scholarly journals Artificial Neural Network Analysis-Based Immune-Related Signatures of Primary Non-Response to Infliximab in Patients With Ulcerative Colitis

2021 ◽  
Vol 12 ◽  
Author(s):  
Xuanfu Chen ◽  
Lingjuan Jiang ◽  
Wei Han ◽  
Xiaoyin Bai ◽  
Gechong Ruan ◽  
...  
Keyword(s):  
Neural Network ◽  
Ulcerative Colitis ◽  
Artificial Neural Network ◽  
Receiver Operating Characteristic ◽  
Clinical Application ◽  
Operating Characteristic ◽  
Protein Detection ◽  
Protein Model ◽  
Artificial Neural ◽  
Receiver Operating

Infliximab (IFX) is an effective medication for ulcerative colitis (UC) patients. However, one-third of UC patients show primary non-response (PNR) to IFX. Our study analyzed three Gene Expression Omnibus (GEO) datasets and used the RobustRankAggreg (RRA) algorithm to assist in identifying differentially expressed genes (DEGs) between IFX responders and non-responders. Then, an artificial intelligence (AI) technology, artificial neural network (ANN) analysis, was applied to validate the predictive value of the selected genes. The results showed that the combination of CDX2, CHP2, HSD11B2, RANK, NOX4, and VDR is a good predictor of patients’ response to IFX therapy. The range of repeated overall area under the receiver-operating characteristic curve (AUC) was 0.850 ± 0.103. Moreover, we used an independent GEO dataset to further verify the value of the six DEGs in predicting PNR to IFX, which has a range of overall AUC of 0.759 ± 0.065. Since protein detection did not require fresh tissue and can avoid multiple biopsies, our study tried to discover whether the key information, analyzed by RNA levels, is suitable for protein detection. Therefore, immunohistochemistry (IHC) staining of colonic biopsy tissues from UC patients treated with IFX and a receiver-operating characteristic (ROC) analysis were used to further explore the clinical application value of the six DEGs at the protein level. The IHC staining of colon tissues from UC patients confirmed that VDR and RANK are significantly associated with IFX efficacy. Total IHC scores lower than 5 for VDR and lower than 7 for RANK had an AUC of 0.828 (95% CI: 0.665–0.991, p = 0.013) in predicting PNR to IFX. Collectively, we identified a predictive RNA model for PNR to IFX and explored an immune-related protein model based on the RNA model, including VDR and RANK, as a predictor of IFX non-response, and determined the cutoff value. The result showed a connection between the RNA and protein model, and both two models were available. However, the composite signature of VDR and RANK is more conducive to clinical application, which could be used to guide the preselection of patients who might benefit from pharmacological treatment in the future.

scholarly journals Systematic Review with Meta-Analysis: Fecal Calprotectin as a Surrogate Marker for Predicting Relapse in Adults with Ulcerative Colitis

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Jiajia Li ◽  
Xiaojing Zhao ◽  
Xueting Li ◽  
Meijiao Lu ◽  
Hongjie Zhang
Keyword(s):  
Ulcerative Colitis ◽  
Receiver Operating Characteristic ◽  
Likelihood Ratio ◽  
Odds Ratio ◽  
Operating Characteristic ◽  
Diagnostic Odds Ratio ◽  
Fecal Calprotectin ◽  
Clinical Relapse ◽  
Sensitivity Specificity ◽  
Receiver Operating

The clinical course of ulcerative colitis (UC) is featured by remission and relapse, which remains unpredictable. Recent studies revealed that fecal calprotectin (FC) could predict clinical relapse for UC patients in remission, which has not yet been well accepted. To detect the predictive value of FC for clinical relapse in adult UC patients based on updated literature, we carried out a comprehensive electronic search of PubMed, Web of Science, Embase, and the Cochrane Library to identify all eligible studies. Diagnostic accuracy including pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and pooled area under the receiver operating characteristic (AUROC) was calculated using a random effects model. Heterogeneity across studies was assessed by the I2 metric. Sources of heterogeneity were detected using subgroup analysis. Metaregression was used to test potential factors correlated to DOR. Publication bias was assessed using Deek’s funnel plots. In our study, 14 articles enrolling a total of 1110 participants were finally included, and all articles underwent a quality assessment. Pooled sensitivity, specificity, PLR, and NLR with 95% confidence intervals (CIs) were 0.75 (95% CI: 0.70–0.79), 0.77 (95% CI: 0.74–0.80), 3.45 (95% CI: 2.31–5.14), and 0.37 (95% CI: 0.28–0.49) respectively. The area under the summary receiver operating characteristic (sROC) curve was 0.82, and the diagnostic odds ratio was 10.54 (95% CI: 6.16–18.02). Our study suggested that FC is useful in predicting clinical relapse for adult UC patients in remission as a simple and noninvasive marker.

