Effects of diarylbutane lignans from Schisandra chinensis fruit on SARS-CoV-2 3CLpro and PLpro proteases and their in vitro anti-inflammatory properties

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Effects of diarylbutane lignans from Schisandra chinensis fruit on SARS-CoV-2 3CLpro and PLpro and their in vitro anti-inflammatory properties.

Background: Schisandra chinensis fruit is a well-known traditional Chinese medicine (TCM), whose extract has a potent inhibitory effect on the severe acute respiratory syndrome-coronavirus-2 (SARS­CoV­2) 3-chymotrypsin-like protease (3CLpro) and papain-like protease (PLpro). Purpose: This work aims to find the active components from the fruit of S. chinensis against SARS­CoV­2 3CLpro and PLpro. Materials and methods: The chemical constituents of the fruit of S. chinensis were retrieved based on the electronic databases, such as Web of Science, PubMed, Medline Plus, and CNKI. Molecular docking was used to screen the active components against SARS­CoV­2 3CLpro and PLpro. Potential hit compounds were further evaluated by enzymatic activity assay. Furthermore, the anti-inflammatory activities of the active compounds were further explored using the phorbol-12-myristate-13-acetate (PMA)-induced THP1 cells model. Results: In this work, we retrieved 75 components of S. chinensis fruit, including 62 dibenzocyclooctadiene-type lignans, 3 diarylbutane-type lignans, 2 tetrahydrofuran-type lignans, and 8 nortriterpenoids. Combining molecular docking study and in vitro experiments, we found that pregomisin (63), meso­dihydroguaiaretic acid (64), and nordihydroguaiaretic acid (65) could potently inhibit 3CLpro with IC50 values of 3.07 ± 0.38, 4.12 ± 0.38, and 6.06 ± 0.62 µM, respectively, and inhibit PLpro with IC50 values of 5.23 ± 0.33, 4.24 ± 0.46, and 16.28 ± 0.54 µM, respectively. Interestingly, compounds 63, 64, and 65 also have potent activities of regulating the inflammatory response in vitro. Conclusion: Our results suggest that compounds 63, 64, and 65 may be promising SARS-CoV-2 3CLpro and PLpro inhibitors and anti-inflammatory.

Real-world effectiveness of primary series and booster doses of inactivated COVID-19 vaccine against Omicron BA.2 variant infection in China: a retrospective cohort study.

BACKGROUND: China has been using inactivated COVID-19 vaccines as primary series and booster doses to protect the population from severe to fatal COVID-19. We evaluated primary and booster vaccine effectiveness (VE) against Omicron BA.2 infection outcomes. METHODS: This was a 13-province retrospective cohort study of quarantined close contacts of BA.2-infected individuals. Outcomes were BA.2 infection, COVID-19 pneumonia or worse, and severe/critical COVID-19. Absolute VE was estimated by comparison with an unvaccinated group. RESULTS: There were 289,427 close-contacts ≥3 years old exposed to Omicron BA.2 cases; 31,831 turned nucleic-acid amplification test (NAAT)-positive during quarantine, 97.2% with mild or asymptomatic infection, 2.6% had COVID-19 pneumonia, and 0.15% had severe/critical COVID-19. None died. Adjusted VE against any infection was 17% for primary series and 22% when boosted. Primary series aVE in adults >18 years was 66% against pneumonia or worse infection and 91% against severe/critical COVID-19. Booster dose aVE was 74% against pneumonia or worse, and 93% against severe/critical COVID-19. CONCLUSIONS: Inactivated COVID-19 vaccines provided modest protection from infection, very good protection against pneumonia, and excellent protection against severe/critical COVID-19. Booster doses are necessary to provide strongest protection.

