scholarly journals Novel candidate colorectal cancer biomarkers identified by methylation microarray-based scanning

2011 ◽  
Vol 18 (4) ◽  
pp. 465-478 ◽  
Author(s):  
Yuriko Mori ◽  
Alexandru V Olaru ◽  
Yulan Cheng ◽  
Rachana Agarwal ◽  
Jian Yang ◽  
...  
Keyword(s):  
Zinc Finger Protein ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Dna Hypermethylation ◽  
Specific Pcr ◽  
A Genome ◽  
Junction Protein ◽  
Genome Wide Search ◽  
Glucagon Like Peptide 1 ◽  
Neuronal Pentraxin

DNA hypermethylation is a common epigenetic abnormality in colorectal cancers (CRCs) and a promising class of CRC screening biomarkers. We conducted a genome-wide search for novel neoplasia-specific hypermethylation events in the colon. We applied methylation microarray analysis to identify loci hypermethylated in 17 primary CRCs relative to eight non-neoplastic colonic mucosae (NCs) from neoplasia-free subjects. These CRC-associated hypermethylation events were then individually evaluated for their ability to discriminate neoplastic from non-neoplastic cases, based on real-time quantitative methylation-specific PCR (qMSP) assays in 113 colonic tissues: 51 CRCs, nine adenomas, 19 NCs from CRC patients (CRC–NCs), and 34 NCs from neoplasia-free subjects (control NCs). A strict microarray data filtering identified 169 candidate CRC-associated hypermethylation events. Fourteen of these 169 loci were evaluated using qMSP assays. Ten of these 14 methylation events significantly distinguished CRCs from age-matched control NCs (P<0.05 by receiver operator characteristic curve analysis); methylation of visual system homeobox 2 (VSX2) achieved the highest discriminative accuracy (83.3% sensitivity and 92.3% specificity, P<1×10−6), followed by BEN domain containing 4 (BEND4), neuronal pentraxin I (NPTX1), ALX homeobox 3 (ALX3), miR-34b, glucagon-like peptide 1 receptor (GLP1R), BTG4, homer homolog 2 (HOMER2), zinc finger protein 583 (ZNF583), and gap junction protein, gamma 1 (GJC1). Adenomas were significantly discriminated from control NCs by hypermethylation of VSX2, BEND4, NPTX1, miR-34b, GLP1R, and HOMER2 (P<0.05). CRC–NCs were significantly distinguished from control NCs by methylation of ALX3 (P<1×10−4). In conclusion, systematic methylome-wide analysis has identified ten novel methylation events in neoplastic and non-neoplastic colonic mucosae from CRC patients. These potential biomarkers significantly discriminate CRC patients from controls. Thus, they merit further evaluation in stool- and circulating DNA-based CRC detection studies.

Molecular detection of pancreatic neuroendocrine tumors using methylated DNA markers: Discovery and tissue validation.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e16705-e16705
Author(s):  
Shounak Majumder ◽  
Calise K. Berger ◽  
Patrick H. Foote ◽  
Xiaoming Cao ◽  
Maria McGlinch ◽  
...  
Keyword(s):  
Small Bowel ◽  
Neuroendocrine Tumors ◽  
Characteristic Curve ◽  
Buffy Coat ◽  
Receiver Operator Characteristic Curve ◽  
P Value ◽  
Pancreatic Neuroendocrine Tumors ◽  
Normal Pancreas ◽  
Methylated Dna ◽  
Specific Pcr

