Biology subjects

Rivera, T.

Publications and source records attributed to Rivera, T..

4 recordsLinked to original sources

bioRxiv2026

BIOTIA-DX RESISTANCE Achieved the Best Antimicrobial Resistance Phenotype Prediction Accuracy at CAMDA 2026

We present BIOTIA-DX RESISTANCE (BDXR), our submission to the CAMDA 2026 AMR Challenge. This work extends our CAMDA 2025 submission [1] to a new set of six species-drug pairs and adds k-mer-based feature engineering (both targeted and whole-genome) for pairs where the 2025 gene-presence base model underperforms. BDXR achieved a mean accuracy of 86.1% across the six pairs on the CAMDA 2026 test set, ranking first on four pairs, tied for first on Streptococcus pneumoniae (penicillin), and second on Campylobacter jejuni (nalidixic acid); per-pair test accuracy ranged from 69.9% (C. jejuni, nalidixic acid) to 98.8% (S. pneumoniae, penicillin). We refer the reader to our 2025 preprint [1] for the underlying workflow, dataset curation, and clinical motivation; this preprint focuses on the results and methodological changes that are new in 2026.

microbiology↗

bioRxiv2025

BIOTIA-DX RESISTANCE Achieved the Best Antimicrobial Resistance Phenotype Prediction Accuracy at CAMDA 2025

We have developed BIOTIA-DX RESISTANCE (BDXR), a bioinformatic tool for predicting antimicrobial resistance (AMR) from whole genome sequencing of microbial isolates. BDXR achieved the best accuracy of any submission to the CAMDA 2025 AMR Challenge. This years challenge focused on predicting AMR phenotype which is a more complex problem than the detection of AMR marker genes, the focus of some prior years. BDXR achieved an overall F1 score of 89% on the training set and 84.1% on the challenge test set. Accuracy varied across the 9 species and drug pairs in the competition from an F1 score of 98.4% (Campylobacter jejuni, tetracycline) to 78.5% (Pseudomonas aeruginosa, cefatzidime). BDXR is based on curation of global datasets, machine learning-based predictions from input data, and highly stringent prepreprocessing of input data and databases.

microbiology↗

bioRxiv2025

A pathway to next-generation mast cell stabilizers identified through the novel Phytomedical Analytics for Research Optimization at Scale data platform.

Mast cell stabilizers (MCS) have the potential to address unmet therapeutic need in allergy and inflammation management. MCS chronically suppress all arms of the pro-inflammatory mast cell response to stimulation (e.g., histamine, protease, lipid mediators, cytokines, chemokines). They may therefore outperform approaches such as H1, H2 and H4 inhibitors (antihistamines) which block only the acute histamine release by mast cells and leave the rest of the functional response untouched. Despite their potential, current MCS (e.g., cromolyn sodium, Tranilast, nedocromil) in clinical use are hindered by poor bioavailability, frequent dosing, long lags to onset of relief, and enigmatic mechanisms of action. MCS have their origins in phytomedicine: cromolyn sodium is the longest standing drug in the class and is a derivatized form of Khellin from Ammi visnaga, used as an anti-inflammatory. Other phytopharmacopeias may offer candidate next generation MCS (ngMCS) and in this study we hypothesized that a coupled pharmacoanalytic and in vitro pharmacology approach could be used to identify, prioritize and derisk additional candidate MCS from phytomedical sources for later pre-clinical and clinical evaluation. Here, we report a novel data analytics workflow starting with a newly developed phytopharmacopeia data platform with >3.5B linkage pathways (country [->] medical system [->] formulation [->] indication [->] ingredient organism [->] chemical component [->] other parameters), covering 22 M sq. miles of biogeography and historical and contemporary timeframes. Additional data layers include druggability indices, target and pathway analyses. The current study validates a subset of candidate phytomedical ngMCS using in silico workflow and in vitro pharmacology, and develops a new harmonic mean-based MCS score for further streamlining of the candidate prioritization process. This proof-of-concept study may have particular relevance for complex presentations such as Mast Cell Activation Syndrome (MCAS) where ngMCS may outperform current management approaches.

immunology↗

bioRxiv2022

Bioinformatic analysis of B and T cell epitopes from SARS-CoV-2 Spike, Membrane and Nucleocapsid proteins as a strategy to assess possible cross-reactivity between emerging variants, including Omicron, and other human coronaviruses

The COVID-19 pandemic caused by SARS-CoV-2 produced a global health emergency since December 2019, that up to the end of January 2022 had caused the death of more than 5.6 million people worldwide. Despite emergence of new variants of concern, vaccination remains one of the most important tools to control the pandemic. All approved vaccines and most of the vaccine candidates use the spike protein of the virus as a target antigen to induce protective immune responses. Several variants of the virus present key mutations in this protein which render the virus, at different rates, to evade the neutralizing antibody response. Although experimental evidence suggests that cross-reactive responses between coronaviruses are present in the population, it is unknown which potential antigens shared between different coronaviruses could be responsible for these responses. This study provides predictions of new potential B and T cell epitopes within SARS-CoV-2 Spike (S), Membrane (M) and Nucleocapsid (N) proteins together with a review of the reported B epitopes of these proteins. We also analyse amino acid changes present in the epitopes of variants of concern (VOC) and variants being monitored (VBM), and how these might affect the immune response, as these changes may alter the peptides immunogenicity index and the antigen presentation by related HLA alleles. Finally, given these observations, we performed an identity analysis between the repertoire of potential epitopes of SARS-CoV-2 and other human coronaviruses to identify which are conserved among them. The results shown here together with the published experimental evidence, allow us to support the hypothesis that antibody and T cell cross-reactive responses to common coronaviruses epitopes, could contribute to broaden the protective response to SARS-CoV-2 and its variants. This evidence could help not only to understand cross-reactive responses among coronaviruses but also contribute to elucidate their role in immunity to SARS-CoV-2 induced by infection and/or vaccination. Finally, these findings could promote targeted analysis of antigen-specific immune responses and might orient and drive the rational development of new SARS-CoV-2 vaccines including candidates that ideally provide "universal" protection against other coronaviruses relevant to human health.

bioinformatics↗