Publications by authors named "Anthony Onoja"

Identification of features with high levels of confidence in liquid chromatography-mass spectrometry (LC-MS) lipidomics research is an essential part of biomarker discovery, but existing software platforms can give inconsistent results, even from identical spectral data. This poses a clear challenge for reproducibility in biomarker identification. In this work, we illustrate the reproducibility gap for two open-access lipidomics platforms, MS DIAL and Lipostar, finding just 14.

View Article and Find Full Text PDF

The constant changes experienced in agricultural activities due to climate change pose a great challenge to melon production. Hence, this research examined the determinants of melon farmers' adaptation strategies to cope with climate change hazards in southern-southern Nigeria. The research ultimately depended on primary data collected by using a set of questionnaires and interviews.

View Article and Find Full Text PDF

The global COVID-19 pandemic resulted in widespread harms but also rapid advances in vaccine development, diagnostic testing, and treatment. As the disease moves to endemic status, the need to identify characteristic biomarkers of the disease for diagnostics or therapeutics has lessened, but lessons can still be learned to inform biomarker research in dealing with future pathogens. In this work, we test five sets of research-derived biomarkers against an independent targeted and quantitative Liquid Chromatography-Mass Spectrometry metabolomics dataset to evaluate how robustly these proposed panels would distinguish between COVID-19-positive and negative patients in a hospital setting.

View Article and Find Full Text PDF

Somatic copy number alterations (SCNA) involving either a whole chromosome or just one of the arms, or even smaller parts, have been described in about 88% of human tumors. This study investigated the SCNA profile in 40 well-characterized sporadic medullary thyroid carcinomas by comparative genomic hybridization array. We found that 26/40 (65%) cases had at least one SCNA.

View Article and Find Full Text PDF
Article Synopsis
  • A multifaceted computational strategy was used to analyze a Whole Exome Sequencing dataset from 2000 Italian patients to identify genetic factors that heighten the risk of severe COVID-19.
  • The study employed stratified k-fold screening and supervised classifiers, finding 16 key variants alongside age and gender that effectively predicted COVID-19 severity with high accuracy.
  • The research highlights genetic signatures related to immune and inflammatory responses to viral infections, revealing pathways that could lead to new therapeutic options and better patient classification.
View Article and Find Full Text PDF