Multi-Scale Feature Fusion using Parallel-Attention Block for COVID-19 Chest X-ray Diagnosis (preprint)

Under the global COVID-19 crisis, accurate diagnosis of COVID-19 from Chest X-ray (CXR) images is critical. To reduce intra- and inter-observer variability, during the radiological assessment, computer-aided diagnostic tools have been utilized to supplement medical decision-making and subsequent disease management. Computational methods with high accuracy and robustness are required for rapid triaging of patients and aiding radiologists in the interpretation of the collected data. In this study, we propose a novel multi-feature fusion network using parallel attention blocks to fuse the original CXR images and local-phase feature-enhanced CXR images at multi-scales. We examine our model on various COVID-19 datasets acquired from different organizations to assess the generalization ability. Our experiments demonstrate that our method achieves state-of-art performance and has improved generalization capability, which is crucial for widespread deployment.

Preparation and identification of monoclonal antibodies against nucleocapsid protein of novel coronavirus (SARS-CoV-2)

[Objective] At present, a novel emerging Coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has caused an epidemic in the world, seriously threatening human life and health. To establish a basis for a rapid detection method for SARS-CoV-2, the monoclonal antibodies targeting nucleocapsid (N)gene were prepared and identified in this study. [Methods] BA LB/c mice were immunized with purified SARS-CoV-2 N recombinant protein. After four times of immunization. the spleen cells of the mice were fused with SP2/0 myeloma cells. The positive hybridoma cell lines stably secreting monoclonal anti-body were screened through ELISA, and the reactivity of the monoclonal antibody was further determined by western blot and indirect immunofluorescence assay. [Results] After three subclones, two hybridoma cell lines designated as 2D11 and 8G6 were obtained. and the prepared antibodies showed good reactivity with the eukaryotic expression of SARS-CoV-2 N protein. [Conclusion] The monoclonal antibody manufactured in this study can be used for SARS-CoV-2 detection.

Integration of protein context improves protein-based COVID-19 patient stratification.

BACKGROUND: Classification of disease severity is crucial for the management of COVID-19. Several studies have shown that individual proteins can be used to classify the severity of COVID-19. Here, we aimed to investigate whether integrating four types of protein context data, namely, protein complexes, stoichiometric ratios, pathways and network degrees will improve the severity classification of COVID-19. METHODS: We performed machine learning based on three previously published datasets. The first was a SWATH (sequential window acquisition of all theoretical fragment ion spectra) MS (mass spectrometry) based proteomic dataset. The second was a TMTpro 16plex labeled shotgun proteomics dataset. The third was a SWATH dataset of an independent patient cohort. RESULTS: Besides twelve proteins, machine learning also prioritized two complexes, one stoichiometric ratio, five pathways, and five network degrees, resulting a 25-feature panel. As a result, a model based on the 25 features led to effective classification of severe cases with an AUC of 0.965, outperforming the models with proteins only. Complement component C9, transthyretin (TTR) and TTR-RBP (transthyretin-retinol binding protein) complex, the stoichiometric ratio of SAA2 (serum amyloid A proteins 2)/YLPM1 (YLP Motif Containing 1), and the network degree of SIRT7 (Sirtuin 7) and A2M (alpha-2-macroglobulin) were highlighted as potential markers by this classifier. This classifier was further validated with a TMT-based proteomic data set from the same cohort (test dataset 1) and an independent SWATH-based proteomic data set from Germany (test dataset 2), reaching an AUC of 0.900 and 0.908, respectively. Machine learning models integrating protein context information achieved higher AUCs than models with only one feature type. CONCLUSION: Our results show that the integration of protein context including protein complexes, stoichiometric ratios, pathways, network degrees, and proteins improves phenotype prediction.

Enhanced inflammation and suppressed adaptive immunity in COVID-19 with prolonged RNA shedding.

