Application of deep learning techniques for biomedical data analysis

Deep learning and machine learning methods have been used for addressing the problems in the biomedical applications, such as diabetic retinopathy assessment and Parkinson's disease diagnosis. The severity of diabetic retinopathy is estimated by the expert's examination of fundus images based on the amount and location of three diabetic retinopathy signs (i.e., exudates, hemorrhages, and microaneurysms). An automatic and accurate system for detection of these signs can significantly help clinicians to make the best possible prognosis can result in reducing the risk of vision loss. For Parkinson's disease diagnosis, analysis of a speech voice is considered as the earliest symptom with the advantage of being non-intrusive and suitable for online applications. While some reported outcomes of the developed techniques have shown the good results and ongoing progress for these two applications, designing new algorithms is a thriving research field to overcome the poor sensitivity and specificity of the outcomes as well as the limitations such as dataset size and heuristic selection of the network parameters. This thesis has comprehensively studied and developed various deep learning frameworks for detection of diabetic retinopathy signs and diagnosis of Parkinson's disease. To improve the performance of the current systems, this work has had an investigation on different techniques: (i) color space investigation, (ii) examination of various deep learning methods, (iii) development of suitable pre/post-processing algorithms and (iv) appropriate selection of deep learning architectures and parameters. For diabetic retinopathy assessment, this thesis has proposed the new color space as the input for the deep learning models that obtained better replicability compared with the conventional color spaces. This has also shown the pre-trained model can extract more relevant features compared to the models which were trained from scratch. This has also presented a deep learning framework combined with the suitable pre and post-processing algorithms that increased the performance of the system. By investigation different architectures and parameters, the suitable deep learning model has been presented to distinguish between Parkinson's disease and healthy speech signal

Evaluation of SD-208, a TGF-β-RI kinase inhibitor, as an anticancer agent in retinoblastoma

Retinoblastoma is the most common intraocular tumor in children resulting from genetic alterations and transformation of mature retinal cells. The objective of this study was to investigate the effects of SD-208, TGF-β-RI kinase inhibitor, on the expression of some miRNAs including a miR-17/92 cluster in retinoblastoma cells. Prior to initiate this work, the cell proliferation was studied by Methyl Thiazolyl Tetrazolium (MTT) and bromo-2�-deoxyuridine (BrdU) assays. Then, the expression patterns of four miRNAs (18a, 20a, 22, and 34a) were investigated in the treated SD-208 (0.0, 1, 2 and 3 μM) and untreated Y-79 cells. A remarkable inhibition of the cell proliferation was found in Y-79 cells treated with SD-208 versus untreated cells. Also, the expression changes were observed in miRNAs 18a, 20a, 22 and 34a in response to SD-208 treatment (P<0.05). The findings of the present study suggest that the anti-cancer effect of SD-208 may be exerted due to the regulation of specific miRNAs, at least in this particular retinoblastoma cell line. To the best of the researchers� knowledge, this is the first report demonstrating that the SD-208 could alter the expression of tumor suppressive miRNAs as well as oncomiRs in vitro. In conclusion, the present data suggest that SD-208 could be an alternative agent in retinoblastoma treatment. © 2016 Tehran University of Medical Sciences. All rights reserved

Title Page: Going beyond common drug metabolizing enzymes: Case studies of biotransformation involving aldehyde oxidase, gamma-glutamyl transpeptidase, cathepsin B, flavin-containing monooxygenase, and ADP-ribosyltransferase Running Title Page: Non-P450 d

adenine dinucleotide and its reduced form, NADP+/H, nicotinamide adenine dinucleotide phosphate and its reduced form, NMN, nicotinamide mononucleotide; PAB, para-aminobenzyl; Val-Cit, valine-citrulline. DMD #70169 3 Abstract The significant roles that cytochrome P450 (P450) and UDP-glucuronosyl transferase (UGT) enzymes play in drug discovery cannot be ignored, and these enzyme systems are commonly examined during drug optimization using liver microsomes or hepatocytes. At the same time, other drug metabolizing enzymes have a role in the metabolism of drugs and can lead to challenges in drug optimization that could be mitigated if the contributions of these enzymes were better understood. This paper presents examples (mostly from Genentech) of five different non-P450 and non-UGT enzymes that contribute to the metabolic clearance or bioactivation of drugs and drug candidates. Aldehyde oxidase mediates a unique amide hydrolysis of GDC-0834, leading to high clearance of the drug. Likewise, the rodent-specific ribose conjugation by ADP-ribosyltransferase leads to high clearance of an interleukin-2-inducible T-cell kinase inhibitor. Metabolic reactions by flavin-containing monooxygenases (FMO) are easily mistaken for P450-mediated metabolism such as oxidative defluorination of 4-fluoro-N-methylaniline by FMO. Gamma-glutamyl transpeptidase is involved in the initial hydrolysis of glutathione metabolites, leading to formation of proximate toxins and nephrotoxicity, as is observed with cisplatin in the clinic, or renal toxicity, as is observed with efavirenz in rodents. Finally, cathepsin B is a lysosomal enzyme that is highly expressed in human tumors and has been targeted to release potent cytotoxins, as in the case of brentuximab vedotin. These examples of non-P450-and non-UGT-mediated metabolism show that a more complete understanding of drug metabolizing enzymes allows for better insight into the fate of drugs and improved design strategies of molecules in drug discovery. DMD #70169

