Background: Prostate cancer (PCa) shows considerable clinical heterogeneity that has been primarily attributed to variable molecular alterations. - fusion is one such molecular subtype that has been associated with predominantly poor prognosis. More recently, a single nucleotide polymorphism (SNP) in the gene rs12329760 C>T (Met160Val) has been shown to positively correlate with the fusion status and also to be associated with increased risk for PCa. The aim of the present study is to determine the frequency of - fusion and association of rs12329760 in Indian PCa patients with fusion status.
Methods: - fusion by fluorescence hybridization was determined in 102 of 150 PCa biopsy-proven cases. Genotyping for rs12329760 was performed on the entire cohort of 150 cases by Sanger sequencing.
Results: - fusion was seen in 27 of 102 (26%) cases. Fusion-positive patterns in this study showed fusion by translocation in nine of 27 cases (33.5%), by deletion in six of 27 (22%) cases, and by insertion in 12 of 27 cases (44.5%). No association of the fusion status with Gleason Score, pattern, or perineural invasion was seen. The SNP rs12329760 'T' allele was prevalent with a frequency of 0.27 in the PCa patients. The SNP was significantly associated with fusion [odds ratio (OR) = 2.176, 95% confidence interval (CI) = 1.012-4.684, = 0.04], more specifically fusion by deletion ( = 0.04).
Conclusion: The results provided here determine the frequency of - fusions (26%) in a fairly large cohort of Indian PCa cases and also the association of rs12329760 SNP with - fusion. No association with other clinico-pathological features was observed. Future studies with clinical outcomes are warranted in this population.
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http://dx.doi.org/10.1016/j.prnil.2018.03.004 | DOI Listing |
Spine (Phila Pa 1976)
January 2025
Indiana Spine Group Location of investigation Indiana Spine Group, 13225 N. Meridian Street, Carmel, IN 46032.
Study Design: Retrospective cohort.
Objective: To compare the clinical outcomes of trial versus standard clinical practice (SCP) patients following cervical disc arthroplasty (CDA).
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Math Biosci Eng
December 2024
School of Information Engineering, Nantong Institute of Technology, Nantong 226002, Jiangsu, China.
As an essential component of mechanical systems, bearing fault diagnosis is crucial to ensure the safe operation of the equipment. However, vibration data from bearings often exhibit non-stationary and nonlinear features, which complicates fault diagnosis. To address this challenge, this paper introduces a novel multi-scale time-frequency and statistical features fusion model (MTSF-FM).
View Article and Find Full Text PDFBMC Med Genomics
January 2025
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, Hubei, China.
Background: Drug and protein targets affect the physiological functions and metabolic effects of the body through bonding reactions, and accurate prediction of drug-protein target interactions is crucial for drug development. In order to shorten the drug development cycle and reduce costs, machine learning methods are gradually playing an important role in the field of drug-target interactions.
Results: Compared with other methods, regression-based drug target affinity is more representative of the binding ability.
BMC Health Serv Res
January 2025
The University of Sydney, Faculty of Medicine and Health, School of Rural Health, Orange, NSW, 2800, Australia.
Background: Low-value care refers to the provision of health services that confer little or no benefit to patients, or have the potential to incur unwarranted harms. A breadth of literature exists investigating geographical variations in rates of potential low-value interventions for musculoskeletal pain. This scoping review aimed to examine the provision of low-value care for osteoarthritis and lower back pain by degree of rurality (e.
View Article and Find Full Text PDFCardiovasc Diabetol
January 2025
Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Background: Diabetic myocardial disorder (DbMD, evidenced by abnormal echocardiography or cardiac biomarkers) is a form of stage B heart failure (SBHF) at high risk for progression to overt HF. SBHF is defined by abnormal LV morphology and function and/or abnormal cardiac biomarker concentrations.
Objective: To compare the evolution of four DbMD groups based on biomarkers alone, systolic and diastolic dysfunction alone, or their combination.
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