ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
A preclinical model to investigate normal tissue damage following fractionated radiotherapy to the head and neck

Radiotherapy (RT) of head and neck (H&N) cancer is known to cause both early- and late-occurring toxicities. To better appraise normal tissue responses and their dependence on treatment parameters such as radiation field and type, as well as dose and fractionation scheme, a preclinical model with relevant endpoints is required. 12-week old female C57BL/6 J mice were irradiated with 100 or 180 kV X-rays to total doses ranging from 30 to 85 Gy, given in 10 fractions over 5 days. The radiation field covered the oral cavity, swallowing structures and salivary glands. Monte Carlo simulations were employed to estimate tissue dose distribution. The follow-up period was 35 days, in order to study the early radiation-induced effects. Baseline and post irradiation investigations included macroscopic and microscopic examinations of the skin, lips, salivary glands and oral mucosa. Saliva sampling was performed to assess the salivary gland function following radiation exposure. A dose dependent radiation dermatitis in the skin was observed for doses above 30 Gy. Oral mucositis in the tongue appeared as ulcerations on the ventral surface of the tongue for doses of 75-85 Gy. The irradiated mice showed significantly reduced saliva production compared to controls. In summary, a preclinical model to investigate a broad panel of normal tissue responses following fractionated irradiation of the H&N region was established. The optimal dose to study early radiation-induced effects was found to be around 75 Gy, as this was the highest tolerated dose that gave acute effects similar to that observed in cancer patients.
MoDL-MUSSELS: Model-Based Deep Learning for Multishot Sensitivity-Encoded Diffusion MRI

We introduce a model-based deep learning architecture termed MoDL-MUSSELS for the correction of phase errors in multishot diffusion-weighted echo-planar MR images. The proposed algorithm is a generalization of the existing MUSSELS algorithm with similar performance but significantly reduced computational complexity. In this work, we show that an iterative re-weighted least-squares implementation of MUSSELS alternates between a multichannel filter bank and the enforcement of data consistency. The multichannel filter bank projects the data to the signal subspace, thus exploiting the annihilation relations between shots. Due to the high computational complexity of the self-learned filter bank, we propose replacing it with a convolutional neural network (CNN) whose parameters are learned from exemplary data. The proposed CNN is a hybrid model involving a multichannel CNN in the k-space and another CNN in the image space. The k-space CNN exploits the annihilation relations between the shot images, while the image domain network is used to project the data to an image manifold. The experiments show that the proposed scheme can yield reconstructions that are comparable to state-of-the-art methods while offering several orders of magnitude reduction in run-time.
Third-degree atrioventricular block caused by intoxication with rhododendron leaves

We report the case of a 41-year-old human with third-degree atrioventricular block caused due to intoxication with water concoction prepared from Rhododendron leaves. Such poisoning is rare. It is prone to arrhythmia with hemodynamic instability and is confused with various diseases. For these reasons, the correct diagnosis and treatment of this poisoning are particularly important. We confirmed it by analyzing the remaining liquid carried by the family members. After symptomatic and supportive treatment, the patient was discharged uneventfully.
Detecting Deficient Coverage in Colonoscopies

Colonoscopy is tool of choice for preventing Colorectal Cancer, by detecting and removing polyps before they become cancerous. However, colonoscopy is hampered by the fact that endoscopists routinely miss 22-28% of polyps. While some of these missed polyps appear in the endoscopist's field of view, others are missed simply because of substandard coverage of the procedure, i.e. not all of the colon is seen. This paper attempts to rectify the problem of substandard coverage in colonoscopy through the introduction of the C2D2 (Colonoscopy Coverage Deficiency via Depth) algorithm which detects deficient coverage, and can thereby alert the endoscopist to revisit a given area. More specifically, C2D2 consists of two separate algorithms: the first performs depth estimation of the colon given an ordinary RGB video stream; while the second computes coverage given these depth estimates. Rather than compute coverage for the entire colon, our algorithm computes coverage locally, on a segment-by-segment basis; C2D2 can then indicate in real-time whether a particular area of the colon has suffered from deficient coverage, and if so the endoscopist can return to that area. Our coverage algorithm is the first such algorithm to be evaluated in a large-scale way; while our depth estimation technique is the first calibration-free unsupervised method applied to colonoscopies. The C2D2 algorithm achieves state of the art results in the detection of deficient coverage. On synthetic sequences with ground truth, it is 2.4 times more accurate than human experts; while on real sequences, C2D2 achieves a 93.0% agreement with experts.
The Renewal of Arts, Lives, and a Community through Social Enterprise: The Case of Oficina de Agosto

