A5020095161- Computational Drug Discovery Methods
- Metabolomics and Mass Spectrometry Studies
- Animal testing and alternatives
- Machine Learning in Materials Science
- Effects and risks of endocrine disrupting chemicals
- Estrogen and related hormone effects
- Alcohol Consumption and Health Effects
- Analytical Chemistry and Chromatography
- Forensic Toxicology and Drug Analysis
- Drug-Induced Hepatotoxicity and Protection
- Cholinesterase and Neurodegenerative Diseases
- Analytical Methods in Pharmaceuticals
- Pharmacogenetics and Drug Metabolism
- Psychedelics and Drug Studies
- Pesticide Exposure and Toxicity
- Pharmacological Effects and Assays
Cuyahoga County Juvenile Court
2023-2024
Rutgers, The State University of New Jersey
2019-2022
Arcadia University
2020
la diffusion de documents scientifiques niveau recherche, publiés ou non, émanant des établissements d'enseignement et recherche français étrangers, laboratoires publics privés.
Traditional experimental testing to identify endocrine disruptors that enhance estrogenic signaling relies on expensive and labor-intensive experiments. We sought design a knowledge-based deep neural network (k-DNN) approach reveal organize public high-throughput screening data for compounds with nuclear estrogen receptor α β (ERα ERβ) binding potentials. The target activity was rodent uterotrophic bioactivity driven by ERα/ERβ activations. After training, the resultant successfully inferred...
Traditional methodologies for assessing chemical toxicity are expensive and time-consuming. Computational modeling approaches have emerged as low-cost alternatives, especially those used to develop quantitative structure-activity relationship (QSAR) models. However, conventional QSAR models limited training data, leading low predictivity new compounds. We developed a data-driven approach constructing carcinogenicity-related these identify potential human carcinogens. To this goal, we probe...
For hazard identification, classification, and labeling purposes, animal testing guidelines are required by law to evaluate the developmental toxicity potential of new existing chemical products. However, guideline studies costly, time-consuming, require many laboratory animals. Computational modeling has emerged as a promising, animal-sparing, cost-effective method for evaluating chemicals, such endocrine disruptors, without use We aimed develop predictive explainable computational model...
Compared to traditional experimental approaches, computational modeling is a promising strategy efficiently prioritize new candidates with low cost. In this study, we developed novel data mining and workflow proven be applicable by screening analgesic opioids. To end, large opioid set was used as the probe automatically obtain bioassay from PubChem portal. There were 114 bioassays selected build quantitative structure-activity relationship (QSAR) models based on testing results across...
Understanding the stability of drugs in a forensic toxicology setting is critical for evaluation drug concentrations. Synthetic cathinones are new psychoactive substances structurally derived from cathinone, component Catha edulis ("khat"), shrub that indigenous to Middle East and Africa. Previous research has evaluated synthetic biological matrices, including blood preserved with combination NaF K2C2O4 used grey-top tubes. However, it does not assess their Na2EDTA, some clinical samples....
Public laboratories must balance innovative and existing methods to keep up with designer drug trends. This article presents a strategy for handling benzodiazepines (DBZDs) in casework from screening interpretation. The cross-reactivity of 22 DBZDs metabolites was tested against the Immunalysis™ Benzodiazepine Direct Enzyme-Linked Immunosorbent Assay kit. kit had high intra-analyte precision (coefficients variation < 15%). Inter-analyte performance varied, triggering confirmation testing at...