Biology subjects

Shaik, M.

Publications and source records attributed to Shaik, M..

4 recordsLinked to original sources

bioRxiv2025

The developing tendon and enthesis are hypoxic and rely on hypoxia-inducible factor 1a (Hif1a) during postnatal development

The tendon-bone enthesis is a specialized fibrocartilaginous tissue crucial for muscle-to-bone force transmission, yet its postnatal development is not fully understood. Emerging evidence suggests hypoxia plays a pivotal role in enthesis maturation akin to its function in skeletal growth plates, with Hypoxia Inducible Factor 1 alpha (HIF-1) acting as a key regulator of cellular adaptation (e.g., cell survival, extracellular matrix (ECM) deposition). Here, we investigated the spatial and temporal dynamics of hypoxia in the murine Achilles tendon enthesis and elucidated the role of Hif1a in enthesis cell survival and ECM formation using Scleraxis-lineage conditional knockout (ScxCre; Hif1a cKO) mice. We found that while neonatal tendons rapidly resolve hypoxia after birth, the enthesis maintains a hypoxic niche through postnatal day 5, mirroring a gradient observed in growth plates. Disruption of HIF-1 in enthesis-resident cells resulted in pronounced deficits in grip strength, abnormal tendon-bone attachment morphology, disrupted calcaneal architecture, impaired mineralization, and significant ECM disorganization. Histological analyses revealed persistent cell death and loss of the characteristic fibrocartilaginous gradient in cKO entheses, including dysregulated collagen alignment. In vitro, HIF-1-deficient tendon fibroblasts exhibited blunted transcriptional responses to hypoxia, altered metabolic gene expression, and changes in ECM deposition. Collectively, our findings illuminate hypoxia as a sustained niche in the postnatal enthesis, with HIF-1 critically required for cell survival, ECM organization, and enthesis structural integrity. This work advances our understanding of enthesis biology and provides insights relevant to tendon-bone attachment disorders and regenerative strategies.

developmental biology↗

bioRxiv2024

Enhancing Biomarker-Based Oncology Trial Matching Using Large Language Models

Clinical trials are an essential component of drug development for new cancer treatments, yet the information required to determine a patients eligibility for enrollment is scattered in large amounts of unstructured text. Genomic biomarkers are especially important in precision medicine and targeted therapies, making them essential for matching patients to appropriate trials. Large language models (LLMs) offer a promising solution for extracting this information from clinical trial data, aiding both physicians and patients in identifying suitable matches. In this study, we explore various LLM strategies for extracting genetic biomarkers from oncology trials to improve patient enrollment rates. Our results show that open-source language models, when applied out-of-the-box, effectively capture complex logical expressions and structure genomic biomarkers in disjunctive normal form, outperforming closed-source models such as GPT-4 and GPT-3.5-Turbo. Furthermore, fine-tuning these open-source models with additional data significantly enhances their performance.

bioinformatics↗

bioRxiv2019

Glioma-induced alterations in neuronal activity and neurovascular coupling during disease progression

Diffusely infiltrating gliomas are known to cause alterations in cortical function, vascular disruption and seizures. These neurological complications present major clinical challenges, yet their underlying mechanisms and causal relationships to disease progression are poorly characterized. Here, we followed glioma progression in awake Thy1-GCaMP6f mice using in-vivo wide-field optical mapping to monitor alterations in both neuronal activity and functional hemodynamics. The bilateral synchrony of spontaneous neuronal activity in glioma-infiltrated cortex gradually decreased, while neurovascular coupling was also progressively disrupted compared to uninvolved cortex. Over time, mice developed diverse patterns of high amplitude discharges and eventually generalized seizures that begin at the infiltrative margin of the tumors. Interictal and seizure events exhibited positive neurovascular coupling in uninfiltrated cortex, however glioma-infiltrated regions exhibited inverted hemodynamic responses driving seizure-evoked hypoxia. These results reveal a landscape of complex physiological interactions occurring during glioma progression and present new opportunities for exploring new biomarkers and therapeutic targets.\n\nHighlights- Glioma disrupts neural synchrony between bilateral cortical regions.\n- WFOM reveals frequent interictal discharges and seizures during glioma progression.\n- Tumor development is accompanied by local changes in neurovascular coupling.\n- Altered neurovascular coupling drives hypoperfusion of the tumor during seizures.

neuroscience↗

bioRxiv2018

Penalized matrix decomposition for denoising, compression, and improved demixing of functional imaging data

Calcium imaging has revolutionized systems neuroscience, providing the ability to image large neural populations with single-cell resolution. The resulting datasets are quite large (with scales of TB/hour in some cases), which has presented a barrier to routine open sharing of this data, slowing progress in reproducible research. State of the art methods for analyzing this data are based on non-negative matrix factorization (NMF); these approaches solve a non-convex optimization problem, and are highly effective when good initializations are available, but can break down e.g. in low-SNR settings where common initialization approaches fail.\n\nHere we introduce an improved approach to compressing and denoising functional imaging data. The method is based on a spatially-localized penalized matrix decomposition (PMD) of the data to separate (low-dimensional) signal from (temporally-uncorrelated) noise. This approach can be applied in parallel on local spatial patches and is therefore highly scalable, does not impose non-negativity constraints or require stringent identifiability assumptions (leading to significantly more robust results compared to NMF), and estimates all parameters directly from the data, so no hand-tuning is required. We have applied the method to a wide range of functional imaging data (including one-photon, two-photon, three-photon, widefield, somatic, axonal, dendritic, calcium, and voltage imaging datasets): in all cases, we observe ~2-4x increases in SNR and compression rates of 20-300x with minimal visible loss of signal, with no adjustment of hyperparameters; this in turn facilitates the process of demixing the observed activity into contributions from individual neurons. We focus on two challenging applications: dendritic calcium imaging data and voltage imaging data in the context of optogenetic stimulation. In both cases, we show that our new approach leads to faster and much more robust extraction of activity from the video data.

neuroscience↗