Nili Tickotsky

Motivation: T lymphocytes (T-cells) major role in adaptive immunity drives efforts to elucidate the mechanisms behind T-cell epitope recognition. Results: We analyzed solved structures of T-cell receptors (TCRs) and their cognate epitopes and used the data to train a set of machine learning models, POP-UP TCR, that predict the binding of any peptide to any TCR, including peptide and TCR sequences that were not included in the training set. We address biological issues that should be considered in the design of machine learning models for TCR-peptide binding and suggest that models trained only on beta chains give satisfactory predictions. Finally, we apply our models to large data set of TCR repertoires from COVID-19 patients and find that TCRs from patients in severe/critical condition have significantly lower scores for binding SARS-coV-2 epitopes compared to TCRs from moderate patients (p-value <0.001). Availability and Implementation: POP-Up TCR is available at: https://github.com/NiliTicko/POP-UP-TCR Contact: nilibrac@bgu.ac.il

The long-term effect of Zinc oxide zinc sulfate (Coltosol^®) dressing material on pulpotomy success and tooth survival has not yet been studied. This study compared the success rates of Zinc oxide zinc sulfate and zinc oxide eugenol as coronal dressing materials post radicular pulp amputation in primary teeth pulpotomies. This study included healthy two- to ten-year-old children who had pulpotomies on primary molars between 2012 and 2018 at the Pediatric Dentistry Clinic of the School of Dental Medicine. Data were analyzed at several follow-ups of up to 60 months. Kaplan-Meier survival curves were used to estimate survival probabilities of Zinc oxide zinc sulfate versus zinc oxide eugenol. In the 107 children included in this study, 54 teeth were filled with Zinc oxide zinc sulfate and 53 were filled with zinc oxide eugenol. Follow-up ranged from 12.2 to 73.3 months. Overall survival of Coltosol^® vs. IRM filled teeth was 87.1% and 79.3%, respectively. Overall survival probabilities for Coltosol^®-filled teeth at 15.5, 24 and 45 months were 95%, 89.8% and 79.7%, respectively, while for IRM they were 93.7%, 83% and 67.7%, respectively. Treatment failure rates and type of treated teeth did not differ between boys and girls (p-value = 0.77 and 0.87, respectively). Zinc oxide zinc sulfate and zinc oxide eugenol exhibited comparable high long-term success rates of up to five years (p = 0.16).

This retrospective cohort study evaluated overall parental satisfaction of zirconia crowns (ZC) placed on primary maxillary anterior teeth with that of two independent, blinded dentists. 131 ZC placed in 37 children, aged 24.8–62.2 months (mean = 42.8), who had at least one recall visit a minimum of 6 months after placement were rated (average = 13.3). Crown colour match, crown contour and crown durability were evaluated by parents and compared to photographic evaluations of two independent raters. Overall parental satisfaction was also evaluated. The overall retention rate was 99.7% and parental satisfaction was 100%. Colour match was rated excellent by 84% of parents and 36% of dental evaluators. Crown contour was rated excellent by 97% of parents and 55% of dental evaluators. The length of follow-up had no effect on colour match or crown contour. ZC comprises an aesthetic and durable option for restoring carious primary maxillary incisors and were well-accepted by parents. Parents were less critical than dental evaluators of crown appearance.

Current sequencing methods allow for detailed samples of T cell receptors (TCR) repertoires. To determine from a repertoire whether its host had been exposed to a target, computational tools that predict TCR-epitope binding are required. Currents tools are based on conserved motifs and are applied to peptides with many known binding TCRs. Given any TCR and peptide, we employ new NLP-based methods to predict whether they bind. We combined large-scale TCR-peptide dictionaries with deep learning methods to produce ERGO (pEptide tcR matchinG predictiOn), a highly specific and generic TCR-peptide binding predictor. A set of standard tests are defined for the performance of peptide-TCR binding, including the detection of TCRs binding to a given peptide/antigen, choosing among a set of candidate peptides for a given TCR and determining whether any pair of TCR-peptide bind. ERGO significantly outperforms current methods in these tests even when not trained specifically for each test. The software implementation and data sets are available at https://github.com/louzounlab/ERGO