The present work investigates, with a cultural approach, the emergence of an art-based social enterprise and an art-entrepreneurial ecosystem in Southeastern semi-rural Brazil, shedding light on how local private initiatives may build stronger communities and vice-versa, in a mutually transformative relationship. The focus lies on Oficina de Agosto, a folk-art studio, school, and shop. Fieldwork design combined ethnography and art-based research. The thick description of the phenomenon is organized under the acronym P.L.A.C.E., a conceptual framework describing five principles of community development. The contributions of this study are three-fold: (1) it illustrates how social enterprise may work as an alternative market model that could support community building; (2) it raises awareness to the possibility that social enterprises' initial social focus may not be perennial or unshakeable, in an undesirable change that might require a both/and mindset and a patient management of paradoxes; and (3) it offers practical managerial recommendations to the SE under focus, which might be extended to other local businesses, or to SEs in other semi-rural Brazilian towns, or even in international settings that might bear economic and social resemblance to our researched context.
Class-Agnostic Weighted Normalization of Staining in Histopathology Images Using a Spatially Constrained Mixture Model

The colorless biopsied tissue samples are usually stained in order to visualize different microscopic structures for diagnostic purposes. But color variations associated with the process of sample preparation, usage of raw materials, diverse staining protocols, and using different slide scanners may adversely influence both visual inspection and computer-aided image analysis. As a result, many methods are proposed for histopathology image stain normalization in recent years. In this study, we introduce a novel approach for stain normalization based on learning a mixture of multivariate skew-normal distributions for stain clustering and parameter estimation alongside a stain transformation technique. The proposed method, labeled "Class-Agnostic Weighted Normalization" (short CLAW normalization), has the ability to normalize a source image by learning the color distribution of both source and target images within an expectation-maximization framework. The novelty of this approach is its flexibility to quantify the underlying both symmetric and nonsymmetric distributions of the different stain components while it is considering the spatial information. The performance of this new stain normalization scheme is tested on several publicly available digital pathology datasets to compare it against state-of-the-art normalization algorithms in terms of ability to preserve the image structure and information. All in all, our proposed method performed superior more consistently in comparison with existing methods in terms of information preservation, visual quality enhancement, and boosting computer-aided diagnosis algorithm performance.
Semantic Cluster Unary Loss for Efficient Deep Hashing

With the rapid development of deep learning, deep hashing methods have achieved promising results in efficient information retrieval. The hashing method maps similar data to binary hashcodes with smaller hamming distance, which has received broad attention due to its low storage cost and fast retrieval speed. Most of the existing deep hashing methods adopt pairwise or triplet losses to deal with similarities underlying the data, but their training is difficult and less efficient because O(n(2)) data pairs and O(n(3)) triplets are involved. To address these issues, we propose a novel deep hashing algorithm with the unary loss which can be trained very efficiently. First, we introduce a Unary Upper Bound of the traditional triplet loss, thus reducing the complexity to O(n) and bridging the classification-based unary loss and the triplet loss. Second, we propose a novel Semantic Cluster Deep Hashing (SCDH) algorithm by introducing a modified Unary Upper Bound loss, called Semantic Cluster Unary Loss. The resultant hashcodes form several compact clusters, which means the hashcodes in the same cluster have similar semantic information. We also demonstrate that the proposed SCDH is easy to extend to semi-supervised settings by incorporating the state-of-the-art semi-supervised learning algorithms. The experiments on large-scale datasets show that the proposed method is superior to the state-of-the-art hashing algorithms.
Edge-aware image filtering using a structure-guided CNN

Image filtering is a fundamental preprocessing step for accurate, robust computer vision applications such as image segmentation, object classification, and reconstruction. However, many convolutional neural network (CNN)-based methods tend to lose significant edge information in the output layer, and generate undesired artefacts in the feature extraction layers. This study presents a deep CNN model for edge-aware image filtering. The proposed network model consists of three sub-networks: (i) feature extraction, (ii) convolution artefact removal, and (iii) structure extraction networks. The proposed network model has an end-to-end trainable architecture that does not need any post-processing steps. Especially, the structure extraction network can successfully preserve significant edges. The proposed filter outperforms state-of-the-art denoising filters in terms of both objective and subjective measures, and can be used for various image enhancement and restoration problems such as edge-preserving smoothing, image denoising, deblurring, and deblocking.
Lightweight and Effective Convolutional Neural Networks for Vehicle Viewpoint Estimation From Monocular Images

