Transplantation

Supervised Learning and Mass Spectrometry Predicts the Fate of Nanomaterials.

The surface of nanoparticles changes immediately after intravenous injection because blood proteins adsorb on the surface. How this interface changes during circulation and its impact on nanoparticle distribution within the body is not understood. Here, we developed a workflow to show that the evolution of proteins on nanoparticle surfaces predicts the biological fate of nanoparticles . This workf...

Changes in clinical indicators related to the transition from dialysis to kidney transplantation-data from the ERA-EDTA Registry.

BACKGROUND: Kidney transplantation should improve abnormalities that are common during dialysis treatment, like anaemia and mineral and bone disorder. However, its impact is incompletely understood. We therefore aimed to assess changes in clinical indicators after the transition from chronic dialysis to kidney transplantation.

Neural Mechanisms for Accepting and Rejecting Artificial Social Partners in the Uncanny Valley.

Artificial agents are becoming prevalent across human life domains. However, the neural mechanisms underlying human responses to these new, artificial...

When Not to Classify: Anomaly Detection of Attacks (ADA) on DNN Classifiers at Test Time.

A significant threat to the recent, wide deployment of machine learning-based systems, including deep neural networks (DNNs), is adversarial learning ...

[Renal graft survival in patients transplanted from organs of deceased donors].

BACKGROUND: In Mexico, out of the total number of transplants it was reported, in 2014, a frequency of 29% of deceased donor renal transplantation (DD...

The Effect of Muscle Graft With Nerve Growth Factor and Laminin on Sciatic Nerve Repair in Rats.

INTRODUCTION: Peripheral nerve injury is one of the most common damages that lead to physical disability. Considering the similarity between the coati...

Using machine learning to characterize heart failure across the scales.

Heart failure is a progressive chronic condition in which the heart undergoes detrimental changes in structure and function across multiple scales in ...

A new blind source separation framework for signal analysis and artifact rejection in functional Near-Infrared Spectroscopy.

In the analysis of functional Near-Infrared Spectroscopy (fNIRS) signals from real-world scenarios, artifact rejection is essential. However, currentl...

VesselNet: A deep convolutional neural network with multi pathways for robust hepatic vessel segmentation.

Extraction or segmentation of organ vessels is an important task for surgical planning and computer-aided diagnoses. This is a challenging task due to...

Intelligent inverse treatment planning via deep reinforcement learning, a proof-of-principle study in high dose-rate brachytherapy for cervical cancer.

Inverse treatment planning in radiation therapy is formulated as solving optimization problems. The objective function and constraints consist of mult...

Predicting Reaction Products and Automating Reactive Trajectory Characterization in Molecular Simulations with Support Vector Machines.

A machine learning-based methodology for the prediction of chemical reaction products, along with automated elucidation of mechanistic details via pha...

Compatibility of Y-Site Injection of Meropenem Trihydrate With 101 Other Injectable Drugs.

The objective of the study was to establish the compatibility of injectable meropenem with 101 other drugs during Y-site administration. Meropenem (...

Progress in the Development of Intrinsically Conducting Polymer Composites as Biosensors.

Biosensors are analytical devices which find extensive applications in fields such as the food industry, defense sector, environmental monitoring, and...

Abdominal multi-organ segmentation with organ-attention networks and statistical fusion.

Accurate and robust segmentation of abdominal organs on CT is essential for many clinical applications such as computer-aided diagnosis and computer-a...

Trace, Machine Learning of Signal Images for Trace-Sensitive Mass Spectrometry: A Case Study from Single-Cell Metabolomics.

Recent developments in high-resolution mass spectrometry (HRMS) technology enabled ultrasensitive detection of proteins, peptides, and metabolites in ...

A Novel CNN-Based CAD System for Early Assessment of Transplanted Kidney Dysfunction.

This paper introduces a deep-learning based computer-aided diagnostic (CAD) system for the early detection of acute renal transplant rejection. For no...

Generating automated kidney transplant biopsy reports combining molecular measurements with ensembles of machine learning classifiers.

We previously reported a system for assessing rejection in kidney transplant biopsies using microarray-based gene expression data, the Molecular Micro...

Identifying Factors That Affect Patient Survival After Orthotopic Liver Transplant Using Machine-Learning Techniques.

OBJECTIVES: Survival after liver transplant depends on pretransplant, peritransplant, and posttransplant factors. Identifying effective factors for pa...

Building the drug-GO function network to screen significant candidate drugs for myasthenia gravis.

Myasthenia gravis (MG) is an autoimmune disease. In recent years, considerable evidence has indicated that Gene Ontology (GO) functions, especially GO...

Smeared multiscale finite element model for electrophysiology and ionic transport in biological tissue.

Basic functions of living organisms are governed by the nervous system through bidirectional signals transmitted from the brain to neural networks. Th...