scholarly journals Investigating Serum and Tissue Expression Identified a Cytokine/Chemokine Signature as a Highly Effective Melanoma Marker

Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3680
Author(s):  
Marco Cesati ◽  
Francesca Scatozza ◽  
Daniela D’Arcangelo ◽  
Gian Carlo Antonini-Cappellini ◽  
Stefania Rossi ◽  
...  
Keyword(s):  
Single Molecule ◽  
Learning Algorithm ◽  
Profile Analysis ◽  
Pearson Correlation ◽  
Tissue Expression ◽  
Support Vector ◽  
Expression Data ◽  
Serum Samples ◽  
Tissue Samples ◽  
Mortality And Morbidity

The identification of reliable and quantitative melanoma biomarkers may help an early diagnosis and may directly affect melanoma mortality and morbidity. The aim of the present study was to identify effective biomarkers by investigating the expression of 27 cytokines/chemokines in melanoma compared to healthy controls, both in serum and in tissue samples. Serum samples were from 232 patients recruited at the IDI-IRCCS hospital. Expression was quantified by xMAP technology, on 27 cytokines/chemokines, compared to the control sera. RNA expression data of the same 27 molecules were obtained from 511 melanoma- and healthy-tissue samples, from the GENT2 database. Statistical analysis involved a 3-step approach: analysis of the single-molecules by Mann–Whitney analysis; analysis of paired-molecules by Pearson correlation; and profile analysis by the machine learning algorithm Support Vector Machine (SVM). Single-molecule analysis of serum expression identified IL-1b, IL-6, IP-10, PDGF-BB, and RANTES differently expressed in melanoma (p < 0.05). Expression of IL-8, GM-CSF, MCP-1, and TNF-α was found to be significantly correlated with Breslow thickness. Eotaxin and MCP-1 were found differentially expressed in male vs. female patients. Tissue expression analysis identified very effective marker/predictor genes, namely, IL-1Ra, IL-7, MIP-1a, and MIP-1b, with individual AUC values of 0.88, 0.86, 0.93, 0.87, respectively. SVM analysis of the tissue expression data identified the combination of these four molecules as the most effective signature to discriminate melanoma patients (AUC = 0.98). Validation, using the GEPIA2 database on an additional 1019 independent samples, fully confirmed these observations. The present study demonstrates, for the first time, that the IL-1Ra, IL-7, MIP-1a, and MIP-1b gene signature discriminates melanoma from control tissues with extremely high efficacy. We therefore propose this 4-molecule combination as an effective melanoma marker.

scholarly journals Key biomarkers within the colorectal cancer related inflammatory microenvironment

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Valentin Calu ◽  
Adriana Ionescu ◽  
Loredana Stanca ◽  
Ovidiu Ionut Geicu ◽  
Florin Iordache ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Tumor Staging ◽  
Pearson Correlation ◽  
Tumor Resection ◽  
Correlation Coefficients ◽  
Tumor Stage ◽  
Magnetic Bead ◽  
Tissue Expression ◽  
Tissue Samples ◽  
Inflammatory Microenvironment

AbstractTherapeutic approaches focused on the inflammatory microenvironment are currently gaining more support, as biomolecules involved in the inflammatory colorectal cancer (CRC) tumor microenvironment are being explored. We analyzed tumor and paired normal tissue samples from CRC patients (n = 22) whom underwent tumor resection surgery. We assessed 39 inflammation-involved biomolecules (multiplex magnetic bead-based immunoassay), CEA and CA19-9 (ELISA assay) and the tissue expression levels of occludin and also pErk, STAT1 and STAT3 transcriptional factors (western blot). Tumor staging has been established by histopathological evaluation of HE stained tumor tissue sections. We report 32 biomarkers displaying statistically significant differences in tumor vs. control. Additionally, positive statistical biomarker correlations were found between MMP2–IL8 and BAFF–IL8 (Pearson correlation coefficients > 0.751), while APRIL–MMP2, APRIL–BAFF and APRIL–IL8 were negatively correlated (correlation coefficients < − 0.650). While APRIL, BAFF, IL8 and MMP2 did not modulate with tumor stage, they were inversely related to the immune infiltrate level and CD163 tissue expression. We conclude that the significantly decreased APRIL and increased BAFF, IL8 and MMP2 expression were tumor-specific and deserve consideration in the development of new treatments. Also, the positive correlation between Chitinase 3-like 1 and IL8 (0.57) or MMP2 (0.50) suggest a role in tumor growth and metastasis pathways.

