Victor V. Calderon

This study compares the feasibility of matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry with whole genome sequencing (WGS) for identifying bacteria in river surface water samples. We collected samples from four rivers in the Dominican Republic and used both techniques to characterize bacterial profiles. MALDI-TOF demonstrated high precision, with 86.2% similarity to WGS results, except for a few discordant cases due to database limitations. MALDI-TOF provided cost-effective and rapid identification, making it a promising alternative to WGS in resource-constrained regions. In particular, good effectiveness of MALDI-TOF in identifying bacteria with a high probability of being resistant to antibiotics was observed, which allows this technology to be used in the monitoring processes of this type of microorganism for their rapid, accurate, and low-cost identification. We found this technology to be advantageous for environmental bacterial profiling, with potential applications in understanding waterborne pathogenic bacteria. Our findings underline the relevance of MALDI-TOF in microbiology and its potential to expand its capabilities in bacterial identification and protein profiling.

We compared the performance of matrix-assisted laser desorption/ionization followed by a time of flight (MALDI-TOF) mass spectrometry and genomic DNA extraction followed by sequencing, assembly, and alignment for phylogenetic assessment (Genomics method). We collected the samples from four contaminated rivers in the Dominican Republic and analyzed MALDI-TOF efficacy and accuracy for identifying bacteria in the samples. We evaluated the results for both methods (MALDI-TOF and Genomics) and reported a similarity percentage between each method's results. The MALDI-TOF method had a 72.41\% of coincidence with the Genomics method. This could have been sequence contamination found in the Genomics method. When it was later filtered, the result's coincidence rate went up to 90\%.

The spread and contamination of antimicrobial-resistant bacteria in ambient waters is an emerging concern in urban, rural, medical, and industrial settings. A large amount of domestic, hospital, and industrial wastewater discharged directly into the rivers through the different channels can turn them into extensive reservoirs of antibiotic-resistant bacteria. In the present study, surface water samples from three collection sites were analyzed, according to different levels of anthropogenic impacts, along the Ozama River, one of the most important rivers in the Dominican metropolitan area, a source of water and food for human consumption. Seventy-six bacterial isolates were selected based on resistance to beta-lactams, using culture media previously enriched with cefotaxime and imipenem. Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry (MALDI-TOF) subsequently identified them. The isolates covered 12 genera of bacteria; more than 30% were clinically relevant, and 43% had phenotypes classified as multidrug resistance. A total of 10 (44%) presented resistance. However, only seven presented resistance to 3 or more of the 14 groups of antibiotics, considered to be a multiresistant phenotype, which was sequenced using the high-throughput sequencing technique or New Generation (NGS). This study is part of the initiative to understand the profiles of the dangers of multidrug resistance in the metropolitan and rural areas of the Dominican Republic and its possible implications for human health.

Our research team compared the performance of matrix-assisted laser desorption/ionization followed by a time of flight (MALDI-TOF) mass spectrometry and genomic DNA extraction followed by sequencing, assembly and alignment for phylogenetic assessment. We performed these comparisons to determine our methodology’s overall efficacy and accuracy for environmental bacteria. In addition, we collected samples from various contaminated rivers in the Dominican Republic. For both methods, we analyzed these results and reported the main differences between each method.

A heterogeneous disease such as cancer is activated through multiple pathways and different perturbations. Depending upon the activated pathway(s), the survival of the patients varies significantly and shows different efficacy to various drugs. Therefore, cancer subtype detection using genomics level data is a significant research problem. Subtype detection is often a complex problem, and in most cases, needs multi-omics data fusion to achieve accurate subtyping. Different data fusion and subtyping approaches have been proposed over the years, such as kernel-based fusion, matrix factorization, and deep learning autoencoders. In this paper, we compared the performance of different deep learning autoencoders for cancer subtype detection. We performed cancer subtype detection on four different cancer types from The Cancer Genome Atlas (TCGA) datasets using four autoencoder implementations. We also predicted the optimal number of subtypes in a cancer type using the silhouette score and found that the detected subtypes exhibit significant differences in survival profiles. Furthermore, we compared the effect of feature selection and similarity measures for subtype detection. For further evaluation, we used the Glioblastoma multiforme (GBM) dataset and identified the differentially expressed genes in each of the subtypes. The results obtained are consistent with other genomic studies and can be corroborated with the involved pathways and biological functions. Thus, it shows that the results from the autoencoders, obtained through the interaction of different datatypes of cancer, can be used for the prediction and characterization of patient subgroups and survival profiles.

Después de su descubrimiento en China, en diciembre de 2019, el virus SARS-CoV-2 causante de la enfermedad COVID-19, comenzó a propagarse por la mayoría de los países del mundo, provocando una cantidad importante de infectados y muertos, situación que ha generado una gran presión en los sistemas de salud y gobiernos de los diferentes países, que en muchos casos no contaban con los recursos e infraestructuras para enfrentar una pandemia provocada por el virus, como fue el caso de República Dominicana. Usando los datos públicos del avance de la pandemia en la Republica Dominicana, desde el 1 de marzo hasta el 30 de abril, se modeló y pronosticó la posible evolución del COVID-19 en este país, utilizando de tres modelos matemáticos SIR, SEIR-Extendido y log-lineal. Estos modelos permitieron proyectar los diferentes escenarios de contagios y evolución de la enfermedad, además de pronosticar el pico y el descenso de la curva de casos. Según los resultados obtenidos, el país se encontraba en un proceso de crecimiento exponencial de los contagios; el pico de los contagios fue pronosticado ocurriría entre la segunda mitad del mes de mayo y la primera mitad del mes de junio, con un estimado de infectados entre 100,000 y 200,000 individuos. Los resultados obtenidos en este estudio permiten obtener una idea de cuál podría ser el impacto humano y social del COVID-19 en la República Dominicana.