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.

Neoadjuvant therapy has become the standard treatment for locally advanced resectable esophageal cancer, significantly improving long-term survival compared to surgery alone. Neoadjuvant therapy has evolved to include various strategies, such as concurrent chemoradiotherapy, chemotherapy, immunotherapy, or targeted combination therapy. This enriches clinical treatment options and provides a more personalized and scientific treatment approach for patients. This article aims to comprehensively summarize current academic research hot topics, review the rationale and evaluation measures of neoadjuvant therapy, discuss challenges in restaging methods after neoadjuvant therapy, and identify the advantages and disadvantages of various neoadjuvant therapeutic strategies.

Objective To design a training device of the flight crew for plateau normobaric low-oxygen acclimatization so as to enhance the flight crew's ability to adapt to the low oxygen environment after rushing into the plateau and reduce the incidence of acute plateau reaction.Methods The training device comprised a plateau environment simulation controller,a multimodal physiological acquisition system and hypoxia exercise training evaluation software.The plateau environment simulation controller was composed of an environment monitor for plateau acclimatization,two composite sensor sets,a control valve and an alarm device;the multimodal physiological acquisition system was made up of 20 groups of vital signs acquisi-tion devices,with a wearable dynamic ECG and respiration recorder,a wrist oximeter and an arm sphygmomano-meter included in each group.The hypoxia exercise training evaluation software was developed with a B/S architecture,Java language and JetBrains 2020.3.Results The training device proved to have the simulation altitude ranging from 0 to 6 000 m and facilitated simultaneous training of 20 persons for normobaric low-oxygen acclimatization,screening for hypoxia endurance,real-time monitoring of physiological parameters and assessment of training effect,with none of the trainees having acute plateau reaction.Conclusion The training device assists the flight crew for plateau normobaric low-oxygen acclimatization,and can be used for acclimatization training before plateau missions.[Chinese Medical Equipment Journal,2025,46(8):18-24]

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.

Objective To investigate the effects of hypoxia on the transcriptional phenotype and ultrastructure of tumor-associated macrophages(TAMs)in glioma.Methods CD14+monocytes were isolated from healthy human peripheral blood samples collected from the Blood Bank of the First Affiliated Hospital of Army Medical University,and the cells were induced to differentiate into TAMs through co-culture with glioma cell-conditioned medium.Hypoxic TAM models were established using varying concentrations of cobalt chloride hexahydrate(CoCl2,50～400 μmol/L)or hypoxic conditions(1%,5%,10%O2)for 48 h,while normoxic TAM models(21%O2)served as controls.RT-qPCR and transcriptome sequencing were employed to analyze transcriptional changes in TAMs under normoxic and hypoxic conditions.Gene set enrichment analysis(GSEA)was applied to compare the differences in angiogenesis,glycolysis and other hypoxia-responsive pathways between the 2 conditions.Transmission electron microscopy(TEM)or immunofluorescence staining was conducted to assess the ultrastructural alterations in cytoskeleton,endoplasmic reticulum(ER),and mitochondria in normoxic and hypoxic TAMs(1%O2).Results Hypoxic TAMs exhibited up-regulated transcription of hypoxia-responsive markers(oxygen transport,glycolysis,pro-angiogenesis),with the effects correlating with hypoxia severity(P<0.05).GSEA revealed significant up-regulation of hypoxia,angiogenesis regulation,glycolysis and gluconeogenesis,and starvation stress pathways,alongside down-regulation of innate immunity,macrophage activation,cytoskeleton,and protein maturation pathways in hypoxic TAMs(P<0.05).TEM and immunofluorescence staining demonstrated obvious ultrastructure changes,including disrupted cytoskeletal organization,shortened rough ER with reduced ribosomes,mitochondrial swelling with cristae damage,and diminished ER-mitochondria contacts in hypoxic TAMs.Conclusion CoCl2 and hypoxia induce a hypoxic transcriptional phenotype in TAMs,which may potentially associated with ultrastructural remodeling of the cytoskeleton,ER,and mitochondria.

