Biology subjects

Eskandari, M.

Publications and source records attributed to Eskandari, M..

5 recordsLinked to original sources

bioRxiv2025

A Machine Learning Approach to Predicting Dyspnea with Noninvasive Biomarkers

RationaleDyspnea is the subjective sensation of breathing discomfort. This symptom is highly prevalent in patients with chronic and critical illness, and its presence is associated with poor clinical outcomes and long-term psychological trauma. The multidimensional nature of the neurophysiological mechanisms underlying dyspnea, paired with individual variation in its presentation, makes identifying and monitoring this symptom difficult, particularly in non-communicative patients. ObjectiveUndetected and untreated dyspnea in critically ill patients is a significant problem contributing to patient suffering. Therefore, the objective of this study was to investigate the feasibility of machine learning methods for assessing and continuously monitoring dyspnea using easily obtained noninvasive biomarkers. MethodsWe recruited healthy participants (N = 60, 35 women) and stimulated dyspnea using a forced end-tidal semi-rebreathing circuit to modulate arterial oxygen and carbon dioxide levels while collecting non-invasive biomarker data and continuous self-reported dyspnea severity scores. This data was used to train machine-learning models to predict the presence or absence of significant dyspnea (Numeric Rating Scale [≥] 3). We then compared the performance of our final model to observational estimates by trained healthcare providers. Measurements and Main ResultsThe final model (Random Forest) performed well (PR-AUC=0.832) and exceeded the accuracy of observation estimates made on the same participants using the Respiratory Distress Observational Scale (RDOS) (accuracy=54%). ConclusionsThese results indicate that machine learning models can utilize non-invasive biomarker inputs to accurately predict carbon dioxide- and hypoxia-induced dyspnea in a healthy population during spontaneous breathing.

physiology↗

bioRxiv2025

Community Challenge towards Consensus on Characterization of Biological Tissue: C4Bios First Findings

This study investigates methodological variability across various expert laboratories worldwide, with regards to characterizing the mechanical properties of biological tissues. Two testing rounds were conducted on the specific use case of uniaxial tensile testing of porcine aorta. In the first round, 24 labs were invited to apply their established methods to assess inter-laboratory variability. This revealed significant methodological diversity and associated variability in the stress-stretch results, underscoring the necessity for a standardized approach. In the second round, a consensus protocol was collaboratively developed and adopted by 19 labs in an attempt to minimize variability. This involved standardized sample preparation and uniformity in testing protocol, including the use of a common cutting and thickness measurement tool. Despite protocol harmonization, significant variability persisted across labs, which could not be solely attributed to inherent biological differences in tissue samples. These results illustrate the challenges in unifying testing methods across different research settings, underlining the necessity for further refinement of testing practices. Enhancing consistency in biomechanical experiments is pivotal when comparing results across studies, as well as when using the resulting material properties for in silico simulations in medical research.

bioengineering↗

bioRxiv2023

Soybean-SCN duel: Novel insight into Soybean's Resistant Responses to Heterodera glycines

Soybean cyst nematodes (SCN, Heterodera glycines Ichinohe) are a significant threat to soybean production globally, causing severe yield losses and necessitating the development of effective strategies to combat this devastating nematode disease. This study presents dual RNA-seq analyses of the three most common SCN-resistant lines (Plant Introduction (PI) 437654, 548402, and 88788) and the susceptible line Lee74 against the SCN HG type 1.2.5.7 to identify the mechanisms of resistance and virulence genes involved in resistance breakdown. Transcriptomic and pathway analyses reveal the activation of the phenylpropanoid pathway, MAPK signaling pathway, plant hormone signal transduction, and secondary metabolite pathways in the resistance mechanisms. PI 437654, which exhibited robust resistance (female index, FI=0%), demonstrated unique gene expression associated with cell wall reinforcement, oxidative enzymes, ROS scavengers, and Ca+2 sensors governing the salicylic acid (SA) biosynthesis process, indicating its key defense mechanism. Moreover, using different hosts with varying levels of immunity and a susceptible line provided insights into SCN pathogenesis and how H. glycine overcomes different layers of host immunity by modulating its virulence genes. This research provides novel insights into the molecular mechanisms underlying soybean-SCN interactions and identifies potential targets for developing strategies to manage this devastating nematode disease.

plant biology↗

bioRxiv2021

Machine Learning based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and its Components

Genome-wide association study (GWAS) is currently one of the important approaches for discovering quantitative trait loci (QTL) associated with traits of interest. However, insufficient statistical power is the limiting factor in current conventional GWAS methods for characterizing quantitative traits, especially in narrow genetic bases plants such as soybean. In this study, we evaluated the potential use of machine learning (ML) algorithms such as support vector machine (SVR) and random forest (RF) in GWAS, compared with two conventional methods of mixed linear models (MLM) and fixed and random model circulating probability unification (FarmCPU), for identifying QTL associated with soybean yield components. In this study, important soybean yield component traits, including the number of reproductive nodes (RNP), non-reproductive nodes (NRNP), total nodes (NP), and total pods (PP) per plant along with yield and maturity were assessed using 227 soybean genotypes evaluated across four environments. Our results indicated SVR-mediated GWAS outperformed RF, MLM and FarmCPU in discovering the most relevant QTL associated with the traits, supported by the functional annotation of candidate gene analyses. This study for the first time demonstrated the potential benefit of using sophisticated mathematical approaches such as ML algorithms in GWAS for identifying QTL suitable for genomic-based breeding programs.

plant biology↗

bioRxiv2020

Synergistic effects of conductivity and cell-imprinted topography of chitosan-polyaniline based scaffolds for neural differentiation of adipose-derived stem cells

Smart nano-environments that mimic the stem cell niche can guide cell behavior to support functional repair and regeneration of tissues. The specific microenvironment of nervous tissue is composed of several physical signaling factors, including proper topography, flexibility, and electric conductance, to support the electrical conduction of neuronal communication. In this study, a cell-imprinting technique was used to obtain a hierarchical topographical conductive scaffold based on chitosan-polyaniline (PANI) hydrogels for directing the neural differentiation of rat adipose-derived stem cells (rADSCs). A chitosan-polyaniline hydrogel was synthesized, followed by characterization tests, such as Fourier transform infrared spectroscopy (FTIR), electrical conductivity, Young modulus, and contact angle measurements. A chitosan-PANI scaffold with a biomimetic topography was fabricated by molding it on a chemically fixed culture of PC12 cells. This substrate was used to test the hypothesis that the PC12 cell-imprinted chitosan-PANI hydrogel provides the required hierarchical topographical surface to induce neural differentiation. To test the importance of spatial imprinting, rADSCs were seeded on these conductive patterned substrates, and the resulting cultures were compared to those of the same cells grown on flat conductive chitosan-polyaniline, and flat pure chitosan substrates for evaluation of adhesion, cell viability, and expression of neural differentiation markers. The morphology of rADSCs grown on conductive patterned scaffolds noticeably was significantly different from that of stem cells cultivated on flat scaffolds. This difference suggests that the change in cell and nuclear shape imposed by the patterned conductive substrate leads to altered gene expression and neural differentiation of cultured cells. In addition, the percentage of rADSCs that differentiated into neural-like cells was almost 80 % on the imprinted chitosan-PANI scaffold. In summary, a conductive chitosan-polyaniline scaffold with biomimetic topography demonstrates a promising method for enhancing the neural differentiation of rADSCs for the treatment of neurodegenerative diseases.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioengineering↗