Biology subjects

Bagheri, A.

Publications and source records attributed to Bagheri, A..

5 recordsLinked to original sources

bioRxiv2026

Visual Complexity, Abstraction, and Human Figuration in Healthcare Wayfinding Symbols: An Eye-Tracking Study

Wayfinding in hospitals is often hindered by ineffective signage; however, the cognitive mechanisms of healthcare wayfinding symbols comprehension remain under-researched. This study utilized eye-tracking and spatial gaze mapping to examine how visual complexity, abstraction, and human figuration modulate perception in 40 healthy adults viewing 24 hospital-related healthcare wayfinding symbols. Results indicate that pupil size is a sensitive physiological marker of cognitive load, significantly influenced by visual complexity ({chi}2 = 11.32, p = .022) and abstraction ({chi}2 = 7.49, p = .027). Human figuration reduced fixation duration and increased saccade amplitude, facilitating efficient semantic integration. Furthermore, human-centric healthcare wayfinding symbols elicited streamlined gaze trajectories, whereas abstract/complex designs induced chaotic scanpaths. These findings suggest that human figuration acts as a cognitive scaffold, reducing mental effort. We provide evidence-based guidelines for optimizing healthcare wayfinding symbols by prioritizing human body representations and balancing abstraction levels. HighlightO_LIPupil size indexes cognitive load during symbol comprehension. C_LIO_LIHuman figuration cuts fixation duration, boosting wayfinding efficiency. C_LIO_LIAbstract symbols increase pupil dilation, raising cognitive load. C_LI

neuroscience↗

bioRxiv2024

Distinct Nuclear and Cytoplasmic Transcriptomic Signatures Reveal Transcription Alone Is Insufficient to Determine Glucose-Induced Transcriptomic Dynamics

Glucose profoundly influences cellular transcriptomes, but whether these changes are primarily driven by transcription remains unclear. Traditional bulk RNA sequencing, which interrogates total mRNA from whole cells, obscures distinct dynamics of nuclear and cytoplasmic transcriptomes. Nuclear RNA levels primarily reflect transcriptional activity, whereas cytoplasmic RNA levels are shaped by both transcription and post-transcriptional processes, such as RNA export, stability, and degradation. In this study, we systematically investigate glucose-induced transcriptomic responses in a subcellular location- and cell type-specific manner using three cell lines: FHC (normal colonic epithelial cells), MCF10A (normal breast epithelial cells), and MCF7 (metastatic breast cancer cells). Our findings reveal that, although nuclear and cytoplasmic mRNA levels show strong global correlations, glucose-induced changes in mRNA abundance exhibit minimal concordance between the nucleus and cytoplasm. Additionally, glucose-induced changes in exon inclusion levels often diverge between the nucleus and cytoplasm, underscoring the importance of post-transcriptional processes in shaping the cytoplasmic transcriptome response to glucose level changes. Glucose-induced differentially expressed genes (DEGs) and differentially spliced exons ({Delta}PSI) are enriched in distinct pathways exhibiting unique enrichment patterns depending on the subcellular location and cell line. These findings underscore the complexity of glucose-induced transcriptomic regulation, demonstrating that transcription alone is insufficient to explain the observed dynamics.

bioinformatics↗

bioRxiv2024

Integrative Analysis of Gene Expression, Protein Abundance, and Metabolomic Profiling Elucidates Complex Relationships in Chronic Hyperglycemia-Induced Changes in Human Aortic Smooth Muscle Cells

Type 2 diabetes mellitus (T2DM) is a major public health concern with significant cardiovascular complications (CVD). Despite extensive epidemiological data, the molecular mechanisms relating hyperglycemia to CVD remain incompletely understood. We here investigated the impact of chronic hyperglycemia on human aortic smooth muscle cells (HASMCs) cultured under varying glucose conditions in vitro, mimicking normal (5 mmol/L), pre-diabetic (10 mmol/L), and diabetic (20 mmol/L) conditions, respectively. Patient-derived T2DM-SMCs served as disease phenotype controls. Results showed significant increases in cellular proliferation, area, perimeter, and F-actin expression with increasing glucose concentration (p < 0.01), albeit not exceeding the levels in T2DM cells. Atomic force microscopy analysis revealed significant decreases in Youngs moduli, membrane tether forces, membrane tension, and surface adhesion in SMCs at higher glucose levels (p < 0.001), with T2DM-SMCs being the lowest among all the cases (p < 0.001). T2DM-SMCs exhibited elevated levels of pro-inflammatory markers such as IL-6, IL-8, and MCP-1 compared to glucose-treated SMCs (p < 0.01). Conversely, growth factors such as FGF-2 and TGF-{beta} were higher in SMCs exposed to 10 mmol/L glucose but lower in T2DM-SMCs (p < 0.01). Pathway enrichment analysis showed significant increases in the expression of inflammatory cytokine-associated pathways, especially involving IL-10, IL-4 and IL-13 signaling in genes that are up-regulated by elevated glucose levels. Differentially regulated gene (DGE) analysis showed that compared to normal glucose receiving SMCs, 513 genes were upregulated and 590 genes were downregulated in T2DM-SMCs; fewer genes were differentially expressed in SMCs receiving higher glucose levels. Finally, the altered levels in genes involved in ECM organization, elastic fiber synthesis and formation, laminin interactions, and ECM proteoglycans were identified, which highlight the role of hyperglycemia in vascular remodeling and CVD progression. Our results collectively suggest that chronic hyperglycemia in vascular SMCs leads to morphological, biomechanical, and functional alterations, potentially contributing to the pathogenesis of T2DM-associated CVD. The observed differences in gene expression patterns between in vitro hyperglycemic models and patient-derived T2DM-SMCs highlight the complexity of T2DM pathophysiology and underline the need for further studies.

