COVID-19 infection with keratitis as the first clinical manifestation

AIM To report a case which keratitis is the first clinical manifestation of COVID-19 that occurred 3d earlier than the common COVID-19 symptoms. METHODS Regular slit lamp examination, corneal scraping test, and chest computed tomography (CT) were performed for patients with COVID-19 infection. The ophthalmologic treatment included ganciclovir eye drop (50 mg/mL, 6 times/d). The treatment for diarrhea included Guifu Lizhong pills (TID). The antiviral therapy consisted of oseltamivir (75 mg capsule Q12H);therapy preventing bacterial infection consisted of azithromycin (250 mg tablet QD) and moxifloxacin (0.4 g tablet Q12H);and therapy for cough relief and fever prevention consisted of Chinese herbal decoction. RESULTS A 35-year-old male suddenly suffered pain, photophobia, and tears in his right eye for one day without systemic COVID-19 symptoms. Patient was diagnosed with keratitis, which was seemingly different from common keratitis. Ganciclovir eye drop was initiated. The corneal scraping test for COVID-19 was positive. The chest CT images were abnormal confirming the diagnosis of COVID-19 infection. The antiviral and antibacterial therapies were initiated. Chinese herbal therapy was used for cough relief and fever prevention. After roughly two weeks, patient recovered from COVID-19. CONCLUSION A new type of keratitis, atypical keratitis, is a clinical manifestation of COVID-19, and this clinical manifestation could appear 3d earlier than fever and cough. The earlier a COVID-19 clinical manifestation is identified, the earlier can a patient be directed to stay at home, and significantly fewer people would be infected.

Virtual Screening and Molecular Docking to Study the Mechanism of Chinese Medicines in the Treatment of Coronavirus Infection.

BACKGROUND Heat-clearing and detoxifying herbs (HDHs) play an important role in the prevention and treatment of coronavirus infection. However, their mechanism of action needs further study. This study aimed to explore the anti-coronavirus basis and mechanism of HDHs. MATERIAL AND METHODS Database mining was performed on 7 HDHs. Core ingredients and targets were screened according to ADME rules combined with Neighborhood, Co-occurrence, Co-expression, and other algorithms. GO enrichment and KEGG pathway analyses were performed using the R language. Finally, high-throughput molecular docking was used for verification. RESULTS HDHs mainly acts on NOS3, EGFR, IL-6, MAPK8, PTGS2, MAPK14, NFKB1, and CASP3 through quercetin, luteolin, wogonin, indirubin alkaloids, ß-sitosterol, and isolariciresinol. These targets are mainly involved in the regulation of biological processes such as inflammation, activation of MAPK activity, and positive regulation of NF-kappaB transcription factor activity. Pathway analysis further revealed that the pathways regulated by these targets mainly include: signaling pathways related to viral and bacterial infections such as tuberculosis, influenza A, Ras signaling pathways; inflammation-related pathways such as the TLR, TNF, MAPK, and HIF-1 signaling pathways; and immune-related pathways such as NOD receptor signaling pathways. These pathways play a synergistic role in inhibiting lung inflammation and regulating immunity and antiviral activity. CONCLUSIONS HDHs play a role in the treatment of coronavirus infection by regulating the body's immunity, fighting inflammation, and antiviral activities, suggesting a molecular basis and new strategies for the treatment of COVID-19 and a foundation for the screening of new antiviral drugs.

Infections Forecasting and Intervention Effect Evaluation for COVID-19 via a Data-Driven Markov Process and Heterogeneous Simulation (preprint)

The Coronavirus Disease 2019 (COVID-19) pandemic has caused tremendous amount of deaths and a devastating impact on the economic development all over the world. Thus, it is paramount to control its further transmission, for which purpose it is necessary to find the mechanism of its transmission process and evaluate the effect of different control strategies. To deal with these issues, we describe the transmission of COVID-19 as an explosive Markov process with four parameters. The state transitions of the proposed Markov process can clearly disclose the terrible explosion and complex heterogeneity of COVID-19. Based on this, we further propose a simulation approach with heterogeneous infections. Experimentations show that our approach can closely track the real transmission process of COVID-19, disclose its transmission mechanism, and forecast the transmission under different non-drug intervention strategies. More importantly, our approach can helpfully develop effective strategies for controlling COVID-19 and appropriately compare their control effect in different countries/cities.

Synchronous distance education vs traditional education for health science students: A systematic review and meta-analysis.

