Clinical Diagnosis Of COVID-19 Research Articles

An anti-fouling surface molecularly imprinted ratiometric electrochemical biosensor for SARS-CoV-2 spike protein

An anti-fouling surface molecularly imprinted (SMI) ratiometric electrochemical biosensor was fabricated for the detection of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) spike protein (S protein). Polypyrrole (PPy) was electrodeposited on the electrode surface by using ferrous phthalocyanine (PcFe) and sodium alginate (SA) as the dopants, which acted as the molecularly imprinted polymers (MIPs). The SMI electrochemical biosensor could be used for the detection of SARS-CoV-2 S protein based on the ratiometric signals of the internal reference (PcFe) and the external probe ([Fe(CN)6]3−/4−). Under optimized conditions, the as-fabricated SMI ratiometric biosensor showed a wide linear range of 100 fg mL−1 ∼ 100 ng mL−1 with a low limit of detection (LOD) of 30.1 fg mL−1. Nonspecific adsorption of interfering substances onto the SMI biosensor surface could be significantly reduced due to the outstanding anti-fouling ability of SA, as revealed by fluorescence microscopy. Finally, the reliability of the SMI ratiometric electrochemical biosensor was demonstrated by the detection of SARS-CoV-2 S protein in clinical serum samples. The findings of this work open a new avenue in clinical diagnosis of COVID-19.

The effectiveness of COVID-19 vaccines to prevent long COVID symptoms: staggered cohort study of data from the UK, Spain, and Estonia

Although vaccines have proved effective to prevent severe COVID-19, their effect on preventing long-term symptoms is not yet fully understood. We aimed to evaluate the overall effect of vaccination to prevent long COVID symptoms and assess comparative effectiveness of the most used vaccines (ChAdOx1 and BNT162b2). We conducted a staggered cohort study using primary care records from the UK (Clinical Practice Research Datalink [CPRD] GOLD and AURUM), Catalonia, Spain (Information System for Research in Primary Care [SIDIAP]), and national health insurance claims from Estonia (CORIVA database). All adults who were registered for at least 180 days as of Jan 4, 2021 (the UK), Feb 20, 2021 (Spain), and Jan 28, 2021 (Estonia) comprised the source population. Vaccination status was used as a time-varying exposure, staggered by vaccine rollout period. Vaccinated people were further classified by vaccine brand according to their first dose received. The primary outcome definition of long COVID was defined as having at least one of 25 WHO-listed symptoms between 90 and 365 days after the date of a PCR-positive test or clinical diagnosis of COVID-19, with no history of that symptom 180 days before SARS-Cov-2 infection. Propensity score overlap weighting was applied separately for each cohort to minimise confounding. Sub-distribution hazard ratios (sHRs) were calculated to estimate vaccine effectiveness against long COVID, and empirically calibrated using negative control outcomes. Random effects meta-analyses across staggered cohorts were conducted to pool overall effect estimates. A total of 1 618 395 (CPRD GOLD), 5 729 800 (CPRD AURUM), 2 744 821 (SIDIAP), and 77 603 (CORIVA) vaccinated people and 1 640 371 (CPRD GOLD), 5 860 564 (CPRD AURUM), 2 588 518 (SIDIAP), and 302 267 (CORIVA) unvaccinated people were included. Compared with unvaccinated people, overall HRs for long COVID symptoms in people vaccinated with a first dose of any COVID-19 vaccine were 0·54 (95% CI 0·44-0·67) in CPRD GOLD, 0·48 (0·34-0·68) in CPRD AURUM, 0·71 (0·55-0·91) in SIDIAP, and 0·59 (0·40-0·87) in CORIVA. A slightly stronger preventative effect was seen for the first dose of BNT162b2 than for ChAdOx1 (sHR 0·85 [0·60-1·20] in CPRD GOLD and 0·84 [0·74-0·94] in CPRD AURUM). Vaccination against COVID-19 consistently reduced the risk of long COVID symptoms, which highlights the importance of vaccination to prevent persistent COVID-19 symptoms, particularly in adults. National Institute for Health and Care Research.

