Statistical Processing of Relationship Between Biomarkers and Disease Severity Caused by COVID-19 Infection-Delta Variant

Severe infectious disease caused by acute respiratory syndrome, COVID-19 (SARS-CoV-2), spread rapidly worldwide, infecting several million people. According to scientific data, the disease develops through several different stages. After 2–4 days of infection and disease development, the lower respiratory tract is attacked and in a relatively short time interstitial pneumonia develops in a certain number of patients (with genetic predisposition between 5 and 10% of cases). Patients infected with COVID-19 have symptoms such as very high temperatures, fever, persistent cough, joint and bone pain, in some cases diarrhea, and loss of appetite and taste. Disease monitoring should primarily include erythrocyte sedimentation rate, leukocyte count, leukocyte count formula, C-reactive protein (CRP), determination of troponin I (hsTnI) and T (cTnT) levels, N-terminal pro-B natriuretic peptide (NT-proBNP), fibrinogen, and D-dimer level. Previous studies have shown that in pneumonia developed from chronic and acute obstructive pulmonary infections, high levels of D-dimer are observed in patients, and it is suggested that this parameter can be used as a specific prognostic biomarker, and the values higher than > 1000 ng/ml represent increased risk factors for mortality in patients with COVID-19. Because vascular thrombosis affects the promotion of an unfavorable clinical progression for the patient, the identification of early and accurate predictors of the worst outcome seems to be essential for timely and appropriate anticoagulant treatment in patients with SARS-CoV-2 infection. Overall, these data suggest that acute myocardial damage, or heart failure, may be an important indicator of disease severity and adverse prognosis in patients with COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Clinical characteristics among patients with COVID­19: A single­center retrospective study.

The aim of the present study was to investigate the clinical features and laboratory parameters of hospitalized patients with coronavirus disease 2019 (COVID-19) and assess the characteristics between severe and non-severe cases. The study retrospectively analyzed the clinical data of 1,096 patients, of which, 626 (57.11%) and 470 (42.89%) were categorized into severe and non-severe groups, respectively. Clinical parameters such as signs and symptoms, comorbidities, levels of D-dimer, C-reactive protein (CRP), interleukin 6 (IL-6) and lactate dehydrogenase were analyzed. The data are presented as frequencies, means and standard deviations. The chi-square test and Mann-Whitney U test were used to assess any significant differences between the severe and non-severe COVID-19 groups. The clinical symptoms in severe COVID-19 cases included anosmia (P≤0.01), sore throat (P≤0.01), fatigue (P≤0.01), headache (P≤0.01), and shortness of breath (P≤0.01). Laboratory findings showed a significant increase in CRP (21.90±40.23 vs. 16.13±21.82; P≤0.01) and IL-6 levels (58.92±55.07 vs. 41.41±38.30; P≤0.01). Patients with severe COVID-19 had significant lymphopenia compared with that in non-severe cases. Among the comorbidities, hypertension (P≤0.01) was significantly more frequent in patients with severe COVID-19. In conclusion, major derangements in laboratory parameters were observed in patients with severe COVID-19 infection.

Towards Automated Multiclass Severity Prediction Approach for COVID-19 Infections Based on Combinations of Clinical Data

The recent dramatic expansion of the COVID-19 outbreak is placing enormous strain on human society as a whole. Numerous biomarkers are being investigated in an effort to track the condition of the patient. This could interfere with signs of many other illnesses, making it more difficult for a specialist to diagnose or predict the severity level of the case. As a result, the focus of this research was on the development of a multiclass prediction system capable of dealing with three severity cases (severe, moderate, and mild). The lymphocyte to CRP ratio (C-reactive protein blood test) and SpO2 (blood oxygen saturation level) indicators were ranked and used as prediction system attributes. A machine learning model based on SVMs is created. A total of 78 COVID-19 patients were recruited from the Azizia primary health care sector/Wasit Health Directorate/Ministry of Health to form different combinations of COVID-19 clinical dataset. The outcomes demonstrate that the proposed approach had an average accuracy of 82%. The established prediction system allows for the early identification of three severity cases, which reduces deaths. © 2022 Ahmed M. Dinar et al.

Ultrasensitive aptamer-functionalized Cu-MOF fluorescent nanozyme as an optical biosensor for detection of C-reactive protein.

