Explore the vast range of titles available.

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by cognitive decline and complex molecular changes. Extracellular vesicles (EVs), particularly exosomes, play a key role in intercellular communication and disease progression, transporting proteins, lipids, and nucleic acids. While altered exosomal mRNA profiles have emerged as potential biomarkers for AD, the relationship between mRNA expression and AD neuropsychological deficits remains unclear. Here, we investigated the correlation between exosomx10-derived mRNA signatures and neuropsychological performance in a cohort from Barranquilla, Colombia. Expression profiles of 16,585 mRNAs in 15 AD patients and 15 healthy controls were analysed using Generalized Linear Models (GLMs) and the Predictive Power Score (PPS). We identified significant correlations between specific mRNA signatures and key neuropsychological variables, including the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Functional Assessment Screening Tool (FAST), Boston Naming Test, and Rey–Osterrieth Figure test. These mRNAs were in key AD-associated genes (i.e., GABRB3 and CADM1), while other genes are novel (i.e., SHROOM3, SLC7A2, GJB4, and XBP1). PPS analyses further revealed predictive relationships between mRNA expression and neuropsychological variables, accounting for non-linear patterns and asymmetric associations. If replicated in more extensive and heterogeneous studies, these findings provide critical insights into the molecular basis governing the natural history of AD, potential personalized and non-invasive diagnosis, prognosis, follow-up, and potential targets for future therapies.

Background 5q‐Spinal muscular atrophy (SMA) is now one of the 5% treatable rare diseases worldwide. As disease‐modifying therapies alter disease progression and patient phenotypes, paediatricians and consulting disciplines face new unknowns in their treatment decisions. Conclusions made from historical patient data sets are now mostly limited, and new approaches are needed to ensure our continued best standard‐of‐care practices for this exceptional patient group. Here, we present a data‐driven machine learning approach to a rare disease data set to predict spinal muscular atrophy (SMA)‐associated scoliosis. Methods We collected data from 84 genetically confirmed 5q‐SMA patients who have received novel SMA therapies. We performed expert domain knowledge‐directed feature engineering, correlation and predictive power score (PPS) analyses for feature selection. To test the predictive performance of the selected features, we trained a Random Forest Classifier and evaluated model performance using standard metrics. Results The SMA data set consisted of 1304 visits and over 360 variables. We performed feature engineering for variables related to ‘interventions’, ‘devices’, ‘orthosis’, ‘ventilation’, ‘muscle contractures’ and ‘motor milestones’. Through correlation and PPS analysis paired with expert domain knowledge feature selection, we identified relevant features for scoliosis prediction in SMA that included disease progression markers: Hammersmith Functional Motor Scale Expanded ‘HFMSE’ (PPS = 0.27) and 6‐Minute Walk Test ‘6MWT’ scores (PPS = 0.44), ‘age’ (PPS = 0.41) and ‘weight’ (PPS = 0.49), ‘contractures’ (PPS = 0.17), the use of ‘assistive devices’ (PPS = 0.39, ‘ventilation’ (PPS = 0.16) and the presence of ‘gastric tubes’ (PPS = 0.35) in SMA patients. These features were validated using expert domain knowledge and used to train a Random Forest Classifier with an observed accuracy of 0.82 and an average receiver operating characteristic (ROC) area of 0.87. Conclusion The introduction of disease‐modifying SMA therapies, followed by the implementation of SMA in newborn screenings, has presented physicians with never‐seen patients. We used feature engineering tools to overcome one of the main challenges when using data‐driven approaches in rare disease data sets. Through predictive modelling of this data, we defined disease progression markers, which are easily assessed during patient visits and can help anticipate scoliosis onset. This highlights the importance of progressive features in the drug‐induced revolution of this rare disease and further supports the ongoing efforts to update the SMA classification. We advocate for the consistent documentation of relevant progression markers, which will serve as a basis for data‐driven models that physicians can use to update their best standard‐of‐care practices.

Probabilistic forecasts of continuous variables take the form of predictive densities or predictive cumulative distribution functions. We propose a diagnostic approach to the evaluation of predictive performance that is based on the paradigm of maximizing the sharpness of the predictive distributions subject to calibration. Calibration refers to the statistical consistency between the distributional forecasts and the observations and is a joint property of the predictions and the events that materialize. Sharpness refers to the concentration of the predictive distributions and is a property of the forecasts only. A simple theoretical framework allows us to distinguish between probabilistic calibration, exceedance calibration and marginal calibration. We propose and study tools for checking calibration and sharpness, among them the probability integral transform histogram, marginal calibration plots, the sharpness diagram and proper scoring rules. The diagnostic approach is illustrated by an assessment and ranking of probabilistic forecasts of wind speed at the Stateline wind energy centre in the US Pacific Northwest. In combination with cross-validation or in the time series context, our proposal provides very general, nonparametric alternatives to the use of information criteria for model diagnostics and model selection.