Особенности поражения легких при коинфекции ВИЧ/SARS-CoV-2 у пациентов с разной степенью иммунодефицита

Introduction.The development of the COVID-19 pandemic along with the ongoing pandemic of HIV infection has raised the question of how these two diseases interact. This study examines morphological changes that occur in lung tissue during HIV/SARS-CoV-2 coinfection in patients with varying levels of immune status. Materials and methods. The study included 6 deceased HIV-infected patients who had a clinical diagnosis of COVID-19, which was confirmed postmortem by the detection of SARS-CoV-2 RNA in lung samples obtained during biopsy using polymerase chain reaction. Three patients were with newly diagnosed HIV infection; three patients had a relatively intact immune status (CD4+ more than 200 cells/μl) and no opportunistic infection and received effective antiretroviral therapy. To perform histologic examination, we used histochemical stains by Van Gieson, Grocott, Ziehl-Neelsen, PAS reaction as well as immunohistochemistry (IHC) with antibodies against SARS-CoV-2 nucleocapsid and cytomegalovirus. Results. The macroscopic picture in all cases was characterized by a decreased airiness and compaction of the lung tissue. Microscopically, patients with newly diagnosed HIV demonstrated diffuse alveolar damage (DAD) with pneumocystis pneumonia and cytomegalovirus infection. Nucleocapsid of SARS-CoV-2 was revealed in hyaline membranes and alveolar cells. In patients with controlled HIV, we detected manifestations of various phases of DAD and/or disseminated intravascular coagulation (DIC) syndrome with a predominant distribution of SARS-CoV-2 in macrophages and blood vessels. Conclusion. Lung damage in severe HIV-associated immunodeficiency was determined by the combination of direct cytopathic effect of coronavirus and opportunistic pathogens. In controlled HIV and relatively intact immune status, immune mediated action of SARS-CoV-2 could take place manifesting in diffuse alveolar damage and making the morphological picture comparable to that in HIV-negative patients.

How predictive of SARS-CoV-2 infection are clinical characteristics at presentation among individuals with COVID-like symptoms treated at the Mexican Institute of Social Security.

The COVID-19 pandemic has progressed rapidly, with the emergence of new virus variants that pose challenges in treating infected individuals. In Mexico, four epidemic waves have been recorded with varying disease severity. To understand the heterogeneity in clinical presentation over time and the sensitivity and specificity of signs and symptoms in identifying COVID-19 cases, an analysis of the changes in the clinical presentation of the disease was conducted. To analyze the changes in the clinical presentation of COVID-19 among 3.38 million individuals tested for SARS-CoV-2 at the Mexican Social Security Institute (IMSS) from March 2020 to October 2021 and evaluate the predictivity of signs and symptoms in identifying COVID-19 cases. A retrospective analysis of clinical presentation patterns of COVID-19 among individuals treated at IMSS was performed, contrasting the signs and symptoms among SARS-CoV-2-positive individuals with those who tested negative for the virus but had respiratory infection symptoms. The sensitivity and specificity of each sign and symptom in identifying SARS-CoV-2 infection were estimated. The set of signs and symptoms reported for COVID-19-suspected patients treated at IMSS were not highly specific for SARS-CoV-2 positivity. The signs and symptoms exhibited variability based on age and epidemic wave. The area under the receiver operating characteristic (ROC) curve was 0.62 when grouping the five main symptoms (headache, dyspnea, fever, arthralgia, and cough). Most of the individual symptoms had ROC values close to 0.5 (16 out of 22 between 0.48 and 0.52), indicating non-specificity. The results highlight the difficulty in making a clinical diagnosis of COVID-19 due to the lack of specificity of signs and symptoms. The variability of clinical presentation over time and among age groups highlights the need for further research to differentiate whether the changes are due to changes in the virus, who is becoming infected, or the population, particularly with respect to prior infection and vaccination status.

When Patients with Plasma Cell Disorders Encountered the Largest Omicron Wave (December 2022) in China: A Real-World Multicenter and Multiregional Study

