The creatine kinase system and pleiotropic effects of creatine

The pleiotropic effects of creatine (Cr) are based mostly on the functions of the enzyme creatine kinase (CK) and its high-energy product phosphocreatine (PCr). Multidisciplinary studies have established molecular, cellular, organ and somatic functions of the CK/PCr system, in particular for cells and tissues with high and intermittent energy fluctuations. These studies include tissue-specific expression and subcellular localization of CK isoforms, high-resolution molecular structures and structure–function relationships, transgenic CK abrogation and reverse genetic approaches. Three energy-related physiological principles emerge, namely that the CK/PCr systems functions as (a) an immediately available temporal energy buffer, (b) a spatial energy buffer or intracellular energy transport system (the CK/PCr energy shuttle or circuit) and (c) a metabolic regulator. The CK/PCr energy shuttle connects sites of ATP production (glycolysis and mitochondrial oxidative phosphorylation) with subcellular sites of ATP utilization (ATPases). Thus, diffusion limitations of ADP and ATP are overcome by PCr/Cr shuttling, as most clearly seen in polar cells such as spermatozoa, retina photoreceptor cells and sensory hair bundles of the inner ear. The CK/PCr system relies on the close exchange of substrates and products between CK isoforms and ATP-generating or -consuming processes. Mitochondrial CK in the mitochondrial outer compartment, for example, is tightly coupled to ATP export via adenine nucleotide transporter or carrier (ANT) and thus ATP-synthesis and respiratory chain activity, releasing PCr into the cytosol. This coupling also reduces formation of reactive oxygen species (ROS) and inhibits mitochondrial permeability transition, an early event in apoptosis. Cr itself may also act as a direct and/or indirect anti-oxidant, while PCr can interact with and protect cellular membranes. Collectively, these factors may well explain the beneficial effects of Cr supplementation. The stimulating effects of Cr for muscle and bone growth and maintenance, and especially in neuroprotection, are now recognized and the first clinical studies are underway. Novel socio-economically relevant applications of Cr supplementation are emerging, e.g. for senior people, intensive care units and dialysis patients, who are notoriously Cr-depleted. Also, Cr will likely be beneficial for the healthy development of premature infants, who after separation from the placenta depend on external Cr. Cr supplementation of pregnant and lactating women, as well as of babies and infants are likely to be of benefit for child development. Last but not least, Cr harbours a global ecological potential as an additive for animal feed, replacing meat- and fish meal for animal (poultry and swine) and fish aqua farming. This may help to alleviate human starvation and at the same time prevent over-fishing of oceans

A novel clinical gland feature for detection of early Barrett’s neoplasia using volumetric laser endomicroscopy

Volumetric laser endomicroscopy (VLE) is an advanced imaging system offering a promising solution for the detection of early Barrett’s esophagus (BE) neoplasia. BE is a known precursor lesion for esophageal adenocarcinoma and is often missed during regular endoscopic surveillance of BE patients. VLE provides a circumferential scan of near-microscopic resolution of the esophageal wall up to 3-mm depth, yielding a large amount of data that is hard to interpret in real time. In a preliminary study on an automated analysis system for ex vivo VLE scans, novel quantitative image features were developed for two previously identified clinical VLE features predictive for BE neoplasia, showing promising results. This paper proposes a novel quantitative image feature for a missing third clinical VLE feature. The novel gland-based image feature called “gland statistics” (GS), is compared to several generic image analysis features and the most promising clinically-inspired feature “layer histogram” (LH). All features are evaluated on a clinical, validated data set consisting of 88 non-dysplastic BE and 34 neoplastic in vivo VLE images for eight different widely-used machine learning methods. The new clinically-inspired feature has on average superior classification accuracy (0.84 AUC) compared to the generic image analysis features (0.61 AUC), as well as comparable performance to the LH feature (0.86 AUC). Also, the LH feature achieves superior classification accuracy compared to the generic image analysis features in vivo, confirming previous ex vivo results. Combining the LH and the novel GS features provides even further improvement of the performance (0.88 AUC), showing great promise for the clinical utility of this algorithm to detect early BE neoplasia

Ensemble of deep convolutional neural networks for classification of early Barrett’s neoplasia using volumetric laser endomicroscopy

Barrett’s esopaghagus (BE) is a known precursor of esophageal adenocarcinoma (EAC). Patients with BE undergo regular surveillance to early detect stages of EAC. Volumetric laser endomicroscopy (VLE) is a novel technology incorporating a second-generation form of optical coherence tomography and is capable of imaging the inner tissue layers of the esophagus over a 6 cm length scan. However, interpretation of full VLE scans is still a challenge for human observers. In this work, we train an ensemble of deep convolutional neural networks to detect neoplasia in 45 BE patients, using a dataset of images acquired with VLE in a multi-center study. We achieve an area under the receiver operating characteristic curve (AUC) of 0.96 on the unseen test dataset and we compare our results with previous work done with VLE analysis, where only AUC of 0.90 was achieved via cross-validation on 18 BE patients. Our method for detecting neoplasia in BE patients facilitates future advances on patient treatment and provides clinicians with new assisting solutions to process and better understand VLE data

Tissue segmentation in volumetric laser endomicroscopy data using FusionNet and a domain-specific loss function

