The Ecology of Economic Distress and Life Expectancy.

Objectives: To determine whether life expectancy (LE) changes between 2000 and 2019 were associated with race, rural status, local economic prosperity, and changes in local economic prosperity, at the county level.

Methods: Between 12/1/22 and 2/28/23, we conducted a retrospective analysis of 2000 and 2019 data from 3,123 United States counties. For Total, White, and Black populations, we compared LE changes for counties across the rural-urban continuum, the local economic prosperity continuum, and for counties in which local economic prosperity dramatically improved or declined.

Known pathogenic gene variants and new candidates detected in sudden unexpected infant death using whole genome sequencing.

The purpose of this study is to gain insights into potential genetic factors contributing to the infant's vulnerability to Sudden Unexpected Infant Death (SUID). Whole Genome Sequencing (WGS) was performed on 144 infants that succumbed to SUID, and 573 healthy adults. Variants were filtered by gnomAD allele frequencies and predictions of functional consequences.

Rapid and accurate prediction of protein homo-oligomer symmetry with Seq2Symm.

The majority of proteins must form higher-order assemblies to perform their biological functions. Despite the importance of protein quaternary structure, there are few machine learning models that can accurately and rapidly predict the symmetry of assemblies involving multiple copies of the same protein chain. Here, we address this gap by training several classes of protein foundation models, including ESM-MSA, ESM2, and RoseTTAFold2, to predict homo-oligomer symmetry.

Democratizing Protein Language Models with Parameter-Efficient Fine-Tuning.

Proteomics has been revolutionized by large pre-trained protein language models, which learn unsupervised representations from large corpora of sequences. The parameters of these models are then fine-tuned in a supervised setting to tailor the model to a specific downstream task. However, as model size increases, the computational and memory footprint of fine-tuning becomes a barrier for many research groups.

PSMA-Hornet: Fully-automated, multi-target segmentation of healthy organs in PSMA PET/CT images.

Background: Prostate-specific membrane antigen (PSMA) PET imaging represents a valuable source of information reflecting disease stage, response rate, and treatment optimization options, particularly with PSMA radioligand therapy. Quantification of radiopharmaceutical uptake in healthy organs from PSMA images has the potential to minimize toxicity by extrapolation of the radiation dose delivery towards personalization of therapy. However, segmentation and quantification of uptake in organs requires labor-intensive organ delineations that are often not feasible in the clinic nor scalable for large clinical trials.

Using Data Science and a Health Equity Lens to Identify Long-COVID Sequelae Among Medically Underserved Populations.

Understanding how post-acute COVID-19 syndrome (PACS or long COVID) manifests among underserved populations, who experienced a disproportionate burden of acute COVID-19, can help providers and policymakers better address this ongoing crisis. To identify clinical sequelae of long COVID among underserved populations treated in the primary care safety net, we conducted a causal impact analysis with electronic health records (EHR) to compare symptoms among community health center patients who tested positive (n=4,091) and negative (n=7,118) for acute COVID-19. We found 18 sequelae with statistical significance and causal dependence among patients who had a visit after 60 days or more following acute COVID-19.

Evaluating the generalizability of deep learning image classification algorithms to detect middle ear disease using otoscopy.

To evaluate the generalizability of artificial intelligence (AI) algorithms that use deep learning methods to identify middle ear disease from otoscopic images, between internal to external performance. 1842 otoscopic images were collected from three independent sources: (a) Van, Turkey, (b) Santiago, Chile, and (c) Ohio, USA. Diagnostic categories consisted of (i) normal or (ii) abnormal.

Predicting locations of cryptic pockets from single protein structures using the PocketMiner graph neural network.

Cryptic pockets expand the scope of drug discovery by enabling targeting of proteins currently considered undruggable because they lack pockets in their ground state structures. However, identifying cryptic pockets is labor-intensive and slow. The ability to accurately and rapidly predict if and where cryptic pockets are likely to form from a structure would greatly accelerate the search for druggable pockets.

Reimagining leprosy elimination with AI analysis of a combination of skin lesion images with demographic and clinical data.