Background: An unprecedented nationwide Omicron outbreak hit China in December 2022. Patients with plasma cell disorders have been confirmed to exhibit worse outcomes following COVID-19 infection, although data on the impact of Omicron on this population in China remain scarce. This study aims to report the clinical and epidemiological characteristics of plasma cell disorder patients during this major Omicron outbreak. It also analyzes the association between patient clinical characteristics and clinical course (infection, severity, hospitalization, and time to recovery) of COVID-19. Methods: We performed a multicenter and multiregional retrospective study primarily involving nine large tertiary hematology centers across nine provinces/cities in China. The study period was limited to December 1 st, 2022 - January 19 th, 2023. A total of 404 plasma cell disorder patients participated in the survey. Results: During follow-up, 342 patients had a laboratory or clinical diagnosis of COVID-19. The prevalence of COVID-19 within the study population was 76.2%. Among those with COVID-19, the majority were with multiple myeloma (325, 95.0%), and over half (201, 58.8%) were unvaccinated. At the time of COVID-19, 121 (40.1%) patients were undergoing maintenance therapy. Ninety (31.3%) patients had prior exposure to anti-CD38 monoclonal antibody, while 25 (8.7%) to CAR-T. The rates of severe illness and hospitalization were 16.4% and 20.5%, respectively. Four (5.7%) patients required ICU support and only two (2/277, 0.7%) deaths were reported. As of the data cutoff date (January 19 th, 2023), 231 (231/277, 83.4%) patients had recovered from COVID-19, with a median time to recovery of 14 days (95% CI: 13-15 days). Multivariate analysis identified age > 65 (OR 1.47, 95% CI 1.05-2.05, P = 0.02) and anti-CD38 monoclonal antibody within six months of COVID-19 (OR 1.47, 95% CI 1.03-2.09, P = 0.03) as independent risk factors for severe COVID-19 illness. Prior CAR-T therapy within six months was correlated with an increased risk of hospitalization (OR 3.49, 95% CI 1.07-11.4, P = 0.04) and prolonged time to recovery (HR 0.38, 95% CI 0.16-0.93, P = 0.03). Compared to maintenance therapy, patients at the induction or consolidation therapy stage had an elevated risk of hospitalization following COVID-19 (OR 1.55, 95% CI 1.04-2.33, P = 0.03). Notably, no significant protective effect of COVID-19 vaccination on infection or severe infection rates was observed ( P > 0.05). Conclusions: Although the impact of Omicron appears attenuated in plasma cell disorder patients, this vulnerable population still exhibits higher rates of severe illness and poorer outcomes compared to the general population. Apart from known risk factor (i.e. older age), certain treatment-related factors, including induction/consolidation therapy stage, recent anti-CD38 monoclonal antibody exposure, and prior CAR-T therapy, are associated with increased risks of severe COVID-19, hospitalization, and prolonged time to recovery. However, the protective effect of vaccination against COVID-19 is not identified. The findings from this work provide implications for the clinical management of PCD patients during the pandemic under the likely scenario of future resurgences of COVID-19.

FP-CNN: Fuzzy pooling-based convolutional neural network for lung ultrasound image classification with explainable AI

The COVID-19 pandemic wreaks havoc on healthcare systems all across the world. In pandemic scenarios like COVID-19, the applicability of diagnostic modalities is crucial in medical diagnosis, where non-invasive ultrasound imaging has the potential to be a useful biomarker. This research develops a computer-assisted intelligent methodology for ultrasound lung image classification by utilizing a fuzzy pooling-based convolutional neural network FP-CNN with underlying evidence of particular decisions. The fuzzy-pooling method finds better representative features for ultrasound image classification. The FPCNN model categorizes ultrasound images into one of three classes: covid, disease-free (normal), and pneumonia. Explanations of diagnostic decisions are crucial to ensure the fairness of an intelligent system. This research has used Shapley Additive Explanation (SHAP) to explain the prediction of the FP-CNN models. The prediction of the black-box model is illustrated using the SHAP explanation of the intermediate layers of the black-box model. To determine the most effective model, we have tested different state-of-the-art convolutional neural network architectures with various training strategies, including fine-tuned models, single-layer fuzzy pooling models, and fuzzy pooling at all pooling layers. Among different architectures, the Xception model with all pooling layers having fuzzy pooling achieves the best classification results of 97.2% accuracy. We hope our proposed method will be helpful for the clinical diagnosis of covid-19 from lung ultrasound (LUS) images.

Rapid protocols to support COVID-19 clinical diagnosis based on hematological parameters

In December 2019, the Covid-19 pandemic began in the world. To reduce mortality, in addiction to mass vaccination, it is necessary to massify and accelerate clinical diagnosis, as well as creating new ways of monitoring patients that can help in the construction of specific treatments for the disease. In this work, we propose rapid protocols for clinical diagnosis of COVID-19 through the automatic analysis of hematological parameters using evolutionary computing and machine learning. These hematological parameters are obtained from blood tests common in clinical practice. We investigated the best classifier architectures. Then, we applied the particle swarm optimization algorithm (PSO) to select the most relevant attributes: serum glucose, troponin, partial thromboplastin time, ferritin, D-dimer, lactic dehydrogenase, and indirect bilirubin. Then, we assessed again the best classifier architectures, but now using the reduced set of features. Finally, we used decision trees to build four rapid protocols for Covid-19 clinical diagnosis by assessing the impact of each selected feature. The proposed system was used to support clinical diagnosis and assessment of disease severity in patients admitted to intensive and semi-intensive care units as a case study in the city of Paudalho, Brazil. We developed a web system for Covid-19 diagnosis support. Using a 100-tree random forest, we obtained results for accuracy, sensitivity, and specificity superior to 99%. After feature selection, results were similar. The four empirical clinical protocols returned accuracies, sensitivities and specificities superior to 98%. By using a reduced set of hematological parameters common in clinical practice, it was possible to achieve results of accuracy, sensitivity, and specificity comparable to those obtained with RT-PCR. It was also possible to automatically generate clinical decision protocols, allowing relatively accurate clinical diagnosis even without the aid of the web decision support system.

