METHODS: This is a retrospective cohort study utilizing data from the National Cardiovascular Disease (NCVD)-PCI registry. The data collected (N = 28,007) were split into training set (n = 24,409) and testing set (n = 3598). Four predictive models (logistic regression [LR], random forest method, support vector machine [SVM], and artificial neural network) were developed and validated. The outcome on risk prediction were compared.
RESULTS: The demographic and clinical features of patients in the training and testing cohorts were similar. Patients had mean age ± standard deviation of 58.15 ± 10.13 years at admission with a male majority (82.66%). In over half of the procedures (50.61%), patients had chronic stable angina. Within 1 year of follow up mortality, target vessel revascularization (TVR), and composite event of mortality and TVR were 3.92%, 9.48%, and 12.98% respectively. LR was the best model in predicting mortality event within 1-year post-PCI (AUC: 0.820). SVM had the highest discrimination power for both TVR event (AUC: 0.720) and composite event of mortality and TVR (AUC: 0.720).
CONCLUSIONS: This study successfully identified optimal prediction models with the good discriminatory ability for mortality outcome and good discrimination ability for TVR and composite event of mortality and TVR with a simple machine learning framework.
METHODS: A total of 612 participants were recruited. A confirmatory factor analysis (CFA) examined construct validity of the ACSID-11. Cronbach's α and McDonald's ω were used to assess reliability of the ACSID-11. Pearson correlations examined relationships between ACSID-11 domains and Internet Gaming Disorder Scale-Short Form (IGDS9-SF) scores.
RESULTS: The CFA supported validity of the Thai version of the ACSID-11 and a four-factor structure. Specific domains of the Thai ACSID-11, particularly gaming, were positively and significantly correlated with IGDS9-SF scores.
CONCLUSIONS: Data indicate that the Thai version of the ACSID-11 is a valid and reliable instrument to assess major types of specific internet use disorders. Additional studies are needed to further examine the validity and reliability of the Thai ACSID-11.
MATERIAL AND METHODS: Differential gene expression was identified using the "limma" package in R. Prognosis-related LncRNAs were identified via univariate Cox regression analysis, while a prognostic model was crafted using multivariate Cox regression analysis. Survival analysis was conducted using Kaplan-Meier curves. The precision of the prognostic model was assessed through ROC analysis. Subsequently, the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm were executed on the TCGA dataset via the TIDE database. Fractions of 24 types of immune cell infiltration were obtained from NCI Cancer Research Data Commons using deconvolution techniques. The protein expression levels encoded by specific genes were obtained through the TPCA database.
RESULTS: In this research, we have identified 85 LncRNAs associated with TP53 mutations and developed a corresponding signature referred to as TP53MLncSig. Kaplan-Meier analysis revealed a lower 3-year survival rate in high-risk patients (46.9%) compared to low-risk patients (74.2%). The accuracy of the prognostic TP53MLncSig was further evaluated by calculating the area under the ROC curve. The analysis yielded a 5-year ROC score of 0.793, confirming its effectiveness. Furthermore, a higher score for TP53MLncSig was found to be associated with an increased response rate to immune checkpoint blocker (ICB) therapy (p = .005). Patients possessing high-risk classification exhibited lower levels of P53 protein expression and higher levels of genomic instability.
CONCLUSION: The present study aimed to identify and validate LncRNAs associated with TP53 mutations. We constructed a prognostic model that can predict chemosensitivity and response to ICB therapy in HCC patients. This novel approach sheds light on the role of LncRNAs in TP53 mutation and provides valuable resources for analyzing patient prognosis and treatment selection.
METHODS: A cross-sectional study on 284 epilepsy patients was performed in a local tertiary centre. The demographic and clinical epilepsy data were collected. The Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS) questionnaires were utilised to determine the quality of life and daytime hypersomnolence of epilepsy patients, respectively.
RESULTS: Poor sleep quality was reported in 78 (27.5%) patients while daytime hypersomnolence was present in 17 (6%) patients. The predictors of poor sleep quality include structural causes (OR = 2.749; 95% CI: 1.436, 5.264, p = 0.002), generalised seizures (OR = 1.959, 95% CI: 1.04, 3.689, p = 0.037), and antiseizure medications such as Carbamazepine (OR = 2.34; 95% CI: 1.095, 5.001, p = 0.028) and Topiramate (OR 2.487; 95% CI: 1.028, 6.014, p = 0.043). Females are 3.797 times more likely score higher in ESS assessment (OR 3.797; 95% CI: 1.064, 13.555 p = 0.04).
DISCUSSION: Sleep disturbances frequently coexist with epilepsy. Patients should be actively evaluated using the PSQI and ESS questionnaires. It is imperative to identify the key factors that lead to reduced sleep quality and heightened daytime sleepiness in patients with epilepsy, as this is essential to properly manage their condition.
METHODS: This retrospective study was conducted in the participating centers of the Pan-Asian Trauma Outcome Study from October 2015 to December 2020. Subjects who reported "school" as the site of injury were included. Major trauma was defined as an Injury Severity Score (ISS) value of ≥16.
RESULTS: In total, 1305 injury cases (1.0% of 127,715 events) occurred at schools. Among these, 68.2% were children. Unintentional injuries were the leading cause and intentional injuries comprised 7.5% of the cohort. Major trauma accounted for 7.1% of those with documented ISS values. Multivariable regression revealed associations between major trauma and factors, including age, intention of injury (self-harm), type of injury (traffic injuries, falls), and body part injured (head, thorax, and abdomen). Twenty-two (1.7%) died, with six deaths related to self-harm. Females represented 28.4% of injuries but accounted for 40.9% of all deaths.
CONCLUSIONS: In Asia, injuries at schools affect a significant number of children. Although the incidence of injuries was higher in males, self-inflicted injuries and mortality cases were relatively higher in females.
IMPACT: Epidemiological data and risk factors for major trauma resulting from school injuries in Asia are lacking. This study identified significant risk factors for major trauma occurring at schools, including age, intention of injury (self-harm), injury type (traffic injuries, falls), and body part injured (head, thoracic, and abdominal injuries). Although the incidence of injuries was higher in males, the incidence of self-harm injuries and mortality rates were higher in females. The results of this would make a significant contribution to the development of prevention strategies and relative policies concerning school injuries.
METHODS: The sources for this MAFLD review following PRISMA protocol were PubMed, Google scholar, Scopus and Science Direct. Quality of evidence was assessed by consistent results with previous studies. Assessment of quality was done by Joanna Briggs Institute criteria. Quality of evidence was assessed by GRADE approach tool.
RESULTS: This review included 12 studies, from which five were qualitative and seven quantitative. The later showed poor dietary habits and sedentary lifestyle exhibiting MAFLD which eventually affect their quality of life. Further studies suggested that by introducing healthy lifestyle in MAFLD group using diet and exercise caused reduction in BMI, obesity levels, improved glycemic control and reversal of liver fat content with improved liver enzymes.
CONCLUSION: Subjects with MAFLD experienced poor quality of life. Altering lifestyle by diet and exercise can improve their physical wellbeing.