Cerenkov luminescence imaging (CLI) for image-guided cancer surgery

Cerenkov luminescence imaging (CLI) is a novel molecular optical imaging technique based on the detection of optical Cerenkov photons emitted by positron emission tomography (PET) imaging agents. The ability to use clinically approved tumour-targeted tracers in combination with small-sized imaging equipment makes CLI a particularly interesting technique for image-guided cancer surgery. The past few years have witnessed a rapid increase in proof-of-concept preclinical studies in this field, and several clinical trials are currently underway. This article provides an overview of the basic principles of Cerenkov radiation and outlines the challenges of CLI-guided surgery for clinical use. The preclinical and clinical trial literature is examined including applications focussed on image-guided lymph node detection and Cerenkov luminescence endoscopy, and the ongoing clinical studies and technological developments are highlighted. By intraoperatively guiding the oncosurgeon towards more accurate and complete resections, CLI has the potential to transform current surgical practice, and improve oncological and cosmetic outcomes for patients.</p

Collaborative Deep Learning for Recommender Systems

Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recent advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix. Extensive experiments on three real-world datasets from different domains show that CDL can significantly advance the state of the art

Psoriasis Skin Disease Classification based on Clinical Images

Psoriasis is an autoimmune skin disorder that causes skin plaques to develop into red and scaly patches. It affects millions of people globally. Dermatologists currently employ visual and haptic methods to determine a medical issue's severity. Intelligent medical imaging-based diagnosis systems are now a possibility because of the relatively recent development of deep learning technologies for medical image processing. These systems can help a human expert make better decisions about a patient's health. Convolutional neural networks, or CNNs, on the other hand, have achieved imaging performance levels comparable to, if not better than, those of humans. In the paper, a Dermnet dataset is used. Image preprocessing, fuzzy c-mean-based segmentation, MobileNet-based feature extraction, and a support vector machine (SVM) classification are used for skin disease classification. Dermnet's dataset was investigated for images of skin conditions using three classes Psoriasis, Dermatofibroma, and Melanoma are studied. The performance metrics such as accuracy, precision-recall, and f1-score are evaluated and compared for three classes of skin diseases. Despite working with a smaller dataset, MobileNet with Support Vector Machine outperforms ResNet in terms of accuracy (99.12%), precision (98.65%), and recall (99.66%)

Photoacoustic Sentinel Lymph Node Imaging with Self-Assembled Copper Neodecanoate Nanoparticles

Photoacoustic tomography (PAT) is emerging as a novel, hybrid, and non-ionizing imaging modality because of its satisfactory spatial resolution and high soft tissue contrast. PAT combines the advantages of both optical and ultrasonic imaging methods. It opens up the possibilities for noninvasive staging of breast cancer and may replace sentinel lymph node (SLN) biopsy in clinic in the near future. In this work, we demonstrate for the first time that copper can be used as a contrast metal for near-infrared detection of SLN using PAT. A unique strategy is adopted to encapsulate multiple copies of Cu as organically soluble small molecule complexes within a phospholipid-entrapped nanoparticle. The nanoparticles assumed a size of 80–90 nm, which is the optimum hydrodynamic diameter for its distribution throughout the lymphatic systems. These particles provided at least 6-fold higher signal sensitivity in comparison to blood, which is a natural absorber of light. We also demonstrated that high SLN detection sensitivity with PAT can be achieved in a rodent model. This work clearly demonstrates for the first time the potential use of copper as an optical contrast agent

Microscopic changes in the spinal extensor musculature in people with chronic spinal pain: a systematic review.

