A caputo fractional order epidemic model for evaluating the effectiveness of high-risk quarantine and vaccination strategies on the spread of COVID-19.

The recent global Coronavirus disease (COVID-19) threat to the human race requires research on preventing its reemergence without affecting socio-economic factors. This study proposes a fractional-order mathematical model to analyze the impact of high-risk quarantine and vaccination on COVID-19 transmission. The proposed model is used to analyze real-life COVID-19 data to develop and analyze the solutions and their feasibilities. Numerical simulations study the high-risk quarantine and vaccination strategies and show that both strategies effectively reduce the virus prevalence, but their combined application is more effective. We also demonstrate that their effectiveness varies with the volatile rate of change in the system's distribution. The results are analyzed using Caputo fractional order and presented graphically and extensively analyzed to highlight potent ways of curbing the virus.

Reverse tracking from drug-induced transcriptomes through multilayer molecular networks reveals hidden drug targets.

Identifying molecular targets of a drug is an essential process for drug discovery and development. The recent in-silico approaches are usually based on the structure information of chemicals and proteins. However, 3D structure information is hard to obtain and machine-learning methods using 2D structure suffer from data imbalance problem. Here, we present a reverse tracking method from genes to target proteins using drug-perturbed gene transcriptional profiles and multilayer molecular networks. We scored how well the protein explains gene expression changes perturbed by a drug. We validated the protein scores of our method in predicting known targets of drugs. Our method performs better than other methods using the gene transcriptional profiles and shows the ability to suggest the molecular mechanism of drugs. Furthermore, our method has the potential to predict targets for objects that do not have rigid structural information, such as coronavirus.

Signatures of Co-Deregulated Genes and Their Transcriptional Regulators in Kidney Cancers.

There are several studies on the deregulated gene expression profiles in kidney cancer, with varying results depending on the tumor histology and other parameters. None of these, however, have identified the networks that the co-deregulated genes (co-DEGs), across different studies, create. Here, we reanalyzed 10 Gene Expression Omnibus (GEO) studies to detect and annotate co-deregulated signatures across different subtypes of kidney cancer or in single-gene perturbation experiments in kidney cancer cells and/or tissue. Using a systems biology approach, we aimed to decipher the networks they form along with their upstream regulators. Differential expression and upstream regulators, including transcription factors [MYC proto-oncogene (MYC), CCAAT enhancer binding protein delta (CEBPD), RELA proto-oncogene, NF-kB subunit (RELA), zinc finger MIZ-type containing 1 (ZMIZ1), negative elongation factor complex member E (NELFE) and Kruppel-like factor 4 (KLF4)] and protein kinases [Casein kinase 2 alpha 1 (CSNK2A1), mitogen-activated protein kinases 1 (MAPK1) and 14 (MAPK14), Sirtuin 1 (SIRT1), Cyclin dependent kinases 1 (CDK1) and 4 (CDK4), Homeodomain interacting protein kinase 2 (HIPK2) and Extracellular signal-regulated kinases 1 and 2 (ERK1/2)], were computed using the Characteristic Direction, as well as GEO2Enrichr and X2K, respectively, and further subjected to GO and KEGG pathways enrichment analyses. Furthermore, using CMap, DrugMatrix and the LINCS L1000 chemical perturbation databases, we highlight putative repurposing drugs, including Etoposide, Haloperidol, BW-B70C, Triamterene, Chlorphenesin, BRD-K79459005 and ß-Estradiol 3-benzoate, among others, that may reverse the expression of the identified co-DEGs in kidney cancers. Of these, the cytotoxic effects of Etoposide, Catecholamine, Cyclosporin A, BW-B70C and Lasalocid sodium were validated in vitro. Overall, we identified critical co-DEGs across different subtypes in kidney cancer, and our results provide an innovative framework for their potential use in the future.

Changes in Daily Occupations Patterns, Optimism, and Positive Affect During COVID-19 Lockdown.

Background. The COVID-19 outbreak caused an initial 2-week lockdown throughout Israel. Purpose. To identify (1) changes in time-usage patterns of daily occupations during the first COVID-19 lockdown, by gender and employment status, and (2) correlations among optimism, positive affect, and daily occupations during the lockdown. Method. In a voluntary, anonymous, retrospective, online cross-sectional survey, 481 participants completed the Life Orientation Test, Positive Affect Questionnaire, and Occupational Questionnaire. Findings. During lockdown, participants spent more time in recreation, rest, and sleep regardless of their employment status, and more women than men lost their employment. Both before and during lockdown, women spent significantly higher percentage of time performing everyday tasks but reported less rest and sleep than men. Recreation was associated with positive affect. Conclusion. The COVID-19 pandemic created a temporary occupational disruption. Although people devoted their time differently, the lockdown forced people to find ways to continue engaging in their occupations.

Long-term high-dose immunoglobulin successfully treats Long COVID patients with pulmonary, neurologic, and cardiologic symptoms.

