Objective To evaluate factors influencing discharge readiness of coronary heart disease after percutaneous coronary intervention(PCI).Methods Studies on factors affecting the discharge preparation of patients with coronary heart disease PCI were searched in English and Chinese databases from database construction to November 2024.Two researchers independently screened articles,extracted data,and assessed quality.A total of 15 articles involving 3019 patients were included,identifying 19 influencing factors.Meta-analysis was performed.Results Meta-analysis showed that support level,discharge guidance quality,actual content obtained,age,and medication types were significant factors(P<0.05).Conclusion Healthcare professionals can use these factors to identify patients with low discharge readiness and implement early interventions to support recovery.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

Objective To evaluate factors influencing discharge readiness of coronary heart disease after percutaneous coronary intervention(PCI).Methods Studies on factors affecting the discharge preparation of patients with coronary heart disease PCI were searched in English and Chinese databases from database construction to November 2024.Two researchers independently screened articles,extracted data,and assessed quality.A total of 15 articles involving 3019 patients were included,identifying 19 influencing factors.Meta-analysis was performed.Results Meta-analysis showed that support level,discharge guidance quality,actual content obtained,age,and medication types were significant factors(P<0.05).Conclusion Healthcare professionals can use these factors to identify patients with low discharge readiness and implement early interventions to support recovery.

Objective To observe the effect of Baduanjin exercise on depression,sleep quality and life quality of patients with breast cancer in the rehabilitation period.Methods A total of 76 breast can-cer patients in postoperative rehabilitation were randomly divided into an intervention group of 38 with 2 dropping out,and a control group of 38 with 3 dropouts.Both groups received routine nursing and rehabilitation after breast cancer surgery,while the intervention group additionally practised Baduanjin for 6 weeks.The Beck Depression Inventory-Ⅱ(BDI-Ⅱ-C),Pittsburgh Sleep Quality Index(PSQI)and Quality of Life Questionnaire Core 30(EORTC QLQ-C30)were used to evaluate both groups be-fore,as well as 3 and 6 weeks after intervention.Results After 3-week intervention,the average BDI-Ⅱ-C score,the total PSQI score and the scores of all dimensions except for the hypnotic drug dimen-sion of the intervention group was significantly lower than before treatment(P<0.05),and the control group at the same time point(P<0.05),while the scores of physical,emotional,cognitive,social and role function in EORTC QLQ-C30 were significantly higher than the latter(P<0.05).Three weeks lat-er,the average BDI-Ⅱ-C score,the total PSQI score and the scores of all dimensions except for the hypnotic drug dimension of the intervention group was significantly lower than before treatment(P<0.05),and the control group at the same time point(P<0.05),while the various scores of EORTC QLQ-C30 were significantly higher than the latter(P<0.05).Compared with after 3-week intervention,after 6-week intervention,the average BDI-Ⅱ-C score,the total PSQI score and the scores of its all dimensions except for the sleep disorder dimension of the intervention group decreased significantly,while all dimension scores of EORTC QLQ-C30 except the cognitive function dimension increased sig-nificantly(P<0.05 for all).Conclusion Baduanjin is feasible in improving the sleep and life quality of patients in the rehabilitation period after breast cancer surgery,and relieving their depression.