scholarly journals Retrospective Validation of a 168-Gene Expression Signature for Glioma Classification on a Single Molecule Counting Platform

Cancers ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 439
Author(s):  
Paul Minh Huy Tran ◽  
Lynn Kim Hoang Tran ◽  
Khaled bin Satter ◽  
Sharad Purohit ◽  
John Nechtman ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Molecule ◽  
Learning Algorithm ◽  
Gene Expression Signature ◽  
Molecular Classification ◽  
The Cancer Genome Atlas ◽  
Support Vector ◽  
Formalin Fixed Paraffin Embedded ◽  
Using Data ◽  
Glioma Classification

Gene expression profiling has been shown to be comparable to other molecular methods for glioma classification. We sought to validate a gene-expression based glioma classification method. Formalin-fixed paraffin embedded tissue and flash frozen tissue collected at the Augusta University (AU) Pathology Department between 2000–2019 were identified and 2 mm cores were taken. The RNA was extracted from these cores after deparaffinization and bead homogenization. One hundred sixty-eight genes were evaluated in the RNA samples on the nCounter instrument. Forty-eight gliomas were classified using a supervised learning algorithm trained by using data from The Cancer Genome Atlas. An ensemble of 1000 linear support vector models classified 30 glioma samples into TP1 with classification confidence of 0.99. Glioma patients in TP1 group have a poorer survival (HR (95% CI) = 4.5 (1.3–15.4), p = 0.005) with median survival time of 12.1 months, compared to non-TP1 groups. Network analysis revealed that cell cycle genes play an important role in distinguishing TP1 from non-TP1 cases and that these genes may play an important role in glioma survival. This could be a good clinical pipeline for molecular classification of gliomas.

Optical Biopsy for Prostate Cancer Diagnosis Using Fluorescence Spectroscopy

2018 ◽  
Vol 27 (03n04) ◽  
pp. 1840026
Author(s):  
Binlin Wu ◽  
Xin Gao ◽  
Jason Smith
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Supervised Machine Learning ◽  
Optical Biopsy ◽  
Support Vector ◽  
Svm Classifier ◽  
Machine Learning Algorithm ◽  
Redox Ratio ◽  
Tissue Samples ◽  
Statistical Measures

Native fluorescence spectra are acquired from fresh normal and cancerous human prostate tissues. The fluorescence data are analyzed using an unsupervised machine learning algorithm such as non-negative matrix factorization. The nonnegative spectral components are retrieved and attributed to the native fluorophores such as collagen, reduced nicotinamide adenine dinucleotide (NADH), and flavin adenine dinucleotide (FAD) in tissue. The retrieved scores of the components are used to estimate the relative concentrations of the native fluorophores such as NADH and FAD and the redox ratio. A supervised machine learning algorithm such as support vector machine (SVM) is used to classify normal and cancerous tissue samples based on either the relative concentrations of NADH and FAD or the redox ratio alone. Various statistical measures such as sensitivity, specificity, and accuracy, along with the area under receiver operating characteristic (ROC) curve are used to show the classification performance. A cross validation method such as leave-one-out is used to further evaluate the predictive performance of the SVM classifier to avoid bias due to overfitting, and the accuracy was found to be 93.3%.

scholarly journals Predictive Modelling of Employee Turnover in Indian IT Industry Using Machine Learning Techniques

2019 ◽  
Vol 23 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Shikha N. Khera ◽  
Divya
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Predictive Modelling ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
It Industry ◽  
Knowledge Based ◽  
Employee Attrition

Information technology (IT) industry in India has been facing a systemic issue of high attrition in the past few years, resulting in monetary and knowledge-based loses to the companies. The aim of this research is to develop a model to predict employee attrition and provide the organizations opportunities to address any issue and improve retention. Predictive model was developed based on supervised machine learning algorithm, support vector machine (SVM). Archival employee data (consisting of 22 input features) were collected from Human Resource databases of three IT companies in India, including their employment status (response variable) at the time of collection. Accuracy results from the confusion matrix for the SVM model showed that the model has an accuracy of 85 per cent. Also, results show that the model performs better in predicting who will leave the firm as compared to predicting who will not leave the company.

