Digital detection of exosomes by interferometric imaging

Exosomes, which are membranous nanovesicles, are actively released by cells and have been attributed to roles in cell-cell communication, cancer metastasis, and early disease diagnostics. The small size (30–100 nm) along with low refractive index contrast of exosomes makes direct characterization and phenotypical classification very difficult. In this work we present a method based on Single Particle Interferometric Reflectance Imaging Sensor (SP-IRIS) that allows multiplexed phenotyping and digital counting of various populations of individual exosomes (>50 nm) captured on a microarray-based solid phase chip. We demonstrate these characterization concepts using purified exosomes from a HEK 293 cell culture. As a demonstration of clinical utility, we characterize exosomes directly from human cerebrospinal fluid (hCSF). Our interferometric imaging method could capture, from a very small hCSF volume (20 uL), nanoparticles that have a size compatible with exosomes, using antibodies directed against tetraspanins. With this unprecedented capability, we foresee revolutionary implications in the clinical field with improvements in diagnosis and stratification of patients affected by different disorders.This work was supported by Regione Lombardia and Fondazione Cariplo through POR-FESR, project MINER (ID 46875467); Italian Ministry of Health, Ricerca Corrente. This work was partially supported by The Scientific and Technological Research Council of Turkey (grant #113E643). (Regione Lombardia; 46875467 - Fondazione Cariplo through POR-FESR, project MINER; Italian Ministry of Health, Ricerca Corrente; 113E643 - Scientific and Technological Research Council of Turkey)Published versio

Standardization of BCR-ABL1 p210 Monitoring: From Nested to Digital PCR

The introduction of tyrosine kinase inhibitors in 2001 as a targeted anticancer therapy has significantly improved the quality of life and survival of patients with chronic myeloid leukemia. At the same time, with the introduction of tyrosine kinase inhibitors, the need for precise monitoring of the molecular response to therapy has emerged. Starting with a qualitative polymerase chain reaction, followed by the introduction of a quantitative polymerase chain reaction to determine the exact quantity of the transcript of interest-p210 BCR-ABL1, molecular monitoring in patients with chronic myeloid leukemia was internationally standardized. This enabled precise monitoring of the therapeutic response, unification of therapeutic protocols, and comparison of results between different laboratories. This review aims to summarize the steps in the diagnosis and molecular monitoring of p210 BCR-ABL1, as well as to consider the possible future application of a more sophisticated method such as digital polymerase chain reaction

Classification Criteria for Vogt-Koyanagi-Harada Disease

PURPOSE: To determine classification criteria for Vogt-Koyanagi-Harada (VKH) disease DESIGN: Machine learning of cases with VKH disease and 5 other panuveitides. METHODS: Cases of panuveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the panuveitides. The resulting criteria were evaluated on the validation set. RESULTS: One thousand twelve cases of panuveitides, including 156 cases of early-stage VKH and 103 cases of late-stage VKH, were evaluated. Overall accuracy for panuveitides was 96.3% in the training set and 94.0% in the validation set (95% confidence interval 89.0, 96.8). Key criteria for early-stage VKH included: 1) exudative retinal detachment with characteristic appearance on fluorescein angiogram or optical coherence tomography or 2) panuveitis with ≥2 of 5 neurologic symptoms/signs. Key criteria for late-stage VKH included history of early-stage VKH and either: 1) sunset glow fundus or 2) uveitis and ≥1 of 3 cutaneous signs. The misclassification rates in the learning and validation sets for early-stage VKH were 8.0% and 7.7%, respectively, and for late-stage VKH 1.0% and 12%, respectively. CONCLUSIONS: The criteria for VKH had a reasonably low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research

Use of Xpert MTB/RIF in Decentralized Public Health Settings and Its Effect on Pulmonary TB and DR-TB Case Finding in India

