Dataset Information

A review on corona virus disease 2019 (COVID-19): current progress, clinical features and bioanalytical diagnostic methods.

ABSTRACT: A new epidemic of acute respiratory viral pneumonia was discovered in central China at the end of 2019. The disease was given the name coronavirus disease 2019 (COVID-19), and the virus that caused this disease was known as severe acute respiratory syndrome coronavirus (SARS-CoV-2). So far, diagnostic methods have been focused on (a) human antibody detection, (b) viral antigen detection and (c) viral gene detection, the latter using RT-PCR being the most accurate approach. In this paper, we present a summary of the COVID-19 pandemic, clinical features and epidemiology and pathogenesis. Also, we focus on the recent advances in bioanalytical diagnostic methods based on various techniques for SARS-CoV-2 sensing that have recently been published (2020-2021). Furthermore, we present the mechanisms, advantages and disadvantages of the most common biosensors for COVID-19 detection, which include optical, electrochemical and piezoelectric biosensors as well as wearable and smart nanobiosensors, immunosensors, aptasensors and genosensors.

SUBMITTER: Mollarasouli F

PROVIDER: S-EPMC8852957 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A review on corona virus disease 2019 (COVID-19): current progress, clinical features and bioanalytical diagnostic methods.

Mollarasouli Fariba F Zare-Shehneh Nader N Ghaedi Mehrorang M

Mikrochimica acta 20220214 3

A new epidemic of acute respiratory viral pneumonia was discovered in central China at the end of 2019. The disease was given the name coronavirus disease 2019 (COVID-19), and the virus that caused this disease was known as severe acute respiratory syndrome coronavirus (SARS-CoV-2). So far, diagnostic methods have been focused on (a) human antibody detection, (b) viral antigen detection and (c) viral gene detection, the latter using RT-PCR being the most accurate approach. In this paper, we pres ...[more]

PMID: 35157153

Similar Datasets

Project description:ObjectiveSince December 2019, an outbreak of corona virus disease 2019 (COVID-19) occurred in Wuhan, and rapidly spread to almost all parts of China. This was followed by prevention programs recommending Chinese medicine (CM) for the prevention. In order to provide evidence for CM recommendations, we reviewed ancient classics and human studies.MethodsHistorical records on prevention and treatment of infections in CM classics, clinical evidence of CM on the prevention of severe acute respiratory syndrome (SARS) and H1N1 influenza, and CM prevention programs issued by health authorities in China since the COVID-19 outbreak were retrieved from different databases and websites till 12 February, 2020. Research evidence included data from clinical trials, cohort or other population studies using CM for preventing contagious respiratory virus diseases.ResultsThe use of CM to prevent epidemics of infectious diseases was traced back to ancient Chinese practice cited in Huangdi's Internal Classic (Huang Di Nei Jing) where preventive effects were recorded. There were 3 studies using CM for prevention of SARS and 4 studies for H1N1 influenza. None of the participants who took CM contracted SARS in the 3 studies. The infection rate of H1N1 influenza in the CM group was significantly lower than the non-CM group (relative risk 0.36, 95% confidence interval 0.24-0.52; n=4). For prevention of COVID-19, 23 provinces in China issued CM programs. The main principles of CM use were to tonify qi to protect from external pathogens, disperse wind and discharge heat, and resolve dampness. The most frequently used herbs included Radix astragali (Huangqi), Radix glycyrrhizae (Gancao), Radix saposhnikoviae (Fangfeng), Rhizoma Atractylodis Macrocephalae (Baizhu), Lonicerae Japonicae Flos (Jinyinhua), and Fructus forsythia (Lianqiao).ConclusionsBased on historical records and human evidence of SARS and H1N1 influenza prevention, Chinese herbal formula could be an alternative approach for prevention of COVID-19 in high-risk population. Prospective, rigorous population studies are warranted to confirm the potential preventive effect of CM.

Project description:IntroductionDiabetes mellitus (DM) is associated with severe forms of COVID-19 but little is known about the diabetes-related phenotype considering pre-admission, on-admission and data covering the entire hospitalization period.MethodsWe analyzed COVID-19 inpatients (n = 3327) aged 61.2(48.2-71.4) years attended from March to September 2020 in a public hospital.ResultsDM group (n = 1218) differed from Non-DM group (n = 2109) by higher age, body mass index (BMI), systolic blood pressure and lower O2 saturation on admission. Gender, ethnicity and COVID-19-related symptoms were similar. Glucose and several markers of inflammation, tissue injury and organ dysfunction were higher among patients with diabetes: troponin, lactate dehydrogenase, creatine phosphokinase (CPK), C-reactive protein (CRP), lactate, brain natriuretic peptide, urea, creatinine, sodium, potassium but lower albumin levels. Hospital (12 × 11 days) and intensive care unit permanence (10 × 9 days) were similar but DM group needed more vasoactive, anticoagulant and anti-platelet drugs, oxygen therapy, endotracheal intubation and dialysis. Lethality was higher in patients with diabetes (39.3% × 30.7%) and increased with glucose levels and age, in male sex and with BMI < 30 kg/m2 in both groups (obesity paradox). It was lower with previous treatment with ACEi/BRA in both groups. Ethnicity and education level did not result in different outcomes between groups. Higher frequency of comorbidities (hypertension, cardiovascular/renal disease, stroke), of inflammatory (higher leucocyte number, RCP, LDH, troponin) and renal markers (urea, creatinine, potassium levels and lower sodium, magnesium) differentiated lethality risk between patients with and without diabetes.ConclusionsComorbidities, inflammatory markers and renal disfunction but not Covid-19-related symptoms, obesity, ethnicity and education level differentiated lethality risk between patients with and without diabetes.

Dataset Information

A review on corona virus disease 2019 (COVID-19): current progress, clinical features and bioanalytical diagnostic methods.

Publications

A review on corona virus disease 2019 (COVID-19): current progress, clinical features and bioanalytical diagnostic methods.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets