Biology subjects

Rezaee, M.

Publications and source records attributed to Rezaee, M..

5 recordsLinked to original sources

bioRxiv2026

Causal Multiscale Modeling of Ocular FLASH Electroretinography: Model Development, Experimental Fitting, and Spin-Glass--Percolation Predictions

\textbf{Purpose:} To develop a causal multiscale model of retinal electroretinography (ERG) after ocular ultrahigh-dose-rate (FLASH) and conventional-dose-rate (CONV) irradiation, evaluate the framework against published ocular ERG data, and derive prospective dose-response predictions for future validation. \textbf{Methods:} The retina was coarse-grained into $N=200$ functional modules. Local rate equations were formulated as Hodgkin--Huxley-inspired conductance-balance/RC reductions of retinal ion-channel and synaptic pathways. Radiation generated an injury field that altered phototransduction, a-wave and b-wave dynamics and, in the network extension, inter-module coupling and bond survival. A reduced amplitude benchmark and nested M0/M1/M2 causal models were evaluated. \textbf{Results:} The reduced benchmark achieved $R^2=0.9721$ and RMSE $=11.94~\mu$V. M0, M1, and M2 yielded $R^2=0.8546$, 0.8559, and 0.8560, respectively, indicating limited independent identifiability of the network terms from amplitude data alone. Under an exploratory 50\% retained-gain criterion, the reduced benchmark predicted a-wave preservation thresholds of 28.6 Gy for FLASH and 23.7 Gy for CONV, and b-wave thresholds of 29.2 Gy and 24.1 Gy, respectively, corresponding to a model-based functional dose-modifying factor (DMF) of 1.21. \textbf{Conclusion:} The ERG amplitude data support the causal radiation-injury-to-ERG framework, while additional observables will be needed to distinguish the predicted collective-network contributions. The model also provides quantitative dose targets for prospective studies of the FLASH retinal-preservation window and a framework for testing how local retinal injury may propagate into collective dysfunction.

animal behavior and cognition↗

bioRxiv2026

Reduced Myocardial Serine Synthesis Impairs Functional, Metabolic, and Redox Adaptations to Cardiac Stress

BackgroundImpaired myocardial metabolism is a defining feature of heart failure, but many defective pathways and mechanisms remain to be identified. Prior studies find phosphoglycerate kinase and its synthesized product 3-phospho-glycerate required for the serine synthetic pathway (SSP) are reduced in human HFpEF myocardium. As serine is also provided exogenously, the impact of SSP reduction is uncertain. Here, we tested if and how SSP decline coupled to phosphoglycerate dehydrogenase (PHGDH) impacts cardiomyocyte (CM) and whole heart metabolic remodeling and stress responses. MethodsStudies were performed in isolated CMs and mice with CM-selective knock-down of PHGDH. Using pharmacological inhibition or genetic silencing of PHGDH, we tested their impact on CM one-carbon metabolism pathways, cell hypertrophic responses, mitochondrial respiration, and in vivo functional, structural, and metabolic adaptations to pressure-overload stress. ResultsIn CMs, PHGDH inhibition caused dose-dependent serine depletion linearly coupled with cytotoxicity, accompanied by NAD/NADH and GSH/GSSG imbalance, reduced ATP, and disruption of one-carbon and nucleotide metabolites. Stable-isotope tracing revealed distinct metabolic fates of glucose-derived (SSP) versus exogenous serine. Exogenous serine did not rescue PHGDH-deficient CMs, whereas combined ribose and an anti-oxidant (DTT) attenuated injury and reduced nucleotide pools. PHGDH suppression reduced amino acid abundance, impaired nascent protein synthesis, and blunted endothelin-1-induced hypertrophic and mitochondrial respiration. In vivo, cardiomyocyte-specific PHGDH heterozygous mice (PHGDH+/-) had no basal phenotype, but amplified chamber dilation, dysfunction, fibrosis, and mortality 4 weeks after transverse aortic constriction (TAC). Corresponding increases in amino acids, one-carbon metabolites, nucleotides, and TCA-cycle intermediates in wild-type TAC hearts were significantly blunted in PHGDH+/- hearts. ConclusionsCardiomyocyte SSP is a critical regulator of redox balance, one-carbon metabolism, purine synthesis, amino acid homeostasis, and growth-related pathways required for cardiac adaptation to pressure overload. It is non-redundant with exogenous serine by providing distinct influences on key metabolic pathways and is a potential therapeutic target.

molecular biology↗

bioRxiv2025

Macrophages Lacking TSC2 have mTORC1-dependent GPNMB Augmentation Ameliorating Cardiac Ischemia-Reperfusion Injury

