Discovery of First-in-Class PROTAC Degraders of SARS-CoV-2 Main Protease (preprint)

We have witnessed three coronavirus (CoV) outbreaks in the past two decades, including the COVID-19 pandemic caused by SARS-CoV-2. Main protease (MPro) is a highly conserved and essential protease that plays key roles in viral replication and pathogenesis among various CoVs, representing one of the most attractive drug targets for antiviral drug development. Traditional antiviral drug development strategies focus on the pursuit of high-affinity binding inhibitors against MPro. However, this approach often suffers from issues such as toxicity, drug resistance, and a lack of broad-spectrum efficacy. Targeted protein degradation represents a promising strategy for developing next-generation antiviral drugs to combat infectious diseases. Here we leverage the proteolysis targeting chimera (PROTAC) technology to develop a new class of small-molecule antivirals that induce the degradation of SARS-CoV-2 MPro. Our previously developed MPro inhibitors MPI8 and MPI29 were used as MPro ligands to conjugate a CRBN E3 ligand, leading to compounds that can both inhibit and degrade SARS-CoV-2 MPro. Among them, MDP2 was demonstrated to effectively reduce MPro protein levels in 293T cells (DC50 = 296 nM), relying on a time-dependent, CRBN-mediated, and proteasome-driven mechanism. Furthermore, MPD2 exhibited remarkable efficacy in diminishing MPro protein levels in SARS-CoV-2-infected A549-ACE2 cells, concurrently demonstrating potent anti-SARS-CoV-2 activity (EC50 = 492 nM). This proof-of-concept study highlights the potential of PROTAC-mediated targeted protein degradation of MPro as an innovative and promising approach for COVID-19 drug discovery.

An Azapeptide Platform in Conjunction with Covalent Warheads to Uncover High-Potency Inhibitors for SARS-CoV-2 Main Protease (preprint)

Main protease (MPro) of SARS-CoV-2, the viral pathogen of COVID-19, is a crucial nonstructural protein that plays a vital role in the replication and pathogenesis of the virus. Its protease function relies on three active site pockets to recognize P1, P2, and P4 amino acid residues in a substrate and a catalytic cysteine residue for catalysis. By converting the P1 C(alpha) atom in an MPro substrate to nitrogen, we showed that a large variety of azapeptide inhibitors with covalent warheads targeting the MPro catalytic cysteine could be easily synthesized. Through the characterization of these inhibitors, we identified several highly potent MPro inhibitors. Specifically, one inhibitor, MPI89 that contained an aza-2,2-dichloroacetyl warhead, displayed a 10 nM EC50 value in inhibiting SARS-CoV-2 from infecting ACE2+ A549 cells and a selectivity index of 875. The crystallography analyses of MPro bound with 6 inhibitors, including MPI89, revealed that inhibitors used their covalent warheads to covalently engage the catalytic cysteine and the aza-amide carbonyl oxygen to bind to the oxyanion hole. MPI89 represents one of the most potent MPro inhibitors developed so far, suggesting that further exploration of the azapeptide platform and the aza-2,2-dichloroacetyl warhead is needed for the development of potent inhibitors for the SARS-CoV-2 MPro as therapeutics for COVID-19.

A Systematic Survey of Reversibly Covalent Dipeptidyl Inhibitors of the SARS-CoV-2 Main Protease (preprint)

SARS-CoV-2 is the coronavirus pathogen of the currently prevailing COVID-19 pandemic. It relies on its main protease (MPro) for replication and pathogenesis. MPro is a demonstrated target for the development of antivirals for SARS-CoV-2. Past studies have systematically explored tripeptidyl inhibitors such as nirmatrelvir as MPro inhibitors. However, dipeptidyl inhibitors especially those with a spiro residue at their P2 position have not been systematically investigated. In this work, we synthesized about 30 reversibly covalent dipeptidyl MPro inhibitors and characterized them on in vitro enzymatic inhibition potency, structures of their complexes with MPro, cellular MPro inhibition potency, antiviral potency, cytotoxicity, and in vitro metabolic stability. Our results indicated that MPro has a flexible S2 pocket that accommodates dipeptidyl inhibitors with a large P2 residue and revealed that dipeptidyl inhibitors with a large P2 spiro residue such as (S)-2-azaspiro[4,4]nonane-3-carboxylate and (S)-2-azaspiro[4,5]decane-3-carboxylate have optimal characteristics. One compound MPI60 containing a P2 (S)-2-azaspiro[4,4]nonane-3-carboxylate displayed high antiviral potency, low cellular cytotoxicity, and high in vitro metabolic stability and can be potentially advanced to further preclinical tests.

