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Consent involving Concentric Rings Technique like a Topographic Way of measuring Retinal Nonperfusion within Ultra-Widefield Fluorescein Angiography.
We found that LINC00662 was frequently highly expressed and related to the malignant phenotype of glioma. LINC00662 knockdown inhibited the proliferation, invasion and glioma genesis of glioma. LINC00662 acted as a ceRNA sponging miR-340-5p to protect the expression of STAT3. In addition, STAT3 was forced to the promoter region of LINC00662 and promoted its transcription. Infigratinib In vivo experiments demonstrated that targeting LINC00662 may be a potential strategy in glioma therapy.

There was a positive regulation loop between LINC00662 and STAT3 in glioma. LINC00662 might be an oncogene in glioma. Targeting LINC00662 was a potential strategy in glioma therapy.
There was a positive regulation loop between LINC00662 and STAT3 in glioma. LINC00662 might be an oncogene in glioma. Targeting LINC00662 was a potential strategy in glioma therapy.Present research aims to develop a finite element computational model to examine delamination-fretting wear behaviour that can suitably mimic actual loading conditions at HAp-Ti-6Al-4V interface of uncemented hip implant femoral stem component. A simple finite element contact configuration model based on fretting fatigue experimental arrangement subjected to different mechanical and tribological properties consist of contact pad (bone), HAp coating and Ti-6Al-4V substrate are developed using adaptive wear modelling approach adopting modified Archard wear equation to be examined under static simulation. The developed finite element model is validated and verified with reported literatures. The findings revealed that significant delamination-fretting wear is recorded at contact edge (leading edge) as a result of substantial contact pressure and contact slip driven by stress singularity effect. The delamination-fretting wear behaviour is promoted under higher delamination length, lower normal loading with higher fatigue loading, increased porous (cancellous) and cortical bone elastic modulus with higher cycle number due to significant relative slip amplitude as the result of reduced interface rigidity. Tensile-compressive condition (R=-1) experiences most significant delamination-fretting wear behaviour (8 times higher) compared to stress ratio R=0.1 and R=10.Adversarial attacks are considered a potentially serious security threat for machine learning systems. Medical image analysis (MedIA) systems have recently been argued to be vulnerable to adversarial attacks due to strong financial incentives and the associated technological infrastructure. In this paper, we study previously unexplored factors affecting adversarial attack vulnerability of deep learning MedIA systems in three medical domains ophthalmology, radiology, and pathology. We focus on adversarial black-box settings, in which the attacker does not have full access to the target model and usually uses another model, commonly referred to as surrogate model, to craft adversarial examples that are then transferred to the target model. We consider this to be the most realistic scenario for MedIA systems. Firstly, we study the effect of weight initialization (pre-training on ImageNet or random initialization) on the transferability of adversarial attacks from the surrogate model to the target model, i.e., ho planned to be deployed in clinical practice. We recommend avoiding using only standard components, such as pre-trained architectures and publicly available datasets, as well as disclosure of design specifications, in addition to using adversarial defense methods. When evaluating the vulnerability of MedIA systems to adversarial attacks, various attack scenarios and target-surrogate differences should be simulated to achieve realistic robustness estimates. The code and all trained models used in our experiments are publicly available.3.The early detection of breast cancer greatly increases the chances that the right decision for a successful treatment plan will be made. Deep learning approaches are used in breast cancer screening and have achieved promising results when a large-scale labeled dataset is available for training. However, they may suffer from a dramatic decrease in performance when annotated data are limited. In this paper, we propose a method called deep adversarial domain adaptation (DADA) to improve the performance of breast cancer screening using mammography. Specifically, our aim is to extract the knowledge from a public dataset (source domain) and transfer the learned knowledge to improve the detection performance on the target dataset (target domain). Because of the different distributions of the source and target domains, the proposed method adopts an adversarial learning technique to perform domain adaptation using the two domains. Specifically, the adversarial procedure is trained by taking advantage of the disagreement of two classifiers. To evaluate the proposed method, the public well-labeled image-level dataset Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) is employed as the source domain. Mammography samples from the West China Hospital were collected to construct our target domain dataset, and the samples are annotated at case-level based on the corresponding pathological reports. The experimental results demonstrate the effectiveness of the proposed method compared with several other state-of-the-art automatic breast cancer screening approaches.Computer-aided diagnosis (CAD) of prostate cancer (PCa) using multiparametric magnetic resonance imaging (mpMRI) is actively being investigated as a means to provide clinical decision support to radiologists. Typically, these systems are trained using lesion annotations. However, lesion annotations are expensive to obtain and inadequate for characterizing certain tumor types e.g. diffuse tumors and MRI invisible tumors. In this work, we introduce a novel patient-level classification framework, denoted PCF, that is trained using patient-level labels only. In PCF, features are extracted from three-dimensional mpMRI and derived parameter maps using convolutional neural networks and subsequently, combined with clinical features by a multi-classifier support vector machine scheme. The output of PCF is a probability value that indicates whether a patient is harboring clinically significant PCa (Gleason score ≥3+4) or not. PCF achieved mean area under the receiver operating characteristic curves of 0.79 and 0.86 on the PICTURE and PROSTATEx datasets respectively, using five-fold cross-validation.
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