Distinct Expression Patterns of Carbonic Anhydrase IX in Clear Cell, Microcystic, and Angiomatous Meningiomas.

Clear cell, microcytic, and angiomatous meningiomas are 3 vasculature-rich variants with overlapping morphological features but different prognostic and treatment implications. Distinction between them is not always straightforward. We compared the expression patterns of the hypoxia marker carbonic anhydrase IX (CA-IX) in meningiomas with predominant clear cell (n = 15), microcystic (n = 9), or angiomatous (n = 11) morphologies, as well as 117 cases of other World Health Organization recognized histological meningioma variants. Immunostaining for SMARCE1 protein, whose loss-of-function has been associated with clear cell meningiomas, was performed on all clear cell meningiomas, and selected variants of meningiomas as controls. All clear cell meningiomas showed absence of CA-IX expression and loss of nuclear SMARCE1 expression. All microcystic and angiomatous meningiomas showed diffuse CA-IX immunoreactivity and retained nuclear SMARCE1 expression. In other meningioma variants, CA-IX was expressed in a hypoxia-restricted pattern and was highly associated with atypical features such as necrosis, small cell change, and focal clear cell change. In conclusion, CA-IX may serve as a useful diagnostic marker in differentiating clear cell, microcystic, and angiomatous meningiomas.

Probabilistic partial least squares regression for quantitative analysis of Raman spectra.

With the latest development of Surface-Enhanced Raman Scattering (SERS) technique, quantitative analysis of Raman spectra has shown the potential and promising trend of development in vivo molecular imaging. Partial Least Squares Regression (PLSR) is state-of-the-art method. But it only relies on training samples, which makes it difficult to incorporate complex domain knowledge. Based on probabilistic Principal Component Analysis (PCA) and probabilistic curve fitting idea, we propose a probabilistic PLSR (PPLSR) model and an Estimation Maximisation (EM) algorithm for estimating parameters. This model explains PLSR from a probabilistic viewpoint, describes its essential meaning and provides a foundation to develop future Bayesian nonparametrics models. Two real Raman spectra datasets were used to evaluate this model, and experimental results show its effectiveness.

Models and methods for quantitative analysis of surface-enhanced Raman spectra.

The quantitative analysis of surface-enhanced Raman spectra using scattering nanoparticles has shown the potential and promising applications in in vivo molecular imaging. The diverse approaches have been used for quantitative analysis of Raman pectra information, which can be categorized as direct classical least squares models, full spectrum multivariate calibration models, selected multivariate calibration models, and latent variable regression (LVR) models. However, the working principle of these methods in the Raman spectra application remains poorly understood and a clear picture of the overall performance of each model is missing. Based on the characteristics of the Raman spectra, in this paper, we first provide the theoretical foundation of the aforementioned commonly used models and show why the LVR models are more suitable for quantitative analysis of the Raman spectra. Then, we demonstrate the fundamental connections and differences between different LVR methods, such as principal component regression, reduced-rank regression, partial least square regression (PLSR), canonical correlation regression, and robust canonical analysis, by comparing their objective functions and constraints.We further prove that PLSR is literally a blend of multivariate calibration and feature extraction model that relates concentrations of nanotags to spectrum intensity. These features (a.k.a. latent variables) satisfy two purposes: the best representation of the predictor matrix and correlation with the response matrix. These illustrations give a new understanding of the traditional PLSR and explain why PLSR exceeds other methods in quantitative analysis of the Raman spectra problem. In the end, all the methods are tested on the Raman spectra datasets with different evaluation criteria to evaluate their performance.

Continuous wavelet transform based partial least squares regression for quantitative analysis of Raman spectrum.

Quantitative analysis of Raman spectra using surface-enhanced Raman scattering (SERS) nanoparticles has shown the potential and promising trend of development in in vivo molecular imaging. Partial least square regression (PLSR) methods have been reported as state-of-the-art methods. However, the approaches fully rely on the intensities of Raman spectra and can not avoid the influences of the unstable background. In this paper we design a new continuous wavelet transform based PLSR (CWT-PLSR) algorithm that uses mixing concentrations and the average CWT coefficients of Raman spectra to carry out PLSR. We elaborate and prove how the average CWT coefficients with a Mexican hat mother wavelet are robust representations of Raman peaks, and the method can reduce the influences of unstable baseline and random noises during the prediction process. The algorithm was tested using three Raman spectra data sets with three cross-validation methods in comparison with current leading methods, and the results show its robustness and effectiveness.

Eigenspectra, a robust regression method for multiplexed Raman spectra analysis.

With the latest development of Surface Enhanced Raman Scattering (SERS) nanoparticles, Raman spectroscopy now can be extended to bioimaging and biosensing. In this study, we demonstrate the ability of Raman spectroscopy to separate multiple spectral fingerprints using Raman nanotags. A machine learning method is proposed to estimate the mixing ratios of sources from mixture signals. It decomposes the mixture signals into components for both best representation and most relating to mixing ratios. Then regression coefficients are calculated for the prediction. The robustness of the method was compared with least squares and weighted least squares methods.