Ali, M. K. | Search bioRxiv

bioRxiv2026

HRV-GUI: A MATLAB Graphical User Interface for Heart Rate Variability Analysis and Validation Using Human, Rodent, and Clinical Diabetic Gastroparesis Data

Background and Objective: Heart rate variability (HRV) analysis provides a non-invasive method for quantifying autonomic modulation from electrocardiographic recordings. However, practical HRV analysis often depends on fragmented workflows, limited signal-quality review, and software tools optimized for either human or preclinical recordings, but not both. This study developed and evaluated HRV-GUI, a MATLAB-based graphical interface for electrocardiogram (ECG)-derived HRV analysis in translational biomedical research. Methods: The HRV-GUI integrates electrocardiographic and RR interval loading, human and rat analysis modes, preprocessing, segment selection, automated R-peak detection, manual peak correction, RR interval generation, multi-domain HRV computation, diagnostic visualization, result export, and session saving/loading. The software was evaluated using deterministic synthetic RR interval datasets, baseline recordings from healthy human controls and healthy rats, and a clinical use-case comparison between healthy controls and patients with diabetic gastroparesis. Results: The HRV-GUI produced expected outputs in synthetic RR validation tests, including constant RR sequences, alternating RR sequences, outlier-containing RR sequences, and low-frequency- or high-frequency-dominant sinusoidal RR modulation. The software generated physiologically plausible HRV profiles in both human and rat recordings. In the clinical use-case analysis, patients with diabetic gastroparesis showed higher heart rate and sympathetic index, together with lower respiratory sinus arrhythmia, absolute low- and high-frequency spectral power, standard deviation of normal-to-normal intervals (SDNN), root mean square of successive differences (RMSSD), percentage of successive RR intervals differing by more than 50 ms (pNN50), Poincare short-term variability (SD1), and Poincare long-term variability (SD2) compared with healthy controls. Conclusions: HRV-GUI provides an integrated biomedical software workflow for ECG-derived HRV analysis. The validation results support its use for controlled RR testing, human and rodent ECG recordings, and clinical autonomic assessment in diabetic gastroparesis.

bioengineering↗

bioRxiv2017

Diabetes mellitus is associated with increased prevalence of latent tuberculosis infection: Results from the National Health and Nutrition Examination Survey

AimsWe aimed to determine the association between prediabetes and diabetes with latent TB using National Health and Nutrition Examination Survey data.\n\nMethodsWe performed a cross-sectional analysis of 2011-2012 National Health and Nutrition Examination Survey data. Participants [≥]20 years were eligible. Diabetes was defined by glycated hemoglobin (HbA1c) as no diabetes ([≤]5.6% [38 mmol/mol]), prediabetes (5.7-6.4% [3946mmol/mol]), and diabetes ([≥]6.5% [48 mmol/mol]) combined with self-reported diabetes. Latent TB infection was defined by the QuantiFERON(R)-TB Gold In Tube (QFT-GIT) test. Adjusted odds ratios (aOR) of latent TB infection by diabetes status were calculated using logistic regression and accounted for the stratified probability sample.\n\nResultsDiabetes and QFT-GIT measurements were available for 4,958 (89.2%) included participants. Prevalence of diabetes was 11.4% (95%CI 9.8-13.0%) and 22.1% (95%CI 20.523.8%) had prediabetes. Prevalence of latent TB infection was 5.9% (95%CI 4.9-7.0%). After adjusting for age, sex, smoking status, history of active TB, and foreign born status, the odds of latent TB infection were greater among adults with diabetes (aOR 1.90, 95%CI 1.15-3.14) compared to those without diabetes. The odds of latent TB in adults with prediabetes (aOR 1.15, 95%CI 0.90-1.47) was similar to those without diabetes.\n\nConclusionsDiabetes is associated with latent TB infection among adults in the United States, even after adjusting for confounding factors. Given diabetes increases the risk of active TB, patients with co-prevalent diabetes and latent TB may be targeted for latent TB treatment.

epidemiology↗