Characterization of the enhanced infectivity and antibody evasion of Omicron BA.2.75

Recently emerged SARS-CoV-2 Omicron subvariant, BA.2.75, displayed a growth advantage over circulating BA.2.38, BA.2.76 and BA.5 in India. However, the underlying mechanisms for enhanced infectivity, especially compared to BA.5, remain unclear. Here we show BA.2.75 exhibits substantially higher affinity for host receptor ACE2 than BA.5 and other variants. Structural analyses of BA.2.75 Spike shows its decreased thermostability and increased frequency of the receptor binding domain (RBD) in the “up” conformation under acidic conditions, suggesting enhanced low-pH-endosomal cell entry. Relative to BA.4/BA.5, BA.2.75 exhibits reduced evasion of humoral immunity from BA.1/BA.2 breakthrough-infection convalescent plasma, but greater evasion of Delta breakthrough-infection convalescent plasma. BA.5 breakthrough infection plasma also exhibits weaker neutralization against BA.2.75 than BA.5, mainly due to BA.2.75’s distinct neutralizing antibody escape pattern. Antibody therapeutics Evusheld and Bebtelovimab remain effective against BA.2.75. These results suggest BA.2.75 may prevail after BA.4/BA.5, and its increased receptor-binding capability could support further immune-evasive mutations. Graphical SARS-CoV-2 BA.2.75 is growing rapidly and globally. Cao et al. solved the structure of BA.2.75 spike and show it has stronger binding to human ACE2 than previous variants. BA.2.75 also exhibited distinct antigenicity compared to BA.5, escaping neutralizing antibodies targeting various epitopes and evading convalescent plasma from BA.5 breakthrough infections.

Pre-Avatar: An Automatic Presentation Generation Framework Leveraging Talking Avatar (preprint)

Since the beginning of the COVID-19 pandemic, remote conferencing and school-teaching have become important tools. The previous applications aim to save the commuting cost with real-time interactions. However, our application is going to lower the production and reproduction costs when preparing the communication materials. This paper proposes a system called Pre-Avatar, generating a presentation video with a talking face of a target speaker with 1 front-face photo and a 3-minute voice recording. Technically, the system consists of three main modules, user experience interface (UEI), talking face module and few-shot text-to-speech (TTS) module. The system firstly clones the target speaker's voice, and then generates the speech, and finally generate an avatar with appropriate lip and head movements. Under any scenario, users only need to replace slides with different notes to generate another new video. The demo has been released here and will be published as free software for use.

[Acupuncture as adjuvant therapy for 32 cases of coronavirus disease 2019].

OBJECTIVE: To observe the clinical effect of acupuncture on coronavirus disease 2019 (COVID-19) based on the conventional treatment. METHODS: A total of 35 patients with COVID-19 of mild or ordinary type were involved (3 cases dropped off). Acupuncture was applied on the basis of western medicine and Chinese materia medica treatment. Dazhui (GV 14), Fengchi (GB 20), Kongzui (LU 6), Hegu (LI 4), etc. were selected as the main acupoints, the supplementary acupoints and the reinforcing and reducing manipulations were selected according to syndrome differentiation. Acupuncture treatment was given once a day, 5 times a week. On day 3 and day 7 of acupuncture, relief condition of the main symptoms was observed. Before acupuncture and on day 3 and day 7 of acupuncture, efficacy evaluation scale of TCM on COVID-19 (efficacy evaluation scale) score was recorded. The effects of different intervention time of acupuncture on patients' hospitalization time were compared, the understanding of acupuncture treatment of patients discharged from hospital was recorded, the clinical efficacy and safety of acupuncture treatment were evaluated. RESULTS: On day 3 and day 7 of acupuncture, the symptoms of lung system and non lung system were both relieved; the scores of efficacy evaluation scale were both decreased compared before acupuncture (P<0.05), and the efficacy evaluation scale score of day 7 of acupuncture were lower than day 3 of acupuncture (P<0.05). The average hospitalization time of patients received early acupuncture was shorter than late acupuncture (P<0.05). The total effective rate was 84.4% (27/32) on day 7 of acupuncture, which was higher than 34.4% (11/32) on day 3 of acupuncture (P<0.05). During the acupuncture treatment, there were neither adverse reactions in patients nor occupational exposures in doctors. The patients generally believed that acupuncture could promote the recovery of COVID-19 and recommended acupuncture treatment. CONCLUSION: On the basis of the conventional treatment, acupuncture can effectively relieve the clinical symptoms in patients with COVID-19, early intervention of acupuncture can accelerate the recovery process. Acupuncture has good safety, clinical compliance and recognition of patients.

