Cotton flower metabolites inhibit SARS-CoV-2 main protease.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been spreading globally for over 2 years, causing serious contagious disease and incalculable damage. The introduction of vaccines has slowed the spread of SARS-CoV-2 to some extent, but there remains a need for specific and effective treatment. The high chemical diversity and safety profiles of natural products make them a potential source of effective anti-SARS-CoV-2 drugs. Cotton plant is one of the most important economic and medical crops and is the source of a large number of antiviral phytochemicals. In this work, we used SARS-CoV-2 main protein (Mpro ) as the target to identify potential anti-SARS-CoV-2 natural products in cotton. An in vitro assay showed that of all cotton tissues examined, cotton flower extracts (CFs) exhibited optimal inhibitory effects against Mpro . We proceeded to use the CF metabolite database to screen natural Mpro inhibitors by combining virtual screening and biochemical assays. We identified that several CF natural products, including astragalin, myricitrin, and astilbin, significantly inhibited Mpro with half-maximal inhibitory concentrations (IC50s) of 0.13, 10.73, and 7.92 µm, respectively. These findings may serve as a basis for further studies into the suitability of cotton as a source of potential therapeutics for SARS-CoV-2.

Impact of COVID-19 pandemic on treatment of colorectal cancer patients.

BACKGROUND: Modifications to practice during COVID pandemic impacted health maintenance and treatment of cancer patients. METHODS: We conducted a retrospective cohort study of all consecutive patients presenting to our institution with a new diagnosis of colorectal cancer pre-COVID (January 2017 to December 2019) and post-COVID (January to December 2020). RESULTS: The total number of patients with a new diagnosis of CRC was 2196. The pre-COVID period had 1891 patients whereas post-COVID period had 305. The median number of patients diagnosed with CRC per month was 50 and 35.5 pre and post-COVID, respectively. Time to treatment initiation was similar with no difference in stage at presentation for the pre and post-COVID periods. CONCLUSION: There was a significant decrease in colorectal cancer diagnosis number and rate (p < 0.01) during the COVID era with no difference in staging at diagnosis or time to treatment initiation.

Risk Factors Associated With Postoperative Mortality Among COVID-19 Positive Patients: Results of 3027 Operations and Procedures.

OBJECTIVE: To investigate the predictors of postoperative mortality in coronavirus disease 2019 (COVID-19)-positive patients. BACKGROUND: COVID-19-positive patients have more postoperative complications. Studies investigating the risk factors for postoperative mortality in COVID-19-positive patients are limited. METHODS: COVID-19-positive patients who underwent surgeries/procedures in Cleveland Clinic between January 2020 and March 2021 were identified retrospectively. The primary outcome was postoperative/procedural 30-day mortality. Secondary outcomes were length of stay, intensive care unit admission, and 30-day readmission. RESULTS: A total of 2543 patients who underwent 3027 surgeries/procedures were included. Total 48.5% of the patients were male. The mean age was 57.8 (18.3) years. A total of 71.2% had at least 1 comorbidity. Total 78.7% of the cases were elective. The median operative time was 94 (47.0-162) minutes and mean length of stay was 6.43 (13.4) days. Postoperative/procedural mortality rate was 4.01%. Increased age [odds ratio (OR): 1.66, 95% CI, 1.4-1.98; P <0.001], being a current smoker [2.76, (1.3-5.82); P =0.008], presence of comorbidity [3.22, (1.03-10.03); P =0.043], emergency [6.35, (3.39-11.89); P <0.001] and urgent versus [1.78, (1.12-2.84); P =0.015] elective surgery, admission through the emergency department [15.97, (2.00-127.31); P =0.009], or inpatient service [32.28, (7.75-134.46); P <0.001] versus outpatients were associated with mortality in the multivariable analysis. Among all specialties, thoracic surgery [3.76, (1.66-8.53); P =0.002] had the highest association with mortality. Total 17.5% of the patients required intensive care unit admission with increased body mass index being a predictor [1.03, (1.01-1.05); P =0.005]. CONCLUSIONS: COVID-19-positive patients have higher risk of postintervention mortality. Risk factors should be carefully evaluated before intervention. Further studies are needed to understand the impact of pandemic on long-term surgical/procedural outcomes.

Longitudinal Serum Proteome Characterization of COVID-19 Patients With Different Severities Revealed Potential Therapeutic Strategies.

The COVID-19 pandemic caused by SARS-CoV-2 is exerting huge pressure on global healthcare. Understanding of the molecular pathophysiological alterations in COVID-19 patients with different severities during disease is important for effective treatment. In this study, we performed proteomic profiling of 181 serum samples collected at multiple time points from 79 COVID-19 patients with different severity levels (asymptomatic, mild, moderate, and severe/critical) and 27 serum samples from non-COVID-19 control individuals. Dysregulation of immune response and metabolic reprogramming was found in severe/critical COVID-19 patients compared with non-severe/critical patients, whereas asymptomatic patients presented an effective immune response compared with symptomatic COVID-19 patients. Interestingly, the moderate COVID-19 patients were mainly grouped into two distinct clusters using hierarchical cluster analysis, which demonstrates the molecular pathophysiological heterogeneity in COVID-19 patients. Analysis of protein-level alterations during disease progression revealed that proteins involved in complement activation, the coagulation cascade and cholesterol metabolism were restored at the convalescence stage, but the levels of some proteins, such as anti-angiogenesis protein PLGLB1, would not recovered. The higher serum level of PLGLB1 in COVID-19 patients than in control groups was further confirmed by parallel reaction monitoring (PRM). These findings expand our understanding of the pathogenesis and progression of COVID-19 and provide insight into the discovery of potential therapeutic targets and serum biomarkers worth further validation.

