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Ninety-six million people are symptomatically infected with Dengue globally every year. Under the current standard-of-care, up to 20% of Dengue patients may be hospitalized, while only 500,000 develop Dengue Haemorrhagic Fever (DHF) and require hospitalization. This leads to unnecessary overwhelming of hospitals in tropical countries during large Dengue epidemics, especially when healthcare systems are grappling with large numbers of COVID-19 patients. Our research team set out to discover biomarkers to prognosticate Dengue patients, and augment the infectious disease clinician's decision-making process to hospitalize Dengue patients. Host biomarkers with concentrations significantly different between pooled serum samples of Dengue Fever (DF) patients and DHF patients were identified using protein array. The prognostication capabilities of selected biomarkers were then validated over 283 adult Dengue patients recruited from three Singapore tertiary hospitals, prior to the diagnosis of DHF. Three biomarkers (A2M, CMA1 and VEGFA) were identified that provide independent prognostication value from one another, and from clinical parameters commonly monitored in Dengue patients. The combination of all three biomarkers was able to identify from as early as Day 1 after the onset of fever, DF patients whose conditions will deteriorate into DHF. The biomarkers are robust and able to predict DHF well when trained on different AI/ML algorithms (logistic regression, support vector machine, decision tree, random forest, AdaBoost and gradient boosting). When stacked, prediction models based on the biomarkers were able to predict DHF with 97.3% sensitivity, 92.7% specificity, 66.7% PPV, 99.6% NPV and an AUC of 0.978. To the best of our knowledge, our panel of three biomarkers offers the highest accuracy in prognosticating Dengue to date. Further studies are required to validate the biomarkers in different geographical settings and pilot their implementation as part of the standard-of-care workflow for Dengue patients. [ FROM AUTHOR] Copyright of International Journal of Infectious Diseases is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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Intro: In Australia, the main methods to diagnose COVID-19 are through rapid antigen tests (RATs) and through nucleic acid amplification testing (NAAT, including polymerase chain reaction) on healthcare worker (HCW)-collected combined nose/throat swabs. With self-collection widely used by the public for RATs, the aim of this study was to evaluate the performance of self-collected samples using commercial NAAT for SARS-CoV-2. Method(s): Consenting participants aged 14 years and older were provided with a self-collection pack containing instructions and either a FLOQSwab (Copan) or a Rhinoswab (Rhinomed). Participants collected their own nasal sample unsupervised prior to having a HCW-collected combined nose and throat swab taken for standard of care NAAT. Paired self-collected and HCW samples were tested on the cobas SARS-CoV-2 assay (Roche) and the Aptima SARS-CoV-2 assay (Hologic). Finding(s): We demonstrated comparable sensitivity, specificity, and agreement between self-collected nasal and Rhinoswab samples, compared to HCW- collected samples tested using the cobas SARS-CoV-2 and Aptima SARS-CoV-2 assays. In our study the clinical performance of self-collected specimens was comparable to HCW-collected samples, with both self-collect nasal and Rhinoswab samples resulting in 90-95% sensitivity, and in most cases >95% specificity. Discussion(s): Without the availability of samples for NAAT the ability to perform genomic testing is limited, reducing surveillance and public health investigations. We showed that genomic sequencing from self-collected samples can correctly identify the virus lineage and that the main determination of successful genomic testing is a high viral load rather than collection method. Conclusion(s): These data support self-collection as an accessible method for community testing for COVID-19 and introduces a novel collection device, the Rhinoswab as an alternative to the standard nasal swab. The testing method of self-collection can be expanded from the widely used RATs to NAAT and genomic testing which may inform the management and public health response to the COVID-19 pandemic.Copyright © 2023
ABSTRACT
Background and Objectives: Due to the high prevalence of COVID-19 disease and its high mortality rate, it is necessary to identify the symptoms, demographic information and underlying diseases that effectively predict COVID-19 death. Therefore, in this study, we aimed to predict the mortality behavior due to COVID-19 in Khorasan Razavi province. Method(s): This study collected data from 51, 460 patients admitted to the hospitals of Khorasan Razavi province from 25 March 2017 to 12 September 2014. Logistic regression and Neural network methods, including machine learning methods, were used to identify survivors and non-survivors caused by COVID-19. Result(s): Decreased consciousness, cough, PO2 level less than 93%, age, cancer, chronic kidney diseases, fever, headache, smoking status, and chronic blood diseases are the most important predictors of death. The accuracy of the artificial neural network model was 89.90% in the test phase. Also, the sensitivity, specificity and area under the rock curve in this model are equal to 76.14%, 91.99% and 77.65%, respectively. Conclusion(s): Our findings highlight the importance of some demographic information, underlying diseases, and clinical signs in predicting survivors and non-survivors of COVID-19. Also, the neural network model provided high accuracy in prediction. However, medical research in this field will lead to complementary results by using other methods of machine learning and their high power.Copyright © 2022 The Authors.
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The analyses of effectiveness of medical means of protection based on virus specific antibodies, intended for special prophylactic and current of COVID-19 is conducted. The plasma of patients, obtained from the blood takes the leading part among these prepares. It is concluded, that convalescents plasma, containing virus neutralizing antibodies, may be used for emergency prevention or in the early stages of the disease. A risk group, that primarily needs in such drugs for special prophylactics, is medical workers. The other prepares, based on virus specific antibodies, including purified prepares of monoclonal antibodies, that may have certain advantages to convalescent's plasma due to their safety and high activity, due to complexity of their production and presumably high cost are unlikely to be available in the near future for mass use in the practice of medicine. The use of convalescents plasma for the prevention and treatment of COVID-19.can be based on the experience of their application in specialized medical centers and summarizing data from randomized clinical trials.Copyright © 2021 Moscow State University of Psychology and Education. All right reserved.
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COVID-19 is characterized by predominant respiratory and gastrointestinal symptoms. Liver enzymes derangement is seen in 15-55% of the patients. Cirrhosis is characterized by immune dysregulation, leading to concerns that these patients may be at increased risk of complications following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Patients with metabolic dysfunction-associated fatty liver (MAFLD) had shown a 4-sixfold increase in severity of COVID-19, and its severity and mortality increased in patients with higher fibrosis scores. Patients with chronic liver disease had shown that cirrhosis is an independent predictor of severity of COVID-19 with increased hospitalization and mortality. An international European registry study included 756 patients with chronic liver disease from 29 countries reports high mortality in patients with cirrhosis (32%). Data of 228 patients collected from 13 Asian countries on patients with CLD, known or newly diagnosed, with confirmed COVID-19 (APCOLIS study) showed that SARSCoV- 2 infection produces acute liver injury in 43% of CLD patients without cirrhosis. Additionally, 20% of compensated cirrhosis patients develop either ACLF or acute decompensation. In decompensated cirrhotics, the liver injury was progressive in 57% of patients, with 43% mortality. Patients with CLD and associated diabetes and obesity had a worse outcome. Liver related complications were seen in nearly half of the decompensated cirrhotics, which were of greater severity and with higher mortality. Increase in Child Turcotte Pugh (CTP) score and model for end-stage liver disease (MELD) score increases the mortality in these patients. In a subsequent study of 532 patients from 17 Asian countries was obtained with 121 cases of cirrhosis. An APCOLIS risk score was developed, which included presence of comorbidity, low platelet count, AKI, HE and respiratory failure predicts poor outcome and an APCOLIS score of 34 gave a sensitivity and specificity of 79.3%, PPV of 54.8% and NPV of 92.4% and predicted higher mortality (54.8% vs 7.6%, OR = 14.3 [95 CI 5.3-41.2], p<0.001) in cirrhosis patients with Covid-19. The APCOLIS score is helpful in triaging and prognostication of cirrhotics with Coivd-19. The impact of COVID-19 on patients with cirrhosis due to non-alcoholic fatty liver disease (NASH-CLD) was separately studied in 177 NASH-CLD patients. Obese patients with diabetes and hypertension had a higher prevalence of symptomatic COVID. Presence of diabetes [HR 2.27], fraility [HR 2.68], leucocyte counts [HR 1.69] and COVID-19 were independent predictors of worsening liver functions in patients with NASH-CLD. Severity of Covid in Cirrhosis could also be assessed by measuring ICAM1 the Intercellular Adhesion Molecule, an indicator of Endothelial Injury Marker. in Cirrhosis with Covid 19 Immunosuppression should be reduced prophylactically in patients with autoimmune liver disease and post-transplantation with no COVID-19. Hydroxychloroquine and remdesivir are found to be safe in limited studies in a patient with cirrhosis and COVID-19. And is safe in cirrhosis patients. However, flare of AIH has been reported in AIH patients. For hepatologists, cirrhosis with COVID-19 is a pertinent issue as the present pandemic cause severe disease in patients with chronic liver disease leading to more hospitalization and decompensation.
