Hydrogel mechanical properties in altered gravity.

Exposure to altered gravity influences cellular behaviour in cell cultures. Hydrogels are amongst the most common materials used to produce tissue-engineering scaffolds, and their mechanical properties play a crucial role in cell-matrix interaction. However, little is known about the influence of altered gravity on hydrogel properties.

APOLLO 11 Project, Consortium in Advanced Lung Cancer Patients Treated With Innovative Therapies: Integration of Real-World Data and Translational Research.

Introduction: Despite several therapeutic efforts, lung cancer remains a highly lethal disease. Novel therapeutic approaches encompass immune-checkpoint inhibitors, targeted therapeutics and antibody-drug conjugates, with different results. Several studies have been aimed at identifying biomarkers able to predict benefit from these therapies and create a prediction model of response, despite this there is a lack of information to help clinicians in the choice of therapy for lung cancer patients with advanced disease.

Laser-induced graphene on cross-linked sodium alginate.

Laser-induced graphene (LIG) possesses desirable properties for numerous applications. However, LIG formation on biocompatible substrates is needed to further augment the integration of LIG-based technologies into nanobiotechnology. Here, LIG formation on cross-linked sodium alginate is reported.

PEOPLE (NTC03447678), a phase II trial to test pembrolizumab as first-line treatment in patients with advanced NSCLC with PD-L1 <50%: a multiomics analysis.

Background: Chemoimmunotherapy represents the standard of care for patients with advanced non-small cell lung cancer (NSCLC) and programmed death-ligand 1 (PD-L1) <50%. Although single-agent pembrolizumab has also demonstrated some activity in this setting, no reliable biomarkers yet exist for selecting patients likely to respond to single-agent immunotherapy. The main purpose of the study was to identify potential new biomarkers associated with progression-free-survival (PFS) within a multiomics analysis.

Artificial Intelligence in Clinical Oncology: From Data to Digital Pathology and Treatment.

Recently, a wide spectrum of artificial intelligence (AI)-based applications in the broader categories of digital pathology, biomarker development, and treatment have been explored. In the domain of digital pathology, these have included novel analytical strategies for realizing new information derived from standard histology to guide treatment selection and biomarker development to predict treatment selection and response. In therapeutics, these have included AI-driven drug target discovery, drug design and repurposing, combination regimen optimization, modulated dosing, and beyond.

A Deep Learning Image-to-Image Translation Approach for a More Accessible Estimator of the Healing Time of Burns.

Objective: An accurate and timely diagnosis of burn severity is critical to ensure a positive outcome. Laser Doppler imaging (LDI) has become a very useful tool for this task. It measures the perfusion of the burn and estimates its potential healing time.

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients.

Introduction: Artificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods.

Methods: We prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy.

A Scalable Framework for Closed-Loop Neuromodulation with Deep Learning.

Closed-loop neuromodulation measures dynamic neural or physiological activity to optimize interventions for clinical and nonclinical behavioral, cognitive, wellness, attentional, or general task performance enhancement. Conventional closed-loop stimulation approaches can contain biased biomarker detection (decoders and error-based triggering) and stimulation-type application. We present and verify a novel deep learning framework for designing and deploying flexible, data-driven, automated closed-loop neuromodulation that is scalable using diverse datasets, agnostic to stimulation technology (supporting multi-modal stimulation: tACS, tDCS, tFUS, TMS), and without the need for personalized ground-truth performance data.

U-Net based Mapping from Digital Images to Laser Doppler Imaging for Burn Assessment.

The incidence of burn injuries is higher in low-and middle-income countries, and particularly in remote areas where the access to specialized burn assessment, care and recovery is limited. Given the high costs associated with one of the most used techniques to evaluate the severity of a burn, namely laser Doppler imaging (LDI), an alternative approach could be beneficial for remote locations. This study proposes a novel approach to estimate the LDI from digital images of a burn.

Printability of Double Network Alginate-Based Hydrogel for 3D Bio-Printed Complex Structures.

Three-dimensional (3D) bio-printing has recently emerged as a crucial technology in tissue engineering, yet there are still challenges in selecting materials to obtain good print quality. Therefore, it is essential to study the influence of the chosen material (i.e.

