Comparative Epidemiological Assessment of Monkeypox Infections on a Global and Continental Scale Using Logistic and Gompertz Mathematical Models.

The monkeypox virus (MPXV) has caused an unusual epidemiological scenario-an epidemic within a pandemic (COVID-19). Despite the inherent evolutionary and adaptive capacity of poxviruses, one of the potential triggers for the emergence of this epidemic was the change in the status of orthopoxvirus vaccination and eradication programs. This epidemic outbreak of HMPX spread worldwide, with a notable frequency in Europe, North America, and South America. Due to these particularities, the objective of the present study was to assess and compare cases of HMPX in these geographical regions through logistic and Gompertz mathematical modeling over one year since its inception. We estimated the highest contagion rates (people per day) of 690, 230, 278, and 206 for the world, Europe, North America, and South America, respectively, in the logistic model. The equivalent values for the Gompertz model were 696, 268, 308, and 202 for the highest contagion rates. The Kruskal-Wallis Test indicated different means among the geographical regions affected by HMPX regarding case velocity, and the Wilcoxon pairwise test indicated the absence of significant differences between the case velocity means between Europe and South America. The coefficient of determination (R2) values in the logistic model varied from 0.8720 to 0.9023, and in the Gompertz model, they ranged from 0.9881 to 0.9988, indicating a better fit to the actual data when using the Gompertz model. The estimated basic reproduction numbers (R0) were more consistent in the logistic model, varying from 1.71 to 1.94 in the graphical method and from 1.75 to 1.95 in the analytical method. The comparative assessment of these mathematical modeling approaches permitted the establishment of the Gompertz model as the better-fitting model for the data and the logistic model for the R0. However, both models successfully represented the actual HMPX case data. The present study estimated relevant epidemiological data to understand better the geographic similarities and differences in the dynamics of HMPX.

Mathematical Modeling of COVID-19 Cases and Deaths and the Impact of Vaccinations during Three Years of the Pandemic in Peru.

The COVID-19 pandemic has caused widespread infections, deaths, and substantial economic losses. Vaccine development efforts have led to authorized candidates reducing hospitalizations and mortality, although variant emergence remains a concern. Peru faced a significant impact due to healthcare deficiencies. This study employed logistic regression to mathematically model COVID-19's dynamics in Peru over three years and assessed the correlations between cases, deaths, and people vaccinated. We estimated the critical time (tc) for cases (627 days), deaths (389 days), and people vaccinated (268 days), which led to the maximum speed values on those days. Negative correlations were identified between people vaccinated and cases (-0.40) and between people vaccinated and deaths (-0.75), suggesting reciprocal relationships between those pairs of variables. In addition, Granger causality tests determined that the vaccinated population dynamics can be used to forecast the behavior of deaths (p-value < 0.05), evidencing the impact of vaccinations against COVID-19. Also, the coefficient of determination (R2) indicated a robust representation of the real data. Using the Peruvian context as an example case, the logistic model's projections of cases, deaths, and vaccinations provide crucial insights into the pandemic, guiding public health tactics and reaffirming the essential role of vaccinations and resource distribution for an effective fight against COVID-19.

Establishment of a 7-gene expression panel to improve the prognosis classification of gastric cancer patients.

Gastric cancer (GC) ranks fifth in incidence and fourth in mortality worldwide. The high death rate in patients with GC requires new biomarkers for improving survival estimation. In this study, we performed a transcriptome-based analysis of five publicly available cohorts to identify genes consistently associated with prognosis in GC. Based on the ROC curve, patients were categorized into high and low-expression groups for each gene using the best cutoff point. Genes associated with survival (AUC > 0.5; univariate and multivariate Cox regressions, p < 0.05) were used to model gene expression-based scores by weighted sum using the pooled Cox ß regression coefficients. Cox regression (p < 0.05), AUC > 0.5, sensitivity > 0.5, and specificity > 0.5 were considered to identify the best scores. Gene set enrichment analysis (KEGG, REACTOME, and Gene Ontology databases), as well as microenvironment composition and stromal cell signatures prediction (CIBERSORT, EPIC, xCell, MCP-counter, and quanTIseq web tools) were performed. We found 11 genes related to GC survival in the five independent cohorts. Then, we modeled scores by calculating all possible combinations between these genes. Among the 2,047 scores, we identified a panel based on the expression of seven genes. It was named GES7 and is composed of CCDC91, DYNC1I1, FAM83D, LBH, SLITRK5, WTIP, and NAP1L3 genes. GES7 features were validated in two independent external cohorts. Next, GES7 was found to recategorize patients from AJCC TNM stages into a best-fitted prognostic group. The GES7 was associated with activation of the TGF-ß pathway and repression of anticancer immune cells. Finally, we compared the GES7 with 30 previous proposed scores, finding that GES7 is one of the most robust scores. As a result, the GES7 is a reliable gene-expression-based signature to improve the prognosis estimation in GC.

Identification of Novel Candidate Epitopes on SARS-CoV-2 Proteins for South America: A Review of HLA Frequencies by Country.

Coronavirus disease (COVID-19), caused by the virus SARS-CoV-2, is already responsible for more than 4.3 million confirmed cases and 295,000 deaths worldwide as of May 15, 2020. Ongoing efforts to control the pandemic include the development of peptide-based vaccines and diagnostic tests. In these approaches, HLA allelic diversity plays a crucial role. Despite its importance, current knowledge of HLA allele frequencies in South America is very limited. In this study, we have performed a literature review of datasets reporting HLA frequencies of South American populations, available in scientific literature and/or in the Allele Frequency Net Database. This allowed us to enrich the current scenario with more than 12.8 million data points. As a result, we are presenting updated HLA allelic frequencies based on country, including 91 alleles that were previously thought to have frequencies either under 5% or of an unknown value. Using alleles with an updated frequency of at least ≥5% in any South American country, we predicted epitopes in SARS-CoV-2 proteins using NetMHCpan (I and II) and MHC flurry. Then, the best predicted epitopes (class-I and -II) were selected based on their binding to South American alleles (Coverage Score). Class II predicted epitopes were also filtered based on their three-dimensional exposure. We obtained 14 class-I and four class-II candidate epitopes with experimental evidence (reported in the Immune Epitope Database and Analysis Resource), having good coverage scores for South America. Additionally, we are presenting 13 HLA-I and 30 HLA-II novel candidate epitopes without experimental evidence, including 16 class-II candidates in highly exposed conserved areas of the NTD and RBD regions of the Spike protein. These novel candidates have even better coverage scores for South America than those with experimental evidence. Finally, we show that recent similar studies presenting candidate epitopes also predicted some of our candidates but discarded them in the selection process, resulting in candidates with suboptimal coverage for South America. In conclusion, the candidate epitopes presented provide valuable information for the development of epitope-based strategies against SARS-CoV-2, such as peptide vaccines and diagnostic tests. Additionally, the updated HLA allelic frequencies provide a better representation of South America and may impact different immunogenetic studies.