Characterization and inhibitory activity of chitosan on hyphae growth and morphology of Botrytis cinerea plant pathogen

Summary. Low and high molecular weight chitosan were tested in different concentrations and growth times with the aim to evaluate the inhibitory activity against Botrytis cinerea, a very important plant pathogen. Tested chitosans were characterized by vibratory spectroscopy and elementary analyzes to determine the deacetylation degree. In addiction molar mass was estimated by viscosity measuring. Scanning electron microscopy was utilized for antimicrobial activity observation. Results showed that both chitosans markedly inhibited fungal growth, which was effected by incubation time and chitosan concentration. Scanning electron microscopy observations revealed that chitosan induced changes in surface morphology. The present study show that chitosan is capable of inhibit the growth and cause serious damage to the cell structure of the B. cinerea, as well as have the ability to form an impervious layer around the cell. Therefore, chitosan could be considered as a potential alternative for synthetic fungicides. Industrial relevance. Ultrastructural analysis showed that chitosan is capable of causing serious damage to the cell structure of the B. cinerea, as well as have the ability to form an impervious layer around the cell. Chitosan could inhibit the growth of B. cinerea in vitro and consequently may be considered as a potential alternative in replacement of synthetic fungicides.info:eu-repo/semantics/publishedVersio

Influence Of Surface Treatments On Enamel Susceptibility To Staining By Cigarette Smoke.

The purpose of this study was to evaluate the influence of remineralizing agents, including artificial saliva, neutral fluoride, and casein phosphopeptide-amorphous calcium phosphate (CPP-ACP), on the susceptibility of bleached enamel to staining by cigarette smoke. Fifty bovine enamel blocks were randomly divided into five groups (n = 10): G1- bleaching; G2- bleaching and immersion in artificial saliva; G3- bleaching and application of CPP-ACP; G4- bleaching and application of neutral fluoride; and G5- untreated (Control). Teeth were bleached with 35% hydrogen peroxide and treated with the appropriate remineralizing agent. After treatment, all groups were exposed to cigarette smoke. Enamel color measurements were performed at three different times: before treatment (T1), after treatment (bleaching and remineralizing agent) (T2), and after staining (T3), by using the CIE Lab method with a spectrophotometer. The data coordinate L* was evaluated by analysis of repeated-measures PROC MIXED and Tukey-Kramer's test, and the ΔE values were submitted to one-way ANOVA and Tukey's test (α = 0.05). The G1 group did not show any statistically significant difference for L* values between times T1 and T2. The G4 and G5 groups showed lower L* values at T3 compared to T2. No significant differences between the groups were observed for ΔE (after treatment and staining). However, G4 showed a clinically apparent color change. Treatment of bleached enamel with neutral fluoride can contribute to the increased staining of enamel due to cigarette smoke. Key words:Spectrophotometer, remineralizing agents, bleaching.5e163-

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

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

Novel Machado-Joseph disease-modifying genes and pathways identified by whole-exome sequencing

Machado-Joseph disease (MJD/SCA3) is a neurodegenerative polyglutamine disorder exhibiting a wide spectrum of phenotypes. The abnormal size of the (CAG)n at ATXN3 explains ~55% of the age at onset variance, suggesting the involvement of other factors, namely genetic modifiers, whose identification remains limited. Our aim was to find novel genetic modifiers, analyse their epistatic effects and identify disease-modifying pathways contributing to MJD variable expressivity. We performed whole-exome sequencing in a discovery sample of four age at onset-concordant and four discordant first-degree relative pairs of Azorean patients, to identify candidate variants which genotypes differed for each discordant pair but were shared in each concordant pair. Variants identified by this approach were then tested in an independent multi-origin cohort of 282 MJD patients. Whole-exome sequencing identified 233 candidate variants, from which 82 variants in 53 genes were prioritized for downstream analysis. Eighteen disease-modifying pathways were identified; two of the most enriched pathways were relevant for the nervous system, namely the neuregulin signaling and the agrin interactions at neuromuscular junction. Variants at PARD3, NFKB1, CHD5, ACTG1, CFAP57, DLGAP2, ITGB1, DIDO1 and CERS4 modulate age at onset in MJD, with those identified in CFAP57, ACTG1 and DIDO1 showing consistent effects across cohorts of different geographical origins. Network analyses of the nine novel MJD modifiers highlighted several important molecular interactions, including genes/proteins previously related with MJD pathogenesis, namely between ACTG1/APOE and VCP/ITGB1. We describe novel pathways, modifiers, and their interaction partners, providing a broad molecular portrait of age at onset modulation to be further exploited as new disease-modifying targets for MJD and related diseases

