Diagnosis and main types of coronavirus in humans: a narrative review / Uma análise dos principais diagnósticos de Sars-cov-2 disponíveis no Brasil: uma revisão de literatura / Diagnóstico y principales tipos de coronavirus en humanos: una revisión narrativa

Introdução: Com a emergência do SARS-CoV-2 foi disponibilizado uma grande quantidade de ferramentas de diagnóstico. Neste contexto, a falta de vacina, de tratamento e o grande número de casos graves e morte, possibilitou a aprovação emergencial de diversos testes, que ainda necessitam de estudos populacionais para seu registro definitivo. Objetivo: Realizar uma revisão de literatura para avaliar as metodologias de diagnóstico disponíveis no Brasil, de acordo com a realidade local de saúde, explorando o momento epidemiológico a complexidade do teste e a finalidade da sua aplicação. Metodologia: Trata-se de um estudo bibliográfico, descritivo do tipo revisão de literatura. Foram utilizadas as seguintes bases de dados científicos para buscas: PUBMED, MEDLINE, LILACS E COCHRANE LIBRARY, através de descritores selecionados na plataforma DECS. Resultados: O cenário de diversos ensaios, baseados em diferentes metodologias, como os testes baseados em RNA viral, em detecção de antígenos virais ou de anticorpos, associados ao conhecimento da história natural do vírus, possibilita uma análise crítica do melhor diagnóstico de acordo com a clínica do paciente, os epidemiológicos, o objetivo do diagnóstico e a acurácia do ensaio. Atualmente, há mudança no padrão imunológico da população e a descrição de tipos e subtipos de SARS-CoV-2 com mudanças gênicas, que podem levar a mudanças na acurácia diagnóstica ou a re-emergência em surtos de doença grave. Conclusão: Ainda é incerto o caminho evolutivo da história natural da Covid-19 e os ensaios diagnósticos estão em diferentes estágios de desenvolvimento, validação e produção e cada tipo de teste tem suas próprias vantagens e desvantagens distintas inerentes a plataforma tecnológica de origem e uma combinação de tipos de testes usados em momentos diferentes pode ser útil para a condução clínica dos pacientes e no controle da pandemia por SARS-CoV-2.

Introduction: With the emergence of SARS-CoV-2, a large number of diagnostic tools were made available. In this context, the lack of vaccine, treatment and the large number of severe cases and death, allowed the emergency approval of several tests, which still require population studies for their definitive registration. Objective: To carry out a literature review to evaluate the diagnostic methodologies available in Brazil, according to the local health reality, exploring the epidemiological moment, the complexity of the test and the purpose of its application. Methodology: This is a bibliographic, descriptive study of the literature review type. The following scientific databases were used for searches: PUBMED, MEDLINE, LILACS AND COCHRANE LIBRARY, through selected descriptors on the DECS platform. Results: The scenario of several tests, based on different methodologies, such as tests based on viral RNA, on detection of viral antigens or antibodies, associated with knowledge of the natural history of the virus, allows a critical analysis of the best diagnosis according to the patient's clinical, epidemiological, diagnostic objective and assay accuracy. Currently, there is a change in the immune pattern of the population and the description of types and subtypes of SARS-CoV-2 with genetic changes, which can lead to changes in diagnostic accuracy or the re-emergence in outbreaks of severe disease. Conclusion: The evolutionary path of the natural history of Covid-19 is still uncertain and diagnostic assays are at different stages of development, validation and production and each type of test has its own distinct advantages and disadvantages inherent in the technology platform of origin and a combination of types of tests used at different times can be useful for the clinical management of patients and in the control of the SARS-CoV-2 pandemic.

