Biology subjects

Ebrahimi, P.

Publications and source records attributed to Ebrahimi, P..

3 recordsLinked to original sources

bioRxiv2025

Arterial Shear Rate Determines the Structure and Mechanical Properties of Whole-Blood Clots

BackgroundArterial thrombosis arises under high shear conditions and is a leading cause of ischemic stroke and myocardial infarction. The local hemodynamic environment during the formation of the thrombus influences its final composition, which in turn influences its mechanical characteristics and the associated risk of embolization. However, how arterial shear rates determine the local mechanical properties, structure and composition of clots remains poorly understood. ObjectiveTo investigate how physiological and pathological arterial shear rates shape blood clot composition, formation dynamics, and regional mechanical properties in a newly-developed microfluidic flow model. MethodsWe used a microfluidic flow model that allows real-time imaging of blood clot formation under controlled arterial shear rates (300, 1000, and 3500s-1). We visualized platelets and fibrin deposition using confocal microscopy and quantified composition-dependent spatial heterogeneities in clot stiffness with microindentation. ResultsPathologically high (3500 s-1) arterial shear rates promoted more rapid platelet aggregation and the formation of taller platelet-rich clots. Fibrin deposition started after about 6 minutes in all shear conditions and coincided with platelet aggregate stabilization at higher shear rates. Microindentation revealed that platelet- and fibrin-rich regions were significantly stiffer than red blood cell-rich regions. Clots formed at 3500s-1 had a > 3-fold higher average stiffness than those formed at 300s-1, reflecting their higher platelet content. ConclusionsThis study demonstrates that arterial shear rate governs clot growth and composition, translating into shear-dependent microscale mechanical properties. These findings provide new mechanistic insights into clot properties that are relevant for clot stability and vulnerability to embolization.

bioengineering↗

bioRxiv2023

Characterizing human postprandial metabolic response using multiway data analysis

Analysis of time-resolved postprandial metabolomics data can enhance our knowledge about human metabolism by providing a better understanding of similarities and differences in postprandial responses of individuals, with the potential to advance precision nutrition and medicine. Traditional data analysis methods focus on clustering methods relying on summaries of data across individuals or use univariate methods analyzing one metabolite at a time. However, they fail to provide a compact summary revealing the underlying patterns, i.e., groups of subjects, clusters of metabolites, and their temporal profiles. In this study, we analyze NMR (Nuclear Magnetic Resonance) spectroscopy measurements of plasma samples collected at multiple time points during a meal challenge test from 299 individuals from the COPSAC2000 cohort. We arrange the data as a three-way array: subjects by metabolites by time, and use the CAN-DECOMP/PARAFAC (CP) tensor factorization model to capture the underlying patterns. We analyze the fasting state data to reveal static patterns of subject group differences, and the fasting state-corrected postprandial data to reveal dynamic markers of group differences. Our analysis demonstrates that the CP model reveals replicable and biologically meaningful patterns capturing certain metabolite groups and their temporal profiles, and showing differences among males according to their body mass index (BMI). Furthermore, we observe that certain lipoproteins relate to the group difference differently in the fasting vs. dynamic state in males. While similar dynamic patterns are observed in response to the challenge test in males and females, the BMI-related group difference is only observed in males in the dynamic state.

systems biology↗

bioRxiv2018

Optimization of swelling response of pH sensitive hydrogels using Box-Behnken design

Hydrogels are three-dimensional crosslinked hydrophilic polymer networks capable of swelling or de-swelling reversibly in water and retaining a large volume of liquid in swollen state. Hydrogels can be designed with controllable responses to shrink or expand with changes in external environmental conditions. Among stimulisensitive hydrogels, the pH-sensitive ones are widely-studied and used. Despite these advances, we currently lack a systematic way to optimize the synthesis protocols. Here we propose to use techniques from chemometrics, namely Box-Behnken statistical screening design to optimize the chemical composition of pH-responsive hydrogels for an improved responsiveness as quantified by swelling index. Polymer blends were prepared by mixing different suitable volume of pvp (poly vinyl pyrrolidone) and chitosan aqueous solution in order to obtain a mixture, glutardialdehyde solution was added as crosslinkers to the chitosan/pvp mixture to form semi-IPN(semi-interpenetrating polymeric hydrogel). The pH-dependent swelling properties have been measured and used to obtain a regression model. We characterized the descriptive and predictive abilities of our model. We found a remarkable correlation (correlation coefficient = 0.954) between the observed responses and the responses predicted by the model. Our results demonstrate that Box-Behnken is an appropriate statistical design that can be successfully used in the development of pH-sensitive hydrogels with a predictable swelling ratio. This technology will remarkably reduce the time and cost that is needed for chemical synthesis of hydrogels with a desired pH-sensitivity.

bioengineering↗