Staphylococcus aureus is a serious pathogen that can survive within host cells after a typical course of treatment completion, leading to chronic infection. Knowledge of host proteomic patterns after clearance of this pathogen from cells is limited. Here, we looked for S. aureus clearance biomarkers produced by in vitro-infected leukocytes. Extracellular proteins from primary human leukocytes infected with S. aureus ATCC 25923 were investigated as possible treatment-monitoring clearance biomarkers by applying a proteomics approach combining liquid chromatography with tandem mass spectrometry (LC-MS/MS) and protein interaction network analysis. It was found that the expression patterns of proteins secreted by S. aureus-infected leukocytes differed among stages of infection. Proteomic profiles showed that an ATPase, aminophospholipid transporter-like, Class I, type 8A, member 2 (ATP8A2) was expressed in the clearance stage and was not detected at any earlier stage or in uninfected controls. Protein network analysis showed that TERF2 (telomeric repeat-binding factor 2), ZNF440 (zinc finger protein 440), and PPP1R14A (phosphatase 1 regulatory subunit 14A) were up-regulated, while GLE1, an essential RNA-export mediator, was suppressed in both infection and clearance stages, suggesting their potential roles in S. aureus infection and clearance. These findings are the first to report that the ATP8A2 has potential as a clearance biomarker for S. aureus infection.
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http://dx.doi.org/10.1007/s00284-023-03450-6 | DOI Listing |
J Drug Target
January 2025
Sunirmal Bhattacharjee, Bharat Pharmaceutical Technology, Amtali, Agartala, Tripura, India.
A significant area of computer science called artificial intelligence (AI) is successfully applied to the analysis of intricate biological data and the extraction of substantial associations from datasets for a variety of biomedical uses. AI has attracted significant interest in biomedical research due to its features: (i) better patient care through early diagnosis and detection; (ii) enhanced workflow; (iii) lowering medical errors; (v) lowering medical costs; (vi) reducing morbidity and mortality; (vii) enhancing performance; (viii) enhancing precision; and (ix) time efficiency. Quantitative metrics are crucial for evaluating AI implementations, providing insights, enabling informed decisions, and measuring the impact of AI-driven initiatives, thereby enhancing transparency, accountability, and overall impact.
View Article and Find Full Text PDFMol Omics
January 2025
Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore 575018, India.
Lung cancer remains the leading cause of cancer-related deaths worldwide due to its poor prognosis. Despite significant advancements in the understanding of cancer development, improvements in diagnostic methods, and multimodal therapeutic regimens, the prognosis of lung cancer has still not improved. Therefore, it is reasonable to look for newer and alternative medicines for treatment.
View Article and Find Full Text PDFExpert Opin Drug Saf
January 2025
Department of Pharmacy, Yantai Yuhuangding Hospital Affiliated to Qingdao University, Yantai, Shandong, China.
Background: Adverse events (AE) in dupilumab induced ocular surface diseases (DIOSD) have raised concerns regarding its safety. The objective of this study was to evaluate DIOSD by employing database analysis and clinical case review, along with mechanism analysis.
Research Design And Methods: Database AE data were extracted from FAERS from 2017 Quarter 1 (Q1) to 2023 Q1.
Front Artif Intell
December 2024
Computer Science and Software Engineering Department, Auckland University of Technology, Auckland, New Zealand.
Introduction: Musical instrument recognition is a critical component of music information retrieval (MIR), aimed at identifying and classifying instruments from audio recordings. This task poses significant challenges due to the complexity and variability of musical signals.
Methods: In this study, we employed convolutional neural networks (CNNs) to analyze the contributions of various spectrogram representations-STFT, Log-Mel, MFCC, Chroma, Spectral Contrast, and Tonnetz-to the classification of ten different musical instruments.
Front Immunol
January 2025
Programa Multicêntrico de Bioquímica e Biologia Molecular/PMBqBM - Universidade Federal de Juiz de Fora - UFJF, Governador Valadares, MG, Brazil.
Introduction: Leprosy, a chronic infectious disease, is closely linked to the host immune response. According to the WHO, leprosy patients (L) and household contacts (HHC) are classified into subgroups: paucibacillary (PB) and multibacillary (MB), witch reflect the degree of infection in patients and the level of exposure of their contacts. The main goal of this study was to: i) establish a comprehensive overview of soluble mediator signatures of PBMCs upon antigen-specific stimuli and ii) identify whether the chemokine (CH) and cytokine (CY) signatures were associated with distinct clinical manifestations in (L) and immune response profiles in (HHC).
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