Spinal cord injury presents a significant challenge in regenerative medicine due to the complex and deli-cate nature of neural tissue repair.This study aims to design a conductive hydrogel embedded with magnetic MgFe_...Spinal cord injury presents a significant challenge in regenerative medicine due to the complex and deli-cate nature of neural tissue repair.This study aims to design a conductive hydrogel embedded with magnetic MgFe_(2)O_(4) nanoparticles to establish a bioelectrically active and spatially stable microenvironment that promotes spinal cord regeneration through computational analysis(BIOVIA Materials Studio).Hydrogels,known for their biocompatibility and extracellular matrix-mimicking properties,support essential cellular behaviors such as adhesion,proliferation,and migration.The integration of MgFe_(2)O_(4) nanoparticles imparts both electrical conductivity and magnetic responsiveness,enabling controlled transmission of electrical signals that are crucial for guiding cellular processes like differentiation and directed migration.Furthermore,the hydrogel acts as a delivery medium,facilitating the adsorption of MgFe_(2)O_(4) nanoparticles onto spinal tissue through strong Van der Waals and intramolecular interactions.The computational simulations revealed a robust adsorption profile,with a binding distance of 20.180Åand a cumulative adsorption energy of 2740.42 kcal/mol,indicating stable nanoparticle-tissue interactions.Pressure-dependent sorption analysis further demonstrated that reduced pressure conditions enhance adsorption strength,promoting tighter material-tissue integration.The adverse Van der Waals energy and increased intramolecular energy observed under these conditions underscore the importance of optimized adsorption settings for functional tissue interface formation.Altogether,the conductive hydrogel-MgFe_(2)O_(4) composite system offers a promising therapeutic platform by combining structural support,electrical stimulation,and magnetic guidance,thereby enhancing cell-material interactions and fostering an environment conducive to spinal cord tissue repair.展开更多
In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects adults.Treatment depends on the type of leukemia...In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects adults.Treatment depends on the type of leukemia and the extent to which cancer has established throughout the body.Identifying leukemia in the initial stage is vital to providing timely patient care.Medical image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive processes.It can be simple to generalize Computer vision(CV)-based and image-processing techniques and eradicate human error.Many researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its subgroups.This study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical Images.The projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical images.The MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature extraction.Lastly,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia cancer.The hyperparameter tuning process using MPA helps enhance leukemia cancer classification performance.Simulation results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.展开更多
基金the“Young Talent Research Grant”:(600-RMC/YTR/5/3(004/2022)Universiti Teknologi Mara(UiTM)for providing the financial support.
文摘Spinal cord injury presents a significant challenge in regenerative medicine due to the complex and deli-cate nature of neural tissue repair.This study aims to design a conductive hydrogel embedded with magnetic MgFe_(2)O_(4) nanoparticles to establish a bioelectrically active and spatially stable microenvironment that promotes spinal cord regeneration through computational analysis(BIOVIA Materials Studio).Hydrogels,known for their biocompatibility and extracellular matrix-mimicking properties,support essential cellular behaviors such as adhesion,proliferation,and migration.The integration of MgFe_(2)O_(4) nanoparticles imparts both electrical conductivity and magnetic responsiveness,enabling controlled transmission of electrical signals that are crucial for guiding cellular processes like differentiation and directed migration.Furthermore,the hydrogel acts as a delivery medium,facilitating the adsorption of MgFe_(2)O_(4) nanoparticles onto spinal tissue through strong Van der Waals and intramolecular interactions.The computational simulations revealed a robust adsorption profile,with a binding distance of 20.180Åand a cumulative adsorption energy of 2740.42 kcal/mol,indicating stable nanoparticle-tissue interactions.Pressure-dependent sorption analysis further demonstrated that reduced pressure conditions enhance adsorption strength,promoting tighter material-tissue integration.The adverse Van der Waals energy and increased intramolecular energy observed under these conditions underscore the importance of optimized adsorption settings for functional tissue interface formation.Altogether,the conductive hydrogel-MgFe_(2)O_(4) composite system offers a promising therapeutic platform by combining structural support,electrical stimulation,and magnetic guidance,thereby enhancing cell-material interactions and fostering an environment conducive to spinal cord tissue repair.
基金funded by Researchers Supporting Program at King Saud University,(RSPD2024R809).
文摘In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects adults.Treatment depends on the type of leukemia and the extent to which cancer has established throughout the body.Identifying leukemia in the initial stage is vital to providing timely patient care.Medical image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive processes.It can be simple to generalize Computer vision(CV)-based and image-processing techniques and eradicate human error.Many researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its subgroups.This study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical Images.The projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical images.The MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature extraction.Lastly,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia cancer.The hyperparameter tuning process using MPA helps enhance leukemia cancer classification performance.Simulation results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.
