Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.64898/2026.10.07.757242

Photoplethysmography-based glucose estimation with feature selection

Singha, S.·Chowdhury, A.
View original source ↗Export metadata ↓

Abstract

Diabetes mellitus is a major global health challenge, and routine blood glucose monitoring still depends on invasive fingerstick sampling. This study examines non-invasive glucose estimation from photoplethysmography (PPG) using a signal quality pipeline and a multi-criteria feature selection framework. PPG recordings from 51 subjects were collected with a custom low-cost system, and 32 passed quality control. Fourteen morphological and autonomic features were extracted and ranked using Pearson and Spearman correlation, partial correlation with confounder adjustment, mutual information and a composite score. The same processing was then applied to a public four-wavelength PPG dataset of 133 subjects with complete metadata, comparing three machine learning input configurations on a held-out test set of 27 subjects. Adding greedy forward-selected PPG features to subject metadata gave a coefficient of determination of 0.410 with Random Forest at 730 nm, compared with 0.355 for metadata alone. Gains at the other wavelengths were at most 0.024, and no significance test was performed. Agreement with the ISO 15197:2013 criterion ranged from 74% to 85%, below the 95% the standard requires. A compact set of amplitude-variability and spectral features may add modest information to demographic predictors, but this needs confirmation in larger cohorts, with longer recordings and independent validation.

Explore related subjects

bioengineeringPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Singha, S., Chowdhury, A.. 2026-10-14. Photoplethysmography-based glucose estimation with feature selection. https://doi.org/10.64898/2026.10.07.757242

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Mesoscale Soft Robotics for Mechanically Perturbing and Functionally Monitoring Responses in Human Organoids

Human organoids represent promising models for studies of significance to the biology and medical communities. Of the existing approaches for monitoring organoids, none offers the ability to apply programmable mechanical forces while simultaneously recording multi-modal physiological responses, of relevance to phenomena spanning from traumatic brain injuries to cardiac rhythm disorders. Here, we present a mesoscale soft robotic organoid interface that enables programmable delivery of forces to organoids of diverse shapes, and to map electrophysiological activity and mechanical responses. The platform combines actuation elements with integrated sensors for quantitative measurement and control. Studies using this technology reveal that spheroidal cortical organoids exhibit distinct responses to mechanical trauma, including silencing, recovery, and changes in neuronal firing patterns. Additional experiments on cardiac ring tissues demonstrate that sustained stretching induces beat-to-beat rhythm variability and altered conduction. The results establish a framework for investigating force-triggered transitions between physiological and disease-like states in human tissue models.

bioengineering↗
bioRxiv2026

Few-shot regulatory sequence design with a genomic language model

A central challenge in generative biology is translating knowledge learned from natural sequences and experiments into designs with desired functions. In-context learning (ICL) is an emergent capability of large language models that allows them to adapt to new tasks from a few examples without additional training, enabling these models to become general-purpose learners. Here, we show that the genome language model, Evo 2, exhibits generative ICL in biological sequence design. Prompting with just tens of examples conditions Evo 2 to generate novel and diverse regulatory DNA sequences without any additional training. Mechanistic analysis suggests that genomic ICL may originate from induction, similar to phenomena observed in natural language. Reinforcement learning leveraging sequence-to-function models as experiment-derived rewards further strengthens ICL. We first validate this approach by generating enhancers that drive cell-type-specific gene expression in three human cell lines. Extending this approach in vivo, enhancers designed using human regulatory element data selectively label layer 5 extratelencephalic neurons in the mouse cortex with high specificity and stronger activity than existing enhancers. Our study provides evidence of emergent properties in genomic sequence models and introduces a promising approach for improving functional conditioning in generative biology.

bioengineering↗
bioRxiv2026

IOP-independent treatment for neurodegeneration in glaucoma using optimized ROS-scavenging microparticles for sustained release of erythropoietin

Glaucoma is a slow-progressing neurodegenerative disease of the optic projection to the brain and a leading cause of blindness worldwide. Current treatments are targeted towards lowering intraocular pressure (IOP), the only known, modifiable risk factor; however, this strategy is ineffective in many patients. An early hallmark of glaucomatous neurodegeneration is increased reactive oxygen species (ROS), representing an alternative opportunity for intervention to halt or slow disease progression. In this work, we optimize a ROS-scavenging copolymer, poly(propylene sulfide-co-ethylene sulfide) (PPSES), for formulation of size-controlled, stabilized, antioxidant microparticles (MPs) for sustained release of the protein erythropoietin (EPO). EPO promotes biological antioxidant defense via activation of NRF2-mediated transcription of antioxidant genes. This MP system overcomes the short half-life of EPO and leverages the combined benefits of bioactive EPO with long-lasting, inherently antioxidant PPSES MPs. The lead EPO-MP formulation preserves neurons in both mouse and non-human primate models of glaucoma through suppression of oxidative stress and optic nerve degeneration. EPO-loaded PPSES MPs represent a translationally relevant therapy for long-term management of glaucoma independent of IOP.

bioengineering↗