RESUMEN
Background: Exposure-response (E-R) models were developed for the primary endpoint of hospitalization or death in COVID-19 patients from the Phase 3 portion of the MOVe-OUT study (Clinicaltrials.gov NCT04577797). Beyond dose, these models can identify other determinants of response, highlight the relationship of virologic response with clinical outcomes, and provide a basis for differential efficacy across trials. Method(s): Logistic regression models were constructed using a multi-step process with influential covariates identified first using placebo arm data only. Subsequently the assessment of drug effect based on drug exposure was determined using placebo and molnupiravir (MOV) arm data. To validate the models, the rate of hospitalization/death was predicted for published studies of COVID-19 treatment. All work was performed using R Version 3.0 or later. Result(s): A total of 1313 participants were included in the E-R analysis, including subjects having received MOV (N=630) and placebo (N=683). Participants with missing baseline RNA or PK were excluded (79 from MOV and 16 from placebo arms). The covariates shown to be significant determinants of response were baseline viral load, baseline disease severity, age, weight, viral clade, and co-morbidities of active cancer and diabetes. Day 5 and Day 10 viral load were identified as strong on-treatment predictors of hospitalization/death, pointing to sustained high viral load as driving negative outcomes. Estimated AUC50 was 19900 nM*hr with bootstrapped 95% C.I. of (9270, 32700). In an external validation exercise based on baseline characteristics, the E-R model predicted the mean (95% CI) placebo hospitalization rates across trials of 9.3% (7.6%, 11.7%) for MOVe-OUT, 7.2% (5.3%, 9.8%) for the nirmatrelvir/ritonavir EPIC-HR trial, and 3.2% (1.9%, 5.5%) for generic MOV trials by Aurobindo and Hetero, consistent with the differing observed placebo rates in these trials. The relative reduction in hospitalization/death rate predicted with MOV treatment (relative to placebo) also varied with the above patient populations. Conclusion(s): Overall, the exposure-response results support the MOV dose of 800 mg Q12H for treatment of COVID-19. The results further support that many clinical characteristics impacted hospitalization rate beyond drug exposures which can vary widely across studies. These characteristics also influenced the magnitude of relative risk reduction achieved by MOV in the MOVe-OUT study.
RESUMEN
BACKGROUND: Exposure-response (E-R) analysis supported molnupiravir phase 3 dose selection based on viral load (VL) and mechanism of action (MOA) markers from phase 2.1 This analysis evaluated how well these biomarkers predict the E-R for hospitalization or death in phase 3. METHOD(S): The following E-R models were developed and compared: (1) logistic regression of the primary outcome (hospitalization or death) from phase 3, (2) VL change from baseline (CFB) from phase 2 and 3, and (3) low frequency nucleotide substitutions (LNS), a measure of MOA, from phase 2. Individual estimates of exposure were derived from population PK modeling of sparse samples collected in all patients. All work was performed using R v3.0 or later. RESULT(S): All E-R relationships were best represented by an Emax model with AUC50 estimates of 19,900, 10,260, and 4,390 nM*hr for hospitalization, day 5 VL CFB, and LNS mutation rate, respectively. Normalized E-R relationships were overlaid, illustrating consistency in E-R shape (Figure). Plasma NHC AUC0-12 was identified as the PK driver. Patients at 800 mg achieved near maximal response. CONCLUSION(S): E-R results support the dose of 800 mg Q12H for treatment of COVID-19. E-R relationships for MOA and virology biomarkers were consistent with the clinical E-R. (Figure Presented).
RESUMEN
BACKGROUND: The goal of this analysis was to investigate the relationship of molnupiravir pharmacokinetics (PK) and clinical outcomes (primary endpoint of hospitalization or death) in patients with COVID-19 in the phase 3 cohort of MOVe-OUT (clinicaltrials.gov NCT04577797).1 METHODS: Logistic regression models were constructed using a multi-step process with influential covariates identified first using placebo arm data only and subsequently assessment of drug effect as a function of exposures evaluated using placebo and MOV arm data. Individual estimates of exposure were derived from population PK modeling of sparse samples collected in all patients. All work was performed using R v3.0 or later. RESULT(S): A total of 1,313 participants were included in the exposure-response (E-R) analysis, including subjects on MOV (N = 630) and placebo (N = 683). Participants with missing PK or baseline RNA were excluded (79 from MOV and 16 from placebo arms). The covariates shown to be significant determinants of response were baseline viral load, baseline disease severity, age, weight, viral clade, active cancer, and diabetic risk factors. An additive AUC-based Emax model with a fixed hill coefficient of 1 best represented exposure-dependency in drug effect. Estimated AUC50 was 19,900 nM*hr with bootstrapped 95% confidence interval of (9,270, 32,700). Patients at 800 mg achieved near maximal response, which was larger than the response projected for 200 or 400 mg. CONCLUSION(S): Overall, the E-R results support the MOV dose of 800 mg Q12H for treatment of COVID-19. Many patient characteristics, beyond drug exposures, impacted the risk of hospitalization or death.
