First Report of Fusarium asiaticum Causing Stem Rot of Ligusticum chuanxiong in China

Ligusticum chuanxiong (known as chuanxiong in China) is a traditional edible and medicinal herb, which has been playing important roles in fighting against COVID-19 (Ma et al. 2020). In March 2021, we investigated stem rot of chuanxiong in six adjacent fields (∼100 ha) in Chengdu, Sichuan Province, China. The disease incidence was above 5% in each field. Symptomatic plants showed stem rot, water-soaked lesions, and blackening with white hyphae present on the stems. Twelve symptomatic chuanxiong plants (two plants per field) were sampled. Diseased tissues from the margins of necrotic lesions were surface sterilized in 75% ethanol for 45 s and 2% NaClO for 5 min. Samples were then rinsed three times in sterile distilled water and cultured on potato dextrose agar (PDA) at 25°C for 72 h. Fourteen fungal cultures were isolated from 18 diseased tissues, of which eight monosporic isolates showed uniform characteristics. The eight fungal isolates showed fluffy white aerial mycelia and produced yellow pigments with age. Mung bean broth was used to induce sporulation. Macroconidia were sickle-shaped, slender, three- to five-septate, and averaged 50 to 70 μm in length. Based on morphological features of colonies and conidia, the isolates were tentatively identified as Fusarium spp. (Leslie and Summerell 2006). To identify the species, the partial translation elongation factor 1 alpha (TEF1-α) gene was amplified and sequenced (O'Donnell et al. 1998). TEF1-α sequences of LCSR01, LCSR02, and LCSR05 isolates (GenBank nos. MZ169386, MZ169388, and MZ169387) were 100, 99.72, and 99.86% identical to that of F. asiaticum strain NRRL 26156, respectively. The phylogenetic tree based on TEF1-α sequences showed these isolates clustered with F. asiaticum using the neighbor-joining algorithm. Furthermore, these isolates were identified using the specific primer pair Fg16 F/R (Nicholson et al. 1998). The results showed these isolates (GenBank nos. MZ164938, MZ164939, and MZ164940) were 100% identical to F. asiaticum NRRL 26156. Pathogenicity testing of the isolate LCSR01 was conducted on chuanxiong. After wounding chuanxiong stalks and rhizomes with a sterile needle, the wounds were inoculated with mycelia PDA plugs. A total of 30 chuanxiong rhizomes and stalks were inoculated with mycelia PDA plugs, and five mock-inoculated chuanxiong rhizomes and stalks served as controls. After inoculation, the stalks and rhizomes were kept in a moist chamber at 25°C in the dark. At 8 days postinoculation (dpi), all inoculated stalks and rhizomes exhibited water-soaked and blackened lesions. At 10 dpi, the stalks turned soft and decayed, and abundant hyphae grew on the exterior of infected plants, similar to those observed in the field. No disease symptoms were observed on the control plants. The pathogen was reisolated from the inoculated tissues, and the identity was confirmed as described above. Ten fungal cultures were reisolated from the 10 inoculated tissues, of which nine fungal cultures were F. asiaticum, fulfilling Koch's postulates. To our knowledge, this is the first report of F. asiaticum causing stem rot of chuanxiong in China. Chuanxiong has been cultivated in rotation with rice over multiple years. This rotation may have played a role in the increase in inoculum density in soil and stem rot epidemics in chuanxiong. Diseased chuanxiong may be contaminated with the mycotoxins produced by F. asiaticum, 3-acetyldeoxynivalenol or nivalenol, which may deleteriously affect human health. Therefore, crop rotations should be considered carefully to reduce disease impacts.

Physical activity and asthma symptom control in children during COVID-19 lockdown: A feasibility study.

