Influences of Excluded Volume of Molecules on Signaling Processes on Biomembrane

We investigate the influences of the excluded volume of molecules on biochemical reaction processes on 2-dimensional surfaces using a model of signal transduction processes on biomembranes. We perform simulations of the 2-dimensional cell-based model, which describes the reactions and diffusion of the receptors, signaling proteins, target proteins, and crowders on the cell membrane. The signaling proteins are activated by receptors, and these activated signaling proteins activate target proteins that bind autonomously from the cytoplasm to the membrane, and unbind from the membrane if activated. If the target proteins bind frequently, the volume fraction of molecules on the membrane becomes so large that the excluded volume of the molecules for the reaction and diffusion dynamics cannot be negligible. We find that such excluded volume effects of the molecules induce non-trivial variations of the signal flow, defined as the activation frequency of target proteins, as follows. With an increase in the binding rate of target proteins, the signal flow varies by i) monotonically increasing; ii) increasing then decreasing in a bell-shaped curve; or iii) increasing, decreasing, then increasing in an S-shaped curve. We further demonstrate that the excluded volume of molecules influences the hierarchical molecular distributions throughout the reaction processes. In particular, when the system exhibits a large signal flow, the signaling proteins tend to surround the receptors to form receptor-signaling protein clusters, and the target proteins tend to become distributed around such clusters. To explain these phenomena, we analyze the stochastic model of the local motions of molecules around the receptor.Comment: 31 pages, 10 figure

Anisotropic diffusion of water molecules in hydroxyapatite nanopores

Funded by EPSRC Grant EP/K000128/1

Double blind, randomized, placebo controlled clinical trial for the treatment of diabetic foot ulcers, using a nitric oxide releasing patch: PATHON

<p>Abstract</p> <p>Background</p> <p>Diabetes Mellitus constitutes one of the most important public health problems due to its high prevalence and enormous social and economic consequences. Diabetic foot ulcers are one of the chronic complications of diabetes mellitus and constitute the most important cause of non-traumatic amputation of inferior limbs. It is estimated that 15% of the diabetic population will develop an ulcer sometime in their lives. Although novel therapies have been proposed, there is no effective treatment for this pathology. Naturally produced nitric oxide participates in the wound healing process by stimulating the synthesis of collagen, triggering the release of chemotactic cytokines, increasing blood vessels permeability, promoting angiogenic activity, stimulating the release of epidermical growth factors, and by interfering with the bacterial mitochondrial respiratory chain. Topically administered nitric oxide has demonstrated to be effective and safe for the treatment of chronic ulcers secondary to cutaneous leishmaniasis. However, due to their unstable nitric oxide release, the topical donors needed to be applied frequently, diminishing the adherence to the treatment. This difficulty has led to the development of a multilayer polymeric transdermal patch produced by electrospinning technique that guarantees a constant nitric oxide release. The main objective of this study is to evaluate the effectiveness and safety of this novel nitric oxide releasing wound dressing for the treatment of diabetic foot ulcers.</p> <p>Methods and design</p> <p>A double-blind, placebo-controlled clinical trial, including 100 diabetic patients was designed. At the time of enrollment, a complete medical evaluation and laboratory tests will be performed, and those patients who meet the inclusion criteria randomly assigned to one of two groups. Over the course of 90 days group 1 will receive active patches and group 2 placebo patches. The patients will be seen by the research group at least every two weeks until the healing of the ulcer or the end of the treatment. During each visit the healing process of the ulcer, the patient's health status and the presence of adverse events will be assessed. Should the effectiveness of the patches be demonstrated an alternative treatment would then be available to patients.</p> <p>Trial registration</p> <p>NCT00428727.</p

The ELBA Force Field for Coarse-Grain Modeling of Lipid Membranes

A new coarse-grain model for molecular dynamics simulation of lipid membranes is presented. Following a simple and conventional approach, lipid molecules are modeled by spherical sites, each representing a group of several atoms. In contrast to common coarse-grain methods, two original (interdependent) features are here adopted. First, the main electrostatics are modeled explicitly by charges and dipoles, which interact realistically through a relative dielectric constant of unity (). Second, water molecules are represented individually through a new parametrization of the simple Stockmayer potential for polar fluids; each water molecule is therefore described by a single spherical site embedded with a point dipole. The force field is shown to accurately reproduce the main physical properties of single-species phospholipid bilayers comprising dioleoylphosphatidylcholine (DOPC) and dioleoylphosphatidylethanolamine (DOPE) in the liquid crystal phase, as well as distearoylphosphatidylcholine (DSPC) in the liquid crystal and gel phases. Insights are presented into fundamental properties and phenomena that can be difficult or impossible to study with alternative computational or experimental methods. For example, we investigate the internal pressure distribution, dipole potential, lipid diffusion, and spontaneous self-assembly. Simulations lasting up to 1.5 microseconds were conducted for systems of different sizes (128, 512 and 1058 lipids); this also allowed us to identify size-dependent artifacts that are expected to affect membrane simulations in general. Future extensions and applications are discussed, particularly in relation to the methodology's inherent multiscale capabilities

Magnetic Resonance-based Response Assessment and Dose Adaptation in Human Papilloma Virus Positive Tumors of the Oropharynx treated with Radiotherapy (MR-ADAPTOR): An R-IDEAL stage 2a-2b/Bayesian phase II trial.

