Rapid video-based deep learning of cognate versus non-cognate T cell-dendritic cell interactions

Identification of cognate interactions between antigen-specific T cells and dendritic cells (DCs) is essential to understanding immunity and tolerance, and for developing therapies for cancer and autoimmune diseases. Conventional techniques for selecting antigen-specific T cells are time-consuming and limited to pre-defined antigenic peptide sequences. Here, we demonstrate the ability to use deep learning to rapidly classify videos of antigen-specific CD8+ T cells. The trained model distinguishes distinct interaction dynamics (in motility and morphology) between cognate and non-cognate T cells and DCs over 20 to 80 min. The model classified high affinity antigen-specific CD8+ T cells from OT-I mice with an area under the curve (AUC) of 0.91, and generalized well to other types of high and low affinity CD8+ T cells. The classification accuracy achieved by the model was consistently higher than simple image analysis techniques, and conventional metrics used to differentiate between cognate and non-cognate T cells, such as speed. Also, we demonstrated that experimental addition of anti-CD40 antibodies improved model prediction. Overall, this method demonstrates the potential of video-based deep learning to rapidly classify cognate T cell-DC interactions, which may also be potentially integrated into high-throughput methods for selecting antigen-specific T cells in the future.

Pedagogical Strategies for the learning of medicine students

With the epidemiological contingency, the learning processes were modified by teaching with virtualization. The teaching staff implemented pedagogical strategies structuring methodologies, interaction dynamics, fulfilling the objectives set out in the educational curriculum. Objective: To implement pedagogical strategies involved in virtualization during the learning process in medical students. Methodology: A descriptive, observational study with a qualitative approach over a period of 8 months. The sample was 225 students from the 5th semester of the Surgeon Degree. Results: It was shown that group activities, synchronous online conferences have 68% approval, dynamics such as discussion forums have 85% approval. Video recording has 45%, essays 25%, and mind maps 30%. Conclusion: Implementing educational strategies allows assessing efficiency and effectiveness promoting learning for the development of professional intellect.

Validation that human microbiome phages use alternative genetic coding with TAG stop read as Q

Metagenomic findings suggesting that bacteriophages (phages) can use genetic codes different from those of their host bacteria reveal a new dimension of phage-host interaction dynamics. Whereas reassignment of stop codons to code for amino acids has been predicted, there has been no proteomic validation of alternative coding in phages. In fact, one code where the stop codon TAG is reassigned to glutamine (code 15) has never been experimentally validated in any biological system. Here, we characterized stop codon reassignment in two crAss-like phages found in the human gut microbiome using LC-MS/MS-based metaproteomics. The proteome data from several phage structural proteins clearly demonstrates reassignment of the TAG stop codon to glutamine, establishing for the first time the expression of genetic code 15.