Objective: The aim was to evaluate the impact of the COVID-19 lockdown on physical activity (PA) and asthma symptom control in children. Methods: We conducted a single-cohort observational study on 22 children with a median age of 9 (8-11) years with a diagnosis of asthma being included in the study. Participants were asked to wear a PA tracker for 3 months; during the same 3-month period, the Paediatric Asthma Diary (PAD) was administered daily and the Asthma Control (AC) Questionnaire together with the mini-Paediatric Asthma Quality of Life (AQoL) Questionnaire administered at weekly intervals. Results: Compared with the pre-lockdown period, there was a significant reduction in PA levels after the lockdown began. Daily total steps reduced by about 3000 steps (p < 0.001), very active minutes by 9 min (p < 0.001) and fairly active minutes almost halved (p < 0.001); while asthma symptom control marginally improved, with the AC and AQoL score improving by 0.56 (p < 0.005) and 0.47 (p < 0.05), respectively. Further, for those with AC score higher than 1, PA was positively associated with asthma control both before and after the lockdown. Conclusions: This feasibility study suggests that PA engagement of children with asthma is negatively affected during the pandemic, but the beneficial effect of PA on asthma symptom control potentially sustains even during a lockdown period. These findings emphasize the importance of wearable device to monitor longitudinal PA and thus better management of PA for achieving the best outcome of asthma symptom control.

First Report of Fusarium asiaticum Causing Stem Rot of Ligusticum chuanxiong in China.

Ligusticum chuanxiong (known as Chuanxiong in China) is a traditional edible-medicinal herb, which has been playing important roles in fighting against COVID-19 (Ma et al. 2020). In March 2021, we investigated stem rot of Chuanxiong in six adjacent fields (~100 ha) in Chengdu, Sichuan Province, China. The disease incidence was above 5% in each field. Symptomatic plants showed stem rot, watersoaked lesions, and blackening with white hyphae present on the stems. Twelve symptomatic Chuanxiong plants (2 plants/field) were sampled. Diseased tissues from the margins of necrotic lesions were surface sterilized in 75% ethanol for 45 s, and 2% NaClO for 5 min. Samples were then rinsed three times in sterile distilled water and cultured on potato dextrose agar (PDA) at 25ºC for 72 h. Fourteen fungal cultures were isolated from 18 diseased tissues, of which eight monosporic isolates showed uniform characteristics. The eight fungal isolates showed fluffy white aerial mycelia and produced yellow pigments with age. Mung bean broth was used to induce sporulation. Macroconidia were sickle-shaped, slender, 3- to 5-septate, and averaged 50 to 70 µm in length. Based on morphological features of colonies and conidia, the isolates were tentatively identified as Fusarium spp. (Leslie and Summerell 2006). To identify the species, the partial translation elongation factor 1 alpha (TEF1-α) gene was amplified and sequenced (O'Donnell et al. 1998). TEF1-α sequences of LCSR01, LCSR02 and LCSR05 isolates (GenBank nos. MZ169386, MZ169388 and MZ169387) were 100%, 99.72% and 99.86% identical to that of F. asiaticum strain NRRL 26156, respectively. The phylogenetic tree based on TEF1-α sequences showed these isolates clustered with F. asiaticum using Neighbor-Joining algorithm. Furthermore, these isolates were identified using the specific primer pair Fg16 F/R (Nicholson et al. 1998). The results showed these isolates (GenBank nos. MZ164938, MZ164939 and MZ164940) were 100% identical to F. asiaticum NRRL 26156. Pathogenicity test of the isolate LCSR01 was conducted on Chuanxiong. After wounding Chuanxiong stalks and rhizomes with a sterile needle, the wounds were inoculated with mycelia PDA plugs. A total of 30 Chuanxiong rhizomes and stalks were inoculated with mycelia PDA plugs, and five mock-inoculated Chuanxiong rhizomes and stalks served as controls. After inoculation, the stalks and rhizomes were kept in a moist chamber at 25°C in the dark. At 8 days post inoculation (dpi), all inoculated stalks and rhizomes exhibited water-soaked and blackened lesions. At 10 dpi, the stalks turned soft and decayed, and abundant hyphae grew on the exterior of infected plants, similar to those observed in the field. No disease symptoms were observed on the control plants. The pathogen was re-isolated from the inoculated tissues and the identity was confirmed as described above. Ten fungal cultures were re-isolated from the 10 inoculated tissues, of which nine fungal cultures were F. asiaticum, fulfilling Koch's postulates. To our knowledge, this is the first report of F. asiaticum causing stem rot of Chuanxiong in China. Chuanxiong has been cultivated in rotation with rice over multiple years. This rotation may have played a role in the increase in inoculum density in soil and stem rot epidemics in Chuanxiong. Diseased Chuanxiong may be contaminated with the mycotoxins produced by F. asciaticum, 3-acetyldeoxynivalenol or nivalenol, which may deleteriously affect human health. Therefore, crop rotations should be considered carefully to reduce disease impacts.

