Notes

Your Info of Biological and Accelerated Aging for you to Cancer Progression By means of Senescence-Induced Swelling.
Parry-Romberg syndrome is a rare degenerative disorder causing progressive atrophy of skin and soft tissues of the face and neck, which is usually unilateral. The mean age of onset is usually in the second decade of life and the disease causes functional, aesthetic and psychological disabilities in the affected individual. We present a 14-year-old boy with this disorder. The diagnosis was based on clinical characteristics. A multidisciplinary team approach involving rheumatologists, dermatologists, maxillofacial surgeons, dentists and psychologists is required for the management of this problem, which is mainly targeted at controlling active inflammation with the use of immunosuppressive agents in addition to possible surgical correction of repositioning of adipose tissue that is lost due to atrophy. © The Author(s) 2020. Published by Oxford University Press.A 72-year-old woman with a known history of breast cancer was treated with adjuvant external beam radiation therapy. RI-1 She initially developed radiation field localized blistering and erosions of the skin, before developing more widespread lesions. Immunofluorescence confirmed pemphigus vulgaris, which was responsive to corticosteroids. Pemphigus vulgaris is an autoimmune blistering disease of the skin that can rarely be associated with exposure to ionizing radiation. © The Author(s) 2020. Published by Oxford University Press.Endoscopic video sequences provide surgeons with direct surgical field or visualisation on anatomical targets in the patient during robotic surgery. Unfortunately, these video images are unavoidably hazy or foggy to prevent surgeons from clear surgical vision due to typical surgical operations such as ablation and cauterisation during surgery. This Letter aims at removing fog or smoke on endoscopic video sequences to enhance and maintain a direct and clear visualisation of the operating field during robotic surgery. The authors propose a new luminance blending framework that integrates contrast enhancement with visibility restoration for foggy endoscopic video processing. The proposed method was validated on clinical endoscopic videos that were collected from robotic surgery. The experimental results demonstrate that their method provides a promising means to effectively remove fog or smoke on endoscopic video images. In particular, the visual quality of defogged endoscopic images was improved from 0.5088 to 0.6475.Surgical instrument detection in robot-assisted surgery videos is an import vision component for these systems. Most of the current deep learning methods focus on single-tool detection and suffer from low detection speed. To address this, the authors propose a novel frame-by-frame detection method using a cascading convolutional neural network (CNN) which consists of two different CNNs for real-time multi-tool detection. An hourglass network and a modified visual geometry group (VGG) network are applied to jointly predict the localisation. The former CNN outputs detection heatmaps representing the location of tool tip areas, and the latter performs bounding-box regression for tool tip areas on these heatmaps stacked with input RGB image frames. The authors' method is tested on the publicly available EndoVis Challenge dataset and the ATLAS Dione dataset. The experimental results show that their method achieves better performance than mainstream detection methods in terms of detection accuracy and speed.Kidney stones are a common urologic condition with a high amount of recurrence. Recurrence depends on a multitude of factors the incidence of precursors to kidney stones, plugs, and plaques. One method of characterising the stone precursors is endoscopic assessment, though it is manual and time-consuming. Deep learning has become a popular technique for semantic segmentation because of the high accuracy that has been demonstrated. The present Letter examined the efficacy of deep learning to segment the renal papilla, plaque, and plugs. A U-Net model with ResNet-34 encoder was tested; the Letter examined dropout (to avoid overtraining) and two different loss functions (to address the class imbalance problem. The models were then trained in 1666 images and tested on 185 images. The Jaccard-cross-entropy loss function was more effective than the focal loss function. The model with the dropout rate 0.4 was found to be more effective due to its generalisability. The model was largely successful at delineating the papilla. The model was able to correctly detect the plaques and plugs; however, small plaques were challenging. Deep learning was found to be applicable for segmentation of an endoscopic image for the papilla, plaque, and plug, with room for improvement.The possibility of axillary lymph node metastasis differs in different breast cancer patients and is the strongest prognostic indicator in breast cancer. The existing studies mainly explored the relationship of axillary ultrasound imaging and axillary lymph node metastasis, without exploring whether ultrasound imaging of breast tumour can affect and perform axillary lymph node prediction. Therefore, this Letter proposes a novel particle space-time distribution model to find the correlation between contrast-enhanced ultrasonography of breast tumour and axillary lymphatic metastasis. Starting from the imaging principle of dynamic contrast-enhanced ultrasonography, the particle space-time distribution model not only comprises space-time features of contrast-enhanced ultrasonography with an encoder-decoder network, but also the flow field information of microbubble particles is integrated into the space-time features that better serves the metastasis prediction by enhancing the particle distribution information. Extensive experiments on real patients have demonstrated that dynamic contrast-enhanced ultrasonography of breast tumour can be used to predict the probability of lymphatic metastasis. This conclusion can be interpretable from the clinical and pathological perspectives.In breast reconstruction following a single mastectomy, the surgeon needs to choose between tens of available implants to find the one that can reproduce the symmetry of the patient's breasts. However, due to the lack of measurement tools this decision is made purely visually, which means the surgeon has to order multiple implants to confirm the size for every single patient. In this Letter, the authors present an augmented reality application, which enables surgeons to see the shape of the implants, as 3D holograms on the patient's body. They custom developed a two-chamber implant that can gain different shapes and be used to test the system. Furthermore, the system was tested in a user study with 13 subjects. The study showed that subjects were able to do a comparison between real and holographic implants and come to a decision about which should be used. This method can be quicker than the traditional way and eliminates sizer implants from the process. Further advantages of the method include the use of a more accurate, user-friendly device, which is easily extendable as new implants that are on the market can be easily added to the system dataset.
Read More: https://www.selleckchem.com/products/ri-1.html