Assessing the role of surface layer and molecular probe size in diffusion within meniscus tissue.

Diffusion within extracellular matrix is essential to deliver nutrients and larger metabolites to the avascular region of the meniscus. It is well known that both structure and composition of the meniscus vary across its regions; therefore, it is crucial to fully understand how the heterogenous meniscal architecture affects its diffusive properties. The objective of this study was to investigate the effect of meniscal region (core tissue, femoral, and tibial surface layers) and molecular weight on the diffusivity of several molecules in porcine meniscus. Tissue samples were harvested from the central area of porcine lateral menisci. Diffusivity of fluorescein (MW 332 Da) and three fluorescence-labeled dextrans (MW 3k, 40k, and 150k Da) was measured via fluorescence recovery after photobleaching. Diffusivity was affected by molecular size, decreasing as the Stokes' radius of the solute increased. There was no significant effect of meniscal region on diffusivity for fluorescein, 3k and 40k dextrans (p>0.05). However, region did significantly affect the diffusivity of 150k Dextran, with that in the tibial surface layer being larger than in the core region (p = 0.001). Our findings contribute novel knowledge concerning the transport properties of the meniscus fibrocartilage. This data can be used to advance the understanding of tissue pathophysiology and explore effective approaches for tissue restoration.

A >50% Intraoperative Parathyroid Hormone Decrease Into Normal Reference Range Predicts Complete Excision of Malignancy in Patients With Parathyroid Carcinoma.

INTRODUCTION: The mainstay of successful treatment for parathyroid carcinoma remains complete surgical excision. Although intraoperative parathyroid hormone (ioPTH) monitoring is a useful adjunct during parathyroidectomy for benign primary hyperparathyroidism, its utility for parathyroid carcinoma remains unclear. METHODS: A retrospective review of 796 patients who underwent parathyroidectomy with ioPTH monitoring for primary hyperparathyroidism revealed 13 patients with parathyroid carcinoma on final pathology from two academic institutions. A systematic review yielded 5 additional parathyroid carcinoma patients. Complete excision of malignancy, or operative success (eucalcemia ≥6 mo. after parathyroidectomy); operative failure (persistent hypercalcemia <6 mo. after parathyroidectomy); and perioperative complications were evaluated. Comparison of the >50% ioPTH decrease alone to >50% ioPTH decrease into normal reference range was analyzed using Chi-squared, Kolmogorov-Smirnov, Kruskal-Wallis tests. RESULTS: All 18 parathyroid carcinoma patients achieved a >50% ioPTH decrease, and 14 patients also had a final ioPTH level decrease into normal reference range. 93% of patients who met normal parathyroid hormone reference range had operative success, whereas only two of the four (50%) patients with parathyroid carcinoma with a >50% ioPTH decrease alone demonstrated operative success. CONCLUSIONS: Parathyroidectomy guided by a >50% ioPTH decrease into normal reference range may better predict complete excision of malignant tissue in patients with parathyroid carcinoma compared to >50% ioPTH decrease alone. IoPTH monitoring should be used in conjunction with clinical judgment and complete en bloc resection for optimal treatment and success.

Recurrence Network Analysis of Histopathological Images for the Detection of Invasive Ductal Carcinoma in Breast Cancer.

The histopathological image analysis is one of the most crucial diagnostic procedures to identify Invasive ductal carcinoma (IDC) in breast cancers. However, this diagnosis process is currently time-consuming and heavily dependent on human expertise. Prior research has shown that different degrees of tumors present various microstructures in the histopathological images. However, very little has been done to utilize spatial recurrence features of microstructures for identifying IDC. This paper presents a novel recurrence analysis methodology for automatic image-guided IDC detection. We first utilize wavelet decomposition to delineate the subtle information in the images. Then, we model the patches with a weighted recurrence network approach to characterize the recurrence patterns of the histopathological images. Finally, we develop automated IDC detection models leveraging machine learning methods with spatial recurrence features extracted. The developed recurrence analysis models successfully characterize the complex microstructures of histopathological images and achieve the IDC detection performances of at least AUC = 0.96. This research developed a spatial recurrence analysis methodology to effectively identify IDC regions in histopathological images for BC. It shows a high potential to assist physicians in the decision-making process. The proposed methodology can further be applicable to image processing for other medical or biological applications.

Heterogeneous recurrence analysis of spatial data.

