Earth's magnetic field spatial-temporal changes in relation to the SARS-CoV-2 virus spreading

The results of a study on the possible connection between the spread of the SARS-CoV-2 virus and the Earth's magnetic field based on the analysis of a large array digital data for 95 countries of the world are presented. The dependence of the spatial SARS-CoV-2 virus spread on the magnitude of the BIGRF Earth's main magnetic field modular induction values was established. The maximum diseases number occurs in countries that are located in regions with reduced (25. 0-30. 0 μT) and increased (48. 0-55. 0 μT) values, with a higher correlation for the first case. The spatial dependence of the SARS-CoV-2 virus spreading on geomagnetic field dynamics over the past 70 years was revealed. The maximum diseases number refers to the areas with maximum changes in it, both in decrease direction (up to - 6500 nT) and increase (up to 2500 nT), with a more significant correlation for countries located in regions with increased geomagnetic field. © 2022 EAGE. All Rights Reserved.

CT chest for COVID-19, a multicenter study—experience with 220 Egyptian patients

BackgroundCOVID-19 has become a national and an international pre-occupation to all doctors. Dealing with patients with clinical suspicion of COVID-19 is a daily markedly growing professional issue for radiologists. The number of COVID-19 cases we deal with is peaking since last March and so is our experience in recognizing the disease patterns and in assessing its severity. The purpose of this study is to assess the role of CT chest in the diagnosis of COVID-19 based on our experience with 220 Egyptian cases.ResultsA cross-sectional multicenter study involving 220 patients;68 (30.9%) females and 152 (69.1%) males, their age range was 10-92 years (average 49.198 years). Non-contrast MSCT chest was done to patients with clinically suspected COVID-19. Data assessment and analysis for lesions probability, pattern, localization, and severity were done.Bilateral affection was seen in 168/220 cases (76.36%). Multilobar affection was noted in 186/220 cases (84.54%). Lower lobes affection was noted in 179/220 cases (81.36%). Peripheral/subpleural affection was noted in 203/220 cases (92.27%). The common CT patterns (ground-glass opacities, consolidation, crazy paving, vascular thickening, traction bronchiectasis, vacuolar sign, architectural distortion signs, and reversed halo sign) and the uncommon CT patterns (halo sign, masses, nodules, lobar affection, tree in-bud-pattern and cysts) were discussed. Associated extra-pulmonary lesions described. Temporal changes, severity scoring, reporting, and possible pitfalls were all assessed.ConclusionIn our experience, CT plays a basic essential role in diagnosing COVID-19 in the current declared pandemic.

Temporal changes of lung computed tomography findings pulmonary COVID-19 infection.

OBJECTIVES: COVID-19 infection demonstrates characteristic findings in chest CT. The optimal timing of repeated CT scans still needs to be clarified, and the optimal time to assess imaging clearance in COVID-19 is still unknown. It is crucial to have a roadmap of the imaging course of COVID-19 pneumonia to develop guidelines for prompt diagnosis of pulmonary complications, especially fibrosis, at the earliest stage. PURPOSE: To assess the temporal changes of chest CT findings in patients with COVID-19 pneumonia and evaluate the rate of a complete resolution and determine the patients are at excessive risk for residual parenchymal abnormalities. MATERIALS AND METHODS: This retrospective observational study included 48 patients with real-time polymerase chain reaction-confirmed COVID-19 who were admitted to three academic hospitals. These patients underwent at least one initial chest CT before or after admission and at least one follow-up CT scan four weeks or more after the onset of the symptoms. All chest CTs were categorized according to time of performance into four groups, including the first week, second week, third-fourth week, and more than 28 days. Lung involvement was categorized as predominantly alveolar (ground-glass opacity and consolidation), organizing pneumonia, and reticular patterns. The severity of involvement was also evaluated by the reader. RESULTS: Forty-eight patients and a total of 130 chest CT scans were evaluated. The alveolar pattern showed a gradual decrease in frequency from 91% in the first week to 9% after the fourth week of the disease but the organizing pneumonia pattern gradually increased with disease progression and the frequency of reticular pattern increased significantly after third week. Complete resolution of CT findings was seen in 17 patients (13.1%) and was significantly more prevalent in patients of younger age (p value<0.001) and with lower initial CT severity scores (p value=0.048). CT severity scores in the second week were significantly higher in ICU admitted patients (p value=0.003). CONCLUSION: There are temporal patterns of lung abnormalities in patients with COVID-19 pneumonia. The predominant CT pattern was alveolar infiltrate in the first and second weeks of the disease, replaced with an organizing pneumonia pattern in the third and fourth weeks. Progression of lung involvement was correlated with ICU admission due to the highest CT severity score in the second and third weeks of presentation but not in the first week in patients who were admitted at ICU. Complete CT resolution was significantly more common in patients of younger age and lower initial CT severity scores.

