Xiaoaiping (XAP) Injection demonstrates the anti-prostate cancer (PCa) effects, yet the underlying mechanism remains unclear. This study aims to investigate the impact of XAP on PCa and elucidate its mechanism of action. PCa cell proliferation was evaluated using a cell counting kit-8 (CCK-8) assay. Cell apoptosis was assessed through Hoechst staining and Western blotting assays. Proteomics technology was employed to identify key molecules and significant signaling pathways modulated by XAP in PCa cells. To further validate potential key genes and important pathways, a series of assays were conducted, including acridine orange (AO) staining, transmission electron microscopy, and immunofluorescence assays. The molecular mechanism of XAP against PCa in vivo was examined using a PC3 xenograft mouse model. Results demonstrated that XAP significantly inhibited cell proliferation in multiple PCa cell lines. In C4-2 and prostate cancer cell line-3 (PC3) cells, XAP induced cellular apoptosis, evidenced by reduced B-cell lymphoma 2 (Bcl-2) levels and elevated Bcl-2-associated X (Bax) levels. Proteomic, immunofluorescence, and quantitative reverse transcription-polymerase chain reaction (qRT-PCR) investigations revealed a strong correlation between forkhead box O3a (FoxO3a) autophagic degradation and the anti-PCa action of XAP. XAP hindered autophagy by reducing the expression levels of autophagy-related protein 5 (Atg5)/autophagy-related protein 12 (Atg12) and enhancing FoxO3a expression and nuclear translocation. Furthermore, XAP exhibited potent anti-PCa action in PC3 xenograft mice and triggered FoxO3a nuclear translocation in tumor tissue. These findings suggest that XAP induces PCa apoptosis via inhibition of FoxO3a autophagic degradation, potentially offering a novel perspective on XAP injection as an effective anticancer therapy for PCa.
Male ; Humans ; Prostatic Neoplasms/physiopathology* ; Autophagy/drug effects* ; Animals ; Drugs, Chinese Herbal/pharmacology* ; Proteomics ; Mice ; Apoptosis/drug effects* ; Cell Line, Tumor ; Cell Proliferation/drug effects* ; Forkhead Box Protein O3/genetics* ; Xenograft Model Antitumor Assays ; Mice, Nude ; Mice, Inbred BALB C

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.

With the changing pattern of disease burden in China and the deepening and acceleration of aging society, healthy aging has become a national strategy. As one of the important roles of the care system, under the opportunity of the action framework for healthy aging, nursing discipline in China has made active exploration in key areas and has achieved research and practice results with Chinese characteristics, including ageism, intrinsic capacity, community development and long-term care system construction. Nursing has demonstrated disciplinary reserves and value in the development of the elderly care industry and the construction of the elderly care service system.

Bilateral medial medullary infarction(MMI)was a rare subtype of stroke.The clinical manifestations of bilateral MMI were complicated,which leaded to misdiagnosis.Patients with bilateral MMI could progress rapidly,and were easy to progress to respiratory failure and even death.Patients with this type of cerebral infarction did not respond well to medication.One case data of a patient with bilateral MMI was reviewed and analyzed.The weakness on the right side of the body was presented,accompanied by slurred speech,and was finally diagnosed with atherosclerosis of the large arteries through cranial MR examination.The patient had occlusion of the left posterior inferior cerebellar artery distal to the left vertebral artery V4 segment on the left side.The patient was treated with stent thrombectomy and balloon angioplasty,and the follow-up phone call at 3 months after discharge was showed good prognosis.Through a review of the literature,combined with the clinical and imaging features of this case,a preliminary analysis suggested that atherosclerosis of the large arteries may be the most common cause of bilateral MMI.Cranial MR imaging is an important method for diagnosing this condition.For patients with bilateral MMI accompanied by occlusion of the vertebral artery V4 segment,endovascular intervention may be attempted.

