Zgli: A Pipeline for Clustering by Compression with Application to Patient Stratification in Spondyloarthritis.

The normalized compression distance (NCD) is a similarity measure between a pair of finite objects based on compression. Clustering methods usually use distances (e.g., Euclidean distance, Manhattan distance) to measure the similarity between objects. The NCD is yet another distance with particular characteristics that can be used to build the starting distance matrix for methods such as hierarchical clustering or K-medoids. In this work, we propose Zgli, a novel Python module that enables the user to compute the NCD between files inside a given folder. Inspired by the CompLearn Linux command line tool, this module iterates on it by providing new text file compressors, a new compression-by-column option for tabular data, such as CSV files, and an encoder for small files made up of categorical data. Our results demonstrate that compression by column can yield better results than previous methods in the literature when clustering tabular data. Additionally, the categorical encoder shows that it can augment categorical data, allowing the use of the NCD for new data types. One of the advantages is that using this new feature does not require knowledge or context of the data. Furthermore, the fact that the new proposed module is written in Python, one of the most popular programming languages for machine learning, potentiates its use by developers to tackle problems with a new approach based on compression. This pipeline was tested in clinical data and proved a promising computational strategy by providing patient stratification via clusters aiding in precision medicine.

Persistence of IgG COVID-19 antibodies: A longitudinal analysis.

Background and aim: The kinetics of antibody production in response to coronavirus disease 2019 (COVID-19) infection is not well-defined yet. This study aimed to evaluate the antibody responses to SARS-CoV-2 and its dynamics during 9-months in a cohort of patients infected during the first phase of the pandemic. As a secondary aim, it was intended to evaluate the factors associated with different concentrations of IgG antibodies. Methods: A prospective cohort study was conducted from June 2020 to January 2021. This study recruited a convenience sample of adult individuals who where recently diagnosed with COVID-19 and were living in mainland Portugal. A total of 1,695 blood samples were collected from 585 recovered COVID-19 patients up to 9 months after SARS-CoV-2 acute infection. A blood sample was collected at baseline and three, 6 and 9 months after SARS-CoV-2 acute infection to assess the concentration of IgG antibody against SARS-CoV-2. Results: The positivity rate of IgG reached 77.7% in the first 3 months after symptom onset. The IgG persists at all subsequent follow-up time-points, which was 87.7 and 89.2% in the 6th and 9th months after symptom onset, respectively. Three distinct kinetics of antibody response were found within the 9 months after infection. Kinetic 1 (K1) was characterized by a constant low IgG antibody concentration kinetic (group size: 65.2%); kinetic 2 (K2), composed by constant moderate IgG kinetic (group size: 27.5%) and kinetic 3 (K3) characterized by higher IgG kinetic (group size: 7.3%). People with ≥56 years old (OR: 3.33; CI 95%: [1.64; 6.67]; p-value: 0.001) and symptomatic COVID-19 (OR: 2.08; CI 95%: [1.08; 4.00]; p-value: 0.031) had higher odds of a "Moderate IgG kinetic." No significant association were found regarding the "Higher IgG kinetic." Conclusion: Our results demonstrate a lasting anti-spike (anti-S) IgG antibody response at least 9 months after infection in the majority of patients with COVID-19. Younger participants with asymptomatic disease have lower IgG antibody positivity and possibly more susceptible to reinfection. This information contributes to expanding knowledge of SARS-CoV-2 immune response and has direct implications in the adoption of preventive strategies and public health policies.

Persistence of IgG COVID-19 antibodies: A longitudinal analysis

Background and aim The kinetics of antibody production in response to coronavirus disease 2019 (COVID-19) infection is not well-defined yet. This study aimed to evaluate the antibody responses to SARS-CoV-2 and its dynamics during 9-months in a cohort of patients infected during the first phase of the pandemic. As a secondary aim, it was intended to evaluate the factors associated with different concentrations of IgG antibodies. Methods A prospective cohort study was conducted from June 2020 to January 2021. This study recruited a convenience sample of adult individuals who where recently diagnosed with COVID-19 and were living in mainland Portugal. A total of 1,695 blood samples were collected from 585 recovered COVID-19 patients up to 9 months after SARS-CoV-2 acute infection. A blood sample was collected at baseline and three, 6 and 9 months after SARS-CoV-2 acute infection to assess the concentration of IgG antibody against SARS-CoV-2. Results The positivity rate of IgG reached 77.7% in the first 3 months after symptom onset. The IgG persists at all subsequent follow-up time-points, which was 87.7 and 89.2% in the 6th and 9th months after symptom onset, respectively. Three distinct kinetics of antibody response were found within the 9 months after infection. Kinetic 1 (K1) was characterized by a constant low IgG antibody concentration kinetic (group size: 65.2%);kinetic 2 (K2), composed by constant moderate IgG kinetic (group size: 27.5%) and kinetic 3 (K3) characterized by higher IgG kinetic (group size: 7.3%). People with ≥56 years old (OR: 3.33;CI 95%: [1.64;6.67];p-value: 0.001) and symptomatic COVID-19 (OR: 2.08;CI 95%: [1.08;4.00];p-value: 0.031) had higher odds of a "Moderate IgG kinetic.” No significant association were found regarding the "Higher IgG kinetic.” Conclusion Our results demonstrate a lasting anti-spike (anti-S) IgG antibody response at least 9 months after infection in the majority of patients with COVID-19. Younger participants with asymptomatic disease have lower IgG antibody positivity and possibly more susceptible to reinfection. This information contributes to expanding knowledge of SARS-CoV-2 immune response and has direct implications in the adoption of preventive strategies and public health policies.

