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.

AIM: To observe the clinical and multimodal imaging features of retinopathy associated with novel coronavirus disease 2019(COVID-19)infection, investigate the related risk factors, and analyze the treatment and prognosis.METHODS: A total of 7 patients(7 eyes)with clinically confirmed COVID-19-associated retinopathy in Tianjin Medical University General Hospital from December 13, 2022 to January 13, 2023 were included in the study. All patients underwent color fundus photography, IR, spectral-domain optical coherence tomography(SD-OCT), fundus autofluorescein(FAF)and other ophthalmic examination and serological examination.RESULTS: Among the included patients, 2 cases(2 eyes)of central retinal vein occlusion(CRVO)appeared differently from previous CRVO. The hemorrhagic features of CRVO were round or fusiform shape hemorrhagic spots with white centers. One of them, the von Willebrand factor antigen(vWF: Ag)level was increased to 161.8%. The other case was positive in serologic test for lupus anticoagulant. In 2 cases(2 eyes)of multiple evanescent white dot syndrome(MEWDS), FAF showed that dots of high spontaneous fluorescence were scattered in the posterior pole. The prognosis of 2 MEWDS were good after the treatment of glucocorticoids. The 3 cases(3 eyes)of acute macular neuroretinopathy(AMN)showed reddened brown lesions in the macular area, hyporeflective lesions with clear boundaries on IR, and high signal intensity in the ONL and ELM, EZ/IZ signal fracture on SD-OCT.CONCLUSION: COVID-19 may cause inflammatory storm, involving all layers of retinal tissues and blood vessels, leading to the occurrence of various retinal lesions. Hormone therapy may be effective and the prognosis is good in most cases. Roth spot can be seen in fundus hemorrhage of CRVO, lupus anticoagulant and increased vWF: Ag may be risk factors for CRVO after COVID-19.

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia seen in clinical settings, which has been associated with substantial rates of mortality and morbidity. However, clinically available drugs have limited efficacy and adverse effects. We aimed to investigate the mechanisms of action of andrographolide (Andr) with respect to AF. We used network pharmacology approaches to investigate the possible therapeutic effect of Andr. To define the role of Andr in AF, HL-1 cells were pro-treated with Andr for 1 h before rapid electronic stimulation (RES) and rabbits were pro-treated for 1 d before rapid atrial pacing (RAP). Apoptosis, myofibril degradation, oxidative stress, and inflammation were determined. RNA sequencing (RNA-seq) was performed to investigate the relevant mechanism. Andr treatment attenuated RAP-induced atrial electrophysiological changes, inflammation, oxidative damage, and apoptosis both in vivo and in vitro. RNA-seq indicated that oxidative phosphorylation played an important role. Transmission electron microscopy and adenosine triphosphate (ATP) content assay respectively validated the morphological and functional changes in mitochondria. The translocation of nuclear factor erythroid 2-related factor 2 (Nrf2) to the nucleus and the molecular docking suggested that Andr might exert a therapeutic effect by influencing the Keap1-Nrf2 complex. In conclusions, this study revealed that Andr is a potential preventive therapeutic drug toward AF via activating the translocation of Nrf2 to the nucleus and the upregulation of heme oxygenase-1 (HO-1) to promote mitochondrial bioenergetics.
Animals ; Rabbits ; Atrial Fibrillation/metabolism* ; Kelch-Like ECH-Associated Protein 1/metabolism* ; Signal Transduction ; NF-E2-Related Factor 2/pharmacology* ; Molecular Docking Simulation ; Oxidative Stress ; Energy Metabolism ; Mitochondria/metabolism* ; Inflammation/metabolism* ; Heme Oxygenase-1

ObjectiveTo investigate the molecular mechanism of the anti-liver fibrosis effect of curcumol by observing the effect of curcumol on the TLR4/NF-κB signaling pathway in liver sinusoidal endothelial cells. MethodsA total of 50 mice were randomly divided into blank group, model group, and curcumol group, and cells were divided into blank control group, LPS positive control group, curcumol intervention group, and PDTC group. HE staining and Masson staining were used to observe the change in liver structure; Western blot and quantitative real-time PCR (RT-PCR) were used to measure the protein and mRNA expression of the key molecules TLR4 and NF-κB in the TLR4/NF-κB signaling pathway; immunofluorescence assay was used to observe the expression and nuclear import of NF-κB in cells. A one-way analysis of variance was used for comparison between multiple 　groups, and the least　significant difference t-test was used for further comparison between two groups.　ResultsRT-PCR showed that compared with the positive control group, the curcumol intervention group had significant reductions in the mRNA expression of TLR4 and NF-κB (both P＜0.05). Western blot showed that compared with the positive control group, the curcumol intervention group had significant reductions in the expression of TLR4 and NF-κB (both P＜005). Immunofluorescence assay showed that compared with the positive control group, the curcumol intervention group had significant improvement in NF-κB nuclear import. ConclusionCurcumol can exert an anti-liver fibrosis effect possibly by inhibiting the activity of the TLR4/NF-κB signaling pathway.

Recently,a fast developing new technology for gene modification named as CRISPR-Cas9 which based on CRISPR-Cas9 system composed of clustered regulatory interspaced short palindromic repeat(CRISPR) and Cas9 nuclease(CRISPR associated system 9,Cas9)has been developed. CRISPR-Cas9 system is a kind of immune mechanism widely found in bacteria and archaea. This mechanism can help bacteria and archaea against exogenous DNA by the approach of specifically breaking DNA. Later,this mechanism was found to be useful for gene modification and gene deletion. At present,this technology has been applied to gene modification and therapy. Many studies have shown that the technology,compared with other genetic technology,has higher efficiency and accuracy,and it has promoted genetic engineering progress. Summarized here is the principle and application advance of CRISPR-Cas9.

OBJECTIVE To observe the effect of the silver sulfadiazine-impregnated hydrocolloid dressing on the pain of nail extraction wound during dressing change and the healing time of wound. METHODS Forty eight patients with nail extraction were randomly divided into two groups: in the study group,whose wound was covered with silver sulfadiazine-impregnated hydrocolloid dressing;in the control group,whose wound was applied vaseline gauze when the nail had been extracted and the wound was applied antibiotic gauze during dressing change.The pain scores of two groups were compared.Two groups were compared with healing time and the times of dressing change. RESULTS The pain scores in the study group were significantly lower than that of the control group.The healing time of wound and the times of dressing change in the study group were less than that of the control one((P