Re-emergence of Severe Acute Diarrhea Syndrome Coronavirus (SADS-CoV) in Guangxi, China, 2021 (preprint)

Severe acute diarrhea syndrome coronavirus (SADS-CoV) has had a major impact on the swine industry in China, but has not been detected since 2019. Using real-time qPCR and metagenomic surveillance we identified SADS-CoV in a pig farm experiencing diarrheal disease. Genomic analysis supported the undetected circulation of SADS-CoV since 2019.

The Differences and Changes of Semi-Quantitative and Quantitative CT Features of Coronavirus Disease 2019 Pneumonia in Patients With or Without Smoking History.

Objective: To assess CT features of COVID-19 patients with different smoking status using quantitative and semi-quantitative technologies and to investigate changes of CT features in different disease states between the two groups. Methods: 30 COVID-19 patients with current smoking status (29 men, 1 woman) admitted in our database were enrolled as smoking group and 56 COVID-19 patients without smoking history (24 men, 32 women) admitted during the same period were enrolled as a control group. Twenty-seven smoking cases and 55 control cases reached recovery standard and were discharged. Initial and follow-up CT during hospitalization and follow-up CT after discharge were acquired. Thirty quantitative features, including the ratio of infection volume and visual-assessed interstitial changes score including total score, score of ground glass opacity, consolidation, septal thickening, reticulation and honeycombing sign, were analyzed. Results: Initial CT images of the smoking group showed higher scores of septal thickening [4.5 (0-5) vs. 0 (0-4), p = 0.001] and reticulation [0 (0-5.25) vs 0 (0-0), p = 0.001] as well as higher total score [7 (5-12.25) vs. 6 (5-7), p = 0.008] with statistical significance than in the control group. The score of reticulation was higher in the smoking group than in the control group when discharged [0.89 (0-0) vs. 0.09 (0-0), p = 0.02]. The score of septal thickening tended to be higher in the smoking group than the control group [4 (0-4) vs. 0 (0-4), p = 0.007] after being discharged. Quantitative CT features including infection ratio of whole lung and left lung as well as infection ratio within HU (-750, -300) and within HU (-300, 49) were higher in the control group of initial CT with statistical differences. The infection ratio of whole lung and left lung, infection ratio within HU (-750), and within HU (-750, -300) were higher in the control group with statistical differences when discharged. This trend turned adverse after discharge and the values of quantitative features were generally higher in the smoking group than in the control group without statistical differences. Conclusions: Patients with a history of smoking presented more severe interstitial manifestations and more residual lesion after being discharged. More support should be given for COVID-19 patients with a smoking history during hospitalization and after discharge.

Deep supervised learning using self-adaptive auxiliary loss for COVID-19 diagnosis from imbalanced CT images.

The outbreak and rapid spread of coronavirus disease 2019 (COVID-19) has had a huge impact on the lives and safety of people around the world. Chest CT is considered an effective tool for the diagnosis and follow-up of COVID-19. For faster examination, automatic COVID-19 diagnostic techniques using deep learning on CT images have received increasing attention. However, the number and category of existing datasets for COVID-19 diagnosis that can be used for training are limited, and the number of initial COVID-19 samples is much smaller than the normal's, which leads to the problem of class imbalance. It makes the classification algorithms difficult to learn the discriminative boundaries since the data of some classes are rich while others are scarce. Therefore, training robust deep neural networks with imbalanced data is a fundamental challenging but important task in the diagnosis of COVID-19. In this paper, we create a challenging clinical dataset (named COVID19-Diag) with category diversity and propose a novel imbalanced data classification method using deep supervised learning with a self-adaptive auxiliary loss (DSN-SAAL) for COVID-19 diagnosis. The loss function considers both the effects of data overlap between CT slices and possible noisy labels in clinical datasets on a multi-scale, deep supervised network framework by integrating the effective number of samples and a weighting regularization item. The learning process jointly and automatically optimizes all parameters over the deep supervised network, making our model generally applicable to a wide range of datasets. Extensive experiments are conducted on COVID19-Diag and three public COVID-19 diagnosis datasets. The results show that our DSN-SAAL outperforms the state-of-the-art methods and is effective for the diagnosis of COVID-19 in varying degrees of data imbalance.

Severity assessment of COVID-19 using CT image features and laboratory indices.

