Lesion-preserving unpaired image-to-image translation between MRI and CT from ischemic stroke patients.

Purpose: Multiple medical imaging modalities are used for clinical follow-up ischemic stroke analysis. Mixed-modality datasets are challenging, both for clinical rating purposes and for training machine learning models. While image-to-image translation methods have been applied to harmonize stroke patient images to a single modality, they have only been used for paired data so far.

An analysis of the effects of limited training data in distributed learning scenarios for brain age prediction.

Objective: Distributed learning avoids problems associated with central data collection by training models locally at each site. This can be achieved by federated learning (FL) aggregating multiple models that were trained in parallel or training a single model visiting sites sequentially, the traveling model (TM). While both approaches have been applied to medical imaging tasks, their performance in limited local data scenarios remains unknown.

The use of acrylic yarn modified with amidoxime and carboxylate-containing polymer for lead removal from drinking water.

Amidoxime and carboxylate-containing polymer adsorbents derived from acrylic yarn exhibit high adsorption capacity for lead(ii) (Pb) ions in water. The adsorption process follows pseudo-second-order kinetics and fits the extended Langmuir isotherm model with the maximum adsorption capacity of Pb with 238 mg lead per gram of the fiber at room temperature. Endothermic (Δ° = 20.

Dementia in Convolutional Neural Networks: Using Deep Learning Models to Simulate Neurodegeneration of the Visual System.

Although current research aims to improve deep learning networks by applying knowledge about the healthy human brain and vice versa, the potential of using such networks to model and study neurodegenerative diseases remains largely unexplored. In this work, we present an in-depth feasibility study modeling progressive dementia in silico with deep convolutional neural networks. Therefore, networks were trained to perform visual object recognition and then progressively injured by applying neuronal as well as synaptic injury.

Modeling Neurodegeneration With Deep Learning.

Deep neural networks, inspired by information processing in the brain, can achieve human-like performance for various tasks. However, research efforts to use these networks as models of the brain have primarily focused on modeling healthy brain function so far. In this work, we propose a paradigm for modeling neural diseases with deep learning and demonstrate its use in modeling posterior cortical atrophy (PCA), an atypical form of Alzheimer's disease affecting the visual cortex.

An Analysis of the Vulnerability of Two Common Deep Learning-Based Medical Image Segmentation Techniques to Model Inversion Attacks.

Recent research in computer vision has shown that original images used for training of deep learning models can be reconstructed using so-called inversion attacks. However, the feasibility of this attack type has not been investigated for complex 3D medical images. Thus, the aim of this study was to examine the vulnerability of deep learning techniques used in medical imaging to model inversion attacks and investigate multiple quantitative metrics to evaluate the quality of the reconstructed images.

Supervised machine learning tools: a tutorial for clinicians.

In an increasingly data-driven world, artificial intelligence is expected to be a key tool for converting big data into tangible benefits and the healthcare domain is no exception to this. Machine learning aims to identify complex patterns in multi-dimensional data and use these uncovered patterns to classify new unseen cases or make data-driven predictions. In recent years, deep neural networks have shown to be capable of producing results that considerably exceed those of conventional machine learning methods for various classification and regression tasks.

Building machine learning models without sharing patient data: A simulation-based analysis of distributed learning by ensembling.

The development of machine learning solutions in medicine is often hindered by difficulties associated with sharing patient data. Distributed learning aims to train machine learning models locally without requiring data sharing. However, the utility of distributed learning for rare diseases, with only a few training examples at each contributing local center, has not been investigated.

Injectable hydrogel enables local and sustained co-delivery to the brain: Two clinically approved biomolecules, cyclosporine and erythropoietin, accelerate functional recovery in rat model of stroke.

Therapeutic delivery to the brain is limited by the blood-brain barrier and is exacerbated by off-target effects associated with systemic delivery, thereby precluding many potential therapies from even being tested. Given the systemic side effects of cyclosporine and erythropoietin, systemic administration would be precluded in the context of stroke, leaving only the possibility of local delivery. We wondered if direct delivery to the brain would allow new reparative therapeutics, such as these, to be identified for stroke.

Local Delivery of Brain-Derived Neurotrophic Factor Enables Behavioral Recovery and Tissue Repair in Stroke-Injured Rats.

