Publications by authors named "S Donya Razavi"

Objective: The foodborne pathogen Salmonella enterica serovar Typhimurium causes self-limiting gastroenteritis in humans and is difficult to eliminate due to its ability to adhere to surfaces and form biofilms that exhibit high resistance to antimicrobial agents. To explore alternative strategies for biofilm treatment, it is essential to investigate novel agents that inhibit Salmonella biofilms.

Method: In this study, we investigated the minimum biofilm inhibitory concentrations (MBICs) and minimum biofilm eradication concentrations (MBECs) of nafcillin and diosmin, both previously identified as Lon protease inhibitors, against biofilms formed by S.

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The viability of probiotic cells decreases during passage through the gastrointestinal tract. The process of probiotics encapsulation with sodium alginate and chitosan polymers was carried out to protect the Lactobacillus plantarum in adverse conditions. Lactobacillus plantarum was entrapped in sodium alginate/chitosan (SA/BChi) and sodium alginate/nano-chitosan (SA/NChi) wall materials.

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is a protozoan parasite that infects approximately one billion people worldwide. In this study, the effects of lycopene on experimental giardiasis in mice were investigated by evaluating cyst shedding rate, weight changes, duodenal antioxidant status, and histopathological alteration. Ninety-five male mice aged four to six weeks were divided into six groups of 15 and one group of 5 as the negative control.

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Background: Bisphosphonate-related osteonecrosis of the jaw (BRONJ) presents significant clinical challenges with uncertain treatment outcomes. Teriparatide, a fragment of human parathyroid hormone, shows potential in prevention strategies for BRONJ.

Objective: This study investigates the impact of a single local dose of teriparatide on BRONJ prevention in an animal model.

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Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts.

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