Little is known regarding why a subset of COVID-19 patients exhibited prolonged positivity of SARS-CoV-2 infection. Here, we found that patients with long viral RNA course (LC) exhibited prolonged high-level IgG antibodies and higher regulatory T (Treg) cell counts compared to those with short viral RNA course (SC) in terms of viral load. Longitudinal proteomics and metabolomics analyses of the patient sera uncovered that prolonged viral RNA shedding was associated with inhibition of the liver X receptor/retinoid X receptor (LXR/RXR) pathway, substantial suppression of diverse metabolites, activation of the complement system, suppressed cell migration, and enhanced viral replication. Furthermore, a ten-molecule learning model was established which could potentially predict viral RNA shedding period. In summary, this study uncovered enhanced inflammation and suppressed adaptive immunity in COVID-19 patients with prolonged viral RNA shedding, and proposed a multi-omic classifier for viral RNA shedding prediction.

Hypertension Exacerbates Severity and Outcomes of COVID-19 in Elderly Patients: A Retrospective Observational Study.

OBJECTIVE: To evaluate the impact of hypertension on the clinical outcome of COVID-19 patients aged 60 years old and older. METHODS: This single-center retrospective cohort study enrolled consecutive COVID-19 patients aged 60 years old and older, who were admitted to Liyuan Hospital from January 1, 2020 to April 25, 2020. All included patients were divided into two groups: hypertension and nonhypertension group. The baseline demographic characteristics, laboratory test results, chest computed tomography (CT) images and clinical outcomes were collected and analyzed. The prognostic value of hypertension was determined using binary logistic regression. RESULTS: Among the 232 patients included in the analysis, 105 (45.3%) patients had comorbid hypertension. Compared to the nonhypertension group, patients in the hypertension group had higher neutrophil-to-lymphocyte ratios, red cell distribution widths, lactate dehydrogenase, high-sensitivity C-reactive protein, D-dimer and severity of lung lesion, and lower lymphocyte counts (all P<0.05). Furthermore, the hypertension group had a higher proportion of intensive care unit admissions [24 (22.9%) vs. 14 (11.0%), P=0.02) and deaths [16 (15.2%) vs. 3 (2.4%), P<0.001] and a significantly lower probability of survival (P<0.001) than the nonhypertension group. Hypertension (OR: 4.540, 95% CI: 1.203-17.129, P=0.026) was independently correlated with all-cause in-hospital death in elderly patients with COVID-19. CONCLUSION: The elderly COVID-19 patients with hypertension tend to have worse conditions at baseline than those without hypertension. Hypertension may be an independent prognostic factor of poor clinical outcome in elderly COVID-19 patients.

Multi-Feature Vision Transformer via Self-Supervised Representation Learning for Improvement of COVID-19 Diagnosis (preprint)

The role of chest X-ray (CXR) imaging, due to being more cost-effective, widely available, and having a faster acquisition time compared to CT, has evolved during the COVID-19 pandemic. To improve the diagnostic performance of CXR imaging a growing number of studies have investigated whether supervised deep learning methods can provide additional support. However, supervised methods rely on a large number of labeled radiology images, which is a time-consuming and complex procedure requiring expert clinician input. Due to the relative scarcity of COVID-19 patient data and the costly labeling process, self-supervised learning methods have gained momentum and has been proposed achieving comparable results to fully supervised learning approaches. In this work, we study the effectiveness of self-supervised learning in the context of diagnosing COVID-19 disease from CXR images. We propose a multi-feature Vision Transformer (ViT) guided architecture where we deploy a cross-attention mechanism to learn information from both original CXR images and corresponding enhanced local phase CXR images. We demonstrate the performance of the baseline self-supervised learning models can be further improved by leveraging the local phase-based enhanced CXR images. By using 10\% labeled CXR scans, the proposed model achieves 91.10\% and 96.21\% overall accuracy tested on total 35,483 CXR images of healthy (8,851), regular pneumonia (6,045), and COVID-19 (18,159) scans and shows significant improvement over state-of-the-art techniques. Code is available https://github.com/endiqq/Multi-Feature-ViT