Introducción: Con la aparición del SARS-CoV-2, se dispuso de un gran número de herramientas diagnósticas. En este contexto, la falta de vacuna, tratamiento y el gran número de casos graves y muerte, permitieron la aprobación de urgencia de varias pruebas, que aún requieren estudios poblacionales para su registro definitivo. Objetivo: Realizar una revisión bibliográfica para evaluar las metodologías diagnósticas disponibles en Brasil, de acuerdo con la realidad sanitaria local, explorando el momento epidemiológico, la complejidad de la prueba y la finalidad de su aplicación. Metodología: Se trata de un estudio bibliográfico, descriptivo, del tipo revisión de literatura. Para las búsquedas se utilizaron las siguientes bases de datos científicas PUBMED, MEDLINE, LILACS Y COCHRANE LIBRARY, a través de descriptores seleccionados en la plataforma DECS. Resultados: El escenario de varias pruebas, basadas en diferentes metodologías, como pruebas basadas en el ARN viral, en la detección de antígenos virales o anticuerpos, asociado al conocimiento de la historia natural del virus, permite un análisis crítico del mejor diagnóstico de acuerdo con la clínica del paciente, epidemiológica, objetivo diagnóstico y precisión de la prueba. Actualmente, hay un cambio en el patrón inmunológico de la población y la descripción de tipos y subtipos de SARS-CoV-2 con cambios genéticos, que pueden conducir a cambios en la precisión diagnóstica o la reaparición en brotes de enfermedad grave. Conclusiones: El camino evolutivo de la historia natural del Covid-19 es aún incierto y los ensayos de diagnóstico se encuentran en diferentes etapas de desarrollo, validación y producción y cada tipo de prueba tiene sus propias ventajas y desventajas distintas inherentes a la plataforma tecnológica de origen y una combinación de tipos de pruebas utilizadas en diferentes momentos puede ser útil para el manejo clínico de los pacientes y en el control de la pandemia de SARS- CoV-2.

A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics.

During the diagnostic process, clinicians leverage multimodal information, such as the chief complaint, medical images and laboratory test results. Deep-learning models for aiding diagnosis have yet to meet this requirement of leveraging multimodal information. Here we report a transformer-based representation-learning model as a clinical diagnostic aid that processes multimodal input in a unified manner. Rather than learning modality-specific features, the model leverages embedding layers to convert images and unstructured and structured text into visual tokens and text tokens, and uses bidirectional blocks with intramodal and intermodal attention to learn holistic representations of radiographs, the unstructured chief complaint and clinical history, and structured clinical information such as laboratory test results and patient demographic information. The unified model outperformed an image-only model and non-unified multimodal diagnosis models in the identification of pulmonary disease (by 12% and 9%, respectively) and in the prediction of adverse clinical outcomes in patients with COVID-19 (by 29% and 7%, respectively). Unified multimodal transformer-based models may help streamline the triaging of patients and facilitate the clinical decision-making process.

Identification of 3 key genes as novel diagnostic and therapeutic targets for OA and COVID-19.

Background: Corona Virus Disease 2019 (COVID-19) and Osteoarthritis (OA) are diseases that seriously affect the physical and mental health and life quality of patients, particularly elderly patients. However, the association between COVID-19 and osteoarthritis at the genetic level has not been investigated. This study is intended to analyze the pathogenesis shared by OA and COVID-19 and to identify drugs that could be used to treat SARS-CoV-2-infected OA patients. Methods: The four datasets of OA and COVID-19 (GSE114007, GSE55235, GSE147507, and GSE17111) used for the analysis in this paper were obtained from the GEO database. Common genes of OA and COVID-19 were identified through Weighted Gene Co-Expression Network Analysis (WGCNA) and differential gene expression analysis. The least absolute shrinkage and selection operator (LASSO) algorithm was used to screen key genes, which were analyzed for expression patterns by single-cell analysis. Finally, drug prediction and molecular docking were carried out using the Drug Signatures Database (DSigDB) and AutoDockTools. Results: Firstly, WGCNA identified a total of 26 genes common between OA and COVID-19, and functional analysis of the common genes revealed the common pathological processes and molecular changes between OA and COVID-19 are mainly related to immune dysfunction. In addition, we screened 3 key genes, DDIT3, MAFF, and PNRC1, and uncovered that key genes are possibly involved in the pathogenesis of OA and COVID-19 through high expression in neutrophils. Finally, we established a regulatory network of common genes between OA and COVID-19, and the free energy of binding estimation was used to identify suitable medicines for the treatment of OA patients infected with SARS-CoV-2. Conclusion: In the present study, we succeeded in identifying 3 key genes, DDIT3, MAFF, and PNRC1, which are possibly involved in the development of both OA and COVID-19 and have high diagnostic value for OA and COVID-19. In addition, niclosamide, ciclopirox, and ticlopidine were found to be potentially useful for the treatment of OA patients infected with SARS-CoV-2.

Diagnostic value and characteristic analysis of serum nucleocapsid antigen in COVID-19 patients.

