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: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 therapeutic efficacy of artesunate(AS)on polycystic ovary syndrome(PCOS)in mice and explore the potential mechanism primarily.Methods Twenty-five female C57BL/6J mice were randomly divided into Control group,model group(PCOS group),low-and high-dose AS groups(AS15 and AS30 groups)and metformin group(Met group).In addition to the Control group,the mouse model of PCOS was established by subcutaneous injection of dehydroepiandrosterone(DHEA,60 mg/kg)following by a high-fat diet for 21 d.After modeling,AS of 15 and 30 mg/kg was intraperitoneally injected into the mice of the AS 15 and AS30 groups,respectively,and 200 mg/kg Met was given to those of the Met group by gavage,once per day,for 6 weeks.ELISA was used to detect serum testosterone(T),fasting insulin(FINS),luteinizing hormone(LH)and follicle-stimulating hormone(FSH),and the LH/FSH ratio was calculated.The levels of fasting blood glucose(FBG),triglyceride(TG)and total cholesterol(TC)were detected by automatic biochemical analyzer,and the homeostasis model assessment of insulin resistance(HOMA-IR)was calculated.The estrous cycle was observed,and HE staining was performed for pathological changes in the ovary and uterus.Immunofluorescence assay was employed to measure the expression of p-eIF2α,ATF4 and CHOP in the ovarian tissue.After steroidogenic human granulosa-like tumor cell line KGN were exposed to 100 μmol/L DHEA to simulate the hyperandrogen environment of PCOS,and then treated with 5 and 10 μg/mL AS for 24 h,the protein levels of endoplasmic reticulum stress signaling pathway was detected by Western blotting.Results Compared with the Control group,the PCOS mice had disturbed estrous cycle,polycystic changes in the ovaries,and significantly increased serum T level and LH/FSH ratio(P＜0.05),and obviously elevated HOMA-IR,TC and TG levels in terms of metabolism(P＜0.01).The expression levels of p-eIF2α,ATF4 and CHOP were notably up-regulated in the ovarian granulosa cells of PCOS mice and KGN cells after DHEA exposure(P＜0.05).Additionally,AS treatment attenuated the pathological changes of ovary and uterine expression,decreased the serum T level and the LH/FSH ratio(P＜0.05),and reduced HOMA-IR,TC and TG levels(P＜0.05)when compared with the PCOS mice.Moreover,the expression levels of p-eIF2α,ATF4 and CHOP were significantly down-regulated after AS treatment in both ovarian granulosa cells of PCOS mice and KGN cells(P＜0.05).Conclusion AS significantly improves glycolipid metabolic disorder and reproductive dysfunction in PCOS mice,which may be associated with its suppressing endoplasmic reticulum stress by inhibiting the PERK/eIF2α/ATF4/CHOP pathway.

Objective To compare the midterm outcomes of the Bentall procedure versus isolated aortic valve replacement(AVR)in patients with bicuspid aortic valve(BAV)complicated with severe stenosis and ascending aortic dilation in order to assess the therapeutic value of these surgical approaches for this complex cardiac condition.Methods A retrospective cohort study was conducted on 96 eligible patients who underwent surgical treatment in our institute between January 2018 and December 2022.According to surgical approaches,they were divided into an AVR group(65 cases)and a Bentall group(31 cases).Demographic features,comorbidities,preoperative status,and echocardiographic parameters were collected in all patients.Propensity score matching(PSM)was applied in a 1:1 ratio to balance baseline characteristics.Perioperative indicators and follow-up data were compared and analyzed between matched cohorts after control of cofounding factors.Results After PSM,25 matched pairs were screened out and analyzed with comparable baseline data(all P>0.05).The Bentall group demonstrated significantly more superior intraoperative effective orifice area(EOA,2.69±0.47 vs 2.35±0.47 cm2,P=0.013)and EOA index(EOAI,1.69±0.30 vs 1.47±0.29 cm2/m2,P=0.010),and obviously longer cardiopulmonary bypass time[190.00(147.00,257.00)vs 101.00(88.50,124.50)min,P<0.01]and aortic cross-clamp time[141.00(120.00,166.00)vs 66.00(55.00,81.50)min,P<0.01]when compared with the AVR group.During a median follow-up of 28 months,the AVR group had notably larger aortic sinus diameter[32.00(30.00,34.00)vs 26.80(26.00,28.00)mm,P<0.01]and ascending aortic diameter[38.00(34.50,42.00)mm vs 26.00(26.00,28.00)mm,P<0.01],with ongoing dilation in the ascending aorta,while the Bentall group maintained stable dimensions.The Bentall group also showed statistically lower peak aortic valve pressure gradients[21.00(15.50,27.00)vs 25.00(19.50,31.00)mmHg,P=0.049].Conclusion Both Bentall procedure and AVR are effective in treatment of BAV complicated with severe stenosis and ascending aortic dilation.But,Bentall procedure offers advantages in hemodynamic optimization and aortic stability.