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Objective: To systematically evaluate the diagnostic value of nucleic acid test in sputum for COVID-19 and to determine the suitable population for sputum specimens. Method(s): PubMed, CNKI, Scopus, Web of Science, medRxiv and bioRxiv databases were searched for the diagnostic value of sputum nucleic acid test for COVID-19 from December 2019 to April 2022. Two researchers independently screened the literature, extracted data, and evaluated the risk of bias with QUADAS-2 in the included studies. We used sensitivity, specificity, AUC and DOR to evaluate the diagnostic value of sputum specimens. Result(s): A total of 25 studies were included, including 10,731 subjects. Meta-analysis results showed that: The combined sensitivity (SEN), specificity (SPE), diagnostic odds ratio (DOR), and area under operating characteristic curve (AUC) of sputum nucleic acid for the diagnosis of COVID-19 were 89.2% (95% CI, 86.6-91.4), 97.5% (95% CI, 97.2-97.8), 41.4 (95% CI, 11.7-145.9), 0.9474 (95% CI, 0.8964-0.9846). The results of subgroup analysis showed that the Asian group's DOR was 36.835 (95% CI, 10.83-134.570), and the Non-Asian group's DOR was 66.294 (95% CI, 0.719-6109.09). The DOR was 27.207 (95% CI, 2.860-258.780) in the OPS group and 44.165 (95% CI, 4.828-403.970) in the NPS group. DOR of mild patients was 84.255 (95% CI, 9.975-711.690), the DOR of the severe group was 14.216 (95% CI, 3.527-57.142) and was 19.464 (95% CI, 0.724-522.920) in the cured group. Conclusion(s): Current evidence shows that sputum nucleic acid test is of high diagnostic value for COVID-19. Study area and severity of disease are the influencing factors for the diagnostic accuracy of the sputum nucleic acid test. Due to the limitations on the number and quality of the included studies, the above conclusions need to be verified by more high-quality studies.Copyright © 2023, E-Century Publishing Corporation. All rights reserved.
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Background: Since its emergence in December 2019, until June 2022, coronavirus 2019 (COVID-19) has impacted populations all around the globe with it having been contracted by ~ 535 M people and leaving ~ 6.31 M dead. This makes identifying and predicating COVID-19 an important healthcare priority. Method and Material: The dataset used in this study was obtained from Shahid Beheshti University of Medical Sciences in Tehran, and includes the information of 29,817 COVID-19 patients who were hospitalized between October 8, 2019 and March 8, 2021. As diabetes has been shown to be a significant factor for poor outcome, we have focused on COVID-19 patients with diabetes, leaving us with 2824 records. Result(s): The data has been analyzed using a decision tree algorithm and several association rules were mined. Said decision tree was also used in order to predict the release status of patients. We have used accuracy (87.07%), sensitivity (88%), and specificity (80%) as assessment metrics for our model. Conclusion(s): Initially, this study provided information about the percentages of admitted Covid-19 patients with various underlying disease. It was observed that diabetic patients were the largest population at risk. As such, based on the rules derived from our dataset, we found that age category (51-80), CPR and ICU residency play a pivotal role in the discharge status of diabetic inpatients.Copyright © 2023, The Author(s), under exclusive licence to Tehran University of Medical Sciences.
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A current COVID-19 detection tool is CXR imaging, which has been developing since 2019 to provide early diagnosis;it can be performed in any health unit and is more affordable than Real Time Polymerase Chain Reaction (RT-PCR) tests. However, diagnosis with Chest X Ray (CXR) images had not achieved the predictive capacity required to replace the RT-PCR test;previous studies with a limited number of images have evaluated their models. This research seeks to contribute to the detection of COVID-19 from CXR images, with the evaluation of a convolutional neural network model from CXR images, through the use of open source code on a free dataset of approximately 30 thousand images. The algorithm and mathematical model used was DenseNet-201. The results of the experiment show a precision and accuracy of more than 95% and specificity, sensitivity, predictive ability and F1 measurement of more than 90%.Copyright © 2022, Anka Publishers. All rights reserved.
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The detection of COVID-19 by CXR imaging is a support tool for physicians and specialists since the pandemic and has been evolving rapidly because it provides early diagnosis, can be performed in any health center, and is more affordable than Real-Time Polymerase Chain Reaction (RT-PCR) tests. However, Chest X-Ray (CXR) imaging had not achieved the predictive capacity needed to replace the RT-PCR test;previous studies have evaluated their models with a limited amount of images. This study aims to contribute to the evaluation of a convolutional neural network (CNN) model to detect COVID-19 from CXR images, using open source and a free dataset containing approximately 30,000 images. The mathematical model or algorithm used was VGGNet-16. The results of the experiments show accuracy and precision of more than 95% and sensitivity, specificity, F1-measure,andthedictive ability of more than 90%.Copyright © 2022, Anka Publishers. All rights reserved.
