In silico and physico-chemical characterization of cluster formation dynamics in peptide solutions.

Although antimicrobial peptides are considered one of the most promising alternatives to conventional antibiotics given the alarming increase in bacterial multidrug resistance, many aspects of their mechanism of action remain unclear, in particular the emergence and role of collective phenomena such as the spontaneous formation of nano-sized unstructured objects (clusters) and their effects on the biodynamics. We study this process using two novel peptides from the mucus of the garden snail as an example to reveal its dynamics and bioactivity implications through coordinated in silico and in vitro techniques - molecular dynamics simulations, UV-Vis and fluorescence spectroscopy, and antibacterial activity tests against two representative bacterial strains - one gram-negative ( 3458) and one gram-positive (). The results obtained confirm the impact of the aggregation processes of the peptides on their biological activity and provide insight into possible synergies in their action.

Expression of concern: Heat-based transdermal delivery of a ramipril loaded cream for treating hypertension.

Expression of concern for 'Heat-based transdermal delivery of a ramipril loaded cream for treating hypertension' by Anna Voronova , , 2022, , 12247-12256, https://doi.org/10.1039/D2NR02295H.

Quantifying performance and joint kinematics in functional tasks crucial for anterior cruciate ligament rehabilitation using smartphone video and pose detection.

Background: The assessment of performance during functional tasks and the quality of movement execution are crucial metrics in the rehabilitation of patients with anterior cruciate ligament (ACL) injuries. While measuring performance is feasible in clinical practice, quantifying joint kinematics poses greater challenges. The aim of this study was to investigate whether smartphone video, using deep neural networks for human pose detection, can enable the clinicians not only to measure performance in functional tasks but also to assess joint kinematics.

Dissociable Effects of Urgency and Evidence Accumulation during Reaching Revealed by Dynamic Multisensory Integration.

When making perceptual decisions, humans combine information across sensory modalities dependent on their respective uncertainties. However, it remains unknown how the brain integrates multisensory feedback during movement and which factors besides sensory uncertainty influence sensory contributions. We performed two reaching experiments on healthy adults to investigate whether movement corrections to combined visual and mechanical perturbations scale with visual uncertainty.

Rapid and Specific Detection of Using Phage Protein-Based Lateral Flow Assays.

Rapid and precise diagnostic techniques are essential for identifying foodborne pathogens, including (), which poses significant challenges to food safety. Traditional detection methods are limited by long incubation times and high costs. In this context, gold nanoparticle (AuNP)-based lateral flow assays (LFAs) are emerging as valuable tools for rapid screening.

A dosimetric and robustness analysis of proton arc therapy with early energy layer and spot assignment for lung cancer versus conventional intensity modulated proton therapy.

Background And Purpose: Intensity Modulated Proton Therapy (IMPT) faces challenges in lung cancer treatment, like maintaining plan robustness for moving tumors against setup, range errors, and interplay effects. Proton Arc Therapy (PAT) is an alternative to maintain target coverage, potentially improving organ at risk (OAR) sparing, reducing beam delivery time (BDT), and enhancing patient experience. We aim to perform a systematic plan comparison study between IMPT and energy layer (EL) and spot assignment algorithm - Proton Arc Therapy (ELSA-PAT) to assess its potential for lung cancer treatment.

Enhancing Sex Estimation Accuracy with Cranial Angle Measurements and Machine Learning.

The development of current sexing methods largely depends on the use of adequate sources of data and adjustable classification techniques. Most sex estimation methods have been based on linear measurements, while the angles have been largely ignored, potentially leading to the loss of valuable information for sex discrimination. This study aims to evaluate the usefulness of cranial angles for sex estimation and to differentiate the most dimorphic ones by training machine learning algorithms.

Identifying the Health Educational Needs of Refugees: Empirical Evidence from a Delphi Study.

Refugees experience poorer health outcomes especially which can be exacerbated by or can be a result of low health literacy of refugee populations. To address poor health outcomes, health literacy, and health usage in refugee populations, it is essential to develop health educational interventions for refugees' healthcare integration. To do so, learning objectives must be identified based on refugees' health knowledge gaps.

Multimodal representations of biomedical knowledge from limited training whole slide images and reports using deep learning.

The increasing availability of biomedical data creates valuable resources for developing new deep learning algorithms to support experts, especially in domains where collecting large volumes of annotated data is not trivial. Biomedical data include several modalities containing complementary information, such as medical images and reports: images are often large and encode low-level information, while reports include a summarized high-level description of the findings identified within data and often only concerning a small part of the image. However, only a few methods allow to effectively link the visual content of images with the textual content of reports, preventing medical specialists from properly benefitting from the recent opportunities offered by deep learning models.

Fast multi-compartment Microstructure Fingerprinting in brain white matter.

We proposed two deep neural network based methods to accelerate the estimation of microstructural features of crossing fascicles in the white matter. Both methods focus on the acceleration of a multi-dictionary matching problem, which is at the heart of Microstructure Fingerprinting, an extension of Magnetic Resonance Fingerprinting to diffusion MRI. The first acceleration method uses efficient sparse optimization and a dedicated feed-forward neural network to circumvent the inherent combinatorial complexity of the fingerprinting estimation.

Definitive engineering strength and fracture toughness of graphene through on-chip nanomechanics.

