Multimodal Deep Learning Improves Recurrence Risk Prediction in Pediatric Low-Grade Gliomas.

Background: Postoperative recurrence risk for pediatric low-grade gliomas (pLGGs) is challenging to predict by conventional clinical, radiographic, and genomic factors. We investigated if deep learning of MRI tumor features could improve postoperative pLGG risk stratification.

Methods: We used pre-trained deep learning (DL) tool designed for pLGG segmentation to extract pLGG imaging features from preoperative T2-weighted MRI from patients who underwent surgery (DL-MRI features).

Automated algorithm for medical data structuring, and segmentation using artificial intelligence within secured environment for dataset creation.

Objective: Routinely collected electronic health records using artificial intelligence (AI)-based systems bring out enormous benefits for patients, healthcare centers, and its industries. Artificial intelligence models can be used to structure a wide variety of unstructured data.

Methods: We present a semi-automatic workflow for medical dataset management, including data structuring, research extraction, AI-ground truth creation, and updates.

Intercellular pathways of cancer treatment-related cardiotoxicity and their therapeutic implications: the paradigm of radiotherapy.

Advances in cancer therapeutics have improved patient survival rates. However, cancer survivors may suffer from adverse events either at the time of therapy or later in life. Cardiovascular diseases (CVD) represent a clinically important, but mechanistically understudied complication, which interfere with the continuation of best-possible care, induce life-threatening risks, and/or lead to long-term morbidity.

Multiphysics simulation of magnetoelectric micro core-shells for wireless cellular stimulation therapy via magnetic temporal interference.

This paper presents an innovative approach to wireless cellular stimulation therapy through the design of a magnetoelectric (ME) microdevice. Traditional electrophysiological stimulation techniques for neural and deep brain stimulation face limitations due to their reliance on electronics, electrode arrays, or the complexity of magnetic induction. In contrast, the proposed ME microdevice offers a self-contained, controllable, battery-free, and electronics-free alternative, holding promise for targeted precise stimulation of biological cells and tissues.

Multimodal deep learning for personalized renal cell carcinoma prognosis: Integrating CT imaging and clinical data.

Background And Objective: Renal cell carcinoma represents a significant global health challenge with a low survival rate. The aim of this research was to devise a comprehensive deep-learning model capable of predicting survival probabilities in patients with renal cell carcinoma by integrating CT imaging and clinical data and addressing the limitations observed in prior studies. The aim is to facilitate the identification of patients requiring urgent treatment.

Optimization of Extracellular Vesicle Release for Targeted Drug Delivery.

Targeted drug delivery is a promising approach for many serious diseases, such as glioblastoma multiforme, one of the most common and devastating brain tumor. In this context, this work addresses the optimization of the controlled release of drugs which are carried by extracellular vesicles. Towards this goal, we derive and numerically verify an analytical solution for the end-to-end system model.

Modeling Extracellular Vesicles-Mediated Interactions of Cells in the Tumor Microenvironment.

Interactions of cells via extracellular vesicles (EVs) manipulate various actions, including cancer initiation and progression, inflammation, anti-tumor signaling and cell migration, proliferation and apoptosis in the tumor microenvironment. EVs as the external stimulus can activate or inhibit some receptor pathways in a way that amplify or attenuate a kind of particle release at target cells. This can also be carried out in a biological feedback-loop where the transmitter is affected by the induced release initiated by the target cell due to the EVs received from the donor cell, to create a bilateral process.

The End-to-End Molecular Communication Model of Extracellular Vesicle-Based Drug Delivery.

A closer look at nature has recently brought more interest in exploring and utilizing intra-body communication networks composed of cells as intrinsic, perfectly biocompatible infrastructures to deliver therapeutics. Naturally occurring cell-to-cell communication systems are being manipulated to release, navigate, and take-up soluble cell-derived messengers that are either therapeutic by nature or carry therapeutic molecular cargo. One example of such structures is extracellular vesicles (EVs) which have been recently proven to have pharmacokinetic properties, opening new avenues for developing the next generation biotherapeutics.

Implants with Sensing Capabilities.

Because of the aging human population and increased numbers of surgical procedures being performed, there is a growing number of biomedical devices being implanted each year. Although the benefits of implants are significant, there are risks to having foreign materials in the body that may lead to complications that may remain undetectable until a time at which the damage done becomes irreversible. To address this challenge, advances in implantable sensors may enable early detection of even minor changes in the implants or the surrounding tissues and provide early cues for intervention.

Regional Left Ventricular Fiber Stress Analysis for Cardiac Resynchronization Therapy Response.

Cardiac resynchronization therapy (CRT) is an effective treatment for a subgroup of heart failure (HF) patients, but more than 30% of those selected do not improve after CRT implantation. Imperfect pre-procedural criteria for patient selection and optimization are the main causes of the high non-response rate. In this study, we evaluated a novel measure for assessing CRT response.

Brain-on-a-chip: Recent advances in design and techniques for microfluidic models of the brain in health and disease.

Recent advances in biomaterials, microfabrication, microfluidics, and cell biology have led to the development of organ-on-a-chip devices that can reproduce key functions of various organs. Such platforms promise to provide novel insights into various physiological events, including mechanisms of disease, and evaluate the effects of external interventions, such as drug administration. The neuroscience field is expected to benefit greatly from these innovative tools.

