Transplantation

AI-enhanced patient-specific dosimetry in I-131 planar imaging with a single oblique view.

This study aims to enhance the dosimetry accuracy in I planar imaging by utilizing a single oblique view and Monte Carlo (MC) validated dose point kernels (DPKs) alongside the integration of artificial intelligence (AI) for accurate dose prediction within planar imaging. Forty patients with thyroid cancers post-thyroidectomy surgery and 30 with neuroendocrine tumors underwent planar and SPECT/CT i...

Machine learning models using non-invasive tests & B-mode ultrasound to predict liver-related outcomes in metabolic dysfunction-associated steatotic liver disease.

Advanced metabolic-dysfunction-associated steatotic liver disease (MASLD) fibrosis (F3-4) predicts liver-related outcomes. Serum and elastography-based non-invasive tests (NIT) cannot yet reliably predict MASLD outcomes. The role of B-mode ultrasound (US) for outcome prediction is not yet known. We aimed to evaluate machine learning (ML) algorithms based on simple NIT and US for prediction of adve...

A novel method to predict the haemoglobin concentration after kidney transplantation based on machine learning: prediction model establishment and method optimization.

BACKGROUND: Anaemia is a common complication after kidney transplantation, and the haemoglobin concentration is one of the main criteria for identifyi...

Current and future applications of robotics in structural heart interventions.

Robotics entered the cardiovascular field in the late 1990s with a robot-assisted coronary artery bypass graft. Since then, the use of robots has beco...

Multiple omics-based machine learning reveals peripheral blood immune cell landscape during acute rejection of kidney transplantation and constructs a precise non-invasive diagnostic strategy.

Kidney transplantation is the optimal treatment for end-stage renal disease (ESRD), but acute rejection (AR) remains a major factor affecting graft su...

Developing a Machine Learning Model for Hydrogen Bond Acceptance Based on Natural Bond Orbital Descriptors.

This study employs machine learning (ML) to assess the predictive power of electronic descriptors derived from natural bond orbital (NBO) analysis for...

A transfer learning based deep neural network adaptive controller for the Furuta pendulum subject to uncertain disturbance signals.

In this study, a control algorithm that integrates a Deep Neural Network (DNN) adaptive control architecture with a robust disturbance rejection mecha...

Machine learning-based prediction of G-CSF-induced hematopoietic stem cell mobilization outcomes in healthy volunteers.

Haematopoietic stem cell transplantation (HSC-T) has been established as a fundamental therapeutic intervention for a wide range of hematological mali...

Prediction of postoperative infection through early-stage salivary microbiota following kidney transplantation using machine learning techniques.

Kidney transplantation (KT) is an effective treatment for end-stage renal disease; however, the lifelong immunosuppressive regimen increases the risk ...

Peripheral HLA-DRCD141 Classical Monocytes Predict Relapse Risk and Worsening in Multiple Sclerosis.

BACKGROUND AND OBJECTIVES: Multiple sclerosis (MS) is an immune-mediated demyelinating disease of the CNS characterized by a heterogeneous disease tra...

Fluorescence Guided Raman Spectroscopy enables the training of robust support vector machines for the detection of tumour marker proteins.

Raman spectroscopy provides comprehensive biochemical information on a sample's composition, yet it is often used to analyze aggregated spectra rather...

Machine learning-assisted multi-dimensional transcriptomic analysis of cytoskeleton-related molecules and their relationship with prognosis in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, with a poor prognosis due to its aggressive nature and limited tr...

Folate metabolism-associated CYP26A1 is a clinico-immune target in colorectal cancer.

Folic acid plays a crucial role in cellular regulation and metabolism, commonly included in dietary supplements. Despite this, its involvement in colo...

Toward developing a compact total artificial heart using a soft robotic fluidic transmission system.

Cardiovascular diseases are a leading cause of mortality, with limited possibilities for transplantation due to a critical shortage of donor hearts. R...

Stochastic Orientational Encoding via Hydrogen Bonding Driven Assembly of Woven-Like Molecular Physically Unclonable Functions.

The prevention of counterfeiting and the assurance of object authenticity require stochastic encoding schemes based on physically unclonable functions...

Differential Phase Contrast Imaging to Predict MSC Immune Function.

Mesenchymal stromal cells (MSCs) exhibit significant immunomodulatory potential, making them promising candidates for cell-based therapies in autoimmu...

Label-Free Evaluation of Lung and Heart Transplant Biopsies Using Tissue Autofluorescence-Based Virtual Staining.

We present a panel of virtual staining neural networks for lung and heart transplant biopsies, providing rapid and high-quality histological staining...

Regulation and induction of fungal secondary metabolites: a comprehensive review.

Fungal secondary metabolites (SMs) represent a vast reservoir of bioactive compounds with immense therapeutic, agricultural, and industrial potential....

Predicting long-term kidney graft failure using novel multi-omic blood-based biomarkers and artificial intelligence tools.

Kidney transplantation (KT) remains the preferred treatment for end-stage renal disease. With advancements in immunosuppressive regimens and KT survei...

Deep learning-based single-shot computational spectrometer using multilayer thin films.

Computational spectrometers hold significant potential for mobile applications, such as on-site detection and self-diagnosis, due to their compact siz...