Some mechanistic underpinnings of molecular adaptations of SARS-COV-2 spike protein by integrating candidate adaptive polymorphisms with protein dynamics (preprint)

We integrate evolutionary predictions based on the neutral theory of molecular evolution with protein dynamics to generate mechanistic insight into the molecular adaptations of the SARS-COV-2 Spike (S) protein. With this approach, we first identified Candidate Adaptive Polymorphisms (CAPs) of the SARS-CoV-2 Spike protein and assessed the impact of these CAPs through dynamics analysis. Not only have we found that CAPs frequently overlap with well-known functional sites, but also, using several different dynamics-based metrics, we reveal the critical allosteric interplay between SARS-CoV-2 CAPs and the S protein binding sites with the human ACE2 (hACE2) protein. CAPs interact far differently with the hACE2 binding site residues in the open conformation of S protein compared to the closed form. In particular, the CAP sites control the dynamics binding residues in the open state, suggesting an allosteric control of hACE2 binding. We also explored the characteristic mutations of different SARS-CoV-2 strains to find dynamic hallmarks and potential effects of future mutations. Our analyses reveal that Delta strain-specific variants have non-additive (i.e., epistatic) interactions with CAP sites, whereas the less pathogenic Omicron strains have mostly compensatory variants. Finally, our dynamics-based analysis suggests that the novel mutations observed in the Omicron strain epistatically interact with the CAP sites to help escape antibody binding.

Mathematical modeling of vaccination as a control measure of stress to fight COVID-19 infections

The world experienced the life-threatening COVID-19 disease worldwide since its inversion. The whole world experienced difficult moments during the COVID-19 period, whereby most individual lives were affected by the disease socially and economically. The disease caused millions of illnesses and hundreds of thousands of deaths worldwide. To fight and control the COVID-19 disease intensity, mathematical modeling was an essential tool used to determine the potentiality and seriousness of the disease. Due to the effects of the COVID-19 disease, scientists observed that vaccination was the main option to fight against the disease for the betterment of human lives and the world economy. Unvaccinated individuals are more stressed with the disease, hence their body’s immune system are affected by the disease. In this study, the SVEIHR deterministic model of COVID-19 with six compartments was proposed and analyzed. Analytically, the next-generation matrix method was used to determine the basic reproduction number (R0). Detailed stability analysis of the no-disease equilibrium (E0) of the proposed model to observe the dynamics of the system was carried out and the results showed that E0 is stable if R0<1 and unstable when R0>1. The Bayesian Markov Chain Monte Carlo (MCMC) method for the parameter identifiability was discussed. Moreover, the sensitivity analysis of R0 showed that vaccination was an essential method to control the disease. With the presence of a vaccine in our SVEIHR model, the results showed that R0=0.208, which means COVID-19 is fading out of the community and hence minimizes the transmission. Moreover, in the absence of a vaccine in our model, R0=1.7214, which means the disease is in the community and spread very fast. The numerical simulations demonstrated the importance of the proposed model because the numerical results agree with the sensitivity results of the system. The numerical simulations also focused on preventing the disease to spread in the community.

Quality improvement initiatives in the care and prevention of fragility fractures in the Asia Pacific region.