Targeted Drug Delivery for Cardiovascular Disease: Modeling of Modulated Extracellular Vesicle Release Rates.

Invasive and medical therapy has led to major improvements in cardiovascular disease management, but important challenges remain open. The discovery of a nano-sized system of extracellular vesicles (EVs) is opening new possibilities for reprogramming malfunctioning cells and indicates that EVs can be employed in therapeutic biomedical applications as engineered drug vehicles. Molecular communication (MC) has applications for treating cells with directed drug delivery, employing special targeting transmembrane proteins.

Visualizing Extracellular Vesicles and Their Function in 3D Tumor Microenvironment Models.

Extracellular vesicles (EVs) are cell-derived nanostructures that mediate intercellular communication by delivering complex signals in normal tissues and cancer. The cellular coordination required for tumor development and maintenance is mediated, in part, through EV transport of molecular cargo to resident and distant cells. Most studies on EV-mediated signaling have been performed in two-dimensional (2D) monolayer cell cultures, largely because of their simplicity and high-throughput screening capacity.

Molecular Communications in Viral Infections Research: Modeling, Experimental Data, and Future Directions.

Hundreds of millions of people worldwide are affected by viral infections each year, and yet, several of them neither have vaccines nor effective treatment during and post-infection. This challenge has been highlighted by the COVID-19 pandemic, showing how viruses can quickly spread and impact society as a whole. Novel interdisciplinary techniques must emerge to provide forward-looking strategies to combat viral infections, as well as possible future pandemics.

Optimal pacing sites in cardiac resynchronization by left ventricular activation front analysis.

Cardiac resynchronization therapy (CRT) can substantially improve dyssynchronous heart failure and reduce mortality. However, about one-third of patients who are implanted, derive no measurable benefit from CRT. Non-response may partly be due to suboptimal activation of the left ventricle (LV) caused by electrophysiological heterogeneities.

Toward real-time polyp detection using fully CNNs for 2D Gaussian shapes prediction.

To decrease colon polyp miss-rate during colonoscopy, a real-time detection system with high accuracy is needed. Recently, there have been many efforts to develop models for real-time polyp detection, but work is still required to develop real-time detection algorithms with reliable results. We use single-shot feed-forward fully convolutional neural networks (F-CNN) to develop an accurate real-time polyp detection system.

On Mathematical Analysis of Active Drug Transport Coupled With Flow-Induced Diffusion in Blood Vessels.

Blood vessels are flow-induced diffusive molecular channels equipped with transport mechanisms across their walls for conveying substances between the organs in the body. Mathematical modeling of the blood vessel as a molecular transport channel can be used for the characterization of the underlying processes and higher-level functions in the circulatory system. Besides, the mathematical model can be utilized for designing and realizing nano-scale molecular communication systems for healthcare applications including drug delivery systems.

Theoretical Aspects of Resting-State Cardiomyocyte Communication for Multi-Nodal Nano-Actuator Pacemakers.

The heart consists of billions of cardiac muscle cells-cardiomyocytes-that work in a coordinated fashion to supply oxygen and nutrients to the body. Inter-connected specialized cardiomyocytes form signaling channels through which the electrical signals are propagated throughout the heart, controlling the heart's beat to beat function of the other cardiac cells. In this paper, we study to what extent it is possible to use ordinary cardiomyocytes as communication channels between components of a recently proposed multi-nodal leadless pacemaker, to transmit data encoded in subthreshold membrane potentials.

Modeling of Modulated Exosome Release From Differentiated Induced Neural Stem Cells for Targeted Drug Delivery.

A novel implantable and externally controllable stem-cell-based platform for the treatment of Glioblastoma brain cancer has been proposed to bring hope to patients who suffer from this devastating cancer type. Induced Neural Stem Cells (iNSCs), known to have potent therapeutic effects through exosomes-based molecular communication, play a pivotal role in this platform. Transplanted iNSCs demonstrate long-term survival and differentiation into neurons and glia which then fully functionally integrate with the existing neural network.

Multisite pacing and myocardial scars: a computational study.

Cardiac resynchronization therapy (CRT) is a frequently effective treatment modality for dyssynchronous heart failure, however, 30% of patients do not respond, usually due to suboptimal activation of the left ventricle (LV). Multisite pacing (MSP) may increase the response rate, but its effect in the presence of myocardial scars is not fully understood. We use a computational model to study the outcome of MSP in an LV with scars in two different locations and of two different sizes.

Evaluation of Secrecy Capacity for Next-Generation Leadless Cardiac Pacemakers.

Secure communication can be considered as an integral part of the next generation implantable medical devices. With the advent of Physical Layer Security (PLS) methods, confidential messages can be transmitted without the use of encryption keys. For analyzing the effectiveness of PLS for next-generation leadless cardiac pacemakers, we provide secrecy analysis using a performance metric of secrecy capacity.

Energy-efficiency of Cardiomyocyte Stimulation with Rectangular Pulses.

In cardiac pacemaker design, energy expenditure is an important issue. This work aims to explore whether varying stimulation pulse configuration is a viable optimization strategy for reducing energy consumption by the pacemaker. A single cardiomyocyte was used as an experimental model.