scholarly journals Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3570
Author(s):  
Daniele Marinucci ◽  
Agnese Sbrollini ◽  
Ilaria Marcantoni ◽  
Micaela Morettini ◽  
Cees A. Swenne ◽  
...  
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Artificial Neural Network ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Portable Devices ◽  
Promising Tool ◽  
Artificial Neural ◽  
Fully Connected ◽  
Electrocardiogram Ecg

Atrial fibrillation (AF) is a common cardiac disorder that can cause severe complications. AF diagnosis is typically based on the electrocardiogram (ECG) evaluation in hospitals or in clinical facilities. The aim of the present work is to propose a new artificial neural network for reliable AF identification in ECGs acquired through portable devices. A supervised fully connected artificial neural network (RSL_ANN), receiving 19 ECG features (11 morphological, 4 on F waves and 4 on heart-rate variability (HRV)) in input and discriminating between AF and non-AF classes in output, was created using the repeated structuring and learning (RSL) procedure. RSL_ANN was created and tested on 8028 (training: 4493; validation: 1125; testing: 2410) annotated ECGs belonging to the “AF Classification from a Short Single Lead ECG Recording” database and acquired with the portable KARDIA device by AliveCor. RSL_ANN performance was evaluated in terms of area under the curve (AUC) and confidence intervals (CIs) of the received operating characteristic. RSL_ANN performance was very good and very similar in training, validation and testing datasets. AUC was 91.1% (CI: 89.1–93.0%), 90.2% (CI: 86.2–94.3%) and 90.8% (CI: 88.1–93.5%) for the training, validation and testing datasets, respectively. Thus, RSL_ANN is a promising tool for reliable identification of AF in ECGs acquired by portable devices.

Clinical and Host Biological Factors Predict Colectomy Risk in Children Newly Diagnosed With Ulcerative Colitis

2021 ◽  
Author(s):  
Jeffrey S Hyams ◽  
Michael Brimacombe ◽  
Yael Haberman ◽  
Thomas Walters ◽  
Greg Gibson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ulcerative Colitis ◽  
Confidence Interval ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Newly Diagnosed ◽  
Receiver Operating Characteristic Area ◽  
Characteristic Area ◽  
Receiver Operating

Abstract Background Develop a clinical and biological predictive model for colectomy risk in children newly diagnosed with ulcerative colitis (UC). Methods This was a multicenter inception cohort study of children (ages 4-17 years) newly diagnosed with UC treated with standardized initial regimens of mesalamine or corticosteroids (CS) depending upon initial disease severity. Therapy escalation to immunomodulators or infliximab was based on predetermined criteria. Patients were phenotyped by clinical activity per the Pediatric Ulcerative Colitis Activity Index (PUCAI), disease extent, endoscopic/histologic severity, and laboratory markers. In addition, RNA sequencing defined pretreatment rectal gene expression and high density DNA genotyping by the Affymetrix UK Biobank Axiom Array. Coprimary outcomes were colectomy over 3 years and time to colectomy. Generalized linear models, Cox proportional hazards multivariate regression modeling, and Kaplan-Meier plots were used. Results Four hundred twenty-eight patients (mean age 13 years) started initial theapy with mesalamine (n = 136), oral CS (n = 144), or intravenous CS (n = 148). Twenty-five (6%) underwent colectomy at ≤1 year, 33 (9%) at ≤2 years, and 35 (13%) at ≤3 years. Further, 32/35 patients who had colectomy failed infliximab. An initial PUCAI ≥ 65 was highly associated with colectomy (P = 0.0001). A logistic regression model predicting colectomy using the PUCAI, hemoglobin, and erythrocyte sedimentation rate had a receiver operating characteristic area under the curve of 0.78 (95% confidence interval [0.73, 0.84]). Addition of a pretreatment rectal gene expression panel reflecting activation of the innate immune system and response to external stimuli and bacteria to the clinical model improved the receiver operating characteristic area under the curve to 0.87 (95% confidence interval [0.82, 0.91]). Conclusions A small group of children newly diagnosed with severe UC still require colectomy despite current therapies. Our gene signature observations suggest additional targets for management of those patients not responding to current medical therapies.

scholarly journals Enhanced neoplasia detection in chronic ulcerative colitis: the ENDCaP-C diagnostic accuracy study

