In the present trial, there was no evidence that implementing a prophylactic PEEP of 5 cmH2O adversely affects short-term hemodynamics or outcome in medical intensive care patients during the postintubation period.

Optical coherence tomography (OCT) yields microscopic volumetric images representing tissue structures based on the contrast provided by elastic light scattering. Multipatient studies using OCT for detection of tissue abnormalities can lead to large datasets making quantitative and unbiased assessment of classification algorithms performance difficult without the availability of automated analytical schemes. We present a mathematical descriptor reducing the dimensionality of a classifier's input data, while preserving essential volumetric features from reconstructed three-dimensional optical volumes. This descriptor is used as the input of classification algorithms allowing a detailed exploration of the features space leading to optimal and reliable classification models based on support vector machine techniques. Using imaging dataset of paraffin-embedded tissue samples from 38 ovarian cancer patients, we report accuracies for cancer detection [Formula: see text] for binary classification between healthy fallopian tube and ovarian samples containing cancer cells. Furthermore, multiples classes of statistical models are presented demonstrating [Formula: see text] accuracy for the detection of high-grade serous, endometroid, and clear cells cancers. The classification approach reduces the computational complexity and needed resources to achieve highly accurate classification, making it possible to contemplate other applications, including intraoperative surgical guidance, as well as other depth sectioning techniques for fresh tissue imaging.

GPU-based nonlinear model fitting optimizer called GPU-LMFit. We demonstrate the applications of GPU-LMFit in super resolution localization microscopy, fluorescence lifetime imaging microscopy, diffusion-weighted MRI (DW-MRI) and myocardial longitudinal relaxation time (T1) MRI using modified Look-Locker inversion recovery (MOLLI) based techniques. The results show that the use of GPU-LMFit can readily result in more than tens of times of speedup of parametric analyses in these techniques, compared with the software using CPU-only processing. An important example will be presented that when GPU-LMFit was used with a medium level GPU like Quadro K2000 for a DW-MRI image data set to reconstruct non-Gaussian diffusion parametric images, the results show that the images can be constructed up to 240x faster than with CPU processing alone. In this application, GPU-LMFit helps to reduce the time for DW-MRI processing from hours to seconds. Our results show the performance of GPU-LMFit is excellent to significantly improve the efficiency of parametric analyses and can thus be a useful tool to enable automated parametric imaging for real-time visualization, analysis and diagnostics.

Background: There is an urgent need for pathologists to better define patients with high-risk prostate cancer. One of the promising tools is Raman micro-spectroscopy, also known as Raman microscopy, a nondestructive and label-free imaging technique based on light scattered after reflection. Our group has recently developed a rapid standardized protocol for the preparation of formalin-fixed, paraffin-embedded (FFPE) diagnostic tissues suitable for Raman microscopy. The objective of this study was to evaluate the potential of Raman microscopy to assess the prognosis of prostate cancer patients with FFPE tissues from radical prostatectomy. Methods: Patients treated by first-line radical prostatectomy between 1994 and 2004 at Centre hospitalier de l’Université de Montréal (CHUM) were included in this study. FFPE prostate cancer tissues from surgery were used for the construction of tissue microarrays (TMAs). To enable Raman microscopy, TMA sections of 4 µm were placed on low-cost aluminum slides. The rapid dewaxing protocol of the hospital was used (8 minutes), followed by 20 minutes of vacuum drying. All Raman spectra were acquired with the Renishaw inVia confocal Raman microscope equipped with a 785-nm line focus laser. After removing background contributions (e.g., autofluorescence) for each spectrum with Wire 4.4 software, a custom toolbox in MATLAB was used to predict biochemical recurrence. Chemometric methods and calculated ratios were used for the analysis of Raman microscopy images. Results: A total of 320 Raman spectra from 80 patients were analyzed from prostate cancer TMAs, representing 25 patients with biochemical recurrence within 18 months after radical prostatectomy and 55 without. Using a Support Vector Machine (SVM) technique and correlation feature selection for classification, our results with Raman microscopy identified biochemical recurrence with an accuracy of 83.7%, a sensitivity of 84.0% and a specificity of 83.6%. Raman peak assignment of features was used to investigate the molecular differences between these two patient groups. We found that the molecular constituents of RNA and phosphorylated proteins were more important in prostate cancer with biochemical recurrence. In contrast, Raman peaks of the phospholipid head of cell membranes, DNA, and collagen were more intense in prostate cancer without biochemical recurrence. For the visualization of these different molecular constituents of prostate cancer, we developed two methods of Raman microscopy imaging. The first method involved analysis of chemometric data (i.e., extraction of chemical information) to identify the whole tissue (phenylalanine), nuclei (DNA), and red blood cells (hemoglobin), followed by background removal. The images of our chemometric analysis created a virtual staining of hematoxylin and eosin (H&E). The second method involved testing several ratios of Raman peaks associated with proteins, lipids, DNA and RNA. Calculated ratios distinguished specific structures of the prostate tissue, such as the cancerous and normal glands, by different colors. Conclusions: This is the first study demonstrating the potential of Raman microscopy for the prediction of biochemical recurrence within 18 months following radical prostatectomy for prostate cancer. Raman microscopy imaging of tissues is a promising method for the recognition of specific structures, which could help pathologists in the accuracy of diagnosis. The accessibility of this technology to clinicians could be useful for patient follow-up and treatment strategies. Citation Format: Andrée-Anne Grosset, Catherine St-Pierre, Karl St-Arnaud, Kelly Aubertin, Michael Jermyn, Frédéric Leblond, Dominique Trudel. Raman microscopy to assess biochemical recurrence risk after radical prostatectomy [abstract]. In: Proceedings of the AACR Special Conference: Prostate Cancer: Advances in Basic, Translational, and Clinical Research; 2017 Dec 2-5; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(16 Suppl):Abstract nr A014.

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