Nonlinear dynamical systems often generate significant amounts of observational data such as time series, as well as high-dimensional spatial data. To delineate recurrence dynamics in the spatial data, prior efforts either extended the recurrence plot, which is a widely used tool for time series, to a four-dimensional hyperspace or utilized the network approach for recurrence analysis. However, very little has been done to differentiate heterogeneous types of recurrences in the spatial data (e.g., recurrence variations of state transitions in the spatial domain). Therefore, we propose a novel heterogeneous recurrence approach for spatial data analysis. First, spatial data are traversed with the Hilbert Space-Filling Curve to transform the variations of recurrence patterns from the spatial domain to the state-space domain. Second, we design an Iterated Function System to derive the fractal representation for the state-space trajectory of spatial data. Such a fractal representation effectively captures self-similar behaviors of recurrence variations and multi-state transitions in the spatial data. Third, we develop the Heterogeneous Recurrence Quantification Analysis of spatial data. Experimental results in both simulation and real-world case studies show that the proposed approach yields superior performance in the extraction of salient features to characterize and quantify heterogeneous recurrence dynamics in spatial data.

Semi-Supervised Learning Algorithm for Identifying High-Priority Drug-Drug Interactions Through Adverse Event Reports.

Identifying drug-drug interactions (DDIs) is a critical enabler for reducing adverse drug events and improving patient safety. Generating proper DDI alerts during prescribing workflow has the potential to prevent DDI-related adverse events. However, the implementation of DDI alerting system remains a challenge as users are experiencing alert overload which causes alert fatigue. One strategy to optimize the current system is to establish a list of high-priority DDIs for alerting purposes, though it is a resource-intensive task. In this study, we propose a machine learning framework to extract useful features from the FDA adverse event reports and then identify potential high-priority DDIs using an autoencoder-based semi-supervised learning algorithm. The experimental results demonstrate the effectiveness of using adverse event feature representations in differentiating high- and low-priority DDIs. Additionally, the proposed algorithm utilizes stacked autoencoders and weighted support vector machine for boosting classification performance, which outperforms other competing methods in terms of F-measure and AUC score. This framework integrates multiple information sources, leverages domain knowledge and clinical evidence, and provides a practical approach for pre-screening high-priority DDI candidates for medication alerts.

Recurrence network modeling and analysis of spatial data.

Nonlinear dynamical systems exhibit complex recurrence behaviors. Recurrence plot is widely used to graphically represent the patterns of recurrence dynamics and further facilitates the quantification of recurrence patterns, namely, recurrence quantification analysis. However, traditional recurrence methods tend to be limited in their ability to handle spatial data due to high dimensionality and geometric characteristics. Prior efforts have been made to generalize the recurrence plot to a four-dimensional space for spatial data analysis, but this framework can only provide graphical visualization of recurrence patterns in the projected reduced-dimension space (i.e., two- or three- dimensions). In this paper, we propose a new weighted recurrence network approach for spatial data analysis. A weighted network model is introduced to represent the recurrence patterns in spatial data, which account for both pixel intensities and spatial distance simultaneously. Note that each network node represents a location in the high-dimensional spatial data. Network edges and weights preserve complex spatial structures and recurrence patterns. Network representation is shown to be an effective means to provide a complete picture of recurrence patterns in the spatial data. Furthermore, we leverage network statistics to characterize and quantify recurrence properties and features in the spatial data. Experimental results in both simulation and real-world case studies show that the generalized recurrence network approach yields superior performance in the visualization of recurrence patterns in spatial data and in the extraction of salient features to characterize recurrence dynamics in spatial systems.

Predicting Patients at Risk for 3-Day Postdischarge Readmissions, ED Visits, and Deaths.

BACKGROUND: Transitional care interventions can be utilized to reduce post-hospital discharge adverse events (AEs). However, no methodology exists to effectively identify high-risk patients of any disease across multiple hospital sites and patient populations for short-term postdischarge AEs. OBJECTIVES: To develop and validate a 3-day (72 h) AEs prediction model using electronic health records data available at the time of an indexed discharge. RESEARCH DESIGN: Retrospective cohort study of admissions between June 2012 and June 2014. SUBJECTS: All adult inpatient admissions (excluding in-hospital deaths) from a large multicenter hospital system. MEASURES: All-cause 3-day unplanned readmissions, emergency department (ED) visits, and deaths (REDD). The REDD model was developed using clinical, administrative, and socioeconomic data, with data preprocessing steps and stacked classification. Patients were divided randomly into training (66.7%), and testing (33.3%) cohorts to avoid overfitting. RESULTS: The derivation cohort comprised of 64,252 admissions, of which 2782 (4.3%) admissions resulted in 3-day AEs and 13,372 (20.8%) in 30-day AEs. The c-statistic (also known as area under the receiver operating characteristic curve) of 3-day REDD model was 0.671 and 0.664 for the derivation and validation cohort, respectively. The c-statistic of 30-day REDD model was 0.713 and 0.711 for the derivation and validation cohort, respectively. CONCLUSIONS: The 3-day REDD model predicts high-risk patients with fair discriminative power. The discriminative power of the 30-day REDD model is also better than the previously reported models under similar settings. The 3-day REDD model has been implemented and is being used to identify patients at risk for AEs.