Real-world evaluation of rapid and laboratory-free COVID-19 triage for emergency care: external validation and pilot deployment of artificial intelligence driven screening.

BACKGROUND: Uncertainty in patients' COVID-19 status contributes to treatment delays, nosocomial transmission, and operational pressures in hospitals. However, the typical turnaround time for laboratory PCR remains 12-24 h and lateral flow devices (LFDs) have limited sensitivity. Previously, we have shown that artificial intelligence-driven triage (CURIAL-1.0) can provide rapid COVID-19 screening using clinical data routinely available within 1 h of arrival to hospital. Here, we aimed to improve the time from arrival to the emergency department to the availability of a result, do external and prospective validation, and deploy a novel laboratory-free screening tool in a UK emergency department. METHODS: We optimised our previous model, removing less informative predictors to improve generalisability and speed, developing the CURIAL-Lab model with vital signs and readily available blood tests (full blood count [FBC]; urea, creatinine, and electrolytes; liver function tests; and C-reactive protein) and the CURIAL-Rapide model with vital signs and FBC alone. Models were validated externally for emergency admissions to University Hospitals Birmingham, Bedfordshire Hospitals, and Portsmouth Hospitals University National Health Service (NHS) trusts, and prospectively at Oxford University Hospitals, by comparison with PCR testing. Next, we compared model performance directly against LFDs and evaluated a combined pathway that triaged patients who had either a positive CURIAL model result or a positive LFD to a COVID-19-suspected clinical area. Lastly, we deployed CURIAL-Rapide alongside an approved point-of-care FBC analyser to provide laboratory-free COVID-19 screening at the John Radcliffe Hospital (Oxford, UK). Our primary improvement outcome was time-to-result, and our performance measures were sensitivity, specificity, positive and negative predictive values, and area under receiver operating characteristic curve (AUROC). FINDINGS: 72 223 patients met eligibility criteria across the four validating hospital groups, in a total validation period spanning Dec 1, 2019, to March 31, 2021. CURIAL-Lab and CURIAL-Rapide performed consistently across trusts (AUROC range 0·858-0·881, 95% CI 0·838-0·912, for CURIAL-Lab and 0·836-0·854, 0·814-0·889, for CURIAL-Rapide), achieving highest sensitivity at Portsmouth Hospitals (84·1%, Wilson's 95% CI 82·5-85·7, for CURIAL-Lab and 83·5%, 81·8-85·1, for CURIAL-Rapide) at specificities of 71·3% (70·9-71·8) for CURIAL-Lab and 63·6% (63·1-64·1) for CURIAL-Rapide. When combined with LFDs, model predictions improved triage sensitivity from 56·9% (51·7-62·0) for LFDs alone to 85·6% with CURIAL-Lab (81·6-88·9; AUROC 0·925) and 88·2% with CURIAL-Rapide (84·4-91·1; AUROC 0·919), thereby reducing missed COVID-19 cases by 65% with CURIAL-Lab and 72% with CURIAL-Rapide. For the prospective deployment of CURIAL-Rapide, 520 patients were enrolled for point-of-care FBC analysis between Feb 18 and May 10, 2021, of whom 436 received confirmatory PCR testing and ten (2·3%) tested positive. Median time from arrival to a CURIAL-Rapide result was 45 min (IQR 32-64), 16 min (26·3%) sooner than with LFDs (61 min, 37-99; log-rank p<0·0001), and 6 h 52 min (90·2%) sooner than with PCR (7 h 37 min, 6 h 5 min to 15 h 39 min; p<0·0001). Classification performance was high, with sensitivity of 87·5% (95% CI 52·9-97·8), specificity of 85·4% (81·3-88·7), and negative predictive value of 99·7% (98·2-99·9). CURIAL-Rapide correctly excluded infection for 31 (58·5%) of 53 patients who were triaged by a physician to a COVID-19-suspected area but went on to test negative by PCR. INTERPRETATION: Our findings show the generalisability, performance, and real-world operational benefits of artificial intelligence-driven screening for COVID-19 over standard-of-care in emergency departments. CURIAL-Rapide provided rapid, laboratory-free screening when used with near-patient FBC analysis, and was able to reduce the number of patients who tested negative for COVID-19 but were triaged to COVID-19-suspected areas. FUNDING: The Wellcome Trust, University of Oxford Medical and Life Sciences Translational Fund.

Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge.

BACKGROUND: Delays in patient flow and a shortage of hospital beds are commonplace in hospitals during periods of increased infection incidence, such as seasonal influenza and the COVID-19 pandemic. The objective of this study was to develop and evaluate the efficacy of machine learning methods at identifying and ranking the real-time readiness of individual patients for discharge, with the goal of improving patient flow within hospitals during periods of crisis. METHODS AND PERFORMANCE: Electronic Health Record data from Oxford University Hospitals was used to train independent models to classify and rank patients' real-time readiness for discharge within 24 hours, for patient subsets according to the nature of their admission (planned or emergency) and the number of days elapsed since their admission. A strategy for the use of the models' inference is proposed, by which the model makes predictions for all patients in hospital and ranks them in order of likelihood of discharge within the following 24 hours. The 20% of patients with the highest ranking are considered as candidates for discharge and would therefore expect to have a further screening by a clinician to confirm whether they are ready for discharge or not. Performance was evaluated in terms of positive predictive value (PPV), i.e., the proportion of these patients who would have been correctly deemed as 'ready for discharge' after having the second screening by a clinician. Performance was high for patients on their first day of admission (PPV = 0.96/0.94 for planned/emergency patients respectively) but dropped for patients further into a longer admission (PPV = 0.66/0.71 for planned/emergency patients still in hospital after 7 days). CONCLUSION: We demonstrate the efficacy of machine learning methods at making operationally focused, next-day discharge readiness predictions for all individual patients in hospital at any given moment and propose a strategy for their use within a decision-support tool during crisis periods.

CT Features of Coronavirus Disease 2019 (COVID-19) Pneumonia in 62 Patients in Wuhan, China.

OBJECTIVE. The purpose of this study was to investigate 62 subjects in Wuhan, China, with laboratory-confirmed coronavirus disease (COVID-19) pneumonia and describe the CT features of this epidemic disease. MATERIALS AND METHODS. A retrospective study of 62 consecutive patients with laboratory-confirmed COVID-19 pneumonia was performed. CT images and clinical data were reviewed. Two thoracic radiologists evaluated the distribution and CT signs of the lesions and also scored the extent of involvement of the CT signs. The Mann-Whitney U test was used to compare lesion distribution and CT scores. The chi-square test was used to compare the CT signs of early-phase versus advanced-phase COVID-19 pneumonia. RESULTS. A total of 62 patients (39 men and 23 women; mean [± SD] age, 52.8 ± 12.2 years; range, 30-77 years) with COVID-19 pneumonia were evaluated. Twenty-four of 30 patients who underwent routine blood tests (80.0%) had a decreased lymphocyte count. Of 27 patients who had their erythrocyte sedimentation rate and high-sensitivity C-reactive protein level assessed, 18 (66.7%) had an increased erythrocyte sedimentation rate, and all 27 (100.0%) had an elevated high-sensitivity C-reactive protein level. Multiple lesions were seen on the initial CT scan of 52 of 62 patients (83.9%). Forty-eight of 62 patients (77.4%) had predominantly peripheral distribution of lesions. The mean CT score for the upper zone (3.0 ± 3.4) was significantly lower than that for the middle (4.5 ± 3.8) and lower (4.5 ± 3.7) zones (p = 0.022 and p = 0.020, respectively), and there was no significant difference in the mean CT score of the middle and lower zones (p = 1.00). The mean CT score for the anterior area (4.4 ± 4.1) was significantly lower than that for the posterior area (7.7 ± 6.3) (p = 0.003). CT findings for the patients were as follows: 25 patients (40.3%) had ground-glass opacities (GGO), 21 (33.9%), consolidation; 39 (62.9%), GGO plus a reticular pattern; 34 (54.8%), vacuolar sign; 28 (45.2%), microvascular dilation sign; 35 (56.5%), fibrotic streaks; 21 (33.9%), a subpleural line; and 33 (53.2%), a subpleural transparent line. With regard to bronchial changes seen on CT, 45 patients (72.6%) had air bronchogram, and 11 (17.7%) had bronchus distortion. In terms of pleural changes, CT showed that 30 patients (48.4%) had pleural thickening, 35 (56.5%) had pleural retraction sign, and six (9.7%) had pleural effusion. Compared with early-phase disease (≤ 7 days after the onset of symptoms), advanced-phase disease (8-14 days after the onset of symptoms) was characterized by significantly increased frequencies of GGO plus a reticular pattern, vacuolar sign, fibrotic streaks, a subpleural line, a subpleural transparent line, air bronchogram, bronchus distortion, and pleural effusion; however, GGO significantly decreased in advanced-phase disease. CONCLUSION. CT examination of patients with COVID-19 pneumonia showed a mixed and diverse pattern with both lung parenchyma and the interstitium involved. Identification of GGO and a single lesion on the initial CT scan suggested early-phase disease. CT signs of aggravation and repair coexisted in advanced-phase disease. Lesions presented with a characteristic multifocal distribution in the middle and lower lung regions and in the posterior lung area. A decreased lymphocyte count and an increased high-sensitivity C-reactive protein level were the most common laboratory findings.

