Perspectives on tracking data reuse across biodata resources

c The Author(s) 2024. Published by Oxford University Press.Motivation: Data reuse is a common and vital practice in molecular biology and enables the knowledge gathered over recent decades to drive discovery and innovation in the life sciences. Much of this knowledge has been collated into molecular biology databases, such as UniProtKB, and these resources derive enormous value from sharing data among themselves. However, quantifying and documenting this kind of data reuse remains a challenge. Results: The article reports on a one-day virtual workshop hosted by the UniProt Consortium in March 2023, attended by representatives from biodata resources, experts in data management, and NIH program managers. Workshop discussions focused on strategies for tracking data reuse, best practices for reusing data, and the challenges associated with data reuse and tracking. Surveys and discussions showed that data reuse is widespread, but critical information for reproducibility is sometimes lacking. Challenges include costs of tracking data reuse, tensions between tracking data and open sharing, restrictive licenses, and difficulties in tracking commercial data use. Recommendations that emerged from the discussion include: development of standardized formats for documenting data reuse, education about the obstacles posed by restrictive licenses, and continued recognition by funding agencies that data management is a critical activity that requires dedicated resources

Evidence for lower CD4 + T cell and higher viral load in asymptomatic HIV-1 infected individuals of India: Implications for therapy initiation

Purpose: We have earlier documented that the south Indian population had lower CD4 counts. The aim of this study was to investigate a previous suggestion on a new CD4+ T cell cut off and association with HIV-1 RNA levels for decision on anti retroviral therapy in India (south). Methods: We evaluated a new methodology i.e., artus real-time PCR and CD4+ T cell count by Guava EasyCD4™ system. From 146 HIV infected individuals seen at a tertiary care centre, blood was collected for CD4+ T cell and HIV-1 RNA estimation. Results: The receiver operating characteristic curve cut off value for the CD4 counts to distinguish between CDC clinical categories A and B was 243 cells/μL, and to distinguish B and C was 153 cells/μL. The RNA level that differentiated CDC A and B was 327473 RNA copies/mL, while for CDC B and C was 688543 copies/mL. There was a significant negative correlation (r = -0.55, P < 0.01) between the RNA estimated and CD4+ T cell counts in HIV infected individuals. Conclusions: A majority with CD4 counts of 201-350 cells/μL in our population had higher viral load than the treatment threshold suggested by the International AIDS society and the above two methodologies are useful in monitoring HIV infections