In the present work, an aptasensing method based on integration of RNA on Cu-MOF was developed for detection of C-Reactive Protein (CRP). Cu-MOF showed stimulated fluorescence and mimetic peroxidase enzymatic activity at the time and can be used as dual-signal transduction. CRP binding RNA was used as a highly selective recognition element and immobilized on the Cu-MOF. The immobilized RNA can block the peroxidase activity and fluorescence of the signal traducer probe. Adding CRP to the RNA/Cu-MOF will release RNA from the surface of Cu-MOF and recover both the stimulated fluorescence and peroxidase activity. A biosensor was built for detection of CRP using the two modes of transduction, either colorimetry or fluorometry. A dynamic linear range was obtained from 0.1 to 50 ng mL -1with a limit of detection (LOD) as small as 40 pg mL -1was calculated in fluorescence mode and 240 pg mL -1 as LOD in colorimetry mode. The LODs are lower than the LOD of nephelometric techniques used in clinical practice and is comparable to the normal clinical cutoff value in high-sensitivity CRP assays (1 µg/mL). The aptasensor was successfully applied for detection of CRP in Covid-19 patients with spike recoveries between 84 and 102% and RSD from 0.94% to 2.05%.

Role of laboratory biomarkers in assessing the severity of COVID-19 disease. A cross-sectional study.

BACKGROUND: Corona virus disease 2019 (Covid-19) has high infectivity and mortality rate. Covid-19 patients can suddenly deteriorate and develop life threatening complications. Hence, there is a need to identify laboratory biomarkers in order to categorize high risk patients. The main purpose of the study is to investigate the role and correlation of laboratory parameters such as total leucocyte count (TLC), absolute lymphocyte count, platelet count, C-Reactive Proteins (CRP), serum ferritin, serum lactate dehydrogenase (LDH), serum procalcitonin and D-dimer in severe and non-severe Covid-19 patients. METHODOLOGY: This retrospective cross-sectional study was conducted at Latifa Women and Child Hospital in the UAE after obtaining ethical committee clearance. Based on the symptoms and the criteria by National Institute of Health, USA, 109 patients were divided into three groups: Non-severe with 75, severe with 18 and critical with 16 patients. Laboratory data of these patients were assessed through the electronic medical records (SALAMA). Statistical analysis was done using Statistical Packages for Social Sciences (SPSS) version 25.0 (SPSS/PC; SPSS-25.0, Chicago, USA). Laboratory test profiles were expressed as mean (SD). Independent 't' test and ANOVA were used to study the significance of means. P value less than 0.05 was considered significant. RESULT: Males were more severely affected than females. Severe and critically ill Covid-19 patients had a significantly higher TLC, serum LDH, ferritin and CRP and lower absolute lymphocyte count. PCT and D-dimer were significantly elevated in critical group. CONCLUSION: Along with clinical presentation and radiological findings, biochemical parameter may also be considered as important predictors for assessing severity in covid-19 patients.

Advancement in biosensors for inflammatory biomarkers of SARS-CoV-2 during 2019-2020.

COVID-19 pandemic has affected everyone throughout the world and has resulted in the loss of lives of many souls. Due to the restless efforts of the researchers working hard day and night, some success has been gained for the detection of virus. As on date, the traditional polymerized chain reactions (PCR), lateral flow devices (LFID) and enzyme linked immunosorbent assays (ELISA) are being adapted for the detection of this deadly virus. However, a more exciting avenue is the detection of certain biomarkers associated with this viral infection which can be done by simply re-purposing our existing infrastructure. SARS-CoV-2 viral infection triggers various inflammatory, biochemical and hematological biomarkers. Because of the infection route that the virus follows, it causes significant inflammatory response. As a result, various inflammatory markers have been reported to be closely associated with this infection such as C-reactive proteins, interleukin-6, procalcitonin and ferritin. Sensing of these biomarkers can simultaneously help in understanding the illness level of the affected patient. Also, by monitoring these biomarkers, we can predict the viral infections in those patients who have low SARS-CoV-2 RNA and hence are missed by traditional tests. This can give more targets to the researchers and scientists, working in the area of drug development and provide better prognosis. In this review, we propose to highlight the conventional as well as the non-conventional methods for the detection of these inflammatory biomarkers which can act as a single platform of knowledge for the researchers and scientists working for the treatment of COVID-19.