Objective:To screen the optimal machine learning model for predicting the recurrence condition of hepatocellular carcinoma (HCC) at different time points post-surgery, based on the cutoff value of the Ki67 positive proliferation index condition calculated from recurrence-free survival and combined with various clinical features.Methods:retrospective study included initially treated patients with solitary HCC who underwent radical surgery at the Fifth Medical Center of the PLA General Hospital from January 2013 to March 2023. Data included general clinical data, preoperative laboratory parameters, and surgical pathology information about the subjects. The postoperative recurrence status was assessed by querying the medical record system or by telephone follow-up. The Ki67 positive index cutoff value was determined by the X-tile software based on the patient's recurrence-free survival status and time analysis. Survival rates were calculated using the Kaplan-Meier method, and survival curves were plotted. The study population was randomly divided into training and testing groups in a 7:3 ratio using a computer-generated random number method. The minimum redundancy maximum relevance (mRMR) method was used for feature variable selection. Predictive models for postoperative HCC recurrence conditions in patients with HCC were constructed using random forest, support vector machine, logistic regression, and gradient boosting decision tree machine learning algorithms. Inter-group comparisons for continuous data were performed using the t-test or Mann-Whitney U test. Inter-group comparisons of enumeration data were performed using the Pearson χ2 test, continuity-corrected χ2 test, or Fisher's exact test. Results:The cutoff values for the Ki67 positivity index were 0.3 and 0.5 in 510 cases, with a follow-up time ranging from 1.2 to 11.4 years (median: 6.2 years). The recurrence-free survival time was between 1 and 135 months (median: 32 months), with recurrence-free survival rates post-surgery at 1, 2, 3, and 5 years were 87.5%, 77.1%, 61.2%, and 54.5%, respectively. The top five variables predicted HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years, in accordance with information obtained by the mRMR screen out. The Ki67 positivity index screened a successfully constructed machine learning model to predict HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years. The machine learning model based on the gradient boosting decision tree algorithm had the best prediction performance among them (areas under the receiver operating characteristic curves for predicting HCC recurrence within six months in the training and validation sets were 0.996 and 0.946, and accuracies were 0.972 and 0.935, respectively).Conclusion:A machine learning model was successfully constructed using the Ki67 positivity index combined with four readily available clinical features to predict HCC recurrence. The machine learning model based on the gradient boosting decision tree algorithm demonstrated the best performance in terms of predicting HCC recurrence within six months after surgery.

Objective:To compare the efficacies of endovascular therapy (EVT) and standard medical therapy in acute ischemic stroke (AIS) patients aged ≥85 years, and analyze the independent influencing factors for poor prognosis of AIS patients after EVT.Methods:Sixty-nine AIS patients aged ≥85 years admitted to Department of Neurology, Xi'an Third Hospital from January 2018 to April 2024, including 40 accepted EVT and 28 accepted standard medicinal therapy, were enrolled. Modified Rankin scale (mRS) was used to evaluate the prognosis of the patients 90 days after onset. General data, prognosis and complications between the EVT group and standard medical therapy group were compared. General data, treatment processes and complications between patients with good prognosis and poor prognosis in the EVT group were compared. Multivariate Logistic regression was used to analyze the independent influencing factors for poor prognosis in AIS patients after EVT.Results:Compared with the standard medical therapy, the EVT group had significantly lower NIHSS score at discharge, greater improvement in NIHSS score (NIHSS score at admission-NIHSS score at discharge), lower mRS score 90 days after onset, higher good prognosis rate, lower mortality rate within 90 days of onset, and longer hospital stay ( P<0.05). In the EVT group, 11 patients (27.5%) had good prognosis and 29 patients (72.5%) had poor prognosis 90 days after onset. Compared with the good prognosis group, the poor prognosis group had significantly higher blood glucose level and lower Alberta Stroke Program Early CT Score (ASPECT) on admission ( P<0.05). Multivariate Logistic regression analysis showed that blood glucose on admission ( OR=2.363, 95% CI: 1.134-4.928, P=0.022) and ASPECT score on admission ( OR=0.273, 95% CI: 0.088-0.854, P=0.026) were independent influencing factors for poor prognosis in AIS patients after EVT. Conclusion:AIS patients aged ≥85 years received EVT have better prognosis compared with those accepted standard medical therapy; these patients with high glucose level and low ASPECT score on admission have poor prognosis.

