QUALITY BY DESIGN (QBD) AS A TOOL FOR THE OPTIMIZATION OF INDOMETHACIN FREEZE-DRIED SUBLINGUAL TABLETS: IN VITRO AND IN VIVO EVALUATION

Objective: This study aims to prepare and optimize indomethacin freeze-dried sublingual tablets (IND-FDST) by utilizing a quality by design (QbD) approach to achieve rapid drug dissolution and simultaneously bypassing the GIT for better patient tolerability. Methods: A screening study was utilized to determine the most significant factors which the quality attributes, namely disintegration time and % friability. Then an optimization study was conducted using a full response surface design to determine the optimized formula by varying the amount of the matrix-forming polymer (gelatin) and super disintegrant (croscarmellose sodium (CCS)). The variables' effect on the % friability, disintegration time, wetting time, and amount of drug release after 10 min (%Q10) was studied. The optimized formula was tested for compatibility, morphology as well as stability studies under accelerated conditions in addition to the in vivo pharmacodynamics in rats. QbD was adopted by utilizing a screening study to identify the significant formulation factors followed by a response surface optimization study to determine the optimized IND-FDST formulation. Results: Optimized IND-FDST comprised of gelatin/CCS combination in a ratio of 1:1 possessed adequate %friability (0.73±0.03%), disintegration time (25.40±1.21 seconds), wetting time (3.49±0.68 seconds), and % Q10 (100.99±5.29%) as well as good stability under accelerated conditions. IND-FDST also showed significant inhibition of edema, tumour necrosis factor-alpha, and interleukin-6 release in vivo compared to the oral market product by 70%, 42%, and 65%, respectively. Conclusion: QbD presents a successful approach in the optimization of a successful IND-FDST formula that showed superior in vivo and in vitro characteristics

Assessment of the Energy Rating of Insulated Wall Assemblies - A Step Towards Building Energy Labeling

Considerable efforts are recently focusing on energy labeling of components and systems in buildings. In Canada, the energy rating of windows was established, which provides a protocol to rate different types of windows with respect to their energy performance. It takes into account the interaction between: solar heat gain, heat loss due to air leakage and due to the thermal properties of the entire window assembly. A major research project, jointly sponsored by NRC-IRC and the polyurethane spray foam industry, was established to assess the thermal and air leakage performance of insulated walls with the focus on developing an energy rating procedure for insulated wall assemblies. This paper is one in a series of publications to present partial results of this project. Experimental data and computer simulation comparison of a set of wall specimens are presented together with a summary of the proposed procedure for the determination of the energy rating of insulated walls (WER)

Hierarchical prediction of registration misalignment using a convolutional LSTM: application to chest CT scans

In this paper we propose a supervised method to predict registration misalignment using convolutional neural networks (CNNs). This task is casted to a classification problem with multiple classes of misalignment: "correct" 0-3 mm, "poor" 3-6 mm and "wrong" over 6 mm. Rather than a direct prediction, we propose a hierarchical approach, where the prediction is gradually refined from coarse to fine. Our solution is based on a convolutional Long Short-Term Memory (LSTM), using hierarchical misalignment predictions on three resolutions of the image pair, leveraging the intrinsic strengths of an LSTM for this problem. The convolutional LSTM is trained on a set of artificially generated image pairs obtained from artificial displacement vector fields (DVFs). Results on chest CT scans show that incorporating multi-resolution information, and the hierarchical use via an LSTM for this, leads to overall better F1 scores, with fewer misclassifications in a well-tuned registration setup. The final system yields an accuracy of 87.1%, and an average F1 score of 66.4% aggregated in two independent chest CT scan studies.Radiolog

Joint registration and segmentation via multi-task learning for adaptive radiotherapy of prostate cancer

Medical image registration and segmentation are two of the most frequent tasks in medical image analysis. As these tasks are complementary and correlated, it would be beneficial to apply them simultaneously in a joint manner. In this paper, we formulate registration and segmentation as a joint problem via a Multi-Task Learning (MTL) setting, allowing these tasks to leverage their strengths and mitigate their weaknesses through the sharing of beneficial information. We propose to merge these tasks not only on the loss level, but on the architectural level as well. We studied this approach in the context of adaptive image-guided radiotherapy for prostate cancer, where planning and follow-up CT images as well as their corresponding contours are available for training. At testing time the contours of the follow-up scans are not available, which is a common scenario in adaptive radiotherapy. The study involves two datasets from different manufacturers and institutes. The first dataset was divided into training (12 patients) and validation (6 patients), and was used to optimize and validate the methodology, while the second dataset (14 patients) was used as an independent test set. We carried out an extensive quantitative comparison between the quality of the automatically generated contours from different network architectures as well as loss weighting methods. Moreover, we evaluated the quality of the generated deformation vector field (DVF). We show that MTL algorithms outperform their Single-Task Learning (STL) counterparts and achieve better generalization on the independent test set. The best algorithm achieved a mean surface distance of 1.06 +/- 0.3 mm, 1.27 +/- 0.4 mm, 0.91 +/- 0.4 mm, and 1.76 +/- 0.8 mm on the validation set for the prostate, seminal vesicles, bladder, and rectum, respectively. The high accuracy of the proposed method combined with the fast inference speed, makes it a promising method for automatic re-contouring of follow-up scans for adaptive radiotherapy, potentially reducing treatment related complications and therefore improving patients quality-of-life after treatment. The source code is available at https://github.com/moelmahdy/JRS-MTL.Biological, physical and clinical aspects of cancer treatment with ionising radiatio

Esophageal tumor segmentation in CT images using a Dilated Dense Attention Unet (DDAUnet)

