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 systematically evaluate the influencing factors for antiretroviral therapy(ART)discontinuation in Chinese human immunodeficiency virus(HIV)/acquired immuno deficiency syn-drome(AIDS)patients.Methods A computer-based search was conducted in PubMed,Web of Science,Em-base,The Cochrane Library,CNKI,Wanfang,VIP,and China Biology Medicine(CBM)databases for studies on influencing factors of ART discontinuation in Chinese HIV/AIDS patients from the establishment of the databases to August 2024.Meta-analysis was performed on the included studies by using Stata16.0.Results A total of 17 studies were included.Meta-analysis showed that the following factors were associated with ART discontinuation:male gender(OR=1.301,95%CI:1.099-1.540),age≥50 years(OR=1.212,95%CI:1.109-1.324),unmarried/divorced/widowed marital status(OR=1.198,95%CI:1.060-1.354),education level was or below senior high school(OR=1.778,95%CI:1.508-2.096),infection route was in-travenous drug use(OR=2.420,95%CI:1.989-2.945),baseline CD4 cell count>500 cells/μL(OR=1.157,95%CI:1.020-1.313),tuberculosis co-infection before ART(OR=1.559,95%CI:1.398-1.739),hepatitis B co-infection before ART(OR=1.554,95%CI:1.305-1.851),AIDS-related symptoms occur be-fore ART(OR=1.245,95%CI:1.148-1.351),time from diagnosis to treatment initiation≥365 days(OR=1.449,95%CI:1.301-1.615),initial treatment regimen containing zidovudine(OR=1.573,95%CI:1.206-2.052),treatment at county-level or lower institutions(OR=1.204,95%CI:1.153-1.257),exist drug adverse reactions(OR=7.043,95%CI:3.142-15.786),and compliance education(OR=0.182,95%CI:0.094-0.352).Conclusion There are multiple factors influencing ART discontinuation in Chinese HIV/AIDS patients.Early identification of individuals at risk of discontinuation and targeted interventions are nec-essary to promote their maintenance of ART.

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.

Keloid, a malignant skin fiber proliferative disease. Pains, itching, non-aesthetics and disorders caused by scar contracture compromise the life of patients. The pathogenesis is not yet fully elucidated. At present, there are many treatment options for keloid, but there is no therapy that can achieve satisfactory result. In this study, literature review for Chinese and foreign researches was conducted to conclude the progress in experimental research progress and clinical treatment of keloid. This provides a novel study review.

Keloid, a malignant skin fiber proliferative disease. Pains, itching, non-aesthetics and disorders caused by scar contracture compromise the life of patients. The pathogenesis is not yet fully elucidated. At present, there are many treatment options for keloid, but there is no therapy that can achieve satisfactory result. In this study, literature review for Chinese and foreign researches was conducted to conclude the progress in experimental research progress and clinical treatment of keloid. This provides a novel study review.

Objective:To explore the effect of cupping therapy on regulating phlegm dampness constitution of elderly patients with hypertension in the community.Methods:From August to November in 2019, 120 elderly patients with phlegm dampness hypertension were randomly divided into control group and intervention group, 60 cases in each. The control group was given traditional Chinese medicine (TCM) health care in the community, and the intervention group was given cupping therapy intervention on this basis, to evaluate the changes of the scores of TCM phlegm wet body mass table, the total score reduction rate of TCM phlegm wet body mass, the conversion rate of TCM phlegm wet body mass, blood pressure, body mass index and waist circumference between the two groups before the intervention, 1 month, 2 months, 3 months after the intervention.Results:Finally, there were 51 cases in the intervention group and 57 cases in the control group. The TCM phlegm dampness constitution sub-scale and total scores of the comparison between groups at four different time points showed that except for the dimension of abdominal hypertrophy, the sub-scale and total scores of TCM phlegm-dampness constitution scale in the two groups were lower than before, and the differences were statistically significant ( F values were 3.450-94.995, P<0.05). The total score reduction rate of phlegm dampness constitution in the intervention group was 0.17 ± 0.10, higher than that in the control group (0.12 ± 0.09), and the difference was statistically significant ( t value was - 2.773, P < 0.05). The transformation rate of phlegm-dampness constitution was 47.1% (24/51) in the intervention group, which was higher than that in the control group (33.3%,19/57). The value of systolic blood pressure was lower in the intervention group [(135.10±2.64) mmHg,1 mmHg=0.133 kPa] than that in the control group [(138.88±2.42) mmHg] after 2 months of intervention, and the difference was statistically significant ( F value was 6.586, P<0.05). The value of diastolic blood pressure was lower in the intervention group [(80.75±3.03) mmHg] than that in the control group [(78.14±3.43) mmHg] after 3 months of intervention, and the difference was statistically significant ( F value was 6.758, P<0.05). Conclusions:On the basis of TCM physical nursing in the community, the effect of cupping therapy on reducing the score of phlegm and dampness physical fitness of elderly hypertension patients in the community, promoting the transformation of phlegm and dampness physical fitness of elderly hypertension patients in the community, and reducing the blood pressure level of elderly hypertension patients in the community is better than that of TCM physical nursing in the community, which is worth being promoted in the community.