This narrative review summarises ongoing challenges and progress in the care and prevention of fragility fractures across the Asia Pacific region since mid-2019. The approaches taken could inform development of national bone health improvement Road Maps to be implemented at scale during the United Nations 'Decade of Healthy Ageing'. PURPOSE: This narrative review summarises recent studies that characterise the burden of fragility fractures, current care gaps and quality improvement initiatives intended to improve the care and prevention of fragility fractures across the Asia Pacific region. METHODS: The review focuses on published studies, reports and quality improvement initiatives undertaken during the period July 2019 to May 2022. RESULTS: Epidemiological studies conducted in countries and regions throughout Asia Pacific highlight the current and projected increasing burden of fragility fractures. Recent studies and reports document a persistent and pervasive post-fracture care gap among people who have sustained fragility fractures. Global initiatives developed by the Fragility Fracture Network and International Osteoporosis Foundation have gained significant momentum in the Asia Pacific region, despite the disruption caused by the COVID-pandemic. The Asia Pacific Fragility Fracture Alliance has developed educational resources including a Hip Fracture Registry Toolbox and a Primary Care Physician Education Toolkit. The Asia Pacific Osteoporosis and Fragility Fractures Society-a new section of the Asia Pacific Orthopaedic Association-is working to engage orthopaedic surgeons across the region in the care and prevention of fragility fractures. The Asia Pacific Consortium on Osteoporosis developed a framework to support national clinical guidelines development groups. Considerable activity at the national level is evident in many countries across the region. CONCLUSION: Development and implementation of national Road Maps informed by the findings of this review are urgently required to respond to the epidemiological emergency posed by fragility fractures during the United Nations 'Decade of Healthy Ageing'.

Within-host genetic diversity of SARS-CoV-2 in the context of large-scale hospital-associated genomic surveillance (preprint)

The COVID-19 pandemic has resulted in extensive surveillance of the genomic diversity of SARS-CoV-2. Sequencing data generated as part of these efforts can also capture the diversity of the SARS-CoV-2 virus populations replicating within infected individuals. To assess this within-host diversity of SARS-CoV-2 we quantified low frequency (minor) variants from deep sequence data of thousands of clinical samples collected by a large urban hospital system over the course of a year. Using a robust analytical pipeline to control for technical artefacts, we observe that at comparable viral loads, specimens from patients hospitalized due to COVID-19 had a greater number of minor variants than samples from outpatients. Since individuals with highly diverse viral populations could be disproportionate drivers of new viral lineages in the patient population, these results suggest that transmission control should pay special attention to patients with severe or protracted disease to prevent the spread of novel variants.

Modeling nosocomial infection of COVID-19 transmission dynamics.

COVID-19 epidemic has posed an unprecedented threat to global public health. The disease has alarmed the healthcare system with the harm of nosocomial infection. Nosocomial spread of COVID-19 has been discovered and reported globally in different healthcare facilities. Asymptomatic patients and super-spreaders are sough to be among of the source of these infections. Thus, this study contributes to the subject by formulating a S E I H R mathematical model to gain the insight into nosocomial infection for COVID-19 transmission dynamics. The role of personal protective equipment θ is studied in the proposed model. Benefiting the next generation matrix method, R 0 was computed. Routh-Hurwitz criterion and stable Metzler matrix theory revealed that COVID-19-free equilibrium point is locally and globally asymptotically stable whenever R 0 < 1 . Lyapunov function depicted that the endemic equilibrium point is globally asymptotically stable when R 0 > 1 . Further, the dynamics behavior of R 0 was explored when varying θ . In the absence of θ , the value of R 0 was 8.4584 which implies the expansion of the disease. When θ is introduced in the model, R 0 was 0.4229, indicating the decrease of the disease in the community. Numerical solutions were simulated by using Runge-Kutta fourth-order method. Global sensitivity analysis is performed to present the most significant parameter. The numerical results illustrated mathematically that personal protective equipment can minimizes nosocomial infections of COVID-19.

Mathematical Approach to Investigate Stress due to Control Measures to Curb COVID-19.