Vehicle viewpoint estimation from monocular images is a crucial component for autonomous driving vehicles and for fleet management applications. In this paper, we make several contributions to advance the state-of-the-art on this problem. We show the effectiveness of applying a smoothing filter to the output neurons of a Convolutional Neural Network (CNN) when estimating vehicle viewpoint. We point out the overlooked fact that, under the same viewpoint, the appearance of a vehicle is strongly influenced by its position in the image plane, which renders viewpoint estimation from appearance an ill-posed problem. We show how, by inserting in the model a CoordConv layer to provide the coordinates of the vehicle, we are able to solve such ambiguity and greatly increase performance. Finally, we introduce a new data augmentation technique that improves viewpoint estimation on vehicles that are closer to the camera or partially occluded. All these improvements let a lightweight CNN reach optimal results while keeping inference time low. An extensive evaluation on a viewpoint estimation benchmark (Pascal3D+) and on actual vehicle camera data (nuScenes) shows that our method significantly outperforms the state-of-the-art in vehicle viewpoint estimation, both in terms of accuracy and memory footprint.
Combined pars plana vitrectomy-scleral buckle versus pars plana vitrectomy for proliferative vitreoretinopathy

The purpose of the study is to evaluate the surgical outcomes of combined pars plana vitrectomy-scleral buckle (PPV-SB) versus pars plana vitrectomy (PPV) for rhegmatogenous retinal detachment complicated with proliferative vitreoretinopathy (PVR). One thousand one hundred and seventy four patients with rhegmatogenous retinal detachment surgery between January 2002 and December 2013 were retrospectively reviewed. Patients with grade C PVR treated with either combined PPV-SB or PPV alone were included in the study. Study outcomes included single surgery anatomic success rate and postoperative visual outcome at 12 months postoperatively. Seventy-seven patients with grade C PVR were identified for analysis. At the end of 12-month follow-up, 80.5 % eyes (33/41) in the PPV-SB group and 58.3 % eyes (21/36) in the PPV group achieved single surgery anatomical success. In a multiple logistic regression model, none of the baseline variables (age, gender, macula status, grade of PVR, extent of detachment, presence of vitreous hemorrhage, lens status, status of high myopia) nor types of retinal detachment surgery (use of scleral buckle, barrier endolaser, 360 degree endolaser, cryopexy, retinectomy, tamponade agent, phacoemulsification) had significant effect on single surgery anatomical success. The post-treatment mean logMAR visual acuity of the PPV-SB group was 1.58 ± 0.58 and the PPV group was 1.57 ± 0.61. There was no significant difference in the postoperative visual acuity between the two groups (P = 0.849). For patients with grade C PVR, PPV-SB did not demonstrate a superiority over PPV alone in achieving single surgery anatomical success.
Visual field asymmetries in numerosity processing

A small number of objects can be rapidly and accurately enumerated, whereas a larger number of objects can only be approximately enumerated. These subitizing and estimation abilities, respectively, are both spatial processes relying on extracting information across spatial locations. Nevertheless, whether and how these processes vary across visual field locations remains unknown. Here, we examined if enumeration displays asymmetries around the visual field. Experiment 1 tested small number (1-6) enumeration at cardinal and non-cardinal peripheral locations while manipulating the spacing among the objects. Experiment 2 examined enumeration at cardinal locations in more detail while minimising crowding. Both experiments demonstrated a Horizontal-Vertical Asymmetry (HVA) where performance was better along the horizontal axis relative to the vertical. Experiment 1 found that this effect was modulated by spacing with stronger asymmetry at closer spacing. Experiment 2 revealed further asymmetries: a Vertical Meridian Asymmetry (VMA) with better enumeration on the lower vertical meridian than on the upper and a Horizontal Meridian Asymmetry (HMA) with better enumeration along the left horizontal meridian than along the right. All three asymmetries were evident for both subitizing and estimation. HVA and VMA have been observed in a range of visual tasks, indicating that they might be inherited from early visual constraints. However, HMA is observed primarily in mid-level tasks, often involving attention. These results suggest that while enumeration processes can be argued to inherit low-level visual constraints, the findings are, parsimoniously, consistent with visual attention playing a role in both subitizing and estimation.
Local application of a transcutaneous carbon dioxide paste prevents excessive scarring and promotes muscle regeneration in a bupivacaine-induced rat model of muscle injury

