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 explore the application value of different Trocar layouts in retroperitoneal laparoscopic partial nephrectomy for localized renal cell carcinoma, so as to provide reference for the layout of Trocar during operation. Methods The data of 137 patients with localized renal cell carcinoma who underwent retroperitoneal laparoscopic partial nephrectomy in our hospital during Sep. 2021 and Sep. 2024 were retrospectively collected, including 68 patients with umbrella layout(umbrella group)and 69 with trapezoidal layout(trapezoidal group). The operation-related indexes, renal function(glomerular filtration rate, serum creatinine), traumatic stress indexes including C-reactive protein(CRP), cortisol, and interleukin-6(IL-6), complications and recurrence rate were compared between the two groups. Results There were no significant differences in pathological results, negative rate of surgical margins, hospitalization time, complications, 1-year recurrence rate, 6-month glomerular filtration rate and serum creatinine between the two groups(P＞0.05). Compared with the trapezoidal group, the umbrella group had less intraoperative blood loss [(72.11±18.30)mL vs.(87.59±20.63)mL], shorter operation time [(88.15±16.92)min vs.(99.40±20.78)min], renal warm ischemia time [(14.68±4.60)vs.(19.27±6.11)min], bowel sound recovery time [(21.00±5.75)h vs.(25.78±4.89)h] and ambulation time [(1.72±0.48)d vs.(2.04±0.55)d](P＜0.05). The umbrella group had lower CRP [(10.02±2.78)mg/ L vs.(12.00±3.15)mg/ L] on the first day after surgery, lower CRP [(7.89±0.76)vs.(8.77±1.25)mg/L] on the third day after surgery, lower cortisol [(0.67±0.20)μmol/L vs.(0.89±0.28)μmol/L] on the first day after surgery, lower cortisol [(0.58±0.15)μmol/L vs.(0.69±0.12)μmol/L] on the third day after surgery, lower IL-6 [(28.20±6.74)ng/L vs.(35.16±7.30)ng/L] on the first day after surgery, and lower IL-6[(21.25±5.39)ng/L vs.(29.78±4.26)ng/L] on the third day after surgery(P＜0.05). Conclusion Compared with the trapezoidal Trocar layout, the umbrella Trocar layout for retroperitoneal laparoscopic partial nephrectomy in patients with localized renal cell carcinoma offers advantages such as minimal surgical trauma, shorter operation and renal warm ischemia time, and faster postoperative recovery.

ObjectiveTo elucidate the potential mechanism of modified Sinisan （MSNS） in alleviating anxiety-like behaviors induced by chronic restraint stress （CRS） in mice at the metabolic level based on serum untargeted metabolomics and identify key metabolites and metabolic pathways regulated by MSNS. MethodsSeventy-two male C57BL/6 mice were randomly assigned into six groups： control， model， high-dose （2.4 g·kg^-1） MSNS， medium-dose （1.2 g·kg^-1） MSNS， low-dose （0.6 g·kg^-1） MSNS， and positive control （fluoxetine， 2.6 mg·kg^-1）. Except the control group， the other groups were subjected to CRS for the modeling of anxiety. Mice were administrated with corresponding agents by gavage 2 h before daily restraint for 14 days. Anxiety-like behaviors were evaluated by the open field test （OFT）， elevated plus maze （EPM） test， and light/dark box （LDB） test. Serum levels of corticotropin-releasing hormone （CRH）， adrenocorticotrophic hormone （ACTH）， and corticosterone （CORT） were measured via ELISA to assess stress levels. Ultra-performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS） was employed to detect 9 metabolites in the brain tissue and serum metabolites. Orthogonal partial least squares-discriminant analysis （OPLS-DA） was adopted to identify differential metabolites （VIP>1.0， P<0.05）. MetaboAnalyst 5.0 was used for metabolic pathway enrichment analysis of the differential metabolites. ResultsCompared with the control group， the model group showed reductions in the central activity time and central distance in the OFT （P<0.05）， the proportions of open-arm residence time and open-arm residence times in the EPM test （P<0.01）， and the proportions of open box activity time and open box activity distance in the LDB test （P<0.05）， which were increased in the medium- and high-dose MSNS groups compared with the model group （P<0.05）. Compared with the control group， the model group showed elevated levels of CRH， ACTH， and CORT in the serum （P<0.01）， and the elevations were diminished in the medium- and high-dose MSNS groups （P<0.05）. UPLC-MS results indicated that compared with the control group， the model group presented declined DA， GABA， 5-HIAA， 5-HT， and 5-HT/Trp levels （P<0.05， P<0.01） and raised Glu， NE， Kyn， and Kyn/Trp levels （P<0.05）. Compared with the model group， high-dose MSNS increased the GABA， 5-HIAA， and 5-HT/Trp levels （P<0.05） and lowered the Glu and Kyn/Trp levels （P<0.05）. Untargeted metabolomics identified that 16 CRS-induced metabolic disturbances were reversed by MSNS. KEGG pathway analysis indicated that MSNS primarily modulated eight core pathways including alanine/aspartate/glutamate metabolism， butyrate metabolism， arginine-proline metabolism， TCA cycle， unsaturated fatty acid biosynthesis， and tryptophan metabolism. The mechanisms involved multidimensional biological processes， including neurotransmitter homeostasis regulation， TCA cycle energy metabolism optimization， and inflammatory response suppression. ConclusionMSNS alleviates CRS-induced anxiety-like behaviors in mice by mitigating hypothalamic-pituitary-adrenal axis hyperactivity， improving hippocampal neurotransmitter and tryptophan metabolic pathways， and regulating alanine/aspartate/glutamate metabolism， butyrate metabolism， arginine-proline metabolism， and TCA cycle.