Thermotropic Liquid Crystals for Temperature Mapping.

Wound management in Space is an important factor to be considered in future Human Space Exploration. It demands the development of reliable wound monitoring systems that will facilitate the assessment and proper care of wounds in isolated environments, such as Space. One possible system could be developed using liquid crystal films, which have been a promising solution for real-time temperature monitoring in healthcare, but they are not yet implemented in clinical practice.

Neural substrates of human fear generalization: A 7T-fMRI investigation.

Fear generalization - the tendency to interpret ambiguous stimuli as threatening due to perceptual similarity to a learned threat - is an adaptive process. Overgeneralization, however, is maladaptive and has been implicated in a number of anxiety disorders. Neuroimaging research has indicated several regions sensitive to effects of generalization, including regions involved in fear excitation (e.

Nondual Awareness and Minimal Phenomenal Experience.

Minimal phenomenal experiences (MPEs) have recently gained attention in the fields of neuroscience and philosophy of mind. They can be thought of as episodes of greatly reduced or even absent phenomenal content together with a reduced level of arousal. It has also been proposed that MPEs are cases of consciousness-as-such.

Glycaemia, renal function and body mass in men and women with type 2 diabetes.

Aim To determine parameters of glycaemic control, renal function and anthropometric measurements in patients with type 2 diabetes in family medicine offices and to examine whether there is a difference in these parameters between genders. Methods This cross-sectional study included 136 patients of both genders diagnosed with type 2 diabetes, with an average age of 69.33±10.

Feedback-Related Negativity and Frontal Midline Theta Reflect Dissociable Processing of Reinforcement.

Prediction errors (PEs) encode representations of rewarding and aversive experiences and are critical to reinforcement processing. The feedback-related negativity (FRN), a component of the event-related potential (ERP) that is sensitive to valenced feedback, is believed to reflect PE signals. Reinforcement is also studied using frontal midline theta (FMΘ) activity, which peaks around the same time as the FRN and increases in response to unexpected events compared to expected events.

Delta phase reset predicts conflict-related changes in P3 amplitude and behavior.

When multiple competing responses are activated, we respond more slowly than if only one response is activated (response conflict). Conflict-induced slowing is reduced for consecutive high-conflict stimuli, an effect known as conflict adaptation. Verguts and Notebaert's (2009) adaptation by binding theory suggests this is due to Hebbian learning of cognitive control, potentiated by the response of the locus coeruleus norepinephrine (NE) system.

Emotional Objectivity: Neural Representations of Emotions and Their Interaction with Cognition.

Recent advances in our understanding of information states in the human brain have opened a new window into the brain's representation of emotion. While emotion was once thought to constitute a separate domain from cognition, current evidence suggests that all events are filtered through the lens of whether they are good or bad for us. Focusing on new methods of decoding information states from brain activation, we review growing evidence that emotion is represented at multiple levels of our sensory systems and infuses perception, attention, learning, and memory.

Dissociation and its disorders: Competing models, future directions, and a way forward.

Dissociative experiences and symptoms have sparked intense scrutiny and debate for more than a century. Two perspectives, the trauma model (TM), which postulates a direct and potent causal link between trauma and dissociation, and the sociocognitive model (SCM), which emphasizes social and cognitive variables (e.g.

Towards a state-space geometry of neural responses to natural scenes: A steady-state approach.

Our understanding of information processing by the mammalian visual system has come through a variety of techniques ranging from psychophysics and fMRI to single unit recording and EEG. Each technique provides unique insights into the processing framework of the early visual system. Here, we focus on the nature of the information that is carried by steady state visual evoked potentials (SSVEPs).

The role of low-level image features in the affective categorization of rapidly presented scenes.

It remains unclear how the visual system is able to extract affective content from complex scenes even with extremely brief (< 100 millisecond) exposures. One possibility, suggested by findings in machine vision, is that low-level features such as unlocalized, two-dimensional (2-D) Fourier spectra can be diagnostic of scene content. To determine whether Fourier image amplitude carries any information about the affective quality of scenes, we first validated the existence of image category differences through a support vector machine (SVM) model that was able to discriminate our intact aversive and neutral images with ~ 70% accuracy using amplitude-only features as inputs.