scholarly journals Early Prediction of Seven-Day Mortality in Intensive Care Unit Using a Machine Learning Model: Results from the SPIN-UTI Project

2021 ◽  
Vol 10 (5) ◽  
pp. 992
Author(s):  
Martina Barchitta ◽  
Andrea Maugeri ◽  
Giuliana Favara ◽  
Paolo Marco Riela ◽  
Giovanni Gallo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Support Vector ◽  
Icu Admission ◽  
Risk Of Death ◽  
Saps Ii ◽  
Svm Algorithm

Patients in intensive care units (ICUs) were at higher risk of worsen prognosis and mortality. Here, we aimed to evaluate the ability of the Simplified Acute Physiology Score (SAPS II) to predict the risk of 7-day mortality, and to test a machine learning algorithm which combines the SAPS II with additional patients’ characteristics at ICU admission. We used data from the “Italian Nosocomial Infections Surveillance in Intensive Care Units” network. Support Vector Machines (SVM) algorithm was used to classify 3782 patients according to sex, patient’s origin, type of ICU admission, non-surgical treatment for acute coronary disease, surgical intervention, SAPS II, presence of invasive devices, trauma, impaired immunity, antibiotic therapy and onset of HAI. The accuracy of SAPS II for predicting patients who died from those who did not was 69.3%, with an Area Under the Curve (AUC) of 0.678. Using the SVM algorithm, instead, we achieved an accuracy of 83.5% and AUC of 0.896. Notably, SAPS II was the variable that weighted more on the model and its removal resulted in an AUC of 0.653 and an accuracy of 68.4%. Overall, these findings suggest the present SVM model as a useful tool to early predict patients at higher risk of death at ICU admission.

scholarly journals Machine learning-based ability to classify psychosis and early stages of disease through parenting and attachment-related variables is associated with social cognition

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Linda A. Antonucci ◽  
Alessandra Raio ◽  
Giulio Pergola ◽  
Barbara Gelao ◽  
Marco Papalino ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Cognition ◽  
Attachment Style ◽  
Learning Algorithm ◽  
Early Recognition ◽  
Emotion Perception ◽  
First Episode ◽  
Support Vector ◽  
Significant Group ◽  
Early Stages

Abstract Background Recent views posited that negative parenting and attachment insecurity can be considered as general environmental factors of vulnerability for psychosis, specifically for individuals diagnosed with psychosis (PSY). Furthermore, evidence highlighted a tight relationship between attachment style and social cognition abilities, a key PSY behavioral phenotype. The aim of this study is to generate a machine learning algorithm based on the perceived quality of parenting and attachment style-related features to discriminate between PSY and healthy controls (HC) and to investigate its ability to track PSY early stages and risk conditions, as well as its association with social cognition performance. Methods Perceived maternal and paternal parenting, as well as attachment anxiety and avoidance scores, were trained to separate 71 HC from 34 PSY (20 individuals diagnosed with schizophrenia + 14 diagnosed with bipolar disorder with psychotic manifestations) using support vector classification and repeated nested cross-validation. We then validated this model on independent datasets including individuals at the early stages of disease (ESD, i.e. first episode of psychosis or depression, or at-risk mental state for psychosis) and with familial high risk for PSY (FHR, i.e. having a first-degree relative suffering from psychosis). Then, we performed factorial analyses to test the group x classification rate interaction on emotion perception, social inference and managing of emotions abilities. Results The perceived parenting and attachment-based machine learning model discriminated PSY from HC with a Balanced Accuracy (BAC) of 72.2%. Slightly lower classification performance was measured in the ESD sample (HC-ESD BAC = 63.5%), while the model could not discriminate between FHR and HC (BAC = 44.2%). We observed a significant group x classification interaction in PSY and HC from the discovery sample on emotion perception and on the ability to manage emotions (both p = 0.02). The interaction on managing of emotion abilities was replicated in the ESD and HC validation sample (p = 0.03). Conclusion Our results suggest that parenting and attachment-related variables bear significant classification power when applied to both PSY and its early stages and are associated with variability in emotion processing. These variables could therefore be useful in psychosis early recognition programs aimed at softening the psychosis-associated disability.