The coronavirus disease 2019 (COVID-19) is now a global pandemic. Tens of millions of people have been confirmed with infection, and also more people are suspected. Chest computed tomography (CT) is recognized as an important tool for COVID-19 severity assessment. As the number of chest CT images increases rapidly, manual severity assessment becomes a labor-intensive task, delaying appropriate isolation and treatment. In this paper, a study of automatic severity assessment for COVID-19 is presented. Specifically, chest CT images of 118 patients (age 46.5 ± 16.5 years, 64 male and 54 female) with confirmed COVID-19 infection are used, from which 63 quantitative features and 110 radiomics features are derived. Besides the chest CT image features, 36 laboratory indices of each patient are also used, which can provide complementary information from a different view. A random forest (RF) model is trained to assess the severity (non-severe or severe) according to the chest CT image features and laboratory indices. Importance of each chest CT image feature and laboratory index, which reflects the correlation to the severity of COVID-19, is also calculated from the RF model. Using three-fold cross-validation, the RF model shows promising results: 0.910 (true positive ratio), 0.858 (true negative ratio) and 0.890 (accuracy), along with AUC of 0.98. Moreover, several chest CT image features and laboratory indices are found to be highly related to COVID-19 severity, which could be valuable for the clinical diagnosis of COVID-19.

The Prediction for Development of COVID-19 in Global Major Epidemic Areas Through Empirical Trends in China by Utilizing State Transition Matrix Model (preprint)

Background: Since pneumonia caused by coronavirus disease 2019 (COVID-19) broke out in Wuhan, Hubei province, China, tremendous infected cases has risen all over the world attributed to its high transmissibility. We aimed to mathematically forecast the inflection point (IFP) of new cases in South Korea, Italy, and Iran, utilizing the transcendental model from China. Methods: : Data from reports released by the National Health Commission of the People’s Republic of China (Dec 31, 2019 to Mar 5, 2020) and the World Health Organization (Jan 20, 2020 to Mar 5, 2020) were extracted as the training set and the data from Mar 6 to 9 as the validation set. New close contacts, newly confirmed cases, cumulative confirmed cases, non-severe cases, severe cases, critical cases, cured cases, and death were collected and analyzed. We analyzed the data above through the State Transition Matrix model. Results: : The optimistic scenario (non-Hubei model, daily increment rate of -3.87%), the cautiously optimistic scenario (Hubei model, daily increment rate of -2.20%), and the relatively pessimistic scenario (adjustment, daily increment rate of -1.50%) were inferred and modeling from data in China. The IFP of time in South Korea would be Mar 6 to 12, Italy Mar 10 to 24, and Iran Mar 10 to 24. The numbers of cumulative confirmed patients will reach approximately 20k in South Korea, 209k in Italy, and 226k in Iran under fitting scenarios, respectively. However, with the adoption of different diagnosis criteria, the variation of new cases could impose various influences in the predictive model. If that happens, the IFP of increment will be earlier than predicted above. Conclusion: The end of the pandemic is still inapproachable, and the number of confirmed cases is still escalating. With the augment of data, the world epidemic trend could be further predicted, and it is imperative to consummate the assignment of global medical resources to curb the development of COVID-19.

Chest CT for Typical Coronavirus Disease 2019 (COVID-19) Pneumonia: Relationship to Negative RT-PCR Testing.

Some patients with positive chest CT findings may present with negative results of real-time reverse-transcription polymerase chain reaction (RT-PCR) tests for coronavirus disease 2019 (COVID-19). In this study, the authors present chest CT findings from five patients with COVID-19 infection who had initial negative RT-PCR results. All five patients had typical imaging findings, including ground-glass opacity (five patients) and/or mixed ground-glass opacity and mixed consolidation (two patients). After isolation for presumed COVID-19 pneumonia, all patients were eventually confirmed to have COVID-19 infection by means of repeated swab tests. A combination of repeated swab tests and CT scanning may be helpful for individuals with a high clinical suspicion of COVID-19 infection but negative findings at RT-PCR screening.

From community-acquired pneumonia to COVID-19: a deep learning-based method for quantitative analysis of COVID-19 on thick-section CT scans.