Background: To date, several types of laboratory tests for coronavirus disease 2019 (COVID-19) diagnosis have been developed. However, the clinical importance of serum severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid antigen (N-Ag) remains to be fully elucidated. In this study, we sought to investigate the value of serum SARS-CoV-2 N-Ag for COVID-19 diagnosis and to analyze N-Ag characteristics in COVID-19 individuals. Methods: Serum samples collected from 215 COVID-19 patients and 65 non-COVID-19 individuals were used to quantitatively detect N-Ag via chemiluminescent immunoassay according to the manufacturer's instructions. Results: The sensitivity and specificity of the N-Ag assay were 64.75% (95% confidence interval (95% CI) [55.94-72.66%]) and 100% (95% CI [93.05-100.00%]), respectively, according to the cut-off value recommended by the manufacturer. The receiver operating characteristic (ROC) curve showed a sensitivity of 100.00% (95% CI [94.42-100.00%]) and a specificity of 71.31% (95% CI [62.73-78.59%]). The positive rates and levels of serum SARS-CoV-2 N-Ag were not related to sex, comorbidity status or disease severity of COVID-19 (all P < 0.001). Compared with RT‒PCR, there was a lower positive rate of serum N-Ag for acute COVID-19 patients (P < 0.001). The positive rate and levels of serum SARS-CoV-2 N-Ag in acute patients were significantly higher than those in convalescent patients (all P < 0.001). In addition, the positive rate of serum SARS-CoV-2 N-Ag in acute COVID-19 patients was higher than that of serum antibodies (IgM, IgG, IgA and neutralizing antibodies (Nab)) against SARS-CoV-2 (all P < 0.001). However, the positive rate of serum SARS-CoV-2 N-Ag in convalescent COVID-19 patients was significantly lower than that of antibodies (all P < 0.001). Conclusion: Serum N-Ag can be used as a biomarker for early COVID-19 diagnosis based on appropriate cut-off values. In addition, our study also demonstrated the relationship between serum N-Ag and clinical characteristics.

COVID-19-Related Testing, Knowledge and Behaviors among Severe and Chronic Non-Communicable Disease Patients in Neno District, Malawi: A Prospective Cohort Study.

COVID-19-related knowledge and behaviors remain essential for controlling the spread of disease, especially among vulnerable patients with advanced, chronic diseases. We prospectively assessed changes over 11 months in COVID-19-related testing, knowledge, and behaviors among patients with non-communicable diseases in rural Malawi using four rounds of telephone interviews between November 2020 to October 2021. The most commonly reported COVID-19-related risks among patients included visiting health facilities (35-49%), attending mass gatherings (33-36%), and travelling outside the district (14-19%). Patients reporting having experienced COVID-like symptoms increased from 30% in December 2020 to 41% in October 2021. However, only 13% of patients had ever received a COVID-19 test by the end of the study period. Respondents answered 67-70% of the COVID-19 knowledge questions correctly, with no significant changes over time. Hand washing, wearing face masks and maintaining a safe distance were the most frequently reported strategies to prevent the spreading of COVID-19. Wearing face masks significantly improved over time (p < 0.001). Although the majority reported accurate knowledge about COVID-19 and enhanced adherence to infection prevention measures over time, patients commonly visited locations where they could be exposed to COVID-19. Government and other stakeholders should increase COVID-19 testing accessibility to primary and secondary facilities.

Discovery of Peptidic Ligands against the SARS-CoV-2 Spike Protein and Their Use in the Development of a Highly Sensitive Personal Use Colorimetric COVID-19 Biosensor.

In addition to efficacious vaccines and antiviral therapeutics, reliable and flexible in-home personal use diagnostics for the detection of viral antigens are needed for effective control of the COVID-19 pandemic. Despite the approval of several PCR-based and affinity-based in-home COVID-19 testing kits, many of them suffer from problems such as a high false-negative rate, long waiting time, and short storage period. Using the enabling one-bead-one-compound (OBOC) combinatorial technology, several peptidic ligands with a nanomolar binding affinity toward the SARS-CoV-2 spike protein (S-protein) were successfully discovered. Taking advantage of the high surface area of porous nanofibers, immobilization of these ligands on nanofibrous membranes allows the development of personal use sensors that can achieve low nanomolar sensitivity in the detection of the S-protein in saliva. This simple biosensor employing naked-eye reading exhibits detection sensitivity comparable to some of the current FDA-approved home detection kits. Furthermore, the ligand used in the biosensor was found to detect the S-protein derived from both the original strain and the Delta variant. The workflow reported here may enable us to rapidly respond to the development of home-based biosensors against future viral outbreaks.

Evaluation of SARS-CoV-2 isolation in cell culture from nasal/nasopharyngeal swabs or saliva specimens of patients with COVID-19.