Fail-safe design of devices requires robust integrity assessment procedures which are still absent for 2D materials, hence affecting transfer to applications. Here, a combined on-chip tension and cracking method, and associated data reduction scheme have been developed to determine the fracture toughness and strength of monolayer-monodomain-freestanding graphene. Myriads of specimens are generated providing statistical data.

The Assessment of the Association of Proton Pump Inhibitor Usage with Chronic Kidney Disease Progression through a Process Mining Approach.

Previous studies have suggested an association between Proton Pump Inhibitors (PPIs) and the progression of chronic kidney disease (CKD). This study aims to assess the association between PPI use and CKD progression by analysing estimated glomerular filtration rate (eGFR) trajectories using a process mining approach. We conducted a retrospective cohort study from 1 January 2006 to 31 December 2011, utilising data from the Stockholm Creatinine Measurements (SCREAM).

Sparse Blind Spherical Deconvolution of diffusion weighted MRI.

Diffusion-weighted magnetic resonance imaging provides invaluable insights into neurological pathways. However, accurate and robust characterization of white matter fibers microstructure remains challenging. Widely used spherical deconvolution algorithms retrieve the fiber Orientation Distribution Function (ODF) by using an estimation of a response function, i.

[The use of medical imaging as a therapeutic patient education tool in radiotherapy : a feasibility study].

Because of its prevalence and high mortality rate, cancer is a major public health challenge. Radiotherapy is an important treatment option, and makes extensive use of medical imaging. Until now, this type of tool has been reserved to professionals, but it is now opening up to wider use, including by patients themselves for educational purposes.

[Contribution of free software to medical imaging].

Medical research uses increasingly massive, complex and interdependent data, the analysis of which requires the use of specialized algorithms. In order to independently reproduce and validate the results of a scientific study, it is no longer sufficient to share the text of the article as an open-access document, together with the raw research data according to the open-data approach. It is now also needed to share the algorithms used to analyze the data with other research teams.

Efficient proton arc optimization and delivery through energy layer pre-selection and post-filtering.

Background: Proton arc therapy (PAT) has emerged as a promising approach for improving dose distribution, but also enabling simpler and faster treatment delivery in comparison to conventional proton treatments. However, the delivery speed achievable in proton arc relies on dedicated algorithms, which currently do not generate plans with a clear speed-up and sometimes even result in increased delivery time.

Purpose: This study aims to address the challenge of minimizing delivery time through a hybrid method combining a fast geometry-based energy layer (EL) pre-selection with a dose-based EL filtering, and comparing its performance to a baseline approach without filtering.

Locus coeruleus features are linked to vagus nerve stimulation response in drug-resistant epilepsy.

The locus coeruleus-norepinephrine system is thought to be involved in the clinical effects of vagus nerve stimulation. This system is known to prevent seizure development and induce long-term plastic changes, particularly with the release of norepinephrine in the hippocampus. However, the requisites to become responder to the therapy and the mechanisms of action are still under investigation.

Interferon- as a Potential Inhibitor of SARS-CoV-2 ORF6 Accessory Protein.

The ORF6 protein of the SARS-CoV-2 virus plays a crucial role in blocking the innate immune response of the infected cells by inhibiting interferon pathways. Additionally, it binds to and immobilises the RAE1 protein on the cytoplasmic membranes, thereby blocking mRNA transport from the nucleus to the cytoplasm. In all these cases, the host cell proteins are tethered by the flexible C-terminus of ORF6.

Design of a Porous Silicon Biosensor: Characterization, Modeling, and Application to the Indirect Detection of Bacteria.

The design of a porous silicon (PSi) biosensor is not often documented, but is of the upmost importance to optimize its performance. In this work, the motivation behind the design choices of a PSi-based optical biosensor for the indirect detection of bacteria via their lysis is detailed. The transducer, based on a PSi membrane, was characterized and models were built to simulate the analyte diffusion, depending on the porous nanostructures, and to optimize the optical properties.

Gradient regularization of Newton method with Bregman distances.

In this paper, we propose a first second-order scheme based on arbitrary non-Euclidean norms, incorporated by Bregman distances. They are introduced directly in the Newton iterate with regularization parameter proportional to the square root of the norm of the current gradient. For the basic scheme, as applied to the composite convex optimization problem, we establish the global convergence rate of the order both in terms of the functional residual and in the norm of subgradients.

Baby HABIT-ILE intervention: study protocol of a randomised controlled trial in infants aged 6-18 months with unilateral cerebral palsy.

Introduction: Research using animal models suggests that intensive motor skill training in infants under 2 years old with cerebral palsy (CP) may significantly reduce, or even prevent, maladaptive neuroplastic changes following brain injury. However, the effects of such interventions to tentatively prevent secondary neurological damages have never been assessed in infants with CP. This study aims to determine the effect of the baby Hand and Arm Bimanual Intensive Therapy Including Lower Extremities (baby HABIT-ILE) in infants with unilateral CP, compared with a control intervention.

Thermokinetic relations.

Thermokinetic relations bound thermodynamic quantities, such as entropy production of a physical system over a certain time interval, with statistics of kinetic (or dynamical) observables, such as mean total variation of the observable over the time interval. We introduce a thermokinetic relation to bound the entropy production or the nonadiabatic (or excess) entropy production for overdamped Markov jump processes, possibly with time-varying rates and nonstationary distributions. For stationary cases, this bound is akin to a thermodynamic uncertainty relation, only involving absolute fluctuations rather than the mean square, thereby offering a better lower bound far from equilibrium.