Factors associated with medication interruption among outpatients’ severe mental illness patients exposed to coronavirus disease 2019 (preprint)

Background. In clinical, many patients with severe mental illness (SMI) have a relapse and deterioration in their illness during COVID-19, with an experienced medication interruption. This study aimed to investigate factors affecting medicine interruption in patients with SMI during the COVID-19 pandemic.Methods. Between 3 September and 7 October 2020, 2,077 patients with SMI participated in an online survey regarding their medication interruption during the COVID-19 outbreak. The questionnaire comprised six parts: basic demographic information, COVID-19 exposure, state of disease, medication compliance pre-COVID-19, medication interruption during COVID-19, and the specific impact and needs.Results. 2,017 valid questionnaires were collected. Nearly 50% of patients with SMI have been affected to varying degrees in their lives and treatment. Among them, 74 patients stopped taking medicines for more than 14 days without a prescription. Binary logistic regression analysis showed that cohabitant exposure [OR = 26.629; 95% CI (3.293-215.323), p = 0.002], medication partial compliance and non-compliance pre-COVID-19 [OR = 11.109; 95% CI (6.093–20.251), p < 0.001; OR = 20.115; 95% CI (10.490-38.571), p < 0.001], and disease status [OR = 0.326; 95% CI (0.188–0.564), p < 0.001] were related to medication interruption. More than 50% of patients wanted help in taking medications, follow-up, and receiving more financial support and protective materials.Conclusions. Patients with a history of partial or non-medication compliance pre-COVID-19 and unstable disease state are more easily affect by epidemics and need extra attention should similar large-scale epidemics occur in future.

Camel nanobodies broadly neutralize SARS-CoV-2 variants (preprint)

With the emergence of SARS-CoV-2 variants, there is urgent need to develop broadly neutralizing antibodies. Here, we isolate two VHH nanobodies (7A3 and 8A2) from dromedary camels by phage display, which have high affinity for the receptor-binding domain (RBD) and broad neutralization activities against SARS-CoV-2 and its emerging variants. Cryo-EM complex structures reveal that 8A2 binds the RBD in its up mode and 7A3 inhibits receptor binding by uniquely targeting a highly conserved and deeply buried site in the spike regardless of the RBD conformational state. 7A3 at a dose of [≥]5 mg/kg efficiently protects K18-hACE2 transgenic mice from the lethal challenge of B.1.351 or B.1.617.2, suggesting that the nanobody has promising therapeutic potentials to curb the COVID-19 surge with emerging SARS-CoV-2 variants.

CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS (preprint)

Background: Limited data was available for rapid and accurate detection of COVID-19 using CT-based machine learning model. This study aimed to investigate the value of chest CT radiomics for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS), and develop an open-source diagnostic tool with the constructed radiomics model. Methods This study enrolled 115 laboratory-confirmed COVID-19 and 435 non-COVID-19 pneunomia patients (training dataset, n = 379; validation dataset, n = 131; testing dataset, n = 40). Key radiomics features extracted from chest CT images were selected to build a radiomics signature using least absolute shrinkage and selection operator (LASSO) regression. Clinical and clinico-radiomics combined models were constructed. The combined model was further validated in the viral pneumonia cohort, and compared with performance of two radiologists with CO-RADS. The diagnostic performance was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). Results Eight radiomics features and 5 clinical variables were selected to construct the combined radiomics model, which outperformed the clinical model in diagnosing COVID-19 pneumonia with an area under the ROC (AUC) of 0.98 and good calibration in the validation cohort. The combined model also performed better in distinguishing COVID-19 from other viral pneumonia with an AUC of 0.93 compared with 0.75 ( P  = 0.03) for clinical model, and 0.69 ( P  = 0.008) or 0.82 ( P  = 0.15) for two trained radiologists using CO-RADS. The sensitivity and specificity of the combined model can be achieved to 0.85 and 0.90. The DCA confirmed the clinical utility of the combined model. An easy-to-use open-source diagnostic tool was developed using the combined model. Conclusions The combined radiomics model outperformed clinical model and CO-RADS for diagnosing COVID-19 pneumonia, which can facilitate more rapid and accurate detection.