It has been revealed that SARS-CoV-2 can be efficiently isolated from clinical specimens such as nasal/nasopharyngeal swabs or saliva in cultured cells. In this study, we examined the efficiency of viral isolation including SARS-CoV-2 mutant strains between nasal/nasopharyngeal swab or saliva specimens. Furthermore, we also examined the comparison of viral isolation rates by sample species using simulated specimens for COVID-19. As a result, it was found that the isolation efficiency of SARS-CoV-2 in the saliva specimens was significantly lower than that in the nasal/nasopharyngeal swab specimens. In order to determine which component of saliva is responsible for the lower isolation rate of saliva specimens, we tested the abilities of lactoferrin, amylase, cathelicidin, and mucin, which are considered to be abundant in saliva, to inhibit the infection of SARS-CoV-2 pseudotyped viruses (SARS-CoV-2pv). Lactoferrin and amylase were found to inhibit SARS-CoV-2pv infection. In conclusion, even if the same number of viral genome copies was detected by the real-time RT-PCR test, infection of SARS-CoV-2 present in saliva is thought to be inhibited by inhibitory factors such as lactoferrin and amylase, compared to nasal/nasopharyngeal swab specimens.

Clinical features, diagnosis, and management of COVID-19 vaccine-associated Vogt-Koyanagi-Harada disease.

Vogt-Koyanagi-Harada (VKH) disease is a rare and serious ocular adverse reaction following COVID-19 vaccination. This study aimed to evaluate the clinical features, diagnosis and management of COVID-19 vaccine-associated VKH disease. Case reports of VKH disease after COVID-19 vaccination were collected up to February 11, 2023 for retrospective analysis. Twenty-one patients (9 males and 12 females) were included, with a median age of 45 years (range 19-78), from three main regions, Asia (12/21), the Mediterranean region (4/21), and South America (5/21). Fourteen patients developed symptoms after the first dose of the vaccine, and 8 after the second dose. Vaccines included mRNA vaccine (10 cases), virus vector vaccine (6 cases), and inactivated vaccine (5 cases). The average time interval from vaccination to onset of symptoms was 7.5 days (range 12 hours to 4 weeks). All 21 patients experienced visual impairment after vaccination, with 20 cases involving both eyes. Sixteen patients showed symptoms of meningitis. Serous retinal detachment was observed in 16 patients, choroidal thickening was observed in 14, aqueous cell in 9, and subretinal fluid in 6. CSF pleocytosis was detected in 7 patients and skin symptoms were found in 3 patients. All patients received corticosteroid therapy, and 8 also received immunosuppressive agents. All patients recovered well, with a mean recovery time of 2 months. Early diagnosis and early treatment are crucial to the prognosis of patients with VKH after vaccination with COVID-19 vaccine. The risk of vaccination against COVID-19 in patients with a history of VKH disease should be evaluated clinically.

Recent COVID-19 infection is associated with increased mortality in the ambulatory surgery population.

BACKGROUND: The effect of COVID-19 infection on post-operative mortality and the optimal timing to perform ambulatory surgery from diagnosis date remains unclear in this population. Our study was to determine whether a history of COVID-19 diagnosis leads to a higher risk of all-cause mortality following ambulatory surgery. METHODS: This cohort constitutes retrospective data obtained from the Optum dataset containing 44,976 US adults who were tested for COVID-19 up to 6 months before surgery and underwent ambulatory surgery between March 2020 to March 2021. The primary outcome was the risk of all-cause mortality between the COVID-19 positive and negative patients grouped according to the time interval from COVID-19 testing to ambulatory surgery, called the Testing to Surgery Interval Mortality (TSIM) of up to 6 months. Secondary outcome included determining all-cause mortality (TSIM) in time intervals of 0-15 days, 16-30 days, 31-45 days, and 46-180 days in COVID-19 positive and negative patients. RESULTS: 44,934 patients (4297 COVID-19 positive, 40,637 COVID-19 negative) were included in our analysis. COVID-19 positive patients undergoing ambulatory surgery had higher risk of all-cause mortality compared to COVID-19 negative patients (OR = 2.51, p < 0.001). The increased risk of mortality in COVID-19 positive patients remained high amongst patients who had surgery 0-45 days from date of COVID-19 testing. In addition, COVID-19 positive patients who underwent colonoscopy (OR = 0.21, p = 0.01) and plastic and orthopedic surgery (OR = 0.27, p = 0.01) had lower mortality than those underwent other surgeries. CONCLUSIONS: A COVID-19 positive diagnosis is associated with significantly higher risk of all-cause mortality following ambulatory surgery. This mortality risk is greatest in patients that undergo ambulatory surgery within 45 days of testing positive for COVID-19. Postponing elective ambulatory surgeries in patients that test positive for COVID-19 infection within 45 days of surgery date should be considered, although prospective studies are needed to assess this.