A Layer-Wise Information Reinforcement Approach to Improve Learning in Deep Belief Networks

With the advent of deep learning, the number of works proposing new methods or improving existent ones has grown exponentially in the last years. In this scenario, "very deep" models were emerging, once they were expected to extract more intrinsic and abstract features while supporting a better performance. However, such models suffer from the gradient vanishing problem, i.e., backpropagation values become too close to zero in their shallower layers, ultimately causing learning to stagnate. Such an issue was overcome in the context of convolution neural networks by creating "shortcut connections" between layers, in a so-called deep residual learning framework. Nonetheless, a very popular deep learning technique called Deep Belief Network still suffers from gradient vanishing when dealing with discriminative tasks. Therefore, this paper proposes the Residual Deep Belief Network, which considers the information reinforcement layer-by-layer to improve the feature extraction and knowledge retaining, that support better discriminative performance. Experiments conducted over three public datasets demonstrate its robustness concerning the task of binary image classification

Intestinal Parasites Classification Using Deep Belief Networks

Currently, approximately $4$ billion people are infected by intestinal parasites worldwide. Diseases caused by such infections constitute a public health problem in most tropical countries, leading to physical and mental disorders, and even death to children and immunodeficient individuals. Although subjected to high error rates, human visual inspection is still in charge of the vast majority of clinical diagnoses. In the past years, some works addressed intelligent computer-aided intestinal parasites classification, but they usually suffer from misclassification due to similarities between parasites and fecal impurities. In this paper, we introduce Deep Belief Networks to the context of automatic intestinal parasites classification. Experiments conducted over three datasets composed of eggs, larvae, and protozoa provided promising results, even considering unbalanced classes and also fecal impurities

Assessing the impact of Bacillus strains mixture probiotic on water quality, growth performance, blood profile and intestinal morphology of Nile tilapia, Oreochromis niloticus

The aim of this study was to assess the impact of a commercial probiotic, Sanolife PRO‐F, on water quality, growth performance, blood profiles and intestinal morphometry of monosex Nile tilapia. A field trial was conducted for 10 weeks in which tilapia fingerlings (20 ± 1.26 g) were randomly distributed into three replicate ponds which were subdivided into three treatment groups, receiving Sanolife PRO‐F at 0 (B0), 0.1 (B1) and 0.2 (B2) g/kg diet, respectively. The results showed a significant improvement in growth performance, feed conversion ratio and blood profiles in tilapia fed on treated diets. The whole intestinal lengths, anterior and terminal intestinal villi heights and anterior goblet cells count were greater in tilapia fed on treated diets. There were no noticeable differences in growth and intestinal morphology between tilapia fed on B1 and B2 diets. The ammonia concentration in water was lower with B1 diet while electric conductivity, salinity and total dissolved solids were higher with the B2 diet. The pH level of pond water was enhanced by both diets, B1 and B2. In conclusion, application of Sanolife PRO‐F at 0.1–0.2 g/kg diet might have beneficial effects on growth, immunity, stress responses and gut health and function as well as the water quality of farmed Nile tilapia

A forward-backward fragment assembling algorithm for the identification of genomic amplification and deletion breakpoints using high-density single nucleotide polymorphism (SNP) array

<p>Abstract</p> <p>Background</p> <p>DNA copy number aberration (CNA) is one of the key characteristics of cancer cells. Recent studies demonstrated the feasibility of utilizing high density single nucleotide polymorphism (SNP) genotyping arrays to detect CNA. Compared with the two-color array-based comparative genomic hybridization (array-CGH), the SNP arrays offer much higher probe density and lower signal-to-noise ratio at the single SNP level. To accurately identify small segments of CNA from SNP array data, segmentation methods that are sensitive to CNA while resistant to noise are required.</p> <p>Results</p> <p>We have developed a highly sensitive algorithm for the edge detection of copy number data which is especially suitable for the SNP array-based copy number data. The method consists of an over-sensitive edge-detection step and a test-based forward-backward edge selection step.</p> <p>Conclusion</p> <p>Using simulations constructed from real experimental data, the method shows high sensitivity and specificity in detecting small copy number changes in focused regions. The method is implemented in an R package FASeg, which includes data processing and visualization utilities, as well as libraries for processing Affymetrix SNP array data.</p