AIM:To investigate the effects of exercise preconditioning(EP)combined with electroacupunc-ture(EA)on learning and memory ability of rats with vascular dementia(VD),and to explore role of hippocampal ferrop-tosis in this process.METHODS:Seventy-two male SD rats were randomly divided into non-EP group and EP group,with 36 rats in each group.The rats were subjected to EP,and subsequently to establish the VD model.The rats from non-EP group were randomly divided into sham group,model group(VD group)and VD-EA group,each with 12 rats,while those in EP group were randomly divided into EP-sham group,EP-VD group and EP-VD-EA group,each with 12 rats.All rats in EP group underwent 4 weeks of swimming exercise training,5 d per week,30 min per day.At the end of the 4th week,the rats in VD,EP-VD,EP-VD-EA and VD-EA groups were used to induce the VD model,and the rats in sham and EP-sham groups received a sham surgery to simulate the VD model.On the 7th day after successful modeling,the rats in EP-VD-EA and VD-EA groups were treated with EA for 4 weeks,6 d per week,30 min per day.At the end of the inter-vention,the learning and memory ability of the rats was evaluated using Morris water maze.Neuron morphology in the CA1 area of rat hippocampus was observed through Nissl staining.Ferrous ion(Fe2+),malondialdehyde(MDA)and re-duced glutathione(GSH)contents in the rat hippocampal tissues were quantified using the colorimetric assay.The expres-sion levels of ferroptosis-related proteins,nuclear factor E2-related factor 2(Nrf2)and glutathione peroxidase 4(GPX4),in the hippocampal tissues were quantified by Western blot method.RESULTS:Compared with sham group,the rats in VD group exhibited longer mean evasion latency and decreased number of traversals across the plateau(P<0.01).The neurons in the CA1 region of the hippocampus were loose and disorganized,exhibiting an irregular cellular morphology.The hippocampal Fe2+and MDA content was elevated,and the GSH content was reduced(P<0.01).The protein levels of hippocampal Nrf2 and GPX4 were decreased(P<0.01).Compared with VD group,the rats in EP-VD,EP-VD-EA and VD-EA groups showed a shorter average escape latency and an increased number of traversals across the plateau(P<0.05).Neurons in the hippocampal CA1 area were more neatly arranged,showing regular cellular morphology.The hip-pocampal Fe2+and MDA contents of the rats in EP-VD group were significantly reduced(P<0.01),while the GSH content was elevated(P<0.05).Hippocampal Fe2+and MDA contents were significantly reduced and GSH contents were signifi-cantly increased in EP-EA and EA groups(P<0.01).The protein levels of hippocampal Nrf2 and GPX4 in EP-VD,EP-VD-EA and VD-EA groups were significantly increased(P<0.01).CONCLUSION:Exercise preconditioning combined with EA improves learning and memory ability in VD rats by reducing hippocampal intra-neuronal iron overload,maintain-ing organismal redox homeostasis,and inhibiting ferroptosis.

Diabetes mellitus is a global epidemic characterized by high incidence and mortality rates. It can lead to both acute and chronic systemic complications. Diabetes-induced male reproductive disorders have become a focal point of concern. Numerous studies have shown that hyperglycemia can impair male fertility through various pathways, including erectile and ejaculatory dysfunction, disruption of the hypothalamic-pituitary-gonadal axis, damage to the epididymis and testis, impaired semen quality, abnormalities in testicular and sperm glucose metabolism, and adverse effects on offspring health. The mechanisms involved include oxidative stress, inflammation, autophagy, apoptosis, epigenetic regulation, and mitochondrial damage. Interventions for male fertility impairment include diabetes treatment drugs, antioxidants, Traditional Chinese Medicine and acupuncture, mesenchymal stem cell therapy, exercise and physical training, as well as gut microbiota interventions. This article aims to review the current research on the relationship between diabetes and male fertility, explore the underlying mechanisms leading to male infertility, and identify potential interventions to improve male reproductive health, which holds significant clinical value.

Diabetes mellitus is a global epidemic characterized by high incidence and mortality rates. It can lead to both acute and chronic systemic complications. Diabetes-induced male reproductive disorders have become a focal point of concern. Numerous studies have shown that hyperglycemia can impair male fertility through various pathways, including erectile and ejaculatory dysfunction, disruption of the hypothalamic-pituitary-gonadal axis, damage to the epididymis and testis, impaired semen quality, abnormalities in testicular and sperm glucose metabolism, and adverse effects on offspring health. The mechanisms involved include oxidative stress, inflammation, autophagy, apoptosis, epigenetic regulation, and mitochondrial damage. Interventions for male fertility impairment include diabetes treatment drugs, antioxidants, Traditional Chinese Medicine and acupuncture, mesenchymal stem cell therapy, exercise and physical training, as well as gut microbiota interventions. This article aims to review the current research on the relationship between diabetes and male fertility, explore the underlying mechanisms leading to male infertility, and identify potential interventions to improve male reproductive health, which holds significant clinical value.