2021 ◽  
Vol 8 (1) ◽  
pp. 1-88
Author(s):  
Ashish Awasthi ◽  
Jamie Barbour ◽  
Andrew Beggs ◽  
Pradeep Bhandari ◽  
Daniel Blakeway ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
Confidence Interval ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Chronic Ulcerative Colitis ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Added Value ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Background Chronic ulcerative colitis is a large bowel inflammatory condition associated with increased colorectal cancer risk over time, resulting in 1000 colectomies per year in the UK. Despite intensive colonoscopic surveillance, 50% of cases progress to invasive cancer before detection. Detecting early (precancer) molecular changes by analysing biopsies from routine colonoscopy should increase neoplasia detection. Objectives To establish a deoxyribonucleic acid (DNA) marker panel associated with early neoplastic changes in ulcerative colitis patients. To develop the DNA methylation test for high-throughput analysis within the NHS. To prospectively evaluate the test within the existing colonoscopy surveillance programme. Design Module 1 analysed 569 stored biopsies from neoplastic and non-neoplastic sites/patients using pyrosequencing for 11 genes that were previously reported to have altered promoter methylation associated with colitis-associated neoplasia. Classifiers were constructed to predict neoplasia based on gene combinations. Module 2 translated analysis to a NHS laboratory, assessing next-generation sequencing to increase speed and reduce cost. Module 3 applied the molecular classifiers within a prospective diagnostic accuracy study, in the existing ulcerative colitis surveillance programme. Comparisons were made between baseline and reference colonoscopies undertaken in a stratified patient sample 6–12 months later. Setting Thirty-one UK hospitals. Participants Patients with chronic ulcerative colitis, either for at least 10 years and extensive disease, or with primary sclerosing cholangitis. Interventions An optimised DNA methylation classifier tested on routine mucosal biopsies taken during colonoscopy. Main outcome Identifying ulcerative colitis patients with neoplasia. Results Module 1 selected five genes with specificity for neoplasia. The optimism-adjusted area under the receiver operating characteristic curve for neoplasia was 0.83 (95% confidence interval 0.79 to 0.88). Precancerous neoplasia showed a higher area under the receiver operating characteristic curve of 0.88 (95% confidence interval 0.84 to 0.92). Background mucosa had poorer discrimination (optimism-adjusted area under the receiver operating characteristic curve was 0.68, 95% confidence interval 0.62 to 0.73). Module 2 was unable to develop a robust next-generation sequencing assay because of the low amplification rates across all genes. In module 3, 818 patients underwent a baseline colonoscopy. The methylation assay (testing non-neoplastic mucosa) was compared with pathology assessments for neoplasia and showed a diagnostic odds ratio of 2.37 (95% confidence interval 1.46 to 3.82; p = 0.0002). The probability of dysplasia increased from 11.1% before testing to 17.7% after testing (95% confidence interval 13.0% to 23.2%), with a positive methylation result suggesting added value in neoplasia detection. To determine added value above colonoscopy alone, a second (reference) colonoscopy was performed in 193 patients without neoplasia. Although the test showed an increased number of patients with neoplasia associated with primary methylation changes, this failed to reach statistical significance (diagnostic odds ratio 3.93; 95% confidence interval 0.82 to 24.75; p = 0.09). Limitations Since the inception of ENDCaP-C, technology has advanced to allow whole-genome or methylome testing to be performed. Conclusions Methylation testing for chronic ulcerative colitis patients cannot be recommended based on this study. However, following up this cohort will reveal further neoplastic changes, indicating whether or not this test may be identifying a population at risk of future neoplasia and informing future surveillance programmes. Trial registration Current Controlled Trials ISRCTN81826545. Funding This project was funded by the Efficacy and Mechanism Evaluation programme, a Medical Research Council and National Institute for Health Research (NIHR) partnership, and will be published in full in Efficacy and Mechanism Evaluation; Vol. 8, No. 1. See the NIHR Journals Library website for further project information.

Validity of diagnosis support for attention deficit/hyperactivity disorders - Model discrimination of machine learning approach (Preprint)

2020 ◽  
Author(s):  
Hsin-Jou Huang ◽  
Hsin-Ke Lu ◽  
Peng-Chun Lin ◽  
Kuo-Chung Chu ◽  
Wei-Chih Chin ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Logistic Regression ◽  
Receiver Operating Characteristic ◽  
Attention Deficit ◽  
Operating Characteristic ◽  
Learning Approach ◽  
Adhd Group ◽  
Machine Learning Approach ◽  
Receiver Operating

BACKGROUND Attention deficit hyperactivity disorder (ADHD) is a common neurobehavioral disorder characterized by inattention, hyperactivity, and impulsivity. It is a chronic disorder and often persists into adulthood. Long-term follow-up studies showed that children with ADHD were more impaired in psychosocial, educational, and neuropsychological functioning, and had higher risks for antisocial disorders, major depression, and anxiety disorders as adults. Proper and early diagnosis would save medical resources and assist policy making for ADHD. OBJECTIVE In this research, we developed a diagnosis decision model using machine learning approach to effectively screen the disorder potentials. The model is based on three machine learning algorithms: logistic regression, classification and regression tree (CART), and neural network. They were compared for analysis of the disorder diagnosis with receiver operating characteristic curve. METHODS There were 74 participants in the ADHD group, while 21 participants in non-ADHD control group. The performance of three algorithms is evaluated by receiver operating characteristic (ROC) curve. RESULTS The results showed that the CART outperformed the other two, and the region values of receiver operating characteristic were in the following order: CART (0.848) > logistic regression model (0.826) > neural network (0.67). The sensitivity and specificity of the CART were 88% and 50%, respectively. CONCLUSIONS In the future, this model can also be used in other neuroscience fields, such as diagnosing Asperger Syndrome, Tourette Syndrome, and Dementia. Thereby, it can exert practical value and benefits of the research results.

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