Development and validation of early warning score systems for COVID-19 patients.

COVID-19 is a major, urgent, and ongoing threat to global health. Globally more than 24 million have been infected and the disease has claimed more than a million lives as of November 2020. Predicting which patients will need respiratory support is important to guiding individual patient treatment and also to ensuring sufficient resources are available. The ability of six common Early Warning Scores (EWS) to identify respiratory deterioration defined as the need for advanced respiratory support (high-flow nasal oxygen, continuous positive airways pressure, non-invasive ventilation, intubation) within a prediction window of 24 h is evaluated. It is shown that these scores perform sub-optimally at this specific task. Therefore, an alternative EWS based on the Gradient Boosting Trees (GBT) algorithm is developed that is able to predict deterioration within the next 24 h with high AUROC 94% and an accuracy, sensitivity, and specificity of 70%, 96%, 70%, respectively. The GBT model outperformed the best EWS (LDTEWS:NEWS), increasing the AUROC by 14%. Our GBT model makes the prediction based on the current and baseline measures of routinely available vital signs and blood tests.

Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test.

BACKGROUND: The early clinical course of COVID-19 can be difficult to distinguish from other illnesses driving presentation to hospital. However, viral-specific PCR testing has limited sensitivity and results can take up to 72 h for operational reasons. We aimed to develop and validate two early-detection models for COVID-19, screening for the disease among patients attending the emergency department and the subset being admitted to hospital, using routinely collected health-care data (laboratory tests, blood gas measurements, and vital signs). These data are typically available within the first hour of presentation to hospitals in high-income and middle-income countries, within the existing laboratory infrastructure. METHODS: We trained linear and non-linear machine learning classifiers to distinguish patients with COVID-19 from pre-pandemic controls, using electronic health record data for patients presenting to the emergency department and admitted across a group of four teaching hospitals in Oxfordshire, UK (Oxford University Hospitals). Data extracted included presentation blood tests, blood gas testing, vital signs, and results of PCR testing for respiratory viruses. Adult patients (>18 years) presenting to hospital before Dec 1, 2019 (before the first COVID-19 outbreak), were included in the COVID-19-negative cohort; those presenting to hospital between Dec 1, 2019, and April 19, 2020, with PCR-confirmed severe acute respiratory syndrome coronavirus 2 infection were included in the COVID-19-positive cohort. Patients who were subsequently admitted to hospital were included in their respective COVID-19-negative or COVID-19-positive admissions cohorts. Models were calibrated to sensitivities of 70%, 80%, and 90% during training, and performance was initially assessed on a held-out test set generated by an 80:20 split stratified by patients with COVID-19 and balanced equally with pre-pandemic controls. To simulate real-world performance at different stages of an epidemic, we generated test sets with varying prevalences of COVID-19 and assessed predictive values for our models. We prospectively validated our 80% sensitivity models for all patients presenting or admitted to the Oxford University Hospitals between April 20 and May 6, 2020, comparing model predictions with PCR test results. FINDINGS: We assessed 155 689 adult patients presenting to hospital between Dec 1, 2017, and April 19, 2020. 114 957 patients were included in the COVID-negative cohort and 437 in the COVID-positive cohort, for a full study population of 115 394 patients, with 72 310 admitted to hospital. With a sensitive configuration of 80%, our emergency department (ED) model achieved 77·4% sensitivity and 95·7% specificity (area under the receiver operating characteristic curve [AUROC] 0·939) for COVID-19 among all patients attending hospital, and the admissions model achieved 77·4% sensitivity and 94·8% specificity (AUROC 0·940) for the subset of patients admitted to hospital. Both models achieved high negative predictive values (NPV; >98·5%) across a range of prevalences (≤5%). We prospectively validated our models for all patients presenting and admitted to Oxford University Hospitals in a 2-week test period. The ED model (3326 patients) achieved 92·3% accuracy (NPV 97·6%, AUROC 0·881), and the admissions model (1715 patients) achieved 92·5% accuracy (97·7%, 0·871) in comparison with PCR results. Sensitivity analyses to account for uncertainty in negative PCR results improved apparent accuracy (ED model 95·1%, admissions model 94·1%) and NPV (ED model 99·0%, admissions model 98·5%). INTERPRETATION: Our models performed effectively as a screening test for COVID-19, excluding the illness with high-confidence by use of clinical data routinely available within 1 h of presentation to hospital. Our approach is rapidly scalable, fitting within the existing laboratory testing infrastructure and standard of care of hospitals in high-income and middle-income countries. FUNDING: Wellcome Trust, University of Oxford, Engineering and Physical Sciences Research Council, National Institute for Health Research Oxford Biomedical Research Centre.

