Error perception classification in Brain-Computer Interfaces using CNN.

Capturing the error perception of a human interacting with a Brain-Computer Interface (BCI) is a key piece in improving the accuracy of these systems and making the interaction more seamless. Convolutional Neural Networks (CNN) have recently been applied for this task rendering the model free of feature-selection. We propose a new model with shorter temporal input trying to approximate its usability to that of a real-time BCI application. We evaluate and compare our model with some other recent CNN models using the Monitoring Error-Related Potential dataset, obtaining an accuracy of 80% with a sensitivity and specificity of 76% and 85%, respectively. These results outperform previous models. All models are made available online for reproduction and peer review.

Topological isoconductance signatures in Majorana nanowires.

We consider transport properties of a hybrid device composed by a quantum dot placed between normal and superconducting reservoirs, and coupled to a Majorana nanowire: a topological superconducting segment hosting Majorana bound states (MBSs) at the opposite ends. It is demonstrated that if highly nonlocal and nonoverlapping MBSs are formed in the system, the zero-bias Andreev conductance through the dot exhibits characteristic isoconductance profiles with the shape depending on the spin asymmetry of the coupling between the dot and the topological superconductor. Otherwise, for overlapping MBSs with less degree of nonlocality, the conductance is insensitive to the spin polarization and the isoconductance signatures disappear. This allows to propose an alternative experimental protocol for probing the nonlocality of the MBSs in Majorana nanowires.

A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence.

COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health Organization (WHO), people who are at least 60 years old or have comorbidities that have primarily been targeted are at the highest risk from SARS-CoV-2. Medical imaging provides a non-invasive, touch-free, and relatively safer alternative tool for diagnosis during the current ongoing pandemic. Artificial intelligence (AI) scientists are developing several intelligent computer-aided diagnosis (CAD) tools in multiple imaging modalities, i.e., lung computed tomography (CT), chest X-rays, and lung ultrasounds. These AI tools assist the pulmonary and critical care clinicians through (a) faster detection of the presence of a virus, (b) classifying pneumonia types, and (c) measuring the severity of viral damage in COVID-19-infected patients. Thus, it is of the utmost importance to fully understand the requirements of for a fast and successful, and timely lung scans analysis. This narrative review first presents the pathological layout of the lungs in the COVID-19 scenario, followed by understanding and then explains the comorbid statistical distributions in the ARDS framework. The novelty of this review is the approach to classifying the AI models as per the by school of thought (SoTs), exhibiting based on segregation of techniques and their characteristics. The study also discusses the identification of AI models and its extension from non-ARDS lungs (pre-COVID-19) to ARDS lungs (post-COVID-19). Furthermore, it also presents AI workflow considerations of for medical imaging modalities in the COVID-19 framework. Finally, clinical AI design considerations will be discussed. We conclude that the design of the current existing AI models can be improved by considering comorbidity as an independent factor. Furthermore, ARDS post-processing clinical systems must involve include (i) the clinical validation and verification of AI-models, (ii) reliability and stability criteria, and (iii) easily adaptable, and (iv) generalization assessments of AI systems for their use in pulmonary, critical care, and radiological settings.

Treatment of early-stage mycosis fungoides: results from the PROspective Cutaneous Lymphoma International Prognostic Index (PROCLIPI) study.

BACKGROUND: The PROspective Cutaneous Lymphoma International Prognostic Index (PROCLIPI) study is a prospective analysis of an international database. Here we examine front-line treatments and quality of life (QoL) in patients with newly diagnosed mycosis fungoides (MF). OBJECTIVES: To identify (i) differences in first-line approaches according to tumour-nodes-metastasis-blood (TNMB) staging; (ii) parameters related to a first-line systemic approach and (iii) response rates and QoL measures. METHODS: In total, 395 newly diagnosed patients with early-stage MF (stage IA-IIA) were recruited from 41 centres in 17 countries between 1 January 2015 and 31 December 2018 following central clinicopathological review. RESULTS: The most common first-line therapy was skin-directed therapy (SDT) (322 cases, 81·5%), while a smaller percentage (44 cases, 11·1%) received systemic therapy. Expectant observation was used in 7·3%. In univariate analysis, the use of systemic therapy was significantly associated with higher clinical stage (IA, 6%; IB, 14%; IIA, 20%; IA-IB vs. IIA, P < 0·001), presence of plaques (T1a/T2a, 5%; T1b/T2b, 17%; P < 0·001), higher modified Severity Weighted Assessment Tool (> 10, 15%; ≤ 10, 7%; P = 0·01) and folliculotropic MF (FMF) (24% vs. 12%, P = 0·001). Multivariate analysis demonstrated significant associations with the presence of plaques (T1b/T2b vs. T1a/T2a, odds ratio 3·07) and FMF (odds ratio 2·83). The overall response rate (ORR) to first-line SDT was 73%, while the ORR to first-line systemic treatments was lower (57%) (P = 0·027). Health-related QoL improved significantly both in patients with responsive disease and in those with stable disease. CONCLUSIONS: Disease characteristics such as presence of plaques and FMF influence physician treatment choices, and SDT was superior to systemic therapy even in patients with such disease characteristics. Consequently, future treatment guidelines for early-stage MF need to address these issues.