\u3cp\u3eVolumetric Laser Endomicroscopy (VLE) is a promising balloon-based imaging technique for detecting early neoplasia in Barretts Esophagus. Especially Computer Aided Detection (CAD) techniques show great promise compared to medical doctors, who cannot reliably find disease patterns in the noisy VLE signal. However, an essential pre-processing step for the CAD system is tissue segmentation. At present, tissue is segmented manually but is not scalable for the entire VLE scan consisting of 1,200 frames of 4,096 × 2,048 pixels. Furthermore, the current CAD methods cannot use the VLE scans to their full potential, as only a small segment of the esophagus is selected for further processing, while an automated segmentation system results in significantly more available data. This paper explores the possibility of automatically segmenting relevant tissue for VLE scans using FusionNet and a domain-specific loss function. The contribution of this work is threefold. First, we propose a tissue segmentation algorithm for VLE scans. Second, we introduce a weighted ground truth that exploits the signal-to-noise ratio characteristics of the VLE data. Third, we compare our algorithm segmentation against two additional VLE experts. The results show that our algorithm annotations are indistinguishable from the expert annotations and therefore the algorithm can be used as a preprocessing step for further classification of the tissue.\u3c/p\u3

Deep principal dimension encoding for the classification of early neoplasia in Barrett's Esophagus with volumetric laser endomicroscopy

Barrett cancer is a treatable disease when detected at an early stage. However, current screening protocols are often not effective at finding the disease early. Volumetric Laser Endomicroscopy (VLE) is a promising new imaging tool for finding dysplasia in Barrett's esophagus (BE) at an early stage, by acquiring cross-sectional images of the microscopic structure of BE up to 3-mm deep. However, interpretation of VLE scans is difficult for medical doctors due to both the size and subtlety of the gray-scale data. Therefore, algorithms that can accurately find cancerous regions are very valuable for the interpretation of VLE data. In this study, we propose a fully-automatic multi-step Computer-Aided Detection (CAD) algorithm that optimally leverages the effectiveness of deep learning strategies by encoding the principal dimension in VLE data. Additionally, we show that combining the encoded dimensions with conventional machine learning techniques further improves results while maintaining interpretability. Furthermore, we train and validate our algorithm on a new histopathologically validated set of in-vivo VLE snapshots. Additionally, an independent test set is used to assess the performance of the model. Finally, we compare the performance of our algorithm against previous state-of-the-art systems. With the encoded principal dimension, we obtain an Area Under the Curve (AUC) and F1 score of 0.93 and 87.4% on the test set respectively. We show this is a significant improvement compared to the state-of-the-art of 0.89 and 83.1%, respectively, thereby demonstrating the effectiveness of our approach

Informative frame classification of endoscopic videos using convolutional neural networks and hidden Markov models

The goal of endoscopic analysis is to find abnormal lesions and determine further therapy from the obtained information. For example, in case of Barrett’s esophagus, the objective of endoscopy is to timely detect dysplastic lesions, before endoscopic resection is no longer possible. However, the procedure produces a variety of non-informative frames and lesions can be missed due to poor video quality. Especially when analyzing entire endoscopic videos made by non-expert endoscopists, informative frame classification is crucial to e.g. video quality grading. This analysis involves classification problems such as polyp detection or dysplasia detection in Barrett’s Esophagus. This work concentrates on the design of an automated indication of informativeness of video frames. We propose an algorithm consisting of state-of-the-art deep learning techniques, to initialize frame-based classification, followed by a hidden Markov model to incorporate temporal information and control consistent decision making. Results from the performed experiments show that the proposed model improves on the state-of-the-art with an F1-score of 91%, and a substantial increase in sensitivity of 10%, thereby indicating improved labeling consistency. Additionally, the algorithm is capable of processing 261 frames per second, which is multiple times faster compared to other informative frame classification algorithms, thus enabling real-time computation

Pseudo-labeled bootstrapping and multi-stage transfer learning for the classification and localization of dysplasia in Barrett’s esophagus

Patients suffering from Barrett’s Esophagus (BE) are at an increased risk of developing esophageal adenocarcinoma and early detection is crucial for a good prognosis. To aid the endoscopists with the early detection for this preliminary stage of esphageal cancer, this work concentrates on improving the state of the art for the computer-aided classification and localization of dysplastic lesions in BE. To this end, we employ a large-scale endoscopic data set, consisting of 494, 355 images, to pre-train several instances of the proposed GastroNet architecture, after which several data sets that are increasingly closer to the target domain are used in a multi-stage transfer learning strategy. Finally, ensembling is used to evaluate the results on a prospectively gathered external test set. Results from the performed experiments show that the proposed model improves on the state-of-the-art on all measured metrics. More specifically, compared to the best performing state-of-the-art model, the specificity is improved by more than 20% while preserving sensitivity at a high level, thereby reducing the false positive rate substantially. Our algorithm also significantly outperforms state-of-the-art on the localization metrics, where the intersection of all experts is correctly indicated in approximately 92% of the cases

Deep learning biopsy marking of early neoplasia in barrett's esophagus by combining wle and BLI modalities

\u3cp\u3eEsophageal cancer is the fastest rising type of cancer in the western world. Also, early neoplasia in Barrett's esophagus (BE) is difficult to detect for endoscopists and research has shown it is similarly complicated for Computer-Aided Detection (CAD) algorithms. For these reasons, further development of CAD algorithms for BE is essential for the wellbeing of patients. In this work we propose a patch-based deep learning algorithm for early neoplasia in BE, utilizing state-of-the-art deep learning techniques on a new prospective data set. The new algorithm yields not only a high detection score but also identifies the ideal biopsy location for the first time. We define specific novel metrics such as sweet-spot flag and softspot flag, to obtain well-defined computation of the biopsy location. Furthermore, we show that combining white light and blue laser imaging improves localization results by 8%.\u3c/p\u3