Publications by authors named "Attia Z"
Aims: Gender-affirming hormone therapy (GAHT) is used by some transgender individuals (TG), who comprise 1.4% of US population. However, the effects of GAHT on electrocardiogram (ECG) remain unknown.
View Article and Find Full Text PDFBackground: The contribution of MTHFR and TP53 genetic variants to breast carcinoma (BC) susceptibility has been examined, but their findings have been inconclusive. This work is designed to explore the potential roles of the MTHFR (rs1801131, rs1801133) and TP53 (rs1042522) variants with increased risk of BC using genetic and bioinformatic approaches.
Methods: This work included a total of 242 female participants [142 BCE patients and 100 healthy controls].
Objective: To test whether an artificial intelligence (AI) deep neural network (DNN)-derived analysis of the 12-lead electrocardiogram (ECG) can distinguish patients with long QT syndrome (LQTS) from those with acquired QT prolongation.
Methods: The study cohort included all patients with genetically confirmed LQTS evaluated in the Windland Smith Rice Genetic Heart Rhythm Clinic and controls from Mayo Clinic's ECG data vault comprising more than 2.5 million patients.
Background/objectives: The circadian clockwork is implicated in the etiology of addiction, with circadian rhythm disruptions bidirectionally linked to substance abuse, but the molecular mechanisms that underlie this connection are not well known.
Methods: Here, we use machine learning to reveal sex- and substance-specific associations with addiction in variants from 51 circadian-related genes (156,702 SNPs) in 98,800 participants from a UK Biobank cohort. We further analyze SNP associations in a subset of the cohort for substance-specific addictions (alcohol, illicit drugs (narcotics), and prescription drugs (opioids)).
Background And Aims: Artificial intelligence (AI) algorithms in 12-lead electrocardiogram (ECG) provides promising age prediction methods. This study investigated whether the discrepancy between ECG-derived AI-predicted age (AI-ECG age) and chronological age, termed electrocardiographic aging (ECG aging), is associated with atrial fibrillation (AF) risk.
Methods: An AI-ECG age prediction model was developed using a large-scale dataset (1 533 042 ECGs from 689 639 participants) and validated with six independent and multi-national datasets (737 133 ECGs from 330 794 participants).
Background: The determination of left ventricular diastolic function (LVDF) in patients with significant (≥moderate) mitral regurgitation (MR) poses a complex challenge. We recently validated an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm to estimate LVDF.
Objectives: This study sought to evaluate the risk of all-cause mortality across AI-ECG LVDF-derived myocardial disease (MD) grades in MR.
Background: Compared with all other cancer types, Breast cancer (BC) among women has now exceeded them all as the primary reason for cancer worldwide. The BC represents 11.7% of all cancer cases and accounts for a predestined 2.
View Article and Find Full Text PDFCharcot-Marie-Tooth Type 4C (CMT4C) is associated with mutations in the SH3 domain and tetratricopeptide repeats 2 () gene, primarily expressed in Schwann cells (SCs). Neurotrophin-3 (NT-3) is an important autocrine factor for SC survival and differentiation, and it stimulates neurite outgrowth and myelination. In this study, scAAV1.
View Article and Find Full Text PDFBackground: While clinical practice guidelines advocate for multidisciplinary heart team (MDHT) discussions in coronary revascularization, variability in implementation across health care settings remains a challenge. This variability could potentially be addressed by language learning models like ChatGPT, offering decision-making support in diverse health care environments. Our study aims to critically evaluate the concordance between recommendations made by MDHT and those generated by language learning models in coronary revascularization decision-making.
View Article and Find Full Text PDFArticle Synopsis
- Soil microorganisms play a vital role in plant health, affecting resistance to pathogens, stress tolerance, and overall yield, but how factors like geography, climate, and plant genetics influence these microbial communities is still not fully understood.
- A study involving 10 different sunflower genotypes across 15 sites in the Great Plains revealed that while location generally had a larger impact on the composition and richness of soil microbial communities, there were significant interactions with plant genotype at specific sites.
- The findings suggest that variations in soil and climate across geographic regions influence microbial communities, which has important implications for improving plant breeding and agricultural practices targeting enhanced soil microbiomes.
Article Synopsis
- An AI-based ECG model is effective in identifying patients at risk for low ejection fraction (EF), particularly noting that those with abnormal AI-ECG scores and normal EF (false positives or FPs) were more likely to develop low EF later on.