We developed a biocomposite that can be mixed with brain-derived neurotrophic factor (BDNF) and dispensed onto the surface of the brain to provide sustained, local release of the protein using a procedure that avoids additional damage to neural tissue. The composite is simple to fabricate, and provides sustained release without nanoparticle encapsulation of BDNF, preserving material and protein bioactivity. We demonstrate that when delivered epicortically to a rat model of stroke, this composite allows BDNF to diffuse into the brain, resulting in enhanced behavioral recovery and synaptic plasticity in the contralesional hemisphere.

Initial cell maturity changes following transplantation in a hyaluronan-based hydrogel and impacts therapeutic success in the stroke-injured rodent brain.

Ischemic stroke results in a loss of neurons for which there are no available clinical strategies to stimulate regeneration. While preclinical studies have demonstrated that functional recovery can be obtained by transplanting an exogenous source of neural progenitors into the brain, it remains unknown at which stage of neuronal maturity cells will provide the most benefit. We investigated the role of neuronal maturity on cell survival, differentiation, and long-term sensorimotor recovery in stroke-injured rats using a population of human cortically-specified neuroepithelial progenitor cells (cNEPs) delivered in a biocompatible, bioresorbable hyaluronan/methylcellulose hydrogel.

Encapsulation-free controlled release: Electrostatic adsorption eliminates the need for protein encapsulation in PLGA nanoparticles.

Encapsulation of therapeutic molecules within polymer particles is a well-established method for achieving controlled release, yet challenges such as low loading, poor encapsulation efficiency, and loss of protein activity limit clinical translation. Despite this, the paradigm for the use of polymer particles in drug delivery has remained essentially unchanged for several decades. By taking advantage of the adsorption of protein therapeutics to poly(lactic-co-glycolic acid) (PLGA) nanoparticles, we demonstrate controlled release without encapsulation.

Circumventing the blood-brain barrier: Local delivery of cyclosporin A stimulates stem cells in stroke-injured rat brain.

Drug delivery to the central nervous system is limited by the blood-brain barrier, which can be circumvented by local delivery. In applications of stroke therapy, for example, stimulation of endogenous neural stem/progenitor cells (NSPCs) by cyclosporin A (CsA) is promising. However, current strategies rely on high systemic drug doses to achieve small amounts of CsA in the brain tissue, resulting in systemic toxicity and undesirable global immunosuppression.

A hydrogel composite system for sustained epi-cortical delivery of Cyclosporin A to the brain for treatment of stroke.

Stimulation of endogenous neural stem/progenitor cells (NSPCs) with therapeutic factors holds potential for the treatment of stroke. Cyclosporin A (CsA) is a particularly promising candidate molecule because it has been shown to act as a survival factor for these cells over a period of weeks both in vitro and in vivo; however, systemically-delivered CsA compromises the entire immune system, necessitating sustained localized delivery. Herein we describe a local delivery strategy for CsA using an epi-cortical hydrogel of hyaluronan-methylcellulose (HAMC) as the drug reservoir.

Co-expression vs. co-infection using baculovirus expression vectors in insect cell culture: Benefits and drawbacks.

The baculovirus expression vector system (BEVS) is a versatile and powerful platform for protein expression in insect cells. With the ability to approach similar post-translational modifications as in mammalian cells, the BEVS offers a number of advantages including high levels of expression as well as an inherent safety during manufacture and of the final product. Many BEVS products include proteins and protein complexes that require expression from more than one gene.

Estimation of mental effort in learning visual search by measuring pupil response.

Perceptual learning refers to the improvement of perceptual sensitivity and performance with training. In this study, we examined whether learning is accompanied by a release from mental effort on the task, leading to automatization of the learned task. For this purpose, we had subjects conduct a visual search for a target, defined by a combination of orientation and spatial frequency, while we monitored their pupil size.

The effect of retinal illuminance on visual motion priming.

The perceived direction of a directionally ambiguous stimulus is influenced by the moving direction of a preceding priming stimulus. Previous studies have shown that a brief priming stimulus induces positive motion priming, in which a subsequent directionally ambiguous stimulus is perceived to move in the same direction as the primer, while a longer priming stimulus induces negative priming, in which the following ambiguous stimulus is perceived to move in the opposite direction of the primer. The purpose of this study was to elucidate the underlying mechanism of motion priming by examining how retinal illuminance and velocity of the primer influences the perception of priming.