Search Results (4)

Background and Objectives: Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of global morbidity and mortality. The cardiometabolic index (CMI) has been shown to be associated with metabolic disorders and mortality in general populations, but its role in ASCVD-specific mortality risk remains unexplored. Materials and Methods: This cohort study was based on the National Health and Nutrition Examination Survey (NHANES). Weighted Cox proportional hazards models were fitted to estimate the associations between CMI and mortality. Restricted cubic splines were used to explore nonlinear relationships. Subgroup analyses were used to investigate potential differences among specific ASCVD patients. Results: A total of 2157 patients with ASCVD were included. Over a median 83-month follow-up, 887 all-cause and 300 cardiovascular deaths occurred. Each unit increase in CMI was associated with an 11.3% increased risk of all-cause mortality (HR = 1.113, 95% CI: 1.112–1.115) and a 6.4% increased risk of cardiovascular mortality (HR = 1.064, 95% CI: 1.062–1.065). There was a nonlinear J-shaped relationship between CMI and all-cause mortality, while the risk of cardiovascular mortality increased linearly with increasing CMI. Conclusions: These findings underscore the importance of monitoring and managing CMI in patients with ASCVD in clinical practice and suggest that optimizing CMI levels may help reduce the risk of death and improve the long-term prognosis of patients. Full article

Background: The implementation of machine learning (ML) models has significantly impacted neuroimaging. Recent data suggest that these models may improve the accuracy of diagnosing and predicting outcomes in patients with Chiari malformation type I (CMI). Methods: A scoping review was conducted according to the guidelines put forth by PRISMA. The literature search was performed in PubMed/MEDLINE, SCOPUS, and ScienceDirect databases. We included observational or experimental studies focusing on the applications of ML in patients with CMI. Results: A total of 9 articles were included. All the included articles were retrospective. Five out of the nine studies investigated the applicability of machine learning models for diagnosing CMI, whereas the remaining studies focused on the prognosis of the patients treated for CM. Overall, the accuracy of the machine learning models utilized for the diagnosis ranged from 0.555 to 1.00, whereas the specificity and sensitivity ranged from 0.714 to 1.00 and 0.690 to 1.00, respectively. The accuracy of the prognostic ML models ranged from 0.402 to 0.820, and the AUC ranged from 0.340 to 0.990. The most utilized ML model for the diagnosis of CMI is logistic regression (LR), whereas the support vector machine (SVM) is the most utilized model for postoperative prognosis. Conclusions: In the present review, both conventional and novel ML models were utilized to diagnose CMI or predict patient outcomes following surgical treatment. While these models demonstrated significant potential, none were highly validated. Therefore, further research and validation are required before their actual implementation in standard medical practice. Full article

Lung adenocarcinoma (LUAD) is the primary histological subtype of lung cancer with a markedly heterogeneous prognosis. Therefore, there is an urgent need to identify optimal prognostic biomarkers. We aimed to explore the value of the circadian miRNA (cmiRNA) pair in predicting prognosis and guiding the treatment of LUAD. We first retrieved circadian genes (Cgenes) from the CGDB database, based on which cmiRNAs were predicted using the miRDB and mirDIP databases. The sequencing data of Cgenes and cmiRNAs were retrieved from TCGA and GEO databases. Two random cmiRNAs were matched to a single cmiRNA pair. Finally, univariate Cox proportional hazard analysis, LASSO regression, and multivariate Cox proportional hazard analysis were performed to develop a prognostic signature consisting of seven cmiRNA pairs. The signature exhibited good performance in predicting the overall and progression-free survival. Patients in the high-risk group also showed lower IC50 values for several common chemotherapy and targeted medicines. In addition, we constructed a cmiRNA–Cgenes network and performed a corresponding Gene Ontology and Gene Set enrichment analysis. In conclusion, the novel circadian-related miRNA pair signature could provide a precise prognostic evaluation with the potential capacity to guide individualized treatment regimens for LUAD. Full article

It is well known that each year the wind sector has profit losses due to wind turbine failures and operation and maintenance costs. Therefore, operations related to these actions are crucial for wind farm operators and linked companies. One of the key points for failure prediction on wind turbine using SCADA data is to select the optimal or near optimal set of inputs that can feed the failure prediction (prognosis) algorithm. Due to a high number of possible predictors (from tens to hundreds), the optimal set of inputs obtained by exhaustive-search algorithms is not viable in the majority of cases. In order to tackle this issue, show the viability of prognosis and select the best set of variables from more than 200 analogous variables recorded at intervals of 5 or 10 min by the wind farm’s SCADA, in this paper a thorough study of automatic input selection algorithms for wind turbine failure prediction is presented and an exhaustive-search-based quasi-optimal (QO) algorithm, which has been used as a reference, is proposed. In order to evaluate the performance, a k-NN classification algorithm is used. Results showed that the best automatic feature selection method in our case-study is the conditional mutual information (CMI), while the worst one is the mutual information feature selection (MIFS). Furthermore, the effect of the number of neighbours (k) is tested. Experiments demonstrate that k = 1 is the best option if the number of features is higher than 3. The experiments carried out in this work have been extracted from measures taken along an entire year and corresponding to gearbox and transmission systems of Fuhrländer wind turbines. Full article