SARS-CoV-2 shedding, infectivity, and evolution in an immunocompromised adult patient.

This study aimed to provide further insight into the evolutionary dynamics of SARS-CoV-2 by analyzing the case of a 40-year-old man who had previously undergone autologous hematopoietic stem cell transplantation due to a diffuse large B-cell lymphoma. He developed a persistent SARS-CoV-2 infection lasting at least 218 days and did not manifest a humoral immune response to the virus during this follow-up period. Whole-genome sequencing and viral cultures confirmed a persistent infection with a replication-positive virus that had undergone genetic variation for at least 196 days after symptom onset.

Machine learning for predicting Chagas disease infection in rural areas of Brazil.

INTRODUCTION: Chagas disease is a severe parasitic illness that is prevalent in Latin America and often goes unaddressed. Early detection and treatment are critical in preventing the progression of the illness and its associated life-threatening complications. In recent years, machine learning algorithms have emerged as powerful tools for disease prediction and diagnosis. METHODS: In this study, we developed machine learning algorithms to predict the risk of Chagas disease based on five general factors: age, gender, history of living in a mud or wooden house, history of being bitten by a triatomine bug, and family history of Chagas disease. We analyzed data from the Retrovirus Epidemiology Donor Study (REDS) to train five popular machine learning algorithms. The sample comprised 2,006 patients, divided into 75% for training and 25% for testing algorithm performance. We evaluated the model performance using precision, recall, and AUC-ROC metrics. RESULTS: The Adaboost algorithm yielded an AUC-ROC of 0.772, a precision of 0.199, and a recall of 0.612. We simulated the decision boundary using various thresholds and observed that in this dataset a threshold of 0.45 resulted in a 100% recall. This finding suggests that employing such a threshold could potentially save 22.5% of the cost associated with mass testing of Chagas disease. CONCLUSION: Our findings highlight the potential of applying machine learning to improve the sensitivity and effectiveness of Chagas disease diagnosis and prevention. Furthermore, we emphasize the importance of integrating socio-demographic and environmental factors into neglected disease prediction models to enhance their performance.

SARS-CoV-2 Detection and Culture in Different Biological Specimens from Immunocompetent and Immunosuppressed COVID-19 Patients Infected with Two Different Viral Strains.

Introduction-The dynamics of SARS-CoV-2 shedding and replication in humans remain incompletely understood. Methods-We analyzed SARS-CoV-2 shedding from multiple sites in individuals with an acute COVID-19 infection by weekly sampling for five weeks in 98 immunocompetent and 25 immunosuppressed individuals. Samples and culture supernatants were tested via RT-PCR for SARS-CoV-2 to determine viral clearance rates and in vitro replication. Results-A total of 2447 clinical specimens were evaluated, including 557 nasopharyngeal swabs, 527 saliva samples, 464 urine specimens, 437 anal swabs and 462 blood samples. The SARS-CoV-2 genome sequences at each site were classified as belonging to the B.1.128 (ancestral strain) or Gamma lineage. SARS-CoV-2 detection was highest in nasopharyngeal swabs regardless of the virus strain involved or the immune status of infected individuals. The duration of viral shedding varied between clinical specimens and individual patients. Prolonged shedding of potentially infectious virus varied from 10 days up to 191 days, and primarily occurred in immunosuppressed individuals. Virus was isolated in culture from 18 nasal swab or saliva samples collected 10 or more days after onset of disease. Conclusions-Our findings indicate that persistent SARS-CoV-2 shedding may occur in both competent or immunosuppressed individuals, at multiple clinical sites and in a minority of subjects is capable of in vitro replication.

HDL proteome remodeling associates with COVID-19 severity.