OBJECTIVE: To develop a fully automated AI system to quantitatively assess the disease severity and disease progression of COVID-19 using thick-section chest CT images. METHODS: In this retrospective study, an AI system was developed to automatically segment and quantify the COVID-19-infected lung regions on thick-section chest CT images. Five hundred thirty-one CT scans from 204 COVID-19 patients were collected from one appointed COVID-19 hospital. The automatically segmented lung abnormalities were compared with manual segmentation of two experienced radiologists using the Dice coefficient on a randomly selected subset (30 CT scans). Two imaging biomarkers were automatically computed, i.e., the portion of infection (POI) and the average infection HU (iHU), to assess disease severity and disease progression. The assessments were compared with patient status of diagnosis reports and key phrases extracted from radiology reports using the area under the receiver operating characteristic curve (AUC) and Cohen's kappa, respectively. RESULTS: The dice coefficient between the segmentation of the AI system and two experienced radiologists for the COVID-19-infected lung abnormalities was 0.74 ± 0.28 and 0.76 ± 0.29, respectively, which were close to the inter-observer agreement (0.79 ± 0.25). The computed two imaging biomarkers can distinguish between the severe and non-severe stages with an AUC of 0.97 (p value < 0.001). Very good agreement (κ = 0.8220) between the AI system and the radiologists was achieved on evaluating the changes in infection volumes. CONCLUSIONS: A deep learning-based AI system built on the thick-section CT imaging can accurately quantify the COVID-19-associated lung abnormalities and assess the disease severity and its progressions. KEY POINTS: • A deep learning-based AI system was able to accurately segment the infected lung regions by COVID-19 using the thick-section CT scans (Dice coefficient ≥ 0.74). • The computed imaging biomarkers were able to distinguish between the non-severe and severe COVID-19 stages (area under the receiver operating characteristic curve 0.97). • The infection volume changes computed by the AI system were able to assess the COVID-19 progression (Cohen's kappa 0.8220).

Construction and Validation of a Machine Learning-based Nomogram: A TOOL to Predict the Risk of Getting Severe Corona Virus Disease 2019 (COVID-19) (preprint)

Background Identifying patients who may develop severe coronavirus disease 2019 (COVID-19) will facilitate personalized treatment and optimize the distribution of medical resources.Methods In this study, 590 COVID-19 patients during hospitalization were enrolled (Training set: n = 285; Internal validation set: n = 127; Prospective set: n = 178). After filtered by 2 machine learning methods in the training set, 5 out of 31 clinical features were selected into model building to predict the risk of developing severe COVID-19 disease. Multivariate logistic regression was applied to build the prediction nomogram and validated in 2 different sets. Receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) were used to evaluate its performance.Results From 31 potential predictors in the training set, 5 independent predictive factors were identified and included in the risk score: C-reactive protein (CRP), Lactate dehydrogenase (LDH), Age, Charlson/Deyo comorbidity score (CDCS) and Erythrocyte sedimentation rate (ESR). Subsequently, we generated the nomogram based on the above features for predicting severe COVID-19. In the training cohort, the Area under curves (AUCs) were 0.822 (95% CI 0.765–0.875) and the internal validation cohort was 0.762 (95% CI 0.768–0.844). Further, we validated it in a prospective cohort with the AUCs of 0.705 (95% CI 0.627–0.778). The internally bootstrapped calibration curve showed favorable consistency between prediction by nomogram and actual situation. And DCA analysis also conferred high clinical net benefit.Conclusion In this study, our predicting model based on 5 clinical characteristics of COVID-19 patients will enable clinicians to predict the potential risk of developing critical illness and thus optimize medical management.

Multistage CT features of coronavirus disease 2019. / 2019å† çŠ¶ç— æ¯’ç— å¤šé˜¶æ®µCT影像学特征.