e16705 Background: The prevalence of pancreatic neuroendocrine tumors (PNETs) has increased in the last decade. Despite being clinically asymptomatic PNETs can be biologically aggressive. There is currently no reliable non-invasive diagnostic biomarker for PNETs. In this study we aimed to identify and validate methylated DNA marker (MDM) candidates that differentiate PNET from normal pancreas. Methods: For discovery, reduced representation bisulfite sequencing (RRBS) was performed on DNA extracted from frozen normal pancreas (n = 13) and PNET (n = 51) tissues. Area under the receiver operator characteristic curve (AUC), fold-change, and p-value criteria selected candidates MDMs for blinded validation in independent FFPE tissues from primary PNET (n = 67; solid = 50, cystic = 17), normal pancreas controls (n = 24), and normal buffy coat (n = 36) using methylation specific PCR. MDM distributions in primary PNETs were compared to primary lung (n = 36) and small bowel (n = 36) NETs and metastatic PNET tissue (n = 25). The discrimination accuracy of candidate markers was summarized as the AUC with corresponding 95% confidence intervals (CI). Results: From the RRBS discovery, 31 candidate MDMs were selected for validation. Four MDMs ( SRRM3, HCN2, SPTBN4 and TMC6) achieved individual AUCs ≥0.95 in the validation set (Table). These MDMs were similarly discriminant in metastatic PNET tissue and in primary lung and small bowel NETs. Three out of these 4 MDMs perfectly differentiated PNET tissue from buffy coat with AUC of 1 and may be ideally suited for further development of a blood-based assay. Conclusions: We identified and validated novel MDMs in tissue that discriminate PNETs from controls with normal pancreas and buffy coat with high accuracy. These MDMs also differentiated metastatic PNETs from normal pancreas tissue. Further exploration of these candidate tissue MDMs in plasma can potentially guide diagnosis and management of PNETs. Funding: P30DK084567. [Table: see text]

AVALIAÇÃO DE UM INSTRUMENTO DE MENSURAÇÃO DE MORBIDADE PSÍQUICA: ESTUDO DE VALIDAÇÃO DO SELF-REPORTING QUESTIONNAIRE (SRQ-20)

1970 ◽  
Vol 34 (3) ◽  
pp. 544 ◽  
Author(s):  
Kionna Oliveira Bernardes Santos ◽  
Tânia Maria de Araújo ◽  
Paloma de Sousa Pinho ◽  
Ana Cláudia Conceição Silva
Keyword(s):  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Clinical Interview ◽  
Receiver Operator Characteristic ◽  
Interview Schedule ◽  
Para Determinar ◽  
Self Reporting Questionnaire

O Self-Reporting Questionnaire (SRQ-20), desenvolvido pela Organização Mundial de Saúde, tem sido utilizado para mensuração de nível de suspeição de transtornos mentais em estudos brasileiros, especialmente em grupos de trabalhadores. O objetivo deste estudo foi avaliar o desempenho do SRQ-20, com base em indicadores de validade (sensibilidade, especificidade, taxa de classificação incorreta e valores preditivos), e determinar o melhor ponto de corte para classificação dos transtornos mentais comuns na população estudada. O estudo incluiu 91 indivíduos selecionados aleatoriamente de um estudo de corte transversal realizado com população residente em áreas urbanas de Feira de Santana (BA). Entrevistas clínicas, realizadas por psicólogas, utilizando o Revised Clinical Interview Schedule (CIS-R), foi adotada como padrão-ouro. Na avaliação do desempenho do SRQ-20 foram estimados indicadores de validade (sensibilidade e especificidade). A curva Receiver Operator Characteristic Curve (ROC) foi utilizada para determinar o melhor ponto de corte para classificação de suspeitos/não suspeitos. O ponto de corte de melhor desempenho foi de 6/7 para a população investigada, revelando desempenho razoável com área sob a curva de 0,789. Os resultados indicam que o SRQ-20 possui característica discriminante regular.

scholarly journals Haemoconcentration as a predictor of severity in acute pancreatitis

2016 ◽  
Vol 4 (1) ◽  
pp. 3-7
Author(s):  
Tanka Prasad Bohara ◽  
Dimindra Karki ◽  
Anuj Parajuli ◽  
Shail Rupakheti ◽  
Mukund Raj Joshi
Keyword(s):  
Acute Pancreatitis ◽  
Pearson Correlation ◽  
Characteristic Curve ◽  
Scoring Systems ◽  
Cost Effective ◽  
Receiver Operator Characteristic Curve ◽  
First Episode ◽  
P Value ◽  
Receiver Operator Characteristic ◽  
Chi Square