In postoperative patients with head and neck cancer, scar tissue formation may interfere with the healing process, resulting in incomplete functional recovery and a reduced quality of life. Percutaneous application of carbon dioxide (CO2 ) has been reported to improve hypoxia, stimulate angiogenesis, and promote fracture repair and muscle damage. However, gaseous CO2 cannot be applied to the head and neck regions. Previously, we developed a paste that holds non-gaseous CO2 in a carrier and can be administered transdermally. Here, we investigated whether this paste could prevent excessive scarring and promote muscle regeneration using a bupivacaine-induced rat model of muscle injury. Forty-eight Sprague Dawley rats were randomly assigned to either a control group or a CO2 group. Both groups underwent surgery to induce muscle injury, but the control group received no treatment, whereas the CO2 group received the CO2 paste daily after surgery. Then, samples of the experimental sites were taken on days 3, 7, 14, and 21 post-surgery to examine the following: (1) inflammatory (interleukin [IL]-1β, IL-6), and transforming growth factor (TGF)-β and myogenic (MyoD and myogenin) gene expression by polymerase chain reaction, (2) muscle regeneration with haematoxylin and eosin staining, and (3) MyoD and myogenin protein expression using immunohistochemical staining. Rats in the CO2 group showed higher MyoD and myogenin expression and lower IL-1β, IL-6, and TGF-β expression than the control rats. In addition, treated rats showed evidence of accelerated muscle regeneration. Our study demonstrated that the CO2 paste prevents excessive scarring and accelerates muscle regeneration. This action may be exerted through the induction of an artificial Bohr effect, which leads to the upregulation of MyoD and myogenin, and the downregulation of IL-1β, IL-6, and TGF-β. The paste is inexpensive and non-invasive. Thus, it may be the treatment of choice for patients with muscle damage.
Identification of Nonhuman Primate Hematopoietic Stem and Progenitor Cells

The preclinical development of hematopoietic stem cell (HSC) gene therapy/editing and transplantation protocols is frequently performed in large animal models such as nonhuman primates (NHPs). Similarity in physiology, size, and life expectation as well as cross-reactivity of most reagents and medications allows for the development of treatment strategies with rapid translation to clinical applications. Especially after the adverse events of HSC gene therapy observed in the late 1990s, the ability to perform autologous transplants and follow the animals long-term make the NHP a very attractive model to test the efficiency, feasibility, and safety of new HSC-mediated gene-transfer/editing and transplantation approaches.This protocol describes a method to phenotypically characterize functionally distinct NHP HSPC subsets within specimens or stem cell products from three different NHP species. Procedures are based on the flow-cytometric assessment of cell surface markers that are cross-reactive in between human and NHP to allow for immediate clinical translation. This protocol has been successfully used for the quality control of enriched, cultured, and gene-modified NHP CD34+ hematopoietic stem and progenitor cells (HSPCs) as well as sort-purified CD34 subsets for transplantation in the pig-tailed, cynomolgus, and rhesus macaque. It further allows the longitudinal assessment of primary specimens taken during the long-term follow-up post-transplantation in order to monitor homing, engraftment, and reconstitution of the bone marrow stem cell compartment.
Malto-oligosaccharides as critical functional ingredient: a review of their properties, preparation, and versatile applications

Malto-oligosaccharides (MOS) are α-1,4 glycosidic linked linear oligosaccharides of glucose, which have a diverse range of functional applications in the food, pharmaceutical, and other industries. They can be used to modify the physicochemical properties of foods thereby improving their quality attributes, or they can be included as prebiotics to improve their nutritional attributes. The degree of polymerization of MOS can be controlled by using specific enzymes, which means their functionality can be tuned for specific applications. In this article, we review the chemical structure, physicochemical properties, preparation, and functional applications of MOS in the food, health care, and other industries. Besides, we offer an overview for this saccharide from the perspective of prospect functional ingredient, which we feel lacks in the current literature. MOS could be expected to provide a novel promising substitute for functional oligosaccharides.
Determining of acceptable limits of uncertainties in radiotoxicology by using a method based only on external quality assessment results

PROCORAD organizes for more than 20 years proficiency tests in radiotoxicology to estimate the performance of the laboratories in term of accuracy. The results of these intercomparisons also show a wide range of uncertainties values provided by these laboratories. The regulatory obligations related to accreditation, which require that the laboratory shall define the performance requirements for measurement uncertainty and regularly review estimates of measurement uncertainty, and the lack of published or required acceptable limits in radiotoxicology led PROCORAD to propose uncertainties acceptable limits from the "state of art". For this purpose, PROCORAD used a new method to estimate the uncertainties based only on external quality assessment results: the Long-Term Uncertainties Method (LTUM). This method was applied for tritium in urines, gamma/X emitters in urines and actinides in fecal ashes. This study allowed validation of LTUM by a simple and reliable method for radiotoxicology uncertainties measurements. Three targets (optimal, desirable and minimal) for acceptable limits were defined by using the first quartiles, medians and third quartiles calculation of uncertainties provided by LTUM: 10%, 15% et 20% for H-3 respectively, 18%, 21% et 27% for g/X emitters respectively, 15%, 25% et 34% for Pu-238 respectively, 11%, 17% et 26% for Pu-239 respectively, 19%, 26% et 38% for Am-241 respectively and 19%, 30% et 52% for Cm-244 respectively. Applying the LTUM method to the results provided by laboratories shows an underestimation of the uncertainties calculated by laboratories, indicating that all influencing factors are not always taken into account in the uncertainties measurements.