Granulation Patterns of Functional Corticotroph Tumors Correlate with Tumor Size, Proliferative Activity, T2 Intensity-to-White Matter Ratio, and Postsurgical Early Biochemical Remission.

Unlike somatotroph tumors, the data on correlates of tumor granulation patterns in functional TPIT lineage pituitary neuroendocrine tumors (corticotroph tumors) have been less uniformly documented in most clinical series. This study evaluated characteristics of 41 well-characterized functional corticotroph tumors consisting of 28 densely granulated corticotroph tumors (DGCTs) and 13 sparsely granulated corticotroph tumors (SGCTs) with respect to preoperative clinical and radiological findings, tumor proliferative activity (including mitotic count and Ki-67 labeling index), and postoperative early biochemical remission rates. The median (interquartile range (IQR)) tumor size was significantly larger in the SGCT group [16.

Identification of the molecular etiology in rare congenital hemolytic anemias using next-generation sequencing with exome-based copy number variant analysis.

Objectives: In congenital hemolytic anemias (CHA), it is not always possible to determine the specific diagnosis by evaluating clinical findings and conventional laboratory tests. The aim of this study is to evaluate the utility of next-generation sequencing (NGS) and clinical-exome-based copy number variant (CNV) analysis in patients with CHA.

Methods: One hundred and forty-three CHA cases from 115 unrelated families referred for molecular analysis were enrolled in the study.

Comparison of Oral Health and Salivary Biomarkers in Children with Juvenile Idiopathic Arthritis and Healthy Individuals.

Aim: This study examines the effects of juvenile idiopathic arthritis (JIA) on the oral health and detectability of inflammatory biomarkers IL-17, tumour necrosis factor-alpha (TNF-α) and total antioxidant status (TAS) in the saliva of children with JIA.

Patients And Methods: This study included 117 participants (39 patients with JIA and 78 systemically healthy subjects aged 8-12 years). Demographic data, responses to an oral health-related questionnaire, saliva samples, periodontal parameters [plaque index (PI), gingival index (GI) and bleeding on probing (BOP)] and dental recordings [facial profile (FP) and dental occlusion relationship (DOR)] were obtained.

SLIViT: a general AI framework for clinical-feature diagnosis from limited 3D biomedical-imaging data.

We present SLIViT, a deep-learning framework that accurately measures disease-related risk factors in volumetric biomedical imaging, such as magnetic resonance imaging (MRI) scans, optical coherence tomography (OCT) scans, and ultrasound videos. To evaluate SLIViT, we applied it to five different datasets of these three different data modalities tackling seven learning tasks (including both classification and regression) and found that it consistently and significantly outperforms domain-specific state-of-the-art models, typically improving performance (ROC AUC or correlation) by 0.1-0.