Mixed-Reality-Enhanced Human–Robot Interaction with an Imitation-Based Mapping Approach for Intuitive Teleoperation of a Robotic Arm-Hand System

This paper presents an integrated mapping of motion and visualization scheme based on a Mixed Reality (MR) subspace approach for the intuitive and immersive telemanipulation of robotic arm-hand systems. The effectiveness of different control-feedback methods for the teleoperation system is validated and compared. The robotic arm-hand system consists of a 6 Degrees-of-Freedom (DOF) industrial manipulator and a low-cost 2-finger gripper, which can be manipulated in a natural manner by novice users physically distant from the working site. By incorporating MR technology, the user is fully immersed in a virtual operating space augmented by real-time 3D visual feedback from the robot working site. Imitation-based velocity-centric motion mapping is implemented via the MR subspace to accurately track operator hand movements for robot motion control and enables spatial velocity-based control of the robot Tool Center Point (TCP). The user control space and robot working space are overlaid through the MR subspace, and the local user and a digital twin of the remote robot share the same environment in the MR subspace. The MR-based motion and visualization mapping scheme for telerobotics is compared to conventional 2D Baseline and MR tele-control paradigms over two tabletop object manipulation experiments. A user survey of 24 participants was conducted to demonstrate the effectiveness and performance enhancements enabled by the proposed system. The MR-subspace-integrated 3D mapping of motion and visualization scheme reduced the aggregate task completion time by 48% compared to the 2D Baseline module and 29%, compared to the MR SpaceMouse module. The perceived workload decreased by 32% and 22%, compared to the 2D Baseline and MR SpaceMouse approaches.

The Unexpected Importance of the Primary Structure of the Hydrophobic Part of One-Component Ionizable Amphiphilic Janus Dendrimers in Targeted mRNA Delivery Activity.

Viral and synthetic vectors for delivery of nucleic acids impacted genetic nanomedicine by aiding the rapid development of the extraordinarily efficient Covid-19 vaccines. Access to targeted delivery of nucleic acids is expected to expand the field of nanomedicine beyond most expectations. Both viral and synthetic vectors have advantages and disadvantages. The major advantage of the synthetic vectors is their unlimited synthetic capability. The four-component lipid nanoparticles (LNPs) are the leading nonviral vector for mRNA used by Pfizer and Moderna in Covid-19 vaccines. Their synthetic capacity inspired us to develop a one-component multifunctional sequence-defined ionizable amphiphilic Janus dendrimer (IAJD) delivery system for mRNA. The first experiments on IAJDs provided, through a rational-library design combined with orthogonal-modular accelerated synthesis and sequence control in their hydrophilic part, some of the most active synthetic vectors for the delivery of mRNA to lung. The second experiments employed a similar strategy, generating, by a less complex hydrophilic structure, a library of IAJDs targeting spleen, liver, and lung. Here, we report preliminary studies designing the hydrophobic region of IAJDs by using dissimilar alkyl lengths and demonstrate the unexpectedly important role of the primary structure of the hydrophobic part of IAJDs by increasing up to 90.2-fold the activity of targeted delivery of mRNA to spleen, lymph nodes, liver, and lung. The principles of the design strategy reported here and in previous publications indicate that IAJDs could have a profound impact on the future of genetic nanomedicine.

Repurposing of clinically approved drugs for treatment of coronavirus disease 2019 in a 2019-novel coronavirus-related coronavirus model.

