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 effect of remote ischemic preconditioning (RIPC) on preoperative heart rate variability in patients with heart valves. Methods Patients scheduled to undergo on-pump cardiac valve surgery in the Department of Cardiovascular Surgery, General Hospital of Northern Theater Command, between January and July 2022 were initially enrolled. Eligible patients were randomly assigned at a 1 : 1 ratio to either the RIPC group or the control group. Relevant indicators of heart rate variability [standard deviation of NN interval (SDNN), standard deviation of mean value of NN interval in every five minutes (SDANN), mean square root of difference between consecutive NN intervals (RMSSD), percentage of adjacent RR interval>50 ms (PNN50), low frequency (LF) component, high frequency (HF) component and LF/HF] at 8 hours in the morning on the surgical day between two groups were compared. Results A total of 118 patients were initially assessed. After screening, 58 patients were excluded, and 60 patients provided written informed consent and were enrolled in the trial, with 30 allocated to the RIPC group and 30 to the control group. Seven patients in the control group and 5 patients in the RIPC group were subsequently excluded due to missing heart rate variability data resulting from cancelled operations. Finally, 23 patients in the control group and 25 patients in the RIPC group were included in the analysis. There was no statistical difference in baseline characteristics between the two groups, and there was no significant difference in heart rate variability 24 hours before intervention (P>0.05). After the intervention measures were taken, the comparison of the results of heart rate variability at 8 hours on the day of operation showed that SDNN and SDANN of patients in the RIPC group were higher than those in the control group, with statistical differences (P<0.05). Conclusion RIPC can stabilize the preoperative heart rate variability of patients undergoing cardiac valve surgery.

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 investigate the causes and management strategies for lower limb ischemic necrosis following xenogeneic heterotopic heart transplantation from a multigene-edited pig to a rhesus monkey. Methods A xenogeneic heterotopic heart transplantation was performed on December 16, 2023, at the Institute of Experimental Animals of Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, using a quintuple-gene-edited pig as the donor and a rhesus monkey as the recipient. On postoperative day (POD) 9, the recipient monkey underwent left lower limb amputation due to ischemic necrosis. Blood samples were collected at various time points after transplantation for analysis of hematologic parameters, liver and renal function, myocardial enzymes, and coagulation profiles. Ultrasound and computed tomography (CT) were used to evaluate anastomotic patency and cardiac structure. Immunological assays, including complement-dependent cytotoxicity (CDC) and IgG/IgM antibody detection, combined with clinical observations, were employed to assess rejection type and therapeutic response. Results The recipient monkey survived for 46 days after transplantation. Echocardiography demonstrated preserved biventricular systolic function in the recipient’s native heart, with left ventricular ejection fraction (LVEF) consistently exceeding 50%. In the donor pig heart, left ventricular endocardial thickening was noted on POD 9, followed by right ventricular endocardial thickening on POD 24, while LVEF remained around 35%. No hyperacute or acute rejection was detected immunologically. CDC positivity ranged between 3.4% and 5.1%, with IgG/IgM antibody binding trends consistent with CDC results. Following amputation, the recipient exhibited elevated inflammatory markers, coagulopathy, and reactive thrombocytosis, which later normalized. Immunohistochemical staining of the necrotic limb revealed arterial and venous thrombosis; however, no T-cell or B-cell infiltration was observed in vascular structures, thrombi, nerves, muscles, fascia, or skin tissues, with CD3 and CD20 staining both negative. Conclusion Limb ischemia after xenogeneic heart transplantation may be associated with lower extremity vascular thrombosis triggered by local trauma in the context of transplantation-induced inflammatory activation and coagulation dysfunction. While no clear lymphocyte-mediated rejection was observed, further studies are needed to explore the potential role of non-lymphocyte-mediated immune mechanisms.

Objective We improved the segmentation model TransUNet based on deep learning methods to construct a multitask model that can both segment regions of interest and predict classification,which made it possible to identify sensitive populations of patients undergoing neoadjuvant therapy for rectal cancer.Methods The multitask model added classification structure on the basis of TransUNet,including the fully connected layer(input of 512,output of 256),the ReLU activation function,and the fully connected layer(input of 256,output of 3),to achieve the prediction of triple classification outcomes(stable disease(SD),partial response(PD),and complete response(CR)).The 3D MRI of 71 rectal cancer patients before neoadjuvant chemotherapy admitted to the Harbin Medical University Cancer Hospital from 2015 to 2017 were extracted into 2D images as data for the study.Dice coefficient and Hausdorff distance were used to evaluate thesegmentation performance,and accuracy,micro-precision,micro-recall,and micro-F1 score were used for classification performance.Results The model was trained for 100 epochs,and the average Dice coefficient and average Hausdorff distance for the segmentation task on the test set were 0.851 and 10.806,respectively.For the classification task,the accuracy,micro-precision,micro-recall,and micro-F1 score on the test set were all 0.651 from the slicing perspective,and all four metrics were 0.857 from the patient perspective.Conclusion Our model works well in the segmentation task.Although the model performs poorly on the classification task at the slice level,the performance was acceptable at the patient level taking into account the tripartite classification results.The multitask model has the potential to be used in the clinic for assisted diagnosis.