Should we be imaging lymph nodes at initial diagnosis of early-stage mycosis fungoides? Results from the PROspective Cutaneous Lymphoma International Prognostic Index (PROCLIPI) international study.

BACKGROUND: Early-stage mycosis fungoides (MF) includes involvement of dermatopathic lymph nodes (LNs) or early lymphomatous LNs. There is a lack of unanimity among current guidelines regarding the indications for initial staging imaging in early-stage presentation of MF in the absence of enlarged palpable LNs. OBJECTIVES: To investigate how often imaging is performed in patients with early-stage presentation of MF, to assess the yield of LN imaging, and to determine what disease characteristics promoted imaging. METHODS: A review of clinicopathologically confirmed newly diagnosed patients with cutaneous patch/plaque (T1/T2) MF from PROspective Cutaneous Lymphoma International Prognostic Index (PROCLIPI) data. RESULTS: PROCLIPI enrolled 375 patients with stage T1/T2 MF: 304 with classical MF and 71 with folliculotropic MF. Imaging was performed in 169 patients (45%): 83 with computed tomography, 18 with positron emission tomography-computed tomography and 68 with ultrasound. Only nine of these (5%) had palpable enlarged (≥ 15 mm) LNs, with an over-representation of plaques, irrespectively of the 10% body surface area cutoff that distinguishes T1 from T2. Folliculotropic MF was not more frequently imaged than classical MF. Radiologically enlarged LNs (≥ 15 mm) were detected in 30 patients (18%); only seven had clinical lymphadenopathy. On multivariate analysis, plaque presentation was the sole parameter significantly associated with radiologically enlarged LNs. Imaging of only clinically enlarged LNs upstaged 4% of patients (seven of 169) to at least IIA, whereas nonselective imaging upstaged another 14% (24 of 169). LN biopsy, performed in eight of 30 patients, identified N3 (extensive lymphomatous involvement) in two and N1 (dermatopathic changes) in six. CONCLUSIONS: Physical examination was a poor determinant of LN enlargement or involvement. Presence of plaques was associated with a significant increase in identification of enlarged or involved LNs in patients with early-stage presentation of MF, which may be important when deciding who to image. Imaging increases the detection rate of stage IIA MF, and identifies rare cases of extensive lymphomatous nodes, upstaging them to advanced-stage IVA2.

Combining Deep Learning with Handcrafted Features for Cell Nuclei Segmentation.

Segmentation of cell nuclei in fluorescence microscopy images provides valuable information about the shape and size of the nuclei, its chromatin texture and DNA content. It has many applications such as cell tracking, counting and classification. In this work, we extended our recently proposed approach for nuclei segmentation based on deep learning, by adding to its input handcrafted features. Our handcrafted features introduce additional domain knowledge that nuclei are expected to have an approximately round shape. For round shapes the gradient vector of points at the border point to the center. To convey this information, we compute a map of gradient convergence to be used by the CNN as a new channel, in addition to the fluorescence microscopy image. We applied our method to a dataset of microscopy images of cells stained with DAPI. Our results show that with this approach we are able to decrease the number of missdetections and, therefore, increase the F1-Score when compared to our previously proposed approach. Moreover, the results show that faster convergence is obtained when handcrafted features are combined with deep learning.

Interphase Cell Cycle Staging using Deep Learning.

