Gelatin-based nanoparticles and antibiotics: a new therapeutic approach for osteomyelitis? DOI Creative Commons

Ali Sherafati Chaleshtori,

Zeynab Marzhoseyni,

Nikoo Saeedi

et al.

Frontiers in Molecular Biosciences, Journal Year: 2024, Volume and Issue: 11

Published: July 30, 2024

The result of infection bone with microorganisms is osteomyelitis and septic arthritis. Methicillin-resistant Staphylococcus aureus (MRSA) responsible for most its cases (more than 50%). Since MRSA resistant to many treatments, it accompanied by high costs numerous complications, necessitating more effective new treatments. Recently, development gelatin nanoparticles have attracted the attention scientists biomedicine itself, been utilized as a delivery vehicle antibiotics because their biocompatibility, biodegradability, cost-effectiveness. Promising results reported modification combinations chemical agents. Although these findings suggested that has potential be suitable option continuous release in arthritis treatment, they still not become routine clinical practices. deliver antibiotic using gelatin-derived composites vancomycin which showed good efficacy. To date, number pre-clinical studies evaluated utility gelatin-based management osteomyelitis. Gelatin-based were found satisfactory performance control infection, well promotion defect repair chronic models. This review summarized available evidence provides insight into controlled antibiotics.

Language: Английский

Microsystem Advances through Integration with Artificial Intelligence DOI Creative Commons
Hsieh‐Fu Tsai, Soumyajit Podder, Pin‐Yuan Chen

et al.

Micromachines, Journal Year: 2023, Volume and Issue: 14(4), P. 826 - 826

Published: April 8, 2023

Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale volume, typically on the of micro- or nanoliters. Under larger surface-to-volume ratio, advantages low reagent consumption, faster reaction kinetics, more compact systems are evident in microfluidics. However, miniaturization microfluidic chips introduces challenges stricter tolerances designing controlling them for interdisciplinary applications. Recent advances artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, optimization bioanalysis data analytics. In microfluidics, Navier-Stokes equations, which partial differential equations describing viscous fluid motion complete form known not general analytical solution, can be simplified fair performance through numerical approximation due inertia laminar flow. Approximation using neural networks trained by rules physical knowledge new possibility predict physicochemical nature. The combination automation produce large amounts data, where features patterns difficult discern human extracted machine learning. Therefore, integration with AI potential revolutionize workflow enabling precision control analysis. Deployment smart may tremendously beneficial various applications future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), personalized medicine. this review, we summarize key integrated discuss outlook possibilities combining

Language: Английский

Citations

24
AI-enhanced biomedical micro/nanorobots in microfluidics DOI Open Access
Hui Dong, Jiawen Lin,

Yihui Tao

et al.

Lab on a Chip, Journal Year: 2024, Volume and Issue: 24(5), P. 1419 - 1440

Published: Jan. 1, 2024

Although developed independently at the beginning, AI, micro/nanorobots and microfluidics have become more intertwined in past few years which has greatly propelled cutting-edge development fields of biomedical sciences.

Language: Английский

Citations

16
Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications DOI Creative Commons
Claudia Arellano, Joseph Govan

Agronomy, Journal Year: 2024, Volume and Issue: 14(2), P. 341 - 341

Published: Feb. 7, 2024

Nanotechnology, nanosensors in particular, has increasingly drawn researchers’ attention recent years since it been shown to be a powerful tool for several fields like mining, robotics, medicine and agriculture amongst others. Challenges ahead, such as food availability, climate change sustainability, have promoted pushed forward the use of agroindustry environmental applications. However, issues with noise confounding signals make these tools non-trivial technical challenge. Great advances artificial intelligence, more particularly machine learning, provided new that allowed researchers improve quality functionality nanosensor systems. This short review presents latest work analysis data from using learning agroenvironmental It consists an introduction topics application field nanosensors. The rest paper examples techniques utilisation electrochemical, luminescent, SERS colourimetric classes. final section discussion conclusion concerning relevance material discussed future sector.

Language: Английский

Citations

13
Machine learning-guided high throughput nanoparticle design DOI Creative Commons
Ana Ortiz‐Perez, Derek van Tilborg, Roy van der Meel

et al.

Digital Discovery, Journal Year: 2024, Volume and Issue: 3(7), P. 1280 - 1291

Published: Jan. 1, 2024

Designing nanoparticles with desired properties is challenging due to the large combinatorial space and complex structure–function relationships. This process can be accelerated by combining microfluidics, high content imaging active learning.

Language: Английский

Citations

10
Controllable Microfluidic System through Intelligent Framework: Data-Driven Modeling, Machine Learning Energy Analysis, Comparative Multiobjective Optimization, and Experimental Study DOI
Afshin Kouhkord, Faridoddin Hassani, Moheb Amirmahani

et al.