A highly efficient oxidase-mimetic silver phosphate/nickel hydroxystannate(Ag3PO4/NiSn(OH)6)composite was synthesized via a precipitation method using nickel hydroxystannate(NiSn(OH)6)as the support.The abundant hydroxyl groups(—OH)on NiSn(OH)6 not only provided nucleation sites for Ag3PO4 nanoparticles but also improved their dispersion and overall material stability.Based on oxidase-like activity of Ag3PO4/NiSn(OH)6 and inhibitory effect of sodium dodecylbenzenesulfonate(SDBS)on this catalytic activity,a novel colorimetric sensing method for SDBS detection was developed.Under optimized experimental conditions,the method exhibited a linear range of 3.69-42.7 μmol/L,with a detection limit of 0.135 μmol/L(S/N=3).The regression equation was ΔA652=0.01125C(μmol/L)+0.1498,with a correlation coefficient(R2)of 0.992.Practical application in dishwashing liquid analysis achieved satisfactory recoveries of 96.9%-106.4%,demonstrating the method's reliability for real sample detection.

Diabetes is one of the top ten fatal diseases globally,and effective diabetes management can significantly reduce the incidence and progression of diabetes-related complications.Traditional blood glucose monitoring relies on fingertip blood sampling to measure glucose concentration,which requires multiple finger pricks per day.However,the long intervals between tests often result in missed hyperglycemic or hypoglycemic events.Therefore,there is an urgent need for non-invasive,continuous,and accurate glucose monitoring technologies to improve patient compliance and provide timely alerts for abnormal glucose levels.Sensors based on electrochemical detection methods,which indirectly estimate glucose levels by analyzing interstitial fluid,sweat,or other bodily fluids,have emerged as a promising direction due to their high sensitivity and low cost.This review focused on recent advancements in non-invasive,continuous glucose monitoring sensors developed using various electrochemical detection methods,with an in-depth analysis of chronoamperometry,impedance spectroscopy,and voltammetry in sensor applications.Finally,the challenges faced by current detection methods in non-invasive continuous glucose monitoring was summarized,and the future directions,including the integration of enzyme-free sensors with deep learning algorithms to enhance accuracy and reliability were proposed.

Objective:To evaluate the relationship between nuclear receptor subfamily 1 group D member 1 (Rev-erbα) and ferroptosis in cardiomyocytes subjected to high-fat/high-glucose (HFHG) and hypoxia-reoxygenation (H/R) injury.Methods:H9c2 cardiomyocytes were cultured under normal conditions. The cells were divided into 4 groups ( n=13 each) using a random number table method: control group (C group), H/R group, HFHG group and HFHG+ H/R1 group. The cells were divided into 3 groups ( n=17 each) using a random number table method: HFHG+ H/R2 group, negative control siRNA + HFHG + H/R group (si-NC+ HFHG+ H/R group), and Rev-erbα gene knockdown + HFHG + H/R group (si-Rev-erbα+ HFHG+ H/R group). The cardiomyocyte model of HFHG combined with H/R injury was established by incubating cells with HFHG medium for 12 h, followed by 6 h of hypoxia and 2 h of reoxygenation. Rev-erbα gene was knocked down using siRNA technology. Cell viability was assessed using CCK-8 and Calcein AM/PI live-dead cell double staining kits. The expression of Rev-erbα, acyl-CoA synthetase long-chain family member 4 (ACSL4), and nuclear receptor coactivator 4 (NCOA4) was detected by Western blot. The levels of lipid peroxide (LPO) were measured by flow cytometry. Results:Compared with C group, the cell viability was significantly decreased, and the expression of Rev-erbα, ACSL4 and NCOA4 was up-regulated in HFHG, H/R and HFHG+ H/R1 groups( P<0.05). Compared with HFHG group or H/R group, the cell viability was significantly decreased, and the expression of Rev-erbα, ACSL4 and NCOA4 was up-regulated in HFHG+ H/R1 group ( P<0.05).There were no significant differences in the cell viability, levels of LPO, or expression of Rev-erbα, ACSL4 and NCOA4 between HFHG+ H/R2 group and si-NC+ HFHG+ H/R group ( P>0.05). Compared with HFHG+ H/R2 group, the cell viability was significantly increased, the levels of LPO were decreased, and the expression of Rev-erbα, ACSL4 and NCOA4 was down-regulated in si-Rev-erbα+ HFHG+ H/R group ( P<0.05). Conclusions:Rev-erbα participates in the process of HFHG and H/R injury to cardiomyocytes by negatively regulating ferroptosis.