Profiling of clinically relevant subgroups of asymptomatic or mild-symptomatic metastatic castration-resistant prostate cancer (mCRPC) patients using PSA and testosterone levels and deep learning

Background: The optimal time to begin therapy in mCRPC patients who have either no, or minimal symptoms is not defined yet. Estimating prognosis of these patients can avoid undertreatment or overtreatment and guide the follow-up intensity. This study has investigated the ability of a deep learning framework (DLF) to identify asymptomatic or mildly symptomatic mCRPC…

Development and validation of a predictive score to identify K-Ras wild-type (WT) metastatic colorectal cancer (mCRC) patients who are likely to benefit from Panitumumab (P) treatment

Background: Practice guidelines recommend using P to treat K-Ras WT mCRC patients where it was shown to significantly extend overall survival (OS). Still, a proportion of patients will not achieve this goal. We propose a simplified predictive score to identify patients who are likely to benefit from P treatment. Methods: NCT00364013 was used as training…

Analysis of transcriptomic responses to SARS-CoV-2 reveals plausible defective pathways responsible for increased susceptibility to infection and complications and helps to develop fast-track repositioning of drugs against COVID-19

Background: To understand the transcriptomic response to SARS-CoV-2 infection, is of the utmost importance to design diagnostic tools predicting the severity of the infection. Methods: We have performed a deep sampling analysis of the viral transcriptomic data oriented towards drug repositioning. Using different samplers, the basic principle of this methodology the biological invariance, which means…