With the global proliferation of wind power, the need for accurate short-term forecasts of wind resources at wind energy sites is becoming paramount. Regime-switching space-time (RST) models merge meteorological and statistical expertise to obtain accurate and calibrated, fully probabilistic forecasts of wind speed and wind power. The model formulation is parsimonious, yet takes into account all of the salient features of wind speed: alternating atmospheric regimes, temporal and spatial correlation, diurnal and seasonal nonstationarity, conditional heteroscedasticity, and non-Gaussianity. The RST method identifies forecast regimes at a wind energy site and fits a conditional predictive model for each regime. Geographically dispersed meteorological observations in the vicinity of the wind farm are used as off-site predictors. The RST technique was applied to 2-hour-ahead forecasts of hourly average wind speed near the Stateline wind energy center in the U. S. Pacific Northwest. The RST point forecasts and distributional forecasts were accurate, calibrated, and sharp, and they compared favorably with predictions based on state-of-the-art time series techniques. This suggests that quality meteorological data from sites upwind of wind farms can be efficiently used to improve short-term forecasts of wind resources.

We propose a method for comparing density forecasts that is based on weighted versions of the continuous ranked probability score. The weighting emphasizes regions of interest, such as the tails or the center of a variable's range, while retaining propriety, as opposed to a recently developed weighted likelihood ratio test, which can be hedged. Threshold- and quantile-based decompositions of the continuous ranked probability score can be illustrated graphically and provide insight into the strengths and deficiencies of a forecasting method. We illustrate the use of the test and graphical tools in case studies on the Bank of England's density forecasts of quarterly inflation rates in the United Kingdom, and probabilistic predictions of wind resources in the Pacific Northwest.

Wind is a highly effective and environmentally friendly renewable energy source. As the global development of wind farms continues, accurate wind power prediction has become essential for ensuring consistent energy production. Machine learning (ML) significantly advances wind power forecasting, improving the reliability and efficiency of wind power systems. This study presents an analysis of ML algorithms applied to four datasets from different geographical locations. The initial step involved the elimination of outliers using the Z-score and IQR methods to maximize the performance of the regression. The three algorithms (XGBoost, XGB, RFR, and Support Vector Regression) with RBF, polynomial, and linear kernels were trained on the same features and evaluated using R 2 , and MAE. XGBoost provided the most effective results with R 2 values 0.99 in all the locations and MAE from 11.10 to 15.94. RFR performed satisfactorily also, but the R 2 values were 0.99 in three sites, but in site 4, the ( R 2 = 0.83), and a much higher MAE of (600.81). The linear kernel was the best among the SVR models, as it attained R 2 values 0.99 and a much lower MAE on all data locations. RBF and polynomial kernels were lagging, with lower R 2 and higher MAE values. These findings highlight XGBoost and linear-kernel SVR as the best to use in wind power forecasting on diverse datasets with high accuracy levels and low error rates that can be used to improve wind farm energy production.

To construct a nomogram for feeding intolerance (FI) during therapeutic hypothermia (TH) in neonates with hypoxic-ischemic encephalopathy (HIE). 179 neonates with HIE were recruited between March 2017 and July 2023 and clinical data subjected to least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analysis. A predictive model was constructed and verified by receiver operating characteristic (ROC) curve analysis, calibration plots and decision curve analysis (DCA). Neonatal infection, 5-min Apgar score, hypoglycemia, time of enteral nutrition initiation, initial enteral feeding volume (15–30 mL/kg/day) and rate of feeding advancement (1–5 mL/kg/day) were found to be independent predictors for FI. Earlier initiation, larger initial volume and rapid feeding progression increased FI risk and slow advancement was protective. ROC analysis gave an area under the curve (AUC) of 0.83 (95% CI: 0.77–0.89) and internal verification concordance index (C-index) was 0.829. DCA showed a favorable net clinical benefit for the FI predictive model. The predictive model may identify the causes of FI at an early stage and inform clinical decisions.