COVID-19 infodemic on Chinese social media: A 4P framework, selective review and research directions

During the outbreak of the COVID-19 (2019 coronavirus disease), misinformation related to the virus spread rapidly online and have led to serious difficulties in controlling the disease. The term infodemic is coined to outline the bad effect from the extensive dissemination of misinformation during the outbreak. With regards to this phenomenon, the World Health Organization emphasized the need to fight against infodemic and asked all countries not only to make efforts in slowing down the spread of the COVID-19 but also in countering the risk caused by infodemic. Due to its negative impact, this paper analyzes infodemic on Chinese social media at the initial stage of the COVID-19 outbreak and presents a 4P framework standing for the four features of Chinese infodemic: Prevention Attention, Problem Orientation, Patterns Interaction and Points Globalization. Furthermore, a selective review of existing datasets in the neural networks domain is synthesized based on the 4P framework. Finally, research directions, including recommendations, about constructing a large-scale dataset for Chinese infodemic automatic detection are proposed.

Abusers indoors and coronavirus outside: an examination of public discourse about COVID-19 and family violence on Twitter using machine learning (preprint)

PurposeThis brief report aims to provide the first large-scale analysis of public discourse regarding family violence and the COVID-19 pandemic on Twitter. Method: We analyzed 301,606 Tweets related to family violence and COVID-19 from April 12 to July 16, 2020, for this study. We used the machine learning approach, Latent Dirichlet Allocation, and identified salient themes, topics, and representative Twitter examples. ResultsWe extracted nine themes on family violence and COVID-19 pandemic, including (1) the Impact of COVID-19 on family violence (e.g., rising rates, hotline calls increased, murder & homicide); (2) the types (e.g., child abuse, domestic violence, sexual violence) and (3) forms of family violence (e.g., physical aggression, coercive control); (4) risk factors of family violence (e.g., alcohol abuse, financial constraints, gun, quarantine); (5) victims of family violence (e.g., LGBTQ, women, and women of color, children); (6) social services of family violence (e.g., hotlines, social workers, confidential services, shelters, funding); (7) law enforcement response (e.g., 911 calls, police arrest, protective orders, abuse reports); (8) Social movement/awareness (e.g., support victims, raise awareness); and (9) domestic violence-related news (e.g., Tara Reade, Melissa Derosa). ConclusionsThe COVID-19 has an impact on family violence. This report overcomes the limitation of existing scholarship that lacks data for consequences of COVID-19 on family violence. We contribute to the understanding of family violence during the pandemic by providing surveillance in Tweets, which is essential to identify potentially effective policy programs in offering targeted support for victims and survivors and preparing for the next wave.

Twitter discussions and emotions about COVID-19 pandemic: a machine learning approach (preprint)

The objective of the study is to examine coronavirus disease (COVID-19) related discussions, concerns, and sentiments that emerged from tweets posted by Twitter users. We analyze 4 million Twitter messages related to the COVID-19 pandemic using a list of 25 hashtags such as "coronavirus," "COVID-19," "quarantine" from March 1 to April 21 in 2020. We use a machine learning approach, Latent Dirichlet Allocation (LDA), to identify popular unigram, bigrams, salient topics and themes, and sentiments in the collected Tweets. Popular unigrams include "virus," "lockdown," and "quarantine." Popular bigrams include "COVID-19," "stay home," "corona virus," "social distancing," and "new cases." We identify 13 discussion topics and categorize them into five different themes, such as "public health measures to slow the spread of COVID-19," "social stigma associated with COVID-19," "coronavirus news cases and deaths," "COVID-19 in the United States," and "coronavirus cases in the rest of the world". Across all identified topics, the dominant sentiments for the spread of coronavirus are anticipation that measures that can be taken, followed by a mixed feeling of trust, anger, and fear for different topics. The public reveals a significant feeling of fear when they discuss the coronavirus new cases and deaths than other topics. The study shows that Twitter data and machine learning approaches can be leveraged for infodemiology study by studying the evolving public discussions and sentiments during the COVID-19. Real-time monitoring and assessment of the Twitter discussion and concerns can be promising for public health emergency responses and planning. Already emerged pandemic fear, stigma, and mental health concerns may continue to influence public trust when there occurs a second wave of COVID-19 or a new surge of the imminent pandemic.

Public discourse and sentiment during the COVID-19 pandemic: using Latent Dirichlet Allocation for topic modeling on Twitter (preprint)

The study aims to understand Twitter users' discourse and psychological reactions to COVID-19. We use machine learning techniques to analyze about 1.9 million Tweets (written in English) related to coronavirus collected from January 23 to March 7, 2020. A total of salient 11 topics are identified and then categorized into ten themes, including "updates about confirmed cases," "COVID-19 related death," "cases outside China (worldwide)," "COVID-19 outbreak in South Korea," "early signs of the outbreak in New York," "Diamond Princess cruise," "economic impact," "Preventive measures," "authorities," and "supply chain." Results do not reveal treatments and symptoms related messages as prevalent topics on Twitter. Sentiment analysis shows that fear for the unknown nature of the coronavirus is dominant in all topics. Implications and limitations of the study are also discussed.