Scar is the result of the tissue repair in wound healing. The treatment of scar has been the keypoint and difficulty in clinical work. Because the pathogenesis of the disease is still unclear, there are many treatment method, such as compression therapy, drug therapy, surgical resection, laser and fat transplantation therapy. How to choose the right treatment at the right time and how to combine multiple treatments correctly will determine the final therapeutic effect. Based on the existing literatures and personal clinical experiences, this paper reviewed the clinical practice of comprehensive scar treatment in order to provide relevant references for the effective treatment of scar.

Scar is the result of the tissue repair in wound healing. The treatment of scar has been the keypoint and difficulty in clinical work. Because the pathogenesis of the disease is still unclear, there are many treatment method, such as compression therapy, drug therapy, surgical resection, laser and fat transplantation therapy. How to choose the right treatment at the right time and how to combine multiple treatments correctly will determine the final therapeutic effect. Based on the existing literatures and personal clinical experiences, this paper reviewed the clinical practice of comprehensive scar treatment in order to provide relevant references for the effective treatment of scar.

OBJECTIVETo construct and characterize the TGF-β1, induced epithelial-mesenchymal transition (EMT) model of keloid epithelial cells in vitro, and to investigate the expression of epithelial stem cells related surface markers in keloid epithelial cells during EMT induction.

METHODSThe epithelial cells from 3 keloid samples of ears were cultured in vitro and induced by transforming growth factor betal (TGF-β1, 1 ng/ml) for 5 days, which was the experimental group, the same cells untreated were considered as the negative control group. The expressions of EMT-associated markers and regulative genes were detected using immunofluorescence staining, real-time PCR and western blot analysis. Then the surface markers of epithelial stem cells were detected using real-time PCR. Statistical significance was determined using Independent-Samples t Test, a p value less than 0. 05 was considered statistically significant.

RESULTSThe mRNA expression of transcription factor snail2 and mesenchymal-specific marker vimentin increased significantly in TGF-β1, induced keloid epithelial cells (P < 0. 05), in which snail2 increasing from 0. 91 ± 0. 23 to 1. 69 ± 0. 10, and vimentin from 5. 86 ± 2. 07 to 24. 29 ± 5. 39. Whereas the mRNA expression of epithelial-specific marker E-cadherin decreased from 1. 06 ± 0. 19 to 0. 65 ± 0. 09. The mRNA expression of CD29 and Lgr6, two surface markers of epithelial stem cells, significantly increased after induction of the TGF-β1, (P < 0. 05), from 0. 55 ± 0. 14 and 1. 61 ± 0. 31 to 1. 19 ± 0. 12 and 3. 84 t 0. 62 respectively. In induced cells, the immunofluorescence results showed staining of E- cadherin became faint, but the number of positive staining cells of vimentin increased. Western blot confirmed the protein expression of E-cadherin weakened, and the vimentin and p-Smad3 enhanced (P < 0. 05).

CONCLUSIONSTGF-β1, initiated EMT in keloid epithelial cells by inducing the up-regulation of snail2, and TGF-β1,/Smad3 signaling pathway was involved in EMT. EMT could change the phenotype of epithelial stem cells in keloid.

Biomarkers ; metabolism ; Cadherins ; genetics ; metabolism ; Epithelial Cells ; drug effects ; physiology ; Epithelial-Mesenchymal Transition ; drug effects ; physiology ; Humans ; In Vitro Techniques ; Keloid ; pathology ; RNA, Messenger ; metabolism ; Signal Transduction ; Smad3 Protein ; genetics ; metabolism ; Snail Family Transcription Factors ; Transcription Factors ; genetics ; metabolism ; Transforming Growth Factor beta1 ; metabolism ; pharmacology ; Up-Regulation ; Vimentin ; genetics ; metabolism

OBJECTIVETo observe the clinical effect of micro-plasma radio frequency treatment of facial post-burn hyperpigmentation.

METHODSFrom June 2012 to August 2013, a total of 25 cases with facial post-burn hyperpigmentation were treated with micro-plasma treatment for 3-5 times. The roller tip was used with setting of 60-90 watts and 3-4 passes were performed in different directions. Treatments were repeated at an interval of 8 weeks.

RESULTSAll patients tolerated the pain. After treatment, the facial hyperpigmentation improved. Complete recovery was achieved in 4 cases; significant improvement in 11 cases; moderate in 7 cases and no effect in 3 cases. No hyperpigmentation, depigmentation and scar formation was observed.

CONCLUSIONMicro-plasma radio frequency treatment is an ideal treatment for post-burn hyperpigmentation with lower side effect.

Aged ; Burns ; complications ; Cicatrix ; Facial Dermatoses ; etiology ; therapy ; Facial Injuries ; complications ; Humans ; Hyperpigmentation ; etiology ; therapy ; Pulsed Radiofrequency Treatment ; instrumentation ; methods