BACKGROUND: Besides the well-accepted role in lipid metabolism, high-density lipoprotein (HDL) also seems to participate in host immune response against infectious diseases. OBJECTIVE: We used a quantitative proteomic approach to test the hypothesis that alterations in HDL proteome associate with severity of Coronavirus disease 2019 (COVID-19). METHODS: Based on clinical criteria, subjects (n=41) diagnosed with COVID-19 were divided into two groups: a group of subjects presenting mild symptoms and a second group displaying severe symptoms and requiring hospitalization. Using a proteomic approach, we quantified the levels of 29 proteins in HDL particles derived from these subjects. RESULTS: We showed that the levels of serum amyloid A 1 and 2 (SAA1 and SAA2, respectively), pulmonary surfactant-associated protein B (SFTPB), apolipoprotein F (APOF), and inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4) were increased by more than 50% in hospitalized patients, independently of sex, HDL-C or triglycerides when comparing with subjects presenting only mild symptoms. Altered HDL proteins were able to classify COVID-19 subjects according to the severity of the disease (error rate 4.9%). Moreover, apolipoprotein M (APOM) in HDL was inversely associated with odds of death due to COVID-19 complications (odds ratio [OR] per 1-SD increase in APOM was 0.27, with 95% confidence interval [CI] of 0.07 to 0.72, P=0.007). CONCLUSION: Our results point to a profound inflammatory remodeling of HDL proteome tracking with severity of COVID-19 infection. They also raise the possibility that HDL particles could play an important role in infectious diseases.

Serological screening for Chagas disease in an endemic region of Northern Minas Gerais, Brazil: the SaMi-Trop project.

Chagas disease (CD) is still a neglected disease. Infected individuals are diagnosed late, being treated in worse clinical conditions. Thus, this study aimed to analyze the prevalence and the factors associated with new confirmed cases of CD identified by serological screening in an endemic region of Minas Gerais State, Brazil. This is an analytical cross-sectional study with data from a project of the Research Center in Tropical Medicine of Sao Paulo- Minas Gerais (SaMi-Trop) conducted in two municipalities. Data collection included a questionnaire with closed questions, a venous blood collection and an ELISA serological test for CD. A total of 2,038 individuals with no previous diagnosis of CD participated in the study. The result of the serological test for CD was adopted as the dependent variable. The independent variables addressed personal issues, health conditions and lifetime housing. A descriptive analysis of individual variables was performed. Subsequently, a bivariate analysis was performed using the Pearson's chi-square test. Households sheltering individuals positive for CD were georeferenced, and the analysis of spatial distribution was performed using the quartic function to estimate the density of the nucleus. Among the participants, 188 (9.2 %) were positive for CD. The profile of participants with CD was associated with place of residence, age, relative/family member with CD and living conditions. It is noteworthy that there are still patients with CD who are unaware of their diagnosis in both, rural and urban areas.

Prognostic accuracy of MALDI-TOF mass spectrometric analysis of plasma in COVID-19.

SARS-CoV-2 infection poses a global health crisis. In parallel with the ongoing world effort to identify therapeutic solutions, there is a critical need for improvement in the prognosis of COVID-19. Here, we report plasma proteome fingerprinting that predict high (hospitalized) and low-risk (outpatients) cases of COVID-19 identified by a platform that combines machine learning with matrix-assisted laser desorption ionization mass spectrometry analysis. Sample preparation, MS, and data analysis parameters were optimized to achieve an overall accuracy of 92%, sensitivity of 93%, and specificity of 92% in dataset without feature selection. We identified two distinct regions in the MALDI-TOF profile belonging to the same proteoforms. A combination of SDS-PAGE and quantitative bottom-up proteomic analysis allowed the identification of intact and truncated forms of serum amyloid A-1 and A-2 proteins, both already described as biomarkers for viral infections in the acute phase. Unbiased discrimination of high- and low-risk COVID-19 patients using a technology that is currently in clinical use may have a prompt application in the noninvasive prognosis of COVID-19. Further validation will consolidate its clinical utility.

Epidemiological and clinical characteristics of the COVID-19 epidemic in Brazil.

The first case of COVID-19 was detected in Brazil on 25 February 2020. We report and contextualize epidemiological, demographic and clinical findings for COVID-19 cases during the first 3 months of the epidemic. By 31 May 2020, 514,200 COVID-19 cases, including 29,314 deaths, had been reported in 75.3% (4,196 of 5,570) of municipalities across all five administrative regions of Brazil. The R0 value for Brazil was estimated at 3.1 (95% Bayesian credible interval = 2.4-5.5), with a higher median but overlapping credible intervals compared with some other seriously affected countries. A positive association between higher per-capita income and COVID-19 diagnosis was identified. Furthermore, the severe acute respiratory infection cases with unknown aetiology were associated with lower per-capita income. Co-circulation of six respiratory viruses was detected but at very low levels. These findings provide a comprehensive description of the ongoing COVID-19 epidemic in Brazil and may help to guide subsequent measures to control virus transmission.