Clinical and inflammation marker features of cancer patients with COVID-19: data of Istanbul, Turkey multicenter cancer patients (2020-2022).

OBJECTIVE: We aimed to identify a rapid, accurate, and accessible biomarker in the early stages of COVID-19 that can determine the prognosis of the disease in cancer patients. METHODS: A total number of 241 patients with solid cancers who had a COVID-19 diagnosis between March 2020 and February 2022 were included in the study. Factors and ten different markers of inflammation were analyzed by year of diagnosis of COVID-19 and grouped by severity of infection. RESULTS: Hospitalization, referral to the intensive care unit (ICU), mechanical ventilation, and death were more frequent in 2020 than in 2021 and 2022 (mortality rates, respectively, were 18.8%, 3.8%, and 2.5%). Bilateral lung involvement and chronic lung disease were independent risk factors for severe disease in 2020. In 2021-2022, only bilateral lung involvement was found as an independent risk factor for severe disease. The neutrophil-to-lymphocyte platelet ratio (NLPR) with the highest area under the curve (AUC) value in 2020 had a sensitivity of 71.4% and specificity of 73.3% in detecting severe disease (cut-off > 0.0241, Area Under the Curve (AUC) = 0.842, p <.001). In 2021-2022, the sensitivity of the C-reactive protein-to-lymphocyte ratio (CRP/L) with the highest AUC value was 70.0%, and the specificity was 73.3% (cut-off > 36.7, AUC = 0.829, p = .001). CONCLUSIONS: This is the first study to investigate the distribution and characteristics of cancer patients, with a focus on the years of their COVID-19 diagnosis. Based on the data from our study, bilateral lung involvement is an independent factor for severe disease, and the CRP/L inflammation index appears to be the most reliable prognostic marker.

Purification/Amplification-Free RNA Detection Platform for Rapid and Multiplex Diagnosis of Plant Viral Infections.

Genetic tests are highly sensitive, and quantitative methods for diagnosing human viral infections, including COVID-19, are also being used to diagnose plant diseases in various agricultural settings. Conventional genetic tests for plant viruses are mostly based on methods that require purification and amplification of viral genomes from plant samples, which generally take several hours in total, making it difficult to use them in rapid detection at point-of-care testing (POCT). In this study, we developed Direct-SATORI, a rapid and robust genetic test that eliminates the purification and amplification processes of viral genomes by extending the recently developed amplification-free digital RNA detection platform called SATORI, allowing the detection of various plant viral genes in a total of less than 15 min with a limit of detection (LoD) of 98 ∼ copies/µL using tomato viruses as an example. In addition, the platform can simultaneously detect eight plant viruses directly from ∼1 mg of tomato leaves with a sensitivity of 96% and a specificity of 99%. Direct-SATORI can be applied to various infections related to RNA viruses, and its practical use is highly anticipated as a versatile platform for plant disease diagnostics in the future.

Machine learning and artificial intelligence for the diagnosis of infectious diseases in immunocompromised patients.

PURPOSE OF REVIEW: Immunocompromised patients are at high risk for infection. During the coronavirus disease (COVID-19) pandemic, immunocompromised patients exhibited increased odds of intensive care unit admission and death. Early pathogen identification is essential to mitigating infection related risk in immunocompromised patients. Artificial intelligence (AI) and machine learning (ML) have tremendous appeal to address unmet diagnostic needs. These AI/ML tools often rely on the wealth of data found in healthcare to enhance our ability to identify clinically significant patterns of disease. To this end, our review provides an overview of the current AI/ML landscape as it applies to infectious disease testing with emphasis on immunocompromised patients. RECENT FINDINGS: Examples include AI/ML for predicting sepsis in high risk burn patients. Likewise, ML is utilized to analyze complex host-response proteomic data to predict respiratory infections including COVID-19. These same approaches have also been applied for pathogen identification of bacteria, viruses, and hard to detect fungal microbes. Future uses of AI/ML may include integration of predictive analytics in point-of-care (POC) testing and data fusion applications. SUMMARY: Immunocompromised patients are at high risk for infections. AI/ML is transforming infectious disease testing and has great potential to address challenges encountered in the immune compromised population.