Objective:To screen the optimal machine learning model for predicting the recurrence condition of hepatocellular carcinoma (HCC) at different time points post-surgery, based on the cutoff value of the Ki67 positive proliferation index condition calculated from recurrence-free survival and combined with various clinical features.Methods:retrospective study included initially treated patients with solitary HCC who underwent radical surgery at the Fifth Medical Center of the PLA General Hospital from January 2013 to March 2023. Data included general clinical data, preoperative laboratory parameters, and surgical pathology information about the subjects. The postoperative recurrence status was assessed by querying the medical record system or by telephone follow-up. The Ki67 positive index cutoff value was determined by the X-tile software based on the patient's recurrence-free survival status and time analysis. Survival rates were calculated using the Kaplan-Meier method, and survival curves were plotted. The study population was randomly divided into training and testing groups in a 7:3 ratio using a computer-generated random number method. The minimum redundancy maximum relevance (mRMR) method was used for feature variable selection. Predictive models for postoperative HCC recurrence conditions in patients with HCC were constructed using random forest, support vector machine, logistic regression, and gradient boosting decision tree machine learning algorithms. Inter-group comparisons for continuous data were performed using the t-test or Mann-Whitney U test. Inter-group comparisons of enumeration data were performed using the Pearson χ2 test, continuity-corrected χ2 test, or Fisher's exact test. Results:The cutoff values for the Ki67 positivity index were 0.3 and 0.5 in 510 cases, with a follow-up time ranging from 1.2 to 11.4 years (median: 6.2 years). The recurrence-free survival time was between 1 and 135 months (median: 32 months), with recurrence-free survival rates post-surgery at 1, 2, 3, and 5 years were 87.5%, 77.1%, 61.2%, and 54.5%, respectively. The top five variables predicted HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years, in accordance with information obtained by the mRMR screen out. The Ki67 positivity index screened a successfully constructed machine learning model to predict HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years. The machine learning model based on the gradient boosting decision tree algorithm had the best prediction performance among them (areas under the receiver operating characteristic curves for predicting HCC recurrence within six months in the training and validation sets were 0.996 and 0.946, and accuracies were 0.972 and 0.935, respectively).Conclusion:A machine learning model was successfully constructed using the Ki67 positivity index combined with four readily available clinical features to predict HCC recurrence. The machine learning model based on the gradient boosting decision tree algorithm demonstrated the best performance in terms of predicting HCC recurrence within six months after surgery.

Objective:To compare the efficacies of endovascular therapy (EVT) and standard medical therapy in acute ischemic stroke (AIS) patients aged ≥85 years, and analyze the independent influencing factors for poor prognosis of AIS patients after EVT.Methods:Sixty-nine AIS patients aged ≥85 years admitted to Department of Neurology, Xi'an Third Hospital from January 2018 to April 2024, including 40 accepted EVT and 28 accepted standard medicinal therapy, were enrolled. Modified Rankin scale (mRS) was used to evaluate the prognosis of the patients 90 days after onset. General data, prognosis and complications between the EVT group and standard medical therapy group were compared. General data, treatment processes and complications between patients with good prognosis and poor prognosis in the EVT group were compared. Multivariate Logistic regression was used to analyze the independent influencing factors for poor prognosis in AIS patients after EVT.Results:Compared with the standard medical therapy, the EVT group had significantly lower NIHSS score at discharge, greater improvement in NIHSS score (NIHSS score at admission-NIHSS score at discharge), lower mRS score 90 days after onset, higher good prognosis rate, lower mortality rate within 90 days of onset, and longer hospital stay ( P<0.05). In the EVT group, 11 patients (27.5%) had good prognosis and 29 patients (72.5%) had poor prognosis 90 days after onset. Compared with the good prognosis group, the poor prognosis group had significantly higher blood glucose level and lower Alberta Stroke Program Early CT Score (ASPECT) on admission ( P<0.05). Multivariate Logistic regression analysis showed that blood glucose on admission ( OR=2.363, 95% CI: 1.134-4.928, P=0.022) and ASPECT score on admission ( OR=0.273, 95% CI: 0.088-0.854, P=0.026) were independent influencing factors for poor prognosis in AIS patients after EVT. Conclusion:AIS patients aged ≥85 years received EVT have better prognosis compared with those accepted standard medical therapy; these patients with high glucose level and low ASPECT score on admission have poor prognosis.

