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.

Aim To explore the protective effect of the isochroman compound Asperisochroman B(AB)on oxygen-glucose deprivation/reoxygenation(OGD/R)injury of neurons based on the PI3K/AKT/Foxo1 path-way and to reveal the related mechanism.Methods Primary neurons were cultured and the OGD/R model was constructed.The primary neurons were divided in-to the blank control group,OGD/R group,and AB low,medium,and high concentration(3,10,30 μmol·L-1)groups.The effects of AB on primary neurons were determined by CCK-8 assay,lactate dehydrogen-ase(LDH)release assay,and Hoechst 33342 stai-ning.The expression levels of PI3K,AKT,and Foxo1-related proteins were detected by Western blot.After intervention with the PI3K inhibitor(LY294002)and re-modeling and intervention with high concentra-tion of AB(30 μmol·L-1),the expression of PI3K/Foxo1 pathway-related proteins was detected by West-ern blot.Results Compared with the OGD/R group,AB could significantly increase the cell survival rate of primary neurons and reduce the release of LDH.The results of Hoechst 33342 and immunofluorescence stai-ning showed that AB reduced apoptosis after OGD/R injury.Western blot results showed that compared with the OGD/R group,after AB intervention,the expres-sion levels of Bcl-2 and NeuN proteins in neurons sig-nificantly increased(P＜0.01),and the expression level of Bax protein significantly decreased(P＜0.01).At the same time,it upregulated the expres-sion levels of p-AKT and PI3K proteins,promoted Foxo1 phosphorylation,and downregulated the expres-sion of Foxo1.Compared with the high-dose AB group,LY294002 could inhibit the changes of the a-bove indicators and reverse the protective effect of AB on OGD/R-injured primary neurons.Conclusions AB can alleviate oxygen-glucose deprivation/reoxygen-ation-induced neuronal injury,and its mechanism may be related to the activation of the PI3K/AKT/Foxo1 signaling pathway.

BACKGROUND:The occurrence of neuronal axonal injury can result in neurological dysfunction,and the facilitation of axonal elongation is anticipated to play a pivotal role in the treatment of diseases affecting the nervous system.OBJECTIVE:To investigate whether ectomesenchymal stem cells-derived extracellular vesicles can promote neuronal axonal elongation.METHODS:(1)Ectomesenchymal stem cells were obtained from nasal mucosa using the tissue adherence method,and the specific markers of were identified through immunofluorescence.Ectomesenchymal stem cells-derived extracellular vesicles were acquired via ultracentrifugation and identified.(2)Ectomesenchymal stem cells-derived extracellular vesicles(0,0.5,1.0,1.5 mg/mL)were incubated with PC12 cells for 72 hours.The cytotoxicity and proliferation of ectomesenchymal stem cells-derived extracellular vesicles on PC12 cells were assessed using the CCK-8 assay.(3)Ectomesenchymal stem cells-derived extracellular vesicles(1.0 mg/mL)were incubated with PC12 cells or neurons for 72 hours.The changes in axon length were observed using microscopic analysis.The expression levels of axon-related markers β3-tubulin(early stage),growth associated protein 43(middle stage),and neurofilament 200(mature stage)were analyzed through real-time fluorescence quantitative PCR and Western blotting.These investigations aimed to explore the potential of ectomesenchymal stem cells-derived extracellular vesicles in promoting neurite elongation within PC12 cells or neurons.RESULTS AND CONCLUSION:(1)The majority of the acquired ectomesenchymal stem cells exhibited a spindle-shaped morphology,while a minority displayed irregular shapes,and demonstrated high expression levels of mesenchymal stem cell-specific markers Nestin,CD44,and Vimentin.The obtained ectomesenchymal stem cells-derived extracellular vesicles fulfilled the biological criteria for extracellular vesicles.(2)Within the detected protein concentration range of 0.5 to 1.5 mg/mL,the proliferation of PC12 cells was promoted by ectomesenchymal stem cells-derived extracellular vesicles,and this effect was further enhanced with increasing concentrations.(3)Ectomesenchymal stem cells-derived extracellular vesicles increased the length of axons in PC12 cells and neurons and the expression of axon-related markers β3-tubulin,growth associated protein 43,and neurofilament 200.Above findings suggest that ectomesenchymal stem cells-derived extracellular vesicles have the potential to enhance neuronal axonal elongation.

We aimed to explore the value of personalized narrative intervention in the nursing care of elderly patients with stroke hemiplegia(SH).A total of 68 elderly patients with SH were selected and divided into two groups using the ball-rolling method,with 34 cases in each group.The control group received routine nursing care,while the observation group received routine care and personalized narrative nursing.The nursing outcomes of the two groups were compared.Compared with the control group,the observation group had lower scores in anxiety and depression(P<0.05),and higher scores in the Functional Evaluation of Cognitive State-Short Screening(FECS-SS)(P<0.05).The rehabilitation effects of normal rate in the observation group was higher than that in the control group(P<0.05).Personalized narrative nursing for SH patients can effectively relieve the patients'negative emotions,enhance their compliance with rehabilitation,and improve the outcomes of rehabilitation.

