In patients recovering from COVID-19 infection, four stages of evolution on chest CT were identified: early stage (0-4 days); progressive stage (5-8 days); peak stage (10-13 days); and absorption stage (≥14 days). Key Results1. In patients who recovered from COVID-19 pneumonia, initial lung findings on chest CT were small subpleural ground glass opacities (GGO) that grew larger with crazy-paving pattern and consolidation.2. Lung involvement increased to consolidation up to two weeks after disease onset.3. After two weeks, the lesions were gradually absorbed leaving extensive GGO and subpleural parenchymal bands.This copy is for personal use only. To order printed copies, contact reprints@rsna.org I n P r e s s Abstract:Background: Chest CT is used to assess the severity of lung involvement in COVID-19 pneumonia. Purpose:To determine the change in chest CT findings associated with COVID-19 pneumonia from initial diagnosis until patient recovery. Materials and Methods:This retrospective review included patients with RT-PCR confirmed COVID-19 infection presenting between 12 January 2020 to 6 February 2020. Patients with severe respiratory distress and/ or oxygen requirement at any time during the disease course were excluded.Repeat Chest CT was obtained at approximately 4 day intervals. The total CT score was the sum of lung involvement (5 lobes, score 1-5 for each lobe, range, 0 none, 25 maximum) was determined.Results: Twenty one patients (6 males and 15 females, age 25-63 years) with confirmed COVID-19 pneumonia were evaluated. These patients underwent a total of 82 pulmonary CT scans with a mean interval of 4±1 days (range: 1-8 days). All patients were discharged after a mean hospitalized period of 17±4 days (range: 11-26 days). Maximum lung involved peaked at approximately 10 days (with the calculated total CT score of 6) from the onset of initial symptoms (R2=0.25), p<0.001). Based on quartiles of patients from day 0 to day 26 involvement, 4 stages of lung CT were defined: Stage 1 (0-4 days): ground glass opacities (GGO) in 18/24 (75%) patients with the total CT score of 2±2; (2) Stage-2 (5-8d days): increased crazy-paving pattern 9/17 patients (53%) with a increase in total CT score (6±4, p=0.002); (3) Stage-3 (9-13days): consolidation 19/21 (91%) patients with the peak of total CT score (7±4) ; (4) Stage-4 (≥14 days): gradual resolution of consolidation 15/20 (75%) patients with a decreased total CT score (6±4) without crazy-paving pattern. Conclusion:In patients recovering from COVID-19 pneumonia (without severe respiratory distress during the disease course), lung abnormalities on chest CT showed greatest severity approximately 10 days after initial onset of symptoms.

Rationale: Up to date, the exploration of clinical features in severe COVID-19 patients were mostly from the same center in Wuhan, China. The clinical data in other centers is limited. This study aims to explore the feasible parameters which could be used in clinical practice to predict the prognosis in hospitalized patients with severe coronavirus disease-19 . Methods: In this case-control study, patients with severe COVID-19 in this newly established isolation center on admission between 27 January 2020 to 19 March 2020 were divided to discharge group and death event group. Clinical information was collected and analyzed for the following objectives: 1. Comparisons of basic characteristics between two groups; 2. Risk factors for death on admission using logistic regression; 3. Dynamic changes of radiographic and laboratory parameters between two groups in the course. Results: 124 patients with severe COVID-19 on admission were included and divided into discharge group (n=35) and death event group (n=89). Sex, SpO2, breath rate, diastolic pressure, neutrophil, lymphocyte, C-reactive protein (CRP), procalcitonin (PCT), lactate dehydrogenase (LDH), and D-dimer were significantly correlated with death events identified using bivariate logistic regression. Further multivariate logistic regression demonstrated a significant model fitting with C-index of 0.845 (p<0.001), in which SpO2≤89%, lymphocyte≤0.64×10 9 /L, CRP>77.35mg/L, PCT>0.20μg/L, and LDH>481U/L were the independent risk factors with the ORs of 2. 959, 4.015, 2.852, 3.554, and 3.185, respectively (p<0.04). In the course, persistently lower lymphocyte with higher levels of CRP, PCT, IL-6, neutrophil, LDH, D-dimer, cardiac troponin I (cTnI), brain natriuretic peptide (BNP), and increased CD4+/CD8+ T-lymphocyte ratio and were observed in death events group, while these parameters stayed stable or improved in discharge group. Conclusions: On admission, the levels of SpO2, lymphocyte, CRP, PCT, and LDH could predict the prognosis of severe COVID-19 patients. Systematic inflammation with induced cardiac dysfunction was likely a primary reason for death events in severe COVID-19 except for acute respiratory distress syndrome.

Extensive planting of crops genetically engineered to produce insecticidal proteins from the bacterium Bacillus thuringiensis (Bt) has suppressed some major pests, reduced insecticide sprays, enhanced pest control by natural enemies, and increased grower profits. However, rapid evolution of resistance in pests is reducing these benefits. Better understanding of the genetic basis of resistance to Bt crops is urgently needed to monitor, delay, and counter pest resistance. We discovered that a point mutation in a previously unknown tetraspanin gene in the cotton bollworm (Helicoverpa armigera), a devastating global pest, confers dominant resistance to Cry1Ac, the sole Bt protein produced by transgenic cotton planted in China. We found the mutation using a genome-wide association study, followed by fine-scale genetic mapping and DNA sequence comparisons between resistant and susceptible strains. CRISPR/Cas9 knockout of the tetraspanin gene restored susceptibility to a resistant strain, whereas inserting the mutation conferred 125-fold resistance in a susceptible strain. DNA screening of moths captured from 23 field sites in six provinces of northern China revealed a 100-fold increase in the frequency of this mutation, from 0.001 in 2006 to 0.10 in 2016. The correspondence between the observed trajectory of the mutation and the trajectory predicted from simulation modeling shows that the dominance of the mutation accelerated adaptation. Proactive identification and tracking of the tetraspanin mutation demonstrate the potential for genomic analysis, gene editing, and molecular monitoring to improve management of resistance.

Therapeutic Level I. See Instructions for Authors for a complete description of levels of evidence.

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