REGEN-COV Antibody Cocktail Clinical Outcomes Study in Covid-19 Outpatients (preprint)

Background: REGEN-COV antibody cocktail (casirivimab with imdevimab) rapidly reduced viral load and decreased medically-attended visits in the phase 1/2 portion of this trial; REGEN-COV, retains activity in vitro against emerging SARS-CoV-2 variants of concern. Methods: The phase 3 portion of this adaptive, randomized, master protocol, included 4,057 Covid-19 outpatients with one or more risk factors for severe disease. Patients were randomized to a single treatment of intravenous placebo, or various doses of REGEN-COV, and followed for 28 days. The prespecified hierarchical analysis first compared REGEN-COV 2400mg dose vs concurrent placebo, then compared the 1200mg dose vs concurrent placebo, for endpoints assessing risk of hospitalization or death, and time to symptom resolution. Safety was evaluated in all treated patients. Results: Both REGEN-COV 2400mg and 1200mg significantly reduced Covid-19-related hospitalization or all-cause death compared to placebo (71.3% reduction [1.3% vs 4.6%; p<0.0001] and 70.4% reduction [1.0% vs 3.2%; p=0.0024], respectively). The median time to resolution of Covid-19 symptoms was 4 days shorter in both dose arms vs placebo (10 vs 14 days; p<0.0001). Efficacy of REGEN-COV was consistent across subgroups, including patients who were SARS-CoV-2 serum antibody-positive at baseline. REGEN-COV more rapidly reduced viral load than placebo. Serious adverse events occurred more frequently in the placebo group (4.0%) than in the 1200mg (1.1%) and 2400mg (1.3%) groups and grade [≥]2 infusion-related reactions were infrequent (<0.3% in all groups). Conclusions: Treatment with REGEN-COV was well-tolerated and significantly reduced Covid-19-related hospitalization or all-cause death, rapidly resolved symptoms, and reduced viral load. (Funded by Regeneron Pharmaceuticals and the Biomedical and Advanced Research and Development Authority of the Department of Health and Human Services; ClinicalTrials.gov number, NCT04425629.)

Gastrointestinal Symptoms Associated With Unfavorable Prognosis of COVID-19 Patients: A Retrospective Study.

Background and Aim: The global pandemic of COVID-19 has posed an enormous threat to the economy and people's lives across various countries. Patients with COVID-19 most commonly present with respiratory symptoms. However, gastrointestinal (GI) symptoms can also occur. We aimed to study the relationship between GI symptoms and disease prognosis in patients with COVID-19. Methods: In a single-center and retrospective cohort study, the outcomes in COVID-19 patients with or without GI symptoms were compared. The propensity score is a conditional probability of having a particular exposure (COVID-19 patients with GI symptoms vs. without GI symptoms) given a set of baseline measured covariates. Survival was estimated using the Kaplan-Meier method, and any differences in survival were evaluated with a stratified log-rank-test. To explore the GI symptoms associated with ARDS, non-invasive ventilator treatment, tracheal intubation, tracheotomy, and CRRT, univariable and multivariable COX regression models were used. Results: Among 1,113 eligible patients, 359 patients with GI symptoms and 718 without GI symptoms had similar propensity scores and were included in the analyses. Patients with GI symptoms, as compared with those without GI symptoms, were associated with a similar risk of death, but with higher risks of ARDS, non-invasive mechanical ventilation in COVID-19 patients, respectively. Conclusions: The presence of GI symptoms was associated with a high risk of ARDS, non-invasive mechanical ventilation and tracheal intubation in patients with COVID-19 but not mortality.