Chronic spinal pain is one the most common musculoskeletal disorders. Previous studies have observed microscopic structural changes in the spinal extensor muscles in people with chronic spinal pain. This systematic review synthesizes and analyses all the existing evidence of muscle microscopic changes in people with chronic spinal pain. To assess the microscopy of spinal extensor muscles including the fiber type composition, the area occupied by fiber types, fiber size/cross sectional area (CSA) and narrow diameter (ND) in people with and without chronic spinal pain. Further, to compare these outcome measures across different regions of the spine in people with chronic neck, thoracic and low back pain. Systematic review with meta-analysis METHODS: MEDLINE (Ovid Interface), Embase, PubMed, CINAHL Plus and Web of Science were searched from inception to October 2020. Key journals, conference proceedings, grey literature and hand searching of reference lists from eligible studies were also searched. Two independent reviewers were involved in the selection process. Only studies examining the muscle microscopy of the spinal extensor muscles (erector spinae (ES) and/or multifidus (MF)) between people with and without chronic spinal pain were selected. The risk of bias from the studies was assessed using modified Newcastle Ottawa Scale and the level of evidence was established using the GRADE approach. Data were synthesized based on homogeneity on the methodology and outcome measures of the studies for ES and MF muscles and only four studies were eligible for analysis. All the five studies included were related to chronic low back pain (CLBP). Meta-analysis (inverse variance method for random effect to calculate mean difference and 95% CI) was performed for the ES fiber type composition by numbers for both type I and type II fibers (I =43% and 0% respectively indicating homogeneity of studies) and showed no difference between the people with and without CLBP with an overall effect estimate Z= 1.49 (p=0.14) and Z=1.06 (p=0.29) respectively. Meta-analysis was performed for ES fiber CSA for both type I and type II fibers (I =0 for both) and showed no difference between people with and without CLBP with an overall effect estimate Z= 0.08 (p=0.43) and Z=0.75 (p=0.45) respectively. Analysis was not performed for ES area occupied by fiber types and ND due to heterogeneity of studies and lack of evidence respectively. Similarly, meta-analysis was not performed for MF fiber type composition by numbers due to heterogeneity of studies. MF analysis for area occupied by fiber type, fiber CSA and ND did not yield sufficient evidence. For the ES muscle, there was no difference in fiber type composition and fiber CSA between people with and without CLBP and no conclusions could be drawn for ND for the ES. For the MF, no conclusions could be drawn for any of the muscle microscopy outcome measures. Overall, the quality of evidence is very low and there is very low evidence that there are no differences in microscopic muscle features between people with and without CLBP. [Abstract copyright: Copyright © 2022. Published by Elsevier Inc.

A tumor DNA complex aberration index is an independent predictor of survival in breast and ovarian cancer

Complex focal chromosomal rearrangements in cancer genomes, also called "firestorms", can be scored from DNA copy number data. The complex arm-wise aberration index (CAAI) is a score that captures DNA copy number alterations that appear as focal complex events in tumors, and has potential prognostic value in breast cancer. This study aimed to validate this DNA-based prognostic index in breast cancer and test for the first time its potential prognostic value in ovarian cancer. Copy number alteration (CNA) data from 1950 breast carcinomas (METABRIC cohort) and 508 high-grade serous ovarian carcinomas (TCGA dataset) were analyzed. Cases were classified as CAAI positive if at least one complex focal event was scored. Complex alterations were frequently localized on chromosome 8p (n = 159), 17q (n = 176) and 11q (n = 251). CAAI events on 11q were most frequent in estrogen receptor positive (ER+) cases and on 17q in estrogen receptor negative (ER) cases. We found only a modest correlation between CAAI and the overall rate of genomic instability (GII) and number of breakpoints (r = 0.27 and r = 0.42, p <0.001). Breast cancer specific survival (BCSS), overall survival (OS) and ovarian cancer progression free survival (PUS) were used as clinical end points in Cox proportional hazard model survival analyses. CAAI positive breast cancers (43%) had higher mortality: hazard ratio (HR) of 1.94 (95%CI, 1.62-2.32) for BCSS, and of 1.49 (95%CI, 1.30-1.71) for OS. Representations of the 70-gene and the 21-gene predictors were compared with CAAI in multivariable models and CAAI was independently significant with a Cox adjusted HR of 1.56 (95%CI, 1.23-1.99) for ER+ and 1.55 (95%CI, 1.11-2.18) for ER disease. None of the expression-based predictors were prognostic in the ER subset. We found that a model including CAM and the two expression-based prognostic signatures outperformed a model including the 21-gene and 70-gene signatures but excluding CAAL Inclusion of CAAI in the clinical prognostication tool PREDICT significantly improved its performance. CAAI positive ovarian cancers (52%) also had worse prognosis: HRs of 1.3 (95%CI, 1.1-1.7) for PFS and 1.3 (95%CI, 1.1-1.6) for OS. This study validates CAM as an independent predictor of survival in both ER+ and ER breast cancer and reveals a significant prognostic value for CAAI in high-grade serous ovarian cancer. (C) 2014 The Authors. Published by Elsevier B.V. on behalf of Federation of European Biochemical Societies. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).Publisher PDFPeer reviewe