RESUMEN
To investigate the impact of the number of hospital beds on the control of infectious diseases and help allocate the limited medical resources in a region, a SEIHR epidemic model including exposed and hospitalized classes is established. Different from available models, the hospitalization rate is expressed as a function of the number of empty beds. The existence and stability of the equilibria are analyzed, and it is proved that the system undergoes backward bifurcation, Hopf bifurcation, and Bogdanov-Takens bifurcation of codimension 2 under certain conditions by using the center manifold theory and normal form theory. In particular, our results show that there is a threshold value for the capacity of hospital beds in a region. If the capacity of hospital beds is lower than this threshold value, there will be a backward bifurcation, which means that even if the basic reproduction number, R0, is less than unity, it is not enough to prevent the outbreaks. Before taking disease control measures, one should compare the number of beds with the threshold value to avoid misjudgment and try to increase the capacity of hospital beds above this threshold value. The method to estimate the threshold value is also given. In addition, the impacts of the duration of the exposed period on the basic reproduction number and disease transmission are investigated. © 2023 American Institute of Mathematical Sciences. All rights reserved.
RESUMEN
To investigate the impact of the number of hospital beds on the control of infectious diseases and help allocate the limited medical resources in a region, a SEIHR epidemic model including exposed and hospitalized classes is established. Different from available models, the hospitalization rate is expressed as a function of the number of empty beds. The existence and stability of the equilibria are analyzed, and it is proved that the system undergoes backward bifurcation, Hopf bifurcation, and Bogdanov-Takens bifurcation of codimension 2 under certain conditions by using the center manifold theory and normal form theory. In particular, our results show that there is a threshold value for the capacity of hospital beds in a region. If the capacity of hospital beds is lower than this threshold value, there will be a backward bifurcation, which means that even if the basic reproduction number, R0, is less than unity, it is not enough to prevent the outbreaks. Before taking disease control measures, one should compare the number of beds with the threshold value to avoid misjudgment and try to increase the capacity of hospital beds above this threshold value. The method to estimate the threshold value is also given. In addition, the impacts of the duration of the exposed period on the basic reproduction number and disease transmission are investigated.
RESUMEN
Background: Ad26.COV2.S is a single-shot vaccine that has demonstrated clinical efficacy against symptomatic COVID-19. In this study, we report the durability of immune responses in 20 rhesus macaques received single-shot Ad26.COV2.S and the immunogenicity of a booster shot at 8-10 months following the initial immunization. Methods: Animals were immunized by intramuscular route with 1011 vp (N=10) or 5x1010 vp (N=10) Ad26.COV2.S and were followed for either 230 or 315 days. Animals were then boosted with 5x1010 vp Ad26.COV2.S (N=10). Humoral immune responses including RBD-specific Ig ELISA and pseudovirus-based virus neutralization response were monitored. Circulating RBD-specific memory B cells and bone marrow plasma cells were assessed by multiparameter flow cytometry. Results: Ad26.COV2.S elicited robust and comparable RBD-specific binding and neutralizing antibody responses in animals that received the 1011 vp and 5x1010 vp doses, which peaked on days 28-56, and then showed a biphasic decay. All animals showed binding antibody responses for the duration of follow-up, and 17 of 20 animals showed neutralizing antibody responses by day 230-315. RBD-specific memory B cell response peaked on day 14-28 followed by a gradual decline, and remained detectable in 17 of 20 animals by day 230-315. On day 315 following vaccination, bone marrow RBD-specific PCs were detected in the majority of vaccinated macaques, including in all animals that received the 1011 vp dose. Following Ad26.COV2.S boost immunization, RBD-specific binding antibody responses increased 31-69 fold compared with pre-boost levels against the ancestral (WA1/2020), alpha (B.1.1.7), beta (B.1.351), kappa (B.1.617.1), and delta (B.1.617.2) SARS-CoV-2 variants. Neutralizing antibody responses increased 23-43 fold compared with pre-boost levels against the ancestral, alpha, beta, gamma (P.1), kappa, and delta SARS-CoV-2 variants. Antigen-specific memory B cell response also increased 8 fold following the boost immunization. Conclusion: Ad26.COV2.S elicited durable antibody and B cell responses, and a late boost with Ad26.COV2.S resulted in a dramatic increase in humoral immunity that were highly cross-reactive across multiple SARS-CoV-2 variants in rhesus macaques. These data contribute to our understanding of Ad26.COV2.S durability and boostability, and provide important data to inform COVID-19 vaccine boosting strategies in humans.