The progression of cells through the cell cycle is a tightly regulated process and is known to be key in maintaining normal tissue architecture and function. Disruption of these orchestrated phases will result in alterations that can lead to many diseases including cancer. Regrettably, reliable automatic tools to evaluate the cell cycle stage of individual cells are still lacking, in particular at interphase. Therefore, the development of new tools for a proper classification are urgently needed and will be of critical importance for cancer prognosis and predictive therapeutic purposes. Thus, in this work, we aimed to investigate three deep learning approaches for interphase cell cycle staging in microscopy images: 1) joint detection and cell cycle classification of nuclei patches; 2) detection of cell nuclei patches followed by classification of the cycle stage; 3) detection and segmentation of cell nuclei followed by classification of cell cycle staging. Our methods were applied to a dataset of microscopy images of nuclei stained with DAPI. The best results (0.908 F1-Score) were obtained with approach 3 in which the segmentation step allows for an intensity normalization that takes into account the intensities of all nuclei in a given image. These results show that for a correct cell cycle staging it is important to consider the relative intensities of the nuclei. Herein, we have developed a new deep learning method for interphase cell cycle staging at single cell level with potential implications in cancer prognosis and therapeutic strategies.

COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review.

Artificial intelligence (AI) has penetrated the field of medicine, particularly the field of radiology. Since its emergence, the highly virulent coronavirus disease 2019 (COVID-19) has infected over 10 million people, leading to over 500,000 deaths as of July 1st, 2020. Since the outbreak began, almost 28,000 articles about COVID-19 have been published (https://pubmed.ncbi.nlm.nih.gov); however, few have explored the role of imaging and artificial intelligence in COVID-19 patients-specifically, those with comorbidities. This paper begins by presenting the four pathways that can lead to heart and brain injuries following a COVID-19 infection. Our survey also offers insights into the role that imaging can play in the treatment of comorbid patients, based on probabilities derived from COVID-19 symptom statistics. Such symptoms include myocardial injury, hypoxia, plaque rupture, arrhythmias, venous thromboembolism, coronary thrombosis, encephalitis, ischemia, inflammation, and lung injury. At its core, this study considers the role of image-based AI, which can be used to characterize the tissues of a COVID-19 patient and classify the severity of their infection. Image-based AI is more important than ever as the pandemic surges and countries worldwide grapple with limited medical resources for detection and diagnosis.

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence.

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors.

Characteristics associated with significantly worse quality of life in mycosis fungoides/Sézary syndrome from the Prospective Cutaneous Lymphoma International Prognostic Index (PROCLIPI) study.

BACKGROUND: Mycosis fungoides (MF) and Sézary Syndrome (SS) are the most common cutaneous T-cell lymphomas. MF/SS is accompanied by considerable morbidity from pain, itching and disfigurement. AIM: To identify factors associated with poorer health-related quality of life (HRQoL) in patients newly diagnosed with MF/SS. METHODS: Patients enrolled into Prospective Cutaneous Lymphoma International Prognostic Index (PROCLIPI; an international observational study in MF/SS) had their HRQoL assessed using the Skindex-29 questionnaire. Skindex-29 scores were analysed in relation to patient- and disease-specific characteristics. RESULTS: The study population consisted of 237 patients [60·3% male; median age 60 years, (interquartile range 49-70)], of whom 179 had early MF and 58 had advanced MF/SS. In univariate analysis, HRQoL, as measured by Skindex-29, was worse in women, SS, late-stage MF, those with elevated lactate dehydrogenase, alopecia, high modified Severity Weighted Assessment Tool and confluent erythema. Linear regression models only identified female gender (ß = 8·61; P = 0·003) and alopecia (ß = 9·71, P = 0·02) as independent predictors of worse global HRQoL. Item-level analysis showed that the severe impairment in symptoms [odds ratio (OR) 2·14, 95% confidence interval (CI) 1·19-3·89] and emotions (OR 1·88, 95% CI 1·09-3·27) subscale scores seen in women was caused by more burning/stinging, pruritus, irritation and greater feelings of depression, shame, embarrassment and annoyance with their diagnosis of MF/SS. CONCLUSIONS: HRQoL is significantly more impaired in newly diagnosed women with MF/SS and in those with alopecia. As Skindex-29 does not include existential questions on cancer, which may cause additional worry and distress, a comprehensive validated cutaneous T-cell lymphoma-specific questionnaire is urgently needed to more accurately assess disease-specific HRQoL in these patients. What's already known about this topic? Cross-sectional studies of mixed populations of known and newly diagnosed patients with mycosis fungoides (MF)/Sézary syndrome (SS) have shown significant impairment in health-related quality of life (HRQoL). Previous studies on assessing gender-specific differences in HRQoL in MF/SS are conflicting. More advanced-stage disease and pruritus is associated with poorer HRQoL in patients with MF/SS. What does this study add? This is the first prospective study to investigate HRQoL in a homogenous group of newly diagnosed patients with MF/SS. In patients newly diagnosed with MF/SS, HRQoL is worse in women and in those with alopecia and confluent erythema. MF/SS diagnosis has a multidimensional impact on patient HRQoL, including a large burden of cutaneous symptoms, as well as a negative impact on emotional well-being.