Stroke Correlates in Chagasic and Non-Chagasic Cardiomyopathies

BACKGROUND: Aging and migration have brought changes to the epidemiology and stroke has been shown to be independently associated with Chagas disease. We studied stroke correlates in cardiomyopathy patients with focus on the chagasic etiology. METHODOLOGY/PRINCIPAL FINDINGS: We performed a cross-sectional review of medical records of 790 patients with a cardiomyopathy. Patients with chagasic (329) and non-chagasic (461) cardiomyopathies were compared. There were 108 stroke cases, significantly more frequent in the Chagas group (17.3% versus 11.1%; p<0.01). Chagasic etiology (odds ratio [OR], 1.79), pacemaker (OR, 2.49), atrial fibrillation (OR, 3.03) and coronary artery disease (OR, 1.92) were stroke predictors in a multivariable analysis of the entire cohort. In a second step, the population was split into those with or without a Chagas-related cardiomyopathy. Univariable post-stratification stroke predictors in the Chagas cohort were pacemaker (OR, 2.73), and coronary artery disease (CAD) (OR, 2.58); while atrial fibrillation (OR, 2.98), age over 55 (OR, 2.92), hypertension (OR, 2.62) and coronary artery disease (OR, 1.94) did so in the non-Chagas cohort. Chagasic stroke patients presented a very high frequency of individuals without any vascular risk factors (40.4%; OR, 4.8). In a post-stratification logistic regression model, stroke remained associated with pacemaker (OR, 2.72) and coronary artery disease (OR, 2.60) in 322 chagasic patients, and with age over 55 (OR, 2.38), atrial fibrillation (OR 3.25) and hypertension (OR 2.12; p = 0.052) in 444 non-chagasic patients. CONCLUSIONS/SIGNIFICANCE: Chagas cardiomyopathy presented both a higher frequency of stroke and an independent association with it. There was a high frequency of strokes without any vascular risk factors in the Chagas as opposed to the non-Chagas cohort. Pacemaker rhythm and CAD were independently associated with stroke in the Chagas group while age over 55 years, hypertension and atrial fibrillation did so in the non-Chagas cardiomyopathies

Mass spectrometry and multivariate analysis to classify cervical intraepithelial neoplasia from blood plasma: an untargeted lipidomic study

Cervical cancer is still an important issue of public health since it is the fourth most frequent type of cancer in women worldwide. Much effort has been dedicated to combating this cancer, in particular by the early detection of cervical pre-cancerous lesions. For this purpose, this paper reports the use of mass spectrometry coupled with multivariate analysis as an untargeted lipidomic approach to classifying 76 blood plasma samples into negative for intraepithelial lesion or malignancy (NILM, n = 42) and squamous intraepithelial lesion (SIL, n = 34). The crude lipid extract was directly analyzed with mass spectrometry for untargeted lipidomics, followed by multivariate analysis based on the principal component analysis (PCA) and genetic algorithm (GA) with support vector machines (SVM), linear (LDA) and quadratic (QDA) discriminant analysis. PCA-SVM models outperformed LDA and QDA results, achieving sensitivity and specificity values of 80.0% and 83.3%, respectively. Five types of lipids contributing to the distinction between NILM and SIL classes were identified, including prostaglandins, phospholipids, and sphingolipids for the former condition and Tetranor-PGFM and hydroperoxide lipid for the latter. These findings highlight the potentiality of using mass spectrometry associated with chemometrics to discriminate between healthy women and those suffering from cervical pre-cancerous lesions