Clinical responses to vemurafenib in postoperative recurrence of papillary thyroid carcinoma with esophageal fistula: A case report.

Background: While papillary thyroid carcinoma (PTC) generally exhibits a favorable prognosis post-surgery, the poorly differentiated subtype presents elevated rates of postoperative recurrence. Certain aggressive cases demonstrate invasive behavior, compromising adjacent structures and leading to a poor prognosis. This study delineates a unique case of postoperative PTC recurrence, complicated by esophageal fistula, that showed favorable outcomes following brief Vemurafenib treatment.

Prognostic value of blood urea nitrogen to serum albumin ratio for acute kidney injury and in-hospital mortality in intensive care unit patients with intracerebral haemorrhage: a retrospective cohort study using the MIMIC-IV database.

Objective: We sought to evaluate the prognostic ability of blood urea nitrogen to serum albumin ratio (BAR) for acute kidney injury (AKI) and in-hospital mortality in patients with intracerebral haemorrhage (ICH) in intensive care unit (ICU).

Design: A retrospective cohort study using propensity score matching.

Setting: ICU of Beth Israel Deaconess Medical Center.

Accelerating vasculature imaging in tumor using mesoscopic fluorescence molecular tomography via a hybrid reconstruction strategy.

Mesoscopic fluorescent molecular tomography (MFMT) enables to image fluorescent molecular probes beyond the typical depth limits of microscopic imaging and with enhanced resolution compared to macroscopic imaging. However, MFMT is a scattering-based inverse problem that is an ill-posed inverse problem and hence, requires relative complex iterative solvers coupled with regularization strategies. Inspired by the potential of deep learning in performing image formation tasks from raw measurements, this work proposes a hybrid approach to solve the MFMT inverse problem.

System configuration optimization for mesoscopic fluorescence molecular tomography.

Tissue engineering applications demand 3D, non-invasive, and longitudinal assessment of bioprinted constructs. Current emphasis is on developing tissue constructs mimicking conditions; however, these are increasingly challenging to image as they are typically a few millimeters thick and turbid, limiting the usefulness of classical fluorescence microscopic techniques. For such applications, we developed a Mesoscopic Fluorescence Molecular Tomography methodology that collects high information content data to enable high-resolution tomographic reconstruction of fluorescence biomarkers at millimeters depths.

Improving mesoscopic fluorescence molecular tomography via preconditioning and regularization.

Mesoscopic fluorescence molecular tomography (MFMT) is a novel imaging technique capable of obtaining 3-D distribution of molecular probes inside biological tissues at depths of a few millimeters with a resolution up to ~100 μm. However, the ill-conditioned nature of the MFMT inverse problem severely deteriorates its reconstruction performances. Furthermore, dense spatial sampling and fine discretization of the imaging volume required for high resolution reconstructions make the sensitivity matrix (Jacobian) highly correlated, which prevents even advanced algorithms from achieving optimal solutions.

Improving mesoscopic fluorescence molecular tomography through data reduction.

Mesoscopic fluorescence molecular tomography (MFMT) is a novel imaging technique that aims at obtaining the 3-D distribution of molecular probes inside biological tissues at depths of a few millimeters. To achieve high resolution, around 100-150μm scale in turbid samples, dense spatial sampling strategies are required. However, a large number of optodes leads to sizable forward and inverse problems that can be challenging to compute efficiently.

High-resolution mesoscopic fluorescence molecular tomography based on compressive sensing.

Mesoscopic fluorescence molecular tomography (MFMT) is new imaging modality aiming at 3-D imaging of molecular probes in a few millimeter thick biological samples with high-spatial resolution. In this paper, we develop a compressive sensing-based reconstruction method with l1-norm regularization for MFMT with the goal of improving spatial resolution and stability of the optical inverse problem. Three-dimensional numerical simulations of anatomically accurate microvasculature and real data obtained from phantom experiments are employed to evaluate the merits of the proposed method.