Health Counseling and COVID-19 Screening Test at Universitas Nasional in 2021

Rapid tests for initial screening of COVID-19 are urgently needed given the very fast spread of the disease. However, WHO emphasized that this examination does not establish a clinical diagnosis of COVID-19 and is not recommended for triaging patients with suspected COVID-19, the rapid test is only an initial screening; Real-Time Reverse-Transcriptase Polymerase Chain Reaction (RT-PCR) must still confirm the results. Meanwhile, triage of patients uses a rapid risk assessment from the Ministry of Health which refers to the WHO Rapid Risk Assessment of Acute Public Health guidelines. In order to assist the government's program in preventing the spread of Covid-19, it is necessary to carry out health education about the behavior of preventing Covid-19 and early detection of Covid-19 by testing rapid antibodies at the National University. The results of the dedication activity showed that the participants were enthusiastic about listening to the material provided and participating in the inspection activities. Participants admitted that this inspection activity provided great benefits for breaking the chain of the spread of the coronavirus in the National University environment.

A surface molecularly imprinted electrochemical biosensor for the detection of SARS-CoV-2 spike protein by using Cu7S4-Au as built-in probe

Sensitive detection of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) spike protein (S protein) is of significant clinical importance in the diagnosis of COVID-19 pandemic. In this work, a surface molecularly imprinted (SMI) electrochemical biosensor is fabricated for the detection of SARS-CoV-2 S protein. Cu7S4-Au is used as the built-in probe and modified on the surface of a screen-printed carbon electrode (SPCE). 4-Mercaptophenylboric acid (4-MPBA) is anchored to the surface of the Cu7S4-Au through Au–SH bonds, which can be used for the immobilization of the SARS-CoV-2 S protein template through boronate ester bonds. After that, 3-aminophenylboronic acid (3-APBA) is electropolymerized on the electrode surface and used as the molecularly imprinted polymers (MIPs). The SMI electrochemical biosensor is obtained after the elution of the SARS-CoV-2 S protein template with an acidic solution by the dissociation of the boronate ester bonds, which can be utilized for sensitive detection of the SARS-CoV-2 S protein. The developed SMI electrochemical biosensor displays high specificity, reproducibility and stability, which might be a potential and promising candidate for the clinical diagnosis of COVID-19.

Development and validation of a respiratory-responsive vocal biomarker-based tool for generalizable detection of respiratory impairment: independent case-control studies in multiple respiratory conditions including asthma, chronic obstructive pulmonary disease, and COVID-19.

Vocal biomarker-based machine learning approaches have shown promising results in detecting various health conditions, including respiratory diseases such as asthma. In this study, we aim to validate a respiratory-responsive vocal-biomarker (RRVB) platform initially trained on an asthma and healthy volunteer dataset for its ability to differentiate, without modification, active COVID-19 infection vs. healthy volunteers in patients presenting to hospitals in the US and India. The objective of this study was to determine whether the RRVB model can differentiate patients with active COVID-19 infection vs. asymptomatic healthy volunteers by assessing its sensitivity, specificity, and odds ratio. Another objective was to evaluate whether the RRVB model outputs correlate with symptom severity in COVID-19. A logistic regression model using a weighted sum of voice acoustic features was previously trained and validated on a dataset of about 1,700 patients with a confirmed asthma diagnosis vs. a similar number of healthy controls. The same model has shown generalizability to patients with chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD), and cough. In the present study, a total of 497 participants (46% male, 54% female; 94% < 65 years, 6% >= 65 years; 51% Marathi, 45% English, 5% Spanish speakers) were enrolled across four clinical sites in US and India and provided voice samples and symptom reports on their personal smartphones. The participants included symptomatic COVID-19 positive and negative patients as well as asymptomatic healthy volunteers. The RRVB model performance was assessed by comparison with clinical diagnosis of COVID-19 confirmed by RT-PCR. The RRVB model's ability to differentiate patients with respiratory conditions vs. healthy controls was previously demonstrated on validation data in asthma, COPD, ILD and cough with odds ratios of 4.3, 9.1, 3.1, and 3.9 respectively. The same RRVB model in the present study in COVID-19 performed with a sensitivity of 73.2%, specificity of 62.9%, and odds ratio of 4.64 (p<0.0001). Patients experiencing respiratory symptoms were detected more frequently than those not experiencing respiratory symptoms and completely asymptomatic patients (78.4% vs. 67.4% vs. 68.0%). The RRVB model has shown good generalizability across respiratory conditions, geographies, and language. Results in COVID-19 demonstrate its meaningful potential to serve as a pre-screening tool for identifying subjects at risk for COVID-19 infection in combination with temperature and symptom reports. Although not a COVID-19 test, these results suggest that the RRVB model could encourage targeted testing. Moreover, the generalizability of this model for detecting respiratory symptoms across different linguistic and geographic contexts suggests a potential path to development and validation of voice-based tools for broader disease surveillance and monitoring applications in the future. ClinicalTrials.gov (NCT04582331.

