OBJECTIVE To study the pharmacokinetic characteristics of antidepressant active components from Jiaotai pills in healthy subjects. METHODS Eight healthy subjects （3 males and 5 females） were recruited and given a single oral dose of 8.55 g of Jiaotai pills. Venous blood samples were collected before administration （0 h） and at intervals from 0.25 to 36.0 hours post- administration. After treating the plasma samples with protein precipitation， the blood concentrations of the antidepressant active ingredients （coptisine， berberine， magnoflorine， and palmatine） in Jiaotai pills were determined using liquid chromatography- tandem mass spectrometry （LC-MS/MS） method. DAS 2.0 software was employed to calculate the pharmacokinetic parameters of healthy subjects [half-life （t1/2）， peak concentration （cmax）， time to peak concentration （tmax）， area under the concentration-time curve （AUC）， and mean residence time （MRT）] using a non-compartmental model. RESULTS After healthy subjects took Jiaotai pills， the drug-time curve of the four antidepressant active ingredients conforms to a two-compartment model and tmax values were similar， with all reaching peak blood concentrations within 2.00 to 4.00 hours post-administration. However， the t1/2 and MRT of coptisine and berberine were significantly longer than that of magnoflorine and palmatine. There were also significant differences in the AUC and cmax among the four antidepressant active ingredients， with magnoflorine exhibiting markedly higher AUC0-t and cmax compared to the other three components. CONCLUSIONS In this study，LC-MS/MS is used to analyze the pharmacokinetic characteristics of the antidepressant active ingredients from Jiaotai pills in healthy subjects， can provide valuable references for the clinical application of Jiaotai pills.

ObjectiveThis paper aims to investigate the effects of icariin on spermatogenesis in mice with exercise-induced fatigue and explore the underlying mechanisms. MethodsICR male mice were screened by swimming and randomly divided into normal group， model group， vitamin C group， icariin groups with low， medium， and high doses， and medium-dose icariin+N-nitro-L-arginine methyl ester （L-NAME） group， with 10 mice per group. Except for the normal group， all the other groups underwent weighted swimming training to establish an exercise-induced fatigue model. No gavage was administered during the first two weeks of the weighted training. From week three to four， the icariin groups with low， medium， and high doses received 0.03， 0.06， and 0.12 g·kg^-1 icariin via gavage， respectively. The vitamin C group received 0.2 g·kg^-1 vitamin C. The L-NAME group received 0.06 g·kg^-1 icariin and 0.01 g·kg^-1 L-NAME via intraperitoneal injection. The normal and model groups received equivalent physiological saline. After the experiment， body weight and the last exhaustive swimming time were recorded. Blood urea nitrogen （BUN）， lactate （LA）， lactate dehydrogenase （LDH）， malondialdehyde （MDA）， testicular testosterone （T）， testicular Ca²⁺/Mg²⁺-adenosine triphosphatase （ATPase） （micro-assay）， and the levels of testicular cyclic guanosine monophosphate （cGMP） were measured by using kits. Sperm CD46 levels were detected by flow cytometry. Testicular seminiferous tubules were observed via hematoxylin-eosin （HE） staining， and the testicular morphometric score （TMS） was used to evaluate the spermatogenic function. Protein expression of regucalcin （RGN， SMP30）， cGMP-dependent protein kinase 1 （PKG）， and cGMP-dependent protein kinase anchoring protein （GKAP1） was detected by Western blot. Testicular regucalcin expression was examined by immunofluorescence （IF）. The epididymal sperm quality of mice was observed under a microscope. Fluorescence-stained sections of stimulated by retinoic acid gene 8 （STRA8）， synaptonemal complex protein 3 （SCP3）， and transition protein 1（TNP1） in testicular seminiferous tubules were assessed by immunohistochemistry （IHC）. ResultsCompared with the normal group， the model group showed decreased body weight and exhaustive swimming time （P<0.01）， significantly increased fatigue markers （LA， LDH， and BUN） and lipid peroxidation product MDA （P<0.01）， reduced testicular RGN， PKG， GKAP1， testosterone， Ca²⁺/Mg²⁺-ATPase， and cGMP levels （P<0.01）， decreased sperm motility， sperm count， and TMS scores， and downregulated the expression of STRA8， SCP3， and TNP1. Compared with the model group， the icariin group with high dose exhibited increased exhaustive swimming time （P<0.01）， reduced LA， LDH， BUN， and MDA levels （P<0.01）， elevated superoxide dismutase （SOD） （P<0.01）， upregulated testicular RGN， PKG， GKAP1， testosterone， Ca²⁺/Mg²⁺-ATPase， and cGMP levels （P<0.01）， improved sperm motility， sperm count， and TMS scores， and enhanced STRA8， SCP3， and TNP1 expression. Compared with the L-NAME group， the icariin group with medium dose showed increased expression of STRA8， SCP3， and TNP1 in the testicular tissue （P<0.01） and elevated cGMP and GKAP1 levels （P<0.01）. ConclusionExercise-induced fatigue reduces the expression of RGN and cGMP/PKG/GKAP1 in mice， thereby causing abnormal spermatogenesis and impairing reproductive function in mice. Icariin ameliorates spermatogenic dysfunction in exercise-induced fatigue mice by promoting the expression of RGN and cGMP/PKG/GKAP1， thereby mitigating the damage of exercise-induced fatigue to the reproductive system.

