1 patients were included in the per-protocol efficacy analyses. 55 (53·4%) of 103 patients with carcinoma in situ (with or without a high-grade Ta or T1 tumour) had a complete response within 3 months of the first dose and this response was maintained in 25 (45·5%) of 55 patients at 12 months. Micturition urgency was the most common grade 3-4 study drug-related adverse event (two [1%] of 157 patients, both grade 3), and there were no treatment-related deaths.
Intravesical nadofaragene firadenovec was efficacious, with a favourable benefitrisk ratio, in patients with BCG-unresponsive non-muscle-invasive bladder cancer. This represents a novel treatment option in a therapeutically challenging disease state.
FKD Therapies Oy.
FKD Therapies Oy.
Cancer services worldwide had to adapt in response to the COVID-19 pandemic to minimise risk to patients and staff. We aimed to assess the national impact of COVID-19 on the prescribing of systemic anticancer treatment in England, immediately after lockdown and after the introduction of new treatments to reduce patient risk.
We did a retrospective analysis using data from a central National Health Service England web database mandated for clinicians to register intention to start all new systemic anticancer treatments approved for use in England since 2016. We analysed the monthly number of treatment registrations in April, 2020, after the implementation of societal lockdown on March 23, 2020, and after implementation of treatment options to reduce patient risk such as oral or less immunosuppressive drugs, in May and June, 2020. We compared the number of registrations in April-June, 2020, with the mean number of registrations and SD during the previous 6 months of unaffected cancer care (September, 2019, and societal risk mitigation factors (such as telephone consultations, facemasks and physical distancing) are likely to have contributed. However, outcomes of providing less treatment or delaying treatment initiation, particularly for advanced cancers and neoadjuvant therapies, require continued assessment.
None.
None.Poly-proline-arginine (poly-PR) and poly-glycine-arginine (poly-GR) proteins are believed to be the most toxic dipeptide repeat (DPR) proteins that are expressed by the hexanucleotide repeat expansion mutation in C9ORF72, which are associated with amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) diseases. Their structural information and mechanisms of toxicity remain incomplete, however. Using molecular dynamics simulation and all-atom model of proteins, we study folding and aggregation of both poly-PR and poly-GR. The results indicate formation of double-helix structure during the aggregation of poly-PR into dimers, whereas no stable aggregate is formed during the aggregation of poly-GR; the latter only folds into α-helix and double-helix structures that are similar to those formed in the folding of poly-glycine-alanine (poly-GA) protein. Our findings are consistent with the experimental data indicating that poly-PR and poly-GR are less likely to aggregate because of the hydrophilic arginine residues within their structures. Such characteristics could, however, in some respect facilitate migration of the DPR proteins between and within cells and, at the same time, give proline residues the benefits of activating the receptors that regulate ionotropic effect in neurons, resulting in death or malfunction of neurons because of the abnormal increase or decrease of the ion transmission. This may explain the neurotoxicities of poly-PR and poly-GR associated with many neurodegenerative diseases. To our knowledge, this is the first molecular dynamics simulation of the phenomena involving poly-PR and poly-GR proteins.Bacteria invest in a slow-growing subpopulation, called persisters, to ensure survival in the face of uncertainty. This hedging strategy is remarkably similar to financial hedging, where diversifying an investment portfolio protects against economic uncertainty. We provide a new, to our knowledge, theoretical foundation for understanding cellular hedging by unifying the study of biological population dynamics and the mathematics of financial risk management through optimal control theory. https://www.selleckchem.com/products/nedisertib.html Motivated by the widely accepted role of volatility in the emergence of persistence, we consider several models of environmental volatility described by continuous-time stochastic processes. This allows us to study an emergent cellular hedging strategy that maximizes the expected per capita growth rate of the population. Analytical and simulation results probe the optimal persister strategy, revealing results that are consistent with experimental observations and suggest new opportunities for experimental investigation and design. Overall, we provide a new, to our knowledge, way of conceptualizing and modeling cellular decision making in volatile environments by explicitly unifying theory from mathematical biology and finance.The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the COVID-19 pandemic. Computer simulations of complete viral particles can provide theoretical insights into large-scale viral processes including assembly, budding, egress, entry, and fusion. Detailed atomistic simulations are constrained to shorter timescales and require billion-atom simulations for these processes. Here, we report the current status and ongoing development of a largely "bottom-up" coarse-grained (CG) model of the SARS-CoV-2 virion. Data from a combination of cryo-electron microscopy (cryo-EM), x-ray crystallography, and computational predictions were used to build molecular models of structural SARS-CoV-2 proteins, which were then assembled into a complete virion model. We describe how CG molecular interactions can be derived from all-atom simulations, how viral behavior difficult to capture in atomistic simulations can be incorporated into the CG models, and how the CG models can be iteratively improved as new data become publicly available. Our initial CG model and the detailed methods presented are intended to serve as a resource for researchers working on COVID-19 who are interested in performing multiscale simulations of the SARS-CoV-2 virion.