- This study analyzed echocardiographic features and all-cause mortality risk in a large cohort of patients, categorizing them into groups like true negatives (TN), false positives (FP), true positives (TP), and false negatives (FN), using these categorization techniques to assess heart health.
- Results showed that 97% of FPs had some echocardiographic abnormality; they faced a significantly higher risk of mortality compared to
Article Synopsis
- Hyperkalemia, a condition of elevated potassium levels, is linked to higher mortality rates in patients in cardiac intensive care units (CICU), and an AI-powered electrocardiogram (ECG) can predict this condition effectively.
- The study involved over 11,000 CICU patients and found that AI-ECG could identify hyperkalemia in patients even when laboratory tests showed normal potassium levels, with a notable percentage predicted to have the condition.
- Results indicated that patients identified by AI-ECG as hyperkalemic faced increased in-hospital mortality and reduced 1-year survival, suggesting AI-ECG offers valuable risk assessment beyond conventional lab measurements.
Article Synopsis
- Loneliness and social isolation are linked to negative health effects like heart disease and may speed up biological aging, as shown by AI-enabled heart studies.
- The study analyzed over 280,000 adults to assess social isolation and its effects, finding that increased social connections led to a healthier biological age and reduced risk of death.
- The findings suggest that improving social connections is essential for health care, as social isolation can significantly impact aging and overall mortality rates.
Background: Pediatric epilepsy is a complicated neuropsychiatric disorder that is characterized by recurrent seizures and unusual synchronized electrical activities within brain tissues. It has a substantial effect on the quality of life of children, thus understanding of the hereditary considerations influencing epilepsy susceptibility and the response to antiepileptic medications is crucial. This study focuses on assessing the correlation of the ABCB1, ABCC2, CYP1A2, and CYP2B6 genetic polymorphisms with the susceptibility to epileptic seizures and their contributions to antiepileptic medication throughout the course of the disease.
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- Nigeria has the highest global rate of peripartum cardiomyopathy, leading to a clinical trial that compared usual care with AI-guided screening for left ventricular systolic dysfunction (LVSD) in pregnant and postpartum women.
- The study involved 1,232 women, using digital stethoscopes and 12-lead electrocardiograms to identify LVSD, which was confirmed by echocardiography.
- Results showed that AI screening enhanced the detection rates of LVSD (4.1% vs. 2.0% for stethoscope AI; 3.4% vs. 2.0% for electrocardiogram AI), with no serious adverse events reported during the trial.
Article Synopsis
- The study evaluates an AI model (A2E) that analyzes ECGs to predict survival in patients with cardiac amyloidosis (CA), focusing on three patient groups: light chain amyloidosis (AL), wild-type transthyretin amyloidosis (ATTRwt), and hereditary transthyretin amyloidosis (ATTRv).
- Data from 2533 CA patients were analyzed, and results showed that those with higher A2E scores had a significantly increased risk of death.
- The findings suggest that the A2E model provides useful prognostic information, helping to better assess the risk of death in patients with both AL and ATTR amyloidosis.
Background: Artificial intelligence (AI) electrocardiogram (ECG) analysis can enable detection of hyperkalemia. In this validation, we assessed the algorithm's performance in two high acuity settings.
Methods: An emergency department (ED) cohort (February to August 2021) and a mixed intensive care unit (ICU) cohort (August 2017 to February 2018) were identified and analyzed separately.
Aims: Recently, deep learning artificial intelligence (AI) models have been trained to detect cardiovascular conditions, including hypertrophic cardiomyopathy (HCM), from the 12-lead electrocardiogram (ECG). In this external validation study, we sought to assess the performance of an AI-ECG algorithm for detecting HCM in diverse international cohorts.
Methods And Results: A convolutional neural network-based AI-ECG algorithm was developed previously in a single-centre North American HCM cohort (Mayo Clinic).
Article Synopsis
- Hypertension is a major risk factor for serious health conditions, and there’s potential for artificial intelligence (AI) to improve how it's diagnosed and managed.* -
- AI technologies, particularly machine learning, could personalize treatment and enhance blood pressure monitoring, but effective collaboration among health professionals and data scientists is crucial.* -
- A workshop by the National Heart, Lung, and Blood Institute highlighted communication gaps in healthcare, innovative methods for managing hypertension, and challenges to implementing AI in real-world settings.*