Objective To assess the nutritional status of patients undergoing preventive ileostomy and identify the factors that influence the status,thereby providing guidance for medical staff to formulate nutritional intervention strategies.Methods Convenient sampling was used to select 239 patients undergoing preventive ileostomy who were attending follow-up visits or hospitalized while awaiting stoma closure at 4 tertiary hospitals in Guangzhou from November 2023 to July 2024.Data was collected by a general information questionnaire and the Patient-Generated Subjective Global Assessment tool.Factors influencing nutritional status were analyzed by multivariable ordinal Logistic regression.Results Of the 239 patients,227 provided valid responses.The nutritional status was categorized as follows:64 patients(28.19％)had good nutrition or were at risk of malnutrition;104(45.81％)exhibited moderate malnutrition;59(25.99％)had severe malnutrition.Multivariable ordinal Logistic regression analysis revealed that preoperative cumulative chemotherapy cycles,postoperative duration,age,current dietaiy type,use of oral nutritional supplements(ONS),enterostomy-related complications,and stoma self-care ability were significant predictors of nutritional status(P＜0.05).Conclusion Patients undergoing preventive ileostomy are at a high risk of moderate to severe malnutrition and ONS provided to patients did not result in a meaningful improvement in their nutritional health.Specifically,those patients with a higher number of cumulative preoperative chemotherapy cycles,shorter postoperative recovery time,aged ≥ 65 years,liquid diet or semi-liquid diet,experiencing enterostomy-related complications,or poor stoma self-care ability,are particularly vulnerable to malnutrition.These findings underscore the need for medical staff to formulate intervention strategies based on these factors to improve nutritional status of patients undergoing preventive ileostomy.

AIM To evaluate the quality of Rubi Fructus.METHODS UPLC-Q-TOF-MS/MS was adopted in the component identification,after which the HPLC fingerprints were established,cluster analysis,principal component analysis and orthogonal partial least squares discriminant analysis were used for chemical pattern recognition.and the contents of chlorogenic acid,ferulic acid,ellagic acid,isoquercitrin,kaempferol-3-O-rutinoside,astragalin,tiliroside quercetin,kaempferol were determined.RESULTS Total 34 constituents were identified.There were 19 common peaks in the fingerprints for 31 batches of medicinal materials with the similarities of more than 0.8.Wild varieties and cultivated varieties,and medicinal materials from different producing areas could be distinguished;4 principal components demonstrated the accumulative variance contribution rate of 84.142％;8 differential components were screened,2 of which were ellagic acid and astragalin.Ellagic acid and astragalin displayed higher contents in the wild varieties than those in the cultivated varieties(P＜0.05,P＜0.01).CONCLUSION UPLC-Q-TOF-MS/MS,HPLC fingerprints combined with content determination can be used for the quality control of Rubi Fructus.

Aim To investigate the protective effect of Gualou Guizhi granules(GLGZG)on neuronal injury induced by LPS-activated microglia based on Notch signaling pathway.Methods LPS-activated microglia were co-cultured with neurons to construct neuron inju-ry models,and the cells were divided into the control group,model group,Notch inhibitor(DAPT)group,GLGZG(50,100,200 mg·L-1)group,DAPT+100 mg·L-1GLGZG group.After intervention,the activity of HT22 cells was detected by CCK-8 method,and rel-ative mRNA expression was detected by real-time PCR.The relative protein expression was detected by Western blot.Results Compared with the model group,after GLGZG intervention,the cell activity was significantly improved,GLGZG decreased IL-6,IL-12,Bax,Notch 1,caspase-3,Delta-1,NICD,RBPSUH,HES1 expression,and increased Bcl-2 expression(P＜0.05).Compared with the model group,the NICD,RBPSUH and HES1 mRNA and protein expressions significantly decreased after DAPT treatment(P＜0.05),and there was no superposition effect with GLG-ZG.Conclusion GLGZG may play a neuroprotective role by inhibiting inflammatory factors and apoptosis,and inhibiting Notch signaling pathway.

AIM To investigate the inhibitory effect of gallic acid on HCC1806 triple-negative breast cancer xenograft growth in nude mice.METHODS The nude mouse models of HCC1806 triple-negative breast cancer xenograft growth were established and randomly divided into the model group,the gallic acid group(200 mg/kg)and the adriamycin group(5 mg/kg),with 6 mice in each group.After 21 days of respective drug administration,the nude mice were killed and had their xenografts procured and measured.The nude mice had their serum levels of IL-6 and TNF-α detected by ELISA;their morphological changes of xenografts observed with HE staining;their protein expressions of p53,PCNA and Ki67 in xenograft detected by immunohistochemistry;their mRNA expressions of PCNA,Ki67,Bcl-2,Bax,Caspase-3,PI3K,Akt,JNK and p38 detected by RT-qPCR;and their protein expressions of Bcl-2,Bax,Caspase-3,cleaved-Caspase-3,PI3K,p-PI3K,Akt,p-Akt,JNK,p-JNK,p38 MAPK and p-p38 MAPK in xenografts detected by Western blot.RESULTS Compared with the model group,the groups intervened with either gallic acid or adriamycin shared decreased tumor weight(P＜0.05);increased serum levels of IL-6 and TNF-α(P＜0.05,P＜0.01);condensed or irregular nuclei of xenograft,and increased number of cell necrosis;increased protein expressions of p53 and Bax and the ratios of cleaved-Caspase-3/Caspase-3,p-JNK/JNK and p-p38/p38 in xenograft(P＜0.01);decreased protein expressions of PCNA,Ki67 and Bcl-2 and the ratios of p-PI3K/PI3K and p-Akt/Akt(P＜0.01);decreased mRNA expressions of PCNA,Ki67,Bcl-2,PI3K and Akt(P＜0.05,P＜0.01);and increased mRNA expressions of Caspase-3,Bax,JNK and p38(P＜0.05,P＜0.01).CONCLUSION Gallic acid can inhibit HCC1806 triple-negative breast cancer xenograft growth in nude mice,and the underlying mechanism may involve suppression of pro-cell proliferation proteins,activation of pro-apoptotic proteins,and modulation of the PI3K/Akt and JNK/p38 signaling pathways.