Stroke-related sleep disorder is a common clinical manifestation after stroke, which has a greater impact on the prognosis and recovery of patients, but it is often overlooked in actual clinical work. Therefore, it is necessary to quantitatively evaluate the severity of stroke-related sleep disorders through relevant assessment scales in order to facilitate clinical intervention and improve patient prognosis. This article reviews the subjective and objective assessment tools of stroke-related sleep disorders, so as to provide certain theoretical support and a reference basis for the diagnosis, intervention and effect evaluation of stroke-related sleep disorders.

Objective:To construct a prediction model for the prognosis of breast cancer patients with long non-coding RNA expression characteristics.Methods:To construct a long non-coding RNA(LncRNA) model for predicting the prognosis of breast cancer patients.Methods Analyzing LncRNA expression profiles and clinical characteristics of 1 081 breast cancer patients in the cancer genome atlas (TCGA) database.Performing differential expression analysis and univariate analysis on 112 paired breast cancer and normal breast tissues′ transcriptome sequencing data in the TCGA database, and screened for differentially expressed (DELncRNAs) that significantly correlated with the prognosis of BRCA (To reduce batch effects, sequencing data has been normalized using the DESeq function). One thousand eighty-one breast cancer patients were randomly divided into two groups: training set (541) and validation set (540). Performing Cox proportional hazard regression using DELncRNAs and establishing a multi-LncRNA prognosis model in the training set, followed by proportional hazards assumption test(PH assumption test). Patients were divided into high-risk and low-risk groups based on calculated risk score.Kaplan-Meier method was used for survival analysis, and 540 patients′ data were used for validation.To evaluate the prognostic value of the model in patients with squamous cell carcinoma of the lung and hepatocarcinoma in TCGA database.Gene Set Enrichment Analysis (GSEA) was used to analyze the specific mechanism of lncrna affecting the survival of patients.Results:There were 2815 differentially expressed genes screened by transcriptome sequencing, 91 of which were significantly related to the prognosis of breast cancer patients ( P<0.05). Based on the Cox regression analysis of 91 delncrna expression data from 541 breast cancer patients in training set, a Cox proportional risk regression model was constructed based on 5 LncRNA (training set AUC=0.746, validation set AUC=0.650): AC004551.1, MTOR-AS1, KCNAB1-AS2, FAM230G and LINC01283, and PH assumption test( P=0.388). K-M survival analysis showed that the survival time of high-risk group was significantly worse than that of low-risk group (median survival time: 7.049 and 12.21 years, HR 0.367, 95% CI0.228-0.597, P<0.001), and the survival time of high-risk group was significantly shorter than that of low-risk group (median survival time: 7.57 and 10.85 years, HR 0.412, 95% CI0.214-0.793, P<0.001). Similar prediction results were also obtained in other cancer species of TCGA: lung squamous cell carcinoma ( HR 0.604, 95% CI0.383-0.951, P=0.007) and liver cell carcinoma ( HR 0.551, 95% CI0.307-0.987, P=0.011). GSEA results suggested that the expression patterns of the above five LncRNA were related to the cell cycle regulation of tumor cells. Conclusion:The prognostic model constructed based on expression profile of AC004551.1, MTOR-AS1, KCNAB1-AS2, FAM230G and LINC01283 can be used to predict the prognosis of breast cancer patients, which is helpful to further guide clinical treatment.

  Objective  To establish a model that can predict the occurrence of acute kidney disease (AKD) in liver cirrhotic patients and evaluate its performance.  Methods  Liver cirrhotic patients who hospitalized in the department of gastroenterology of the First Hospital of Lanzhou University from January 2017 to January 2022 were retrospectively included. They were divided into AKD and non-AKD groups according to whether they were combined with AKD during hospitalization, and were randomized into training and validation sets in a 7∶3 ratio. The clinical data of patients in the two groups were collected, and LASSO regression and multifactorial Logistic regression were used to screen the influencing factors for the occurrence of AKD in patients with liver cirrhosis and to establish a prediction model. The model was then evaluated by using the receiver operating characteristic curve, the calibration curve and the clinical decision curve.  Results  A total of 796 cases of liver cirrhotic patients who met the inclusion and exclusion criteria were enrolled. Among them, 103 cases were in the AKD group and 693 cases were in the non-AKD group; 561 cases were in the training set and 235 cases were in the validation set. The results of LASSO regression and multifactorial Logistic regression showed that a history of diabetes (OR=2.922, 95% CI: 1.290-6.564, P=0.009), hepatic encephalopathy (OR=6.210, 95% CI: 2.278-17.479, P < 0.001), gastrointestinal bleeding (OR=2.501, 95% CI: 1.236-5.073, P=0.011), ascites (OR=3.219, 95% CI: 1.664-6.539, P < 0.001), male (OR=0.477, 95% CI: 0.254-0.879, P=0.019), hemoglobin (OR=0.987, 95% CI: 0.975-0.999, P=0.044), albumin (OR=0.952, 95% CI: 0.911-0.991, P=0.023), and prothrombin time (OR=0.865, 95% CI: 0.779-0.920, P < 0.001) were the independent influences on the occurrence of AKD in liver cirrhotic patients, and were used to construct a prediction model. The area under the curve of the model in the training set and validation set for predicting the occurrence of AKD in liver cirrhotic patients was 0.895 (95% CI: 0.865-0.925) and 0.869 (95% CI: 0.807-0.930), respectively. The calibration curves showed that the model had good fit and consistency and the clinical decision curves showed that the use of the model for predicting the risk of AKD could benefit liver cirrhotic patients overall.  Conclusions  A prediction model for the occurrence of AKD in liver cirrhotic patients was established based on eight influencing factors, including gender, history of diabetes, and hepatic encephalopathy. It was validated to have good discrimination, calibration, and clinical utility, and is expected to assist in the clinical early screening and identification of liver cirrhosis-associated AKD.