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Mohammadreza Akbari on Muck Rack

Mohammadreza Akbari

As seen in: Nature, Wiley Online Library, Newsweek, MDPI, SAGE Publishing, Emerald Publishing, emerald insight, Springer Professional, Business Process Management Journal, Foresight : The Journal of Futures Studies, Strategic Thinking and Policy and
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Articles

Genetic contributors to premature coronary artery disease identified by whole-exome sequencing

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By Mahsa Tahmasebivand, Kaveh S. Hosseini, Marzieh Mohseni, Saeed Sadeghian, Elham Rostami, Reza Malekzadeh, Reza Najafipour, Mohammadreza Akbari, Yasser Riazalhosseini, Hossein Najmabadi, Mark Lathrop, Kimia Kahrizi, Ebrahim Shokouhian, Sanaz Arzhangi, Fatemeh Ghodratpour, Mohammadali Boroumand, Fatemeh Shokohizadeh, Hamidreza Khorram Khorshid
| Nature Verified
Abstract Coronary artery disease (CAD) is a leading global cause of mortality, with genetic predisposition conferring a more than two-fold increased risk in individuals with a family history of premature disease. This study aimed to identify rare genetic variants underlying inherited premature CAD (PCAD) in familial cases. Fifty unrelated Iranian families with early-onset familial CAD/MI were recruited.
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Clinical drug report generation using multi-phase prompt large language models

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By Mohammadreza Akbari
| TechTrends
Generation quality: structured table reproduction The inference pipeline was evaluated for its capability to reproduce structured drug safety and efficacy tables from clinical literature on anti-migraine serotonin receptor agonists (Triptans), a therapeutic class widely used for acute migraine treatment. The benchmark reference was a manually curated clinical summary derived from FDA-approved labeling and established medical guidelines.
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MDF-MLLM: Deep Fusion Through Cross-Modal Feature Alignment for Contextually Aware Fundoscopic Image Classification

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By Mohammadreza Akbari, Peter Koulen, Mohammadreza Akbari Lor, Mei-Ling Shyu
| arXiv
[Submitted on 21 Sep 2025] Title:MDF-MLLM: Deep Fusion Through Cross-Modal Feature Alignment for Contextually Aware Fundoscopic Image Classification View a PDF of the paper titled MDF-MLLM: Deep Fusion Through Cross-Modal Feature Alignment for Contextually Aware Fundoscopic Image Classification, by Jason Jordan and 4 other authors View PDF HTML (experimental) Abstract:This study aimed to enhance disease classification accuracy from retinal fundus images by integrating fine-grained image...
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