A history of high-risk and destructive behaviors predict COVID-19 infection: A preliminary study.

The COVID-19 pandemic has heightened mental health concerns, but the temporal relationship between these conditions and SARS-CoV-2 infection has not yet been investigated. Specifically, psychological issues, violent behaviors, and substance abuse problems were reported higher during the COVID-19 pandemic when compared to pre-pandemic levels. However, it is unknown whether a pre-pandemic history of these conditions increase an individual's susceptibility for SARS-CoV-2. To better understand the psychological risks underlying COVID-19, it is important to investigate how destructive and risky behaviors (related to externalizing behaviors, substance use, and crime and violence) may increase a person's susceptibility to COVID-19. Here, we analyzed data from a survey of 366 adults across the United States (ages 18-70); this survey was administered between February and March of 2021. Participants were asked to complete the Global Appraisal of Individual Needs-Short Screener (GAIN-SS) questionnaire which indicates a history of high-risk and destructive behaviors and high likelihood of meeting diagnostic criteria. The GAIN-SS includes seven questions related to externalizing behaviors, eight related to substance use issues, and five related to crime and violence. Responses were given on a temporal scale of "0" (never a given behavior) to "4" (experienced a behavior in the past month). Participants were also asked if they ever tested positive for COVID-19 and/or if they ever received a clinical diagnosis of COVID-19. GAIN-SS responses were compared between those reporting and not reporting COVID-19 to deduce if those reporting COVID-19 experienced GAIN-SS behaviors more recently (Wilcoxon Rank Sum test, α=0.05). Three hypotheses surrounding the temporal relationships between the recency of GAIN-SS behaviors to COVID-19 infection were tested using proportions tests (α=0.05). GAIN-SS behaviors that significantly differed (proportions tests, α=0.05) between COVID-19 responses were included as independent variables in multivariable logistic regression (MVLR) models with iterative downsampling. This was performed to assess how well a history of GAIN-SS behaviors statistically discriminated between those reporting, and not reporting, COVID-19. Those reporting COVID-19 more frequently indicated past GAIN-SS behaviors (Q<.05). Further, the proportion of those reporting COVID-19 was higher (Q<.05) in those also reporting a history of GAIN-SS behaviors; specifically, gambling and selling drugs were common across the three proportion tests. MVLR revealed that GAIN-SS behaviors, particularly gambling, selling drugs, and attention problems, accurately modeled self-reported COVID-19 with model accuracies ranging from 77.42-99.55% ± 4.49-5.86%. That is, those that exhibited destructive and high-risk behaviors prior to, and during, the pandemic could be discriminated from those that did not exhibit these behaviors when modeling self-reported COVID-19. Together, this preliminary study provides insight into how a history of destructive and risky behaviors influence infection susceptibility - offering possible explanations for why some persons may be more susceptible to COVID-19, potentially in relation to reduced adherence to prevention guidelines or seeking vaccination.

COVID-19's influence on cardiac function: a machine learning perspective on ECG analysis.

In December 2019, the spread of the SARS-CoV-2 virus to the world gave rise to probably the biggest public health problem in the world: the COVID-19 pandemic. Initially seen only as a disease of the respiratory system, COVID-19 is actually a blood disease with effects on the respiratory tract. Considering its influence on hematological parameters, how does COVID-19 affect cardiac function? Is it possible to support the clinical diagnosis of COVID-19 from the automatic analysis of electrocardiography? In this work, we sought to investigate how COVID-19 affects cardiac function using a machine learning approach to analyze electrocardiography (ECG) signals. We used a public database of ECG signals expressed as photographs of printed signals, obtained in the context of emergency care. This database has signals associated with abnormal heartbeat, myocardial infarction, history of myocardial infarction, COVID-19, and healthy heartbeat. We propose a system to support the diagnosis of COVID-19 based on hybrid deep architectures composed of pre-trained convolutional neural networks for feature extraction and Random Forests for classification. We investigated the LeNet, ResNet, and VGG16 networks. The best results were obtained with the VGG16 and Random Forest network with 100 trees, with attribute selection using particle swarm optimization. The instance size has been reduced from 4096 to 773 attributes. In the validation step, we obtained an accuracy of 94%, kappa index of 0.91, and sensitivity, specificity, and area under the ROC curve of 100%. This work showed that the influence of COVID-19 on cardiac function is quite considerable: COVID-19 did not present confusion with any heart disease, nor with signs of healthy individuals. It is also possible to build a solution to support the clinical diagnosis of COVID-19 in the context of emergency care from a non-invasive and technologically scalable solution, based on hybrid deep learning architectures.