Background: Acute pancreatitis is usually a mild and self-limiting disease. About 25 % of patients have severe episode with mortality up to 30%. Early identification of these patients has potential advantages of aggressive treatment at intensive care unit or transfer to higher centre. Several scoring systems are available to predict severity of acute pancreatitis but are cumbersome, take 24 to 48 hours and are dependent on tests that are not universally available. Haematocrit has been used as a predictor of severity of acute pancreatitis but some have doubted its role.Objectives: To study the significance of haematocrit in prediction of severity of acute pancreatitis.Methods: Patients admitted with first episode of acute pancreatitis from February 2014 to July 2014 were included. Haematocrit at admission and 24 hours of admission were compared with severity of acute pancreatitis. Mean, analysis of variance, chi square, pearson correlation and receiver operator characteristic curve were used for statistical analysis.Results: Thirty one patients were included in the study with 16 (51.61%) male and 15 (48.4%) female. Haematocrit at 24 hours of admission was higher in severe acute pancreatitis (P value 0.003). Both haematocrit at admission and at 24 hours had positive correlation with severity of acute pancreatitis (r: 0.387; P value 0.031 and r: 0.584; P value 0.001) respectively.Area under receiver operator characteristic curve for haematocrit at admission and 24 hours were 0.713 (P value 0.175, 95% CI 0.536 - 0.889) and 0.917 (P value 0.008, 95% CI 0.813 – 1.00) respectively.Conclusion: Haematocrit is a simple, cost effective and widely available test and can predict severity of acute pancreatitis.Journal of Kathmandu Medical College, Vol. 4(1) 2015, 3-7

scholarly journals Identification of candidate biomarkers of liver hydatid disease via microarray profiling, bioinformatics analysis, and machine learning

2021 ◽  
Vol 49 (3) ◽  
pp. 030006052199398
Author(s):  
Jinwu Peng ◽  
Zhili Duan ◽  
Yamin Guo ◽  
Xiaona Li ◽  
Xiaoqin Luo ◽  
...  
Keyword(s):  
Random Forest ◽  
Hydatid Disease ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Random Forest Model ◽  
Hepatic Hydatid Disease ◽  
Forest Model ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Microarray Profiling

Objectives Liver echinococcosis is a severe zoonotic disease caused by Echinococcus (tapeworm) infection, which is epidemic in the Qinghai region of China. Here, we aimed to explore biomarkers and establish a predictive model for the diagnosis of liver echinococcosis. Methods Microarray profiling followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis was performed in liver tissue from patients with liver hydatid disease and from healthy controls from the Qinghai region of China. A protein–protein interaction (PPI) network and random forest model were established to identify potential biomarkers and predict the occurrence of liver echinococcosis, respectively. Results Microarray profiling identified 1152 differentially expressed genes (DEGs), including 936 upregulated genes and 216 downregulated genes. Several previously unreported biological processes and signaling pathways were identified. The FCGR2B and CTLA4 proteins were identified by the PPI networks and random forest model. The random forest model based on FCGR2B and CTLA4 reliably predicted the occurrence of liver hydatid disease, with an area under the receiver operator characteristic curve of 0.921. Conclusion Our findings give new insight into gene expression in patients with liver echinococcosis from the Qinghai region of China, improving our understanding of hepatic hydatid disease.

scholarly journals Epigenome-Wide Study Identified Methylation Sites Associated with the Risk of Obesity

Nutrients ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1984
Author(s):  
Majid Nikpay ◽  
Sepehr Ravati ◽  
Robert Dent ◽  
Ruth McPherson
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait ◽  
Rare Variants ◽  
Genome Wide ◽  
A Genome ◽  
Genome Wide Search ◽  
Risk Snps ◽  
Trait Locus ◽  
Genomic Regions ◽  
Eqtl Data

Here, we performed a genome-wide search for methylation sites that contribute to the risk of obesity. We integrated methylation quantitative trait locus (mQTL) data with BMI GWAS information through a SNP-based multiomics approach to identify genomic regions where mQTLs for a methylation site co-localize with obesity risk SNPs. We then tested whether the identified site contributed to BMI through Mendelian randomization. We identified multiple methylation sites causally contributing to the risk of obesity. We validated these findings through a replication stage. By integrating expression quantitative trait locus (eQTL) data, we noted that lower methylation at cg21178254 site upstream of CCNL1 contributes to obesity by increasing the expression of this gene. Higher methylation at cg02814054 increases the risk of obesity by lowering the expression of MAST3, whereas lower methylation at cg06028605 contributes to obesity by decreasing the expression of SLC5A11. Finally, we noted that rare variants within 2p23.3 impact obesity by making the cg01884057 site more susceptible to methylation, which consequently lowers the expression of POMC, ADCY3 and DNAJC27. In this study, we identify methylation sites associated with the risk of obesity and reveal the mechanism whereby a number of these sites exert their effects. This study provides a framework to perform an omics-wide association study for a phenotype and to understand the mechanism whereby a rare variant causes a disease.

