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Colorectal cancer is of particular concern due to its high mortality rate count. Recent investigations on targeted phototherapy involving novel photosensitizers and drug-delivery systems have provided promising results and realistic prospects for a successful medical treatment. New research trends have been focused particularly on development of advanced molecular systems offering effective photoactive species which could be selectively delivered directly into the affected cells. Porphyrins and phthalocyanines have been considered extremely attractive for this purpose due to their molecular versatility, excellent photochemical properties and multifunctional nature. In this review it has been demonstrated that such macrocyclic compounds may effectively contribute to the inhibition of the growth of colon cancer cells and eventually to their photonecrosis. Purposely designed and tailored porphyrin and phthalocyanine derivatives in combination with smart drug-carriers have proved suitable for photodynamic therapy (PDT) and related antitumor treatments. This survey comprises a choice of potentially applicable ideas developed since 2010 involving 9 different tumor cell lines and featuring 32 photosensitizers.Visible nursing work is usually associated with formal work and physician-delegated tasks which are protocolised and usually well documented. Nevertheless, nurses carry out many actions and display specific attitudes and behaviours which, despite contributing to the well-being, recovery of patients and satisfaction with the attention received, are not as visible. Previous studies have been conducted in order to define 'invisible nursing interventions', but no quantitative instruments focused on measuring invisible nursing interventions have been found in the literature.

To test the psychometric properties of the Perception of Invisible Nursing Care-Hospitalisation (PINC-H) questionnaire.

Cross-sectional survey design. A self-administered questionnaire was completed by 381 participants recruited consecutively after discharge from a Spanish hospital. Data were collected from 2012 to 2020.

Three factors were identified from exploratory factor analysis,namely'Caring for the person','Caring for the environmenseful to evaluate the quality of invisible nursing care to oncology inpatients.Convolutional Neural Networks (CNNs) work very well for supervised learning problems when the training dataset is representative of the variations expected to be encountered at test time. In medical image segmentation, this premise is violated when there is a mismatch between training and test images in terms of their acquisition details, such as the scanner model or the protocol. Remarkable performance degradation of CNNs in this scenario is well documented in the literature. To address this problem, we design the segmentation CNN as a concatenation of two sub-networks a relatively shallow image normalization CNN, followed by a deep CNN that segments the normalized image. We train both these sub-networks using a training dataset, consisting of annotated images from a particular scanner and protocol setting. Now, at test time, we adapt the image normalization sub-network for each test image, guided by an implicit prior on the predicted segmentation labels. We employ an independently trained denoising autoencoder (DAE) in order to model such an implicit prior on plausible anatomical segmentation labels. We validate the proposed idea on multi-center Magnetic Resonance imaging datasets of three anatomies brain, heart and prostate. The proposed test-time adaptation consistently provides performance improvement, demonstrating the promise and generality of the approach. Being agnostic to the architecture of the deep CNN, the second sub-network, the proposed design can be utilized with any segmentation network to increase robustness to variations in imaging scanners and protocols. Our code is available at https//github.com/neerakara/test-time-adaptable-neural-networks-for-domain-generalization.We address the problem of reconstructing high quality images from undersampled MRI data. This is a challenging task due to the highly ill-posed nature of the problem. In particular, in dynamic MRI scans, the interaction between the target structure and the physical motion affects the acquired measurements leading to blurring artefacts and loss of fine details. check details In this work, we propose a framework for dynamic MRI reconstruction framed under a new multi-task optimisation model called Compressed Sensing Plus Motion (CS + M). Firstly, we propose a single optimisation problem that simultaneously computes the MRI reconstruction and the physical motion. Secondly, we show our model can be efficiently solved by breaking it up into two computationally tractable problems. The potentials and generalisation capabilities of our approach are demonstrated in different clinical applications including cardiac cine, cardiac perfusion and brain perfusion imaging. We show, through numerical experiments, that the proposed scheme reduces blurring artefacts, and preserves the target shape and fine details in the reconstruction. We also report the highest quality reconstruction under high undersampling rates in comparison to several state of the art techniques.Gross tumor volume (GTV) and clinical target volume (CTV) delineation are two critical steps in the cancer radiotherapy planning. GTV defines the primary treatment area of the gross tumor, while CTV outlines the sub-clinical malignant disease. Automatic GTV and CTV segmentation are both challenging for distinct reasons GTV segmentation relies on the radiotherapy computed tomography (RTCT) image appearance, which suffers from poor contrast with the surrounding tissues, while CTV delineation relies on a mixture of predefined and judgement-based margins. High intra- and inter-user variability makes this a particularly difficult task. We develop tailored methods solving each task in the esophageal cancer radiotherapy, together leading to a comprehensive solution for the target contouring task. Specifically, we integrate the RTCT and positron emission tomography (PET) modalities together into a two-stream chained deep fusion framework taking advantage of both modalities to facilitate more accurate GTV segmentation.