A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation.

OBJECTIVE: To propose a new decision algorithm combining biomarkers measured in a tumor biopsy with clinical variables, to predict recurrence after liver transplantation (LT). BACKGROUND: Liver cancer is one of the most frequent causes of cancer-related mortality. LT is the best treatment for hepatocellular carcinoma (HCC) patients but the scarcity of organs makes patient selection a critical step. In addition, clinical criteria widely applied in patient eligibility decisions miss potentially curable patients while selecting patients that relapse after transplantation. METHODS: A literature systematic review singled out candidate biomarkers whose RNA levels were assessed by quantitative PCR in tumor tissue from 138 HCC patients submitted to LT (>5 years follow up, 32% beyond Milan criteria). The resulting 4 gene signature was combined with clinical variables to develop a decision algorithm using machine learning approaches. The method was named HepatoPredict. RESULTS: HepatoPredict identifies 99% disease-free patients (>5 year) from a retrospective cohort, including many outside clinical criteria (16%-24%), thus reducing the false negative rate. This increased sensitivity is accompanied by an increased positive predictive value (88.5%-94.4%) without any loss of long-term overall survival or recurrence rates for patients deemed eligible by HepatoPredict; those deemed ineligible display marked reduction of survival and increased recurrence in the short and long term. CONCLUSIONS: HepatoPredict outperforms conventional clinical-pathologic selection criteria (Milan, UCSF), providing superior prognostic information. Accurately identifying which patients most likely benefit from LT enables an objective stratification of waiting lists and information-based allocation of optimal versus suboptimal organs.

A novel platform for distributed and remote real-time monitoring of animal model behavior in a bioterium.

Animal models are an important resource in life sciences research; however, many of the procedures involving vivo models are complex and time consuming. A common problem while conducting experiments is observing the behavior of the models throughout their stay in the bioterium. Ideally, behavioral assessment should be frequent and rigorous, as a way of more accurately characterizing the animal model. However, to date, few suitable automated solutions can be found within the state-of-the-art. In this paper, we propose an autonomous platform for distributed behavioral data acquisition from individual animal habitats in bioteriums, with remote and online access to the data. This approach allows real-time observation of the status of the habitats, and retrieval of the logged data for post-processing. The work focuses on the use case of motion, temperature and water intake monitoring in small rodents, although the platform was designed to be general-purpose and extensible to other types of habitats and sensing configurations.

Quantitative in vivo longitudinal nerve excursion and strain in response to joint movement: A systematic literature review.

BACKGROUND: Neural system mobilization is widely used in the treatment of several painful conditions. Data on nerve biomechanics is crucial to inform the design of mobilization exercises. Therefore, the aim of this review is to characterize normal nervous system biomechanics in terms of excursion and strain. METHODS: Studies were sought from Pubmed, Physiotherapy Evidence Database, Cochrane Library, Web of Science and Scielo. Two reviewers' screened titles and abstracts, assessed full reports for potentially eligible studies, extracted information on studies' characteristics and assessed its methodological quality. FINDINGS: Twelve studies were included in this review that assessed the median nerve (n=8), the ulnar nerve (n=1), the tibial nerve (n=1), the sciatic nerve (n=1) and both the tibial and the sciatic nerves (n=1). All included studies assessed longitudinal nerve excursion and one assessed nerve strain. Absolute values varied between 0.1mm and 12.5mm for median nerve excursion, between 0.1mm and 4.0mm for ulnar nerve excursion, between 0.7 mm and 5.2mm for tibial nerve excursion and between 0.1mm and 3.5mm for sciatic nerve excursion. Maximum reported median nerve strain was 2.0%. INTERPRETATION: Range of motion for the moving joint, distance from the moving joint to the site of the lesion, position of adjacent joints, number of moving joints and whether joint movement stretches or shortens the nerve bed need to be considered when designing neural mobilization exercises as all of these factors seem to have an impact on nerve excursion.