scholarly journals Development and External Validation of Deep-Learning-Based Tumor Grading Models in Soft-Tissue Sarcoma Patients Using MR Imaging

Cancers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2866
Author(s):  
Fernando Navarro ◽  
Hendrik Dapper ◽  
Rebecca Asadpour ◽  
Carolin Knebel ◽  
Matthew B. Spraker ◽  
...  
Keyword(s):  
Deep Learning ◽  
Soft Tissue ◽  
Mr Imaging ◽  
Characteristic Curve ◽  
External Validation ◽  
Soft Tissue Sarcomas ◽  
Treatment Decision ◽  
Receiver Operator Characteristic Curve ◽  
Low Grade ◽  
Tumor Grading

Background: In patients with soft-tissue sarcomas, tumor grading constitutes a decisive factor to determine the best treatment decision. Tumor grading is obtained by pathological work-up after focal biopsies. Deep learning (DL)-based imaging analysis may pose an alternative way to characterize STS tissue. In this work, we sought to non-invasively differentiate tumor grading into low-grade (G1) and high-grade (G2/G3) STS using DL techniques based on MR-imaging. Methods: Contrast-enhanced T1-weighted fat-saturated (T1FSGd) MRI sequences and fat-saturated T2-weighted (T2FS) sequences were collected from two independent retrospective cohorts (training: 148 patients, testing: 158 patients). Tumor grading was determined following the French Federation of Cancer Centers Sarcoma Group in pre-therapeutic biopsies. DL models were developed using transfer learning based on the DenseNet 161 architecture. Results: The T1FSGd and T2FS-based DL models achieved area under the receiver operator characteristic curve (AUC) values of 0.75 and 0.76 on the test cohort, respectively. T1FSGd achieved the best F1-score of all models (0.90). The T2FS-based DL model was able to significantly risk-stratify for overall survival. Attention maps revealed relevant features within the tumor volume and in border regions. Conclusions: MRI-based DL models are capable of predicting tumor grading with good reproducibility in external validation.

scholarly journals Recurrent Clostridioides difficile infection can be predicted using inflammatory mediator and toxin activity levels

2020 ◽  
Vol 41 (S1) ◽  
pp. s77-s78
Author(s):  
Jonathan Motyka ◽  
Aline Penkevich ◽  
Vincent Young ◽  
Krishna Rao
Keyword(s):  
Machine Learning ◽  
Inflammatory Mediators ◽  
Inflammatory Mediator ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Sensitivity Analyses ◽  
Activity Levels ◽  
Clinical Criteria ◽  
Clostridioides Difficile ◽  
Potential Biomarker

