Hybridization of particle swarm optimization algorithm with neural network for COVID-19 using computerized tomography scan and clinical parameters

The 2019 coronavirus disease began in Wuhan, China, and spread worldwide. This pandemic was concerning, given its significant and worrying impact on human health. Strategies to manage the disease begin with diagnosing the infection, often using the real-time reverse transcription polymerase chain reaction (RT-PCR) assay. However, this process is time intensive. Therefore, alternative rapid methods to diagnose the coronavirus with high accuracy are needed. X-ray and computerized tomography (CT) scans are reasonable solutions for rapid coronavirus diagnosis. The dataset of 500 patients was tested, including 286 uninfected patients and 214 infected with COVID-19. Clinical parameters, including heart rate (HR), temperature (T), blood oxygen level, D-dimer, and CT scan, including red-green-blue (RGB) pixel values of the left and right lungs, were collected from 500 patients and used to train an artificial neural network (ANN) to diagnose coronavirus. The ANN was hybridized with a particle swarm optimization (PSO) algorithm to improve diagnosis accuracy. The results show that the proposed PSO-ANN method significantly improved diagnosis accuracy (98.93%), sensitivity (100%), and specificity (98.13%). The effectiveness of the proposed method was confirmed by comparing the findings with those of previous studies.

Clinical and laboratory features of COVID-19 patients infected with SARS-CoV-2 variant B.1.1.7 versus those infected with other SARS-CoV-2 strains: A retrospective observational study

Objective: To investigate the clinical symptoms of coronavirus disease 2019 (COVID-19) patients with and without B.1.1.7 mutation. Method(s): This retrospective observational study included COVID-19 patients who were divided into two groups, the mutation and the non-mutation group. Demographics characteristics, clinical characteristics, laboratory parameters, and mortality rates were recorded and compared between the two groups. Result(s): A total of 196 patients were included in the study. The relationship between the mutant virus status and sex, age, comorbidity, survival status, and disease severity was not significant (P>0.05). No significant differences were found in duration of hospitalization between the mutation and the non-mutation group (P>0.05). However, there was a statistically significant difference between patients with and without mutant viruses in hemoglobin, mean platelet volume, procalcitonin, low density lipoprotein, ironbinding capacity, potassium, calcium, C-reactive protein, folate, creatine kinase myocardial band, D-dimer, and international normalized ratio (P<0.05). Conclusion(s): No significant difference is found in mortality rate, disease severity or duration of hospitalization between the patients with and without variant B.1.1.7. Careful monitoring of COVID-19 patients is required for all variants. Copyright ©2022 Journal of Acute Disease Produced by Wolters Kluwer-Medknow.

COVID-19 in the Emergency Department of the Primary Healthcare Centre Banja Luka

Background / Aim: COVID-19 is acute virus disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS–CoV-2). It was proclaimed as pandemic starting from March 2020 and is still ongoing. COVID-19 pandemic forced all segments of the society, particularly the health sector, to function in changed and aggravating circumstances and because of the transmission and new strains of the virus it resulted in the change of the number of infected people with peaks and oscillations. Aim of this study was to make analysis of the data related to COVID-19 positive/suspect patients examined in the Primary Healthcare Centre Banja Luka in the period 15 March 2020 – 15 March 2021, which refers to the incidence of the infected persons, sex and age representation, laboratory diag-nostics and clinical parameters, applied therapy, as well as the number of patients sent for the hospital treatment. Methods: Data for the analysis were obtained by the retrospective analysis of the statistical data from the electronic medical record of the examined COVID-19 positive/suspect patients in the Outpatient Clinic for Acute Respiratory Infections (ARI) and in the field. Pearson’s χ2 test of contingency was used for the comparison of differences of the observed characteristics of the examined groups. Results: Personnel of the Emergency Department (ED) of the Primary Health-care Centre Banja Luka, in the period 15 March 2020 – 15 March 2021, examined the total of 3,937 COVID-19 positive patients and patients suspect of COVID-19. Out of that number, 3,601 patients were examined in the ED – ARI and 336 patients were examined in the field. The biggest number of patients was registered in November 2020 (768). Male sex prevailed (55.50 %) and patients of 20-50 years of age were most represented. There were 3.10 % of those highly febrile patients and 2.5 % of those with low SpO2 of under 90 %. 14.90 % of patients had higher values of troponin T and 45.50 % of them had higher values of D-dimer. In the field, 69.60 % of patients had pathological changes on lungs and 33.30 % had pathological ECG report. The number of patients sent from the ARI for further diagnostic procedure or hospitalisation to the Clinic for Infectious Diseases of the University Clinical Centre of Banja Luka was 1,191 and 258 patients were sent from the field. Conclusion: For the purpose of preventing the spread of epidemics, the ED reor-ganised the existing space by introducing temporary clinics – containers for the patients with acute respiratory infections and febrile status, COVID-19 suspects. Clinical parameters changed depending on the new virus strains, as well as on age distribution and infection complications. © 2022 Banjac et al.