Objective To investigate the effect of ubiquitin-specific peptidase(USP)44 and nuclear receptor co-inhibitor 1(NCOR1)expression on prognosis in non-small cell lung cancer.Methods A total of 98 pa-tients with non-small cell lung cancer admitted to a hospital from May 2019 to May 2021 were selected as the study objects,and non-small cell lung cancer tissues and adjacent tissues were collected to detect the expres-sion levels of USP44 and NCOR1 in these tissues by immunohistochemical staining.The relationship between USP44 and NCOR1 expression and pathological features of non-small cell lung cancer patients was analyzed,and the prognostic factors of non-small cell lung cancer patients were analyzed by multivariate Cox regression.Results The positive expression rates of USP44 and NCOR1 in non-small cell lung cancer tissues were higher than those in adjacent tissues,the difference was statistically significant(P<0.05).The positive expression rates of USP44 and NCOR1 in patients with medium-low differentiation,lymph node metastasis,clinical stageⅢ to Ⅳ,and pleural metastasis were higher than those in patients with highly differentiated,no lymph node metastasis,clinical stage Ⅰ to Ⅱ,and no pleural metastasis,and the difference was statistically significant(P<0.05).The 3-year overall survival rate of USP44 and NCOR1 negative non-small cell lung cancer patients was higher than that of USP44 and NCOR1 positive non-small cell lung cancer patients,and the difference was statistically significant(all P<0.05).Multivariate Cox regression analysis showed that pleural metastasis,USP44 positive and NCOR1 positive were prognostic factors in non-small cell lung cancer patients(P<0.05).Conclusion The expression of USP44 and NCOR1 in patients with non-small cell lung cancer can be used as biomarkers for prognosis assessment,and provide evidence for progression assessment and clinical de-cision making of non-small cell lung cancer.

In recent years, a variety of innovative fast algorithm techniques have emerged in the field of transcranial magnetic stimulation (TMS) electric field solving, which show great potential to meet the requirements of real-time clinical applications. In this review, the crucial processes of TMS electric field modeling were summarized, focusing on two prominent fast algorithm techniques, namely the basis function method and the deep neural network (DNN)-based method. The advantages and limitations of these two techniques were analyzed in detail. Commonly used software tools for electric field modeling were discussed, and a prospective discussion of future developments was offered, aiming to provide a reference for the further development of TMS electric field modeling technology.

Alzheimer′s disease (AD) is a degenerative disorder of the central nervous system. In the early stages of AD, i.e. when cognitive impairment is mild or absent but biomarkers are present, patients show abnormal neuroelectric activity. Repetitive transcranial magnetic stimulation (rTMS) is an important method of non-invasively regulating neuroelectric activity in the brain. It does so by inducing microcurrents to change the membrane potential of nerve cells in the brain based on electromagnetic induction. In this review, the characteristics of neuroelectric activity of AD patients and AD model mice in the early stage of AD were mainly introduced, and the regulation of rTMS on the neuroelectric activity of early AD was discussed.