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Background/Aims In April 2020 the British Society for Rheumatology (BSR) issued a risk stratification guide to identify patients at the highest risk of COVID-19 requiring shielding. This guidance was based on patients' age, comorbidities, and immunosuppressive therapies - including biologics that are not captured in primary care records. This meant rheumatologists needed to manually review outpatient letters to score patients' risk. The process required considerable clinician time, with shielding decisions not always transparently communicated. Our aim was to develop an automated shielding algorithm by text-mining outpatient letter diagnoses and medications, reducing the need for future manual review. Methods Rheumatology outpatient letters from Salford Royal Hospital, a large UK tertiary hospital, were retrieved between 2013-2020. The two most recent letters for each patient were extracted, created before 01.04.2020 when BSR guidance was published. Free-text diagnoses were processed using Intelligent Medical Objects software1 (Concept Tagger), which utilised interface terminology for each condition mapped to a SNOMED-CT code. We developed the Medication Concept Recognition tool (MedCore Named Entity Recognition) to retrieve medications type, dose, duration and status (active/past) at the time of the letter. The medication status was established based on the heading where they appeared (e.g. past medications, current medications), but incorporated additional information such as medication stop dates. The age, diagnosis and medication variables were then combined to output the BSR shielding score. The algorithm's performance was calculated using clinical review as the gold standard. Results To allow for the comparison with manual decisions, we focused on all 895 patients who were reviewed clinically. 64 patients (7.1%) had not consented for their data to be used for research as part of the national opt-out scheme. After removing duplicates, 803 patients were used to run the algorithm. 11,558 free-text diagnoses were extracted and mapped to SNOMED CT, with 15,003 free-text medications (that included past, present and any planned treatment). The automated shielding algorithm demonstrated a sensitivity of 80.3% (95% CI: 74.7, 85.2%) and specificity of 92.2% (95% CI: 89.7, 94.2%). Positive likelihood ratio was 10.3 (95% CI: 7.7, 13.7), negative likelihood ratio was 0.21 (95% CI: 0.16, 0.28), F1 score was 0.81. False positive rate was 7.9%, whilst false negative rate was 19.7%. Further evaluation of false positives/negatives revealed clinician interpretation of BSR guidance and misclassification of medications status were important contributing factors. Conclusion An automated algorithm for risk stratification has several advantages including reducing clinician time for manual review to allow more time for direct care, improving efficiency and transparently communicating decisions based on individual risk. With further development, it has the potential to be adapted for future public health initiatives that requires prompt automated review of hospital outpatient letters.
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The emerging virus SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2 virus), agent of COVID-19, appeared in December 2019 in Wuhan, China, and became a serious threat to global health and public safety. Many COVID-19 vaccines have been approved and licensed around the world. Most of the developed vaccines include S protein and induce an antibody-based immune response. Additionally, T-cell response to the SARS-CoV-2 antigens could be beneficial for combating the infection. The type of immune response is greatly dependent not only on the antigen, but also on adjuvants used in vaccine formulation. Here, we compared the effect of four different adjuvants (AddaS03, Alhydrogel/MPLA, Alhydrogel/ODN2395, Quil A) on the immunogenicity of a mixture of recombinant RBD and N SARS-CoV-2 proteins. We have analyzed the antibody and T-cell response specific to RBD and N proteins and assessed the impact of adjuvants on virus neutralization. Our results clearly indicated that Alhydrogel/MPLA and Alhydrogel/ODN2395 adjuvants elicited the higher titers of specific and cross-reactive antibodies to S protein variants from various SARS-CoV-2 and SARS-CoV-1 strains. Moreover, Alhydrogel/ODN2395 stimulated high cellular response to both antigens, as assessed by IFN-γ production. Importantly, sera collected from mice immunized with RBD/N cocktail in combination with these adjuvants exhibited neutralizing activity against the authentic SARS-CoV-2 virus as well as particles pseudotyped with S protein from various virus variants. The results from our study demonstrate the immunogenic potential of RBD and N antigens and point out the importance of adjuvants selection in vaccine formulation in order to enhance the immunological response. IMPORTANCE Although several COVID-19 vaccines have been approved worldwide, continuous emergence of new SARS-CoV-2 variants calls for new efficient vaccines against them, providing long-lasting immunity. As the immune response after vaccination is dependent not only on antigen used, but also on other vaccine components, e.g., adjuvants, the purpose of this work was to study the effect of different adjuvants on the immunogenicity of RBD/N SARS-CoV-2 cocktail proteins. In this work, it has been shown that immunization with both antigens plus the different adjuvants studied elicited higher Th1 and Th2 responses against RBD and N, which contributed to higher neutralization of the virus. The obtained results can be used for design of new vaccines, not only against SARS-CoV-2, but also against other important viral pathogens.
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We reviewed the diagnostic accuracy of SARS-CoV-2 serological tests. Random-effects models yielded a summary sensitivity of 82% for IgM, and 85% for IgG and total antibodies. For specificity, the pooled estimate were 98% for IgM and 99% for IgG and total antibodies. In populations with ≤ 5% of seroconverted individuals, unless the assays have perfect (i.e. 100%) specificity, the positive predictive value would be ≤ 88%. Serological tests should be used for prevalence surveys only in hard-hit areas.