Background: Currently, there are a few studies on the clinical characteristics of the pediatric population with This study aimed to analyze data associated with the development of pneumonia in children and adolescents with SARS-CoV-2 infection throughout Mexico. Methods: We conducted a secondary analysis of the database of the Dirección General de Epidemiología of the Mexican Government. We included children under the age of 19 who were confirmed with SARS-CoV-2 infection by reverse transcription-polymerase chain reaction (RT-PCR) test. The dependent variable was the diagnosis of pneumonia. Odds ratios (OR) and 95% confidence intervals (95% CI) were calculated. Multiple logistic regression models were performed to adjust factors associated with pneumonia. Results: A total of 1443 children with a COVID-19 diagnosis were included. The median age of the participants was 12 years (interquartile range 25, 75: 5, 16). Pneumonia occurred in 141 children (9.8%). The main risk factors were age < 3 years (OR, 3.5; 95%CI,; diabetes or obesity (OR, 12.6; 95%CI, and immunocompromise (OR, 7.03; 95%CI,. Conclusions: Children < 3 years with COVID-19 and comorbidities, especially diabetes or obesity, and immunocompromised patients have a higher risk of developing pneumonia.

Los gráficos constituyen una ayuda visual que usan los artículos para resaltar los resultados de una investigación. Estos permiten ilustrar los resultados con el fin de hacerlos más claros. Los gráficos, al igual que las pruebas estadísticas, se seleccionan a partir del objetivo del estudio, de los tipos de variable y de los análisis estadísticos que se desee ilustrar. Algunos de los gráficos más usados en la práctica clínica son los histogramas de frecuencia que ilustran las variables cualitativas o frecuencias, los gráficos de error se usan para variables cuantitativas con distribución normal, el gráfico de cajas o gráfico de violín para variables cuantitativas de libre distribución y las curvas de supervivencia para las variables que incluyen la variable tiempo/persona. Estos mismos gráficos pueden ser usados para ilustrar las comparaciones entre maniobras y desenlace dependiendo del tipo de variable que se analice. Cuando se comparan dos grupos y la variable dependiente es dicotómica se usan gráficos de bosque. Para los modelos multivariados los gráficos dependen del tipo de análisis, en el caso de la regresión logística se utilizan gráficos de árbol y para la regresión lineal de dispersión y para los riesgos proporcionales de Cox, gráficos de supervivencia. Si bien los gráficos son de gran utilidad, mal utilizados pueden mostrar diferencias donde no las hay, provocando una errónea interpretación de los estudios. En este artículo complementaremos con ejemplos los temas abordados con anterioridad en los artículos de esta misma serie.

No abstract

Periodontal disease is caused by different gram-negative anaerobic bacteria; however, Escherichia coli has also been isolated from periodontitis and its role in periodontitis is less known. This study aimed to determine the variability in virulence genotype, antibiotic resistance phenotype, biofilm formation, phylogroups, and serotypes in different emerging periodontal strains of Escherichia coli, isolated from patients with periodontal disease and healthy controls. E. coli, virulence genes, and phylogroups, were identified by PCR, antibiotic susceptibility by the Kirby-Bauer method, biofilm formation was quantified using polystyrene microtiter plates, and serotypes were determined by serotyping. Although E. coli was not detected in the controls (n = 70), it was isolated in 14.7% (100/678) of the patients. Most of the strains (n = 81/100) were multidrug-resistance. The most frequent adhesion genes among the strains were fimH and iha, toxin genes were usp and hlyA, iron-acquisition genes were fyuA and irp2, and protectin genes were ompT, and KpsMT. Phylogroup B2 and serotype O25:H4 were the most predominant among the strains. These findings suggest that E. coli may be involved in periodontal disease due to its high virulence, multidrug-resistance, and a wide distribution of phylogroups and serotypes.

To determine the efficacy and safety of fixed combination of hydroxychloroquine/azithromycin (HCQ+AZT) compared to hydroxychloroquine (HCQ) alone or placebo in mild COVID-19 outpatients to avoid hospitalization.Materials and methods                                                                            This randomized, parallel, double-blind clinical trial included male and female patients aged 18 and 76 years non COVID vaccinated, who were diagnosed with mild COVID-19 infection. All patients underwent liver and kidney profile test, as well as a health questionnaire and clinical revision to document that they did not have uncontrolled comorbidities. They were randomly assigned to one of the three treatment arms: 1) hydroxychloroquine with azithromycin 200 mg/250 mg every 12 hours for five days followed by hydroxychloroquine 200 mg every 12 hours for 5 days; 2) hydroxychloroquine 200 mg every 12 hours for ten days; or 3) placebo every 12 hours for ten days. The primary outcome of the study was hospitalization, while the secondary outcomes were disease progression, pneumonia, use of supplemental oxygen, and adverse events. This study was registered in clinicaltrials.gov with the NCT number of 04964583.ResultsA total of 92 participants were randomized. Of whom, 30 received HCQ+AZT, 31 received HCQ, and 31 received placebo. The median age was 37 years, 27.2% of the participants had comorbidities, and the global incidence of hospitalization was 2.2%. The incidence of hospitalization was 6.7% (2/30) in the HCQ+AZT group compared to the HCQ or placebo groups, in which there were no hospitalizations. Progression of disease was higher in the HCQ group [RR=3.25 (95% CI, 1.19-8.87)] compared with placebo group. There was no statistical difference between the HCQ+AZT group and the placebo group in progression of disease. The incidence of pneumonia was 30% in the HCQ+AZT group, 32.2% in the HCQ group, and 9.6% in the placebo group (HCQ + AZT vs Placebo; p=0.06). There was a significant risk of pneumonia versus placebo only in the HCQ group [RR=3.33 (95% CI, 1.01-10.9)]. Supplemental oxygen was required by 20% (6/30) of the patients in the HCQ+AZT group, 6.4 (2/31) of the patients in the HCQ group, and 3.2% (1/31) of the patients in the placebo group,[(HCQ + AZT vs Placebo; p=0.100), (HCQ vs Placebo, p=0.610)]. There was no statistical difference between groups for negative test (PCR) on day 11. The most frequent adverse events were gastrointestinal symptoms. No lengthening of the QT interval was observed in patients receiving HCQ+AZT or HCQ.ConclusionThe use of HCQ+AZT does not decrease the risk of hospitalization in patients with mild COVID-19. The use of HCQ increases the risk of progression and pneumonia.