Objective:To explore any effect of combining Liuzijue training with pulmonary rehabilitation training on functional recovery after pulmonary nodule resection.Methods:Sixty patients after pulmonary nodule resection were randomly divided into an observation group and a control group, each of 30.Both groups were given pulmonary rehabilitation training, but the observation group also received Liuzijue training for 4 weeks. Before and after the treatment and at follow-up 3 months after the surgery, both groups′ forced vital capacity (FVC) and forced expiratory volume in one second (FEV1) were evaluated. Exercise endurance and somatic functioning were quantified using the 6-minute walk test (6MWT) and the short physical performance battery (SPPB), respectively. The 36-item short form survey (SF-36) was administered to assess their life quality.Results:After 4 weeks the pulmonary function index, 6-minute walking distance, SPPB scores and SF-36 scores of both groups were significantly improved. Both groups′ improvements had persisted 2 months later. The observation group′s average FVC and FEV1 values, as well as their average scores on some components of the SF-36 instrument were significantly better than the control group′s averages after 4 weeks. After 3 months their average 6MWT distance and SPPB score was also significantly better.Conclusions:Combining Liuzijue training with pulmonary rehabilitation training can significantly improve the pulmonary function of patients after pulmonary nodule resection. It can further improve their exercise endurance, somatic function and life quality. The combination is worthy of clinical promotion and application.

ObjectiveTo elucidate the potential mechanism of modified Sinisan （MSNS） in alleviating anxiety-like behaviors induced by chronic restraint stress （CRS） in mice at the metabolic level based on serum untargeted metabolomics and identify key metabolites and metabolic pathways regulated by MSNS. MethodsSeventy-two male C57BL/6 mice were randomly assigned into six groups： control， model， high-dose （2.4 g·kg^-1） MSNS， medium-dose （1.2 g·kg^-1） MSNS， low-dose （0.6 g·kg^-1） MSNS， and positive control （fluoxetine， 2.6 mg·kg^-1）. Except the control group， the other groups were subjected to CRS for the modeling of anxiety. Mice were administrated with corresponding agents by gavage 2 h before daily restraint for 14 days. Anxiety-like behaviors were evaluated by the open field test （OFT）， elevated plus maze （EPM） test， and light/dark box （LDB） test. Serum levels of corticotropin-releasing hormone （CRH）， adrenocorticotrophic hormone （ACTH）， and corticosterone （CORT） were measured via ELISA to assess stress levels. Ultra-performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS） was employed to detect 9 metabolites in the brain tissue and serum metabolites. Orthogonal partial least squares-discriminant analysis （OPLS-DA） was adopted to identify differential metabolites （VIP>1.0， P<0.05）. MetaboAnalyst 5.0 was used for metabolic pathway enrichment analysis of the differential metabolites. ResultsCompared with the control group， the model group showed reductions in the central activity time and central distance in the OFT （P<0.05）， the proportions of open-arm residence time and open-arm residence times in the EPM test （P<0.01）， and the proportions of open box activity time and open box activity distance in the LDB test （P<0.05）， which were increased in the medium- and high-dose MSNS groups compared with the model group （P<0.05）. Compared with the control group， the model group showed elevated levels of CRH， ACTH， and CORT in the serum （P<0.01）， and the elevations were diminished in the medium- and high-dose MSNS groups （P<0.05）. UPLC-MS results indicated that compared with the control group， the model group presented declined DA， GABA， 5-HIAA， 5-HT， and 5-HT/Trp levels （P<0.05， P<0.01） and raised Glu， NE， Kyn， and Kyn/Trp levels （P<0.05）. Compared with the model group， high-dose MSNS increased the GABA， 5-HIAA， and 5-HT/Trp levels （P<0.05） and lowered the Glu and Kyn/Trp levels （P<0.05）. Untargeted metabolomics identified that 16 CRS-induced metabolic disturbances were reversed by MSNS. KEGG pathway analysis indicated that MSNS primarily modulated eight core pathways including alanine/aspartate/glutamate metabolism， butyrate metabolism， arginine-proline metabolism， TCA cycle， unsaturated fatty acid biosynthesis， and tryptophan metabolism. The mechanisms involved multidimensional biological processes， including neurotransmitter homeostasis regulation， TCA cycle energy metabolism optimization， and inflammatory response suppression. ConclusionMSNS alleviates CRS-induced anxiety-like behaviors in mice by mitigating hypothalamic-pituitary-adrenal axis hyperactivity， improving hippocampal neurotransmitter and tryptophan metabolic pathways， and regulating alanine/aspartate/glutamate metabolism， butyrate metabolism， arginine-proline metabolism， and TCA cycle.

