Publications by authors named "R B Rattan"
Ovarian cancer is one of the deadliest gynecologic cancers affecting the female reproductive tract. This is largely attributed to frequent recurrence and development of resistance to the platinum-based drugs cisplatin and carboplatin. One of the major contributing factors to increased cancer progression and resistance to chemotherapy is the tumor microenvironment (TME).
View Article and Find Full Text PDFThe study by Cao aimed to identify early second-trimester biomarkers that could predict gestational diabetes mellitus (GDM) development using advanced proteomic techniques, such as Isobaric tags for relative and absolute quantitation isobaric tags for relative and absolute quantitation and liquid chromatography-mass spectrometry liquid chromatography-mass spectrometry. Their analysis revealed 47 differentially expressed proteins in the GDM group, with retinol-binding protein 4 and angiopoietin-like 8 showing significantly elevated serum levels compared to controls. Although these findings are promising, the study is limited by its small sample size ( = 4 per group) and lacks essential details on the reproducibility and reliability of the protein quantification methods used.
View Article and Find Full Text PDFBackground: There is evidence indicating that chemoresistance in tumor cells is mediated by the reconfiguration of the tricarboxylic acid cycle, leading to heightened mitochondrial activity and oxidative phosphorylation (OXPHOS). Previously, we have shown that ovarian cancer cells that are resistant to chemotherapy display increased OXPHOS, mitochondrial function, and metabolic flexibility. To exploit this weakness in chemoresistant ovarian cancer cells, we examined the effectiveness of the mitochondrial inhibitor CPI-613 in treating preclinical ovarian cancer.
View Article and Find Full Text PDFArticle Synopsis
- Multiple sclerosis (MS) is difficult to diagnose and manage, often leading to late treatment; however, artificial intelligence (AI) shows promise in analyzing patient data to improve diagnosis.* -
- This study employed a machine-learning approach to analyze metabolite profiles in MS patients and healthy controls, uncovering unique biochemical changes linked to disease severity.* -
- A trained AI model achieved high accuracy rates (87% overall, with good sensitivity, specificity, and precision), indicating potential for clinical use, but further validation with larger studies is required.*
Article Synopsis
- Multiple sclerosis (MS) is the most common inflammatory neurodegenerative disease affecting young adults, manifesting primarily as relapsing-remitting MS (RRMS) and progressing to secondary progressive MS (SPMS) or existing as primary progressive MS (PPMS), which has a steady decline without remission.
- Researchers conducted a study using global untargeted metabolomics to identify specific altered metabolites in the serum of patients with RRMS, PPMS, and healthy subjects (HS), analyzing a total of 235 metabolites.
- The study found significant differences in metabolite profiles between RRMS and HS (22 metabolites) as well as PPMS and HS (28 metabolites), and identified key metabolic pathways involved, suggesting that these unique altered metabolites could help differentiate