Manual or automatic delineation of the esophageal tumor in CT images is known to be very challenging. This is due to the low contrast between the tumor and adjacent tissues, the anatomical variation of the esophagus, as well as the occasional presence of foreign bodies (e.g. feeding tubes). Physicians therefore usually exploit additional knowledge such as endoscopic findings, clinical history, additional imaging modalities like PET scans. Achieving his additional information is time-consuming, while the results are error-prone and might lead to non-deterministic results. In this paper we aim to investigate if and to what extent a simplified clinical workflow based on CT alone, allows one to automatically segment the esophageal tumor with sufficient quality. For this purpose, we present a fully automatic end-to-end esophageal tumor segmentation method based on convolutional neural networks (CNNs). The proposed network, called Dilated Dense Attention Unet (DDAUnet), leverages spatial and channel attention gates in each dense block to selectively concentrate on determinant feature maps and regions. Dilated convolutional layers are used to manage GPU memory and increase the network receptive field. We collected a dataset of 792 scans from 288 distinct patients including varying anatomies with air pockets, feeding tubes and proximal tumors. Repeatability and reproducibility studies were conducted for three distinct splits of training and validation sets. The proposed network achieved a DSC value of 0.79 +/- 0.20, a mean surface distance of 5.4 +/- 20.2mm and 95% Hausdorff distance of 14.7 +/- 25.0mm for 287 test scans, demonstrating promising results with a simplified clinical workflow based on CT alone. Our code is publicly available via https://github.com/yousefis/DenseUnet_Esophagus_Segmentation.Biological, physical and clinical aspects of cancer treatment with ionising radiatio

Robust contour propagation using deep learning and image registration for online adaptive proton therapy of prostate cancer

Purpose To develop and validate a robust and accurate registration pipeline for automatic contour propagation for online adaptive Intensity‐Modulated Proton Therapy (IMPT) of prostate cancer using elastix software and deep learning. Methods A three‐dimensional (3D) Convolutional Neural Network was trained for automatic bladder segmentation of the computed tomography (CT) scans. The automatic bladder segmentation alongside the computed tomography (CT) scan is jointly optimized to add explicit knowledge about the underlying anatomy to the registration algorithm. We included three datasets from different institutes and CT manufacturers. The first was used for training and testing the ConvNet, where the second and the third were used for evaluation of the proposed pipeline. The system performance was quantified geometrically using the dice similarity coefficient (DSC), the mean surface distance (MSD), and the 95% Hausdorff distance (HD). The propagated contours were validated clinically through generating the associated IMPT plans and compare it with the IMPT plans based on the manual delineations. Propagated contours were considered clinically acceptable if their treatment plans met the dosimetric coverage constraints on the manual contours. Results The bladder segmentation network achieved a DSC of 88% and 82% on the test datasets. The proposed registration pipeline achieved a MSD of 1.29 ± 0.39, 1.48 ± 1.16, and 1.49 ± 0.44 mm for the prostate, seminal vesicles, and lymph nodes, respectively, on the second dataset and a MSD of 2.31 ± 1.92 and 1.76 ± 1.39 mm for the prostate and seminal vesicles on the third dataset. The automatically propagated contours met the dose coverage constraints in 86%, 91%, and 99% of the cases for the prostate, seminal vesicles, and lymph nodes, respectively. A Conservative Success Rate (CSR) of 80% was obtained, compared to 65% when only using intensity‐based registration. Conclusion The proposed registration pipeline obtained highly promising results for generating treatment plans adapted to the daily anatomy. With 80% of the automatically generated treatment plans directly usable without manual correction, a substantial improvement in system robustness was reached compared to a previous approach. The proposed method therefore facilitates more precise proton therapy of prostate cancer, potentially leading to fewer treatment‐related adverse side effects

Ferritin immobilization on patterned poly(2-hydroxyethyl methacrylate) brushes on silicon surfaces from colloid system

In this paper, we describe a graft polymerization/solvent immersion method for generating poly(2-hydroxyethyl methacrylate) (PHEMA) brushes in various patterns. We used a novel fabrication process, involving very-large-scale integration and oxygen plasma treatment, to generate well-defined patterns of polymerized PHEMA on patterned Si(100) surfaces. We observed brush- and mushroom-like regions for the PHEMA brushes, with various pattern resolutions, after immersing wafers presenting lines of these polymers in MeOH and n-hexane, respectively. The interaction between PHEMA and ferritin protein sheaths in MeOH and n-hexane (good and poor solvent for PHEMA, respectively) was used to capture and release ferritins from fluidic system. The “tentacles” behaver for PHEMA brushes was found through various solvents in fluidic system. Using high-resolution scanning electron microscopy, we observed patterned ferritin Fe cores on the Si surface after pyrolysis of the patterned PHEMA brushes and ferritin protein sheaths, which verify the “tentacles” behaver for PHEMA brushes

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

Annual Energy Consumption Data on Supermarkets in Ontario and Quebec

This note is a compilation of data on energy consumption in supermarkets in Ontario and Quebec in the period 1977 to 1979. The information is presented in a series of tables and graphs and indicates wide variations in energy use.Cette note est une compilation de donn\ue9es sur l'\ue9nergie consomm\ue9e dans des supermarch\ue9s de l'Ontario et du Qu\ue9bec entre 1977 et 1979. Ces informations sont pr\ue9sent\ue9es sous forme de tableaux et de graphiques montrant de nettes variations de la consommation.Peer reviewed: NoNRC publication: Ye

To seal or not to seal? A critical look at the effects of air leakage on the condensation resistance of windows

Peer reviewed: YesNRC publication: Ye