Wilson's disease: A new perspective review on its genetics, diagnosis and treatment.

Wilson's disease (WD) is an autosomal recessive disorder which is caused by poor excretion of copper in mammalian cells. In this review, various issues such as effective characterization of ATP7B genes, scope of gene network topology in genetic analysis, pattern recognition using different computing approaches and fusion possibilities in imaging and genetic dataset are discussed vividly. We categorized this study into three major sections: (A) WD genetics, (B) diagnosis guidelines and (3) treatment possibilities. We addressed the scope of advanced mathematical modelling paradigms for understanding common genetic sequences and dominating WD imaging biomarkers. We have also discussed current state-of-the-art software models for genetic sequencing. Further, we hypothesized that involvement of machine and deep learning techniques in the context of WD genetics and image processing for precise classification of WD. These computing procedures signify changing roles of various data transformation techniques with respect to supervised and unsupervised learning models.

Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm.

Background and Objective Fatty Liver Disease (FLD) - a disease caused by deposition of fat in liver cells, is predecessor to terminal diseases such as liver cancer. The machine learning (ML) techniques applied for FLD detection and risk stratification using ultrasound (US) have limitations in computing tissue characterization features, thereby limiting the accuracy. Methods Under the class of Symtosis for FLD detection and risk stratification, this study presents a Deep Learning (DL)-based paradigm that computes nearly seven million weights per image when passed through a 22 layered neural network during the cross-validation (training and testing) paradigm. The DL architecture consists of cascaded layers of operations such as: convolution, pooling, rectified linear unit, dropout and a special block called inception model that provides speed and efficiency. All data analysis is performed in optimized tissue region, obtained by removing background information. We benchmark the DL system against the conventional ML protocols: support vector machine (SVM) and extreme learning machine (ELM). Results The liver US data consists of 63 patients (27 normal/36 abnormal). Using the K10 cross-validation protocol (90% training and 10% testing), the detection and risk stratification accuracies are: 82%, 92% and 100% for SVM, ELM and DL systems, respectively. The corresponding area under the curve is: 0.79, 0.92 and 1.0, respectively. We further validate our DL system using two class biometric facial data that yields an accuracy of 99%. Conclusion DL system shows a superior performance for liver detection and risk stratification compared to conventional machine learning systems: SVM and ELM.

Author Correction to: Extreme Learning Machine Framework for Risk Stratification of Fatty Liver Disease Using Ultrasound Tissue Characterization.

The original version of this article unfortunately contained a mistake. The family name of Rui Tato Marinho was incorrectly spelled as Marinhoe.

Global patterns of care in advanced stage mycosis fungoides/Sezary syndrome: a multicenter retrospective follow-up study from the Cutaneous Lymphoma International Consortium.

BACKGROUND: Advanced-stage mycosis fungoides (MF)/Sézary syndrome (SS) patients are weighted by an unfavorable prognosis and share an unmet clinical need of effective treatments. International guidelines are available detailing treatment options for the different stages but without recommending treatments in any particular order due to lack of comparative trials. The aims of this second CLIC study were to retrospectively analyze the pattern of care worldwide for advanced-stage MF/SS patients, the distribution of treatments according to geographical areas (USA versus non-USA), and whether the heterogeneity of approaches has potential impact on survival. PATIENTS AND METHODS: This study included 853 patients from 21 specialist centers (14 European, 4 USA, 1 each Australian, Brazilian, and Japanese). RESULTS: Heterogeneity of treatment approaches was found, with up to 24 different modalities or combinations used as first-line and 36% of patients receiving four or more treatments. Stage IIB disease was most frequently treated by total-skin-electron-beam radiotherapy, bexarotene and gemcitabine; erythrodermic and SS patients by extracorporeal photochemotherapy, and stage IVA2 by polychemotherapy. Significant differences were found between USA and non-USA centers, with bexarotene, photopheresis and histone deacetylase inhibitors most frequently prescribed for first-line treatment in USA while phototherapy, interferon, chlorambucil and gemcitabine in non-USA centers. These differences did not significantly impact on survival. However, when considering death and therapy change as competing risk events and the impact of first treatment line on both events, both monochemotherapy (SHR = 2.07) and polychemotherapy (SHR = 1.69) showed elevated relative risks. CONCLUSION: This large multicenter retrospective study shows that there exist a large treatment heterogeneity in advanced MF/SS and differences between USA and non-USA centers but these were not related to survival, while our data reveal that chemotherapy as first treatment is associated with a higher risk of death and/or change of therapy and thus other therapeutic options should be preferable as first treatment approach.

