Publications by authors named "Faris F Gulamali"
Background: Acute kidney injury (AKI) is common in hospitalized patients with SARS-CoV2 infection despite vaccination and leads to long-term kidney dysfunction. However, peripheral blood molecular signatures in AKI from COVID-19 and their association with long-term kidney dysfunction are yet unexplored.
Methods: In patients hospitalized with SARS-CoV2, we performed bulk RNA sequencing using peripheral blood mononuclear cells(PBMCs).
Article Synopsis
- Acute kidney injury (AKI) is a serious complication of COVID-19, leading to higher in-hospital death rates; researchers used proteomics to find markers for COVID-AKI and long-term kidney issues.
- In a study with two groups of COVID-19 hospitalized patients, they identified 413 proteins with elevated levels and 30 with decreased levels tied to AKI, validating 62 of these in a second group.
- The findings reveal that proteins indicating kidney and heart injury correlate with acute and long-term kidney dysfunction, suggesting that AKI is influenced by various factors, including blood flow issues and heart damage.
Article Synopsis
- Researchers identified four distinct subphenotypes of Acute Kidney Injury (AKI) using a comprehensive approach that included various biomarkers and clinical evaluations.
- Each subphenotype exhibited unique characteristics: Subphenotype 1 showed high kidney injury and cardiac biomarkers, Subphenotype 2 had elevated uromodulin levels, Subphenotype 3 was linked to severe heart-related markers, and Subphenotype 4 was mainly associated with sepsis and significant inflammation.
- Findings indicated that Subphenotypes 3 and 4 had a notably higher risk of death compared to Subphenotype 2, suggesting that a more nuanced understanding of AKI could improve patient outcomes.
Sample size estimation is a crucial step in experimental design but is understudied in the context of deep learning. Currently, estimating the quantity of labeled data needed to train a classifier to a desired performance, is largely based on prior experience with similar models and problems or on untested heuristics. In many supervised machine learning applications, data labeling can be expensive and time-consuming and would benefit from a more rigorous means of estimating labeling requirements.
View Article and Find Full Text PDFArticle Synopsis
- The main issue in machine learning is getting models to perform well on new, unseen data, particularly when there’s a shift in the dataset.
- The researchers used generative adversarial networks (GANs) to enhance model performance on two datasets: handwritten digits and chest X-rays from different medical centers.
- They found that their adaptations significantly improved the accuracy of the models on external tests, suggesting that this method could be beneficial in various medical imaging areas.
Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. Using measurements of ∼4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction.
View Article and Find Full Text PDFMachine learning for risk stratification in kidney disease.
Curr Opin Nephrol Hypertens
November 2022
Purpose Of Review: Risk stratification for chronic kidney is becoming increasingly important as a clinical tool for both treatment and prevention measures. The goal of this review is to identify how machine learning tools contribute and facilitate risk stratification in the clinical setting.
Recent Findings: The two key machine learning paradigms to predictively stratify kidney disease risk are genomics-based and electronic health record based approaches.
Article Synopsis
- Clinical EHR data is diverse and complex, which makes it difficult for machine learning models to accurately predict outcomes due to high variability within the same category.
- To improve prediction accuracy, a new supervised pre-training model using an embedded k-nearest-neighbor positive sampling strategy is proposed.
- This approach demonstrates strong performance, achieving an AUROC score of 0.872 in predicting patient mortality from COVID-19 data, outperforming existing methods, especially in cases with limited training data.
Background: Modern machine learning and deep learning algorithms require large amounts of data; however, data sharing between multiple healthcare institutions is limited by privacy and security concerns.
Summary: Federated learning provides a functional alternative to the single-institution approach while avoiding the pitfalls of data sharing. In cross-silo federated learning, the data do not leave a site.
Food intake behavior is regulated by a network of appetite-inducing and appetite-suppressing neuronal populations throughout the brain. The parasubthalamic nucleus (PSTN), a relatively unexplored population of neurons in the posterior hypothalamus, has been hypothesized to regulate appetite due to its connectivity with other anorexigenic neuronal populations and because these neurons express Fos, a marker of neuronal activation, following a meal. However, the individual cell types that make up the PSTN are not well characterized, nor are their functional roles in food intake behavior.
View Article and Find Full Text PDF