Rapid method using two microbial enzymes for detection of L-abrine in food as a marker for the toxic protein abrin.

Abrin is a toxic protein produced by the ornamental plant Abrus precatorius, and it is of concern as a biothreat agent. The small coextracting molecule N-methyl-l-tryptophan (l-abrine) is specific to members of the genus Abrus and thus can be used as a marker for the presence or ingestion of abrin. Current methods for the detection of abrin or l-abrine in foods and other matrices require complex sample preparation and expensive instrumentation. To develop a fast and portable method for the detection of l-abrine in beverages and foods, the Escherichia coli proteins N-methyltryptophan oxidase (MTOX) and tryptophanase were expressed and purified. The two enzymes jointly degraded l-abrine to products that included ammonia and indole, and colorimetric assays for the detection of those analytes in beverage and food samples were evaluated. An indole assay using a modified version of Ehrlich's/Kovac's reagent was more sensitive and less subject to negative interferences from components in the samples than the Berthelot ammonia assay. The two enzymes were added into food and beverage samples spiked with l-abrine, and indole was detected as a degradation product, with the visual lower detection limit being 2.5 to 10.0 µM (∼0.6 to 2.2 ppm) l-abrine in the samples tested. Results could be obtained in as little as 15 min. Sample preparation was limited to pH adjustment of some samples. Visual detection was found to be about as sensitive as detection with a spectrophotometer, especially in milk-based matrices.

Evaluation of AIML + HDR-A Course to Enhance Data Science Workforce Capacity for Hispanic Biomedical Researchers.

Artificial intelligence (AI) and machine learning (ML) facilitate the creation of revolutionary medical techniques. Unfortunately, biases in current AI and ML approaches are perpetuating minority health inequity. One of the strategies to solve this problem is training a diverse workforce. For this reason, we created the course "Artificial Intelligence and Machine Learning applied to Health Disparities Research (AIML + HDR)" which applied general Data Science (DS) approaches to health disparities research with an emphasis on Hispanic populations. Some technical topics covered included the Jupyter Notebook Framework, coding with R and Python to manipulate data, and ML libraries to create predictive models. Some health disparities topics covered included Electronic Health Records, Social Determinants of Health, and Bias in Data. As a result, the course was taught to 34 selected Hispanic participants and evaluated by a survey on a Likert scale (0-4). The surveys showed high satisfaction (more than 80% of participants agreed) regarding the course organization, activities, and covered topics. The students strongly agreed that the activities were relevant to the course and promoted their learning (3.71 ± 0.21). The students strongly agreed that the course was helpful for their professional development (3.76 ± 0.18). The open question was quantitatively analyzed and showed that seventy-five percent of the comments received from the participants confirmed their great satisfaction.

Discovery of Ancestry-specific Variants Associated with Clopidogrel Response among Caribbean Hispanics.

Background: High on-treatment platelet reactivity (HTPR) with clopidogrel is predictive of ischemic events in adults with coronary artery disease. Despite strong data suggesting HTPR varies with ethnicity, including clinical and genetic variables, no genome-wide association study (GWAS) of clopidogrel response has been performed among Caribbean Hispanics. This study aimed to identify genetic predictors of HTPR in a cohort of Caribbean Hispanic cardiovascular patients from Puerto Rico. Methods: Local Ancestry inference (LAI) and traditional GWASs were performed on a cohort of 511 clopidogrel-treated patients, stratified based on their P2Y12 reaction units (PRU) into responders and non-responders (HTPR). Results: The LAI GWAS identified variants within the CYP2C19 region associated with HTPR, predominantly driven by individuals of European ancestry and absent in those with native ancestry. Incorporating local ancestry adjustment notably enhanced our ability to detect associations. While no loci reached traditional GWAS significance, three variants showed suggestive significance at chromosomes 3, 14 and 22 (OSBPL10 rs1376606, DERL3 rs5030613, and RGS6 rs9323567). In addition, a variant in the UNC5C gene on chromosome 4 was associated with an increased risk of HTPR. These findings were not identified in other cohorts, highlighting the unique genetic landscape of Caribbean Hispanics. Conclusion: This is the first GWAS of clopidogrel response in Hispanics, confirming the relevance of the CYP2C19 cluster, particularly among those with European ancestry, and also identifying novel markers in a diverse patient population. Further studies are warranted to replicate our findings in other diverse cohorts and meta-analyses.

CYP2C9*61, a rare missense variant identified in a Puerto Rican patient with low warfarin dose requirements.

Warfarin continues to be the mainstay therapy for preventing thrombus formation. Although pharmacogenetic algorithms have shown higher predictability of the optimal warfarin dose and lower occurrence of bleeding episodes, they often do not include ethno-specific genetic variants relevant to non-Europeans. This case report describes a rare missense variant at exon 9 of CYP2C9 (rs202201137; c.1370A>G transition; p.Asn457Ser) found in a Puerto Rican patient with low warfarin dose requirements (3 mg/day). The haplotype characterized by two amino acid changes, Asn457Ser and Arg144Cys (rs1799853; c.430C>T), has been designated CYP2C9*61 by the Pharmacogene Variation Consortium. According to prediction scores assessed with the Combined Annotation Dependent Depletion tool, CYP2C9*61 (p.Asn457Ser) was classified as nondeleterious, therefore its impact on CYP2C9 enzymatic activity cannot be postulated.