Uncovering the risk factors of pulmonary hypertension and its mechanisms is crucial for the prevention and treatment of the disease.In the current study,we showed that experimental periodontitis,which was established by ligation of molars followed by orally smearing subgingival plaques from patients with periodontitis,exacerbated hypoxia-induced pulmonary hypertension in mice.Mechanistically,periodontitis dysregulated the pulmonary microbiota by promoting ectopic colonization and enrichment of oral bacteria in the lungs,contributing to pulmonary infiltration of interferon gamma positive(IFNγ+)T cells and aggravating the progression of pulmonary hypertension.In addition,we identified Prevotella zoogleoformans as the critical periodontitis-associated bacterium driving the exacerbation of pulmonary hypertension by periodontitis,and the exacerbation was potently ameliorated by both cervical lymph node excision and IFNγ neutralizing antibodies.Our study suggests a proof of concept that the combined prevention and treatment of periodontitis and pulmonary hypertension are necessary.

Objective:To investigate the value of radiomics based on three-dimensional high resolution MR vessel wall imaging (3D HRMR-VWI) for identifying culprit plaques in symptomatic patients with middle cerebral atherosclerosis.Methods:The clinical and imaging features of 117 patients (139 middle cerebral artery plaques) with cerebrovascular diseases in Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology from October 2018 to October 2020 were respectively reviewed. Stratified random sampling was used to divide 139 plaques into training set (97 plaques) and validation set (42 plaque) at the ratio of 7∶3. The plaques were divided into 69 culprit plaques and 70 non-culprit plaques based on plaque MR features and clinical symptoms. The clinical and imaging characteristics of culprit plaques and non-culprit plaques were compared by independent sample t-test, Mann-Whitney U test and χ 2 test, and factors with significant difference between two groups in univariate analysis were further analyzed by multivariate logistic regression to find out the independent predictors of culprit plaques. Radiomics features were extracted, screened and radiomics model was constructed using pre-and post-contrast 3D HRMR-VWI based on the training set. The combined model was constructed by combining the independent predictors and radiomics model. Receiver operating characteristic curve and area under curve (AUC) were used to evaluate the efficacy of each model, and DeLong test was used to compare the efficacy of different models. Results:Significant difference was found in intraplaque hemorrhage, lumen area of stenosis, stenosis diameter, stenosis rate, plaque burden and enhancement rate between culprit and non-culprit plaques (all P<0.05). Multivariate logistic regression analysis confirmed that only intraplaque hemorrhage was the independent predictor for culprit plaques (OR=7.045,95%CI 1.402-35.397, P=0.018). In the validation set, the AUC of the pre-contrast 3D HRMR-VWI model was lower than that of the post-contrast 3D HRMR-VWI model ( Z=-2.01, P=0.044). The AUC of pre+post-contrast 3D HRMR-VWI model was not significantly different from that of post-contrast 3D HRMR-VWI model ( Z=0.79, P=0.427). The AUC showed no significant difference between combined model and pre+post-contrast 3D HRMR-VWI model ( Z=-0.59, P>0.05). The combined model showed the best performance in predicting culprit plaques of middle cerebral artery (AUC=0.939), with the sensitivity, specificity and accuracy of 95.24%, 76.19% and 85.71%. Conclusion:Radiomics based on 3D HRMR-VWI has potential values in identifying culprit plaques in symptomatic patients with middle cerebral atherosclerosis.