Deep learning-based triage and analysis of lesion burden for COVID-19: a retrospective study with external validation.

Background: Prompt identification of patients suspected to have COVID-19 is crucial for disease control. We aimed to develop a deep learning algorithm on the basis of chest CT for rapid triaging in fever clinics. Methods: We trained a U-Net-based model on unenhanced chest CT scans obtained from 2447 patients admitted to Tongji Hospital (Wuhan, China) between Feb 1, 2020, and March 3, 2020 (1647 patients with RT-PCR-confirmed COVID-19 and 800 patients without COVID-19) to segment lung opacities and alert cases with COVID-19 imaging manifestations. The ability of artificial intelligence (AI) to triage patients suspected to have COVID-19 was assessed in a large external validation set, which included 2120 retrospectively collected consecutive cases from three fever clinics inside and outside the epidemic centre of Wuhan (Tianyou Hospital [Wuhan, China; area of high COVID-19 prevalence], Xianning Central Hospital [Xianning, China; area of medium COVID-19 prevalence], and The Second Xiangya Hospital [Changsha, China; area of low COVID-19 prevalence]) between Jan 22, 2020, and Feb 14, 2020. To validate the sensitivity of the algorithm in a larger sample of patients with COVID-19, we also included 761 chest CT scans from 722 patients with RT-PCR-confirmed COVID-19 treated in a makeshift hospital (Guanggu Fangcang Hospital, Wuhan, China) between Feb 21, 2020, and March 6, 2020. Additionally, the accuracy of AI was compared with a radiologist panel for the identification of lesion burden increase on pairs of CT scans obtained from 100 patients with COVID-19. Findings: In the external validation set, using radiological reports as the reference standard, AI-aided triage achieved an area under the curve of 0·953 (95% CI 0·949-0·959), with a sensitivity of 0·923 (95% CI 0·914-0·932), specificity of 0·851 (0·842-0·860), a positive predictive value of 0·790 (0·777-0·803), and a negative predictive value of 0·948 (0·941-0·954). AI took a median of 0·55 min (IQR: 0·43-0·63) to flag a positive case, whereas radiologists took a median of 16·21 min (11·67-25·71) to draft a report and 23·06 min (15·67-39·20) to release a report. With regard to the identification of increases in lesion burden, AI achieved a sensitivity of 0·962 (95% CI 0·947-1·000) and a specificity of 0·875 (95 %CI 0·833-0·923). The agreement between AI and the radiologist panel was high (Cohen's kappa coefficient 0·839, 95% CI 0·718-0·940). Interpretation: A deep learning algorithm for triaging patients with suspected COVID-19 at fever clinics was developed and externally validated. Given its high accuracy across populations with varied COVID-19 prevalence, integration of this system into the standard clinical workflow could expedite identification of chest CT scans with imaging indications of COVID-19. Funding: Special Project for Emergency of the Science and Technology Department of Hubei Province, China.