BACKGROUND: Medicines for the treatment of 2019-novel coronavirus (2019-nCoV) infections are urgently needed. However, drug screening using live 2019-nCoV requires high-level biosafety facilities, which imposes an obstacle for those institutions without such facilities or 2019-nCoV. This study aims to repurpose the clinically approved drugs for the treatment of coronavirus disease 2019 (COVID-19) in a 2019-nCoV-related coronavirus model. METHODS: A 2019-nCoV-related pangolin coronavirus GX_P2V/pangolin/2017/Guangxi was described. Whether GX_P2V uses angiotensin-converting enzyme 2 (ACE2) as the cell receptor was investigated by using small interfering RNA (siRNA)-mediated silencing of ACE2. The pangolin coronavirus model was used to identify drug candidates for treating 2019-nCoV infection. Two libraries of 2406 clinically approved drugs were screened for their ability to inhibit cytopathic effects on Vero E6 cells by GX_P2V infection. The anti-viral activities and anti-viral mechanisms of potential drugs were further investigated. Viral yields of RNAs and infectious particles were quantified by quantitative real-time polymerase chain reaction (qRT-PCR) and plaque assay, respectively. RESULTS: The spike protein of coronavirus GX_P2V shares 92.2% amino acid identity with that of 2019-nCoV isolate Wuhan-hu-1, and uses ACE2 as the receptor for infection just like 2019-nCoV. Three drugs, including cepharanthine (CEP), selamectin, and mefloquine hydrochloride, exhibited complete inhibition of cytopathic effects in cell culture at 10 µmol/L. CEP demonstrated the most potent inhibition of GX_P2V infection, with a concentration for 50% of maximal effect [EC50] of 0.98 µmol/L. The viral RNA yield in cells treated with 10 µmol/L CEP was 15,393-fold lower than in cells without CEP treatment ([6.48 ±â€Š0.02] × 10vs. 1.00 ±â€Š0.12, t = 150.38, P < 0.001) at 72 h post-infection (p.i.). Plaque assays found no production of live viruses in media containing 10 µmol/L CEP at 48 h p.i. Furthermore, we found CEP had potent anti-viral activities against both viral entry (0.46 ±â€Š0.12, vs.1.00 ±â€Š0.37, t = 2.42, P < 0.05) and viral replication ([6.18 ±â€Š0.95] × 10vs. 1.00 ±â€Š0.43, t = 3.98, P < 0.05). CONCLUSIONS: Our pangolin coronavirus GX_P2V is a workable model for 2019-nCoV research. CEP, selamectin, and mefloquine hydrochloride are potential drugs for treating 2019-nCoV infection. Our results strongly suggest that CEP is a wide-spectrum inhibitor of pan-betacoronavirus, and further study of CEP for treatment of 2019-nCoV infection is warranted.

Using different machine learning models to classify patients into mild and severe cases of COVID-19 based on multivariate blood testing.

COVID-19 is a serious respiratory disease. The ever-increasing number of cases is causing heavier loads on the health service system. Using 38 blood test indicators on the first day of admission for the 422 patients diagnosed with COVID-19 (from January 2020 to June 2021) to construct different machine learning (ML) models to classify patients into either mild or severe cases of COVID-19. All models show good performance in the classification between COVID-19 patients into mild and severe disease. The area under the curve (AUC) of the random forest model is 0.89, the AUC of the naive Bayes model is 0.90, the AUC of the support vector machine model is 0.86, and the AUC of the KNN model is 0.78, the AUC of the Logistic regression model is 0.84, and the AUC of the artificial neural network model is 0.87, among which the naive Bayes model has the best performance. Different ML models can classify patients into mild and severe cases based on 38 blood test indicators taken on the first day of admission for patients diagnosed with COVID-19.

Targeted Delivery of mRNA with One-Component Ionizable Amphiphilic Janus Dendrimers.