Background Tools to identify patients at high risk for subclinical atherosclerosis (subAS) are lacking. We evaluated the Framingham risk score (FRS) for identifying high-risk subAS patients. Methods We enrolled 68 subAS patients and 150 healthy controls. Carotid intima-media thickness (cIMT) was measured via ultrasound in both common carotid arteries, and FRS was calculated. Results The FRS was low (median, 3.98%). Mean cIMTs for both long and short axes in subAS patients were higher than in healthy individuals (𝑃 < 0.001). In subAS patients, FRS significantly correlated with increased cIMT in long (high-risk OR = 11.61, 95% CI = 2.66–65.95) and short (high-risk OR = 7.11, 95% CI = 1.73–37.79) axes. FRS predicted increased cIMT in long (AUC = 0.713) and short (AUC = 0.673) axes in subAS patients, but not in healthy controls (𝑃 > 0.05). Conclusions FRS is associated with high-risk subAS patients, but complementary noninvasive imaging data are needed.

Allostatic load refers to the physiological \"wear and tear\" that results from adaptation to stressors over the lifespan. In this study, we integrated lung function (LF) parameters into the calculation of the allostatic load score (ALS) to evaluate changes in its performance for predicting all-cause mortality. Data from 8,775 participants (aged 25–82 years; 52% female) who participated in the first wave of the Austrian LEAD cohort were used. “ALS without LF” was calculated using 12 parameters, including cardiovascular, metabolic, body composition, and bone mineral density measures. Z-scores of forced expiratory volume in 1 s (FEV 1 ) and forced vital capacity (FVC) were integrated with the aforementioned parameters for the calculation of “ALS with LF”. Participants scored 1 point for each at-risk marker and 1 point for medication use for hypertension, diabetes, and dyslipidaemia. The total points constituted the ALS. Participants were followed-up for death for an average of 7.7 years. The association of ALS, with and without LF, with mortality was investigated using Cox proportional hazards models. ALS increased with age and was higher in males compared to females across all age categories. ALS (as a continuous variable) with LF [hazard ratio (HR): 1.19 (95% confidence interval (CI) 1.14–1.24)] and without LF [HR: 1.16 (95% CI 1.11–1.21)] showed a significant association with mortality in sex- and age-adjusted models. The adjusted models incorporating ALS as a categorical variable showed that individuals with high ALS, with [HR: 2.44 (95% CI 1.41–4.20)] and without [HR: 2.47 (95% CI 1.46–4.80)] LF parameters, had a higher risk of mortality compared to those in the low ALS group. Cox models incorporating “ALS with LF” parameters exhibited higher concordance index and R 2 values, along with a lower Akaike’s information criterion indicating superior predictive power compared to models that included “ALS without LF”. ALS is strongly associated with mortality, with higher ALS linked to an increased risk of mortality across both continuous and categorical analyses. Models that incorporate ALS with LF parameters demonstrated superior predictive performance and greater robustness, underscoring the added value of including LF in the models.

Background The study aimed to introduce a machine learning model that predicts in-hospital mortality in patients on mechanical ventilation (MV) following moderate to severe traumatic brain injury (TBI). Methods A retrospective analysis was conducted for all adult patients who sustained TBI and were hospitalized at the trauma center from January 2014 to February 2019 with an abbreviated injury severity score for head region (HAIS) ≥ 3. We used the demographic characteristics, injuries and CT findings as predictors. Logistic regression (LR) and Artificial neural networks (ANN) were used to predict the in-hospital mortality. Accuracy, area under the receiver operating characteristics curve (AUROC), precision, negative predictive value (NPV), sensitivity, specificity and F-score were used to compare the models` performance. Results Across the study duration; 785 patients met the inclusion criteria (581 survived and 204 deceased). The two models (LR and ANN) achieved good performance with an accuracy over 80% and AUROC over 87%. However, when taking the other performance measures into account, LR achieved higher overall performance than the ANN with an accuracy and AUROC of 87% and 90.5%, respectively compared to 80.9% and 87.5%, respectively. Venous thromboembolism prophylaxis, severity of TBI as measured by abbreviated injury score, TBI diagnosis, the need for blood transfusion, heart rate upon admission to the emergency room and patient age were found to be the significant predictors of in-hospital mortality for TBI patients on MV. Conclusions Machine learning based LR achieved good predictive performance for the prognosis in mechanically ventilated TBI patients. This study presents an opportunity to integrate machine learning methods in the trauma registry to provide instant clinical decision-making support.