Objective:To construct and verify a nomogram prediction model basing on the influencing factors of the ac-tivity of daily living(ADL)disorders in ischemic stroke(IS),providing evidences for the early clinical imple-mentation of efficient rehabilitation therapies.Method:We retrospectively collected clinical data of 401 IS patients hospitalized in the department of Rehabili-tation of Guangdong Provincial Hospital of Chinese Medicine from January 2019 to December 2022,focusing on factors that may influence their ADL.These patients were randomly divided into the training(280 cases)and the validation(121 cases)sets according to 7∶3.We used univariate and multivariate logistic regression to analyze the influencing factors for the occurrence of ADL disorders in IS patients,and established the risk pre-diction model for the occurrence of ADL disorders by a visualized nomogram.The performance of this predic-tion model was assessed by the area under the curve(AUC),specificity,sensitivity,and calibration curve.Result:The results of multivariate logistic regression analysis in the training sets showed that age,diabetes mellitus,NIHSS score,FMA score,Hb,PLT,and HDL were independent influencing factors for the occur-rence of ADL disorders in IS patients.The nomogram model was constructed with the above 7 factors.The AUC,specificity,and sensitivity of this model were 0.875,82.8%,and 75.4%in the validation set,respec-tively,indicating that the model has a good discriminative ability.The calibration curve showed that the mod-el agrees well with the actual predicted probability.Conclusion:The nomogram risk prediction model constructed in this study can effectively predict the probabili-ty of ADL disorder in IS patients,aiding medical staff in implementing early rehabilitation intervention as early as possible and reduce the occurrence of ADL disorder.

Objective:To construct and verify a nomogram prediction model basing on the influencing factors of the ac-tivity of daily living(ADL)disorders in ischemic stroke(IS),providing evidences for the early clinical imple-mentation of efficient rehabilitation therapies.Method:We retrospectively collected clinical data of 401 IS patients hospitalized in the department of Rehabili-tation of Guangdong Provincial Hospital of Chinese Medicine from January 2019 to December 2022,focusing on factors that may influence their ADL.These patients were randomly divided into the training(280 cases)and the validation(121 cases)sets according to 7∶3.We used univariate and multivariate logistic regression to analyze the influencing factors for the occurrence of ADL disorders in IS patients,and established the risk pre-diction model for the occurrence of ADL disorders by a visualized nomogram.The performance of this predic-tion model was assessed by the area under the curve(AUC),specificity,sensitivity,and calibration curve.Result:The results of multivariate logistic regression analysis in the training sets showed that age,diabetes mellitus,NIHSS score,FMA score,Hb,PLT,and HDL were independent influencing factors for the occur-rence of ADL disorders in IS patients.The nomogram model was constructed with the above 7 factors.The AUC,specificity,and sensitivity of this model were 0.875,82.8%,and 75.4%in the validation set,respec-tively,indicating that the model has a good discriminative ability.The calibration curve showed that the mod-el agrees well with the actual predicted probability.Conclusion:The nomogram risk prediction model constructed in this study can effectively predict the probabili-ty of ADL disorder in IS patients,aiding medical staff in implementing early rehabilitation intervention as early as possible and reduce the occurrence of ADL disorder.

Objective:Cognitive impairment is one of the common complications of stroke,which can affect the rehabili-tation and their quality of life.It is very important to build reliable risk prediction model tools to detect post-stroke cognitive impairment(PSCI)in advance,but there is still no clinical risk prediction model for PSCI.Our aim was to identify the influencing factors of PSCI in convalescent stroke patients and construct a nomo-gram model for predicting the risk of PSCI based on these factors.Method:We retrospectively collected the demographic characteristics and clinically relevant data of convales-cent stroke patients hospitalized in Guangdong Provincial Hospital of Chinese Medicine from December 2019 to December 2022.Then we randomly divided the whole data set into the training set and the validation set according to 7:3,the former data was used to construct a nomogram model for predicting the risk of PSCI,and the latter data was used to evaluate the model performance.Univariate and multivariate logistic regression were used to analyze the factors affecting PSCI in convalescent stroke patients.Based on these factors,we used the R software to construct a PSCI risk prediction model who was visualized through a nomogram.The model performance was evaluated using the area under the curve(AUC),sensitivity,specificity,calibration curve,and decision curve analysis(DCA).Result:Our prediction model indicated that age,right hemiparesis,hypertension,coronary heart disease,hyper-homocysteinemia,Fugl-Meyer assessment scale(FMA)score,modified Barthel index(MBI)score and,mean cor-puscular hemoglobin were independent factors influencing the occurrence of PSCI in convalescent stroke pa-tients.The AUC,sensitivity and specificity of the model were 0.804,75.5％and 73.7％in the training set,and 0.737,82.9％and 62.8％in the validation set,suggesting that the model had a good discrimination.The calibra-tion curve of the training and validation sets indicated a good consistency between the prediction and the real observation.The decision curve analysis of the training and validation sets showed that the PSCI risk prediction model performed well in terms of the net clinical benefit.Conclusion:The PSCI risk prediction nomogram model constructed in this study can be personalize prediction of cognitive impairment probabilities in convalescent stroke patients,which can help healthcare providers to de-tect and treat PSCI early and improve patient outcome.

