Endoscopic submucosal dissection with versus without traction for pathologically staged T1B esophageal cancer: a multicenter retrospective study.

Background And Aims: Positive vertical margins (VMs) are common after endoscopic submucosal dissection (ESD) of T1b esophageal cancer (EC) and are associated with an increased risk of recurrence. Traction during ESD provides better exposure of the submucosa and may allow deeper dissection, potentially reducing the risk of positive VMs. We conducted a retrospective multicenter study to compare the proportion of resections with positive VMs in ESD performed with versus without traction in pathologically staged T1b EC.

Outcomes for endoscopic submucosal dissection of pathologically staged T1b esophageal cancer: a multicenter study.

Background And Aims: The outcomes of endoscopic submucosal dissection (ESD) for T1b esophageal cancer (EC) and its recurrence rates remain unclear in the West. Using a multicenter cohort, we evaluated technical outcomes and recurrence rates of ESD in the treatment of pathologically staged T1b EC.

Methods: We included patients who underwent ESD of T1b EC at 7 academic tertiary referral centers in the United States (n = 6) and Brazil (n = 1).

Early-Onset Pancreas Cancer: Clinical Descriptors, Genomics, and Outcomes.

Background: Recent evidence suggests a rising incidence of cancer in younger individuals. Herein, we report the epidemiologic, pathologic, and molecular characteristics of a patient cohort with early-onset pancreas cancer (EOPC).

Methods: Institutional databases were queried for demographics, treatment history, genomic results, and outcomes.

Detection of atrial fibrillation and other abnormal rhythms from ECG using a multi-layer classifier architecture.

Objective: Atrial fibrillation (AF) and other types of abnormal heart rhythm are related to multiple fatal cardiovascular diseases that affect the quality of human life. Hence the development of an automated robust method that can reliably detect AF, in addition to other non-sinus and sinus rhythms, would be a valuable addition to medicine. The present study focuses on developing an algorithm for the classification of short, single-lead electrocardiogram (ECG) recordings into normal, AF, other abnormal rhythms and noisy classes.

Pattern Analysis in Physiological Pulsatile Signals: An Aid to Personalized Healthcare.

We present a system to analyze patterns inside pulsatile signals and discover repetitions inside signals. We measure dominance of the repetitions using morphology and discrete nature of the signals by exploiting machine learning and information theoretic concepts. Patterns are represented as combinations of the basic features and derived features.

Multiplexed identification, quantification and genotyping of infectious agents using a semiconductor biochip.

The emergence of pathogens resistant to existing antimicrobial drugs is a growing worldwide health crisis that threatens a return to the pre-antibiotic era. To decrease the overuse of antibiotics, molecular diagnostics systems are needed that can rapidly identify pathogens in a clinical sample and determine the presence of mutations that confer drug resistance at the point of care. We developed a fully integrated, miniaturized semiconductor biochip and closed-tube detection chemistry that performs multiplex nucleic acid amplification and sequence analysis.

A Fully Integrated CMOS Fluorescence Biochip for DNA and RNA Testing.

Design and successful implementation of a fully-integrated CMOS fluorescence biochip for DNA/RNA testing in molecular diagnostics (MDx) is presented. The biochip includes a 32×32 array of continuous wave fluorescence detection biosensing elements. Each biosensing element is capable of having unique DNA probe sequences, wavelength-selective multi-dielectric emission filter (OD of 3.

Analysis of phonocardiogram signals through proactive denoising using novel self-discriminant learner.

Phonocardiogram (PCG) records heart sound and murmurs, which contains significant information of cardiac health. Analysis of PCG signal has the potential to detect abnormal cardiac condition. However, the presence of noise and motion artifacts in PCG hinders the accuracy of clinical event detection.

An unsupervised learning for robust cardiac feature derivation from PPG signals.

We propose here derivation algorithms for physiological parameters like beat start point, systolic peak, pulse duration, peak-to-peak distance related to heart rate, dicrotic minima, diastolic peak from Photoplethysmogram (PPG) signals robustly. Our methods are based on unsupervised learning mainly following morphology as well as discrete nature of the signal. Statistical learning has been used as a special aid to infer most probable feature values mainly to cope up with presence of noise, which is assumed to be insignificant compared to signal values at each investigation window.