BackgroundWorldwide, it has been observed that there is a strong association between the severity of COVID-19 and with being over 40 years of age, having diabetes mellitus (DM), hypertension and/or obesity.ObjectiveTo compare the probability of death caused by COVID-19 in patients with comorbidities during three periods defined for this study as follows: first wave (March 23 to July 12, 2020), interwave period (July 13 to October 25, 2020), and the second wave (October 26, 2020, to March 29, 2021) using the different fatality rates observed in Mexico City.MethodsThe cohort studied included individuals over 20 years of age. During the first wave (symptomatic), the interwave period, and the second wave (symptomatic and asymptomatic), participants were diagnosed using nasopharyngeal swabs taken in kiosks. Symptomatic individuals with risk factors for serious disease or death were referred to hospital. SARS-CoV-2 infection was defined by real time polymerase chain reaction in all hospitalized patients. All data from hospitalized patients and outpatients were added to the SISVER database.ResultsThe total cohort size for this study was 2,260,156 persons (having a mean age of 43.1 years). Of these, 8.6% suffered from DM, 11.6% from hypertension, and 9.7% from obesity. Of the total of 2,260,156 persons, 666,694 tested positive (29.5%) to SARS CoV-2, (with a mean age of 45). During the first wave, 82,489 tested positive; in the interwave period, 112,115; and during the second wave, 472,090. That is, a considerable increase in the number of cases of infection was observed in all age groups between the first and second waves (an increase of +472% on the first wave).Of the infected persons, a total of 85,587 (12.8%) were hospitalized: 24,023 in the first wave (29.1% of those who tested positive in this period); 16,935 (15.1%) during the interwave period, and 44,629 (9.5%) in the second wave, which represents an increase of 85.77% on the first wave.Of the hospitalized patients, there were 42,979 deaths (50.2% of those hospitalized), in the first wave, 11,964 (49.8% of those hospitalized in this period), during the interwave period, 6,794 (40.1%), and in the second wave 24,221 (54.3%), an increase of +102.4% between the first wave and the second.While within the general population, the probability of a patient dying having both COVID-19 and one of the specified comorbidities (DM, obesity, or arterial hypertension) showed a systematic reduction across all age groups, the probability of death for a hospitalized patient with comorbidities increased across all age groups during the second wave. When comparing the fatality rate of hospitalized COVID-19 patients in the second wave with those of the first wave and the interwave period, a significant increase was observed across all age groups, even in individuals without comorbidities.ConclusionThe data from this study show a considerable increase in the number of detected cases of infection in all age groups between the first and second waves. In addition, 12.8% of those infected were hospitalized for severe COVID-19, representing an increase of +85.9% from the first wave to the second. A high mortality rate was observed among hospitalized patients (>50%), as was a higher probability of death in hospitalized COVID-19 patients with comorbidities for all age groups during the second wave, although there had been a slight decrease during the interwave period.SUMMARY BOXWhat is already known?Worldwide the resurging of COVID-19 cases in waves has been observed. In Mexico, like in the rest of the world, we have observed surges of SARS CoV-2 infections, COVID-19 hospitalizations and fatal outcomes followed by decreases leading to local minima. Pre-existing health conditions such as being older, having diabetes mellitus (DM), hypertension and/or obesity has been observed to be associated with an increase in the severity of COVID-19.What are the new findings?Between the first and second waves, considerable increases were observed in the number of detected cases of infection (+472%), in the number of hospitalized subjects (+85.9%), and the number of hospitalized subjects and deaths (+102.4%) in all age groups.When analysing only hospitalized individuals, with or without comorbidities, the Case Fatality Rate was high (50.2%), the probability of death increased considerably in all age groups between the first and second waves. This increase was more noticeable in those individuals with previously identified comorbidities (DM, hypertension, or obesity).An increased probability of death among individuals without comorbidities was observed between the first and second waves.What do the new findings imply?During the second wave, demand for hospitalization increased, magnifying the impact of age and comorbidities as risk factors. This situation highlights the importance of decreasing the prevalence of comorbidities among the population.