Development and validation of a CT radiomics signature for diagnosing COVID-19 pneumonia compared with CO-RADS (preprint)

Objectives: To develop and validate a CT radiomics signature for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS).Methods: This two-center retrospective study enrolled 115 laboratory-confirmed COVID-19 patients with 1127 lesions and 435 non-COVID-19 pneumonia patients with 842 lesions. In study 1, a radiomics signature and a clinical model was developed and validated in the training and internal validation cohorts (patient/lesion [n] = 379/1325, n = 131/505) for identifying COVID-19 pneumonia. In study 2, the developed radiomics signature was tested in another independent cohort including all viral pneumonia (n = 40/139), compared with clinical model and CO-RADS approach. The predictive performance was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). Results: Twenty-three texture features were selected to construct the radiomics model. Radiomics model outperformed the clinical model in diagnosing COVID-19 pneumonia with an area under the ROC (AUC) of 0.98 and good calibration in the internal validation cohort. Radiomics model also performed better in the testing cohort to distinguish COVID-19 from other viral pneumonia with an AUC of 0.96 compared with 0.75 (P=0.007) for clinical model, and 0.69 (P=0.002) or 0.82 (P=0.04) for two trained radiologists using CO-RADS approach. The sensitivity and specificity of radiomics model can be improved to 0.90 and 1.00. The DCA confirmed the clinical utility of radiomics model. Conclusions: The proposed radiomics signature outperformed clinical model and CO-RADS approach for diagnosing COVID-19, which can facilitate rapid and accurate detection of COVID-19 pneumonia.

Cytokine biomarkers of COVID-19 (preprint)

We used a new strategy to screen cytokines associated with SARS-CoV-2 infection. Cytokines that can classify populations in different states of SARS-CoV-2 infection were first screened in cross-sectional serum samples from 184 subjects by 2 statistical analyses. The resultant cytokines were then analyzed for their interrelationships and fluctuating features in sequential samples from 38 COVID-19 patients. Three cytokines, M-CSF, IL-8 and SCF, which were clustered into 3 different correlation groups and had relatively small fluctuations during SARS-CoV-2 infection, were selected for the construction of a multiclass classification model. This model discriminated healthy individuals and asymptomatic and nonsevere patients with accuracy of 77.4% but was not successful in classifying severe patients. Further searching led to a single cytokine, hepatocyte growth factor (HGF), which classified severe from nonsevere COVID-19 patients with a sensitivity of 84.6% and a specificity of 97.9% under a cutoff value of 1128 pg/ml. The level of this cytokine did not increase in nonsevere patients but was significantly elevated in severe patients. Considering its potent antiinflammatory function, we suggest that HGF might be a new candidate therapy for critical COVID-19. In addition, our new strategy provides not only a rational and effective way to focus on certain cytokine biomarkers for infectious diseases but also a new opportunity to probe the modulation of cytokines in the immune response.

Antibody responses to SARS-CoV-2 in COVID-19 patients: the perspective application of serological tests in clinical practice (preprint)

Background We aim to investigate the profile of acute antibody response in COVID-19 patients, and provide proposals for the usage of antibody test in clinical practice. Methods A multi-center cross-section study (285 patients) and a single-center follow-up study (63 patients) were performed to investigate the feature of acute antibody response to SARS-CoV-2. A cohort of 52 COVID-19 suspects and 64 close contacts were enrolled to evaluate the potentiality of the antibody test. Results The positive rate for IgG reached 100% around 20 days after symptoms onset. The median day of serocon-version for both lgG and IgM was 13 days after symptoms onset. Seroconversion of IgM occurred at the same time, or earlier, or later than that of IgG. IgG levels in 100% patients (19/19) entered a platform within 6 days after seroconversion. The criteria of IgG seroconversion and [≥] 4-fold increase in the IgG titers in sequential samples together diagnosed 82.9% (34/41) of the patients. Antibody test aided to confirm 4 patients with COVID-19 from 52 suspects who failed to be confirmed by RT-PCR and 7 patients from 148 close contacts with negative RT-PCR. Conclusion IgM and IgG should be detected simultaneously at the early phase of infection. The serological diagnosis criterion of seroconversion or [≥] 4-fold increase in the IgG titer is suitable for a majority of COVID-19 patients. Serologic test is helpful for the diagnosis of SARS-CoV-2 infection in suspects and close contacts.