Efficient detection of hacker community based on twitter data using complex networks and machine learning algorithm

2021 ◽  
pp. 1-17
Author(s):  
Ahmed Al-Tarawneh ◽  
Ja’afer Al-Saraireh
Keyword(s):  
Machine Learning ◽  
Complex Networks ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Efficient Detection ◽  
Suggested Keywords

Twitter is one of the most popular platforms used to share and post ideas. Hackers and anonymous attackers use these platforms maliciously, and their behavior can be used to predict the risk of future attacks, by gathering and classifying hackers’ tweets using machine-learning techniques. Previous approaches for detecting infected tweets are based on human efforts or text analysis, thus they are limited to capturing the hidden text between tweet lines. The main aim of this research paper is to enhance the efficiency of hacker detection for the Twitter platform using the complex networks technique with adapted machine learning algorithms. This work presents a methodology that collects a list of users with their followers who are sharing their posts that have similar interests from a hackers’ community on Twitter. The list is built based on a set of suggested keywords that are the commonly used terms by hackers in their tweets. After that, a complex network is generated for all users to find relations among them in terms of network centrality, closeness, and betweenness. After extracting these values, a dataset of the most influential users in the hacker community is assembled. Subsequently, tweets belonging to users in the extracted dataset are gathered and classified into positive and negative classes. The output of this process is utilized with a machine learning process by applying different algorithms. This research build and investigate an accurate dataset containing real users who belong to a hackers’ community. Correctly, classified instances were measured for accuracy using the average values of K-nearest neighbor, Naive Bayes, Random Tree, and the support vector machine techniques, demonstrating about 90% and 88% accuracy for cross-validation and percentage split respectively. Consequently, the proposed network cyber Twitter model is able to detect hackers, and determine if tweets pose a risk to future institutions and individuals to provide early warning of possible attacks.

scholarly journals Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tianqi Tu ◽  
Xueling Wei ◽  
Yue Yang ◽  
Nianrong Zhang ◽  
Wei Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Renal Biopsy ◽  
Membranous Nephropathy ◽  
Learning Algorithm ◽  
Hyperspectral Imagery ◽  
Chinese Patients ◽  
Support Vector ◽  
Deep Learning Algorithm ◽  
The Difference ◽  
Complex Deposition

Abstract Background Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues. Methods We propose here a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results The proposed framework can achieve an overall accuracy of 95.04% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms. Conclusion IMN and HBV-MN can be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid in the diagnosis of MN.

scholarly journals Atypical anti-glomerular basement membrane disease with anti-GBM antibody negativity and ANCA positivity: a case report

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Na Guo ◽  
Qinghua Yin ◽  
Song Lei ◽  
Yanjun He ◽  
Ping Fu
Keyword(s):  
Basement Membrane ◽  
Glomerular Basement Membrane ◽  
Rare Case ◽  
Test Results ◽  
Serum Samples ◽  
Tissue Samples ◽  
Case Presentation ◽  
Severe Renal Insufficiency ◽  
Laboratory Test Results ◽  
Organ Specific

Abstract Background Anti-glomerular basement membrane (anti-GBM) disease is an organ-specific autoimmune disease that involves the lung and kidneys and leads to rapid glomerulonephritis progression, with or without diffuse alveolar hemorrhage, and even respiratory failure. Classic cases of anti-GBM disease are diagnosed based on the presence of the anti-GBM antibody in serum samples and kidney or lung biopsy tissue samples. However, atypical cases of anti-GBM disease are also seen in clinical practice. Case presentation We herein report the rare case of a patient with atypical anti-GBM disease whose serum was negative for the anti-GBM antibody but positive for the myeloperoxidase (MPO) anti-neutrophil cytoplasmic antibody (p-ANCA) and another atypical ANCA. Laboratory test results showed severe renal insufficiency with a creatinine level of 385 μmol/L. Renal biopsy specimen analysis revealed 100% glomeruli with crescents; immunofluorescence showed immunoglobulin G (IgG) linearly deposited alongside the GBM. Finally, the patient was discharged successfully after treatment with plasmapheresis, methylprednisolone and prednisone. Conclusion This patient, whose serum was negative for the anti-GBM antibody but positive for p-ANCA and another atypical ANCA, had a rare case of anti-GBM disease. Insights from this unusual case might help physicians diagnose rare forms of glomerulonephritis and treat affected patients in a timely manner.

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