Multiple imputation to minimise bias from missing stage information in estimates of early cancer diagnosis in England: a population-based study.

INTRODUCTION: Monitoring early diagnosis is a priority of cancer policy in England. Information on stage has not always been available for a large proportion of patients, however, which may bias temporal comparisons. We previously estimated that early-stage diagnosis of colorectal cancer rose from 32% to 44% during 2008-2013, using multiple imputation. Here we examine the underlying assumptions of multiple imputation for missing stage using the same dataset. METHODS: Individually-linked cancer registration, Hospital Episode Statistics (HES), and audit data were examined. Six imputation models including different interaction terms, post-diagnosis treatment, and survival information were assessed, and comparisons drawn with the a priori optimal model. Models were further tested by setting stage values to missing for some patients under one plausible mechanism, then comparing actual and imputed stage distributions for these patients. Finally, a pattern-mixture sensitivity analysis was conducted. RESULTS: Data from 196,511 colorectal patients were analysed, with 39.2% missing stage. Inclusion of survival time increased the accuracy of imputation: the odds ratio for change in early-stage diagnosis during 2008-2013 was 1.7 (95% CI: 1.6, 1.7) with survival to 1 year included, compared to 1.9 (95% CI 1.9-2.0) with no survival information. Imputation estimates of stage were accurate in one plausible simulation. Pattern-mixture analyses indicated our previous analysis conclusions would only change materially if stage were misclassified for 20% of the patients who had it categorised as late. INTERPRETATION: Multiple imputation models can substantially reduce bias from missing stage, but data on patient's one-year survival should be included for highest accuracy.

Temporal changes of quantitative CT findings from 102 patients with COVID-19 in Wuhan, China: A longitudinal study.

BACKGROUND: Computed tomography (CT) imaging combined with artificial intelligence is important in the diagnosis and prognosis of lung diseases. OBJECTIVE: This study aimed to investigate temporal changes of quantitative CT findings in patients with COVID-19 in three clinic types, including moderate, severe, and non-survivors, and to predict severe cases in the early stage from the results. METHODS: One hundred and two patients with confirmed COVID-19 were included in this study. Based on the time interval between onset of symptoms and the CT scan, four stages were defined in this study: Stage-1 (0 ∼7 days); Stage-2 (8 ∼ 14 days); Stage-3 (15 ∼ 21days); Stage-4 (> 21 days). Eight parameters, the infection volume and percentage of the whole lung in four different Hounsfield (HU) ranges, ((-, -750), [-750, -300), [-300, 50) and [50, +)), were calculated and compared between different groups. RESULTS: The infection volume and percentage of four HU ranges peaked in Stage-2. The highest proportion of HU [-750, 50) was found in the infected regions in non-survivors among three groups. CONCLUSIONS: The findings indicate rapid deterioration in the first week since the onset of symptoms in non-survivors. Higher proportion of HU [-750, 50) in the lesion area might be a potential bio-marker for poor prognosis in patients with COVID-19.

Temporal changes in laboratory markers of survivors and non-survivors of adult inpatients with COVID-19.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2, and outbreaks have occurred worldwide. Laboratory test results are an important basis for clinicians to determine patient condition and formulate treatment plans. METHODS: Fifty-two thousand six hundred forty-four laboratory test results with continuous values of adult inpatients who were diagnosed with COVID-19 and hospitalized in the Fifth Hospital in Wuhan between 16 January 2020 and 18 March 2020 were compiled. The first and last test results were compared between survivors and non-survivors with variance test or Welch test. Laboratory test variables with significant differences were then included in the temporal change analysis. RESULTS: Among 94 laboratory test variables in 82 survivors and 25 non-survivors with COVID-19, white blood cell count, neutrophil count/percentage, mean platelet volume, platelet distribution width, platelet-large cell percentage, hypersensitive C-reactive protein, procalcitonin, D-dimer, fibrin (ogen) degradation product, middle fluorescent reticulocyte percentage, immature reticulocyte fraction, lactate dehydrogenase were significantly increased (P < 0.05), and lymphocyte count/percentage, monocyte percentage, eosinophil percentage, prothrombin activity, low fluorescent reticulocyte percentage, plasma carbon dioxide, total calcium, prealbumin, total protein, albumin, albumin-globulin ratio, cholinesterase, total cholesterol, nonhigh-density/low-density/small-dense-low-density lipoprotein cholesterol were significantly decreased in non-survivors compared with survivors (P < 0.05), in both first and last tests. Prothrombin time, prothrombin international normalized ratio, nucleated red blood cell count/percentage, high fluorescent reticulocyte percentage, plasma uric acid, plasma urea nitrogen, cystatin C, sodium, phosphorus, magnesium, myoglobin, creatine kinase (isoenzymes), aspartate aminotransferase, alkaline phosphatase, glucose, triglyceride were significantly increased (P < 0.05), and eosinophil count, basophil percentage, platelet count, thrombocytocrit, antithrombin III, red blood cell count, haemoglobin, haematocrit, total carbon dioxide, acidity-basicity, actual bicarbonate radical, base excess in the extracellular fluid compartment, estimated glomerular filtration rate, high-density lipoprotein cholesterol, apolipoprotein A1/ B were significantly decreased in non-survivors compared with survivors (P < 0.05), only in the last tests. Temporal changes in 26 variables, such as lymphocyte count/percentage, neutrophil count/percentage, and platelet count, were obviously different between survivors and non-survivors. CONCLUSIONS: By the comprehensive usage of the laboratory markers with different temporal changes, patients with a high risk of COVID-19-associated death or progression from mild to severe disease might be identified, allowing for timely targeted treatment.