Indocyanine green has been a popular near-infrared fluorescent reagent in recent years, and indocyanine green fluorescence imaging navigation surgery technology has been widely used in hepatobiliary surgery. Many factors affect imaging in various clinical scenarios, which are roughly divided into liver background fluorescence interference and penetrating tissue thickness, including the dose and timing of the drug, liver lesions of the patient, and individualization of the patient. Under the concept of precise surgery, we should individualise the treatment of patients to minimise the influence of unfavourable factors and further improve the safety and efficacy of surgery. Currently, there is no reliable research evidence to guide the use of indocyanine green in terms of influencing factors, and clinical studies are urgently needed to optimise the use of indocyanine green in hepatobiliary surgery.

This review systematically elucidates recent advances in the therapeutic application of Erianin,a natural compound derived from Dendrobium,a traditional Chinese medicine,in the treatment of breast cancer,with particular emphasis on triple-negative breast cancer(TNBC).TNBC is characterized by the absence of estrogen receptor(ER),progesterone receptor(PR),and human epidermal growth factor receptor 2(HER2)expression,which presents significant clinical challenges due to limited therapeutic targets and continued reliance on conventional chemotherapy.Erianin exhibits notable anticancer potential through the induction of apoptosis in breast cancer cells.Its primary mechanisms involve sensitizing cancer cells to apoptotic signals via activation of both intrinsic and extrinsic apoptotic pathways,particularly through mitochondrial dysfunction-mediated cytochrome c release and subsequent activation of caspase-dependent pathways.At the molecular level,Erianin effectively modulates key oncogenic signaling pathways,including PI3K/Akt,MAPK,and NFATc1 cascades,thereby sup-pressing cell proliferation and migration while promoting apoptosis.However,current research priorities center on investigating its synergistic effects with chemotherapeutic agents and assessing its radiosensitization potential to further enhance its clinical utility.Notably,Erianin demonstrates unique advantages in overcoming drug resistance in TNBC by modulating apoptotic regulatory networks,particularly through regulation of the Bax/Bcl-2 protein ratio,positioning it as a promising multi-target therapeutic candidate.Although existing evidence largely stems from in vitro and animal studies,future research should prioritize human clinical trials to validate its efficacy and safety,along-side pharmaceutical optimization strategies such as the development of nanodelivery systems and exploration of structural derivatives.This review systematically clarifies the core mechanism and therapeutic potential of pilanin-induced apoptosis,and provides theoretical basis for developing innovative therapeutic regimens for TNBC.

Objective To explore the effects of individualized nutrition intervention combined with one-day outpatient mode on blood glucose management and pregnancy outcomes in pregnant women with gestational diabetes mellitus(GDM).Methods A total of 395 pregnant women diagnosed with GDM who underwent prenatal examination in Maternity and Child Health Care of Guangxi Zhuang Autonomous Region from January 2023 to January 2024 were selected as study objects.According to the nutritional intervention measures,the pregnant women were divided into control group(102 cases),nutrition intervention group(141 cases)and combined intervention group(152 cases).The control group was given routine diet education,and nutrition intervention group received individualized nutrition intervention on the basis of control group,and combined intervention group received one-day outpatient intervention on the basis of nutrition intervention group.All pregnant women in three groups were intervened until delivery.The general data,blood glucose indexes and pregnancy outcome of three groups were compared.Results After the intervention,the levels of preprandial blood glucose and 2h postprandial blood glucose on the day of delivery,glycated hemoglobin in late pregnancy and postpartum fasting blood glucose in combined intervention group were significantly lower than those in nutrition intervention group and control group(P＜0.05).The levels of preprandial blood glucose on the day of delivery and postpartum fasting blood glucose in nutrition intervention group were significantly lower than those in control group(P＜0.05).The incidence of preterm birth,low birth weight infants,neonatal hyperbilirubinemia and neonatal hypoglycemia in nutrition intervention group and combined intervention group were significantly lower than those in control group(P＜0.05).Conclusion Individualized nutrition intervention combined with one-day outpatient mode can help to manage blood glucose in pregnant women with GDM and improve pregnancy outcome.