Targeted and efficient delivery of nucleic acids with viral and synthetic vectors is the key step of genetic nanomedicine. The four-component lipid nanoparticle synthetic delivery systems consisting of ionizable lipids, phospholipids, cholesterol, and a PEG-conjugated lipid, assembled by microfluidic or T-tube technology, have been extraordinarily successful for delivery of mRNA to provide Covid-19 vaccines. Recently, we reported a one-component multifunctional sequence-defined ionizable amphiphilic Janus dendrimer (IAJD) synthetic delivery system for mRNA relying on amphiphilic Janus dendrimers and glycodendrimers developed in our laboratory. Amphiphilic Janus dendrimers consist of functional hydrophilic dendrons conjugated to hydrophobic dendrons. Co-assembly of IAJDs with mRNA into dendrimersome nanoparticles (DNPs) occurs by simple injection in acetate buffer, rather than by microfluidic devices, and provides a very efficient system for delivery of mRNA to lung. Here we report the replacement of most of the hydrophilic fragment of the dendron from IAJDs, maintaining only its ionizable amine, while changing its interconnecting group to the hydrophobic dendron from amide to ester. The resulting IAJDs demonstrated that protonated ionizable amines play dual roles of hydrophilic fragment and binding ligand for mRNA, changing delivery from lung to spleen and/or liver. Replacing the interconnecting ester with the amide switched the delivery back to lung. Delivery predominantly to liver is favored by pairs of odd and even alkyl groups in the hydrophobic dendron. This simple structural change transformed the targeted delivery of mRNA mediated with IAJDs, from lung to liver and spleen, and expands the utility of DNPs from therapeutics to vaccines.

One-Component Multifunctional Sequence-Defined Ionizable Amphiphilic Janus Dendrimer Delivery Systems for mRNA.

Efficient viral or nonviral delivery of nucleic acids is the key step of genetic nanomedicine. Both viral and synthetic vectors have been successfully employed for genetic delivery with recent examples being DNA, adenoviral, and mRNA-based Covid-19 vaccines. Viral vectors can be target specific and very efficient but can also mediate severe immune response, cell toxicity, and mutations. Four-component lipid nanoparticles (LNPs) containing ionizable lipids, phospholipids, cholesterol for mechanical properties, and PEG-conjugated lipid for stability represent the current leading nonviral vectors for mRNA. However, the segregation of the neutral ionizable lipid as droplets in the core of the LNP, the "PEG dilemma", and the stability at only very low temperatures limit their efficiency. Here, we report the development of a one-component multifunctional ionizable amphiphilic Janus dendrimer (IAJD) delivery system for mRNA that exhibits high activity at a low concentration of ionizable amines organized in a sequence-defined arrangement. Six libraries containing 54 sequence-defined IAJDs were synthesized by an accelerated modular-orthogonal methodology and coassembled with mRNA into dendrimersome nanoparticles (DNPs) by a simple injection method rather than by the complex microfluidic technology often used for LNPs. Forty four (81%) showed activity in vitro and 31 (57%) in vivo. Some, exhibiting organ specificity, are stable at 5 °C and demonstrated higher transfection efficiency than positive control experiments in vitro and in vivo. Aside from practical applications, this proof of concept will help elucidate the mechanisms of packaging and release of mRNA from DNPs as a function of ionizable amine concentration, their sequence, and constitutional isomerism of IAJDs.

Clinical diagnosis of severe COVID-19: A derivation and validation of a prediction rule.

