Pittsburg State University

Phenolic compounds are pivotal in food preservation due to their potent antioxidant properties. Among these, p-coumaric acid (p-CA), a hydroxycinnamic acid widely present in fruits, vegetables, and cereals, was selected for this study due to its natural abundance and the limited mechanistic understanding of its radical-scavenging behavior. In this study, we investigated the reaction mechanism and fragmentation pattern of p-CA with 2,2-diphenyl-1-picrylhydrazyl (DPPH^•) using ultra-high-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC-ESI-Q-TOF-MS/MS) in negative ion mode. The analysis revealed the formation of three distinct products: a p-coumaric acid-p-coumaric acid dimer (m/z 325.0709, RT 1.91 min), a p-coumaric acid-DPPH radical adduct (m/z 556.1093, RT 8.83 min), and a third compound with structural features consistent with a dimer-radical (m/z 718.1403, RT 7.68 min), called radimerSB-type structure. These data provide the first direct experimental validation of such fragmentation pathways for p-CA derivatives. According to the reaction mechanism, the p-CA may provide antioxidant activity mainly by radical adduct formation (RAF). Although similar radical-derived adducts have been previously reported for ferulic acid, this is the first study to describe the formation of such a structure from p-CA, highlighting a novel antioxidant pathway and expanding the understanding of phenolic reactivity. MS/MS fragmentation confirmed the unique structural features of each product. Furthermore, the antioxidant activity of p-CA, as measured by DPPH radical scavenging and IC₅₀ determination, was comparable to that of the standard gallic acid and sinapic acid. These findings contribute to a deeper understanding of the antioxidant mechanisms of phenolic acids and support the relevance of p-CA as a functional ingredient in food systems aimed at oxidative stability.

Tuberculosis (TB), a highly contagious disease, remains one of the leading causes of death globally. The proposed computer-assisted TB detection system enhances diagnostic accuracy and efficiency by integrating deep learning and segmentation techniques.

It consists of two key subsystems: Automated field-ofview (FOV) recognition and TB bacilli segmentation. Using a motorized microscopic stage, the system systematically captures Ziehl-Neelsen-stained sputum smear images at 100x magnification. A customized Inception V3 model with transfer learning identifies FOVs containing TB bacilli, reducing variability and manual effort. Segmentation techniques, including coarse-level thresholding and shape descriptors like area, perimeter, and eccentricity, refine bacilli detection and eliminate artifacts.

This study highlights the significant potential of deep learning and image processing techniques in advancing medical diagnostics, particularly TB detection. This framework has the potential to improve clinical outcomes and support global TB eradication efforts by providing a reliable tool for early TB diagnosis.

The system achieved a mean receiver operating characteristic score of 0.9505, a precision of 0.924, a recall of 0.882, and an F1 score of 0.902, demonstrating its potential to improve TB screening, particularly in resource-limited settings. By minimizing reliance on skilled technicians and enhancing diagnostic reliability, this approach offers a scalable solution for effective TB detection and severity assessment.

This article provides a brief overview of the incidence, pathophysiology, diagnosis, and management of neonatal abstinence syndrome. Areas of discussion include genetics, pathophysiology, diagnostic tools, and pharmacologic and nonpharmacologic management of neonatal abstinence syndrome.