scholarly journals Immune profiling of plasma-derived extracellular vesicles identifies Parkinson disease

2020 ◽  
Vol 7 (6) ◽  
pp. e866 ◽  
Author(s):  
Elena Vacchi ◽  
Jacopo Burrello ◽  
Dario Di Silvestre ◽  
Alessio Burrello ◽  
Sara Bolis ◽  
...  
Keyword(s):  
Parkinson Disease ◽  
Diagnostic Performance ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Diagnostic Model ◽  
Surface Markers ◽  
Model Based ◽  
Immune Profiling

ObjectiveTo develop a diagnostic model based on plasma-derived extracellular vesicle (EV) subpopulations in Parkinson disease (PD) and atypical parkinsonism (AP), we applied an innovative flow cytometric multiplex bead-based platform.MethodsPlasma-derived EVs were isolated from PD, matched healthy controls, multiple system atrophy (MSA), and AP with tauopathies (AP-Tau). The expression levels of 37 EV surface markers were measured by flow cytometry and correlated with clinical scales. A diagnostic model based on EV surface markers expression was built via supervised machine learning algorithms and validated in an external cohort.ResultsDistinctive pools of EV surface markers related to inflammatory and immune cells stratified patients according to the clinical diagnosis. PD and MSA displayed a greater pool of overexpressed immune markers, suggesting a different immune dysregulation in PD and MSA vs AP-Tau. The receiver operating characteristic curve analysis of a compound EV marker showed optimal diagnostic performance for PD (area under the curve [AUC] 0.908; sensitivity 96.3%, specificity 78.9%) and MSA (AUC 0.974; sensitivity 100%, specificity 94.7%) and good accuracy for AP-Tau (AUC 0.718; sensitivity 77.8%, specificity 89.5%). A diagnostic model based on EV marker expression correctly classified 88.9% of patients with reliable diagnostic performance after internal and external validations.ConclusionsImmune profiling of plasmatic EVs represents a crucial step toward the identification of biomarkers of disease for PD and AP.

scholarly journals A Multivariate Diagnostic Model Based on Urinary EpCAM-CD9-Positive Extracellular Vesicles for Prostate Cancer Diagnosis

2021 ◽  
Vol 11 ◽  
Author(s):  
Yibei Dai ◽  
Yiyun Wang ◽  
Ying Cao ◽  
Pan Yu ◽  
Lingyu Zhang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Extracellular Vesicles ◽  
Diagnostic Performance ◽  
Specific Antigen ◽  
Transrectal Ultrasound ◽  
Area Under The Curve ◽  
Diagnostic Model ◽  
Model Based ◽  
Applied Machine Learning ◽  
Trus Biopsy

IntroductionProstate cancer (PCa) is one of the most frequently diagnosed cancers and the leading cause of cancer death in males worldwide. Although prostate-specific antigen (PSA) screening has considerably improved the detection of PCa, it has also led to a dramatic increase in overdiagnosing indolent disease due to its low specificity. This study aimed to develop and validate a multivariate diagnostic model based on the urinary epithelial cell adhesion molecule (EpCAM)-CD9–positive extracellular vesicles (EVs) (uEVEpCAM-CD9) to improve the diagnosis of PCa.MethodsWe investigated the performance of uEVEpCAM-CD9 from urine samples of 193 participants (112 PCa patients, 55 benign prostatic hyperplasia patients, and 26 healthy donors) to diagnose PCa using our laboratory-developed chemiluminescent immunoassay. We applied machine learning to training sets and subsequently evaluated the multivariate diagnostic model based on uEVEpCAM-CD9 in validation sets.ResultsResults showed that uEVEpCAM-CD9 was able to distinguish PCa from controls, and a significant decrease of uEVEpCAM-CD9 was observed after prostatectomy. We further used a training set (N = 116) and constructed an exclusive multivariate diagnostic model based on uEVEpCAM-CD9, PSA, and other clinical parameters, which showed an enhanced diagnostic sensitivity and specificity and performed excellently to diagnose PCa [area under the curve (AUC) = 0.952, P < 0.0001]. When applied to a validation test (N = 77), the model achieved an AUC of 0.947 (P < 0.0001). Moreover, this diagnostic model also exhibited a superior diagnostic performance (AUC = 0.917, P < 0.0001) over PSA (AUC = 0.712, P = 0.0018) at the PSA gray zone.ConclusionsThe multivariate model based on uEVEpCAM-CD9 achieved a notable diagnostic performance to diagnose PCa. In the future, this model may potentially be used to better select patients for prostate transrectal ultrasound (TRUS) biopsy.