BACKGROUND: The widespread coronavirus disease 2019 (COVID-19) has led to high morbidity and mortality. Therefore, early risk identification of critically ill patients remains crucial. AIM: To develop predictive rules at the time of admission to identify COVID-19 patients who might require intensive care unit (ICU) care. METHODS: This retrospective study included a total of 361 patients with confirmed COVID-19 by reverse transcription-polymerase chain reaction between January 19, 2020, and March 14, 2020 in Shenzhen Third People's Hospital. Multivariate logistic regression was applied to develop the predictive model. The performance of the predictive model was externally validated and evaluated based on a dataset involving 126 patients from the Wuhan Asia General Hospital between December 2019 and March 2020, by area under the receiver operating curve (AUROC), goodness-of-fit and the performance matrix including the sensitivity, specificity, and precision. A nomogram was also used to visualize the model. RESULTS: Among the patients in the derivation and validation datasets, 38 and 9 participants (10.5% and 2.54%, respectively) developed severe COVID-19, respectively. In univariate analysis, 21 parameters such as age, sex (male), smoker, body mass index (BMI), time from onset to admission (> 5 d), asthenia, dry cough, expectoration, shortness of breath, asthenia, and Rox index < 18 (pulse oxygen saturation, SpO2)/(FiO2 × respiratory rate, RR) showed positive correlations with severe COVID-19. In multivariate logistic regression analysis, only six parameters including BMI [odds ratio (OR) 3.939; 95% confidence interval (CI): 1.409-11.015; P = 0.009], time from onset to admission (≥ 5 d) (OR 7.107; 95%CI: 1.449-34.849; P = 0.016), fever (OR 6.794; 95%CI: 1.401-32.951; P = 0.017), Charlson index (OR 2.917; 95%CI: 1.279-6.654; P = 0.011), PaO2/FiO2 ratio (OR 17.570; 95%CI: 1.117-276.383; P = 0.041), and neutrophil/lymphocyte ratio (OR 3.574; 95%CI: 1.048-12.191; P = 0.042) were found to be independent predictors of COVID-19. These factors were found to be significant risk factors for severe patients confirmed with COVID-19. The AUROC was 0.941 (95%CI: 0.901-0.981) and 0.936 (95%CI: 0.886-0.987) in both datasets. The calibration properties were good. CONCLUSION: The proposed predictive model had great potential in severity prediction of COVID-19 in the ICU. It assisted the ICU clinicians in making timely decisions for the target population.

Multi-Feature Semi-Supervised Learning for COVID-19 Diagnosis from Chest X-ray Images (preprint)

Computed tomography (CT) and chest X-ray (CXR) have been the two dominant imaging modalities deployed for improved management of Coronavirus disease 2019 (COVID-19). Due to faster imaging, less radiation exposure, and being cost-effective CXR is preferred over CT. However, the interpretation of CXR images, compared to CT, is more challenging due to low image resolution and COVID-19 image features being similar to regular pneumonia. Computer-aided diagnosis via deep learning has been investigated to help mitigate these problems and help clinicians during the decision-making process. The requirement for a large amount of labeled data is one of the major problems of deep learning methods when deployed in the medical domain. To provide a solution to this, in this work, we propose a semi-supervised learning (SSL) approach using minimal data for training. We integrate local-phase CXR image features into a multi-feature convolutional neural network architecture where the training of SSL method is obtained with a teacher/student paradigm. Quantitative evaluation is performed on 8,851 normal (healthy), 6,045 pneumonia, and 3,795 COVID-19 CXR scans. By only using 7.06% labeled and 16.48% unlabeled data for training, 5.53% for validation, our method achieves 93.61\% mean accuracy on a large-scale (70.93%) test data. We provide comparison results against fully supervised and SSL methods. Code: https://github.com/endiqq/Multi-Feature-Semi-Supervised-Learning-for-COVID-19-CXR-Images

Multi-organ proteomic landscape of COVID-19 autopsies.

The molecular pathology of multi-organ injuries in COVID-19 patients remains unclear, preventing effective therapeutics development. Here, we report a proteomic analysis of 144 autopsy samples from seven organs in 19 COVID-19 patients. We quantified 11,394 proteins in these samples, in which 5,336 were perturbed in the COVID-19 patients compared to controls. Our data showed that cathepsin L1, rather than ACE2, was significantly upregulated in the lung from the COVID-19 patients. Systemic hyperinflammation and dysregulation of glucose and fatty acid metabolism were detected in multiple organs. We also observed dysregulation of key factors involved in hypoxia, angiogenesis, blood coagulation, and fibrosis in multiple organs from the COVID-19 patients. Evidence for testicular injuries includes reduced Leydig cells, suppressed cholesterol biosynthesis, and sperm mobility. In summary, this study depicts a multi-organ proteomic landscape of COVID-19 autopsies that furthers our understanding of the biological basis of COVID-19 pathology.