Portable Optical Fiber Probe-Based Spectroscopic Scanner for Rapid Cancer Diagnosis: A New Tool for Intraoperative Margin Assessment

There continues to be a significant clinical need for rapid and reliable intraoperative margin assessment during cancer surgery. Here we describe a portable, quantitative, optical fiber probe-based, spectroscopic tissue scanner designed for intraoperative diagnostic imaging of surgical margins, which we tested in a proof of concept study in human tissue for breast cancer diagnosis. The tissue scanner combines both diffuse reflectance spectroscopy (DRS) and intrinsic fluorescence spectroscopy (IFS), and has hyperspectral imaging capability, acquiring full DRS and IFS spectra for each scanned image pixel. Modeling of the DRS and IFS spectra yields quantitative parameters that reflect the metabolic, biochemical and morphological state of tissue, which are translated into disease diagnosis. The tissue scanner has high spatial resolution (0.25 mm) over a wide field of view (10 cm×10 cm), and both high spectral resolution (2 nm) and high spectral contrast, readily distinguishing tissues with widely varying optical properties (bone, skeletal muscle, fat and connective tissue). Tissue-simulating phantom experiments confirm that the tissue scanner can quantitatively measure spectral parameters, such as hemoglobin concentration, in a physiologically relevant range with a high degree of accuracy (<5% error). Finally, studies using human breast tissues showed that the tissue scanner can detect small foci of breast cancer in a background of normal breast tissue. This tissue scanner is simpler in design, images a larger field of view at higher resolution and provides a more physically meaningful tissue diagnosis than other spectroscopic imaging systems currently reported in literatures. We believe this spectroscopic tissue scanner can provide real-time, comprehensive diagnostic imaging of surgical margins in excised tissues, overcoming the sampling limitation in current histopathology margin assessment. As such it is a significant step in the development of a platform technology for intraoperative management of cancer, a clinical problem that has been inadequately addressed to date.Case Comprehensive Cancer Center. Tissue Procurement, Histology and Immunohistochemistry Core Facility (P30 CA43703)National Cancer Institute (U.S.) (R01-CA140288)National Cancer Institute (U.S.) (R01-CA97966)National Center for Research Resources (U.S.) (S10-RR031845)National Center for Research Resources (U.S.) (P41-RR02594

Voter information campaigns and political accountability: cumulative findings from a preregistered meta-analysis of coordinated trials

Voters may be unable to hold politicians to account if they lack basic information about their representatives’ performance. Civil society groups and international donors therefore advocate using voter information campaigns to improve democratic accountability. Yet, are these campaigns effective? Limited replication, measurement heterogeneity, and publication biases may undermine the reliability of published research. We implemented a new approach to cumulative learning, coordinating the design of seven randomized controlled trials to be fielded in six countries by independent research teams. Uncommon for multisite trials in the social sciences, we jointly preregistered a meta-analysis of results in advance of seeing the data. We find no evidence overall that typical, nonpartisan voter information campaigns shape voter behavior, although exploratory and subgroup analyses suggest conditions under which informational campaigns could be more effective. Such null estimated effects are too seldom published, yet they can be critical for scientific progress and cumulative, policy-relevant learning

Application of spectral derivative data in visible and near-infrared spectroscopy

The use of the spectral derivative method in visible and near-infrared optical spectroscopy is presented, whereby instead of using discrete measurements around several wavelengths, the difference between nearest neighbouring spectral measurements is utilized. The proposed technique is shown to be insensitive to the unknown tissue and fibre contact coupling coefficients providing substantially increased accuracy as compared to more conventional techniques. The self-calibrating nature of the spectral derivative techniques increases its robustness for both clinical and industrial applications, as is demonstrated based on simulated results as well as experimental data

Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes

Un des enjeux majeurs de spectroscopie NIR est le transfert des méthodes développées en laboratoire vers le domaine industriel pour des applications en ligne ou portable. Il apparaît ainsi de nouvelles problématiques telles que la nécessité de robustesse ou, l'aptitude des modèles à prendre en compte les non-linéarités présentes dans les données. Les méthodes semi-paramétrique ont été suggérées comme outils pour compenser ce type de problèmes.Dans cet article, les régressions 'Least-Squared Support Vector Machine (LS-SVM)' sont mis en oeuvre pour la prédir l'acidité de trois différentes variétés de raisins à partir de leur spectre NIR. Les performances sont comparés au modèles utilisant des techniques classiques (PLSR, MLR). Les LS-SVM permettent s'obtenir de meilleures prédictions. Néanmoins un prétraitement par SNV est nécéssaire afin d'améliorer la robustesse des modèles / Nowadays, near infrared (NIR)technology is being transferred from the laboratory to the industrial world for on-line and portable applications. As a result, new issues are arising, such as the need for increased robustness, or the ability to compensate for non-linearities in the calibration or instrument. Semi-parametric modeling has been suggested as a means for adapting to these complications. In this article, Least-Squared Support Vector Machine (LS-SVM) regression, a semi-parametric modeling technique, is used to predict the acidity of three different grape varieties using NIR spectra. The performance and robustness of LS-SVM regression are compared to Partial Least Square Regression (PLSR) and Multivariate Linear Regression (MLR). LS-SVM regression produces more accurate prediction. However SNV pretreatment is required to improve the model robustnes