OBJECTIVES: To determine imaging features of coronavirus disease 2019 (COVID-19) in different stages, and to provide foundations for early diagnosis and treatment. METHODS: CT image data of 187 COVID-19 patients were analyzed in the period of hospitalization. CT scanning was performed on admission and repeated every 3 days. The improvement time of clinical symptoms and the image changes of follow-up CT were statistically analyzed. RESULTS: All 187 patients' nucleic acid test were positive to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The early CT images of lung in 187 cases (100%) showed multiple patchy and ground-glass opacities with fine mesh and consolidation shade, which mainly distributed in pulmonary band or under the pleura. In the progressive stage, the pulmonary lesions in 146 cases (78.1%) were mainly consolidation, accompanied by air bronchogram, thickening of blood vessels, and interstitial changes. Severe pulmonary CT images in 15 cases (8%) showed diffuse lesions in both lungs, displaying consolidation, or "white lung". The CT imaging features in 185 cases (98.9%) at the absorptive period showed that the lesions diminished and fibrogenesis. The imaging features of 6 times of lung CT examination in one case showed continuous progress. The original lesion in one case was obviously absorbed, but new lesions appeared under the pleura of both lungs at the third review of CT scanning. The changes of CT imaging lesions during follow-up were significantly different in different clinical symptoms improvement time (P< 0.01). CONCLUSIONS: Images of COVID-19 in various stages have special characteristics. The change of clinical symptoms is synchronous with the change of reexamination CT. Follow-up CT can reflect the trend of clinical changes. Repeat CT examination plays an important role in the early clinical diagnosis and the evaluation for the therapeutic effect on COVID-19 patient.

Chest CT findings and clinical features of coronavirus disease 2019 in children. / 儿童2019å† çŠ¶ç— æ¯’ç— æ‚£è€ çš„èƒ¸éƒ¨CT表现和临床特征.

OBJECTIVES: To describe the CT features and clinical characteristics of pediatric patients with coronavirus disease 2019 (COVID-19). METHODS: A total of 9 COVID-19 infected pediatric patients were included in this study. Clinical history, laboratory examination, and detailed CT imaging features were analyzed. All patients underwent the first CT scanning on the same day of being diagnosed by real-time reverse-transcription polymerase chain reaction (rRT-PCR). A low-dose CT scan was performed during follow-up. RESULTS: All the child patients had positive results. Four patients had cough and one patient had fever. One patient presented both cough and fever. Two children presented other symptoms like sore throat and stuffy nose. One child showed no clinical symptom. Five patients had positive initial CT findings with subtle lesions like ground-glass opacity (GGO) or spot-like mixed consolidation. Three patients were reported with negative results in the initial and follow-up CT examination. One patient was reported with initial negative CT findings but turning positive during the first follow-up. All patients had absorbed lesions on follow-up CT images after treatment. CONCLUSIONS: Pediatric COVID-19 patients have certain imaging and clinical features as well as disease prognosis. Children with COVID-19 tend to have normal or subtle CT findings and relatively better outcome.

CT Scans of Patients with 2019 Novel Coronavirus (COVID-19) Pneumonia.

Rationale: The increasing speed of confirmed 2019 novel coronavirus (COVID-19) cases is striking in China. The purpose of this study is to summarize the outcomes of patients with novel COVID-19 pneumonia (NCP) at our institution. Methods: In this single-center study, we retrospectively included 118 cases of NCP, from January 16, 2020 to February 4, 2020. The clinical outcomes were monitored up to February 11, 2020. The outcomes of NCP patients were phase summarized at our institution. Three kinds of responses to clinical treatment were defined and evaluated: 1) good, symptoms continually improved; 2) fair, symptoms not improved or relapsed; 3) poor, symptoms aggravated. The risk factors, including basal clinical characteristics, CT imaging features, and follow-up CT changes (no change, progress, and improvement) related to poor/fair outcomes, were also investigated. Results: Six patients were improved to no-emergency type, 2 remained the same, and 2 progressed to fatal type. Besides, 13 patients progressed from the common type group to the emergency group (3 in fatal type and 10 in severe type). Forty-two (35.6%) patients were discharged with a median hospital stay of 9.5 days (range, 4.0-15.0 days). Thus, the numbers in different responses were, 73 patients in good response group (4 emergency cases, 69 no-emergency cases), 28 in fair response group (3 emergency cases, 25 no-emergency cases), and 17 in poor response group (3 emergency cases, 14 no-emergency cases). No patient has died in our hospital to date. The median duration of progress observed from CT scans was 6 days (range, 2-14 days). The progression in abnormal imaging findings indicate a poor/fair response, whereas the alleviated symptoms seen from CT suggest a good response. Conclusion: Most cases are no-emergency type and have a favorable response to clinical treatment. Follow-up CT changes during the treatment can help evaluate the treatment response of patients with NCP.

