Barbara Schmidt

TL;DR: The significance of sequence variations in the protease and reverse transcriptase genes for drug resistance and derived models that predict phenotypic resistance from genotypes are analyzed with a machine learning approach.

TL;DR: The European HIV Drug Resistance Panel was established to make recommendations to clinicians and virologists on this topic and to propose quality control measures as mentioned in this paper, and the panel recommended resistance testing for the following indications: i) drug-naive patients with acute or recent infection; ii) therapy failure, including suboptimal treatment response, when treatment change is considered; iii) pregnant HIV-1-infected women and paediatric patients with detectable viral load when treatment initiation or change is considering; and iv) genotype source patient when post-exposure prophylaxis is considered

TL;DR: Genotypic and phenotypic resistance of viral reverse transcriptase (RT) and protease (PR) was determined for 64 therapy‐naive, HIV‐1‐infected seroconverters of the German Seroconverter Study coordinated by the Robert Koch‐Institut, Berlin.

TL;DR: Due to high specificity and strong correlation with virus neutralization, the RBD ELISA holds great potential to become a preferred tool to assess thresholds of protective immunity after infection and vaccination.

TL;DR: This intelligent system uses information encoded in the HIV genomic sequence to predict the virus's resistance to drugs and employs decision tree classifiers and support vector machines.