Analysis of clinical features and related factors of coronavirus disease 2019 in Shanghai

Objective • To explore the common clinical features of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)-infected local patients in Shanghai and their related influencing factors. Methods • A total of 320 patients admitted to Shanghai Public Health Clinical Center from January to March 2020 and diagnosed as having coronavirus disease 2019(COVID-19) were selected. Clinical data of the patients were collected to analyze their characteristics. Using the statistical operation formula of R language, the correlation analysis of hospitalization days, days of increased hypersensitive C-reactive protein concentration (allergic days), days of lung CT improvement (CT days), and days required for nucleic acid turning negative with the main clinical manifestations and laboratory data was carried out. The correlation factors affecting the above four variables were analyzed. Results • Among the 320 patients, the proportions of mild type, moderate type, serious type and critical type were 6.25%, 83.44%, 6.88% and 3.44%, respectively;91.25% of them had a history of exposure to Hubei. The proportions of fever, cough, sputum and fatigue were 79.06%, 46.56%, 21.56% and 15.31%, respectively. Spearman correlation analysis showed that the concentrations of lactate dehydrogenase, interleukin-2(IL-2) and IL-6 were positively correlated with the above four variables, respectively (all P<0.05), albumin concentration was negatively correlated with allergic days (P=0.018), and CD4+ cell count was negatively correlated with CT days and days required for nucleic acid turning negative (both P<0.05). Stepwise multiple linear regression analysis showed that procalcitonin (PCT) concentration was negatively correlated with hospitalization days, CT days and allergic days (both P<0.05), and disease type was positively correlated with hospitalization days, allergic days, CT days and days required for nucleic acid turning negative (all P<0.05). Conclusion • Moderate type is common in the local patients in Shanghai;fever, cough and fatigue are common symptoms, and most of the patients are accompanied by lung CT abnormalities. The therapeutic effect and prognosis of these patients are closely related to disease type, concentrations of PCT and IL-6, as well as CD4+ cell count.

Imaging features and evolution on CT in 100 COVID-19 pneumonia patients in Wuhan, China.

OBJECTIVES: To investigate CT images of 100 confirmed COVID-19 pneumonia patients to describe the lesion distribution, CT signs, and evolution during different courses. METHODS: A retrospective study of 100 COVID-19 pneumonia patients without ARDS was performed, and CT scans were reviewed. A COVID-19 pneumonia course diagram was drawn. Mann-Whitney U test was used to compare the lesion distribution and CT scores, χ2 test was used to compare the CT findings between different stages. RESULTS: A total of 272 CT scans from 100 patients (mean age, 52.3 years ± 13.1) were investigated. Four patients with lung abnormalities on CT first showed negative RT-PCR result and turned positive afterwards. One hundred sixty-nine (62.1%) showed predominantly peripheral distribution. The CT scores of the upper zone (3.4 ± 3.6) were significantly lower than those of the middle (5.0 ± 3.9) and lower (4.8 ± 3.6) zones (p < 0.001). The CT scores of the anterior zones (4.9 ± 4.7) were significantly lower than those of the posterior zones (8.4 ± 6.2) (p < 0.001). In the early rapid progressive stage (1~7 days), ground glass opacity (GGO) plus reticular pattern (58.1%), GGO plus consolidation (43.0%), and GGO (41.9%) were all common. In the advanced stage (8~14 days), GGO plus consolidation (79.8%) and repairing CT signs (subpleural line, bronchus distortion, and fibrotic strips) showed a significant increase (p < 0.05). In the absorption stage, GGO plus consolidation (9.1%) sharply decreased (p < 0.05). CONCLUSION: CT imaging of COVID-19 pneumonia showed a predominantly peripheral, middle and lower, and posterior distribution. The early rapid progressive stage is 1~7 days from symptom onset, the advanced stage with peak levels of abnormalities on CT is 8~14 days, and the abnormalities started to improve after 14 days. KEY POINTS: • The course of COVID-19 pneumonia consists of three stages: 1~7 days is the early rapid progressive stage, 8~14 days is the advanced stage, and after 14 days, the abnormalities started to decrease. • In the early rapid progressive stage, GGO plus a reticular pattern, GGO plus consolidation, and GGO were all common signs; in the advanced stage, signs of progression and absorption coexisted; lung abnormalities showed an asynchronous process with parts with absorption and parts progressing. • Lung abnormalities mainly showed predominantly peripheral, middle, and lower distribution.

