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. PATIENT DESCRIPTION: A 64-year-old female patient underwent surgical resection for PTC, subsequently experiencing rapid tumor recurrence and development of an esophageal fistula. DIAGNOSIS: The patient was confirmed to have locally advanced PTC through intraoperative cytopathology. The cancer recurred postoperatively, culminating in the formation of an esophageal fistula. METHODS: The patient was administered Vemurafenib at a dosage of 960 mg twice daily following tumor recurrence. RESULTS: A 12-month regimen of targeted Vemurafenib therapy led to a substantial reduction in tumor size. Concurrently, the esophageal fistula underwent complete healing, facilitating successful removal of the gastrostomy tube. The tumor response was classified as stable disease. CONCLUSION SUBSECTIONS: Vemurafenib demonstrates potential as a targeted therapeutic strategy for recurrent PTC harboring the BRAFV600E mutation. This approach may effectively mitigate tumor dimensions and the associated risk of esophageal and tracheal fistulas.

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. PARTICIPANTS: The data of patients with ICH were obtained from the Medical Information Mart for Intensive Care IV (V.1.0) database. A total of 1510 patients with ICH were enrolled in our study. MAIN OUTCOME AND MEASURE: The optimal threshold value of BAR is determined by the means of X-tile software (V.3.6.1) and the crude cohort was categorised into two groups on the foundation of the optimal cut-off BAR (6.0 mg/g). Propensity score matching and inverse probability of treatment weighting were performed to control for confounders. The predictive performance of BAR for AKI was tested using univariate and multivariate logistic regression analyses. Multivariate Cox regression analysis was used to investigate the association between BAR and in-hospital mortality. RESULTS: The optimal cut-off value for BAR was 6.0 mg/g. After matching, multivariate logistic analysis showed that the high-BAR group had a significantly higher risk of AKI (OR, 2.60; 95% confidence index, 95% CI, 1.86 to 3.65, p<0.001). What's more, a higher BAR was also an independent risk factor for in-hospital mortality (HR, 2.84; 95% confidence index, 95% CI, 1.96 to 4.14, p<0.001) in terms of multivariate Cox regression analysis. These findings were further demonstrated in the validation cohort. CONCLUSIONS: BAR is a promising and easily available biomarker that could serve as a prognostic predictor of AKI and in-hospital mortality in patients with ICH in the ICU.

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. This methodology combines a convolutional symmetric network and a conventional iterative algorithm to accelerate the reconstruction procedure. By the proposed deep neural network, the principal components of the sensitivity matrix are extracted and the accompanying noise in measurements is suppressed, which helps to accelerate the reconstruction and improve the accuracy of results. We apply the proposed method to reconstruct in silico and vascular tree models. The results demonstrate that reconstruction accuracy and speed are highly improved due to the reduction of redundant entries of the sensitivity matrix and noise suppression.

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 in vivo 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. This imaging approach is based on an inverse problem; hence, its imaging performances are dependent on critical technical considerations including optode sampling, forward model design and inverse solver parameters. Herein, we investigate the impact of the optical system configuration parameters, including detector layout, number of detectors, combination of detector and source numbers, and scanning mode with uncoupled or coupled source and detector array, on the 3D imaging performances. Our results establish that an MFMT system with a 2D detection chain implemented in a de-scanned mode provides the optimal imaging reconstruction performances.

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. In this work, we propose two computational methods to respectively increase the incoherence of the sensitivity matrix and improve the convergence rate of the inverse solver. We first apply a compressed sensing (CS) based preconditioner on either the whole sensitivity matrix or sub sensitivity matrices to reduce the coherence between columns of the sensitivity matrix. Then we employed a regularization method based on the weight iterative improvement method (WIIM) to mitigate the ill-condition of the sensitivity matrix and to drive the iterative optimization process towards convergence at a faster rate. We performed numerical simulations and phantom experiments to validate the effectiveness of the proposed strategies. In both in silico and in vitro cases, we were able to improve the quality of MFMT reconstructions significantly.

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. In this work, we propose a two-step data reduction strategy to accelerate the inverse problem and improve robustness. First, data selection is performed via signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) criteria. Then principal component analysis (PCA) is applied to further reduce the size of the sensitivity matrix. We perform numerical simulations and phantom experiments to validate the effectiveness of the proposed strategy. In both in silico and in vitro cases, we are able to significantly improve the quality of MFMT reconstructions while reducing the computation times by close to a factor of two.

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. Experimental results show that the proposed method can achieve 80 µm spatial resolution for a biological sample of 3 mm thickness and more accurate quantifications of concentrations and locations for the fluorophore distribution than those of the conventional methods.