Long-gradient separations coupled with selected reaction monitoring for highly sensitive, large scale targeted protein quantification in a single analysis.

Long-gradient separations coupled to tandem mass spectrometry (MS) were recently demonstrated to provide a deep proteome coverage for global proteomics; however, such long-gradient separations have not been explored for targeted proteomics. Herein, we investigate the potential performance of the long-gradient separations coupled with selected reaction monitoring (LG-SRM) for targeted protein quantification. Direct comparison of LG-SRM (5 h gradient) and conventional liquid chromatography (LC)-SRM (45 min gradient) showed that the long-gradient separations significantly reduced background interference levels and provided an 8- to 100-fold improvement in limit of quantification (LOQ) for target proteins in human female serum. On the basis of at least one surrogate peptide per protein, an LOQ of 10 ng/mL was achieved for the two spiked proteins in nondepleted human serum. The LG-SRM detection of seven out of eight endogenous plasma proteins expressed at ng/mL or subng/mL levels in clinical patient sera was also demonstrated. A correlation coefficient of >0.99 was observed for the results of LG-SRM and enzyme-linked immunosorbent assay (ELISA) measurements for prostate-specific antigen (PSA) in selected patient sera. Further enhancement of LG-SRM sensitivity was achieved by applying front-end IgY14 immunoaffinity depletion. Besides improved sensitivity, LG-SRM potentially offers much higher multiplexing capacity than conventional LC-SRM due to an increase in average peak widths (~3-fold) for a 300 min gradient compared to a 45 min gradient. Therefore, LG-SRM holds great potential for bridging the gap between global and targeted proteomics due to its advantages in both sensitivity and multiplexing capacity.

Structural Patterns in Chronic Disease Clinical Practice Guidelines Formalized for Clinical Decision Support.

Making recommendations from clinical practice guidelines (CPGs) computable for clinical decision support (CDS) has typically been a laborious and costly process. Identifying domain-specific regularities helps clinicians and knowledge engineers conceptualize, extract, and encode evidence-based recommendations. Based on our work to provide complex CDS in the management of multiple chronic diseases, we propose nine chronic disease CPG structural patterns, discuss considerations in representing the necessary knowledge, and illustrate them with the solutions that our CDS system provides.

PortFindr: A Novel, More Accurate, and Precise Device for Locating Tissue Expander Ports.

Two-stage breast reconstruction is the most common method of reconstructive modality following mastectomy and requires serial saline infusions into temporary tissue expanders through subcutaneous ports. Historically, these ports were located using a small magnet attached to a string or support structure. Magnetic force decreases exponentially as tissue thickness or fluid accumulation creates more distance between the TE port and skin. We developed a prototype handheld electronic device, the PortFindr, which more accurately and precisely locates the centre of subcutaneous ports. This device may lead to more confident localization of ports, less inadvertent puncture of tissue expanders, and thus less complications during infusions.

La reconstruction mammaire en deux étapes est la principale intervention reconstructive utilisée après une mastectomie. Elle exige plusieurs perfusions de soluté physiologique dans des expanseurs tissulaires temporaires par l'entremise de chambres sous-cutanées. Par le passé, ces chambres étaient localisées à l'aide d'un petit aimant fixé à une cordelette ou une structure de soutien. La force magnétique diminuait exponentiellement à mesure que l'épaisseur du tissu ou l'accumulation de liquide accroissait la distance entre la chambre et la peau. Les chercheurs ont créé un prototype électronique manuel, le PortFindr, qui situe le centre de la chambre sous-cutanée avec plus d'exactitude et de précision. Ce dispositif pourrait permettre de localiser les chambres de manière plus objective, de réduire les perforations accidentelles des expanseurs tissulaires et donc de limiter les complications pendant les perfusions.

Three-Dimensionally-Printed Hand Surgical Simulator for Resident Training.

Reduced work hours and funding have fueled an increase in simulation-based training for plastic and orthopedic surgery residency programs. Unfortunately, certain simulation training can fail to enhance surgical skills because of availability, cost, or low fidelity. There is a growing interest among training programs for a cost-effective surgical simulator to improve basic skills and muscle memory of residents. The authors developed a three-dimensionally-printed, malleable, and anatomically accurate hand surgery simulator from a computed tomographic scan of an adult male subject. The bone matrix was specifically designed to provide proprioceptive feedback to hone drilling skills used in fracture repair and arthrodesis. The silicone soft-tissue covering provides excellent malleability to dissect and perform fracture-reducing maneuvers. Three-dimensional printing of "fracture bridges" allows the design of on-demand polyfracture models so the trainee can practice multiple types and locations of repairs as skills progress. To summarize, the authors' hand simulator is an anatomical, low-cost, multiprocedure tool that can be used to improve the muscle memory and basic surgery skills of residents in training.

Selecting Test Cases from the Electronic Health Record for Software Testing of Knowledge-Based Clinical Decision Support Systems.

Software testing of knowledge-based clinical decision support systems is challenging, labor intensive, and expensive; yet, testing is necessary since clinical applications have heightened consequences. Thoughtful test case selection improves testing coverage while minimizing testing burden. ATHENA-CDS is a knowledge-based system that provides guideline-based recommendations for chronic medical conditions. Using the ATHENA-CDS diabetes knowledgebase, we demonstrate a generalizable approach for selecting test cases using rules/ filters to create a set of paths that mimics the system's logic. Test cases are allocated to paths using a proportion heuristic. Using data from the electronic health record, we found 1,086 cases with glycemic control above target goals. We created a total of 48 filters and 50 unique system paths, which were used to allocate 200 test cases. We show that our method generates a comprehensive set of test cases that provides adequate coverage for the testing of a knowledge-based CDS.

Natural language processing for lines and devices in portable chest x-rays.

Radiology reports are unstructured free text documents that describe abnormalities in patients that are visible via imaging modalities such as X-ray. The number of imaging examinations performed in clinical care is enormous, and mining large repositories of radiology reports connected with clinical data such as patient outcomes could enable epidemiological studies, such as correlating the frequency of infections to the presence or length of time medical devices are present in patients. We developed a natural language processing (NLP) system to recognize device mentions in radiology reports and information about their state (insertion or removal) to enable epidemiological research. We tested our system using a reference standard of reports that were annotated to indicate this information. Our system performed with high accuracy (recall and precision of 97% and 99% for device mentions and 91-96% for device insertion status). Our methods are generalizable to other types of radiology reports as well as to other information extraction tasks and could provide the foundation for tools that enable epidemiological research exploration based on mining radiology reports.