Biology subjects

Kamal, S.

Publications and source records attributed to Kamal, S..

4 recordsLinked to original sources

bioRxiv2026

Molecular characterization of spiny lobsters from the Saint Martins Island, Bangladesh: a new record of Panulirus longipes (A. Milne-Edwards, 1868)

In Bangladesh, spiny lobsters are vital to the economy as a key export. A total of 4 species of spiny lobsters from the genus Panulirus have been identified in Bangladeshi waters (Panulirus homarus, P. ornatus, P. polyphagus and P. versicolor). This study aimed to identify and update the existing list of spiny lobster species found in Bangladeshi waters by examining their morphological characteristics and the phylogenetic profile of the cytochrome c oxidase I (COI) from mitochondrial DNA (mtDNA) gene marker. A new species, Panulirus longipes, was recorded for the first time from the Saint Martins Island, Bay of Bengal, Bangladesh. This species is characterized by white spots and longitudinal orange stripes on its walking legs. In the Neighbor-joining (NJ) tree, the sequences of the same species were grouped together under a single clade for COI and demonstrating the effectiveness of marker genes in distinguishing between lobster species. The results indicate a new lobster species in Bangladesh, enhancing the known diversity of lobsters in the region and revealing previously undocumented species.

genetics↗

bioRxiv2025

A prioritized medium-throughput screen on human stem cell derived insulin secreting beta cells identifies FGF4, FGF5, FGF8F, FGF19 and FGF21 as protective factors

AbstractStem cell-derived {beta}-like cells (SC{beta} cells) are a potential alternative to cadaveric {beta} cells for replacement therapy in type 1 diabetes. However, SC{beta} cells face a multitude of stresses that must be overcome. Both {beta} cells and SC{beta} cells reside in complex microtissues and can therefore be modulated by hundreds of autocrine/paracrine signals within these islets or spheroids. Here, we leveraged multi-omics data from late-stage SC{beta} cells and human islets to map ligand-receptor pairs and generate a prioritized list of ligands for high-content SC{beta} cell survival screening. Our medium-throughput screen tracked cell number, cell death, and INS production over several days using automated, high-content imaging. Members of the fibroblast growth factor (FGF) family significantly prevented cytokine-induced cell death, with the top validated hits being FGF4, FGF5, FGF19, FGF21, and FGF8F. With these results, there is the potential to improve SC{beta} cell survival in vitro via readily targetable pathways and to produce a more robust product for translation to the clinic.

molecular biology↗

bioRxiv2024

Modeling differences in neurodevelopmental maturity of the reading network using support vector regression on functional connectivity data

The construction of growth charts trained to predict age or developmental deviation (the brain-age index) based on structural/functional properties of the brain may be informative of childrens neurodevelopmental trajectories. When applied to both typically and atypically developing populations, results may indicate that a particular condition is associated with atypical maturation of certain brain networks. Here, we focus on the relationship between reading disorder (RD) and maturation of functional connectivity (FC) patterns in the prototypical reading/language network using a cross-sectional sample of N = 742 participants aged 6-21 years. A support vector regression model is trained to predict chronological age from FC data derived from a whole-brain model as well as multiple reduced models, which are trained on FC data generated from a successively smaller number of regions in the brains reading network. We hypothesized that the trained models would show systematic underestimation of brain network maturity for poor readers, particularly for the models trained with reading/language regions. Comparisons of the different models predictions revealed that while the whole-brain model outperforms the others in terms of overall prediction accuracy, all models successfully predicted brain maturity, including the one trained with the smallest amount of FC data. In addition, all models showed that reading ability affected the brain-age gap, with poor readers ages being underestimated and advanced readers ages being overestimated. Exploratory results demonstrated that the most important regions and connections for prediction were derived from the default mode and frontoparietal control networks. GlossaryDevelopmental dyslexia / reading disorder (RD): A specific learning disorder affecting reading ability in the absence of any other explanatory condition such as intellectual disability or visual impairment Support vector regression (SVR): A supervised machine learning technique which predicts continuous outcomes (such as chronological age) rather than classifying each observation; finds the best-fit function within a defined error margin Principal component analysis (PCA): A dimensionality reduction technique that transforms a high-dimensional dataset with many features per observation into a reduced set of principal components for each observation; each component is a linear combination of several original (correlated) features, and the final set of components are all orthogonal (uncorrelated) to one another Brain-age index: A numerical index quantifying deviation from the brains typical developmental trajectory for a single individual; may be based on a variety of morphometric or functional properties of the brain, resulting in different estimates for the same participant depending on the imaging modality used Brain-age gap (BAG): The difference, given in units of time, between a participants true chronological age and a predictive models estimated age for that participant based on brain data (Actual - Predicted); may be used as a brain-age index HighlightsO_LIA machine learning model trained on functional data predicted participants ages C_LIO_LIThe model showed variability in age prediction accuracy based on reading skills C_LIO_LIThe model highly weighted data from frontoparietal and default mode regions C_LIO_LINeural markers of reading and language are diffusely represented in the brain C_LI

neuroscience↗

bioRxiv2019

Building Functional Connectivity Neuromarkers of Behavioral Self-Regulation in Children with and without Autism Spectrum Disorder

Children with Autism Spectrum Disorder (ASD) are known to struggle with behavioral self-regulation, which associates with greater daily-life challenges and an increased risk for psychiatric comorbidities. Despite these negative outcomes, little is known about the neural expression of behavioral regulation in children with and without ASD. Here, we examined whole-brain linear associations between brain functional correlations (FC) and behavioral regulation through connectome predictive modelling (CPM), a data-driven protocol for developing predictive models of brain-behavior relationships from data, assessing neuromarkers using cross-validation. Using data from two sites of the ABIDE II dataset comprising 276 children with and without ASD (8-13 years), we identified functional brain networks whose FC predicted individual differences in two, of three, behavioral regulation subdomains. These distributed network models predicted novel individuals inhibition and shifting from resting-state FC data both in a leave-one-out, as well as split halves, cross-validation. We observed commonalities and differences in the functional networks associating with these subdomains, with inhibition relying on more posterior networks, shifting relying on more anterior networks, and both involving regions of the DMN. Our findings present a substantial addition to our knowledge on the neural expressions of inhibition and shifting across the spectrum of children with and without ASD, demonstrating the utility of this trans-diagnostic modelling approach. Given the numerous cognitive and behavioral issues that can be quantified dimensionally in neurodevelopmental disorders, further refinement of whole-brain neuromarker techniques may thus pave a way for functional neuroimaging to meaningfully contribute to individualized medicine.

neuroscience↗