Background:Clostridioides difficile infection (CDI) frequently recurs after initial treatment. Predicting recurrent CDI (rCDI) early in the disease course can assist clinicians in their decision making and improve outcomes. However, predictions based on clinical criteria alone are not accurate and/or do not validate other results. Here, we tested the hypothesis that circulating and stool-derived inflammatory mediators predict rCDI. Methods: Consecutive subjects with available specimens at diagnosis were included if they tested positive for toxigenic C. difficile (+enzyme immunoassay [EIA] for glutamate dehydrogenase and toxins A/B, with reflex to PCR for the tcdB gene for discordants). Stool was thawed on ice, diluted 1:1 in PBS with protease inhibitor, centrifuged, and used immediately. A 17-plex panel of inflammatory mediators was run on a Luminex 200 machine using a custom antibody-linked bead array. Prior to analysis, all measurements were normalized and log-transformed. Stool toxin activity levels were quantified using a custom cell-culture assay. Recurrence was defined as a second episode of CDI within 100 days. Ordination characterized variation in the panel between outcomes, tested with a permutational, multivariate ANOVA. Machine learning via elastic net regression with 100 iterations of 5-fold cross validation selected the optimal model and the area under the receiver operator characteristic curve (AuROC) was computed. Sensitivity analyses excluding those that died and/or lived >100 km away were performed. Results: We included 186 subjects, with 95 women (51.1%) and average age of 55.9 years (±20). More patients were diagnosed by PCR than toxin EIA (170 vs 55, respectively). Death, rCDI, and no rCDI occurred in 32 (17.2%), 36 (19.4%), and 118 (63.4%) subjects, respectively. Ordination revealed that the serum panel was associated with rCDI (P = .007) but the stool panel was not. Serum procalcitonin, IL-8, IL-6, CCL5, and EGF were associated with recurrence. The machine-learning models using the serum panel predicted rCDI with AuROCs between 0.74 and 0.8 (Fig. 1). No stool inflammatory mediators independently predicted rCDI. However, stool IL-8 interacted with toxin activity to predict rCDI (Fig. 2). These results did not change significantly upon sensitivity analysis. Conclusions: A panel of serum inflammatory mediators predicted rCDI with up to 80% accuracy, but the stool panel alone was less successful. Incorporating toxin activity levels alongside inflammatory mediator measurements is a novel, promising approach to studying stool-derived biomarkers of rCDI. This approach revealed that stool IL-8 is a potential biomarker for rCDI. These results need to be confirmed both with a larger dataset and after adjustment for clinical covariates.Funding: NoneDisclosure: Vincent Young is a consultant for Bio-K+ International, Pantheryx, and Vedanta Biosciences.

scholarly journals Development and validation of an early warning tool for sepsis and decompensation in children during emergency department triage

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Louis Ehwerhemuepha ◽  
Theodore Heyming ◽  
Rachel Marano ◽  
Mary Jane Piroutek ◽  
Antonio C. Arrieta ◽  
...  
Keyword(s):  
Emergency Department ◽  
Severe Sepsis ◽  
Early Warning ◽  
Characteristic Curve ◽  
Vital Signs ◽  
Warning System ◽  
Receiver Operator Characteristic Curve ◽  
Warning Signs ◽  
Gradient Boosting ◽  
Stochastic Gradient Boosting

AbstractThis study was designed to develop and validate an early warning system for sepsis based on a predictive model of critical decompensation. Data from the electronic medical records for 537,837 visits to a pediatric Emergency Department (ED) from March 2013 to December 2019 were collected. A multiclass stochastic gradient boosting model was built to identify early warning signs associated with death, severe sepsis, non-severe sepsis, and bacteremia. Model features included triage vital signs, previous diagnoses, medications, and healthcare utilizations within 6 months of the index ED visit. There were 483 patients who had severe sepsis and/or died, 1102 had non-severe sepsis, 1103 had positive bacteremia tests, and the remaining had none of the events. The most important predictors were age, heart rate, length of stay of previous hospitalizations, temperature, systolic blood pressure, and prior sepsis. The one-versus-all area under the receiver operator characteristic curve (AUROC) were 0.979 (0.967, 0.991), 0.990 (0.985, 0.995), 0.976 (0.972, 0.981), and 0.968 (0.962, 0.974) for death, severe sepsis, non-severe sepsis, and bacteremia without sepsis respectively. The multi-class macro average AUROC and area under the precision recall curve were 0.977 and 0.316 respectively. The study findings were used to develop an automated early warning decision tool for sepsis. Implementation of this model in pediatric EDs will allow sepsis-related critical decompensation to be predicted accurately after a few seconds of triage.

scholarly journals Genome-wide methylation sequencing identifies progression-related epigenetic drivers in myelodysplastic syndromes

2020 ◽  
Vol 11 (11) ◽  
Author(s):  
Jing-dong Zhou ◽  
Ting-juan Zhang ◽  
Zi-jun Xu ◽  
Zhao-qun Deng ◽  
Yu Gu ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Myelodysplastic Syndromes ◽  
Bisulfite Sequencing ◽  
De Novo ◽  
Dna Hypermethylation ◽  
Reduced Representation ◽  
Targeted Bisulfite Sequencing ◽  
Specific Pcr ◽  
Genome Wide ◽  
Potential Biomarker