Objective:To explore the diagnosis and treatment strategy of emphysematous pyelonephritis (EPN).Methods:The clinical data of 11 cases patients with EPN admitted to 3 hospitals from March 2016 to June 2022 were retrospectively analyzed, among them, 4 cases from Qianhai Shekou Free Trade Zone Hospital, 5 cases from the Second Hospital Affiliated to Kunming Medical University, 2 cases from the Second Affiliated Hospital of Hubei University of Scinece and Technology. Among the 11 patients, 2 were males and 9 were females, aged 50-82 years; the lesions were located on the left side in 6 cases, right side in 4 cases and bilateral in 1 case; all patients had type 2 diabetes and poor glycemic control. The clinical manifestations at admission including back pain in 8 cases, fever in 11 cases, nausea and vomiting in 5 cases, disturbance of consciousness in 3 cases, septic shock in 3 cases, accompanied with ureteral or kidney stones in 5 cases. The pathogenic bacteria were Escherichia coli in 8 cases, Klebsiella pneumoniae in 2 cases and Proteus mirabilis in 1 case. All patients received minimally invasive surgery, anti-infection, subcutaneous injection of insulin, fluid rehydration and nutritional support after admission. 2 cases had a combination of an initial ureteral stenting and second stage percutaneous drainage, 3 patients underwent ureteral stent implantation, 6 patients underwent percutaneous drainage. According to the CT classification of EPN, there were 1 case of type Ⅰ, 3 cases of type Ⅱ, 2 cases of type ⅢA, 4 cases of type ⅢB, and 1 case of type Ⅳ. Results:All 11 cases were cured, 4 cases were admitted to intensive care unit for 2-7 days, 1 case underwent nephrectomy during hospitalization, and 1 case underwent nephrectomy due to renal atrophy during follow-up. After 12 to 18 months of follow-up with urinary CT or B-ultrasound, there were no recurrence cases.Conclusions:EPN is a rare and serious renal parenchymal necrotic infection. Early urinary CT examination is necessary for the diagnosis, and positive minimally invasive surgery combined with comprehensive medical treatment is the preferred treatment strategy. If those above treatment does not work, nephrectomy should be performed.

Objective:To evaluate the diagnostic value of non-targeted detection of metabolic fingerprinting in pulmonary nodules and to analyze the clinical effective model of multi-omics for assessing the malignant risk of pulmonary nodules.Methods:A total of 73 patients who underwent chest CT and completed pathological diagnosis and non-targeted detection of metabolic fingerprinting at Shandong Provincial Hospital Affiliated to Shandong First Medical University from November 2021 to October 2024 were selected as the research subjects. According to the postoperative histopathological diagnosis, the patients were divided into the lung malignant nodule group (61 cases) and the lung benign nodule group (12 cases). General clinical data of the patients, including sex, age, smoking history, and family history of tumors, as well as imaging data, including nodule density, nodule size, nodule location, nodule number, and special imaging manifestations (spiculation, lobulation, vacuole sign, vascular convergence sign, etc.), and non-targeted detection of metabolic fingerprinting results were collected. The above data were compared between the two groups of patients, and the receiver operator characteristic (ROC) curve was drawn to evaluate the predictive value of each model. Results:There were statistically significant differences in age ( t=4.41, P<0.001), nodule size ( Z=2.67, P=0.008), nodule density ( χ2=4.64, P=0.031), and spiculation ( χ2=7.67, P=0.006) between the lung malignant nodule group and the lung benign nodule group. There were no statistically significant differences in sex, smoking history, family history of lung cancer, nodule number, nodule location, lobulation, vacuole sign, vascular convergence sign, pleural indentation sign, calcification sign, bronchial truncation sign, vascular supply sign, and bronchial air sign (all P>0.05). The number of non-targeted detection of metabolic fingerprinting high-risk patients in the lung malignant nodule group (36 cases) was significantly higher than that in the lung benign nodule group (0 case) ( χ2=13.97, P<0.001). ROC curve analysis showed that the area under the curve of the Brock model combined with non-targeted detection of metabolic fingerprinting was 0.930 (95% CI: 0.872-0.988), which was greater than that of the Brock model (0.856, 95% CI: 0.769-0.942, Z=0.27, P=0.040) and non-targeted detection of metabolic fingerprinting (0.768, 95% CI: 0.650-0.887, Z=0.30, P=0.004) alone. Conclusions:Non-targeted detection of metabolic fingerprinting risk assessment may serve as a non-invasive method to assist the Brock model in the diagnosis of pulmonary nodules and has good application value. The combination of the Brock model and non-targeted detection of metabolic fingerprinting can more accurately distinguish the benign and malignant nature of pulmonary nodules.