Background: Leprosy is an infectious disease that mostly affects underserved populations. Although it has been largely eliminated, still about 200'000 new patients are diagnosed annually. In the absence of a diagnostic test, clinical diagnosis is often delayed, potentially leading to irreversible neurological damage and its resulting stigma, as well as continued transmission.

Identifying long-term effects of SARS-CoV-2 and their association with social determinants of health in a cohort of over one million COVID-19 survivors.

Background: Despite an abundance of information on the risk factors of SARS-CoV-2, there have been few US-wide studies of long-term effects. In this paper we analyzed a large medical claims database of US based individuals to identify common long-term effects as well as their associations with various social and medical risk factors.

Methods: The medical claims database was obtained from a prominent US based claims data processing company, namely Change Healthcare.

The Risk of Hospitalization and Mortality After Breakthrough SARS-CoV-2 Infection by Vaccine Type: Observational Study of Medical Claims Data.

Background: Several risk factors have been identified for severe COVID-19 disease by the scientific community. In this paper, we focus on understanding the risks for severe COVID-19 infections after vaccination (ie, in breakthrough SARS-CoV-2 infections). Studying these risks by vaccine type, age, sex, comorbidities, and any prior SARS-CoV-2 infection is important to policy makers planning further vaccination efforts.

Machine learning-based derivation and external validation of a tool to predict death and development of organ failure in hospitalized patients with COVID-19.

COVID-19 mortality risk stratification tools could improve care, inform accurate and rapid triage decisions, and guide family discussions regarding goals of care. A minority of COVID-19 prognostic tools have been tested in external cohorts. Our objective was to compare machine learning algorithms and develop a tool for predicting subsequent clinical outcomes in COVID-19.

An Artificial Intelligence Dataset for Solar Energy Locations in India.

Rapid development of renewable energy sources, particularly solar photovoltaics (PV), is critical to mitigate climate change. As a result, India has set ambitious goals to install 500 gigawatts of solar energy capacity by 2030. Given the large footprint projected to meet renewables energy targets, the potential for land use conflicts over environmental values is high.

BindVAE: Dirichlet variational autoencoders for de novo motif discovery from accessible chromatin.

We present a novel unsupervised deep learning approach called BindVAE, based on Dirichlet variational autoencoders, for jointly decoding multiple TF binding signals from open chromatin regions. BindVAE can disentangle an input DNA sequence into distinct latent factors that encode cell-type specific in vivo binding signals for individual TFs, composite patterns for TFs involved in cooperative binding, and genomic context surrounding the binding sites. On the task of retrieving the motifs of expressed TFs in a given cell type, BindVAE is competitive with existing motif discovery approaches.

Testing the Ability of Convolutional Neural Networks to Learn Radiomic Features.

Background And Objective: Radiomics and deep learning have emerged as two distinct approaches to medical image analysis. However, their relative expressive power remains largely unknown. Theoretically, hand-crafted radiomic features represent a mere subset of features that neural networks can approximate, thus making deep learning a more powerful approach.

Effective deep learning approaches for predicting COVID-19 outcomes from chest computed tomography volumes.

The rapid evolution of the novel coronavirus disease (COVID-19) pandemic has resulted in an urgent need for effective clinical tools to reduce transmission and manage severe illness. Numerous teams are quickly developing artificial intelligence approaches to these problems, including using deep learning to predict COVID-19 diagnosis and prognosis from chest computed tomography (CT) imaging data. In this work, we assess the value of aggregated chest CT data for COVID-19 prognosis compared to clinical metadata alone.

Height Estimation of Children under Five Years using Depth Images.

Malnutrition is a global health crisis and is a leading cause of death among children under 5 years. Detecting malnutrition requires anthropometric measurements of weight, height, and middle-upper arm circumference. However, measuring them accurately is a challenge, especially in the global south, due to limited resources.

Machine Learning-based Derivation and External Validation of a Tool to Predict Death and Development of Organ Failure in Hospitalized Patients with COVID-19.

BackgroundCOVID-19 mortality risk stratification tools could improve care, inform accurate and rapid triage decisions, and guide family discussions regarding goals of care. A minority of COVID-19 prognostic tools have been tested in external cohorts. Our objective was to compare machine learning algorithms and develop a tool for predicting subsequent clinical outcomes in COVID-19.