Real world experience of drug-drug interactions for HCV patients receiving pan-genotypic direct-acting antivirals in the era of COVID-19 pandemic

Background and Aims: The treatment of chronic hepatitis C (CHC) has evolved from genotype-specific to pan-genotypic direct acting antivirals (DAAs) with high efficacy and safety. However, drug-drug interactions (DDIs) must be avoided when used in combination with other medications, especially with the possible concomitant use of COVID-19 infection antivirals during the COVID-19 pandemic. This study aimed to access the potential DDIs of concomitant drugs with pan-genotypic DAAs and COVID-19 infection antivirals, and actual incidence of DDIs in real-world experience. Method(s): From January 2022 to October 2022, consecutive 116 HCV patients receiving pan-genotypic DAAs were retrospectively enrolled in Taipei Veterans General Hospital. The number of comedications and their potential DDIs with three pan-genotypic DAA regimens and three COVID-19 infection antivirals were analyzed. The actual incidence of DDIs during DAAs treatment were also investigated. Result(s): The mean age was 60.9 years old, with male predominant (55.2%). Of them, 12 (10.3%) patients had cirrhosis, and 24 (20.7%) patients had diabetes mellitus. Most patients were within Child-Pugh class A (109/116, 94.0%). The distribution of HCV genotypes was 8.6% in GT 1a, 36.2% in GT 1b, 39.7% in GT 2, 6.9% in GT 6, and 8.6% in indeterminate genotype, respectively. Of them, 43 (37.1%) patients received GLE/PIB, 69 (59.5%) received SOF/VEL 7plusmn;RBV, and 4 (3.4%) received SOF/VEL/VOX as DAAs regimen. Noteworthy, four patients had COVID-19 infection during DAAs treatment course. The rates of ETVR and SVR12 were 97.6% and 95.3%. The mean number of concomitant medications was 2.01. The distribution of concomitant drugs was 64.7% with no concomitant drug, 11.2% with 1-3 drugs, 11.2% with 4-6 drugs, 9.5% with 7-9 drugs, and 3.4% had more than 9 drugs, respectively. In potential contraindicated (red) DDI class, GLE/PIB was the most prevalent (7.3%), followed by SOF/VEL/VOX (6.4%), and SOF/VEL (1.8%) for non-cirrhosis and compensated cirrhosis patients;and no red DDI occurred in decompensated cirrhosis patients. In addition, the percentage of patients without potential DDIs was higher with SOF/VEL (79.8%) than with the other regimens. The potential red DDIs were predominantly with lipid-lowering agents for DAAs. For potential red DDI class with COVID-19 infection antivirals, Nirmatrelvir/Ritonavir was the most prevalent (6%), followed by Remdesivir (0.9%), and no potential DDIs with Molnupiravir. For COVID-19 antivirals, the potential red DDIs was mainly with central nervous system drugs. Finally, the actual incidence of DDIs during DAAs treatment showed no red DDI occurred for all patients, and GLE/PIB was the most prevalent (93%) of no potential DDIs. Conclusion(s): The potential DDIs between these comedications differed, with the most potential DDIs occurring with GLE/PIB and Nirmatrelvir/Ritonavir. After careful assessment of comedications and their potential DDIs, the actual incidence of DDIs could be reduced, and optimize safety in real-world practice.

Collaborative Cohort of Cohorts for COVID-19 Research (C4R) Study: Study Design.

The Collaborative Cohort of Cohorts for COVID-19 Research (C4R) is a national prospective study of adults comprising 14 established US prospective cohort studies. Starting as early as 1971, investigators in the C4R cohort studies have collected data on clinical and subclinical diseases and their risk factors, including behavior, cognition, biomarkers, and social determinants of health. C4R links this pre-coronavirus disease 2019 (COVID-19) phenotyping to information on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and acute and postacute COVID-related illness. C4R is largely population-based, has an age range of 18-108 years, and reflects the racial, ethnic, socioeconomic, and geographic diversity of the United States. C4R ascertains SARS-CoV-2 infection and COVID-19 illness using standardized questionnaires, ascertainment of COVID-related hospitalizations and deaths, and a SARS-CoV-2 serosurvey conducted via dried blood spots. Master protocols leverage existing robust retention rates for telephone and in-person examinations and high-quality event surveillance. Extensive prepandemic data minimize referral, survival, and recall bias. Data are harmonized with research-quality phenotyping unmatched by clinical and survey-based studies; these data will be pooled and shared widely to expedite collaboration and scientific findings. This resource will allow evaluation of risk and resilience factors for COVID-19 severity and outcomes, including postacute sequelae, and assessment of the social and behavioral impact of the pandemic on long-term health trajectories.

Vaccination with Span, an antigen guided by SARS-CoV-2 S protein evolution, protects against challenge with viral variants in mice

SARS-CoV-2 continues to accumulate mutations to evade immunity, leading to breakthrough infections after vaccination. How researchers can anticipate the evolutionary trajectory of the virus in advance in the design of next-generation vaccines requires investigation. Here, we performed a comprehensive study of 11,650,487 SARS-CoV-2 sequences, which revealed that the SARS-CoV-2 spike (S) protein evolved not randomly but into directional paths of either high infectivity plus low immune resistance or low infectivity plus high immune resistance. The viral infectivity and immune resistance of variants are generally incompatible, except for limited variants such as Beta and Kappa. The Omicron variant has the highest immune resistance but showed high infectivity in only one of the tested cell lines. To provide cross-clade immunity against variants that undergo diverse evolutionary pathways, we designed a new pan-vaccine antigen (Span). Span was designed by analyzing the homology of 2675 SARS-CoV-2 S protein sequences from the NCBI database before the Delta variant emerged. The refined Span protein harbors high-frequency residues at given positions that reflect cross-clade generality in sequence evolution. Compared with a prototype wild-type (Swt) vaccine, which, when administered to mice, induced serum with decreased neutralization activity against emerging variants, Span vaccination of mice elicited broad immunity to a wide range of variants, including those that emerged after our design. Moreover, vaccinating mice with a heterologous Span booster conferred complete protection against lethal infection with the Omicron variant. Our results highlight the importance and feasibility of a universal vaccine to fight against SARS-CoV-2 antigenic drift.