Development of a Prediction Model for COVID-19 Acute Respiratory Distress Syndrome in Patients With Rheumatic Diseases: Results From the Global Rheumatology Alliance Registry.

OBJECTIVE: Some patients with rheumatic diseases might be at higher risk for coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS). We aimed to develop a prediction model for COVID-19 ARDS in this population and to create a simple risk score calculator for use in clinical settings. METHODS: Data were derived from the COVID-19 Global Rheumatology Alliance Registry from March 24, 2020, to May 12, 2021. Seven machine learning classifiers were trained on ARDS outcomes using 83 variables obtained at COVID-19 diagnosis. Predictive performance was assessed in a US test set and was validated in patients from four countries with independent registries using area under the curve (AUC), accuracy, sensitivity, and specificity. A simple risk score calculator was developed using a regression model incorporating the most influential predictors from the best performing classifier. RESULTS: The study included 8633 patients from 74 countries, of whom 523 (6%) had ARDS. Gradient boosting had the highest mean AUC (0.78; 95% confidence interval [CI]: 0.67-0.88) and was considered the top performing classifier. Ten predictors were identified as key risk factors and were included in a regression model. The regression model that predicted ARDS with 71% (95% CI: 61%-83%) sensitivity in the test set, and with sensitivities ranging from 61% to 80% in countries with independent registries, was used to develop the risk score calculator. CONCLUSION: We were able to predict ARDS with good sensitivity using information readily available at COVID-19 diagnosis. The proposed risk score calculator has the potential to guide risk stratification for treatments, such as monoclonal antibodies, that have potential to reduce COVID-19 disease progression.

SARS-CoV-2 Antibody Prevalence in the Portuguese Municipality of Vila Nova de Gaia after the First Wave of the Pandemic.

INTRODUCTION: Assessment of SARS-CoV-2 seroprevalence may detect the real spread of the virus because antibody data can provide a long-lasting measure of infection. Existing serological studies in Portugal have tested new serology methods, albeit with small sample sizes and a lack the focus on geographical regions with a high rate of infection cases. The aim of this study was to estimate the serological prevalence of SARS-CoV-2 in Vila Nova de Gaia, the most populous municipality in the north of Portugal and one of those most affected during the first pandemic wave. MATERIAL AND METHODS: A cross-sectional observational study was conducted between June 23rd and July 17th, 2020. Included in the cohort were 18- to 74-year-old men and women living in the municipality of Vila Nova de Gaia, who were sampled through a nonprobabilistic quota-based approach. Cases with a previous RT-PCR diagnosis of COVID-19 were excluded. Sociodemographic and clinical information was collected using a self-administered, written questionnaire. Blood samples were collected for serological laboratory analysis to detect and quantify SARS-CoV-2 anti-IgG antibodies. RESULTS: We tested 2754 participants. Our results show a SARS-CoV-2 seroprevalence of 3.03% (95% confidence interval: 2.37% - 3.87%). Being a smoker (odds ratio: 0.382, 95% confidence interval: 0.147 - 0.99) and having symptoms of COVID-19 (odds ratio: 2.480, 95% confidence interval: 1.360 - 4.522) were consistently associated with lower and higher odds of SARS-CoV-2 antibody presence, respectively, regardless of the analytic design. Moreover, without adjusting for any variables, having had contact with an infected person within the household was associated with increased odds of a positive test (odds ratio: 9.684, 95% confidence interval: 4.06 - 23.101); after adjusting, having self-reported chronic diseases (odds ratio: 0.448, 95% confidence interval: 0.213 - 0.941) was associated with decreased odds. CONCLUSION: This was the first study to estimate the serological prevalence of SARS-CoV-2 in one of the most populous municipalities in Portugal, representing the first step in the development of an epidemiological surveillance system in Portugal, which can help to improve the diagnosis of COVID-19.