Semi-quantitative analysis for the dynamic chest CT imaging features from onset to recovery in severe and critical COVID-19

Automatically transferring supervised targets method for segmenting lung lesion regions with CT imaging

BACKGROUND

To present an approach that autonomously identifies and selects a self-selective optimal target for the purpose of enhancing learning efficiency to segment infected regions of the lung from chest computed tomography images. We designed a semi-supervised dual-branch framework for training, where the training set consisted of limited expert-annotated data and a large amount of coarsely annotated data that was automatically segmented based on Hu values, which were used to train both strong and weak branches. In addition, we employed the Lovasz scoring method to automatically switch the supervision target in the weak branch and select the optimal target as the supervision object for training. This method can use noisy labels for rapid localization during the early stages of training, and gradually use more accurate targets for supervised training as the training progresses. This approach can utilize a large number of samples that do not require manual annotation, and with the iterations of training, the supervised targets containing noise become closer and closer to the fine-annotated data, which significantly improves the accuracy of the final model.

RESULTS

The proposed dual-branch deep learning network based on semi-supervision together with cost-effective samples achieved 83.56 ± 12.10 and 82.67 ± 8.04 on our internal and external test benchmarks measured by the mean Dice similarity coefficient (DSC). Through experimental comparison, the DSC value of the proposed algorithm was improved by 13.54% and 2.02% on the internal benchmark and 13.37% and 2.13% on the external benchmark compared with U-Net without extra sample assistance and the mean-teacher frontier algorithm, respectively.

CONCLUSION

The cost-effective pseudolabeled samples assisted the training of DL models and achieved much better results compared with traditional DL models with manually labeled samples only. Furthermore, our method also achieved the best performance compared with other up-to-date dual branch structures.

Dihydroartemisinin attenuates pulmonary inflammation and fibrosis in rats by suppressing JAK2/STAT3 signaling

Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has induced a worldwide pandemic since early 2020. COVID-19 causes pulmonary inflammation, secondary pulmonary fibrosis (PF); however, there are still no effective treatments for PF. The present study aimed to explore the inhibitory effect of dihydroartemisinin (DHA) on pulmonary inflammation and PF, and its molecular mechanism. Morphological changes and collagen deposition were analyzed using hematoxylin-eosin staining, Masson staining, and the hydroxyproline content. DHA attenuated early alveolar inflammation and later PF in a bleomycin-induced rat PF model, and inhibited the expression of interleukin (IL)-1β, IL-6, tumor necrosis factor α (TNFα), and chemokine (C-C Motif) Ligand 3 (CCL3) in model rat serum. Further molecular analysis revealed that both pulmonary inflammation and PF were associated with increased transforming growth factor-β1 (TGF-β1), Janus activated kinase 2 (JAK2), and signal transducer and activator 3(STAT3) expression in the lung tissues of model rats. DHA reduced the inflammatory response and PF in the lungs by suppressing TGF-β1, JAK2, phosphorylated (p)-JAK2, STAT3, and p-STAT3. Thus, DHA exerts therapeutic effects against bleomycin-induced pulmonary inflammation and PF by inhibiting JAK2-STAT3 activation. DHA inhibits alveolar inflammation, and attenuates lung injury and fibrosis, possibly representing a therapeutic candidate to treat PF associated with COVID-19.

Efficacy and safety of colchicine treatment in patients with COVID-19: A prospective, multicenter, randomized clinical trial

Colchicine has shown clinical benefits in the management of COVID-19 via its anti-inflammatory effect. However, the exact role of colchicine in COVID-19 patients is unknown. The current clinical trial was performed on 202 patients with moderate to severe COVID-19. Patients were randomly assigned in a 1:1 ratio to receive up to a 3-day course of 0.5 mg colchicine followed by a 12-day course of 1 mg colchicine in combination with standard care or a 15-day course of standard care. Among 202 randomized patients, 153 completed the study and received colchicine/standard care or continued standard care (M age, 54.72 [SD, 15.03] years; 93 [63.1%] men). On day 14, patients in the colchicine/standard care group had significantly higher odds of a better clinical status distribution on chest CT evaluation (p = .048). Based on NYHA classification, the percentage change of dyspnea on day 14 between groups was statistically significant (p = .026), indicating a mean of 31.94% change in the intervention group when compared with 19.95% in the control group. According to this study, colchicine can improve clinical outcomes and reduce pulmonary infiltration in COVID-19 patients if contraindications and precautions are considered and it is prescribed at the right time and in appropriate cases.

Long-term chest CT follow-up in COVID-19 Survivors: 102-361 days after onset

Background

The aim of this study was to evaluate long-term longitudinal changes in chest computed tomography (CT) findings in coronavirus disease 2019 (COVID-19) survivors and their correlations with dyspnea after discharge.

Methods

A total of 337 COVID-19 survivors who underwent CT scan during hospitalization and between 102 and 361 days after onset were retrospectively included. Subjective CT findings, lesion volume, therapeutic measures and laboratory parameters were collected. The severity of the survivors' dyspnea was determined by follow-up questionnaire. The evolution of the CT findings from the peak period to discharge and throughout follow-up and the abilities of CT findings and clinical parameters to predict survival with and without dyspnea were analyzed.

Results

Ninety-one COVID-19 survivors still had dyspnea at follow-up. The age, comorbidity score, duration of hospital stays, receipt of hormone administration, receipt of immunoglobulin injections, intensive care unit (ICU) admission, receipt of mechanical ventilation, laboratory parameters, clinical classifications and parameters associated with lesion volume of the survivors with dyspnea were significantly different from those of survivors without dyspnea. Among the clinical parameters and CT parameters used to identify dyspnea, parameters associated with lesion volume showed the largest area under the curve (AUC) values, with lesion volume at discharge showing the largest AUC (0.820). Lesion volume decreased gradually from the peak period to discharge and through follow-up, with a notable decrease observed after discharge. Absorption of lesions continued 6 months after discharge.

Conclusions

Among the clinical parameters and subjective CT findings, CT findings associated with lesion volume were the best predictors of post-discharge dyspnea in COVID-19 survivors.