Infection associated with severe malnutrition among hspitalised children in East Africa

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Distributed cross-learning for equitable federated models - privacy-preserving prediction on data from five California hospitals.

Quality improvement, clinical research, and patient care can be supported by medical predictive analytics. Predictive models can be improved by integrating more patient records from different healthcare centers (horizontal) or integrating parts of information of a patient from different centers (vertical). We introduce Distributed Cross-Learning for Equitable Federated models (D-CLEF), which incorporates horizontally- or vertically-partitioned data without disseminating patient-level records, to protect patients privacy. We compared D-CLEF with centralized/siloed/federated learning in horizontal or vertical scenarios. Using data of more than 15,000 patients with COVID-19 from five University of California (UC) Health medical centers, surgical data from UC San Diego, and heart disease data from Edinburgh, UK, D-CLEF performed close to the centralized solution, outperforming the siloed ones, and equivalent to the federated learning counterparts, but with increased synchronization time. Here, we show that D-CLEF presents a promising accelerator for healthcare systems to collaborate without submitting their patient data outside their own systems

Recent Approaches of Forecasting and Optimal Economic Dispatch to Overcome Intermittency of Wind and Photovoltaic (PV) Systems:A Review

Renewable energy sources (RESs) are the replacement of fast depleting, environment polluting, costly, and unsustainable fossil fuels. RESs themselves have various issues such as variable supply towards the load during different periods, and mostly they are available at distant locations from load centers. This paper inspects forecasting techniques, employed to predict the RESs availability during different periods and considers the dispatch mechanisms for the supply, extracted from these resources. Firstly, we analyze the application of stochastic distributions especially the Weibull distribution (WD), for forecasting both wind and PV power potential, with and without incorporating neural networks (NN). Secondly, a review of the optimal economic dispatch (OED) of RES using particle swarm optimization (PSO) is presented. The reviewed techniques will be of great significance for system operators that require to gauge and pre-plan flexibility competence for their power systems to ensure practical and economical operation under high penetration of RESs

Classification and Analysis of HIV Neurocognitive MRI Images using Support Vector Machine

Medical imaging has expanded thanks to advances in processing power and advanced image analysis techniques, especially with magnetic resonance imaging (MRI), which offers comprehensive body scans for diagnosis. This work proposes a simple yet efficient method to use a support vector machine (SVM) to classify HIV neurocognitive MRI pictures into normal and pathological categories. The model consists of four steps: data pre-processing, feature extraction, SVM classification, and model evaluation. To separate desired and undesired elements, such as the scalp and skull, pre-processed images were converted from grayscale to colour using support vector machines. The discrete wavelet transform (DWT) was used in the feature extraction stage to extract image properties. Colour moments (CMs) were then used to optimize the feature collection. Afterwards, the SVM classifier was used to determine the ideal feature set to classify images. For example, a dataset is used for training and testing, with a split ratio of 75% to 25% respectively. The experimental results show that the proposed model has a high classification accuracy of 94.4

Deep learning enhanced transmembranous electromyography in the diagnosis of sleep apnea

Obstructive sleep apnea (OSA) is widespread, under-recognized, and under-treated, impacting the health and quality of life for millions. The current gold standard for sleep apnea testing is based on the in-lab sleep study, which is costly, cumbersome, not readily available and represents a well-known roadblock to managing this huge societal burden. Assessment of neuromuscular function involved in the upper airway using electromyography (EMG) has shown potential to characterize and diagnose sleep apnea, while the development of transmembranous electromyography (tmEMG), a painless surface probe, has made this opportunity practical and highly feasible. However, experience and ability to interpret electrical signals from the upper airway are scarce, and much of the pertinent information within the signal is likely difficult to detect visually. To overcome this issue, we explored the use of transformers, a deep learning (DL) model architecture with attention mechanisms, to model tmEMG data and distinguish between electromyographic signals from a cohort of control, neurogenic, and sleep apnea patients. Our approach involved three strategies to train a generalizable model on a relatively small dataset including, (1) transfer learning using an audio spectral transformer (AST), (2) the use of 6,000 simulated EMG recordings, converted to spectrograms and using standard backpropagation for fine-tuning, and (3) application of regularization to prevent overfitting and enhance generalizability. This DL approach was tested using 177 transoral EMG recordings from a prior study's database that included six healthy controls, five moderate to severe OSA patients, and five amyotrophic lateral sclerosis (ALS) patients with evidence of bulbar involvement (neurogenic injury). Sensitivity and specificity for classifying neurogenic cases from controls were 98% and 73%, respectively, while classifying OSA from controls were 88% and 64%, respectively. Notably, by averaging the predicted probabilities of each segment for individual patients, the model correctly classified up to 82% of control and OSA patients. These results not only suggest a potential to diagnose OSA patients accurately, but also to identify OSA endotypes that involve neuromuscular pathology, which has major implications for clinical management, patient outcomes, and research

Association between adverse childhood experiences and suicidal behavior in schizophrenia spectrum disorders: A systematic review and meta-analysis

