Publications

A multiplexed microarray chip (Immuno-mu SARS2) aiming at providing information on the prognosis of the COVID-19 has been developed. The diagnostic technology records information related to the profile of the immunological response of patients infected by the SARS-CoV-2 virus. The diagnostic technology delivers information on the avidity of the sera against 28 different peptide epitopes and 7 proteins printed on a 25 mm2 area of a glass slide. The peptide epitopes (12-15 mer) derived from structural proteins (Spike and Nucleocapsid) have been rationally designed, synthesized, and used to develop Immuno-mu SARS2 as a multiplexed and high-throughput fluorescent microarray platform. The analysis of 755 human serum samples (321 from PCR+ patients; 288 from PCR- patients; 115 from prepandemic individuals and classified as hospitalized, admitted to intensive-care unit (ICU), and exitus) from three independent cohorts has shown that the chips perform with a 98% specificity and 91% sensitivity identifying RT-PCR+ patients. Computational analysis utilized to correlate the immunological signatures of the samples analyzed indicate significant prediction rates against exitus conditions with 82% accuracy, ICU admissions with 80% accuracy, and 73% accuracy over hospitalization requirement compared to asymptomatic patients' fingerprints. The miniaturized microarray chip allows simultaneous determination of 96 samples (24 samples/slide) in 90 min and requires only 10 mu L of sera. The diagnostic approach presented for the first time here could have a great value in assisting clinicians in decision-making based on the information provided by the Immuno-mu SARS2 regarding progression of the disease and could be easily implemented in diagnostics of other infectious diseases.

JTD Keywords: Antibodies, Clinical diagnostic, Diagnosis, High-throughput, Machine learning, Microarray, Multiplexation, Nucleocapsid protein, Peptide epitopes, Sars-cov-, Sars-cov-2, Serological signature, Seroprevalence, Severity prediction, Spik

ReViTA (Reverse in VitroTranscription Assay) is a novel in vitro transcription-based method to study gene expression under the regulation of specific transcription factors. The ReViTA system uses a plasmid with a control sequence, the promoter region of the studied gene, the transcription factor of interest, and an RNA polymerase saturated with σ70. The main objective of this study was to evaluate the method; thus, as a proof of concept, two different transcription factors were used, a transcriptional inducer, AlgR, and a repressor, LexA, from Pseudomonas aeruginosa. After the promoters were incubated with the transcription factors, the plasmid was transcribed into RNA and reverse transcribed to cDNA. Gene expression was measured using qRTPCR. Using the ReViTA plasmid, transcription induction of 55% was observed when AlgR protein was added and a 27% transcription reduction with the repressor LexA, compared with the samples without transcription factors. The results demonstrated the correct functioning of ReViTA as a novel method to study transcription factors and gene expression. Thus, ReViTA could be a rapid and accessible in vitro method to evaluate genes and regulators of various species.Crown Copyright © 2023. Published by Elsevier B.V. All rights reserved.

JTD Keywords: binding, dna-polymerase-iv, gene expression, in vitro transcription, lexa, rpos, transcription factor, transcriptional regulation, Gene expression, Response sigma-factor, Transcriptional regulation

This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.

JTD Keywords: Feedback control, Feed-forward control, Supervised learning, Unsupervised learning, Reinforcement, Learning, Classical conditioning, Operant conditioning, Reflex, Anticipatory reflex

We have developed a new liquid chromatography-electrospray ionization tandem mass spectrometry methodology based on 2-picolylamine derivatization and positive ion mode detection for abscisic acid (ABA) identification. The selected reaction leads to the formation of an amide derivative which contains a highly active pyridyl group. The enhanced ionization allows for a 700-fold increase over commonly monitored unmodified ABA, which in turn leads to excellent limits of detection and quantification values of 0.03 and 0.15 ng mL-1, respectively. This method has been validated in the highly complex matrix of a red blood cell extract. In spite of the high sensitivity achieved, ABA could not be detected in Plasmodium falciparum-infected red blood cells, suggesting that, if present, it will be found either in ultratrace amounts or as brief bursts at defined time points within the intraerythrocytic cycle and/or in the form of a biosynthetic analogue.

JTD Keywords: Abscisic acid, Apicomplexa, Liquid chromatography-electrospray ionization tandem mass spectrometry, Malaria, Picolylamine, Plasmodium falciparum