AbstractThe potential mechanism of myelodysplastic syndromes (MDS) progressing to acute myeloid leukemia (AML) remains poorly elucidated. It has been proved that epigenetic alterations play crucial roles in the pathogenesis of cancer progression including MDS. However, fewer studies explored the whole-genome methylation alterations during MDS progression. Reduced representation bisulfite sequencing was conducted in four paired MDS/secondary AML (MDS/sAML) patients and intended to explore the underlying methylation-associated epigenetic drivers in MDS progression. In four paired MDS/sAML patients, cases at sAML stage exhibited significantly increased methylation level as compared with the matched MDS stage. A total of 1090 differentially methylated fragments (DMFs) (441 hypermethylated and 649 hypomethylated) were identified involving in MDS pathogenesis, whereas 103 DMFs (96 hypermethylated and 7 hypomethylated) were involved in MDS progression. Targeted bisulfite sequencing further identified that aberrant GFRA1, IRX1, NPY, and ZNF300 methylation were frequent events in an additional group of de novo MDS and AML patients, of which only ZNF300 methylation was associated with ZNF300 expression. Subsequently, ZNF300 hypermethylation in larger cohorts of de novo MDS and AML patients was confirmed by real-time quantitative methylation-specific PCR. It was illustrated that ZNF300 methylation could act as a potential biomarker for the diagnosis and prognosis in MDS and AML patients. Functional experiments demonstrated the anti-proliferative and pro-apoptotic role of ZNF300 overexpression in MDS-derived AML cell-line SKM-1. Collectively, genome-wide DNA hypermethylation were frequent events during MDS progression. Among these changes, ZNF300 methylation, a regulator of ZNF300 expression, acted as an epigenetic driver in MDS progression. These findings provided a theoretical basis for the usage of demethylation drugs in MDS patients against disease progression.

Adaptation and Validation of the Mini-Addenbrooke’s Cognitive Examination in Dementia in Arabic Speakers in Egypt

2020 ◽  
Vol 49 (6) ◽  
pp. 611-616
Author(s):  
Tarik Qassem ◽  
Mohamed S. Khater ◽  
Tamer Emara ◽  
Doha Rasheedy ◽  
Heba M. Tawfik ◽  
...  
Keyword(s):  
Screening Tool ◽  
Characteristic Curve ◽  
High Sensitivity ◽  
Receiver Operator Characteristic Curve ◽  
Cutoff Score ◽  
Arabic Speakers ◽  
Dementia Patients ◽  
Significant Difference ◽  
Sensitivity Specificity ◽  
Addenbrooke’S Cognitive Examination

<b><i>Background:</i></b> The mini-Addenbrooke’s Cognitive Examination (m-ACE) is a brief cognitive battery that assesses 5 subdomains of cognition (attention, memory, verbal fluency, visuospatial abilities, and memory recall). It is scored out of 30 and can be administered in under 5 min providing a quick screening tool for assessment of cognition. <b><i>Objectives:</i></b> We aimed to adapt the m-ACE in Arabic speakers in Egypt and to validate it in dementia patients to provide cutoff scores. <b><i>Methods:</i></b> We included 37 patients with dementia (Alzheimer’s disease [<i>n</i> = 25], vascular dementia [<i>n</i> = 8], and dementia with Lewy body [<i>n</i> = 4]) and 43 controls. <b><i>Results:</i></b> There was a statistically significant difference (<i>p</i> &#x3c; 0.001) on the total m-ACE score between dementia patients (mean 10.54 and standard deviation [SD] 5.83) and controls (mean 24.02 and SD 2.75). There was also a statistically significant difference between dementia patients and controls on all sub-score domains of the m-ACE (<i>p</i> &#x3c; 0.05). Performance on the m-ACE significantly correlated with both the Mini-Mental State Examination (MMSE) and the Addenbrooke’s Cognitive Examination-III (ACE-III). Using a receiver operator characteristic curve, the optimal cutoff score for dementia on the m-ACE total score was found to be 18 (92% sensitivity, 95% specificity, and 94% accuracy). <b><i>Conclusions:</i></b> We adapted the m-ACE in Arabic speakers in Egypt and provided objective validation of it as a screening tool for dementia, with high sensitivity, specificity, and accuracy.

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