bioengineering↗

bioRxiv2022

CRSP: Comparative RNA-seq pipeline for species lacking both of sequenced genomes and reference transcripts

RNA sequencing (RNA-seq) has been a widely used high-throughput method to characterize transcriptomic dynamics spatiotemporally. However, typical RNA-seq data analysis pipelines depend on either a sequenced genome or corresponding reference transcripts or them both. This restriction makes the use of RNA-seq for species lacking both of sequenced genomes and corresponding reference transcripts challenging. Nile grass rat (Arvicanthis niloticus) is a diurnal rodent species with several unique characteristics making it as a useful model to study diet-induced type 2 diabetes and other physiological or behavioral processes due to its diurnal nature. However, there is neither a high-quality annotated Nile grass rat genome nor a reference transcript sets available so far, making it technically challenging to perform large-scale RNA-seq based transcriptomic studies. Although we are working on the first draft of Nile grass rat genome, a well annotated genome typically requires several rounds of manually reviewing curated transcripts and can take years to achieve. To solve this problem, we developed a Comparative RNA-Seq Pipeline (CRSP), integrating a comparative species strategy but not depending on a specific sequenced genome or species-matched reference transcripts. Benchmarking suggests the CRSP tool can achieve high accuracy to quantify gene expression levels. In this study, we generated the first ultra-deep (2.3 billion x 2 paired-end) Nile grass rat RNA-seq data from 59 biopsy samples representing 22 major organs, providing a unique resource and spatial gene expression reference for using Nile grass rat as a model to study human diseases. To facilitate a general use of CRSP, we also characterized the number of RNA-seq reads required for accurate estimation via simulation studies. CRSP and documents are available at: https://github.com/pjiang1105/CRSP. HighlightsO_LICRSP is a novel software tool which can quantify gene expression levels from RNA-seq data for species lacking both a sequenced genome and corresponding reference transcripts. C_LIO_LINile grass rat is a unique diurnal rodent species (day active but not night active) with several unique characteristics making it as a useful model to study diet-induced type 2 diabetes and other physiological or behavioral processes due to its diurnal nature. C_LIO_LIWe generated the first ultra-deep (2.3 billion x 2 paired-end reads) Nile grass rat RNA-seq data from 59 biopsy samples representing 22 major organs, providing a unique resource and spatial transcriptomic reference (e.g., tissue gene expression baseline) for using Nile grass rat as a model to study human diseases. C_LI

bioinformatics↗

bioRxiv2019

SALTClass: classifying clinical short notes using background knowledge from unlabeled data

BackgroundWith the increasing use of unstructured text in electronic health records, extracting useful related information has become a necessity. Text classification can be applied to extract patients medical history from clinical notes. However, the sparsity in clinical short notes, that is, excessively small word counts in the text, can lead to large classification errors. Previous studies demonstrated that natural language processing (NLP) can be useful in the text classification of clinical outcomes. We propose incorporating the knowledge from unlabeled data, as this may alleviate the problem of short noisy sparse text.\n\nResultsThe software package SALTClass (short and long text classifier) is a machine learning NLP toolkit. It uses seven clustering algorithms, namely, latent Dirichlet allocation, K-Means, MiniBatchK-Means, BIRCH, MeanShift, DBScan, and GMM. Smoothing methods are applied to the resulting cluster information to enrich the representation of sparse text. For the subsequent prediction step, SALTClass can be used on either the original document-term matrix or in an enrichment pipeline. To this end, ten different supervised classifiers have also been integrated into SALTClass. We demonstrate the effectiveness of the SALTClass NLP toolkit in the identification of patients family history in a Dutch clinical cardiovascular text corpus from University Medical Center Utrecht, the Netherlands.\n\nConclusionsThe considerable amount of unstructured short text in healthcare applications, particularly in clinical cardiovascular notes, has created an urgent need for tools that can parse specific information from text reports. Using machine learning algorithms for enriching short text can improve the representation for further applications.\n\nAvailabilitySALTClass can be downloaded as a Python package from Python Package Index (PyPI) website at https://pypi.org/project/saltclass and from GitHub at https://github.com/bagheria/saltclass.

bioinformatics↗