COVID-19 is a world pandemic that has affected and continues to affect the social lives of people. Due to its social and economic impact, different countries imposed preventive measures that are aimed at reducing the transmission of the disease. Such control measures include physical distancing, quarantine, hand-washing, travel and boarder restrictions, lockdown, and the use of hand sanitizers. Quarantine, out of the aforementioned control measures, is considered to be more stressful for people to manage. When people are stressed, their body immunity becomes weak, which leads to multiplying of coronavirus within the body. Therefore, a mathematical model consisting of six compartments, Susceptible-Exposed-Quarantine-Infectious-Hospitalized-Recovered (SEQIHR) was developed, aimed at showing the impact of stress on the transmission of COVID-19 disease. From the model formulated, the positivity, bounded region, existence, uniqueness of the solution, the model existence of free and endemic equilibrium points, and local and global stability were theoretically proved. The basic reproduction number (R 0) was derived by using the next-generation matrix method, which shows that, when R 0 < 1, the disease-free equilibrium is globally asymptotically stable whereas when R 0 > 1 the endemic equilibrium is globally asymptotically stable. Moreover, the Partial Rank Correlation Coefficient (PRCC) method was used to study the correlation between model parameters and R 0. Numerically, the SEQIHR model was solved by using the Rung-Kutta fourth-order method, while the least square method was used for parameter identifiability. Furthermore, graphical presentation revealed that when the mental health of an individual is good, the body immunity becomes strong and hence minimizes the infection. Conclusively, the control parameters have a significant impact in reducing the transmission of COVID-19.

Creating a healthcare variant CYNEFIN framework to improve leadership and urgent decision-making in times of crisis.

PURPOSE: The complex and occasionally chaotic nature of health care has been previously described in the literature, as has the broadening recognition that different management approaches are required for different types of problems rather than a "one size fits all" approach. The CYNEFIN framework from Snowden outlines a consistent cognitive approach that offers the leader and leadership team an ability to urgently apply the correct actions to a given situation. This paper proposes a variant CYNEFIN approach for healthcare. DESIGN/METHODOLOGY/APPROACH: Consistent and accurate decision-making within health care is the hallmark of an effective and pragmatic leader and leadership team. An awareness of how one's cognitive biases and heuristics may adversely impact on this cognitive process is paramount, as is an understanding of the calibration between fast and slow thinking. FINDINGS: The authors propose a variant CYNEFIN approach for health care of "act-probe-sense-respond" to resolve complex and time-critical emergency scenarios, using the differing contexts of a cardiac arrest and an evolving crisis management problem as examples. The variant serves as a pragmatic sense-making framework for the health-care leader and leadership team that can be adopted for many time-critical crisis situations. ORIGINALITY/VALUE: The variant serves as a pragmatic sense-making framework for the health-care leader that can be adopted for many crisis situations.

Post Discharge after Surgery Virtual Care with Remote Automated Monitoring Technology (PVC-RAM): protocol for a randomized controlled trial.

BACKGROUND: After nonelective (i.e., semiurgent, urgent and emergent) surgeries, patients discharged from hospitals are at risk of readmissions, emergency department visits or death. During the coronavirus disease 2019 (COVID-19) pandemic, we are undertaking the Post Discharge after Surgery Virtual Care with Remote Automated Monitoring Technology (PVC-RAM) trial to determine if virtual care with remote automated monitoring (RAM) compared with standard care will increase the number of days adult patients remain alive at home after being discharged following nonelective surgery. METHODS: We are conducting a randomized controlled trial in which 900 adults who are being discharged after nonelective surgery from 8 Canadian hospitals are randomly assigned to receive virtual care with RAM or standard care. Outcome adjudicators are masked to group allocations. Patients in the experimental group learn how to use the study's tablet computer and RAM technology, which will measure their vital signs. For 30 days, patients take daily biophysical measurements and complete a recovery survey. Patients interact with nurses via the cellular modem-enabled tablet, who escalate care to preassigned and available physicians if RAM measurements exceed predetermined thresholds, patients report symptoms, a medication error is identified or the nurses have concerns they cannot resolve. The primary outcome is number of days alive at home during the 30 days after randomization. INTERPRETATION: This trial will inform management of patients after discharge following surgery in the COVID-19 pandemic and offer insights for management of patients who undergo nonelective surgery in a nonpandemic setting. Knowledge dissemination will be supported through an online multimedia resource centre, policy briefs, presentations, peer-reviewed journal publications and media engagement. TRIAL REGISTRATION: ClinicalTrials.gov, no. NCT04344665.