Background:The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is a current public health concern.Rapid diagnosis is crucial, and reverse transcription polymerase chain reaction (RT-PCR) is presently the reference standard for SARS-CoV-2 detection. Objective: Automated RT-PCR analysis (ARPA) is a software designed to analyze RT-PCR data for SARS-CoV-2 detection. ARPA loads the RT-PCR data, classifies each sample by assessing its amplification curve behavior, evaluates the experiment's quality, and generates reports. Methods: ARPA was implemented in the R language and deployed as a Shiny application. We evaluated the performance of ARPA in 140 samples. The samples were manually classified and automatically analyzed using ARPA. Results: ARPA had a true-positive rate = 1, true-negative rate = 0.98, positive-predictive value = 0.95, and negative-predictive value = 1, with 36 samples correctly classified as positive, 100 samples correctly classified as negative, and two samples classified as positive even when labeled as negative by manual inspection. Two samples were labeled as invalid by ARPA and were not considered in the performance metrics calculation. Conclusions: ARPA is a sensitive and specific software that facilitates the analysis of RT-PCR data, and its implementation can reduce the time required in the diagnostic pipeline. (REV INVEST CLIN. [AHEAD OF PRINT]

Follicle-stimulating hormone exists as different major glycoforms defined by distinct glycosylation patterns of the hormone-specific β-subunit. It has been documented that variations in glycosylation confer differential biological effects to the glycoforms when multiple in vitro biochemical readings are analyzed. We here applied Next Generation Sequencing (NGS) to explore changes in the transcriptome of rat granulosa cells exposed for 0, 6, and 12 h to 100 ng/ml of four highly purified FSH glycoforms, each exhibiting distinctly different glycosylation patterns: human pituitary FSH21 and equine FSH (eFSH) (hypo-glycosylated), and human FSH24 and CHO cell-derived human recombinant FSH (recFSH) (fully-glycosylated). Total RNA from triplicate incubations was prepared from FSH glycoform-exposed cultured granulosa cells obtained from DES-pretreated immature female rats, and total RNA libraries were sequenced in a HighSeq 2500 sequencer (2 x 125 bp paired-end format, 10–15 x 106 reads/sample). The computational workflow was focused on investigating differences among the four FSH glycoforms at three levels: gene expression (Salmon and DESeq2 bioinformatic tools), enriched biological processes (DAVID tool), and perturbed pathways (GAGE tool). Among the top 200 differentially expressed genes, only 4 (0.6%) were shared by all 4 glycoforms at 6 h, whereas 118 genes (40%) were shared at 12 h. At 6 h, up-regulated genes in recFSH were associated with cell response, angiogenesis, extracellular matrix organization, and mitosis; eFSH with sex hormones (shared with FSH21); FSH21 with cellular response and response to drugs (shared with recFSH); and FSH24 with cAMP-related processes. There were more shared biological processes at 12 h, with fewer treatment-specific ones, except for recFSH, which exhibited stronger responses with more specifically associated processes. Similar results were found for down-regulated cell processes, with a greater number of processes at 6 h or 12 h, depending on the particular glycoform. In general, there were fewer down-regulated than up-regulated processes at both 6 h and 12 h, with FSH21 exhibiting the largest number of down-regulated associated processes at 6 h (10 vs 3 processes for eFSH, one process for FSH24, and one for recFSH), while eFSH exhibited the greatest number at 12 h (19 processes vs 4 for FSH21, 13 for FSH24, and 7 for recFSH). Two signaling cascades, largely linked to Rap-1 and cAMP pathways, were differentially activated by the glycoforms, with each glycoform exhibiting its own molecular signature. These transcriptomic data support previous biochemical observations demonstrating glycosylation-dependent differential regulation of intracellular signaling pathways triggered by FSH in granulosa cells.