A single-center review of pre-exposure prophylaxis with tixagevimab-cilgavimab in solid organ transplant recipients.

BACKGROUND: Coronavirus disease 2019 (COVID-19) continues to negatively impact solid organ transplant recipients (SOTr). Data on the use of tixagevimab-cilgavimab (tix-cil) in vaccinated SOTr during circulation of Omicron and its subvariants are limited. Therefore, this single-center review was conducted to evaluate tix-cil efficacy in multiple organ transplant groups during a study period where Omicron B.1.1.529, BA.2.12.1, and BA.5 predominated. METHODS: In this single-center retrospective study, we evaluated the incidence of COVID-19 infection in adult SOTr who did or did not receive pre-exposure prophylaxis (PrEP) with tix-cil. SOTr were included if they were at least 18 years of age and met emergency use authorization criteria for tix-cil use. The primary outcome analyzed was the incidence of COVID-19 infection. RESULTS: Ninety SOTr met inclusion criteria and comprised of two groups, tix-cil PrEP (n = 45) and no tix-cil PrEP (n = 45). Of SOTr who received tix-cil PrEP, three (6.7%) developed COVID-19 infection, compared to eight (17.8%) in the no tix-cil PrEP group (p = .20). Of the 11 SOTr diagnosed with COVID-19, 15 (82.2%) were fully vaccinated against COVID-19 prior to transplantation. Moreover, 18.2% and 81.8% of the COVID-19 cases observed were asymptomatic and mild-to-moderate, respectively. DISCUSSION: Our study results, which included months when BA.5 was in increased circulation, suggest no significant difference in COVID-19 infection with or without use of tix-cil PrEP in our solid organ transplant groups. As the COVID-19 pandemic continues to evolve, clinical utility of tix-cil should be evaluated against new, emerging strains.

Algal bioplastics: current market trends and technical aspects.

Abstract: Plastics are undebatably a hot topic of discussion across international forums due to their huge ecological footprint. The onset of COVID-19 pandemic has exacerbated the issue in an irreversible manner. Bioplastics produced from renewable sources are a result of lookout for sustainable alternatives. Replacing a ton of synthetic plastics with biobased ones reduces 1.8 tons CO2 emissions. Here, we begin with highlighting the problem statement-Plastic accumulation and its associated negative impacts. Microalgae outperforms plants and microbes, when used to produce bioplastic due to superior growth rate, non-competitive nature to food, and simultaneous wastewater remediation. They have minimal nutrient requirements and less dependency on climatic conditions for cultivation. These are the reasons for current boom in the algal bioplastic market. However, it is still not at par in price with the petroleum-based plastics. A brief market research has been done to better evaluate the current global status and future scope of algal bioplastics. The objective of this review is to propose possible solutions to resolve the challenges in scale up of bioplastic industry. Various bioplastic production technologies have been comprehensively discussed along with their optimization strategies. Overall studies discussed show that in order to make it cost competitive adopting a multi-dimensional approach like algal biorefinery is the best way out. A holistic comparison of any bio-based alternative with its conventional counterpart is imperative to assess its impact upon commercialization. Therefore, the review concludes with the life cycle assessment of bioplastics and measures to improve their inclusivity in a circular economy.

Predicting clinical outcomes in COVID-19 using radiomics on chest radiographs.

OBJECTIVES: For optimal utilization of healthcare resources, there is a critical need for early identification of COVID-19 patients at risk of poor prognosis as defined by the need for intensive unit care and mechanical ventilation. We tested the feasibility of chest X-ray (CXR)-based radiomics metrics to develop machine-learning algorithms for predicting patients with poor outcomes. METHODS: In this Institutional Review Board (IRB) approved, Health Insurance Portability and Accountability Act (HIPAA) compliant, retrospective study, we evaluated CXRs performed around the time of admission from 167 COVID-19 patients. Of the 167 patients, 68 (40.72%) required intensive care during their stay, 45 (26.95%) required intubation, and 25 (14.97%) died. Lung opacities were manually segmented using ITK-SNAP (open-source software). CaPTk (open-source software) was used to perform 2D radiomics analysis. RESULTS: Of all the algorithms considered, the AdaBoost classifier performed the best with AUC = 0.72 to predict the need for intubation, AUC = 0.71 to predict death, and AUC = 0.61 to predict the need for admission to the intensive care unit (ICU). AdaBoost had similar performance with ElasticNet in predicting the need for admission to ICU. Analysis of the key radiomic metrics that drive model prediction and performance showed the importance of first-order texture metrics compared to other radiomics panel metrics. Using a Venn-diagram analysis, two first-order texture metrics and one second-order texture metric that consistently played an important role in driving model performance in all three outcome predictions were identified. CONCLUSIONS: Considering the quantitative nature and reliability of radiomic metrics, they can be used prospectively as prognostic markers to individualize treatment plans for COVID-19 patients and also assist with healthcare resource management. ADVANCES IN KNOWLEDGE: We report on the performance of CXR-based imaging metrics extracted from RT-PCR positive COVID-19 patients at admission to develop machine-learning algorithms for predicting the need for ICU, the need for intubation, and mortality, respectively.

