezocine injection was formed finally,including three dimensions of medication indications,medication process and medication results,with a total of 11 first-level indicators(such as indications,usage and dosage)and 33 second-level indicators(such as labor analgesia and the management of severe pain following major or moderate surgeries combined with other analgesic drugs).The average importance scores for each indicator ranged from 4.08 to 5.00 points,with an overall average score of 4.61 points and coefficient of variation ranging from 0 to 0.19.CONCLUSIONS The drug utilization evaluation criteria for Dezocine injection established based on evidence-based methodology and Delphi method is authoritative and scientific,which provides a reference for subsequent evaluation of the rationality of clinical medication.

The application of sensors to the monitoring of spinal morphology and motion was reviewed in terms of the research object and monitoring index.The present situation of the application of sensors was introduced,such as inertial sensor,stretchable strain sensor and electromagnetic sensor.The deficiencies of sensors applied to the monitoring of spinal morphology and motion were analyzed,and the future directions of the application were pointed out.[Chinese Medical Equipment Journal,2025,46(6):105-110]