ObjectiveThis paper aims to investigate the effects of icariin on spermatogenesis in mice with exercise-induced fatigue and explore the underlying mechanisms. MethodsICR male mice were screened by swimming and randomly divided into normal group， model group， vitamin C group， icariin groups with low， medium， and high doses， and medium-dose icariin+N-nitro-L-arginine methyl ester （L-NAME） group， with 10 mice per group. Except for the normal group， all the other groups underwent weighted swimming training to establish an exercise-induced fatigue model. No gavage was administered during the first two weeks of the weighted training. From week three to four， the icariin groups with low， medium， and high doses received 0.03， 0.06， and 0.12 g·kg^-1 icariin via gavage， respectively. The vitamin C group received 0.2 g·kg^-1 vitamin C. The L-NAME group received 0.06 g·kg^-1 icariin and 0.01 g·kg^-1 L-NAME via intraperitoneal injection. The normal and model groups received equivalent physiological saline. After the experiment， body weight and the last exhaustive swimming time were recorded. Blood urea nitrogen （BUN）， lactate （LA）， lactate dehydrogenase （LDH）， malondialdehyde （MDA）， testicular testosterone （T）， testicular Ca²⁺/Mg²⁺-adenosine triphosphatase （ATPase） （micro-assay）， and the levels of testicular cyclic guanosine monophosphate （cGMP） were measured by using kits. Sperm CD46 levels were detected by flow cytometry. Testicular seminiferous tubules were observed via hematoxylin-eosin （HE） staining， and the testicular morphometric score （TMS） was used to evaluate the spermatogenic function. Protein expression of regucalcin （RGN， SMP30）， cGMP-dependent protein kinase 1 （PKG）， and cGMP-dependent protein kinase anchoring protein （GKAP1） was detected by Western blot. Testicular regucalcin expression was examined by immunofluorescence （IF）. The epididymal sperm quality of mice was observed under a microscope. Fluorescence-stained sections of stimulated by retinoic acid gene 8 （STRA8）， synaptonemal complex protein 3 （SCP3）， and transition protein 1（TNP1） in testicular seminiferous tubules were assessed by immunohistochemistry （IHC）. ResultsCompared with the normal group， the model group showed decreased body weight and exhaustive swimming time （P<0.01）， significantly increased fatigue markers （LA， LDH， and BUN） and lipid peroxidation product MDA （P<0.01）， reduced testicular RGN， PKG， GKAP1， testosterone， Ca²⁺/Mg²⁺-ATPase， and cGMP levels （P<0.01）， decreased sperm motility， sperm count， and TMS scores， and downregulated the expression of STRA8， SCP3， and TNP1. Compared with the model group， the icariin group with high dose exhibited increased exhaustive swimming time （P<0.01）， reduced LA， LDH， BUN， and MDA levels （P<0.01）， elevated superoxide dismutase （SOD） （P<0.01）， upregulated testicular RGN， PKG， GKAP1， testosterone， Ca²⁺/Mg²⁺-ATPase， and cGMP levels （P<0.01）， improved sperm motility， sperm count， and TMS scores， and enhanced STRA8， SCP3， and TNP1 expression. Compared with the L-NAME group， the icariin group with medium dose showed increased expression of STRA8， SCP3， and TNP1 in the testicular tissue （P<0.01） and elevated cGMP and GKAP1 levels （P<0.01）. ConclusionExercise-induced fatigue reduces the expression of RGN and cGMP/PKG/GKAP1 in mice， thereby causing abnormal spermatogenesis and impairing reproductive function in mice. Icariin ameliorates spermatogenic dysfunction in exercise-induced fatigue mice by promoting the expression of RGN and cGMP/PKG/GKAP1， thereby mitigating the damage of exercise-induced fatigue to the reproductive system.