Objective:Cognitive impairment is one of the common complications of stroke,which can affect the rehabili-tation and their quality of life.It is very important to build reliable risk prediction model tools to detect post-stroke cognitive impairment(PSCI)in advance,but there is still no clinical risk prediction model for PSCI.Our aim was to identify the influencing factors of PSCI in convalescent stroke patients and construct a nomo-gram model for predicting the risk of PSCI based on these factors.Method:We retrospectively collected the demographic characteristics and clinically relevant data of convales-cent stroke patients hospitalized in Guangdong Provincial Hospital of Chinese Medicine from December 2019 to December 2022.Then we randomly divided the whole data set into the training set and the validation set according to 7:3,the former data was used to construct a nomogram model for predicting the risk of PSCI,and the latter data was used to evaluate the model performance.Univariate and multivariate logistic regression were used to analyze the factors affecting PSCI in convalescent stroke patients.Based on these factors,we used the R software to construct a PSCI risk prediction model who was visualized through a nomogram.The model performance was evaluated using the area under the curve(AUC),sensitivity,specificity,calibration curve,and decision curve analysis(DCA).Result:Our prediction model indicated that age,right hemiparesis,hypertension,coronary heart disease,hyper-homocysteinemia,Fugl-Meyer assessment scale(FMA)score,modified Barthel index(MBI)score and,mean cor-puscular hemoglobin were independent factors influencing the occurrence of PSCI in convalescent stroke pa-tients.The AUC,sensitivity and specificity of the model were 0.804,75.5％and 73.7％in the training set,and 0.737,82.9％and 62.8％in the validation set,suggesting that the model had a good discrimination.The calibra-tion curve of the training and validation sets indicated a good consistency between the prediction and the real observation.The decision curve analysis of the training and validation sets showed that the PSCI risk prediction model performed well in terms of the net clinical benefit.Conclusion:The PSCI risk prediction nomogram model constructed in this study can be personalize prediction of cognitive impairment probabilities in convalescent stroke patients,which can help healthcare providers to de-tect and treat PSCI early and improve patient outcome.

Background and purpose:MicroRNA-340 (miR-340) has been demonstrated to play a role of negative regulation in many kinds of tumor, however, there are few reports about the relationship between miR-340 in proliferation and apoptosis of breast cancer cell. This study was aimed to explore the effect of miR-340 on proliferation and apoptosis of breast cancer cell MDA-MB231. Methods: The pre-miR-340 or anti-miR-340 were transiently transfected into breast cancer cell MDA-MB231 with LipofectamineTM2000. miR-340 level was detected by RT-PCR. The Western blot was performed to detect the protein level of cleaved-caspase-3. The inhibition rate of cell proliferation was evaluated by MTT assay. The cell apoptosis was studied by lfow cytometry. Results:The pre-miR-340 facilitated the expression of miR-340 in MDA-MB231 cells. The pre-miR-340 enhanced the protein level of cleaved-caspase-3, inhibited the proliferation of MDA-MB231 cells and increased its apoptosis. On the contrary, the expression of miR-340 was inhibited by anti-miR-340 in MDA-MB231 cells. The protein level of cleaved-caspase-3 was reduced after the anti-miR-340-transfected MDA-MB231 cells. Anti-miR-340 promoted the proliferation of MDA-MB231 cells. Decreased apoptosis of MDA-MB231 cells was observed by lfow cytometry. Conclusion:The overexpression of miR-340 can effectively inhibit the proliferation and increase the apoptosis of MDA-MB231 cells, which may be explained by up-regulating of protein cleaved-caspase-3 level.