A Comparative Study of Chest Computed Tomography Features in Young and Older Adults With Corona Virus Disease (COVID-19).

OBJECTIVE: To analyze the most common computed tomography (CT) findings of pneumonia caused by new coronavirus in younger patients (60 and younger) and older adults (older than 60). MATERIALS AND METHODS: The chest CT images of 72 symptomatic patients with corona virus disease (COVID-19) were analyzed retrospectively, including 44 younger patients (47.5±8.7 y old) and 28 older patients (68.4±6.0 y old). CT findings including density (pure ground-glass opacities, ground-glass opacities with consolidation, consolidation), the number of lobes involved, lesion distribution, and the main accompanying signs were analyzed and compared. RESULTS: Characteristic CT findings included the lobes of bilateral lung extensively involved, ground-glass opacity and ground-glass opacity with consolidation in the peripheral area, sometimes accompanied by interlobular septal thickening, and subpleural line and pleural thickening. Compared with the younger group, the proportion of extensive involvement of lung lobes was higher in the elderly group (71.4% vs. 36.4%, P=0.009), and subpleural line and pleural thickening were more likely to occur (50.0% vs. 25.0%, and 71.4% vs. 40.9%, P=0.030 and 0.011, respectively). CONCLUSION: Elderly and younger patients with corona virus disease have some common CT features, but older patients are more likely to have extensive lung lobe involvement, and subpleural line and pleural thickening. These differentiated characteristics may be related to the progress and prognosis of the disease.

Initial CT findings and temporal changes in patients with the novel coronavirus pneumonia (2019-nCoV): a study of 63 patients in Wuhan, China.

OBJECTIVES: The purpose of this study was to observe the imaging characteristics of the novel coronavirus pneumonia. METHODS: Sixty-three confirmed patients were enrolled from December 30, 2019 to January 31, 2020. High-resolution CT (HRCT) of the chest was performed. The number of affected lobes, ground glass nodules (GGO), patchy/punctate ground glass opacities, patchy consolidation, fibrous stripes and irregular solid nodules in each patient's chest CT image were recorded. Additionally, we performed imaging follow-up of these patients. RESULTS: CT images of 63 confirmed patients were collected. M/F ratio: 33/30. The mean age was 44.9 ± 15.2 years. The mean number of affected lobes was 3.3 ± 1.8. Nineteen (30.2%) patients had one affected lobe, five (7.9%) patients had two affected lobes, four (6.3%) patients had three affected lobes, seven (11.1%) patients had four affected lobes while 28 (44.4%) patients had 5 affected lobes. Fifty-four (85.7%) patients had patchy/punctate ground glass opacities, 14 (22.2%) patients had GGO, 12 (19.0%) patients had patchy consolidation, 11 (17.5%) patients had fibrous stripes and 8 (12.7%) patients had irregular solid nodules. Fifty-four (85.7%) patients progressed, including single GGO increased, enlarged and consolidated; fibrous stripe enlarged, while solid nodules increased and enlarged. CONCLUSIONS: Imaging changes in novel viral pneumonia are rapid. The manifestations of the novel coronavirus pneumonia are diverse. Imaging changes of typical viral pneumonia and some specific imaging features were observed. Therefore, we need to strengthen the recognition of image changes to help clinicians to diagnose quickly and accurately. KEY POINTS: • High-resolution CT (HRCT) of the chest is critical for early detection, evaluation of disease severity and follow-up of patients with the novel coronavirus pneumonia. • The manifestations of the novel coronavirus pneumonia are diverse and change rapidly. • Radiologists should be aware of the various features of the disease and temporal changes.