Secondary Endothelial Keratoplasty—A Narrative Review of the Outcomes of Secondary Corneal Endothelial Allografts

Background: We review the literature on the efficacy and safety outcomes of secondary Descemet stripping endothelial keratoplasty (DSEK) and Descemet membrane endothelial keratoplasty (DMEK). Methods: Literature search of English-written publications up to September 27, 2020 in PubMed database, using the terms "endothelial keratoplasty" in combination with keywords "secondary" or "repeat." In addition, we manually searched the references of the primary articles. Results: Twenty-seven studies (n = 651 eyes) were retained and reviewed, including 10 studies on repeat DSEK, 8 studies on repeat DMEK, 6 studies of DMEK following DSEK, and 3 studies of DSEK after failed DMEK. All studies reported significant improvement in visual acuity after secondary endothelial keratoplasty (EK). Twelve studies compared visual outcomes between primary and secondary EK, reporting conflicting findings. Sixteen studies reported endothelial cell loss rates after secondary EK, and only 1 study reported significantly increased endothelial cell loss rates compared with primary EK. Allograft rejection episodes occurred in 1.8% of eyes (range, 0%-50%). Six studies compared complication rates between primary and secondary EK eyes, and only 1 study found a higher median number of complications. However, 2 studies reported higher regraft failure rates compared with primary EK eyes. Conclusions: Secondary EK is surgically feasible and renders significant visual improvement after failed primary EK, although it is not clear whether visual outcomes and allograft survival are comparable with primary EK, raising the question of whether secondary EK eyes are "low risk" as primary EK eyes. Further larger, prospective studies are encouraged to obtain additional quality data on secondary corneal endothelial allotransplantation.info:eu-repo/semantics/publishedVersio

Pre-training autoencoder for lung nodule malignancy assessment using CT images

Lung cancer late diagnosis has a large impact on the mortality rate numbers, leading to a very low five-year survival rate of 5%. This issue emphasises the importance of developing systems to support a diagnostic at earlier stages. Clinicians use Computed Tomography (CT) scans to assess the nodules and the likelihood of malignancy. Automatic solutions can help to make a faster and more accurate diagnosis, which is crucial for the early detection of lung cancer. Convolutional neural networks (CNN) based approaches have shown to provide a reliable feature extraction ability to detect the malignancy risk associated with pulmonary nodules. This type of approach requires a massive amount of data to model training, which usually represents a limitation in the biomedical field due to medical data privacy and security issues. Transfer learning (TL) methods have been widely explored in medical imaging applications, offering a solution to overcome problems related to the lack of training data publicly available. For the clinical annotations experts with a deep understanding of the complex physiological phenomena represented in the data are required, which represents a huge investment. In this direction, this work explored a TL method based on unsupervised learning achieved when training a Convolutional Autoencoder (CAE) using images in the same domain. For this, lung nodules from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) were extracted and used to train a CAE. Then, the encoder part was transferred, and the malignancy risk was assessed in a binary classification—benign and malignant lung nodules, achieving an Area Under the Curve (AUC) value of 0.936. To evaluate the reliability of this TL approach, the same architecture was trained from scratch and achieved an AUC value of 0.928. The results reported in this comparison suggested that the feature learning achieved when reconstructing the input with an encoder-decoder based architecture can be considered an useful knowledge that might allow overcoming labelling constraints.This work is financed by National Funds through the Portuguese funding agency, FCT—Fundação para a Ciência e a Tecnologia within project UIDB/50014/2020

Infective Endocarditis Complicated by Large Aortic Pseudoaneurysm after Cardiac Surgery

A 66-year-old female with Streptococcus viridans aortic and tricuspid infective endocarditis develops, during the course of antibiotic therapy, rupture of a right coronary sinus of Valsalva aneurysm to the right ventricle. An urgent cardiac surgery is preformed with implantation of a mechanical aortic prosthesis and a right coronary sinus plasty. Six months later a huge aortic pseudoaneurysm is diagnosed and she is submitted to a second uneventful surgery. A review is done for the significant features with discussion of diagnosis and therapy

The Role of Liquid Biopsy in Early Diagnosis of Lung Cancer

Liquid biopsy is an emerging technology with a potential role in the screening and early detection of lung cancer. Several liquid biopsy-derived biomarkers have been identified and are currently under ongoing investigation. In this article, we review the available data on the use of circulating biomarkers for the early detection of lung cancer, focusing on the circulating tumor cells, circulating cell-free DNA, circulating micro-RNAs, tumor-derived exosomes, and tumor-educated platelets, providing an overview of future potential applicability in the clinical practice. While several biomarkers have shown exciting results, diagnostic performance and clinical applicability is still limited. The combination of different biomarkers, as well as their combination with other diagnostic tools show great promise, although further research is still required to define and validate the role of liquid biopsies in clinical practice.This work is financed by the ERDF—European Regional Development Fund through the Operational Programme for Competitiveness and Internationalization—COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT—Fundação para a Ciência e a Tecnologia within project POCI-01-0145-FEDER-030263. Authors thank Abílio Cunha and Francisco Correia for the illustration work. NC-M acknowledges the Portuguese Foundation for Science and Technology under Horizon 2020 Program (PTDC/PSI-GER/28076/2017)

