A digital pathology model for predicting radioiodine-avid metastases on initial post-therapeutic I scan in patients with papillary thyroid cancer.

Accurate postoperative assessment is critical for optimizing I therapy in patients with papillary thyroid cancer (PTC). This study aimed to develop a pathology model utilizing postoperative digital pathology slides to predict lymph node and/or distant metastases on post-therapeutic I scan after initial I treatment in PTC patients. A retrospective analysis was conducted on 229 PTC patients who underwent total or near-total thyroidectomy and subsequent I treatment after levothyroxine (LT4) withdrawal between January 2022 and August 2023.

Research trends and hotspots of radioiodine-refractory thyroid cancer treatment in the twenty-first century: a bibliometric analysis.

The treatment of radioiodine-refractory differentiated thyroid cancer (RAIR-DTC) has made significant advancements in the twenty-first century. This study aimed to assess the current state of research and identify potential new directions by conducting a bibliometric analysis of scientific publications on RAIR-DTC treatment. Publications relevant to RAIR-DTC, published from January 1, 2000, to December 31, 2023, were retrieved from the Web of Science Core Collection.

Dual-locked fluorescent probes for precise diagnosis and targeted treatment of tumors.

Cancer continues to pose a significant threat to global health, with its high mortality rates largely attributable to delayed diagnosis and non-specific treatments. Early and accurate diagnosis is crucial, yet it remains challenging due to the subtle and often undetectable early molecular changes. Traditional single-target fluorescent probes often fail to accurately identify cancer cells, relying solely on single biomarkers and consequently leading to high rates of false positives and inadequate specificity.

Convolutional Recurrent Neural Network for Dynamic Functional MRI Analysis and Brain Disease Identification.

Dynamic functional connectivity (dFC) networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) help us understand fundamental dynamic characteristics of human brains, thereby providing an efficient solution for automated identification of brain diseases, such as Alzheimer's disease (AD) and its prodromal stage. Existing studies have applied deep learning methods to dFC network analysis and achieved good performance compared with traditional machine learning methods. However, they seldom take advantage of sequential information conveyed in dFC networks that could be informative to improve the diagnosis performance.

Distribution-Guided Network Thresholding for Functional Connectivity Analysis in fMRI-Based Brain Disorder Identification.

Functional connectivity (FC) networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used in automated identification of brain disorders, such as Alzheimer's disease (AD) and attention deficit hyperactivity disorder (ADHD). To generate compact representations of FC networks, various thresholding methods have been designed for FC network analysis. However, these studies usually use a pre-defined threshold or connection percentage to threshold whole FC networks, thus ignoring the diversity of temporal correlation (e.

Convergent Evolution in Breadth of Two V6-1-Encoded Influenza Antibody Clonotypes from a Single Donor.

Understanding how broadly neutralizing antibodies (bnAbs) to influenza hemagglutinin (HA) naturally develop in humans is critical to the design of universal influenza vaccines. Several classes of bnAbs directed to the conserved HA stem were found in multiple individuals, including one encoded by heavy-chain variable domain V6-1. We describe two genetically similar V6-1 bnAb clonotypes from the same individual that exhibit different developmental paths toward broad neutralization activity.

A novel artificial immune algorithm for spatial clustering with obstacle constraint and its applications.

An important component of a spatial clustering algorithm is the distance measure between sample points in object space. In this paper, the traditional Euclidean distance measure is replaced with innovative obstacle distance measure for spatial clustering under obstacle constraints. Firstly, we present a path searching algorithm to approximate the obstacle distance between two points for dealing with obstacles and facilitators.

Probability method for Cerenkov luminescence tomography based on conformance error minimization.

Cerenkov luminescence tomography (CLT) was developed to reconstruct a three-dimensional (3D) distribution of radioactive probes inside a living animal. Reconstruction methods are generally performed within a unique framework by searching for the optimum solution. However, the ill-posed aspect of the inverse problem usually results in the reconstruction being non-robust.