Enzymatic production of dietary nucleotides from low-soluble purine bases by an efficient, thermostable and alkali-tolerant biocatalyst.

Traditionally, enzymatic synthesis of nucleoside-5'-monophosphates (5'-NMPs) using low water-soluble purine bases has been described as less efficient due to their low solubility in aqueous media. The use of enzymes from extremophiles, such as thermophiles or alkaliphiles, offers the potential to increase solubilisation of these bases by employing high temperatures or alkaline pH. This study describes the cloning, expression and purification of hypoxanthine-guanine-xanthine phosphoribosyltransferase from Thermus thermophilus (TtHGXPRT). Biochemical characterization indicates TtHGXPRT as a homotetramer with excellent activity and stability across a broad range of temperatures (50-90°C) and ionic strengths (0-500mMNaCl), but it also reveals an unusually high activity and stability under alkaline conditions (pH range 8-11). In order to explore the potential of TtHGXPRT as an industrial biocatalyst, enzymatic production of several dietary 5'-NMPs, such as 5'-GMP and 5'-IMP, was carried out at high concentrations of guanine and hypoxanthine.

Extreme Learning Machine Framework for Risk Stratification of Fatty Liver Disease Using Ultrasound Tissue Characterization.

Fatty Liver Disease (FLD) is caused by the deposition of fat in liver cells and leads to deadly diseases such as liver cancer. Several FLD detection and characterization systems using machine learning (ML) based on Support Vector Machines (SVM) have been applied. These ML systems utilize large number of ultrasonic grayscale features, pooling strategy for selecting the best features and several combinations of training/testing. As result, they are computationally intensive, slow and do not guarantee high performance due to mismatch between grayscale features and classifier type. This study proposes a reliable and fast Extreme Learning Machine (ELM)-based tissue characterization system (a class of Symtosis) for risk stratification of ultrasound liver images. ELM is used to train single layer feed forward neural network (SLFFNN). The input-to-hidden layer weights are randomly generated reducing computational cost. The only weights to be trained are hidden-to-output layer which is done in a single pass (without any iteration) making ELM faster than conventional ML methods. Adapting four types of K-fold cross-validation (K = 2, 3, 5 and 10) protocols on three kinds of data sizes: S0-original, S4-four splits, S8-sixty four splits (a total of 12 cases) and 46 types of grayscale features, we stratify the FLD US images using ELM and benchmark against SVM. Using the US liver database of 63 patients (27 normal/36 abnormal), our results demonstrate superior performance of ELM compared to SVM, for all cross-validation protocols (K2, K3, K5 and K10) and all types of US data sets (S0, S4, and S8) in terms of sensitivity, specificity, accuracy and area under the curve (AUC). Using the K10 cross-validation protocol on S8 data set, ELM showed an accuracy of 96.75% compared to 89.01% for SVM, and correspondingly, the AUC: 0.97 and 0.91, respectively. Further experiments also showed the mean reliability of 99% for ELM classifier, along with the mean speed improvement of 40% using ELM against SVM. We validated the symtosis system using two class biometric facial public data demonstrating an accuracy of 100%.

2'-Deoxyribosyltransferase from Leishmania mexicana, an efficient biocatalyst for one-pot, one-step synthesis of nucleosides from poorly soluble purine bases.