Temporal changes of COVID-19 pneumonia by mass evaluation using CT: a retrospective multi-center study.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has widely spread worldwide and caused a pandemic. Chest CT has been found to play an important role in the diagnosis and management of COVID-19. However, quantitatively assessing temporal changes of COVID-19 pneumonia over time using CT has still not been fully elucidated. The purpose of this study was to perform a longitudinal study to quantitatively assess temporal changes of COVID-19 pneumonia. METHODS: This retrospective and multi-center study included patients with laboratory-confirmed COVID-19 infection from 16 hospitals between January 19 and March 27, 2020. Mass was used as an approach to quantitatively measure dynamic changes of pulmonary involvement in patients with COVID-19. Artificial intelligence (AI) was employed as image segmentation and analysis tool for calculating the mass of pulmonary involvement. RESULTS: A total of 581 confirmed patients with 1,309 chest CT examinations were included in this study. The median age was 46 years (IQR, 35-55; range, 4-87 years), and 311 (53.5%) patients were male. The mass of pulmonary involvement peaked on day 10 after the onset of initial symptoms. Furthermore, the mass of pulmonary involvement of older patients (>45 years) was significantly severer (P<0.001) and peaked later (day 11 vs. day 8) than that of younger patients (≤45 years). In addition, there were no significant differences in the peak time (day 10 vs. day 10) and median mass (P=0.679) of pulmonary involvement between male and female. CONCLUSIONS: Pulmonary involvement peaked on day 10 after the onset of initial symptoms in patients with COVID-19. Further, pulmonary involvement of older patients was severer and peaked later than that of younger patients. These findings suggest that AI-based quantitative mass evaluation of COVID-19 pneumonia hold great potential for monitoring the disease progression.

Temporal changes in psychobehavioural responses during the early phase of the COVID-19 pandemic in Malaysia.

Monitoring public psychological and behavioural responses during the early phase of the coronavirus disease 2019 (COVID-19) outbreak is important for the management and control of infection. This study aims to investigate the temporal trend in (1) avoidance and protective behaviors, (2) fear, (3) socio-economic impact, and (4) anxiety levels during the early phase of the COVID-19 pandemic. As a high level of anxiety may have a detrimental impact during an infectious disease outbreak, factors associated with anxiety were also explored. The survey was carried out for 10 weeks and the responses were divided into three periods of around 3 weeks: 25 January-21 February, 22 February-17 March and 18 March-3 April (the period the Malaysian Government issued Movement Control Order). Findings revealed that most of the pyschobehavioural variables showed small increases during first (25 January-21 February) and second (22 February-17 March) periods, and high psychobehavioral responses were reported during the third period. A total of 72.1% (95%CI = 69.2-75.0) reported moderate to severe anxiety as measured by the State-Trait Anxiety Inventory. Factor influencing moderate to severe anxiety is a high perception of severity (OR = 2.09; 95%CI = 1.48-2.94), high perceived susceptibility (OR = 1.71; 95%CI = 1.17-2.50), high impact score (OR = 1.63; 95%CI = 1.17-2.26) and high fear score (OR = 1.47; 95%CI = 1.01-2.14). In conclusion, the psychological and behavioural responses were found to increase with the progression of the outbreak. High anxiety levels found in this study warrant provision of mental health intervention during the early phase of COVID-19 outbreak.