Assessing and managing suicide behaviors is highly relevant to individuals with schizophrenia spectrum disorders. Our study aims to assess the association between adverse childhood experiences and suicidal behaviors in individuals with schizophrenia spectrum disorders. We included observational studies comparing the probability of suicide behaviors in adults with schizophrenia spectrum disorders exposed and unexposed to adverse childhood experiences. Odds ratio estimates were obtained by pooling data using a random-effects pairwise meta-analysis. Standardized criteria were used to assess the strength of the association of the pooled estimate. We found 21 eligible studies reporting outcomes for 6257 individuals from 11 countries. The primary outcome revealed an association between any suicidal behavior and adverse childhood experiences, which resulted "highly suggestive" according to validated Umbrella Criteria. Similarly, a positive association was confirmed for suicidal ideation and suicide attempt and for any subtype of adverse childhood experience. This meta-analysis showed that exposure to adverse childhood experiences strongly increases the probability of suicide behaviors in people with schizophrenia spectrum disorders

Simulating complex patient populations with hierarchical learning effects to support methods development for post-market surveillance

Funding Information: This work was funded by a grant from the National Heart, Lung, and Blood Institute (NHLBI; grant number 1R01HL149948). The funding agency was not involved in the design of the study, collection and analysis of data, interpretation of results, or writing of the manuscript. Publisher Copyright: © 2023, The Author(s).Peer reviewedPublisher PD

Adaptive and Behavioral Changes in Kynurenine 3-Monooxygenase Knockout Mice:Relevance to Psychotic Disorders

BACKGROUND: Kynurenine 3-monooxygenase converts kynurenine to 3-hydroxykynurenine, and its inhibition shunts the kynurenine pathway-which is implicated as dysfunctional in various psychiatric disorders-toward enhanced synthesis of kynurenic acid, an antagonist of both α7 nicotinic acetylcholine and N-methyl-D-aspartate receptors. Possibly as a result of reduced kynurenine 3-monooxygenase activity, elevated central nervous system levels of kynurenic acid have been found in patients with psychotic disorders, including schizophrenia. METHODS: In the present study, we investigated adaptive-and possibly regulatory-changes in mice with a targeted deletion of Kmo (Kmo-/-) and characterized the kynurenine 3-monooxygenase-deficient mice using six behavioral assays relevant for the study of schizophrenia. RESULTS: Genome-wide differential gene expression analyses in the cerebral cortex and cerebellum of these mice identified a network of schizophrenia- and psychosis-related genes, with more pronounced alterations in cerebellar tissue. Kynurenic acid levels were also increased in these brain regions in Kmo-/- mice, with significantly higher levels in the cerebellum than in the cerebrum. Kmo-/- mice exhibited impairments in contextual memory and spent less time than did controls interacting with an unfamiliar mouse in a social interaction paradigm. The mutant animals displayed increased anxiety-like behavior in the elevated plus maze and in a light/dark box. After a D-amphetamine challenge (5 mg/kg, intraperitoneal), Kmo-/- mice showed potentiated horizontal activity in the open field paradigm. CONCLUSIONS: Taken together, these results demonstrate that the elimination of Kmo in mice is associated with multiple gene and functional alterations that appear to duplicate aspects of the psychopathology of several neuropsychiatric disorders

Experimental investigation of different geometries of fixed oscillating water column devices

publisher: Elsevier articletitle: Experimental investigation of different geometries of fixed oscillating water column devices journaltitle: Renewable Energy articlelink: http://dx.doi.org/10.1016/j.renene.2016.11.061 content_type: article copyright: © 2016 Elsevier Ltd. All rights reserved

Clinical Characteristics and Outcomes of Drug-Induced Acute Kidney Injury Cases

Introduction Drug-induced acute kidney injury (DI-AKI) is a frequent adverse event. The identification of DI-AKI is challenged by competing etiologies, clinical heterogeneity among patients, and a lack of accurate diagnostic tools. Our research aims to describe the clinical characteristics and predictive variables of DI-AKI. Methods We analyzed data from the DIRECT study (NCT02159209), an international, multi-center, observational cohort study of enriched clinically adjudicated DI-AKI cases. Cases met the primary inclusion criteria if the patient was exposed to at least one nephrotoxic drug for a minimum of 24 hours prior to acute kidney injury (AKI) onset. Cases were clinically adjudicated and inter-rater reliability (IRR) was measured using Krippendorff's alpha. Variables associated with DI-AKI were identified using L1 regularized multivariable logistic regression. Model performance was assessed using the area under the receiver operating characteristic curve (ROC AUC). Results 314 AKI cases met the eligibility criteria for this analysis, and 271 (86%) cases were adjudicated as DI-AKI. The majority of the AKI cases were recruited from the United States (68%). The most frequent causal nephrotoxic drugs were vancomycin (48.7%), non-steroidal anti-inflammatory drugs (18.2%), and piperacillin/tazobactam (17.8%). The IRR for DI-AKI adjudication was 0.309. The multivariable model identified age, vascular capacity, hyperglycemia, infections, pyuria, serum creatinine trends, and contrast media as significant predictors of DI-AKI with good performance, ROC AUC 0.86. Conclusions The identification of DI-AKI is challenging even with comprehensive adjudication by experienced nephrologists. Our analysis identified key clinical characteristics and outcomes of DI-AKI compared to other AKI etiologies