Use of Machine Learning and Routine Laboratory Tests for Diabetes Mellitus Screening.

Most patients with diabetes mellitus are asymptomatic, which leads to delayed and more complex treatment. At the same time, most individuals are routinely subjected to standard clinical laboratory examinations, which create large health datasets over a lifetime. Computer processing has been used to search for health anomalies and predict diseases using clinical examinations. This work studied machine learning models to support the screening of diabetes through routine laboratory tests using data from laboratory tests of 62,496 patients. The classification and regression models used were the K-nearest neighbor, support vector machines, Bayes naïve, random forest models, and artificial neural networks. Glycated hemoglobin, a test used for diabetes diagnosis, was used as the target. Regression models calculated glycated hemoglobin directly and were later classified. The performance of classification computer models has been studied under various subdataset partitions and combinations (e.g., healthy, prediabetic, and diabetes, as well as no healthy and no diabetes). The best single performance was achieved with the artificial neural network model when detecting prediabetes or diabetes. The artificial neural network classification model scored 78.1%, 78.7%, and 78.4% for sensitivity, precision, and F1 scores, respectively, when identifying no healthy group. Other models also had good results, depending on what is desired. Machine learning-based models can predict glycated hemoglobin values from routine laboratory tests and can be used as a screening tool to refer a patient for further testing.

SARS-CoV-2 saliva testing is a useful tool for Covid-19 diagnosis.

SARS-CoV-2 is the etiologic agent of coronavirus disease 2019 (COVID-19) and is mainly detected by RT-PCR methods from upper respiratory specimens, as recommended by the World Health Organization. Oro/nasopharyngeal swabbing can be discomfortable to the patients, requires trained healthcare personnel and may generate aerosol, increasing the risk of nosocomial infections. In this study, we describe two SARS-CoV-2 RNA extraction-free single RT-PCR protocols on saliva samples and compared the results with the paired oro/nasopharyngeal swab specimens from 400 patients. The two saliva protocols demonstrated a substantial agreement when compared to the oro/nasopharyngeal swab protocol. Moreover, the positivity rate of saliva protocols increased according to the disease period. The 95 % limit of detection of one of the therefore implemented saliva protocol was determined as 9441 copies/mL. Our results support the conclusion that RNA extraction-free RT-PCR using self-collected saliva specimens is an alternative to nasopharyngeal swabs, especially in the early phase of symptom onset.

Dynamics of SARS-CoV-2 Variants of Concern in Brazil, Early 2021.

Brazil is the country with the second-largest number of deaths due to the coronavirus disease-2019 (COVID-19). Two variants of concern (VOCs), Alpha (B.1.1.7) and Gamma (P.1), were first detected in December 2020. While Alpha expanded within an expected rate in January and February 2021, its prevalence among new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases started to decrease in March, which coincided with the explosion of Gamma variant incidence all over the country, being responsible for more than 95% of the new cases over the following months. A significantly higher viral load [i.e., mean cycle threshold (Ct) values] for Gamma in comparison to non-VOC samples was verified by the analysis of a large data set of routine reverse transcription-PCR (RT-PCR) exams. Moreover, the rate of reinfections greatly increased from March 2021 onward, reinforcing the enhanced ability of Gamma to escape the immune response. It is difficult to predict the outcomes of competition between variants since local factors like frequency of introduction and vaccine coverage play a key role. Genomic surveillance is of uttermost importance for the mitigation of the pandemic.