The time to offer treatments for COVID-19.

Background: COVID-19 has several overlapping phases. Treatments to date have focused on the late stage of disease in hospital. Yet, the pandemic is by propagated by the viral phase in out-patients. The current public health strategy relies solely on vaccines to prevent disease.Methods: We searched the major national registries, pubmed.org, and the preprint servers for all ongoing, completed and published trial results.Results: As of 2/15/2021, we found 111 publications reporting findings on 14 classes of agents, and 9 vaccines. There were 62 randomized controlled studies, the rest retrospective observational analyses. Only 21 publications dealt with outpatient care. Remdesivir and high titer convalescent plasma have emergency use authorization for hospitalized patients in the U.S.A. There is also support for glucocorticoid treatment of the COVID-19 respiratory distress syndrome. Monoclonal antibodies are authorized for outpatients, but supply is inadequate to treat all at time of diagnosis. Favipiravir, ivermectin, and interferons are approved in certain countries.Expert Opinion: Vaccines and antibodies are highly antigen specific, and new SARS-Cov-2 variants are appearing. We call on public health authorities to authorize treatments with known low-risk and possible benefit for outpatients in parallel with universal vaccination.

Machine learning based predictors for COVID-19 disease severity.

Predictors of the need for intensive care and mechanical ventilation can help healthcare systems in planning for surge capacity for COVID-19. We used socio-demographic data, clinical data, and blood panel profile data at the time of initial presentation to develop machine learning algorithms for predicting the need for intensive care and mechanical ventilation. Among the algorithms considered, the Random Forest classifier performed the best with [Formula: see text] for predicting ICU need and [Formula: see text] for predicting the need for mechanical ventilation. We also determined the most influential features in making this prediction, and concluded that all three categories of data are important. We determined the relative importance of blood panel profile data and noted that the AUC dropped by 0.12 units when this data was not included, thus indicating that it provided valuable information in predicting disease severity. Finally, we generated RF predictors with a reduced set of five features that retained the performance of the predictors trained on all features. These predictors, which rely only on quantitative data, are less prone to errors and subjectivity.

The Importance of Understanding the Stages of COVID-19 in Treatment and Trials.

COVID-19, caused by SARS-CoV-2, continues to be a major health problem since its first description in Wuhan, China, in December 2019. Multiple drugs have been tried to date in the treatment of COVID-19. Critical to treatment of COVID-19 and advancing therapeutics is an appreciation of the multiple stages of this disease and the importance of timing for investigation and use of various agents. We considered articles related to COVID-19 indexed on PubMed published January 1, 2020-November 15, 2020, and considered papers on the medRxiv preprint server. We identified relevant stages of COVID-19 including three periods: pre-exposure, incubation, and detectable viral replication; and five phases: the viral symptom phase, the early inflammatory phase, the secondary infection phase, the multisystem inflammatory phase, and the tail phase. This common terminology should serve as a framework to guide when COVID-19 therapeutics being studied or currently in use is likely to provide benefit rather than harm.

A Cross-Sectional Study Examining the Seroprevalence of Severe Acute Respiratory Syndrome Coronavirus 2 Antibodies in a University Student Population.

PURPOSE: The aim of the study was to determine the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies in a university student population. METHODS: This was a cross-sectional survey study based on the World Health Organization population-based seroepidemiological investigational protocol for SARS-CoV-2 conducted between April 29, 2020, and May 8, 2020, examining SARS-CoV-2 antibody prevalence among 790 university students in Los Angeles, CA. Participants completed a questionnaire on potential risk factors before blood sampling. Samples were analyzed using the EUROIMMUN Anti-SARS-CoV-2 ELISA (IgG) for the qualitative detection of IgG class antibodies to SARS-CoV-2 in human serum or plasma. RESULTS: The estimated prevalence of SARS-CoV-2 antibody was 4.0% (3.0%, 5.1%). Factors associated with having a positive test included history of anosmia and/or loss of taste (95% CI: 1.4-9.6). A history of respiratory symptoms, with or without fever, was not associated with a positive antibody test. CONCLUSIONS: Prevalence of SARS-CoV-2 antibodies in the undergraduate and graduate student university population was similar to community prevalence.