Clinical monitored in subjects metabolically healthy and unhealthy before and during a SARS-CoV-2 infection- A cross-sectional study in Mexican population.

The COVID-19 disease has forced us to consider the physiologic role of obesity and metabolically healthy and unhealthy status in response to SARS-CoV-2 infection. Hematological, coagulation, biochemical, and immunoinflammatory changes have been informed with a disparity in morbidity and mortality. Therefore, we aimed to investigate the influence of metabolic health on clinical features in a cross-sectional study in Mexican subjects with and without SARS-CoV-2 infection in non-severe stages after a rigorous classification of obese and non-obese subjects who were metabolically healthy and unhealthy. Four groups were formed: 1) metabolically healthy with normal BMI (MHN); 2) metabolically unhealthy with normal BMI (MUN); 3) metabolically healthy obese (MHO); 4) metabolically unhealthy obese (MUO). Serum proinflammatory (TNF-α, MCP-1, IL-1ß, and IL-6) and anti-inflammatory (TGF-ß, IL-1Ra, IL-4, and IL-10) cytokines, hematological parameters, coagulation, and acute phase components were evaluated. Our results showed that MHO people live with inflammaging. Meanwhile, MUN and MUO subjects develop metaflammation. Both inflammaging and metaflammation cause imperceptible modifications on hematological parameters, mainly in leukocyte populations and platelets, as well as acute phase and coagulation components. The statistical analysis revealed that many clinical features are dependent on metabolic health. In conclusion, MHO subjects seem to be transitioning from metabolically healthy to unhealthy, which is accelerated in acute processes, such as SARS-CoV-2 infection. Meanwhile, metabolically unhealthy subjects independently of BMI have a deteriorating immunometabolic status associated with a hyperinflammatory state leading to multi-organ dysfunction, treatment complications, and severe COVID-19 disease.

Evaluation serum levels of Insulin Growth Factor-1 (IGF-1) and its association with clinical parameters in severe COVID-19.

BACKGROUND: Severe coronavirus disease-2019 (COVID-19) is associated with dysregulated immune response and extreme inflammatory injury. Considering the role of insulin growth factor-1 (IGF-1) in immune-mediated and inflammatory reactions, this study was conducted to investigate the IGF-1 contribution to the pathogenesis of severe form of COVID-19. MATERIAL AND METHODS: Sixty-two patients with severe COVID-19 and 52 healthy subjects were enrolled in this study. The serum levels of IGF-1 were measured using a solid-phase enzyme-linked chemiluminescent immunoassay on an Immulite 2000 system (Siemens Healthcare Diagnostics. RESULT: The serum levels of IGF-1 had no significant difference in COVID-19 patients compared to the healthy subjects (p = 0.359). There was a positive correlation between IGF-1 and age in the severe COVID-19 patients, while a negative correlation was observed for the serum levels of IGF-1 and age in the control group (r = 0.364, p = 0.036, r = - 0.536, p = 0.001, respectively). Moreover, IGF-1 was remarkably associated with hypertension, neurogenic disease, shock, and nausea in patients with the severe form of COVID-19 (p = 0.031, p = 0.044, p = 0.01, p = 0.03, respectively). CONCLUSION: Our results pointed to the complex role of IGF-1 in the severe form of COVID-19, and its association with clinical parameters, and some risk factors in the severe form of COVID-19.