INTRODUCTION: Creatinine has limitations in identifying and predicting acute kidney injury (AKI). Our study examined the utility of neutrophil gelatinase-associated lipocalin (NGAL) in predicting AKI in patients presenting to the emergency department (ED), and in predicting the need for renal replacement therapy (RRT), occurrence of major adverse cardiac events (MACE) and all-cause mortality at three months post visit. METHODS: This is a single-centre prospective cohort study conducted at Singapore General Hospital (SGH). Patients presenting to SGH ED from July 2011 to August 2012 were recruited. They were aged ≥21 years, with an estimated glomerular filtration rate <60 mL/min/1.73 m², and had congestive cardiac failure, systemic inflammatory response syndrome or required hospital admission. AKI was diagnosed by researchers blinded to experimental measurements. Serum NGAL was measured as a point-of-care test. RESULTS: A total of 784 patients were enrolled, of whom 107 (13.6%) had AKI. Mean serum NGAL levels were raised (P < 0.001) in patients with AKI (670.0 ± 431.9 ng/dL) compared with patients without AKI (490.3 ± 391.6 ng/dL). The sensitivity and specificity of NGAL levels >490 ng/dL for AKI were 59% (95% confidence interval [CI] 49%-68%) and 65% (95% CI 61%-68%), respectively. Need for RRT increased 21% per 100 ng/dL increase in NGAL (P < 0.001), whereas odds of death in three months increased 10% per 100 ng/dL increase in NGAL (P = 0.028). No clear relationship was observed between NGAL levels and MACE. CONCLUSION Serum NGAL identifies AKI and predicts three-month mortality.
Humans ; Lipocalin-2 ; Prospective Studies ; Lipocalins ; Proto-Oncogene Proteins ; Acute-Phase Proteins ; Biomarkers ; Acute Kidney Injury/diagnosis* ; Emergency Service, Hospital ; Predictive Value of Tests

Objective Apply modified bilateral carotid artery ligation to establish a VD rat model to observe changes in cerebral blood flow and expression of angiogenic proteins.Methods Thirty-six SD male rats were randomly divided into a sham group(n = 18)and model group(n = 18).In the sham group,only the bilateral carotid artery was isolated without ligation,whereas in the model group,the bilateral carotid artery was ligated to establish the VD model.The Morris water maze behavior test was applied before and 14 days after modeling.Variation in cerebral blood flow was detected by laser speckle contrast imaging.Protein expression of HIF-1α,VEGF,and HO-1 was detected by Western Blot.IL-4 and IL-10 contents were measured by ELISA.Results At 14 days after modeling,escape latency was significantly prolonged and the frequency of crossing the platform had significantly decreased in the model group compared with the sham group(P<0.05).At 2 hours,3 days,and 7 days after modeling,cerebral blood flow in the model group was significantly lower than that in the sham group(P<0.05).At 14 and 21 days after modeling,no significant difference was found in cerebral blood flow between sham and model groups(P>0.05).In the model group,cerebral blood flow was decreased to a minimum at 2 hours after modeling(P<0.05)and then began to recover.The peak of recovery occurred at 3～7 days after modeling and returned to the level before modeling on day 14 after modeling.At postoperative day 21,expression of HIF-1α,VEGF,and HO-1 proteins in the hippocampus of the model group was increased remarkably(P<0.05)and the serum contents of IL-4 and IL-10 in the model group were significantly increased compared with those in the sham group(P<0.05).Conclusions The variation in cerebral blood flow in the VD rat model established by the modified bilateral carotid artery ligation was dependent on time.At postoperative day 21,HIF-1α,VEGF,and HO-1 in the hippocampus were increased significantly,which was accompanied by increased levels of IL-4 and IL-10.