Zinc and manganese homeostasis closely interact in mammalian cells.

Understanding the homeostatic interactions among essential trace metals is important for explaining their roles in cellular systems. Recent studies in vertebrates suggest that cellular Mn metabolism is related to Zn metabolism in multifarious cellular processes. However, the underlying mechanism remains unclear.

Differentiating primary central nervous system lymphoma from glioblastoma by time-dependent diffusion using oscillating gradient.

Background: This study aimed to elucidate the impact of effective diffusion time setting on apparent diffusion coefficient (ADC)-based differentiation between primary central nervous system lymphomas (PCNSLs) and glioblastomas (GBMs) and to investigate the usage of time-dependent diffusion magnetic resonance imaging (MRI) parameters.

Methods: A retrospective study was conducted involving 21 patients with PCNSLs and 66 patients with GBMs using diffusion weighted imaging (DWI) sequences with oscillating gradient spin-echo (Δ = 7.1 ms) and conventional pulsed gradient (Δ = 44.

Differentiating brain metastasis from glioblastoma by time-dependent diffusion MRI.

Background: This study was designed to investigate the use of time-dependent diffusion magnetic resonance imaging (MRI) parameters in distinguishing between glioblastomas and brain metastases.

Methods: A retrospective study was conducted involving 65 patients with glioblastomas and 27 patients with metastases using a diffusion-weighted imaging sequence with oscillating gradient spin-echo (OGSE, 50 Hz) and a conventional pulsed gradient spin-echo (PGSE, 0 Hz) sequence. In addition to apparent diffusion coefficient (ADC) maps from two sequences (ADC and ADC), we generated maps of the ADC change (cADC): ADC - ADC and the relative ADC change (rcADC): (ADC - ADC)/ ADC × 100 (%).

The usefulness of machine-learning-based evaluation of clinical and pretreatment F-FDG-PET/CT radiomic features for predicting prognosis in patients with laryngeal cancer.

Objective: To examine whether machine learning (ML) analyses involving clinical and F-FDG-PET-based radiomic features are helpful in predicting prognosis in patients with laryngeal cancer.

Methods: This retrospective study included 49 patients with laryngeal cancer who underwentF-FDG-PET/CT before treatment, and these patients were divided into the training ( = 34) and testing ( = 15) cohorts.Seven clinical (age, sex, tumor size, T stage, N stage, Union for International Cancer Control stage, and treatment) and 40 F-FDG-PET-based radiomic features were used to predict disease progression and survival.