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We investigated the role of tumor copy number (CN)-altered genome (CN-AG) in the carcinogenesis of cervical cancer (CC), especially its effect on gene expression, biological processes, and patient survival. Fifty-nine human papillomavirus 16 (HPV16)-positive CCs were investigated with microarrays-31 for mapping CN-AG and 55 for global gene expression, with 27 CCs in common. Five-year survival was investigated in 55 patients. Deletions and amplifications >2.5 Mb were defined as CN alterations. The %CN-AG varied from 0 to 32.2% (mean = 8.1±8.9). Tumors were classified as low (mean = 0.5±0.6, n = 11), medium (mean = 5.4±2.4, n = 10), or high (mean = 19.2±6.6, n = 10) CN. The highest %CN-AG was found in 3q, which contributed an average of 55% of all CN alterations. Genome-wide, only 5.3% of CN-altered genes were deregulated directly by gene dosage. In contrast, the rate in fully duplicated 3q was twice as high. Amplification of 3q explained 23.2% of deregulated genes in whole tumors (r2 = 0.232, p = 0.006; analysis of variance), including genes located in 3q and other chromosomes. A total of 862 genes were deregulated exclusively in high-CN tumors, but only 22.9% were CN altered. This suggests that the remaining genes are not deregulated directly by gene dosage, but by mechanisms induced in trans by CN-altered genes. Anaphase-promoting complex/cyclosome (APC/C)-dependent proteasome proteolysis, glycolysis, and apoptosis were upregulated, whereas cell adhesion and angiogenesis were downregulated exclusively in high-CN tumors. The high %CN-AG and upregulated gene expression profile of APC/C-dependent proteasome proteolysis were associated with poor patient survival (p0.38, p

Several copy number-altered regions (CNAs) have been identified in the genome of cervical cancer, notably, amplifications of 3q and 5p. However, the contribution of copy-number alterations to cervical carcinogenesis is unresolved because genome-wide there exists a lack of correlation between copy-number alterations and gene expression. In this study, we investigated whether CNAs in the cell lines CaLo, CaSki, HeLa, and SiHa were associated with changes in gene expression. On average, 19.2% of the cell-line genomes had CNAs. However, only 2.4% comprised minimal recurrent regions (MRRs) common to all the cell lines. Whereas 3q had limited common gains (13%), 5p was entirely duplicated recurrently. Genome-wide, only 15.6% of genes located in CNAs changed gene expression; in contrast, the rate in MRRs was up to 3 times this. Chr 5p was confirmed entirely amplified by FISH; however, maximum 33.5% of the explored genes in 5p were deregulated. In 3q, this rate was 13.4%. Even in 3q26, which had 5 MRRs and 38.7% recurrently gained SNPs, the rate was only 15.1%. Interestingly, up to 19% of deregulated genes in 5p and 73% in 3q26 were downregulated, suggesting additional factors were involved in gene repression. The deregulated genes in 3q and 5p occurred in clusters, suggesting local chromatin factors may also influence gene expression. In regions amplified discontinuously, downregulated genes increased steadily as the number of amplified SNPs increased (p

Background Coronavirus disease 2019 (COVID-19) has created the need to evaluate drugs such as favipiravir (FPV), an antiviral inhibitor of RNA-dependent RNA-polymerase (RdRp), and Maraviroc (MVC), an antiretroviral that antagonizes the chemokine receptor CCR5, which could affect the modulation of inflammation and viral replication in the treatment of COVID-19. We sought to evaluate the effect of MVC and/or FPV plus systemic steroid (SS) vs. SS alone on the viral load and progression to critical disease. Methods Sixteen patients with severe COVID-19 were evaluated in three treatment arms: 1) SS only (n=6), 2) SS plus one test drug MVC or FPV (n=5), and 3) SS plus both test drugs (MVC and FPV, n=5). The viral load was determined for N, E, and RdRp viral genes. Results A significant decrease in viral load was observed in the three treatment groups, with a larger effect size in the group that combined SS with both test drugs. The E, N, and RdRp genes with Cohen’s d were 120%, 123%, and 50%, respectively. Conclusions The largest effect on viral load reduction, as measured by effect size, was observed in the combination treatment group; however, no statistical significance was found, and it did not prevent progression to critical illness.