Objective: To develop a model incorporating radiomic features and clinical factors to accurately predict acute ischemic stroke (AIS) outcomes. Materials and Methods: Data from 522 AIS patients (382 male [73.2%]; mean age ± standard deviation, 58.9 ± 11.5 years) were randomly divided into the training (n = 311) and validation cohorts (n = 211). According to the modified Rankin Scale (mRS) at 6 months after hospital discharge, prognosis was dichotomized into good (mRS ≤ 2) and poor (mRS > 2); 1310 radiomics features were extracted from diffusion-weighted imaging and apparent diffusion coefficient maps. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator logistic regression method were implemented to select the features and establish a radiomics model. Univariable and multivariable logistic regression analyses were performed to identify the clinical factors and construct a clinical model. Ultimately, a multivariable logistic regression analysis incorporating independent clinical factors and radiomics score was implemented to establish the final combined prediction model using a backward step-down selection procedure, and a clinical-radiomics nomogram was developed. The models were evaluated using calibration, receiver operating characteristic (ROC), and decision curve analyses. Results: Age, sex, stroke history, diabetes, baseline mRS, baseline National Institutes of Health Stroke Scale score, and radiomics score were independent predictors of AIS outcomes. The area under the ROC curve of the clinical-radiomics model was 0.868 (95% confidence interval, 0.825–0.910) in the training cohort and 0.890 (0.844–0.936) in the validation cohort, which was significantly larger than that of the clinical or radiomics models. The clinical radiomics nomogram was well calibrated (p > 0.05). The decision curve analysis indicated its clinical usefulness. Conclusion The clinical-radiomics model outperformed individual clinical or radiomics models and achieved satisfactory performance in predicting AIS outcomes.

Objective:To explore the effect of Teach-Back model on oral health behavior among the middle-aged and aged.Methods:From March 2017 to June 2018, 150 middle-aged and elderly patients undergoing oral examinations from the Department of Preventive Dentistry, Hospital of Stomatology, Sun Yat-sen University were selected as the survey subject by convenience sampling. Patients were divided into experimental group ( n=70) and control group ( n=80) . Control group was given regular oral health guidance, and experimental group implemented Teach-Back nursing care on the basis of control group. After 6 months of intervention, we compared the cognition of periodontal disease, oral health behavior, and periodontal health status of middle-aged and elderly patients between two groups. Results:After 6 months of intervention, the scores of daily oral hygiene habits, periodontal disease cognition and periodontal disease treatment behavior of experimental group were higher than those of control group, and the differences were statistically significant ( P<0.05) . The Debris Index-Simplified (DI-S) , Calculus Index-Simplified (CI-S) , Gingival Index (GI) , Bleeing Index (BI) and Depth of Periodontal Pocket (PD) of experimental group were lower than those of control group with statistical differences ( P<0.05) . Conclusions:Middle-aged and elderly people have poor periodontal disease cognition and need nursing intervention. Teach-Back model can improve the daily oral hygiene habits of middle-aged and elderly patients, improve their correct understanding of periodontal disease, and improve their oral health behavior and quality of life.

Objective To evaluate the attitudes of clinical nurses toward caring for the dying patients, and possible influencing factors concerning the attitudes were investigated, so as to provide a scientific basis for further intervention, thus improving the positive attitudes toward care of the dying patients in the future research. Methods A convenience sampling method was used to recruit 770 nurses from 15 hospitals located in 5 provinces in China. A demographic survey, Chinese version of Frommelt Attitudes Toward Care of the Dying Scale Form B (FATCOD-B-C) and Chinese version Death Attitude Profile-Revised were employed in the survey. Results FATCOD-B-C scale was used to evaluate the attitudes of nurses toward caring for dying patients, with the mean score of all FATCOD-B-C item being 95.62 ± 7.45. To analyze relationship among demographic variables and the total score of FATCOD-B-C. Univariate analysis revealed that age group (F=2.285), years employed as a nurse (F=3.353), educational background (F=5.581), technical title (F=5.692), level of hospital (t=2.058), religious beliefs (t=-2.788), previous education on death and dying(F=9.743), previous experience in dealing with terminally ill persons (t=2.761) had significant influence on the nurses' attitudes toward caring for dying patients and families(P<0.05). Multiple linear regression analysis indicated that nurses' attitudes toward caring for dying patients had been affected by those factors, among which the most influential factor was educational background. Conclusions It shows that nurses'FATCOD-B-C scores are at a low level. It is suggested to improve nurses' positive attitudes of caring for dying patients and their families by making the specific methods based on the influencing factors, thus improving the development of palliative care.

MicroRNA is a class of short-chain non-coding RNA that regulates gene expression at post-transcriptional level.It can participate in the pathophysiology processes of tumor regulation,neurodegenerative disease,and cardiovascular disease.Recent studies have shown that microRNA can play a reguhtory role in ischemic brain damage through autophagy.This article reviews the effect of microRNA on autophagy after cerebral ischamia and its possible mechanisms.