Introduction: Long COVID is the overarching name for a wide variety of disorders that may follow the diagnosis of acute SARS-COVID-19 infection and persist for weeks to many months. Nearly every organ system may be affected. Methods: We report nine patients suffering with Long COVID for 101 to 547 days. All exhibited significant perturbations of their immune systems, but only one was known to be immunodeficient prior to the studies directed at evaluating them for possible treatment. Neurological and cardiac symptoms were most common. Based on this data and other evidence suggesting autoimmune reactivity, we planned to treat them for 3 months with long-term high-dose immunoglobulin therapy. If there was evidence of benefit at 3 months, the regimen was continued. Results: The patients' ages ranged from 34 to 79 years-with five male and four female patients, respectively. All nine patients exhibited significant immune perturbations prior to treatment. One patient declined this treatment, and insurance support was not approved for two others. The other six have been treated, and all have had a significant to remarkable clinical benefit. Conclusion: Long-term high-dose immunoglobulin therapy is an effective therapeutic option for treating patients with Long COVID.

C-Terminal Lipidation of SARS-CoV-2 Fusion Peptide Reinstates Superior Membrane Fusion Catalytic Ability.

The Spike (S) protein of severe acute respiratory syndrome-associated coronavirus-2 (SARS-CoV-2) mediates a critical stage in infection, the fusion between viral and host membranes. The protein is categorized as a class I viral fusion protein and has two distinct cleavage sites that can be activated by proteases. The activation deploys the fusion peptide (FP) for insertion into the target cell membranes. Recent studies including our experiments showed that the FP was unable to modulate the kinetics of fusion at a low peptide-to-lipid ratio akin to the spike density at the viral surface. Therefore, we have modified the C-terminus of FP and attached a myristoyl chain (C-myr-FP) to restrict the C-terminus near to interface, bridge both membranes, and increase the effective local concentration. The lipidated FP (C-myr-FP) of SARS-CoV-2 greatly accelerates membrane fusion at a low peptide-to-lipid ratio as compared to the FP with no lipidation. Biophysical experiments suggest that C-myr-FP adopts a helical structure, perturbs the membrane interface, and increases water penetration to catalyze fusion. Scrambled peptide (C-myr-sFP) and truncated peptide (C-myr-8FP) couldn't significantly catalyze the fusion suggesting the important role of myristoylation and the N-terminus. C-myr-FP enhances the murine coronavirus infection by promoting syncytia formation in L2 cells. The C-terminal lipidation of the FP may be a useful strategy to induce artificial fusion in biomedical applications.

Numerical analysis of fractional coronavirus model with Atangana-Baleanu derivative in Liouville-Caputo sense.

The novel coronavirus which emerged at the end of the year 2019 has made a huge impact on the population in all parts of the world. The causes of the outbreak of this deadliest virus in human beings are not yet known to the full extent. In this paper, an investigation is carried out for a new convergent solution of the time-fractional coronavirus model and a reliable homotopy perturbation transform method (HPTM) is used to explore the possible solution. In the presented model, the Atangana-Baleanu derivative in the Liouville-Caputo sense is used. The variations of the susceptible, the exposed, the infected, the quarantined susceptible (isolated and exposed), the hospitalized and the recovered population with time are presented through figures and are further discussed. The effects of selected parameters on the population with the time are also shown through figures. The convergence of solution by the HPTM is shown through tables. The results reveal that the HPTM is efficient, systematic, very effective, and easy to use in getting a solution to this new time-fractional mathematical model of coronavirus disease.

Improving Effectiveness of Simulation-Based Inference in the Massively Parallel Regime

Simulation-based Inference (SBI) is a widely used set of algorithms to learn the parameters of complex scientific simulation models. While primarily run on CPUs in High-Performance Compute clusters, these algorithms have been shown to scale in performance when developed to be run on massively parallel architectures such as GPUs. While parallelizing existing SBI algorithms provides us with performance gains, this might not be the most efficient way to utilize the achieved parallelism. This work proposes a new parallelism-aware adaptation of an existing SBI method, namely approximate Bayesian computation with Sequential Monte Carlo(ABC-SMC). This new adaptation is designed to utilize the parallelism not only for performance gain, but also toward qualitative benefits in the learnt parameters. The key idea is to replace the notion of a single ‘step-size’hyperparameter, which governs how the state space of parameters is explored during learning, with step-sizes sampled from a tuned Beta distribution. This allows this new ABC-SMC algorithm to more efficiently explore the state-space of the parameters being learned. We test the effectiveness of the proposed algorithm to learn parameters for an epidemiology model running on a Tesla T4 GPU. Compared to the parallelized state-of-the-art SBI algorithm, we get similar quality results in <inline-formula><tex-math notation="LaTeX">$\sim 100 \times$</tex-math></inline-formula> fewer simulations and observe <inline-formula><tex-math notation="LaTeX">$\sim 80 \times$</tex-math></inline-formula> lower run-to-run variance across 10 independent trials. IEEE