Industrial & Engineering Chemistry Research, Journal Year: 2024, Volume and Issue: 63(30), P. 13326 - 13344

Published: June 27, 2024

Intelligent microfluidics in nanoparticle synthesis embodies a comprehensive synergistic approach that merges numerical modeling, artificial intelligence, and experimental analysis, striving for controllability over an energy-efficient microfluidic device designed with desired physical properties. This study delves into mass transfer system, employing innovative methodology combines data-driven machine learning-based comparative multiobjective optimization, analysis to model micromixing system. A surrogate is employed the considering four critical geometrical parameters inlet Reynolds as design variables. The provides insights mixer's functionality. It observed at lower numbers, increasing NoT increases mixing efficiency by more than 20%. Moreover, altering SNDi value leads significant 80% reduction pressure drop. Identifying optimal system from numerous challenging but accomplished through learning. Two distinct learning algorithms were integrated mathematical modeling optimize mixer three objectives. RSM-Differential Evolution significantly outperforms RSM-NSGA-II enhancing characteristics reducing mechanical energy consumption 85%. Additionally, improvement dissipation effective of microsystem was made, alongside comparable enhancement index management Fabrication two designs confirms drop increase 20% low Reynolds, outperforming conventional mixers. intelligent micromixer allows precise control adjusting microtransfer parameters. controlled process crucial tissue engineering hydrogel synthesis, nanotechnology, targeted drug delivery.

Language: Английский

Citations

10
Enhancement of the mixing efficiency of double T-shaped micromixers using a combined passive scheme DOI

Komeil Mehdizadeh,

Mohamad Hamed Hekmat,

Mohamad Ali Aghasi

et al.

Chemical Engineering and Processing - Process Intensification, Journal Year: 2024, Volume and Issue: unknown, P. 109682 - 109682

Published: Jan. 1, 2024

Language: Английский

Citations

9
Insight into microbial synthesis of metal nanomaterials and their environmental applications: Exploration for enhanced controllable synthesis DOI
Yuqing Liu, Yang Yu,

E Yuhan

et al.

Chinese Chemical Letters, Journal Year: 2024, Volume and Issue: 35(11), P. 109651 - 109651

Published: Feb. 10, 2024

Language: Английский

Citations

9
Ytterbium-Doped Lead–Halide Perovskite Nanocrystals: Synthesis, Near-Infrared Emission, and Open-Source Machine Learning Model for Prediction of Optical Properties DOI Creative Commons
Yuliya A. Timkina, Vladislav S. Tuchin, Aleksandr P. Litvin

et al.

Nanomaterials, Journal Year: 2023, Volume and Issue: 13(4), P. 744 - 744

Published: Feb. 16, 2023

Lead–halide perovskite nanocrystals are an attractive class of materials since they can be easily fabricated, their optical properties tuned all over the visible spectral range, and possess high emission quantum yields narrow photoluminescence linewidths. Doping perovskites with lanthanides is one ways to widen range emission, making them for further applications. Herein, we summarize recent progress in synthesis ytterbium-doped terms varying parameters such as temperature, ligand molar ratio, ytterbium precursor type, dopant content. We consider dependence morphology (size content) (photoluminescence yield near-infrared ranges) on parameters. The developed open-source code approximates those dependencies multiple-parameter linear regression allows us estimate value from synthesis. Further use promotion database will expand possibilities predict protocols doped nanocrystals.

Language: Английский

Citations

14
Knowledge Gaps in Generating Cell-Based Drug Delivery Systems and a Possible Meeting with Artificial Intelligence DOI
Negin Mozafari, Niloofar Mozafari, Ali Dehshahri

et al.

Molecular Pharmaceutics, Journal Year: 2023, Volume and Issue: 20(8), P. 3757 - 3778

Published: July 10, 2023

Cell-based drug delivery systems are new strategies in targeted which cells or cell-membrane-derived used as carriers and release their cargo a controlled manner. Recently, great attention has been directed to carrier for treating several diseases. There various challenges the development of cell-based systems. The prediction properties these platforms is prerequisite step reduce undesirable effects. Integrating nanotechnology artificial intelligence leads more innovative technologies. Artificial quickly mines data makes decisions accurately. Machine learning subset broader nanomedicine design safer nanomaterials. Here, how developing can be solved with potential predictive models machine portrayed. most famous described. Last but not least, its types highlighted. present Review shown derivatives they learning.

Language: Английский

Citations

14
B‐Site Doping of Metal Halide Perovskite Nanoplatelets Influences Their Optical Properties DOI Open Access
Aleksandr P. Litvin, Igor V. Margaryan,

Wenxu Yin

et al.

Advanced Optical Materials, Journal Year: 2023, Volume and Issue: 12(8)

Published: Aug. 21, 2023

Abstract Metal halide perovskite nanoplatelets (NPls) have recently joined a rich family of 2D semiconductor nanomaterials. Quantum and dielectric confinement in these nanostructures endow them with useful optical properties, which include, but are not limited to, high linear nonlinear absorption coefficients, narrow tunable emission bands, photoluminescence quantum yield. These characteristics render NPls promising for applications lighting, photodetection, optics, photocatalysis. Doping is universal approach tuning electronic properties materials, the B‐site doping allows further improvement adjustment on demand above‐mentioned may result appearance fundamentally new behavior through embedding optically active dopants. In this mini‐review, basic knowledge about shortly summarized terms their colloidal synthesis, then considered existing approaches (in situ post‐synthetic doping) its effect (doping self‐emitting ions such as Mn 2+ rare‐earth elements; consequences response).

Language: Английский

Citations

14