Cerebral ischemia/reperfusion injury(CIRI)is a pathophysiological process affecting the prognosis of patients with acute ischemic stroke(AIS).Its mechanism is complex and remains unclear.Long non-coding RNA(LncRNA)are a class of non-coding RNA(ncRNA).Early studies of LncRNA focused on their relationship with tumor-related diseases,but recent studies have found that they are also closely related to the pathological process of CIRI.LncRNA participate in the damage and repair processes of CIRI by affecting oxidative stress,autophagy,and apoptosis of the nervous system,as well as the inflammatory response and other mechanisms.They can regulate the progression of CIRI in a positive or negative way,and they play an important role in the related signaling pathways.This review focuses on the mechanisms bv which LncRNA regulate CIRI.

Perception of recurrence risk in cardiovascular disease (CVD) patients plays a significant role in aspects such as their quality of life and treatment adherence. This paper reviews the theoretical foundations of recurrence risk perception, the conceptual origins and developmental process, measurement tools, influencing factors of recurrence risk perception in CVD patients, and research progress of recurrence risk perception in CVD management. The aim is to provide a basis for developing scientifically effective intervention measures for CVD patients in the future.

Objective To evaluate the clinical efficacy of hip-knee-ankle active and passive exercise therapy in patients with early-to mid-stage knee osteoarthritis(KOA).Methods A total of 180 patients with early to mid-stage knee osteoarthritis(KOA)were recruited from the First Affiliated Hospital of Hebei University of Tradi-tional Chinese Medicine between March 2023 and March 2024.Patients were randomly assigned to one of four groups:active movement group,passive movement group,combined movement group,and control group,with 45 patients in each group.The active movement group received hip-knee-ankle active movement therapy daily until the end of follow-up.The passive movement group underwent hip-knee-ankle passive movement therapy three times per week for two weeks.The combined movement group received both active and passive therapies.The control group was administered oral celecoxib capsules(200 mg once daily for two weeks).Joint function was assessed in all four groups before treatment,at two weeks post-treatment,and at 14 weeks post-treatment.The primary outcome measure was the WOMAC joint function score,while secondary outcomes included the WOMAC pain score,stiffness score,and quality of life score(SF-12).Results A total of 160 patients completed the trial,with 39 in the active group,42 in the passive group,40 in the combined group,and 39 in the control group.There were no significant differences in baseline characteristics among the groups(P>0.05).Compared to baseline,the WOMAC scores for function,pain,and stiffness in the passive,combined,and control groups decreased significantly at both 2 and 14 weeks post-treatment(P<0.05),while the SF-12 scores increased significantly(P<0.05).Between 2 and 14 weeks post-treat-ment,the active and combined groups showed further significant decreases in WOMAC function,pain,and stiffness scores(P<0.05)and increases in SF-12 scores(P<0.05).At 2 weeks post-treatment,compared to the control group,the passive and combined groups exhibited significantly lower WOMAC function scores(P<0.05),with no significant difference between the passive and combined groups(P>0.05).By 14 weeks post-treatment,the active and combined groups demonstrated significantly lower WOMAC function scores(P<0.05),with the combined group showing a significantly lower score than the active group(P<0.05).Conclusion The four therapeutic approaches demonstrate a certain degree of efficacy in improving joint function for patients with early and mid-stage KOA.The passive therapy group exhibits superior short-term outcomes,while the active therapy group shows better long-term benefits.The combined therapy group presents notable advantages in both short-term and long-term effi-cacy,although its short-term effectiveness does not surpass that of the passive therapy group.It is recommended for patients with early and mid-stage KOA who have underlying gastrointestinal and cardiovascular conditions.