Consequences of chronic diseases and other limitations associated with old age - A scoping review

Funding Information: This work supported in part by the LTC INTER COST, Evaluation of the Potential for Reducing Health and Social Expenses for Elderly People Using the Smart Environment, through the Ministry of Education, Youth and Sports, Czech Republic, under Project LTC18035; and in part by the project of Excellence, University of Hradec Kralove, FIM, Czech Republic (ID: 2205–2019). First author – Petra Maresova is principle investigator of LTC18035 INTER COST project, from which Petra Maresova, Ondrej Krejcar and Kamil Kuca are funded for all expenses including personal costs. Ehsan Javanmardi is funded from project of Excellence ID: 2205–2019 for personal costs. Sabina Barakovic, Jasmina Barakovic Husic and Signe Tomsone are members of COST ACTION 16226 of which also Petra Maresova and Ondrej Krejcar are paticipants, while this article also ACKnowledge this project CA16226. Funding Information: The authors would like to hereby acknowledge COST Action CA16226 for their networking support. The Indoor Living Space Improvement: Smart Habitat for the Elderly played a role of networking platform for knowledge sharing and interchanging ideas for joint research and publication, what was the base for creating this study. Based on CA16226 project LTC18035 INTER COST was proposed for national funding support of COST ACTION Framework. COST is a funding agency that helps innovation and research networks. Our Action was instrumental in connecting research programmes throughout the EU region. Their contribution has made it possible for scientists to connect with each other and share their ideas and findings. This allows for more research and better innovation. More information can be found at www.cost.eu. The authors would also like to acknowledge the Excellence 2019 internal research project, Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic. Publisher Copyright: © 2019 The Author(s).Background: The phenomenon of the increasing number of ageing people in the world is arguably the most significant economic, health and social challenge that we face today. Additionally, one of the major epidemiologic trends of current times is the increase in chronic and degenerative diseases. This paper tries to deliver a more up to date overview of chronic diseases and other limitations associated with old age and provide a more detailed outlook on the research that has gone into this field. Methods: First, challenges for seniors, including chronic diseases and other limitations associated with old age, are specified. Second, a review of seniors' needs and concerns is performed. Finally, solutions that can improve seniors' quality of life are discussed. Publications obtained from the following databases are used in this scoping review: Web of Science, PubMed, and Science Direct. Four independent reviewers screened the identified records and selected relevant publications published from 2010 to 2017. A total of 1916 publications were selected. In all, 52 papers were selected based on abstract content. For further processing, 21 full papers were screened." Results: The results indicate disabilities as a major problem associated with seniors' activities of daily living dependence. We founded seven categories of different conditions - psychological problems, difficulties in mobility, poor cognitive function, falls and incidents, wounds and injuries, undernutrition, and communication problems. In order to minimize ageing consequences, some areas require more attention, such as education and training; technological tools; government support and welfare systems; early diagnosis of undernutrition, cognitive impairment, and other diseases; communication solutions; mobility solutions; and social contributions. Conclusions: This scoping review supports the view on chronic diseases in old age as a complex issue. To prevent the consequences of chronic diseases and other limitations associated with old age related problems demands multicomponent interventions. Early recognition of problems leading to disability and activities of daily living (ADL) dependence should be one of essential components of such interventions.publishersversionPeer reviewe

Arsenic-related DNA copy-number alterations in lung squamous cell carcinomas

BACKGROUND: Lung squamous cell carcinomas (SqCCs) occur at higher rates following arsenic exposure. Somatic DNA copy-number alterations (CNAs) are understood to be critical drivers in several tumour types. We have assembled a rare panel of lung tumours from a population with chronic arsenic exposure, including SqCC tumours from patients with no smoking history. METHODS: Fifty-two lung SqCCs were analysed by whole-genome tiling-set array comparative genomic hybridisation. Twenty-two were derived from arsenic-exposed patients from Northern Chile (10 never smokers and 12 smokers). Thirty additional cases were obtained for comparison from North American smokers without arsenic exposure. Twenty-two blood samples from healthy individuals from Northern Chile were examined to identify germline DNA copy-number variations (CNVs) that could be excluded from analysis. RESULTS: We identified multiple CNAs associated with arsenic exposure. These alterations were not attributable to either smoking status or CNVs. DNA losses at chromosomes 1q21.1, 7p22.3, 9q12, and 19q13.31 represented the most recurrent events. An arsenic-associated gain at 19q13.33 contains genes previously identified as oncogene candidates. CONCLUSIONS: Our results provide a comprehensive approach to molecular characteristics of the arsenic-exposed lung cancer genome and the non-smoking lung SqCC genome. The distinct and recurrent arsenic-related alterations suggest that this group of tumours may be considered as a separate disease subclass