Implications of SARS-CoV-2 infection on the clinical, hematological, and inflammatory parameters in COVID-19 patients: A retrospective cross-sectional study.

BACKGROUND: The current coronavirus pandemic (COVID-19) was caused by severe acute respiratory syndrome virus 2 (SARS-CoV-2). COVID-19 is characterized by atypical pneumonia, mild colds, and more severe illnesses, such as severe acute respiratory distress, thrombosis, organ failure, and various secondary bacterial and fungal infections. Notably, the severity of COVID-19 in different age groups is not well known, and the validity of clinical laboratory data remains unclear. METHODS: In this retrospective cross-sectional study, we examined differential regulation of clinical, hematologic, and inflammatory biomarkers in COVID-19 patients. We divided 104 COVID-19 patients into five different groups according to age (0-17, 18-45, 46-65, 66-79, and >80 years). Baseline data (sex, comorbidities, intensive care admission, and medications), hematologic markers, liver, and renal function tests, coagulation, and inflammatory markers were examined in these groups. Receiver operator characteristic (ROC) analysis was used to determine the optimal threshold for predicting COVID-19 biological markers. RESULTS: We found that the highest percentage (45%) of COVID-19 patients was in the age group of 46-65 years. The hematologic parameters (WBC, HB, and PLT) were normal between the patient groups. The area under the curve in ROC analysis showed significant differences in the levels of creatine, GGT, BUN, CRP, D-dimer, ferritin, AST, and procalcitonin between the patients of age groups 46-65 and 66-79 years. Renal biomarkers were significantly high in most patients, regardless of age. In contrast, the liver biomarkers, did not differ significantly between patient groups. CONCLUSION: The main finding of our study is that laboratory parameters such as GGT, creatinine, BUN, CRP, procalcitonin, ferritin and D-dimer were differentially regulated in COVID -19 patients of different age groups. Importantly, these laboratory parameters may help as clinical predictors to assess the severity of the disease in the population. We conclude here that age is an important factor influencing COVID-19 severity.

Intensive Care Risk Estimation in COVID-19 Pneumonia Based on Clinical and Imaging Parameters: Experiences from the Munich Cohort.

The evolving dynamics of coronavirus disease 2019 (COVID-19) and the increasing infection numbers require diagnostic tools to identify patients at high risk for a severe disease course. Here we evaluate clinical and imaging parameters for estimating the need of intensive care unit (ICU) treatment. We collected clinical, laboratory and imaging data from 65 patients with confirmed COVID-19 infection based on polymerase chain reaction (PCR) testing. Two radiologists evaluated the severity of findings in computed tomography (CT) images on a scale from 1 (no characteristic signs of COVID-19) to 5 (confluent ground glass opacities in over 50% of the lung parenchyma). The volume of affected lung was quantified using commercially available software. Machine learning modelling was performed to estimate the risk for ICU treatment. Patients with a severe course of COVID-19 had significantly increased interleukin (IL)-6, C-reactive protein (CRP), and leukocyte counts and significantly decreased lymphocyte counts. The radiological severity grading was significantly increased in ICU patients. Multivariate random forest modelling showed a mean ± standard deviation sensitivity, specificity and accuracy of 0.72 ± 0.1, 0.86 ± 0.16 and 0.80 ± 0.1 and a receiver operating characteristic-area under curve (ROC-AUC) of 0.79 ± 0.1. The need for ICU treatment is independently associated with affected lung volume, radiological severity score, CRP, and IL-6.