Evaluation of sound pressure levels in a pediatric intensive care unit

This study objectives were to measure the sound pressure levels found in the pediatric intensive care unit in a federal institution of Rio de Janeiro; to verify differences in noise levels during the morning and afternoon; to confront the sound pressure levels found against acceptable levels according to national and international noise organizations; to count the quantity of alarms triggered by each type of medical care equipment selected (multiparameter monitor, mechanical ventilator and infusion pump); to verify the relevance in the scientific world about pediatric intensive care unit noise through bibliometrics and to address the trinomial care technology - noise - implications on care. It’s an observational, exploratory, quantitative study, organized in three steps: Parameter collection and decibel meter calibration - data were based on the study by Salú, et al (2015) ; Data collection: 40 hours of discontinued observation (8am to 16pm) on different days for a period two months using two decibel meters; Data processing: An Excel spreadsheet was created for the database and data analysis was performed with the help of Microsoft Office Excel 2010 and Program R, organized into graphs and tables. 61% of the alarms corresponded to the mechanical ventilator; Bed E had the lowest standard deviation (SD = 2.945) and the highest median (69.5dBA). Even by removing the E bed from the analysis, there is a significant difference (p <0.001) between sound pressure levels. The median of the afternoon (28.2dBA); and morning (26.1dBA). Mechanical fan and monitor generated higher sounds; the pediatric intensive care unit has considerably exceeded that recommended by national and international noise organizations; afternoon generated higher sounds than morning. Keywords: Noise Meters; Noise; Intensive care; Pediatric

Rare genetic variants involved in multisystem inflammatory syndrome in children: a multicenter Brazilian cohort study

IntroductionDespite the existing data on the Multisystem Inflammatory Syndrome in Children (MIS-C), the factors that determine these patients evolution remain elusive. Answers may lie, at least in part, in genetics. It is currently under investigation that MIS-C patients may have an underlying innate error of immunity (IEI), whether of monogenic, digenic, or even oligogenic origin.MethodsTo further investigate this hypothesis, 30 patients with MIS-C were submitted to whole exome sequencing. ResultsAnalyses of genes associated with MIS-C, MIS-A, severe covid-19, and Kawasaki disease identified twenty-nine patients with rare potentially damaging variants (50 variants were identified in 38 different genes), including those previously described in IFNA21 and IFIH1 genes, new variants in genes previously described in MIS-C patients (KMT2D, CFB, and PRF1), and variants in genes newly associated to MIS-C such as APOL1, TNFRSF13B, and G6PD. In addition, gene ontology enrichment pointed to the involvement of thirteen major pathways, including complement system, hematopoiesis, immune system development, and type II interferon signaling, that were not yet reported in MIS-C.DiscussionThese data strongly indicate that different gene families may favor MIS- C development. Larger cohort studies with healthy controls and other omics approaches, such as proteomics and RNAseq, will be precious to better understanding the disease dynamics

Brazilians perceived trustworthiness of sources of information about the vaccine for the prevention of COVID-19 according to vaccine hesitancy (n = 173,178, with 18,250 hesitant and 154,928 non-hesitant).

Some participants did not report trustworthiness in some information sources.</p

Strategies indicated to increase the percentage of vaccine adherence to prevent COVID-19 according to the intention to vaccinate.

Strategies indicated to increase the percentage of vaccine adherence to prevent COVID-19 according to the intention to vaccinate.</p

Clarity perceived about vaccine information between hesitant and non-hesitant (N = 172,314).

Clarity perceived about vaccine information between hesitant and non-hesitant (N = 172,314).</p

Number of participants who gave their opinion on the understanding of vaccine efficacy data according to vaccine hesitancy.

(N = 172,366 respondents, with 154,237 not hesitant and 18,129 hesitant).</p