Brain MRI in the Decision for Liver Transplantation in Pediatric Neurological Wilson's Disease

Background Neurological Wilson's disease (WD) presentation in the pediatric population is rare, and liver transplantation (LT) in these patients remains controversial. The aim of the present study was to assess the role of brain magnetic resonance imaging (MRI) in predicting reversion of brain lesions and neurological outcomes in pediatric WD patients after LT. Methods Patients with confirmed WD (Leipzig score ≥4), disease onset in pediatric age (<18 years), neurological involvement, and submitted to LT were selected. Clinical records and pre- and post-LT brain MRI were evaluated. Results Six patients met the pre-defined inclusion criteria, one of whom died shortly after LT and was excluded. The indication for LT was end-stage liver disease in two patients and neurological worsening despite optimized treatment in three patients. After LT, the neurological picture progressively improved in all patients. Pre-LT brain MRI showed T1-weighted hyperintensities in four patients, which quickly resolved afterward. T2-weighted hyperintensities were observed in four patients before LT, completely resolving in one patient, stabilizing in two, and improving in one after LT. A direct correlation could not be found between clinical and neuroradiological improvement. Progressive clinical improvement was observed even in patients with irreversible brain MRI changes. Conversely, some patients with normal MRI had only slight neurological improvement. Conclusions The pattern of T2-weighted hyperintensities after LT was unpredictable and did not correlate with neurological outcomes, suggesting that these changes may not entail irreversible clinical damage. Therefore, brain MRI does not seem to have prognostic value for assessing clinical response to LT

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

Spinor-Vector Duality in Heterotic String Orbifolds

The three generation heterotic-string models in the free fermionic formulation are among the most realistic string vacua constructed to date, which motivated their detailed investigation. The classification of free fermion heterotic string vacua has revealed a duality under the exchange of spinor and vector representations of the SO(10) GUT symmetry over the space of models. We demonstrate the existence of the spinor-vector duality using orbifold techniques, and elaborate on the relation of these vacua to free fermionic models.Comment: 20 pages. v2 minor corrections. Version to appear on JHEP. v3 misprints correcte

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

Cloning approach and functional analysis of anti-intimin single-chain variable fragment (scFv)

<p>Abstract</p> <p>Background</p> <p>Intimin is an important virulence factor involved in the pathogenesis of enteropathogenic <it>Escherichia coli </it>(EPEC) and enterohemorrhagic <it>Escherichia coli </it>(EHEC). Both pathogens are still important causes of diarrhea in children and adults in many developing and industrialized countries. Considering the fact that antibodies are important tools in the detection of various pathogens, an anti-intimin IgG2b monoclonal antibody was previously raised in immunized mice with the conserved sequence of the intimin molecule (int_388-667). In immunoblotting assays, this monoclonal antibody showed excellent specificity. Despite good performance, the monoclonal antibody failed to detect some EPEC and EHEC isolates harboring variant amino acids within the 338-667 regions of intimin molecules. Consequently, motivated by its use for diagnosis purposes, in this study we aimed to the cloning and expression of the single-chain variable fragment from this monoclonal antibody (scFv).</p> <p>Findings</p> <p>Anti-intimin hybridoma mRNA was extracted and reversely transcripted to cDNA, and the light and heavy chains of the variable fragment of the antibody were amplified using commercial primers. The amplified chains were cloned into <it>pGEM-T Easy </it>vector. Specific primers were designed and used in an amplification and chain linkage strategy, obtaining the scFv, which in turn was cloned into pAE vector. <it>E. coli </it>BL21(DE3)pLys strain was transformed with pAE scFv-intimin plasmid and subjected to induction of protein expression. Anti-intimin scFv, expressed as inclusion bodies (insoluble fraction), was denatured, purified and submitted to refolding. The protein yield was 1 mg protein per 100 mL of bacterial culture. To test the functionality of the scFv, ELISA and immunofluorescence assays were performed, showing that 275 ng of scFv reacted with 2 mg of purified intimin, resulting in an absorbance of 0.75 at 492 nm. The immunofluorescence assay showed a strong reactivity with EPEC E2348/69.</p> <p>Conclusion</p> <p>This study demonstrated that the recombinant anti-intimin antibody obtained is able to recognize the conserved region of intimin (Int_388-667) in purified form and the EPEC isolate.</p