Processes catalyzed by enzymes offer numerous advantages over chemical methods although in many occasions the stability of the biocatalysts becomes a serious concern. Traditionally, synthesis of nucleosides using poorly water-soluble purine bases, such as guanine, xanthine, or hypoxanthine, requires alkaline pH and/or high temperatures in order to solubilize the substrate. In this work, we demonstrate that the 2'-deoxyribosyltransferase from Leishmania mexicana (LmPDT) exhibits an unusually high activity and stability under alkaline conditions (pH 8-10) across a broad range of temperatures (30-70 °C) and ionic strengths (0-500 mM NaCl). Conversely, analysis of the crystal structure of LmPDT together with comparisons with hexameric, bacterial homologues revealed the importance of the relationships between the oligomeric state and the active site architecture within this family of enzymes. Moreover, molecular dynamics and docking approaches provided structural insights into the substrate-binding mode. Biochemical characterization of LmPDT identifies the enzyme as a type I NDT (PDT), exhibiting excellent activity, with specific activity values 100- and 4000-fold higher than the ones reported for other PDTs. Interestingly, LmPDT remained stable during 36 h at different pH values at 40 °C. In order to explore the potential of LmPDT as an industrial biocatalyst, enzymatic production of several natural and non-natural therapeutic nucleosides, such as vidarabine (ara A), didanosine (ddI), ddG, or 2'-fluoro-2'-deoxyguanosine, was carried out using poorly water-soluble purines. Noteworthy, this is the first time that the enzymatic synthesis of 2'-fluoro-2'-deoxyguanosine, ara G, and ara H by a 2'-deoxyribosyltransferase is reported.

Atomic force microscopy and graph analysis to study the P-cadherin/SFK mechanotransduction signalling in breast cancer cells.

Physical forces mediated by cell-cell adhesion molecules, as cadherins, play a crucial role in preserving normal tissue architecture. Accordingly, altered cadherins' expression has been documented as a common event during cancer progression. However, in most studies, no data exist linking pro-tumorigenic signaling and variations in the mechanical balance mediated by adhesive forces. In breast cancer, P-cadherin overexpression increases in vivo tumorigenic ability, as well as in vitro cell invasion, by activating Src family kinase (SFK) signalling. However, it is not known how P-cadherin and SFK activation impact cell-cell biomechanical properties. In the present work, using atomic force microscopy (AFM) images, cell stiffness and cell-cell adhesion measurements, and undirected graph analysis based on microscopic images, we have demonstrated that P-cadherin overexpression promotes significant alterations in cell's morphology, by decreasing cellular height and increasing its area. It also affects biomechanical properties, by decreasing cell-cell adhesion and cell stiffness. Furthermore, cellular network analysis showed alterations in intercellular organization, which is associated with cell-cell adhesion dysfunction, destabilization of an E-cadherin/p120ctn membrane complex and increased cell invasion. Remarkably, inhibition of SFK signaling, using dasatinib, reverted the pathogenic P-cadherin induced effects by increasing cell's height, cell-cell adhesion and cell stiffness, and generating more compact epithelial aggregates, as quantified by intercellular network analysis. In conclusion, P-cadherin/SFK signalling induces topological, morphological and biomechanical cell-cell alterations, which are associated with more invasive breast cancer cells. These effects could be further reverted by dasatinib treatment, demonstrating the applicability of AFM and cell network diagrams for measuring the epithelial biomechanical properties and structural organization.

Preventing E-cadherin aberrant N-glycosylation at Asn-554 improves its critical function in gastric cancer.

E-cadherin is a central molecule in the process of gastric carcinogenesis and its posttranslational modifications by N-glycosylation have been described to induce a deleterious effect on cell adhesion associated with tumor cell invasion. However, the role that site-specific glycosylation of E-cadherin has in its defective function in gastric cancer cells needs to be determined. Using transgenic mice models and human clinical samples, we demonstrated that N-acetylglucosaminyltransferase V (GnT-V)-mediated glycosylation causes an abnormal pattern of E-cadherin expression in the gastric mucosa. In vitro models further indicated that, among the four potential N-glycosylation sites of E-cadherin, Asn-554 is the key site that is selectively modified with ß1,6 GlcNAc-branched N-glycans catalyzed by GnT-V. This aberrant glycan modification on this specific asparagine site of E-cadherin was demonstrated to affect its critical functions in gastric cancer cells by affecting E-cadherin cellular localization, cis-dimer formation, molecular assembly and stability of the adherens junctions and cell-cell aggregation, which was further observed in human gastric carcinomas. Interestingly, manipulating this site-specific glycosylation, by preventing Asn-554 from receiving the deleterious branched structures, either by a mutation or by silencing GnT-V, resulted in a protective effect on E-cadherin, precluding its functional dysregulation and contributing to tumor suppression.