Biology subjects

Monfared, V.

Publications and source records attributed to Monfared, V..

3 recordsLinked to original sources

bioRxiv2026

Convergent Mitochondrial and Oxidative Stress Signatures in Spaceflight and Tauopathy: A Comparative Multi-Omics Reanalysis in Drosophila melanogaster

Mitochondrial dysfunction is a shared biological thread between terrestrial Alzheimer's disease (AD) and in the unique stress environment of spaceflight, making mitochondrial pathophysiology a compelling target for comparative investigation. In AD, mitochondrial impairment is closely linked to defective oxidative phosphorylation, reactive oxygen species accumulation, calcium dysregulation, and neuronal injury, while spaceflight stressors such as microgravity and radiation disrupt mitochondrial homeostasis and increase oxidative stress. Understanding how the extreme stressors of spacelight affect mitochondrial function may provide insights into conserved mitochondrial stress responses and their implications for neurodegenerative diseases, including AD. Our study compares datasets from ground control Drosophila melanogaster RNA-seq with isolated radiation and microgravity exposure to discern conserved mitochondrial pathways and molecular signatures that may provide insights into AD mechanisms.

cell biology↗

bioRxiv2026

Efficacy of postmenopausal estrogen replacement in SIV-infected female macaques on antiretroviral therapy.

The success of modern antiretroviral therapy (ART) has increased the life expectancy of people living with HIV to levels approaching that of uninfected individuals. For women living with HIV (WLWH), this means that more will survive to undergo menopause and experience the consequences of decreased ovarian hormone levels, particularly estrogen (E2). The recent change in federal guidance for use of postmenopausal hormone therapy is increasing demand for both E2-alone and E2+progestogen formulations to control adverse symptoms of menopause. The consequences and efficacy of hormone therapy in WLWH are thus an important issue for WLWH and their healthcare providers. The role of E2 replacement in postmenopausal WLWH is a significant issue because of its potential effects on control the viral reservoir and its demonstrated beneficial metabolic effects in uninfected postmenopausal women. To address these questions, we employed a novel nonhuman primate model of postmenopausal WLWH undergoing E2 replacement. Reproductively competent female rhesus macaques were infected with simian immunodeficiency virus (SIV) and then subjected to a daily ART regimen. After complete suppression of plasma viremia, all animals were ovariectomized (OVX) and then implanted with Silastic capsules containing either cholesterol vehicle or sufficient E2 to restore pre-OVX plasma levels. Plasma and cell-associated viral dynamics, immune responses, body composition, systemic and tissue-specific metabolic parameters, cytokine profiles, and parameters of bone health were followed longitudinally from baseline through 34 weeks of E2 deficiency or replacement. We found that E2 status did not significantly affect plasma or tissue viral dynamics or overall metabolic homeostasis. However, E2 replacement exerted beneficial effects on several aspects of bone health in spite of a chronic inflammatory state that persisted following effective ART suppression of the SIV reservoir. Our findings suggest that hormone therapy, specifically E2 replacement, offers benefit to WLWH, particularly with respect to bone loss.

physiology↗

bioRxiv2023

Prediction Analysis of Preterm Neonates Mortality using Machine Learning Algorithms via Python Programming

Prediction analysis of preterm neonate mortality is necessary and significant for benchmarking and evaluating healthcare services in Hospitals and other medical centers. Application of artificial intelligence and machine learning models, which is a hot topic in medicine/healthcare and engineering, may improve physicians skill to predict the preterm neonatal deaths. The main purpose of this research article is to introduce a preterm neonatal mortality risk prediction by means of machine learning/ML predictive models to survive infants using supervised ML models if possible. Moreover, this paper presents some effective parameters and features which affect to survive the infants directly. It means, the obtained model has an accuracy of about 91.5% to predict the status of infant after delivery. After recognizing the critical status for an infant, physicians and other healthcare personnel can help to infant for possible surviving using special medical NICU cares. It has been tried to get some suitable models with high accuracy and comparing the results. In a word, a survival prediction analysis of preterm neonate mortality has been carried out using machine learning methods via Python programming (possible surviving infants after delivery in the hospital).

bioinformatics↗