Phylogenetic Changes in SARS-CoV-2 Virus in Bosnian-Herzegovinian Population Over the Period of Two Years.

All viral genomes, including the SARS-CoV-2 virus, mutate over time, and some of these mutations can affect the characteristics of the virus, such as the ease of spread, the severity of the patient's clinical picture, or the effect of vaccines, therapeutic drugs, diagnostic tools or other measures of public health and social protection. Because of all the above, it is imperative to carry out continuous sequencing of this pathogen. The main goal of this research was to obtain the highest quality genomic sequences of the SARS-CoV-2 virus, to compare the obtained sequences with the reference Wuhan-Hu-1 sequence and to obtain a high-quality genomic alignment in order to reconstruct the appropriate phylogenetic tree. For the purposes of this research, a next-generation semiconductor sequencing method was chosen. In this research, a total of 47 samples of nasopharyngeal and oropharyngeal swabs from patients from the human population of Bosnia and Herzegovina with a clinical diagnosis of COVID-19 were collected. In the processed 47 samples, there are several monophyletic groups on the constructed phylogenetic tree, of which one sample belongs to the same monophyletic group as the Wuhan-Hu-1 reference sequence. The greater number of samples is needed for a more comprehensive approach. Therefore, the results of this research can act as a guideline for the design of effective measures and strategies in order to solve problems regarding future pandemics as efficiently as possible.

Applying fuzzy qualitative comparative analysis to identify typical symptoms of COVID-19 infection in a primary care unit, Rio de Janeiro, Brazil

This study aims to identify a set of symptoms that could be predictive of SARS-CoV-2 cases in the triage of Primary Care services with the contribution of Qualitative Comparative Analysis (QCA) using Fuzzy Sets (fsQCA). A cross-sectional study was carried out in a Primary Health Care Unit/FIOCRUZ from 09/17/2020 to 05/05/2021. The study population was suspect cases that performed diagnostic tests for COVID-19. We collected information about the symptoms to identify which configurations are associated with positive and negative cases. For analysis, we used fsQCA to explain the outcomes “being a positive case” and “not being a positive case”. The solution term “loss of taste or smell and no headache” showed the highest degree of association with the positive result (consistency = 0.81). The solution term “absence of loss of taste or smell combined with the absence of fever” showed the highest degree of association (consistency = 0,79) and is the one that proportionally best explains the negative result. Our results may be useful to the presumptive clinical diagnosis of COVID-19 in scenarios where access to diagnostic tests is not available. We used an innovative method used in complex problems in Public Health, the fsQCA.

CdTe QDs-sensitized TiO2 nanocomposite for magnetic-assisted photoelectrochemical immunoassay of SARS-CoV-2 nucleocapsid protein

A sensitive, reliable, and cost-effective detection for SARS-CoV-2 was urgently needed due to the rapid spread of COVID-19. Here, a “signal-on” magnetic-assisted PEC immunosensor was constructed for the quantitative detection of SARS-CoV-2 nucleocapsid (N) protein based on Z-scheme heterojunction. Fe3O4@SiO2@Au was used to connect the capture antibody to act as a capture probe (Fe3O4@SiO2@Au/Ab1). It can extract target analytes selectively in complex samples and multiple electrode rinsing and assembly steps were avoided effectively. CdTe QDs sensitized TiO2 coated on the surface of SiO2 spheres to form Z-scheme heterojunction (SiO2@TiO2@CdTe QDs), which broadened the optical absorption range and inhibited the quick recombination of photogenerated electron/hole of the composite. With fascinating photoelectric conversion performance, SiO2@TiO2@CdTe QDs were utilized as a signal label, thus further realizing signal amplification. The migration mechanism of photogenerated electrons was further deduced by active material quenching experiment and electron spin resonance (ESR) measurement. The elaborated immunosensor can detect SARS-CoV-2 N protein in the linear range of 0.005–50 ng mL−1 with a low detection limit of 1.8 pg mL−1 (S/N = 3). The immunosensor displays extraordinary sensitivity, strong anti-interference, and high reproducibility in detecting SARS-CoV-2 N protein, which envisages its potential application in the clinical diagnosis of COVID-19.