Sensitive and quantitative detection of SARS-CoV-2 antibodies from vaccinated serum by MoS2-field effect transistor

Recently, the coronavirus disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread globally with major impact on public health. Novel methods that enable fast and efficient detection of the virus and the associated biomarkers, such as SARS-CoV-2 antibodies, may provide alterative opportunities for early diagnosis, disease status monitoring, and the development of vaccines. Here, we report the fabrication of a functionalized MoS2-field effect transistor (FET) for sensitive and quantitative detection of antibodies against SARS-CoV-2 spike protein receptor binding domain (S-RBD) in vaccinated serum specimens. The device was modified by SARS-CoV-2 S-RBD fusion protein on the surface and enabled rapid detection of SARS-CoV-2 antibodies. In addition, an on-chip calibration method was established for quantitative measurement. Furthermore, this method was applied to measure the levels of S-RBD antibodies in serum specimens from vaccinated donors. The devices showed no response to negative control samples from individuals who did not receive vaccination, suggesting the high specificity of this method. This study illustrated the successful fabrication of S-RBD functionalized MoS2-FET with potential clinical applications to facilitate vaccine development and efficacy evaluation.

Collaborative transnational doctoral education: Insights from a Philippines-Australia partnership: Journal of the Association for Programmed Learning

This paper describes the experiences of doctoral candidates involved in the first year of a transnational research training partnership between Australia and the Philippines. It aims to ascertain how the partnership model of the programme has been perceived by doctoral candidates, and to understand its associated benefits and challenges. Semi-structured interviews were undertaken with the six candidates enrolled in the programme, revealing five key themes: the importance of place, of planned face-to-face interactions, of diversity in research cultures, managing distance communications, and the impact of the COVID-19 pandemic. The consideration of these themes led to two key conclusions. First, in the online context, interpersonal relationships with and between supervisors and programme administrators provided a trusted reference point that became part of the candidates’ sense of belonging. Second, that the treatment of the candidates as a cohort was crucial to their wellbeing and progress in the first year of candidature.

Evaluating factors mediating the relationship between male sex and COVID-19 severity

Background: Males have experienced higher rates of severe COVID-19 outcomes compared to females but the underlying causal mechanisms of this relationship are not well understood. We leveraged existing electronic medical records (EMR) to evaluate associations between sex and COVID-19 test positivity, disease severity, viral burden, and death, and assess factors that mediate the relationship between male sex and severe COVID-19 disease. Methods: We conducted a retrospective cohort study with data collected from University of Washington Medicine EMR from March 1 to September 29, 2020. All persons, regardless of age, were included if they had a conclusive diagnostic COVID-19 PCR test result. We defined severe COVID-19 disease as a score >5 on the WHO clinical progression scale. We used Poisson regression to assess sex differences in risk for COVID-19 test positivity, disease severity and COVID-19 related death, and linear regression to compare viral cycle threshold at the first positive test. We conducted mediation analyses to assess interventional indirect effects of male sex on severe COVID-19 risk through socioeconomic status (SES, based on area deprivation and insurance type), comorbidities, and inflammation status. Models controlled for age and race/ethnicity. Results: Of individuals with SARS-CoV-2 testing records, 32,919 males and 34,733 females had a conclusive PCR test during our observation period. Males were 13% more likely to test positive than females in multivariable analysis (RR=1.13;95% CI: 1.04-1.24;Table). Males had 85% higher risk for severe COVID-19 disease (RR=1.85;95% CI: 1.33-2.62) and 66% higher risk for COVID-19 related death (RR=1.66;95% CI: 0.95-2.98) than females following a positive test result. No difference was observed in cycle threshold at first positive test between males and females (p=0.69). Mediation analyses indicated a significant interventional indirect effect of male sex on severe COVID-19 disease through inflammation status (RR=1.07;95% CI: 1.01-1.13), and less so through SES or comorbidities. Conclusion: In our cohort, males had higher test positivity and greater risk of COVID-19 severity and death. This relationship between male sex and severe COVID-19 seems to act in part through inflammation status. Additional analyses in larger cohorts are needed to better understand the full range of socio-behavioral and biologic factors that mediate the relationship between sex and poor COVID-19 outcomes. (Figure Presented).

Evolutionary Simulation of Online Public Opinion Based on the BERT-LDA Model under COVID-19

The construction of a large-scale online public opinion evolution simulation model has guidance value for differentiated emergency management and public opinion guidance in the worst-hit areas in Wuhan and the other areas in China during the outbreak of the COVID-19. In order to realize the fine-grained simulation of the public sentiment evolution of the topic, the LDA topic model is deeply integrated with BERT word vector to optimize the topic vector and power the text topic clustering. At the same time, on the basis of improving BERT pre-training task, the deep pre-training task is superimposed to improve the accuracy of the model in emotion classification. The results show that the NPMI value of the improved BERT-LDA model is 0.357 higher than that of the original LDA model during the topic vector training. In terms of the emotional classification task of epidemic events, the AUC value exceeds 99.6%, which proves that the improved BERT-LDA model can be effectively applied to large-scale internet public opinion evolution simulation. © 2021, The Editorial Board of Journal of System Simulation. All right reserved.