Background Current standard radiotherapy for oropharynx cancer (OPC) is associated with high rates of severe toxicities, shown to adversely impact patients' quality of life. Given excellent outcomes of human papilloma virus (HPV)-associated OPC and long-term survival of these typically young patients, treatment de-intensification aimed at improving survivorship while maintaining excellent disease control is now a central concern. The recent implementation of magnetic resonance image - guided radiotherapy (MRgRT) systems allows for individual tumor response assessment during treatment and offers possibility of personalized dose-reduction. In this 2-stage Bayesian phase II study, we propose to examine weekly radiotherapy dose-adaptation based on magnetic resonance imaging (MRI) evaluated tumor response. Individual patient's plan will be designed to optimize dose reduction to organs at risk and minimize locoregional failure probability based on serial MRI during RT. Our primary aim is to assess the non-inferiority of MRgRT dose adaptation for patients with low risk HPV-associated OPC compared to historical control, as measured by Bayesian posterior probability of locoregional control (LRC).Methods Patients with T1-2 N0-2b (as per AJCC 7th Edition) HPV-positive OPC, with lymph node <3 cm and <10 pack-year smoking history planned for curative radiotherapy alone to a dose of 70 Gy in 33 fractions will be eligible. All patients will undergo pre-treatment MRI and at least weekly intra-treatment MRI. Patients undergoing MRgRT will have weekly adaptation of high dose planning target volume based on gross tumor volume response. The stage 1 of this study will enroll 15 patients to MRgRT dose adaptation. If LRC at 6 months with MRgRT dose adaptation is found sufficiently safe as per the Bayesian model, stage 2 of the protocol will expand enrollment to an additional 60 patients, randomized to either MRgRT or standard IMRT.Discussion Multiple methods for safe treatment de-escalation in patients with HPV-positive OPC are currently being studied. By leveraging the ability of advanced MRI techniques to visualize tumor and soft tissues through the course of treatment, this protocol proposes a workflow for safe personalized radiation dose-reduction in good responders with radiosensitive tumors, while ensuring tumoricidal dose to more radioresistant tumors. MRgRT dose adaptation could translate in reduced long term radiation toxicities and improved survivorship while maintaining excellent LRC outcomes in favorable OPC.Trial registration ClinicalTrials.gov ID: NCT03224000; Registration date: 07/21/2017

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Combination of searches for heavy spin-1 resonances using 139 fb−1 of proton-proton collision data at s = 13 TeV with the ATLAS detector

A combination of searches for new heavy spin-1 resonances decaying into different pairings of W, Z, or Higgs bosons, as well as directly into leptons or quarks, is presented. The data sample used corresponds to 139 fb−1 of proton-proton collisions at = 13 TeV collected during 2015–2018 with the ATLAS detector at the CERN Large Hadron Collider. Analyses selecting quark pairs (qq, bb, , and tb) or third-generation leptons (τν and ττ) are included in this kind of combination for the first time. A simplified model predicting a spin-1 heavy vector-boson triplet is used. Cross-section limits are set at the 95% confidence level and are compared with predictions for the benchmark model. These limits are also expressed in terms of constraints on couplings of the heavy vector-boson triplet to quarks, leptons, and the Higgs boson. The complementarity of the various analyses increases the sensitivity to new physics, and the resulting constraints are stronger than those from any individual analysis considered. The data exclude a heavy vector-boson triplet with mass below 5.8 TeV in a weakly coupled scenario, below 4.4 TeV in a strongly coupled scenario, and up to 1.5 TeV in the case of production via vector-boson fusion

Accuracy versus precision in boosted top tagging with the ATLAS detector

Abstract The identification of top quark decays where the top quark has a large momentum transverse to the beam axis, known as top tagging, is a crucial component in many measurements of Standard Model processes and searches for beyond the Standard Model physics at the Large Hadron Collider. Machine learning techniques have improved the performance of top tagging algorithms, but the size of the systematic uncertainties for all proposed algorithms has not been systematically studied. This paper presents the performance of several machine learning based top tagging algorithms on a dataset constructed from simulated proton-proton collision events measured with the ATLAS detector at √ s = 13 TeV. The systematic uncertainties associated with these algorithms are estimated through an approximate procedure that is not meant to be used in a physics analysis, but is appropriate for the level of precision required for this study. The most performant algorithms are found to have the largest uncertainties, motivating the development of methods to reduce these uncertainties without compromising performance. To enable such efforts in the wider scientific community, the datasets used in this paper are made publicly available.</jats:p