arXiv subjects

Lobat Tayebi

Publications and source records attributed to Lobat Tayebi.

4 recordsLinked to original sources

arXiv2026

Efficacy of 3D-Printed chitosan-cerium oxide dressings coated with vancomycin-loaded alginate for chronic wounds management

Multifunctional wound dressings with antibacterial and antioxidant properties hold significant promise for treating chronic wounds; however, achieving a balance of these characteristics while maintaining biocompatibility is challenging. To enhance this balance, this study focuses on the design and development of 3D-printed chitosan-matrix composite scaffolds, which are incorporated with varying amounts of cerium oxide nanoparticles (0, 1, 3, 5, and 7 wt%) and subsequently coated with a vancomycin-loaded alginate layer. The structure, antibiotic drug delivery kinetics, biodegradation, swelling, biocompatibility, antibacterial, antioxidant, and cell migration behaviors of the fabricated dressings were evaluated in-vitro. The findings reveal that all of the formulations demonstrated a robust antibacterial effect against S. aureus bacterial strains in disk diffusion tests. Furthermore, the dressings containing cerium oxide nanoparticles exhibited proper antioxidant capabilities, with over 78.1% reactive oxygen species (ROS) scavenging efficiency achieved with 7% cerium oxide nanoparticles. The sample containing 5% cerium oxide nanoparticles was identified as the optimal formulation, characterized by the most favorable cell biocompatibility, an ROS scavenging ability of over 73.4%, and the potential to close the wound bed within 24 h. This study highlights that these dressings are promising for managing chronic wounds by preventing infection and oxidative stress in a correct therapeutic sequence.

physics.app-ph↗

arXiv2025

Enhanced Vascularity in Gelatin Scaffolds via Copper-Doped Magnesium-Calcium Silicates Incorporation: In-Vitro and Ex-Ovo Insights

Addressing a critical challenge in current tissue-engineering practices, this study aims to enhance vascularization in 3D porous scaffolds by incorporating bioceramics laden with pro-angiogenic ions. Specifically, freeze-dried gelatin-based scaffolds were infused with sol-gel-derived powders of Cu-doped akermanite (Ca2MgSi2O7) and bredigite (Ca7MgSi4O16) at various concentrations (10, 20, and 30 wt%). The scaffolds were initially characterized for their structural integrity, biodegradability, swelling behavior, impact on physiological pH, and cytocompatibility with human umbilical vein endothelial cells (HUVECs). The silicate incorporation effectiveness in promoting vascularity was then assessed through HUVEC attachment, capillary tube formation, and ex-ovo chick embryo chorioallantoic membrane assays. The findings revealed significant improvements in both in-vitro and ex-ovo vascularity of the gelatin scaffolds upon the addition of Cu-doped akermanite. The most effective concentrations were determined to be 10 and 20%, which led to notable HUVEC metabolic activity, a well-spread morphology with extensive peripheral filopodia and lamellipodia at 10% and a cobblestone phenotype indicative of in-vivo endothelium at 20% during cell attachment, the formation of complex networks of tubular structures, and robust vascularization in chick embryo development. Moving forward, the incorporation of Cu-doped akermanite into tissue-engineering scaffolds shows great potential for addressing the limitations of vascularization, especially for critical-sized bone defects, by facilitating the controlled release of pro-angiogenic and pro-osteogenic ions.

physics.med-ph↗

arXiv2025

Proof-of-TBI -- Fine-Tuned Vision Language Model Consortium and OpenAI-o3 Reasoning LLM-Based Medical Diagnosis Support System for Mild Traumatic Brain Injury (TBI) Prediction

Mild Traumatic Brain Injury (TBI) detection presents significant challenges due to the subtle and often ambiguous presentation of symptoms in medical imaging, making accurate diagnosis a complex task. To address these challenges, we propose Proof-of-TBI, a medical diagnosis support system that integrates multiple fine-tuned vision-language models with the OpenAI-o3 reasoning large language model (LLM). Our approach fine-tunes multiple vision-language models using a labeled dataset of TBI MRI scans, training them to diagnose TBI symptoms effectively. The predictions from these models are aggregated through a consensus-based decision-making process. The system evaluates the predictions from all fine-tuned vision language models using the OpenAI-o3 reasoning LLM, a model that has demonstrated remarkable reasoning performance, to produce the most accurate final diagnosis. The LLM Agents orchestrates interactions between the vision-language models and the reasoning LLM, managing the final decision-making process with transparency, reliability, and automation. This end-to-end decision-making workflow combines the vision-language model consortium with the OpenAI-o3 reasoning LLM, enabled by custom prompt engineering by the LLM agents. The prototype for the proposed platform was developed in collaboration with the U.S. Army Medical Research team in Newport News, Virginia, incorporating five fine-tuned vision-language models. The results demonstrate the transformative potential of combining fine-tuned vision-language model inputs with the OpenAI-o3 reasoning LLM to create a robust, secure, and highly accurate diagnostic system for mild TBI prediction. To the best of our knowledge, this research represents the first application of fine-tuned vision-language models integrated with a reasoning LLM for TBI prediction tasks.

cs.AI↗

arXiv2024

Unraveling the Molecular Magic: AI Insights on the Formation of Extraordinarily Stretchable Hydrogels

The deliberate manipulation of ammonium persulfate, methylenebisacrylamide, dimethyleacrylamide, and polyethylene oxide concentrations resulted in the development of a hydrogel with an exceptional stretchability, capable of extending up to 260 times its original length. This study aims to elucidate the molecular architecture underlying this unique phenomenon by exploring potential reaction mechanisms, facilitated by an artificial intelligence prediction system. Artificial intelligence predictor introduces a novel approach to interlinking two polymers, involving the formation of networks interconnected with linear chains following random chain scission. This novel configuration leads to the emergence of a distinct type of hydrogel, herein referred to as a "Span Network." Additionally, Fourier-transform infrared spectroscopy (FTIR) is used to investigate functional groups that may be implicated in the proposed mechanism, with ester formation confirmed among numerous hydroxyl end groups obtained from chain scission of PEO and carboxyl groups formed on hydrogel networks.

cond-mat.soft↗