A5049879984- Nanoparticle-Based Drug Delivery
- Air Quality and Health Impacts
- Field-Flow Fractionation Techniques
- Cancer Risks and Factors
- Radiation Therapy and Dosimetry
- Graphene and Nanomaterials Applications
- Nanoplatforms for cancer theranostics
- Bacterial Identification and Susceptibility Testing
- Sleep and Work-Related Fatigue
- Sexual function and dysfunction studies
- Metabolism and Genetic Disorders
- Radiomics and Machine Learning in Medical Imaging
- Peroxisome Proliferator-Activated Receptors
- Vitamin D Research Studies
- Pharmaceutical and Antibiotic Environmental Impacts
- Global Cancer Incidence and Screening
- Effects and risks of endocrine disrupting chemicals
- Ovarian function and disorders
- Endometriosis Research and Treatment
- Metabolomics and Mass Spectrometry Studies
- Gold and Silver Nanoparticles Synthesis and Applications
- Microbial infections and disease research
- Veterinary medicine and infectious diseases
University of Florida
2022-2024
Kansas State University
2019-2020
University of Georgia
2014-2017
The critical barrier for clinical translation of cancer nanomedicine stems from the inefficient delivery nanoparticles (NPs) to target solid tumors. Rapid growth computational power, new machine learning and artificial intelligence (AI) approaches provide tools address this challenge. In study, we established an AI-assisted physiologically based pharmacokinetic (PBPK) model by integrating AI-based quantitative structure-activity relationship (QSAR) with a PBPK simulate tumor-targeted...
Background: Low delivery efficiency of nanoparticles (NPs) to the tumor is a critical barrier in field cancer nanomedicine. Strategies on how improve NP remain be determined. Methods: This study analyzed roles physicochemical properties, models, and types using multiple machine learning artificial intelligence methods, data from recently published Nano-Tumor Database that contains 376 datasets generated physiologically based pharmacokinetic (PBPK) model. Results: The deep neural network...
Nanoparticles (NPs) can be designed for targeted delivery in cancer nanomedicine, but the challenge is a low efficiency (DE) to tumor site. Understanding impact of NPs' physicochemical properties on target tissue distribution and DE help improve design nanomedicines. Multiple machine learning artificial intelligence models, including linear regression, support vector machine, random forest, gradient boosting, deep neural networks (DNN), were trained validated predict based therapeutic...
Many physiologically based pharmacokinetic (PBPK) models for environmental chemicals, drugs, and nanomaterials have been developed to aid risk safety assessments using acslX. However, acslX has rendered sunset since November 2015. Alternative modeling tools tutorials are needed future PBPK applications. This forum article aimed to: (1) demonstrate the performance of 4 software packages (acslX, Berkeley Madonna, MATLAB, R language) tested 2 existing (oxytetracycline gold nanoparticles); (2)...
The objectives of this study were to evaluate the injection site pathology and determine tissue residue depletion tulathromycin in calves following pneumatic dart administration calculate associated extralabel withdrawal interval (WDI). Castrated male Holstein injected with ~2.6 mg/kg via administration. At 1 (n = 2), 6, 12, 18, 24 d after drug 3/time point), euthanized, muscle, liver, kidney, fat, samples harvested analyzed for concentrations using a LC-MS/MS method. Gross histopathology...
Background: The relationship between shift work or exposure to light at night and breast cancer risk in women remains unclear. Aims: To evaluate the statistical association night, as surrogate markers of circadian rhythm disruption, through a comprehensive meta-analysis. Methods: PubMed database was searched for observational epidemiologic studies on published January 1980 December 2012. Publication bias examined by funnel plot Egger's test. Cochran Q test I2 statistics were used assess...