CONTEXT: Synchronous distance education (SDE) has been widely used for health science students in recent years. This study examined the effectiveness and acceptance of SDE compared with traditional education for health science students and explored the potential moderators that could impact the pooled results. METHODS: A systematic review and meta-analysis was conducted of randomised controlled trials (RCTs) from January 2000 to March 2020 searched on nine electronic databases, including Web of Science, PubMed, Cochrane Library, Scopus, EMBASE, CINAHL, ERIC, PsycINFO, and ProQuest Dissertations and Theses. The outcomes measured were knowledge, skills with objective assessments and overall satisfaction with subjective evaluations. The pooled results were calculated using random-model effects, and moderators were explored through meta-regression. RESULTS: A total of seven RCTs with 594 participants were included. At the post-test level, the pooled effect size of knowledge acquisitions (SMD 0.12, 95% CI -0.07-0.32) showed insignificant difference between the SDE and traditional education groups (P = .207), with low heterogeneity (I2  = 17.6%). Subgroup analyses observed no factors that significantly impacted the pooled results of knowledge acquisition at the post-test levels (P for interaction > 0.05). Knowledge gains from pretest to post-test in SDE groups also did not differ significantly between groups (SMD 0.15, 95% CI -0.22-0.53; P = .428). The pooled effect size of skills (SMD 0.02, 95% CI -0.24-0.28; P = .735) was similarly insignificant. The pooled effect size of overall satisfaction (SMD 0.60, 95% CI 0.38-0.83; P < .001) significantly favoured SDE over traditional education. Incorporating two-group studies without randomisations did not significantly change the overall results of knowledge acquisition at the post-test level (SMD -0.002, 95% CI -0.11-0.10; P = .994), with moderate heterogeneity (I2  = 61.9%). CONCLUSIONS: Synchronous distance education was not significantly different from traditional education in effectiveness and had higher satisfaction ratings. Our findings might provide indications for adoptions of online remote education in health science education centres.

An increased pretreatment neutrophil-to-lymphocyte ratio predicts severe novel coronavirus-infected pneumonia (preprint)

Objective The aim of this study was to identify early warning signs for severe novel coronavirus-infected pneumonia (COVID-19).Methods We retrospectively analyzed the clinical data of 90 patients with COVID-19 at the Guanggu District of Hubei Women and Children Medical and Healthcare Center comprising 60 mild cases and 30 severe cases. The demographic data, underlying diseases, clinical manifestations and laboratory blood test results were compared between the two groups. Logistic regression analysis was performed to identify the independent risk factors that predicted severe COVID-19. The receiver-operating characteristic (ROC) curve of independent risk factors was calculated, and the area under the curve (AUC) was used to evaluate the efficiency of the prediction of severe COVID-19.Results The patients with mild and severe COVID-19 showed significant differences in terms of cancer incidence, age, pretreatment neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP) and the serum albumin (ALB) level (P<0.05). The severity of COVID-19 was correlated positively with the comorbidity of cancer, age, NLR, and CRP but was negatively correlated with the ALB level (P<0.05). Multivariate logistic regression analysis showed that the NLR and ALB level were independent risk factors for severe COVID-19 (OR=1.319, 95% CI: 1.043-1.669, P=0.021; OR=0.739, 95% CI: 0.616-0.886, P=0.001), with AUCs of 0.851 and 0.128, respectively. An NLR of 4.939 corresponded to the maximum joint sensitivity and specificity according to the ROC curve (0.700 and 0.917, respectively).Conclusion An increased NLR can serve as an early warning sign of severe COVID-19.

Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Scans (preprint)

Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the training of a deep model. To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net, a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit edge-attention are utilized to model the boundaries and enhance the representations. Moreover, to alleviate the shortage of labeled data, we present a semi-supervised segmentation framework based on a randomly selected propagation strategy, which only requires a few labeled images and leverages primarily unlabeled data. Our semi-supervised framework can improve the learning ability and achieve a higher performance. Extensive experiments on our COVID-SemiSeg and real CT volumes demonstrate that the proposed Inf-Net outperforms most cutting-edge segmentation models and advances the state-of-the-art performance.

Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images (preprint)

Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the training of a deep model. To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net, a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit edge-attention are utilized to model the boundaries and enhance the representations. Moreover, to alleviate the shortage of labeled data, we present a semi-supervised segmentation framework based on a randomly selected propagation strategy, which only requires a few labeled images and leverages primarily unlabeled data. Our semi-supervised framework can improve the learning ability and achieve a higher performance. Extensive experiments on our COVID-SemiSeg and real CT volumes demonstrate that the proposed Inf-Net outperforms most cutting-edge segmentation models and advances the state-of-the-art performance.

JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation (preprint)

Recently, the coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries, influencing billions of humans. To control the infection, identifying and separating the infected people is the most crucial step. The main diagnostic tool is the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Still, the sensitivity of the RT-PCR test is not high enough to effectively prevent the pandemic. The chest CT scan test provides a valuable complementary tool to the RT-PCR test, and it can identify the patients in the early-stage with high sensitivity. However, the chest CT scan test is usually time-consuming, requiring about 21.5 minutes per case. This paper develops a novel Joint Classification and Segmentation (JCS) system to perform real-time and explainable COVID-19 chest CT diagnosis. To train our JCS system, we construct a large scale COVID-19 Classification and Segmentation (COVID-CS) dataset, with 144,167 chest CT images of 400 COVID-19 patients and 350 uninfected cases. 3,855 chest CT images of 200 patients are annotated with fine-grained pixel-level labels of opacifications, which are increased attenuation of the lung parenchyma. We also have annotated lesion counts, opacification areas, and locations and thus benefit various diagnosis aspects. Extensive experiments demonstrate that the proposed JCS diagnosis system is very efficient for COVID-19 classification and segmentation. It obtains an average sensitivity of 95.0% and a specificity of 93.0% on the classification test set, and 78.5% Dice score on the segmentation test set of our COVID-CS dataset. The COVID-CS dataset and code are available at https://github.com/yuhuan-wu/JCS.