Biology subjects

Bhatta, D.

Publications and source records attributed to Bhatta, D..

2 recordsLinked to original sources

bioRxiv2025

Shear stress targeted delivery of nitroglycerin to brain collaterals improves ischaemic stroke outcome

In patients with ischaemic stroke, retrograde perfusion of the penumbra by the leptomeningeal collateral vessels (LMCs) is a strong predictor of clinical outcome, thus raising the possibility that enhancing LMC flow could offer a novel therapeutic approach. Here, using computational modelling we show that LMCs experience elevated fluid shear stress that is significantly higher than that in other blood vessels during ischaemic stroke in animals and humans. We take advantage of this to selectively enhance flow in LMCs using shear-activated nanoparticle aggregates carrying the vasodilator nitroglycerin (NG-NPAs) that specifically release drug in regions of vessels with high shear stress ([≥]100 dyne/cm2). The NG-NPAs significantly increased LMC-mediated penumbral perfusion, decreased infarct volume, and reduced neurological deficit without altering systemic blood pressure in a rat ischaemic stroke model. The NG-NPAs also did not cause known common side effects of systemic nitrate administration, such as systemic hypotension, cerebral vascular steal, cortical vein dilation, or intracranial pressure elevation. Systemic administration of free NG at the maximal tolerated dose, which was ten times higher than the dose of NG used in the NG-NPAs, did not enhance LMC perfusion and dropped blood pressure. Thus, packaging NG within shear-activated NPAs can potentially enable this widely available vasodilator to become a highly effective therapeutic for ischaemic stroke.

physiology↗

bioRxiv2021

Automated identification of chicken distress vocalisations using deep learning models

The annual global production of chickens exceeds 25 billion birds, and they are often housed in very large groups, numbering thousands. Distress calling triggered by various sources of stress has been suggested as an "iceberg indicator" of chicken welfare. However, to date, the identification of distress calls largely relies on manual annotations, which is very labour-intensive and time-consuming. Thus, a novel light-VGG11 was developed to automatically identify chicken distress calls using recordings (3,363 distress calls and 1,973 natural barn sounds) collected on intensive chicken farms. The light-VGG11 was modified from VGG11 with a significantly smaller size in parameters (9.3 million vs 128 million) and 55.88% faster detection speed while displaying comparable performance, i.e., precision (94.58%), recall (94.89%), F1-score (94.73%), and accuracy (95.07%), therefore more useful for model deployment in practice. To further improve the light-VGG11s performance, we investigated the impacts of different data augmentation techniques (i.e., time masking, frequency masking, mixed spectrograms of the same class, and Gaussian noise) and found that they could improve distress calls detection by up to 1.52%. In terms of precision livestock farming, our research opens new opportunities for developing technologies used to monitor the output of distress calls in large, commercial chicken flocks.

animal behavior and cognition↗