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.

ObjectiveTo analyze the distribution characteristics of traditional Chinese medicine （TCM） syndrome elements in different risk populations of heart failure complicated with type 2 diabetes. MethodsClinical data of 675 type 2 diabetes patients were retrospectively collected. Lasso-multivariate Logistic regression was used to construct a clinical prediction nomogram model. Based on this， 441 non-heart failure patients were divided into a low-risk group （325 cases） and a high-risk group （116 cases） according to the median risk score of heart failure complicated with type 2 diabetes. TCM diagnostic information （four diagnostic methods） was collected for both groups， and factor analysis was applied to summarize the distribution of TCM syndrome elements in different risk populations. ResultsLasso-multivariate Logistic regression analysis identified age， disease duration， coronary heart disease， old myocardial infarction， arrhythmia， absolute neutrophil count， activated partial thromboplastin time， and α-hydroxybutyrate dehydrogenase as independent risk factors for heart failure complicated with type 2 diabetes. These were used as final predictive factors to construct the nomogram model. Model validation results showed that the area under the curve （AUC） of the receiver operating characteristic （ROC） curve for the modeling group and validation group were 0.934 and 0.935， respectively. The Hosmer-Lemeshow test （modeling group P = 0.996， validation group P = 0.121） indicated good model discrimination. Decision curve analysis showed that the curves for All and None crossed in the upper right corner， indicating high clinical utility. The low-risk and high-risk groups each obtained 14 common factors. Preliminary analysis revealed that the main disease elements in the low-risk group were qi deficiency （175 cases， 53.85%）， dampness （118 cases， 36.31%）， and heat （118 cases， 36.31%）， with the primary locations in the spleen （125 cases， 38.46%） and lungs （99 cases， 30.46%）. In the high-risk group， the main disease elements were yang deficiency （73 cases， 62.93%）， blood stasis （68 cases， 58.62%）， and heat （49 cases， 42.24%）， with the primary locations in the kidney （84 cases， 72.41%） and heart （70 cases， 60.34%）. ConclusionThe overall disease characteristics in different risk populations of type 2 diabetes patients with heart failure are a combination of deficiency and excess， with deficiency being predominant. Deficiency and heat are present throughout. The low-risk population mainly shows qi deficiency with dampness and heat， related to the spleen and lungs. The high-risk population shows yang deficiency with blood stasis and heat， related to the kidneys and heart.