Clinical Score and Machine Learning-Based Model to Predict Diagnosis of Primary Aldosteronism in Arterial Hypertension

Hypertension ◽  
2021 ◽  
Vol 78 (5) ◽  
pp. 1595-1604
Author(s):  
Fabrizio Buffolo ◽  
Jacopo Burrello ◽  
Alessio Burrello ◽  
Daniel Heinrich ◽  
Christian Adolf ◽  
...  
Keyword(s):  
Machine Learning ◽  
Arterial Hypertension ◽  
Primary Aldosteronism ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Clinical Score ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Individual Risk ◽  
The Individual

Primary aldosteronism (PA) is the cause of arterial hypertension in 4% to 6% of patients, and 30% of patients with PA are affected by unilateral and surgically curable forms. Current guidelines recommend screening for PA ≈50% of patients with hypertension on the basis of individual factors, while some experts suggest screening all patients with hypertension. To define the risk of PA and tailor the diagnostic workup to the individual risk of each patient, we developed a conventional scoring system and supervised machine learning algorithms using a retrospective cohort of 4059 patients with hypertension. On the basis of 6 widely available parameters, we developed a numerical score and 308 machine learning-based models, selecting the one with the highest diagnostic performance. After validation, we obtained high predictive performance with our score (optimized sensitivity of 90.7% for PA and 92.3% for unilateral PA [UPA]). The machine learning-based model provided the highest performance, with an area under the curve of 0.834 for PA and 0.905 for diagnosis of UPA, with optimized sensitivity of 96.6% for PA, and 100.0% for UPA, at validation. The application of the predicting tools allowed the identification of a subgroup of patients with very low risk of PA (0.6% for both models) and null probability of having UPA. In conclusion, this score and the machine learning algorithm can accurately predict the individual pretest probability of PA in patients with hypertension and circumvent screening in up to 32.7% of patients using a machine learning-based model, without omitting patients with surgically curable UPA.

scholarly journals Profiling Inflammatory Extracellular Vesicles in Plasma and Cerebrospinal Fluid: An Optimized Diagnostic Model for Parkinson’s Disease

Biomedicines ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 230
Author(s):  
Elena Vacchi ◽  
Jacopo Burrello ◽  
Alessio Burrello ◽  
Sara Bolis ◽  
Silvia Monticone ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cerebrospinal Fluid ◽  
Extracellular Vesicles ◽  
High Sensitivity ◽  
Diagnostic Model ◽  
Surface Markers ◽  
Model Based ◽  
Flow Cytometric ◽  
Supervised Learning Algorithms

Extracellular vesicles (EVs) play a central role in intercellular communication, which is relevant for inflammatory and immune processes implicated in neurodegenerative disorders, such as Parkinson’s Disease (PD). We characterized and compared distinctive cerebrospinal fluid (CSF)-derived EVs in PD and atypical parkinsonisms (AP), aiming to integrate a diagnostic model based on immune profiling of plasma-derived EVs via artificial intelligence. Plasma- and CSF-derived EVs were isolated from patients with PD, multiple system atrophy (MSA), AP with tauopathies (AP-Tau), and healthy controls. Expression levels of 37 EV surface markers were measured by a flow cytometric bead-based platform and a diagnostic model based on expression of EV surface markers was built by supervised learning algorithms. The PD group showed higher amount of CSF-derived EVs than other groups. Among the 17 EV surface markers differentially expressed in plasma, eight were expressed also in CSF of a subgroup of PD, 10 in MSA, and 6 in AP-Tau. A two-level random forest model was built using EV markers co-expressed in plasma and CSF. The model discriminated PD from non-PD patients with high sensitivity (96.6%) and accuracy (92.6%). EV surface marker characterization bolsters the relevance of inflammation in PD and it underscores the role of EVs as pathways/biomarkers for protein aggregation-related neurodegenerative diseases.

Utility of Routine Frozen Section of Thyroid Nodules Classified as Follicular Neoplasm

2019 ◽  
Author(s):  
Jhorman Grisales ◽  
Alvaro Sanabria
Keyword(s):  
Quality Assessment ◽  
Methodological Quality ◽  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Frozen Section ◽  
Meta Analysis ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Follicular Neoplasm ◽  
Summary Receiver Operating Characteristic

Abstract Objectives To evaluate the diagnostic performance of frozen section in thyroid nodules classified as follicular neoplasm. Methods A diagnostic test meta-analysis was designed. Studies that assessed frozen section in patients with thyroid nodules and a fine-needle aspiration biopsy result of Bethesda IV were selected. The outcomes measured were the number of false- and true-positive and -negative results. We used the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) instrument for methodological quality assessment and a bivariate mixed-effects regression framework and a likelihood-based estimation of the exact binomial approach. Results Forty-six studies from 1991 to 2018 were included. Most studies had moderate methodological quality. The overall sensitivity and specificity were 43% (95% confidence internal [CI], 0.34-0.53) and 100% (95% CI, 0.99-1.00), respectively. The hierarchic summary receiver operating characteristic curve showed an area under the curve of 0.91 (95% CI, 0.80-0.97). Conclusions Frozen section demonstrates moderate diagnostic performance in patients with follicular neoplasm, and its utility for making intraoperative decisions is limited. Its routine use should be discouraged.