Kerr-CFT From Black-Hole Thermodynamics

We analyze the near-horizon limit of a general black hole with two commuting killing vector fields in the limit of zero temperature. We use black hole thermodynamics methods to relate asymptotic charges of the complete spacetime to those obtained in the near-horizon limit. We then show that some diffeomorphisms do alter asymptotic charges of the full spacetime, even though they are defined in the near horizon limit and, therefore, count black hole states. We show that these conditions are essentially the same as considered in the Kerr/CFT corresponcence. From the algebra constructed from these diffeomorphisms, one can extract its central charge and then obtain the black hole entropy by use of Cardy's formula.Comment: 19 pages, JHEP3, no figures. V2: References added, small typos fixe

Anesthesia of Epinephelus marginatus with essential oil of Aloysia polystachya: an approach on blood parameters

This study investigated the anesthetic potential of the essential oil (EO) of Aloysia polystachya in juveniles of dusky grouper (Epinephelus marginatus). Fish were exposed to different concentrations of EO of A. polystachya to evaluate time of induction and recovery from anesthesia. In the second experiment, fish were divided into four groups: control, ethanol and 50 or 300 mu L L-1 EO of A. polystachya, and each group was submitted to induction for 3.5 min and recovery for 5 or 10 min. The blood gases and glucose levels showed alterations as a function of the recovery times, but Na+ and K+ levels did not show any alteration. In conclusion, the EO from leaves of A. polystachya is an effective anesthetic for dusky grouper, because anesthesia was reached within the recommended time at EO concentrations of 300 and 400 mu L L-1. However, most evaluated blood parameters showed compensatory responses due to EO exposure.Fundacao de Amparo a Pesquisa do Estado do Rio Grande do Sul/Programa de Apoio a Nucleos de Excelencia (FAPERGS/PRONEX) [10/0016-8]; Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) [470964/2009-0]; Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior, Brazil (CAPES)info:eu-repo/semantics/publishedVersio

Machine learning and feature selection methods for egfr mutation status prediction in lung cancer

The evolution of personalized medicine has changed the therapeutic strategy from classical chemotherapy and radiotherapy to a genetic modification targeted therapy, and although biopsy is the traditional method to genetically characterize lung cancer tumor, it is an invasive and painful procedure for the patient. Nodule image features extracted from computed tomography (CT) scans have been used to create machine learning models that predict gene mutation status in a noninvasive, fast, and easy-to-use manner. However, recent studies have shown that radiomic features extracted from an extended region of interest (ROI) beyond the tumor, might be more relevant to predict the mutation status in lung cancer, and consequently may be used to significantly decrease the mortality rate of patients battling this condition. In this work, we investigated the relation between image phenotypes and the mutation status of Epidermal Growth Factor Receptor (EGFR), the most frequently mutated gene in lung cancer with several approved targeted-therapies, using radiomic features extracted from the lung containing the nodule. A variety of linear, nonlinear, and ensemble predictive classification models, along with several feature selection methods, were used to classify the binary outcome of wild-type or mutant EGFR mutation status. The results show that a comprehensive approach using a ROI that included the lung with nodule can capture relevant information and successfully predict the EGFR mutation status with increased performance compared to local nodule analyses. Linear Support Vector Machine, Elastic Net, and Logistic Regression, combined with the Principal Component Analysis feature selection method implemented with 70% of variance in the feature set, were the best-performing classifiers, reaching Area Under the Curve (AUC) values ranging from 0.725 to 0.737. This approach that exploits a holistic analysis indicates that information from more extensive regions of the lung containing the nodule allows a more complete lung cancer characterization and should be considered in future radiogenomic studies.This work is financed by the ERDF—European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation—COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT—Fundação para a Ciência e a Tecnologia within project POCI-01-0145-FEDER-030263

Comprehensive perspective for lung cancer characterisation based on AI solutions using CT images

Lung cancer is still the leading cause of cancer death in the world. For this reason, novel approaches for early and more accurate diagnosis are needed. Computer-aided decision (CAD) can be an interesting option for a noninvasive tumour characterisation based on thoracic computed tomography (CT) image analysis. Until now, radiomics have been focused on tumour features analysis, and have not considered the information on other lung structures that can have relevant features for tumour genotype classification, especially for epidermal growth factor receptor (EGFR), which is the mutation with the most successful targeted therapies. With this perspective paper, we aim to explore a comprehensive analysis of the need to combine the information from tumours with other lung structures for the next generation of CADs, which could create a high impact on targeted therapies and personalised medicine. The forthcoming artificial intelligence (AI)-based approaches for lung cancer assessment should be able to make a holistic analysis, capturing information from pathological processes involved in cancer development. The powerful and interpretable AI models allow us to identify novel biomarkers of cancer development, contributing to new insights about the pathological processes, and making a more accurate diagnosis to help in the treatment plan selection.This work is financed by the ERDF–European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation–COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT–Fundação para a Ciência e a Tecnologia within project POCI-01-0145-FEDER-030263