OBJECTIVE: To observe the therapeutic effect of electroacupuncture at acupoints of liver meridian in patients with diminished ovarian reserve (DOR) of liver depression. METHODS: A total of 62 patients with DOR of liver depression were randomly divided into an electroacupuncture group (31 cases, 1 case discontinued) and a western medication group (31 cases, 1 case was eliminated). Electroacupuncture was applied at bilateral Taichong (LR 3), Ligou (LR 5), Ququan (LR 8), Jimai (LR 12) in the electroacupuncture group, with continuous wave, in frequency of 2 Hz and current of 0.5-1.0 mA, 30 min each time, once every other day, 3 times a week. Femoston was taken orally in the western medication group, oral estradiol tablets were taken for the first 14 days, followed by oral estradiol/progesterone complex tablets for the rest 14 days, 1 tablet a day. Both groups were treated for 3 consecutive menstrual cycles. Before and after treatment, the scores of TCM syndrome, self-rating anxiety scale (SAS) and self-rating depression scale (SDS) were observed, serum levels of follicle stimulating hormone (FSH) and anti-Müllerian hormone (AMH) were detected, and antral follicle count (AFC), peak systolic velocity (PSV) and resistance index (RI) of ovarian artery were measured by color Doppler ultrasound in the two groups, and the clinical efficacy was evaluated after treatment. RESULTS: After treatment, the scores of primary symptom and secondary symptom, as well as the total scores of TCM syndrome were decreased compared with those before treatment (P<0.01), the scores of SAS and SDS, as well as the serum FSH levels and RI of ovarian artery were decreased compared with those before treatment (P<0.01), while the serum AMH levels, AFC and PSV of ovarian artery were increased compared with those before treatment (P<0.05, P<0.01) in the two groups. After treatment, in the electroacupuncture group, the primary symptom score of TCM syndrome was higher than that in the western medication group (P<0.01), the secondary symptom score of TCM syndrome and the scores of SAS and SDS were lower than those in the western medication group (P<0.05, P<0.01). The total effective rate was 70.0% (21/30) in the electroacupuncture group and 73.3% (22/30) in the western medication group respectively, there was no significant difference in the total effective rate between the two groups (P>0.05). CONCLUSION Electroacupuncture at acupoints of liver meridian can effectively improve the clinical symptoms, anxiety and depression, regulate the serum sex hormone levels, increase AFC and improve ovarian blood supply in DOR patients of liver depression.
Humans ; Female ; Electroacupuncture ; Adult ; Acupuncture Points ; Meridians ; Ovarian Reserve ; Young Adult ; Liver Diseases/physiopathology* ; Liver/metabolism* ; Ovary/physiopathology* ; Treatment Outcome ; Depression/therapy*

Objective To investigate the influences of different matrix stiffness on proliferation ability and glucose metabolism of hepatocellular carcinoma (HCC) cells and to explore the correlation between metabolism and biological behavior changes of HCC cells resulted from the stiffness of extracellular matrix (ECM)．Methods The proliferation changes of HepG2 cells cultured on matrix with different stiffness were detected by CCK-8 assay and cell count assay. 2-NBDG and flow cytometry were used to detect the effect of matrix stiffness on glucose uptake. Real-time fluorescence quantitative polymerase chain reaction (qRT-PCR) was used to detect the expression level of Glut1. Then, 2-DG was used to inhibit glycolysis, and the influences of matrix stiffness on proliferation of HepG2 cells were detected. Results The proliferation ability, glucose uptake and the expression of Glut1 of HepG2 cells increased with the matrix stiffness increasing. When glycolysis was inhibited, the proliferation ability of HepG2 cells grown on matrix with different stiffness was similar. Conclusions The mechanical microenvironment had an important effect on proliferation of HCC cells; matrix with a larger stiffness might promote proliferation of HCC cells through regulating glycolysis. The research findings provide a corresponding experimental basis for the clinical treatment of HCC cells and drug development targeting glucose metabolism.