IntroductionMacrophages (M{Phi}) modulate both myocardial inflammatory and reparative phases following ischemia-reperfusion (I/R) injury. The mechanistic target of rapamycin (mTOR) is thought to play an important role in M{Phi} phenotype and functionality, but studies report conflicting net influences suggesting dependence on disease context and downstream signaling. Here, we tested the impact of M{Phi} with constitutive mTORC1 activation induced by targeted deletion of tuberous sclerosis complex 2 (TSC2) on cardiac responses to I/R injury. Methods/ResultsMyeloid TSC2 depleted (M{Phi}TSC2-/-) mice were generated by crossing Lys2Cre x TSC2flx/flx. Bone-marrow derived M{Phi}TSC2-/- vs control M{Phi} had basal increased mTORC1 and reduced mTORC2 activity. M{Phi}TSC2-/- were differentially responsive to stimulation by lipopoly- saccharide/IFN-{gamma} or IL-4 in vitro, and all disparities were prevented by rapamycin confirming the model. In vivo, M{Phi}TSC2-/- mice were strongly protected against I/R injury, with minimal change in ejection fraction, less LV dilation, hypertrophy, lung edema, or activation of stress/pro fibrotic genes. Mice pre-treated with anti-LY6G Ab to deplete neutrophils were still similarly protected, suggesting that the impact was primarily related to M{Phi}. M{Phi}TSC2-/- mice had less myocardial pro- inflammatory macrophages (CCR2+MHC-IIhi), LY6C+ monocytes, neutrophils, and CD8+ T cells 5 days post-I/R, and fewer CCR2+ but more CCR2- M{Phi} 2 weeks post I/R. Both M{Phi}TSC2-/- in vitro and in vivo post I/R phenotypes were converted to WT by rapamycin, supporting mTORC1 dependence. Lastly, synthesis of glycoprotein nonmetastatic melanoma protein B (GPNMB), a principally M{Phi} anti-inflammatory secreted protein protective against myocardial infarction was enhanced in M{Phi}TSC2-/- macrophages and hearts following I/R in an mTORC1 dependent manner. Conclusion: Constitutive macrophage-specific mTORC1 activation via TSC2 deletion reduces pro-inflammatory cell infiltration, increases GPNMB protein expression and preserves heart function following I/R injury. Rapamycin eliminates these effects. These results identify a cardioprotective mTORC1-GPNMB signaling nexus in M{Phi} in vivo.

immunology↗

bioRxiv2019

Prediction performance of a cardiovascular risk assessment tool using Stanford EHR data repository

BackgroundStratification of individuals for their risk to develop cardiovascular diseases can be used for effective prevention and intervention. A significant amount of information for risk assessment can be obtained through repurposing electronic health records (EHR). The objective of this study is to derive and assess the performance of prediction models for cardiovascular outcomes by using EHR-derived data.\n\nMethodsWe used the Stanford Medicine Research Data Repository (STARR) data from 2000-2017, containing over 2.1 million patients. A subset of 762,372 individuals with complete International Classification of Diseases (ICD) data was used to fit Cox proportional hazard models for prediction of six cardiovascular-related diseases and type 2 diabetes.\n\nResultsThe derived prediction models indicated consistent high discrimination performance (C-index) for all diseases examined: coronary artery disease (0.85), hypertension (0.82), type 2 diabetes (0.77), stroke (0.76), atrial fibrillation (0.82) and abdominal aortic aneurysm (0.77). Lower prediction abilities were observed for deep vein thrombosis (0.67). These results were consistent across age groups and maintained good prediction abilities among individuals with pre-existing diabetes or hypertension. Assessment of model calibration is ongoing.\n\nConclusionsWe proposed new prediction models for the seven diseases using ICD codes derived from EHR data. EHR data can be used for health risk assessment, but challenges related to data quality and model generalizability and calibration remain to be solved.

bioinformatics↗

bioRxiv2019

Development and validation of a next-gen health stratification engine to determine risk for multiple cardiovascular diseases

Cardiometabolic diseases (CMD) impose greater impact on every aspect of health care than any other disease group. Accurate and in-time risk assessment of individuals for their propensity to develop CMD events is one of the most critical paths in preventing these conditions. The principal objective of the present study is to report the development, and validation of a next generation risk engine to predict CMD. UK Biobank population data was used to derive predictive models for six CMD. Missing data were imputed using imputation algorithms. Cox proportional hazard models were used to estimate annual absolute risk and relative risk of different risk factors for these conditions. In addition to conventional risk factors, the applied model included socioeconomic data, lifestyle factors and comorbidities as predictors of outcomes. In total, 416,936 individuals were included in the analysis. The derived prediction models achieved consistent and moderate-to-high discrimination performance (C-index) for all diseases: coronary artery disease (0.79), hypertension (0.82), type 2 diabetes mellitus (0.87), stroke (0.79), deep vein thrombosis (0.75), and abdominal aortic aneurysm (0.90). These results were consistent across age groups (37-73 years) and showed similar predictive abilities amongst those with pre-existing diabetes or hypertension. Calibration of risk scores showed that there was moderate overestimation of CMD-related conditions only in the highest decile of risk scores for all models. In summary, the newly developed algorithms, based on Cox proportional models, resulted in high disclination and good calibration for several CMD. The integrations of these algorithms on a single platform may have direct clinical impact.

bioinformatics↗