Cardiovascular diseases and psychological disorders represent two major threats to human physical and mental health. Research on electrocardiogram (ECG) signals offers valuable opportunities to address these issues. However, existing methods are constrained by limitations in understanding ECG features and transferring knowledge across tasks. To address these challenges, this study developed a multi-resolution feature encoding network based on residual networks, which effectively extracted local morphological features and global rhythm features of ECG signals, thereby enhancing feature representation. Furthermore, a model compression-based continual learning method was proposed, enabling the structured transfer of knowledge from simpler tasks to more complex ones, resulting in improved performance in downstream tasks. The multi-resolution learning model demonstrated superior or comparable performance to state-of-the-art algorithms across five datasets, including tasks such as ECG QRS complex detection, arrhythmia classification, and emotion classification. The continual learning method achieved significant improvements over conventional training approaches in cross-domain, cross-task, and incremental data scenarios. These results highlight the potential of the proposed method for effective cross-task knowledge transfer in ECG analysis and offer a new perspective for multi-task learning using ECG signals.
Humans ; Electrocardiography/methods* ; Mental Health ; Algorithms ; Signal Processing, Computer-Assisted ; Machine Learning ; Arrhythmias, Cardiac/diagnosis* ; Cardiovascular Diseases ; Neural Networks, Computer ; Mental Disorders

OBJECTIVE: To investigate the application of three-dimensional (3D) reconstruction technology in preoperative planning for anterolateral thigh flap transplantation. METHODS: A retrospective analysis was performed on the clinical data of 11 patients with skin and soft tissue defects treated with free anterolateral thigh flap transplantation between January 2022 and January 2024, who met the selection criteria. There were 8 males and 3 females, aged 34-70 years (mean, 50.8 years). Causes of injury included traffic accidents (4 cases), machine trauma (3 cases), heavy object crush injury (3 cases), and tumor (1 case). The time from injury to flap repair ranged from 7 to 35 days (mean, 23 days). Preoperatively, the patients' CT angiography images were imported into Mimics21.0 software. Through the software's segmentation, editing, and reconstruction functions, 3D visualization and measurement of the vascular pedicle, perforators, wound size, and morphology were performed to plan the flap harvest area, contour, vascular pedicle length, and anastomosis site, guiding the implementation of flap transplantation. RESULTS: The length of the vascular pedicle needed by the recipient site was (9.1±0.9) cm, and the maximum length of vascular pedicle in the donor area was (10.6±0.6) cm, with a significant difference ( t=4.230, P<0.001). The operation time ranged from 220 to 600 minutes (mean, 361.9 minutes). One patient had poor wound healing at the recipient site, which healed after dressing changes. All 11 flaps survived well without necrosis. All patients were followed up 6-19 months (mean, 11 months). Four flaps showed bulkiness and underwent secondary debulking; the remaining flaps had good contour and soft texture. The donor sites healed well, with no sensory disturbance around the incision or complications such as walking impairment. CONCLUSION Preoperative planning using CT angiography data and 3D reconstruction software can effectively determine the flap area, contour, required vascular pedicle length, anastomosis site, and whether vascular grafting is needed, thereby guiding the successful execution of anterolateral thigh flap transplantation.
Humans ; Middle Aged ; Male ; Female ; Adult ; Thigh/diagnostic imaging* ; Aged ; Plastic Surgery Procedures/methods* ; Retrospective Studies ; Imaging, Three-Dimensional/methods* ; Soft Tissue Injuries/surgery* ; Surgical Flaps ; Computed Tomography Angiography ; Free Tissue Flaps/blood supply* ; Preoperative Care

A 72-year-old patient with quadricuspid aortic valve underwent transcatheter aortic valve replacement due to severe valve stenosis accompanied by moderate insufficiency. As initially planned, the right coronary artery was protected during the procedure. However, after the artificial valve was released, the left coronary artery was found to be blocked, so a coronary protection stent was implanted in the left coronary artery ostium under the guidance of intravascular ultrasonography. This case indicates that for patients with a quadricuspid aortic valve undergoing transcatheter aortic valve replacement, in addition to preoperative measurement of the aortic root, attention should also be paid to the coronary artery obstruction caused by the displacement of the artificial valve frame during the procedure.
Aged ; Humans ; Aortic Valve/surgery* ; Aortic Valve Stenosis/surgery* ; Coronary Vessels ; Stents ; Transcatheter Aortic Valve Replacement/methods*