Radiologic Risk Factors for Mortality of Patients with COVID-19 Pneumonia in Wuhan, China: A Retrospective Study (preprint)

Background: Computed tomography (CT) characteristics associated with critical outcomes of patients with coronavirus disease 2019 (COVID-19) have been reported. However, CT risk factors for mortality are poorly understood. We aimed to investigate the automatically quantified CT imaging predictors for COVID-19 mortality.Methods: In this retrospective study, laboratory-confirmed COVID-19 patients at Wuhan Central Hospital between December 9, 2019, and March 19, 2020, were included. A novel prognostic biomarker, V-HU score, depicting the volume of total pneumonia infection and the average Hounsfield unit (HU) value of consolidation areas was quantified from CT by an artificial intelligence (AI) system. Cox proportional hazards models were used to investigate risk factors for mortality.Findings: This study included 238 patients (126 survivors and 112 non-survivors). The V-HU marker was an independent predictor (hazard ratio [HR] 2·78, 95% CI 1·50-5·17; p=0·0012) after adjusting for several COVID-19 prognostic indicators significant in univariable analysis. The prognostic performance of the model containing clinical and outpatient laboratory factors was improved by integrating the V-HU marker (c-index: 0·695 versus 0·728; p<0·0001). Older patients (age>=65 years; HR 3·56, 95% CI 1·64-7·71; p=0·0006) and younger patients (age<65 years; HR 4·60, 95% CI 1·92-10·99; p<0·0001) could be risk-stratified by the V-HU marker.Interpretation: A combination of an increased volume of total pneumonia infection and high HU value of consolidation areas showed a strong correlation to COVID-19 mortality, as determined by AI quantified CT. The novel radiologic marker may be used for early risk assessment to prioritize critical care resources for patients at a high risk of mortality.Funding: None.Declaration of Interests: The authors declare no competing interests.Ethics Approval Statement: The study was approved by the Research Ethics Commission of Wuhan Central Hospital, and the requirement for writing informed consent was waived by the Ethics Commission for the emergence of infectious diseases.

Dual Encoder Fusion U-Net (DEFU-Net) for Cross-manufacturer Chest X-ray Segmentation (preprint)

A number of methods based on deep learning have been applied to medical image segmentation and have achieved state-of-the-art performance. Due to the importance of chest x-ray data in studying COVID-19, there is a demand for state-of-the-art models capable of precisely segmenting soft tissue on the chest x-rays. The dataset for exploring best segmentation model is from Montgomery and Shenzhen hospital which had opened in 2014. The most famous technique is U-Net which has been used to many medical datasets including the Chest X-rays. However, most variant U-Nets mainly focus on extraction of contextual information and skip connections. There is still a large space for improving extraction of spatial features. In this paper, we propose a dual encoder fusion U-Net framework for Chest X-rays based on Inception Convolutional Neural Network with dilation, Densely Connected Recurrent Convolutional Neural Network, which is named DEFU-Net. The densely connected recurrent path extends the network deeper for facilitating contextual feature extraction. In order to increase the width of network and enrich representation of features, the inception blocks with dilation are adopted. The inception blocks can capture globally and locally spatial information from various receptive fields. At the same time, the two paths are fused by summing features, thus preserving the contextual and spatial information for decoding part. This multi-learning-scale model is benefiting in Chest X-ray dataset from two different manufacturers (Montgomery and Shenzhen hospital). The DEFU-Net achieves the better performance than basic U-Net, residual U-Net, BCDU-Net, R2U-Net and attention R2U-Net. This model has proved the feasibility for mixed dataset and approaches state-of-the-art. The source code for this proposed framework is public https://github.com/uceclz0/DEFU-Net.