The Prediction for Development of COVID-19 in Global Major Epidemic Areas Through Empirical Trends in China by Utilizing State Transition Matrix Model (preprint)

Background: Since pneumonia caused by coronavirus disease 2019 (COVID-19) broke out in Wuhan, Hubei province, China, tremendous infected cases has risen all over the world attributed to high transmissibility. We managed to mathematically forecast the inflection point (IFP) of new cases in South Korea, Italy, and Iran, utilizing the transcendental model from Hubei and non-Hubei in China. Methods: We extracted data from reports released by the National Health Commission of the People's Republic of China (Dec 31, 2019 to Mar 5, 2020) and World Health Organization (Jan 20, 2020 to Mar 5, 2020) as the training set to deduce the arrival of the IFP of new cases in Hubei and non-Hubei on subsequent days and the data from Mar 6 to Mar 9 as validation set. New close contacts, newly confirmed cases, cumulative confirmed cases, non-severe cases, severe cases, critical cases, cured cases, and death data were collected and analyzed. Using this state transition matrix model, the horizon of the IFP of time (the rate of new increment reaches zero) could be predicted in South Korean, Italy, and Iran. Also, through this model, the global trend of the epidemic will be decoded to allocate international medical resources better and instruct the strategy for quarantine. Results: the optimistic scenario (non-Hubei model, daily increment rate of -3.87%), the relative pessimistic scenario (Hubei model, daily increment rate of -2.20%), and the relatively pessimistic scenario (adjustment, daily increment rate of -1.50%) were inferred and modeling from data in China. Matching and fitting with these scenarios, the IFP of time in South Korea would be Mar 6-Mar 12, Italy Mar 10-Mar 24, and Iran is Mar 10-Mar 24. The numbers of cumulative confirmed patients will reach approximately 20k in South Korea, 209k in Italy, and 226k in Iran under fitting scenarios, respectively. There should be room for improvement if these metrics continue to improve. In that case, the IFP will arrive earlier than our estimation. However, with the adoption of different diagnosis criteria, the variation of new cases could impose various influences in the predictive model. If that happens, the IFP of increment will be higher than predicted above. Conclusion: We can affirm that the end of the burst of the epidemic is still inapproachable, and the number of confirmed cases is still escalating. With the augment of data, the world epidemic trend could be further predicted, and it is imperative to consummate the assignment of global medical resources to manipulate the development of COVID-19.

Relation Between Chest CT Findings and Clinical Conditions of Coronavirus Disease (COVID-19) Pneumonia: A Multicenter Study.

OBJECTIVE. The increasing number of cases of confirmed coronavirus disease (COVID-19) in China is striking. The purpose of this study was to investigate the relation between chest CT findings and the clinical conditions of COVID-19 pneumonia. MATERIALS AND METHODS. Data on 101 cases of COVID-19 pneumonia were retrospectively collected from four institutions in Hunan, China. Basic clinical characteristics and detailed imaging features were evaluated and compared between two groups on the basis of clinical status: nonemergency (mild or common disease) and emergency (severe or fatal disease). RESULTS. Patients 21-50 years old accounted for most (70.2%) of the cohort, and five (5.0%) patients had disease associated with a family outbreak. Most patients (78.2%) had fever as the onset symptom. Most patients with COVID-19 pneumonia had typical imaging features, such as ground-glass opacities (GGO) (87 [86.1%]) or mixed GGO and consolidation (65 [64.4%]), vascular enlargement in the lesion (72 [71.3%]), and traction bronchiectasis (53 [52.5%]). Lesions present on CT images were more likely to have a peripheral distribution (88 [87.1%]) and bilateral involvement (83 [82.2%]) and be lower lung predominant (55 [54.5%]) and multifocal (55 [54.5%]). Patients in the emergency group were older than those in the non-emergency group. Architectural distortion, traction bronchiectasis, and CT involvement score aided in evaluation of the severity and extent of the disease. CONCLUSION. Patients with confirmed COVID-19 pneumonia have typical imaging features that can be helpful in early screening of highly suspected cases and in evaluation of the severity and extent of disease. Most patients with COVID-19 pneumonia have GGO or mixed GGO and consolidation and vascular enlargement in the lesion. Lesions are more likely to have peripheral distribution and bilateral involvement and be lower lung predominant and multifocal. CT involvement score can help in evaluation of the severity and extent of the disease.