Social determinants of health associated with COVID-19 severity during pregnancy: a multinational cohort study (in the International Registry of Coronavirus Exposure in Pregnancy)

BackgroundThe COVID-19 pandemic has caused morbidity and mortality, particularly among vulnerable populations. We aimed to assess social and demographic characteristics associated with COVID-19 severity among symptomatic participants during pregnancy.MethodsThe International Registry of Coronavirus Exposure in Pregnancy is a multinational, longitudinal observational cohort study of adult participants tested for SARS-CoV-2 or who received clinical diagnosis of COVID-19 during pregnancy (NCT04366986). Disease severity status of mild, moderate, or severe was determined based on symptoms and healthcare utilization. Stratified by current versus recent pregnancy at enrollment, univariate mixed-effects logistic regression modeling was used to characterize association between social and demographic characteristics with COVID-19 severity, using a cumulative mixed effect model with country as a random effect.ResultsThe odds of developing more severe COVID-19 (odds ratio [95% confidence interval]) were higher among participants with lower socioeconomic status (poor: 2.72 [2.01,3.69]; lower-middle class: 2.07 [1.62,2.65] vs wealthy), among participants with lower educational attainment (high school: 1.68 [1.39,2.03]; < high school (1.77 [1.25,2.51] vs graduate education). Participants over 25 years of age had lower odds of severe COVID-19 versus participants < 25 years (25–34: 0.69 [0.56,0.85]; 35–50: 0.62 [0.48,0.80]). Employment in food services was also associated with increased odds of more severe COVID-19, whereas employment in healthcare and within home, and primiparity were associated with lower severity.ConclusionsFindings suggest that employment setting and economic status have strong associations with COVID-19 severity, which warrants considering social determinants of health in the context of assessing risk factors of more severe COVID-19 during pregnancy.Trial registrationIRCEP was registered with the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP) [EUPAS37360] and clinicaltrials.gov [NCT04366986].

Long-term validation of a reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay for the rapid detection of SARS-CoV-2 from March 2020 to October 2021 in Central Africa, Gabon.

Despite the development of several methods for diagnosing COVID-19, long-term validation of such methods remains limited. In the early phase of the COVID-19 pandemic, we developed a rapid and sensitive diagnostic method based on reverse transcription loop-mediated isothermal amplification (RT-LAMP) methodology, which is suitable for point-of-care application or for use in resource-limited settings to detect SARS-CoV-2. To assess the applicability of the RT-LAMP assay technique to resource-limited regions, such as rural areas in Africa, and to verify the usability of the method against various SARS-CoV-2 variants, the method was validated using clinical samples collected longitudinally during the pandemic. First, the sensitivity of the RT-LAMP assay for detecting 10 SARS-CoV-2 variants was evaluated using viral RNA samples extracted from cell culture with a portable battery-supported device, resulting in the successful detection of 20-50 copies of the viral genome within 15 min, regardless of the variant. COVID-19 positive samples collected in Gabon between March 2020 and October 2021 were used to evaluate the sensitivity of the assay and to calculate the copy number of the SARS-CoV-2 genome. More than 292 copies of the viral genome were detected with 100% probability within 15 min in almost all tests. This long-term validation study clearly demonstrated the applicability of the RT-LAMP assay for the clinical diagnosis of COVID-19 in resource-limited settings of Africa, such as rural areas in Gabon. The results show the potential of the assay as a promising COVID-19 diagnostic method, especially in rural and remote regions located far from the official diagnosis facilities in urban or semi-urban areas.

Identifying MicroRNA Markers That Predict COVID-19 Severity Using Machine Learning Methods.

Individuals with the SARS-CoV-2 infection may experience a wide range of symptoms, from being asymptomatic to having a mild fever and cough to a severe respiratory impairment that results in death. MicroRNA (miRNA), which plays a role in the antiviral effects of SARS-CoV-2 infection, has the potential to be used as a novel marker to distinguish between patients who have various COVID-19 clinical severities. In the current study, the existing blood expression profiles reported in two previous studies were combined for deep analyses. The final profiles contained 1444 miRNAs in 375 patients from six categories, which were as follows: 30 patients with mild COVID-19 symptoms, 81 patients with moderate COVID-19 symptoms, 30 non-COVID-19 patients with mild symptoms, 137 patients with severe COVID-19 symptoms, 31 non-COVID-19 patients with severe symptoms, and 66 healthy controls. An efficient computational framework containing four feature selection methods (LASSO, LightGBM, MCFS, and mRMR) and four classification algorithms (DT, KNN, RF, and SVM) was designed to screen clinical miRNA markers, and a high-precision RF model with a 0.780 weighted F1 was constructed. Some miRNAs, including miR-24-3p, whose differential expression was discovered in patients with acute lung injury complications brought on by severe COVID-19, and miR-148a-3p, differentially expressed against SARS-CoV-2 structural proteins, were identified, thereby suggesting the effectiveness and accuracy of our framework. Meanwhile, we extracted classification rules based on the DT model for the quantitative representation of the role of miRNA expression in differentiating COVID-19 patients with different severities. The search for novel biomarkers that could predict the severity of the disease could aid in the clinical diagnosis of COVID-19 and in exploring the specific mechanisms of the complications caused by SARS-CoV-2 infection. Moreover, new therapeutic targets for the disease may be found.