The Impact of Psychology Interventions on Changing Mental Health Status and Sleep Quality in University Students during the COVID-19 Pandemic (preprint)

Abstract Objective We evaluated the change in mental health and sleep quality of college students at four time periods. Methods Mental health status and sleep quality were using the Pittsburgh Sleep Quality Index (PSQI) and Symptom Checklist-90-Revised (SCL-90-R) questionnaire across four time periods. Psychology interventions were carried out from the third period. Results Students in the third period had higher PSQI total scores [mean (SD), 6.01 (3.27)] than those in the first period [5.60 (3.11)], second period [4.17 (2.10)] and fourth period [4.09 (2.80)]. After adjustment for covariates there was a decline of 1.89 points in the PSQI in the fourth period compared with the highest period. The SCL-90-R scores were highest in the second period [121.19 (47.83)], and were higher than the scores in the first [107.60 (52.21)] and second period [107.79 (27.20)] and lowest in the fourth period [97.82 (17.12)]. The decline in scores was 23.38 points after adjustment for covariates. The prevalence of psychological distress and sleep disturbances respectively decreased from 28.6% to 11.7% and from 10.4% to 2.6% comparing to the highest period. Sleep quality showed a significant positive correlation with mental health status. Conclusions The pattern of change in mental health status was different to that of sleep quality. The implementation of comprehensive psychology intervention may improve mental health and sleep quality. These findings may inform public health policy during the reopening of schools in other regions.

A Real-time Robot-based Auxiliary System for Risk Evaluation of COVID-19 Infection (preprint)

In this paper, we propose a real-time robot-based auxiliary system for risk evaluation of COVID-19 infection. It combines real-time speech recognition, temperature measurement, keyword detection, cough detection and other functions in order to convert live audio into actionable structured data to achieve the COVID-19 infection risk assessment function. In order to better evaluate the COVID-19 infection, we propose an end-to-end method for cough detection and classification for our proposed system. It is based on real conversation data from human-robot, which processes speech signals to detect cough and classifies it if detected. The structure of our model are maintained concise to be implemented for real-time applications. And we further embed this entire auxiliary diagnostic system in the robot and it is placed in the communities, hospitals and supermarkets to support COVID-19 testing. The system can be further leveraged within a business rules engine, thus serving as a foundation for real-time supervision and assistance applications. Our model utilizes a pretrained, robust training environment that allows for efficient creation and customization of customer-specific health states.

From Community Acquired Pneumonia to COVID-19: A Deep Learning Based Method for Quantitative Analysis of COVID-19 on thick-section CT Scans (preprint)

Background: Thick-section CT scanners are more affordable for the developing countries. Considering the widely spread COVID-19, it is of great benefit to develop an automated and accurate system for quantification of COVID-19 associated lung abnormalities using thick-section chest CT images. Purpose: To develop a fully automated AI system to quantitatively assess the disease severity and disease progression using thick-section chest CT images. Materials and Methods: In this retrospective study, a deep learning based system was developed to automatically segment and quantify the COVID-19 infected lung regions on thick-section chest CT images. 531 thick-section CT scans from 204 patients diagnosed with COVID-19 were collected from one appointed COVID-19 hospital from 23 January 2020 to 12 February 2020. The lung abnormalities were first segmented by a deep learning model. To assess the disease severity (non-severe or severe) and the progression, two imaging bio-markers were automatically computed, i.e., the portion of infection (POI) and the average infection HU (iHU). The performance of lung abnormality segmentation was examined using Dice coefficient, while the assessment of disease severity and the disease progression were evaluated using the area under the receiver operating characteristic curve (AUC) and the Cohen's kappa statistic, respectively. Results: Dice coefficient between the segmentation of the AI system and the manual delineations of two experienced radiologists for the COVID-19 infected lung abnormalities were 0.74 {+/-} 0.28 and 0.76 {+/-} 0.29, respectively, which were close to the inter-observer agreement, i.e., 0.79 {+/-} 0.25. The computed two imaging bio-markers can distinguish between the severe and non-severe stages with an AUC of 0.9680 (p-value < 0.001). Very good agreement ({kappa} = 0.8220) between the AI system and the radiologists were achieved on evaluating the changes of 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, assess the disease severity and its progressions.