Phylogenetic relationships of cone snails endemic to Cabo Verde based on mitochondrial genomes

Background: Due to their great species and ecological diversity as well as their capacity to produce hundreds of different toxins, cone snails are of interest to evolutionary biologists, pharmacologists and amateur naturalists alike. Taxonomic identification of cone snails still relies mostly on the shape, color, and banding patterns of the shell. However, these phenotypic traits are prone to homoplasy. Therefore, the consistent use of genetic data for species delimitation and phylogenetic inference in this apparently hyperdiverse group is largely wanting. Here, we reconstruct the phylogeny of the cones endemic to Cabo Verde archipelago, a well-known radiation of the group, using mitochondrial (mt) genomes. Results: The reconstructed phylogeny grouped the analyzed species into two main clades, one including Kalloconus from West Africa sister to Trovaoconus from Cabo Verde and the other with a paraphyletic Lautoconus due to the sister group relationship of Africonus from Cabo Verde and Lautoconus ventricosus from Mediterranean Sea and neighboring Atlantic Ocean to the exclusion of Lautoconus endemic to Senegal (plus Lautoconus guanche from Mauritania, Morocco, and Canary Islands). Within Trovaoconus, up to three main lineages could be distinguished. The clade of Africonus included four main lineages (named I to IV), each further subdivided into two monophyletic groups. The reconstructed phylogeny allowed inferring the evolution of the radula in the studied lineages as well as biogeographic patterns. The number of cone species endemic to Cabo Verde was revised under the light of sequence divergence data and the inferred phylogenetic relationships. Conclusions: The sequence divergence between continental members of the genus Kalloconus and island endemics ascribed to the genus Trovaoconus is low, prompting for synonymization of the latter. The genus Lautoconus is paraphyletic. Lautoconus ventricosus is the closest living sister group of genus Africonus. Diversification of Africonus was in allopatry due to the direct development nature of their larvae and mainly triggered by eustatic sea level changes during the Miocene-Pliocene. Our study confirms the diversity of cone endemic to Cabo Verde but significantly reduces the number of valid species. Applying a sequence divergence threshold, the number of valid species within the sampled Africonus is reduced to half.Spanish Ministry of Science and Innovation [CGL2013-45211-C2-2-P, CGL2016-75255-C2-1-P, BES-2011-051469, BES-2014-069575, Doctorado Nacional-567]info:eu-repo/semantics/publishedVersio

EGFR Assessment in Lung Cancer CT Images: Analysis of Local and Holistic Regions of Interest Using Deep Unsupervised Transfer Learning

Statistics have demonstrated that one of the main factors responsible for the high mortality rate related to lung cancer is the late diagnosis. Precision medicine practices have shown advances in the individualized treatment according to the genetic profile of each patient, providing better control on cancer response. Medical imaging offers valuable information with an extensive perspective of the cancer, opening opportunities to explore the imaging manifestations associated with the tumor genotype in a non-invasive way. This work aims to study the relevance of physiological features captured from Computed Tomography images, using three different 2D regions of interest to assess the Epidermal growth factor receptor (EGFR) mutation status: nodule, lung containing the main nodule, and both lungs. A Convolutional Autoencoder was developed for the reconstruction of the input image. Thereafter, the encoder block was used as a feature extractor, stacking a classifier on top to assess the EGFR mutation status. Results showed that extending the analysis beyond the local nodule allowed the capture of more relevant information, suggesting the presence of useful biomarkers using the lung with nodule region of interest, which allowed to obtain the best prediction ability. This comparative study represents an innovative approach for gene mutations status assessment, contributing to the discussion on the extent of pathological phenomena associated with cancer development, and its contribution to more accurate Artificial Intelligence-based solutions, and constituting, to the best of our knowledge, the first deep learning approach that explores a comprehensive analysis for the EGFR mutation status classification.The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health for the free publicly available LIDC-IDRI Database used in this work. They also acknowledge The Cancer Imaging Archive (TCIA) for the open-access NSCLC-Radiogenomics dataset publicly available. This work was supported in part by the European Regional Development Fund (ERDF) through the Operational Program for Competitiveness and Internationalization—COMPETE 2020 Program, and in part by the National Funds through the Portuguese Funding Agency, Fundação para a Ciência e a Tecnologia (FCT), under Project POCI-01-0145-FEDER-030263