Objective This study aimed to investigate the regulatory effect of electroacupuncture(EA)intervention on the protein kinase B(Akt)/mammalian target of rapamycin(mTOR)/hypoxia-inducible factor 1α(HIF-1α)signaling pathway in the hippocampal tissue of Alzheimer's disease(AD)model mice and its effect on astrocytic glycolytic function,further exploring how EA ameliorates AD-related cognitive impairment.Methods Eighteen APP/PS1 mice were randomly divided into model,EA,and sham EA groups(n=6)using the random number table method.Six wild-type C57BL/6J mice served as the control group.The EA group received EA stimulation at acupoints"Shenshu"(BL23),"Baihui"(GV20),and"Zusanli"(ST36)(administered every other day,20 min per session,for 4 weeks).The sham EA group received identical needle insertions at the same acupoints without electrical stimulation.The control and model groups were only restrained.Cognitive function was assessed using the Morris water maze and Y-maze spontaneous alternation tests.Hippocampal morphology was observed via hematoxylin and eosin staining.Hippocampal β-amyloid peptide 1-42(Aβ1-42)deposition was detected using immunohistochemistry.HIF-1α protein expression,the p-Akt/Akt,and p-mTOR/mTOR ratios were measured using Western blotting.Pyruvate kinase M2(PKM2)and lactate dehydrogenase A(LDHA)activities were quantified using enzyme-linked immunosorbent assay.Hexokinase(HK)activity and L-lactate content were determined using a colorimetric assay.Co-localization of LDHA with the astrocyte marker glial fibrillary acidic protein was quantitatively analyzed using immunofluorescence double-labeling combined with Pearson's correlation coefficient.Results Compared with the control group,the model group mice exhibited cognitive decline,as shown by prolonged escape latency(P<0.01),reduced number of platform crossings,lower time spent in the target quadrant,and decreased spontaneous alternation accuracy(P<0.01).The hippocampal neurons showed cell body swelling,deeper nuclear staining,enlarged intercellular spaces,and increased average optical density of Aβ1-42(P<0.01).The p-Akt/Akt and p-mTOR/mTOR ratios,as well as HIF-1α protein expression,were elevated(P<0.01).PKM2,LDHA,HK,and L-lactic acid levels were significantly increased(P<0.01),and the co-localization coefficient of LDHA with astrocytes was enhanced.Compared to the model group,the EA group of mice showed improved cognitive function.The hippocampal neurons had more intact structures,with a more uniform cell distribution.The average optical density of Aβ1-42 decreased(P<0.01),and the p-Akt/Akt and p-mTOR/mTOR ratios,as well as HIF-1α protein expression,decreased(P<0.01).PKM2,LDHA,HK,and L-lactic acid levels decreased(P<0.05),and the co-localization coefficient of LDHA with astrocytes significantly decreased(P<0.01).No significant improvement was observed in any of the indicators in the sham EA group compared with the EA group.Conclusion EA at"Shenshu"(BL23),"Baihui"(GV20),and"Zusanli"(ST36)ameliorates cognitive dysfunction in AD model mice.The underlying mechanism may involve suppressing the overactivation of the hippocampal Akt/mTOR/HIF-1α signaling pathway,thereby downregulating key glycolytic enzyme activities and reducing abnormal lactate accumulation.Furthermore,the astrocytic glycolytic metabolic pathway may constitute a key therapeutic target for this intervention.

Objective To explore the effect of young plasma intraperitoneal injection on the fertility and ovarian function of aging mice and analyze its potential molecular mechanism.Methods Fifty-four-week-old female C57BL/6 mice and 8-week-old male C57BL/6 mice were selected.Among them,the female mice were randomly divided into three groups:the young plasma group,the aging plasma group and the normal saline group.The young plasma group and the aging plasma group received intraperitoneal injection of plasma from young(25-29 years old)and elderly(45-49 years old)female donors,respectively.Each injection was 500 μL,administered every other day for 2 weeks.The saline group received an equal volume of saline.After the last injection,mating experiments were conducted to evaluate fertility.Ovarian histopathological changes were observed by HE staining.Oocytes and fertilized eggs were collected after superovulation and cultured in vitro to assess oocyte quality and embryo developmental potential.Transcriptomic analysis of ovarian tissue was performed,followed by KEGG and GO enrichment analysis.Results Compared with the normal saline group and the aging plasma group,the number of offspring increased in the young plasma group,which reflected higher extrusion rate of first polar body(PB1),decreased fragmentation rate of oocytes and increased conversion rate of two-cell embryos and increased formation rate of blastocysts.There were no significant differences in these indicators between the aging plasma group and the normal saline group.Transcriptomic sequencing revealed that the differentially expressed genes in ovarian tissue of the young plasma group were mainly involved in steroid biosynthesis and metabolic pathways.Among them,the expression level of steroid sulfatase protein was significantly upregulated.Conclusion Systemic infusion of young plasma enhances the reproductive potential of aging ovaries in elderly mice.The sulfated steroid metabolites in plasma may be key substances in restoring ovarian function and delaying the process of ovarian aging.