scholarly journals Combination of Blood Routine Examination and T-SPOT.TB Assay for Distinguishing Between Active Tuberculosis and Latent Tuberculosis Infection

2021 ◽  
Vol 11 ◽  
Author(s):  
Ying Luo ◽  
Guoxing Tang ◽  
Xu Yuan ◽  
Qun Lin ◽  
Liyan Mao ◽  
...  
Keyword(s):  
Latent Tuberculosis Infection ◽  
Area Under The Curve ◽  
Latent Tuberculosis ◽  
Active Tuberculosis ◽  
Tuberculosis Infection ◽  
Diagnostic Model ◽  
Characteristic Analysis ◽  
Routine Examination ◽  
Model Based ◽  
Blood Routine

BackgroundDistinguishing between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains challenging.MethodsBetween 2013 and 2019, 2,059 (1,097 ATB and 962 LTBI) and another 883 (372 ATB and 511 LTBI) participants were recruited based on positive T-SPOT.TB (T-SPOT) results from Qiaokou (training) and Caidian (validation) cohorts, respectively. Blood routine examination (BRE) was performed simultaneously. Diagnostic model was established according to multivariate logistic regression.ResultsSignificant differences were observed in all indicators of BRE and T-SPOT assay between ATB and LTBI. Diagnostic model built on BRE showed area under the curve (AUC) of 0.846 and 0.850 for discriminating ATB from LTBI in the training and validation cohorts, respectively. Meanwhile, TB-specific antigens spot-forming cells (SFC) (the larger of early secreted antigenic target 6 and culture filtrate protein 10 SFC in T-SPOT assay) produced lower AUC of 0.775 and 0.800 in the training and validation cohorts, respectively. The diagnostic model based on combination of BRE and T-SPOT showed an AUC of 0.909 for differentiating ATB from LTBI, with 78.03% sensitivity and 90.23% specificity when a cutoff value of 0.587 was used in the training cohort. Application of the model to the validation cohort showed similar performance. The AUC, sensitivity, and specificity were 0.910, 78.23%, and 90.02%, respectively. Furthermore, we also assessed the performance of our model in differentiating ATB from LTBI with lung lesions. Receiver operating characteristic analysis showed that the AUC of established model was 0.885, while a threshold of 0.587 yield a sensitivity of 78.03% and a specificity of 85.69%, respectively.ConclusionsThe diagnostic model based on combination of BRE and T-SPOT could provide a reliable differentiation between ATB and LTBI.

scholarly journals Double Contrast-enhanced Ultrasound for the Preoperative Gross Classification of Gastric Cancer: a Comparison With Multidetector Computed Tomography

2021 ◽  
Author(s):  
Ping He ◽  
Lan Zeng ◽  
Liying Miao ◽  
Tianli Wang ◽  
Juxiang Ye ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Computed Tomography ◽  
Diagnostic Performance ◽  
Imaging Modality ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Contrast Enhanced Ultrasound ◽  
Double Contrast ◽  
Contrast Enhanced

Abstract Purpose To compare the diagnostic performance of double contrast-enhanced ultrasound (DCEUS) and multi-detector row computed tomography (MDCT) in the gross classification of gastric cancer (GC) preoperatively. Methods 54 patients with GC proved by histology were included in this study. The sensitivity and specificity of DCEUS and MDCT for gross classification were calculated and compared. The area under the curve (AUC) from a receiver operating characteristic curve analysis was used to evaluate the difference of the diagnostic performance between these two methods.Results There were no significant differences between DCEUS and MDCT in terms of AUC values for early gastric cancer (EGC) and Borrmann Ⅰ-Ⅲ (P = 0.248, 0.317, 0.717 and 0.464, respectively). However, the sensitivities of DCEUS for EGC, Borrmann Ⅰ and Borrmann Ⅲ were higher than those of MDCT (75% versus 62%; 100% versus 50%; 90% versus 73%). The specificity of DCEUS for Borrmann Ⅲ was lower than that of MDCT (50% versus 75%). The AUC value of MDCT for Borrmann Ⅳ was significantly higher than that of DCEUS (0.927 versus 0.625; P=0.001). The accuracy and specificity of DCEUS and MDCT for Borrmann Ⅳ were similar, but the sensitivity of MDCT was significantly higher than that of DCEUS (88% versus 25%).Conclusion DCEUS may be considered as a useful complementary imaging modality to MDCT for the evaluation of the gross classification of GC preoperatively.