Comparison of clinical characteristics and outcome measures of PCR-positive and PCR-negative patients diagnosed as COVID-19: Analyses focusing on the older adults

PurposeWhile the definitive diagnosis of COVID-19 relies on PCR confirmation of the virus, the sensitivity of this technique is limited. The clinicians had to go on with the clinical diagnosis of COVID-19 in selected cases. We aimed to compare PCR-positive and PCR-negative patients diagnosed as COVID-19 with a specific focus on older adults. MethodsWe studied 601 hospitalized adults. The demographics, co-morbidities, triage clinical, laboratory characteristics, and outcomes were noted. Differences between the PCR (+) and (−) cases were analyzed. An additional specific analysis focusing on older adults (≥65 years) (n = 184) was performed. ResultsThe PCR confirmation was present in 359 (59.7 %). There was not any difference in terms of age, sex, travel/contact history, hospitalization duration, ICU need, the time between first symptom/hospitalization to ICU need, ICU days, or survival between PCR-positive and negative cases in the total study group and older adults subgroup. The only symptoms that were different in prevalence between PCR-confirmed and unconfirmed cases were fever (73.3 % vs. 64 %, p = 0.02) and fatigue/myalgia (91.1 % vs. 79.3 %, p = 0.001). Bilateral diffuse pneumonia was also more prevalent in PCR-confirmed cases (20 % vs. 13.3 %, p = 0.03). In older adults, the PCR (−) cases had more prevalent dyspnea (72.2 % vs. 51.4 %, p = 0.004), less prevalent fatigue/myalgia (70.9 % vs. 88.6 %, p = 0.002). ConclusionThe PCR (+) and (−) cases displayed very similar disease phenotypes, courses, and outcomes with few differences between each other. The presence of some worse laboratory findings may indicate a worse immune protective response in PCR (−) cases.

Likelihood of COVID-19 reinfection in an urban community cohort in Massachusetts.

BackgroundUnderstanding the association of prior SARS-CoV-2 infection with subsequent reinfection has public health relevance.ObjectiveTo explore COVID-19 severity and SARS-CoV-2 infection and reinfection rates.DesignRetrospective cohort study.SettingBoston, Massachusetts, during the first COVID-19 surge (01/01/2020–05/31/2020; Period-1) and after the first surge (06/01/2020–02/28/2021; Period-2); Period-2 included the second surge (11/01/2020–02/28/2021).ParticipantsPatients in an academic medical center and six community health centers who received a clinical diagnosis of COVID-19 between 01/01/2020 and 05/31/2020 or SARS-CoV-2 testing between 01/01/2020 and 02/28/2021.MeasurementsCOVID-19 severity was compared between Period-1 and Period-2. Poisson regression models adjusted for demographic variables, medical comorbidities, and census tract were used to assess reinfection risk among patients with COVID-19 diagnoses or SARS-CoV-2 testing during Period-1 and additional SARS-CoV-2 testing during Period-2.ResultsAmong 142,047 individuals receiving SARS-CoV-2 testing or clinical diagnoses during the study period, 15.8% were infected. Among COVID-19 patients, 22.5% visited the emergency department, 13% were hospitalized, and 4% received critical care. Healthcare utilization was higher during Period-1 than Period-2 (22.9% vs. 18.9% emergency department use, 14.7% vs. 9.9% hospitalization, 5.5% vs. 2.5% critical care; p < 0.001). Reinfection was assessed among 8961 patients with a SARS-CoV-2 test or COVID-19 diagnosis in Period-1 who underwent additional testing in Period-2. A total of 2.7% (n = 65/2431) with SARS-CoV-2 in Period-1 tested positive in Period-2, compared with 12.6% (n = 821/6530) of those who initially tested negative (IRR of reinfection = 0.19, 95% CI: 0.15–0.25).ConclusionsPrior SARS-CoV-2 infection among this observational cohort was associated with an 81% lower reinfection rate.