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

Inhibition of Macrophage Migration Inhibitory Factor Activity Attenuates Haemorrhagic Shock-Induced Multiple Organ Dysfunction in Rats

OBJECTIVE: The aim of this study was to investigate (a) macrophage migration inhibitory factor (MIF) levels in polytrauma patients and rats after haemorrhagic shock (HS), (b) the potential of the MIF inhibitor ISO-1 to reduce multiple organ dysfunction syndrome (MODS) in acute (short-term and long-term follow-up) HS rat models and (c) whether treatment with ISO-1 attenuates NF-κB and NLRP3 activation in HS. BACKGROUND: The MODS caused by an excessive systemic inflammatory response following trauma is associated with a high morbidity and mortality. MIF is a pleiotropic cytokine which can modulate the inflammatory response, however, its role in trauma is unknown. METHODS: The MIF levels in plasma of polytrauma patients and serum of rats with HS were measured by ELISA. Acute HS rat models were performed to determine the influence of ISO-1 on MODS. The activation of NF-κB and NLRP3 pathways were analysed by western blot in the kidney and liver. RESULTS: We demonstrated that (a) MIF levels are increased in polytrauma patients on arrival to the emergency room and in rats after HS, (b) HS caused organ injury and/or dysfunction and hypotension (post-resuscitation) in rats, while (c) treatment of HS-rats with ISO-1 attenuated the organ injury and dysfunction in acute HS models and (d) reduced the activation of NF-κB and NLRP3 pathways in the kidney and liver. CONCLUSION: Our results point to a role of MIF in the pathophysiology of trauma-induced organ injury and dysfunction and indicate that MIF inhibitors may be used as a potential therapeutic approach for MODS after trauma and/or haemorrhage

High-Yield Hydrogen Production from Starch and Water by a Synthetic Enzymatic Pathway

BACKGROUND: The future hydrogen economy offers a compelling energy vision, but there are four main obstacles: hydrogen production, storage, and distribution, as well as fuel cells. Hydrogen production from inexpensive abundant renewable biomass can produce cheaper hydrogen, decrease reliance on fossil fuels, and achieve zero net greenhouse gas emissions, but current chemical and biological means suffer from low hydrogen yields and/or severe reaction conditions. METHODOLOGY/PRINCIPAL FINDINGS: Here we demonstrate a synthetic enzymatic pathway consisting of 13 enzymes for producing hydrogen from starch and water. The stoichiometric reaction is C(6)H(10)O(5) (l)+7 H(2)O (l)→12 H(2) (g)+6 CO(2) (g). The overall process is spontaneous and unidirectional because of a negative Gibbs free energy and separation of the gaseous products with the aqueous reactants. CONCLUSIONS: Enzymatic hydrogen production from starch and water mediated by 13 enzymes occurred at 30°C as expected, and the hydrogen yields were much higher than the theoretical limit (4 H(2)/glucose) of anaerobic fermentations. SIGNIFICANCE: The unique features, such as mild reaction conditions (30°C and atmospheric pressure), high hydrogen yields, likely low production costs ($∼2/kg H(2)), and a high energy-density carrier starch (14.8 H(2)-based mass%), provide great potential for mobile applications. With technology improvements and integration with fuel cells, this technology also solves the challenges associated with hydrogen storage, distribution, and infrastructure in the hydrogen economy

Non-supersymmetric heterotic model building

We investigate orbifold and smooth Calabi-Yau compactifications of the non-supersymmetric heterotic SO(16)xSO(16) string. We focus on such Calabi-Yau backgrounds in order to recycle commonly employed techniques, like index theorems and cohomology theory, to determine both the fermionic and bosonic 4D spectra. We argue that the N=0 theory never leads to tachyons on smooth Calabi-Yaus in the large volume approximation. As twisted tachyons may arise on certain singular orbifolds, we conjecture that such tachyonic states are lifted in the full blow-up. We perform model searches on selected orbifold geometries. In particular, we construct an explicit example of a Standard Model-like theory with three generations and a single Higgs field.Comment: 1+30 pages latex, 11 tables; v2: references and minor revisions added, matches version published in JHE