RESUMEN
Background: Molnupiravir (MOV), the orally administered prodrug of the antiviral ribonucleoside analogue, N-hydroxycytidine (NHC) has received emergency use authorization for treatment of COVID-19. NHC inhibits viral replication by introduction of random transition errors across the viral genome, resulting in non-infectious virus. In the Phase II/III (MOVe-OUT) study, non-hospitalized participants received MOV or placebo (PBO) for 5 days and followed to Day 29. Viral RNA was sequenced to determine the rate, distribution and type of viral errors observed. Methods: SARS-CoV-2 RNA isolated from nasopharyngeal swabs was quantified by RT-PCR followed by complete genome NGS using the Ion AmpliSeq SARS-CoV-2 Research panel and Ion Torrent sequencing. To distinguish between nucleotide errors resulting from the mechanism of action of MOV and those potentially associated with reduced susceptibility to NHC, two different analyses were used. To measure impact of MOV on accumulation of low-frequency errors in the viral quasispecies, nucleotide variants were identified using VarScan 2.4 mutation caller with 0.4% minimum variant allele frequency cut-off. Resistance-associated changes were identified as amino acid substitutions occurring in D3 or D5 samples from ≥2 participants with a frequency of ≥5% of NGS reads. Phenotypic analysis of selected amino acid substitutions was performed using a replicon model. Results: NGS results showed a relationship between the number of random errors across the viral genome with increasing MOV dose. By Day 5 the mean number of viral genome errors were 21, 83, 129 and 223 in the PBO, 200, 400 and 800 mg groups, respectively. Among the sequence changes observed, the majority were transitions errors, consistent with MOV's mechanism of action. After MOV treatment, few treatment-emergent amino acid substitutions were identified in the viral replicase genes. These included nsp12 (T731I) and nsp14 (A220S/T/V, V466I, S503L/P);none associated with loss of susceptibility to MOV. Changes in spike protein in both PBO and MOV groups were at sites previously described in circulating variants. Conclusion: Consistent with the mechanism of action, MOV treatment resulted in a dose-dependent increase in transition errors across the SARS-CoV-2 genome. No resistance-associated mutations were identified in the viral replicase and no evidence that MOV treatment selected for unique mutations in spike protein not previously observed in circulating variants.
RESUMEN
In the context of pandemic, COVID-19, recognition of masked face images is a challenging problem, as most of the facial components become invisible. By utilizing prior information that mask-occlusion is located in the lower half of the face, we propose a dual-branch training strategy to guide the model to focus on the upper half of the face to extract robust features for Masked face recognition (MFR). During training, the features learned at the intermediate layers of the global branch are fed to our proposed attention module, named Upper Patch Attention (UPA), which acts as a local branch. Both branches are jointly optimized to enhance the feature extraction from non-occluded regions. We also propose a self-attention module, which integrates into the backbone network to enhance the interaction between the channels and spatial locations in the learning process. Extensive experiments on synthetic and real-masked face datasets demonstrate the effectiveness of our method. © 2022 Elsevier Ltd
RESUMEN
COVID-2019 outbroke in China at the beginning of 2020. Traditional face-to-face teaching cannot he achieved during the epidemic, and all schools across the country turned to online teaching. However, online education still faces many difficulties, and the most important question is how to ensure teaching quality. In this paper, we discussed some pedagogical approaches, including online teaching with the enhanced design of interactions, learning theories and problem-based learning, which are useful for designing courses. Then, a computer foundation course was taken as an example to show how to transit a traditional face-to-face course into an online course utilizing the pedagogical approaches and conduct teaching adjustment based on students' learning behavior.
RESUMEN
Objective: To investigate the epidemiological characteristics and transmission chain of COVID-19 in two families, and to provide scientific evidence for effective prevention and control measures. Methods: Field epidemiological investigation was conducted for the COVID-19 cases occurred in two families and the close contacts in a county of Baotou city in Inner Mongolia Autonomous Region. Descriptive statistical analysis on epidemiological data was conducted. Results: The infection source of the COVID-19 cases in the two families was a man who had living history in Wuhan. After his return, his parents were infected by him. A few days later, the members of a neighbor family were found to be infected, and relatives of this family were also infected after dining together repeatedly. Finally, ten confirmed cases and three suspected cases of COVID-19 were detected in the two families. Conclusions: Human-to-human transmission of COVID-19 can occur not only in a family but also in neighborhoods. The cases in two families had close relationship, indicating the necessity to strengthen the health education about COVID-19 prevention and control and the management of groups at high risk to reduce the incidence of COVID-19 in families and neighborhoods.