Background Coronavirus disease 2019 (COVID-19) has created the need to evaluate drugs such as favipiravir (FPV), an antiviral inhibitor of RNA-dependent RNA-polymerase (RdRp), and Maraviroc (MVC), an antiretroviral that antagonizes the chemokine receptor CCR5, which could affect the modulation of inflammation and viral replication in the treatment of COVID-19. We sought to evaluate the effect of MVC and/or FPV plus systemic steroid (SS) vs. SS alone on the viral load and progression to critical disease. Methods Sixteen patients with severe COVID-19 were evaluated in three treatment arms: 1) SS only (n=6), 2) SS plus one test drug MVC or FPV (n=5), and 3) SS plus both test drugs (MVC and FPV, n=5). The viral load was determined for N, E, and RdRp viral genes. Results A significant decrease in viral load was observed in the three treatment groups, with a larger effect size in the group that combined SS with both test drugs. The E, N, and RdRp genes with Cohen’s d were 120%, 123%, and 50%, respectively. Conclusions The largest effect on viral load reduction, as measured by effect size, was observed in the combination treatment group; however, no statistical significance was found, and it did not prevent progression to critical illness.

Background: Phase III clinical trials have documented the efficacy of the SARS-CoV-2 vaccines in preventing symptomatic COVID-19. Nonetheless, it is imperative to continue analyzing the clinical response to different vaccines in real-life studies. Our objective was to evaluate the effectiveness of five different vaccines in hospitalized patients with COVID-19 during the third COVID-19 outbreak in Mexico dominated by the Delta variant. Methods: A test-negative case–control study was performed in nine tertiary-care hospitals for COVID-19. We estimated odds ratios (OR) adjusted by variables related a priori with the likelihood of SARS-CoV-2 infection and its severity. Results: We studied 761 subjects, 371 cases, and 390 controls with a mean age of 53 years (SD, 17 years). Overall, 51% had a complete vaccination scheme, and an incomplete scheme (one dose from a scheme of two), 14%. After adjustment for age, gender, obesity, and diabetes mellitus, we found that the effectiveness of avoiding a SARS-CoV-2 infection when hospitalized with at least one vaccination dose was 71% (OR 0.29, 95% CI 0.19–0.45), that of an incomplete vaccination scheme, 67% (OR 0.33, 95% CI 0.18–0.62), and that of any complete vaccination scheme, 73% (OR 0.27, 95% CI 0.17–0.43). Conclusions: The SARS-CoV-2 vaccination program showed effectiveness in preventing SARS-CoV-2 infection in hospitalized patients during a Delta variant outbreak.

The cyclin-dependent kinase inhibitor 3 (CDKN3) gene, involved in mitosis, is upregulated in cervical cancer (CC). We investigated CDKN3 mRNA as a survival biomarker and potential therapeutic target for CC. CDKN3 mRNA was measured in 134 CC and 25 controls by quantitative PCR. A 5-year survival study was conducted in 121 of these CC patients. Furthermore, CDKN3-specific siRNAs were used to investigate whether CDKN3 is involved in proliferation, migration, and invasion in CC-derived cell lines (SiHa, CaSki, HeLa). CDKN3 mRNA was on average 6.4-fold higher in tumors than in controls (p = 8 x 10-6, Mann-Whitney). A total of 68.2% of CC patients over expressing CDKN3 gene (fold change ≥ 17) died within two years of diagnosis, independent of the clinical stage and HPV type (Hazard Ratio = 5.0, 95% CI: 2.5-10, p = 3.3 x 10-6, Cox proportional-hazards regression). In contrast, only 19.2% of the patients with lower CDKN3 expression died in the same period. In vitro inactivation of CDKN3 decreased cell proliferation on average 67%, although it had no effect on cell migration and invasion. CDKN3 mRNA may be a good survival biomarker and potential therapeutic target in CC.

Influenza has been a stationary disease in Mexico since 2009, and this causes a high cost for the national public health system, including its detection using RT-qPCR tests, treatments, and absenteeism in the workplace. Despite influenza’s relevance, the main clinical features to detect the disease defined by international institutions like the World Health Organization (WHO) and the United States Centers for Disease Control and Prevention (CDC) do not follow the same pattern in all populations. The aim of this work is to find a machine learning method to facilitate decision making in the clinical differentiation between positive and negative influenza patients, based on their symptoms and demographic features. The research sample consisted of 15480 records, including clinical and demographic data of patients with a positive/negative RT-qPCR influenza tests, from 2010 to 2020 in the public healthcare institutions of Mexico City. The performance of the methods for classifying influenza cases were evaluated with indices like accuracy, specificity, sensitivity, precision, the f1-measure and the area under the curve (AUC). Results indicate that random forest and bagging classifiers were the best supervised methods; they showed promise in supporting clinical diagnosis, especially in places where performing molecular tests might be challenging or not feasible.