Objective:To analyze the expression of the four mismatch repair genes protein (hMLH1,hMSH2,hMSH6 and hPMS2) of patients with colorectal cancer and its clinical significance.Methods:177 cases of patients with colorectal caner in the Third Affiliated Hospital of Guangzhou Medical University from January 2013 to December 2015 were randomly selected.Tested the expression of the hMLH1,hMSH2,hMSH6 and hPMS2 by immunohistochemistry,the relationship between protein expression and clinical parameters was analyzed.Results:Among 177 cases ofcolorectal cancer tissue,the deletion rate ofhMLH1 protein was 6.2％ (11/177),the deletion rate of hMLH2 protein was 4.0％(7/177),the deletion rate of hMSH6 protein was 1.7％(3/177),the deletion rate of hPMS2 protein was 8.0％(14/177),the sum of the four values accounted for 19.8％(35/177) of all cases of colorectal cancer.The loss of expression of the four mismatch repair genes protein were correlated to tumor location (P＜0.05),besides,the loss of expression of the hMLH1 and hPMS2 protein were correlated to degree of tumor differentiation (P＜0.05),he loss of expression of the hMSH6 protein were correlated to depth of tumor invasion(P＜0.05);But the loss was not correlated to age,sexes,lymph node metastasis and distant metastasis(P＞0.05).Conclusion:The expression of loss phenomenon with mismatch repair protein appears in part of colorectal cancer,the loss phenomenon with mis match repair protein were correlated to tumor location and degree of tumor differentiation.Mutations of four genes in hMLH1,hPMS2,hMSH6 and bMSH2,to provide a reference value for the clinical judgment of prognosis and to develop a treatment plan.

Objective: To investigate the impact of thyroid hormone on atrial ifbrillation (AF) prevalence in patients with chronic heart failure (CHF).
Methods: A total of 322 non-valvular heart disease CHF patients treated in our hospital from 2011-0-01 to 2012-10-01 were retrospectively studied. Based on previous history and the ECG at admission, the patients were divided into 2 groups: AF group,n=187 and Sinus rhythm group,n=135. The proifle of serum levels of free thyroxine (FT4), free triiodothyronine (FT3), hyroid stimulating hormone (TSH) and LDL-C were examined within 24 hours of admission; 12 lead ECG and echocardiography were conducted to analyze the related factor for AF occurrence.
Results: Compared with Sinus rhythm group, AF group had increased FT4 level as 14.52 (12.74, 15.85) pmol/L vs 13.11 (11.68, 14.85) pmol/L,P<0.001, while FT3 level was similar between 2 groups as 4.06 (3.76, 4.44) pmol/L vs 4.1(3.80, 4.52) pmol/L,P=0.250. Spearman related analysis indicated that FT4 level was positively related to left atrial dimension (LAD) (r=0.134,P=0.016), FT3 level was positively related to LDL-C (r=0.235,P<0.001) and negatively related to age (r=-0.268, P<0.001). Taking FT4 as the continuous variable, Logistic regression analysis indicated that FT4 level [OR=1.227, 95% CI (1.112-1.354),P<0.001], LAD [OR=1.154, 95% CI (1.105-1.205),P<0.001], age [OR=1.038, 95% CI (1.012-1.064),P=0.003], CHF duration [OR=1.111, 95% CI (1.021-1.209),P=0.015], LDL-C level [OR=0.689, 95% CI (0.493-0.963),P=0.029] were related to AF prevalence in CHF patients.
Conclusion: High serum level of FT4 may increase the risk AF occurrence in CHF patients.

OBJECTIVE: To explore the efficacy of the cognitive behavior therapy (CBT) for the treatment of chronic subjective tinnitus. METHOD: One hundred and fifty-seven patients were randomly divided into two groups. Sixty-eight patients of the control group were treated by masking therapy; and the other 89 patients of the experimental group were treated by CBT therapy. The score of tinnitus handicap inventory (THI) was utilized to analyze the treatment efficacy in the two groups respectively. RESULT: The effective rate assessed by of THI score in the experimental group was not significantly higher than the control group 2 months after treatment (P > 0.05), but was significantly higher than the control group 6 months and 12 months after treatment (P < 0.05 respectively). CONCLUSION The CBT therapy contributed to achieve rapid adaptation of tinnitus feeling, which shows great value of further clinical application.
Cognitive Behavioral Therapy ; Humans ; Tinnitus ; psychology ; therapy ; Treatment Outcome