scholarly journals RADT-14. COMPARISON OF MACHINE LEARNING ALGORITHMS IN DISTINGUISHING RADIATION NECROSIS FROM PROGRESSION OF BRAIN METASTASES TREATED WITH STEREOTACTIC RADIOSURGERY (SRS)

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii184-ii184
Author(s):  
Xuguang Chen ◽  
Vishwa Parekh ◽  
Luke peng ◽  
Michael Chan ◽  
Michael Soike ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Metastases ◽  
Radiation Necrosis ◽  
Learning Algorithms ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Post Contrast ◽  
Testing Set

Abstract PURPOSE To test the effectiveness of machine learning algorithms in distinguishing radiation necrosis (RN) from tumor progression (TP) using MRI radiomic features. METHODS Brain metastases were treated with SRS to a median dose of 18Gy. Lesions that showed evidence of progression on follow-up MRI were sampled surgically, and diagnoses confirmed by histopathology. Cases from 2 institutions were combined and randomly assigned for training (70%) and testing (30%). T1 post-contrast (T1c) and T2 fluid attenuated inversion recovery (T2 FLAIR) MRI were used for radiomic feature extraction (50 features each). Three subsets of radiomic features were obtained and tested: Signature #1 included 10 previously published features that correlated with diagnosis on T test; signature #2 and #3 included 5 and 12 features obtained through recursive elimination using random forest (RF) and support vector machine (SVM), respectively. Supervised machine learning models were trained using RF, SVM (radial kernel) and regularized discriminant analysis (RDA) algorithms based on all three radiomics signatures. Receiver operator characteristics (ROC) were compared between signatures and algorithms. RESULTS A total of 135 individual lesions (37 RN and 98 TP) were included. Signature #3 demonstrated the highest area under the curve in the training set (average AUC=0.98, vs 0.95 and 0.92 for signature #1 and #2), as well as the testing set (average AUC=0.83, vs 0.74 and 0.79 for signature #1 and #2). RF and SVM demonstrated similar performance in both training (average AUC 0.99-1) and testing datasets (average AUC 0.79-0.80) among all three signatures. Both RF and SVM were superior to RDA in performance (average training AUC 0.83, testing AUC 0.77). The greatest sensitivity (83%) and specificity (100%) in the testing set were achieved using signature #3 and SVM. CONCLUSION RF and SVM are effective in distinguishing RN from TP in a multi-institution dataset using radiomic signatures.

scholarly journals An Interactive and Predictive Pre-diagnostic Model for Healthcare based on Data Provenance

2019 ◽  
Vol 3 (2) ◽  
pp. 59
Author(s):  
Zhwan Namiq Ahmed ◽  
Jamal Ali Hussien
Keyword(s):  
Predictive Model ◽  
Data Model ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Diagnostic Model ◽  
Data Provenance ◽  
Hand Gestures ◽  
Hands On ◽  
Network Metrics ◽  
Future Plans

The future of healthcare may look completely different from the current clinic-center services.  Rapidly growing and developing technologies are expected to change clinics throughout the world. However, the healthcare delivered to impaired patients, such as elderly and disabled people, possibly still requires hands-on human expertise. The aim of this study is to propose a predictive model that pre-diagnose illnesses by analyzing symptoms that are interactively taken from patients via several hand gestures during a period of time. This is particularly helpful in assisting clinicians and doctors to gain better understanding and make more accurate decisions about future plans for their patients’ situations. The hand gestures are detected, the time of the gesture is recorded and then they are associated to their designated symptoms. This information is captured in the form of provenance graphs constructed based on the W3C PROV data model. The provenance graph is analyzed by extracting several network metrics and then supervised machine-learning algorithms are used to build a predictive model. The model is used to predict diseases from the symptoms with a maximum accuracy of 84.5%.

scholarly journals Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data

2012 ◽  
Vol 9 (73) ◽  
pp. 1934-1942 ◽  
Author(s):  
Philip J. Hepworth ◽  
Alexey V. Nefedov ◽  
Ilya B. Muchnik ◽  
Kenton L. Morgan
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Important Indicator ◽  
Unseen Data

Machine-learning algorithms pervade our daily lives. In epidemiology, supervised machine learning has the potential for classification, diagnosis and risk factor identification. Here, we report the use of support vector machine learning to identify the features associated with hock burn on commercial broiler farms, using routinely collected farm management data. These data lend themselves to analysis using machine-learning techniques. Hock burn, dermatitis of the skin over the hock, is an important indicator of broiler health and welfare. Remarkably, this classifier can predict the occurrence of high hock burn prevalence with accuracy of 0.78 on unseen data, as measured by the area under the receiver operating characteristic curve. We also compare the results with those obtained by standard multi-variable logistic regression and suggest that this technique provides new insights into the data. This novel application of a machine-learning algorithm, embedded in poultry management systems could offer significant improvements in broiler health and welfare worldwide.

Sign in / Sign up

Export Citation Format

Share Document