Background Xpert MTB/RIF, the first automated molecular test for tuberculosis, is transforming the diagnostic landscape in high-burden settings. This study assessed the impact of up-front Xpert MTB/RIF testing on detection of pulmonary tuberculosis (PTB) and rifampicin-resistant PTB (DR-TB) cases in India. Methods This demonstration study was implemented in 18 sub-district level TB programme units (TUs) in India in diverse geographic and demographic settings covering a population of 8.8 million. A baseline phase in 14 TUs captured programmatic baseline data, and an intervention phase in 18 TUs had Xpert MTB/RIF offered to all presumptive TB patients. We estimated changes in detection of TB and DR-TB, the former using binomial regression models to adjust for clustering and covariates. Results In the 14 study TUs, which participated in both phases, 10,675 and 70,556 presumptive TB patients were enrolled in the baseline and intervention phase, respectively, and 1,532 (14.4%) and 14,299 (20.3%) bacteriologically confirmed PTB cases were detected. The implementation of Xpert MTB/RIF was associated with increases in both notification rates of bacteriologically confirmed TB cases (adjusted incidence rate ratio [aIRR] 1.39; CI 1.18-1.64), and proportion of bacteriological confirmed TB cases among presumptive TB cases (adjusted risk ratio (aRR) 1.33; CI 1.6-1.52). Compared with the baseline strategy of selective drug-susceptibility testing only for PTB cases at high risk of drug-resistant TB, Xpert MTB/RIF implementation increased rifampicin resistant TB case detection by over fivefold. Among, 2765 rifampicin resistance cases detected, 1055 were retested with conventional drug susceptibility testing (DST). Positive predictive value (PPV) of rifampicin resistance detected by Xpert MTB/RIF was 94.7% (CI 91.3-98.1), in comparison to conventional DST. Conclusion Introduction of Xpert MTB/RIF as initial diagnostic test for TB in public health facilities significantly increased case-notification rates of all bacteriologically confirmed TB by 39% and rifampicin-resistant TB case notification by fivefold

Classification Criteria for Sympathetic Ophthalmia

PURPOSE: To determine classification criteria for sympathetic ophthalmia DESIGN: Machine learning of cases with sympathetic ophthalmia and 5 other panuveitides. METHODS: Cases of panuveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the panuveitides. The resulting criteria were evaluated on the validation set. RESULTS: One thousand twelve cases of panuveitides, including 110 cases of sympathetic ophthalmia, were evaluated by machine learning. The overall accuracy for panuveitides was 96.3% in the training set and 94.0% in the validation set (95% confidence interval 89.0, 96.8). Key criteria for sympathetic ophthalmia included bilateral uveitis with 1) a history of unilateral ocular trauma or surgery and 2) an anterior chamber and vitreous inflammation or a panuveitis with choroidal involvement. The misclassification rates for sympathetic ophthalmia were 4.2 % in the training set and 6.7% in the validation set, respectively. CONCLUSIONS: The criteria for sympathetic ophthalmia had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research

Classification Criteria for Intermediate Uveitis, Non–Pars Planitis Type

Purpose: To determine classification criteria for intermediate uveitis, non-pars planitis type (IU- NPP, also known as undifferentiated intermediate uveitis) / Design: Machine learning of cases with IU-NPP and 4 other intermediate uveitides. / Methods: Cases of intermediate uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the intermediate uveitides. The resulting criteria were evaluated on the validation set. / Results: Five hundred eighty-nine of cases of intermediate uveitides, including 114 cases of IU-NPP, were evaluated by machine learning. The overall accuracy for intermediate uveitides was 99.8% in the training set and 99.3% in the validation set (95% confidence interval 96.1, 99.9). Key criteria for IU-NPP included unilateral or bilateral intermediate uveitis with neither 1) snowballs in the vitreous nor 2) snowbanks on the pars plana. Other key exclusions included: 1) multiple sclerosis, 2) sarcoidosis, and 3) syphilis. The misclassification rates for pars planitis were 0 % in the training set and 0% in the validation set, respectively. / Conclusions: The criteria for IU-NPP had a low misclassification rate and appeared to perform well enough for use in clinical and translational research

Classification Criteria for Multiple Sclerosis-Associated Intermediate Uveitis

PURPOSE: The purpose of this study was to determine classification criteria for multiple sclerosis-associated intermediate uveitis. DESIGN: Machine learning of cases with multiple sclerosis-associated intermediate uveitis and 4 other intermediate uveitides. METHODS: Cases of intermediate uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used in the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the intermediate uveitides. The resulting criteria were evaluated in the validation set. RESULTS: A total of 589 cases of intermediate uveitides, including 112 cases of multiple sclerosis-associated intermediate uveitis, were evaluated by machine learning. The overall accuracy for intermediate uveitides was 99.8% in the training set and 99.3% in the validation set (95% confidence interval: 96.1-99.9). Key criteria for multiple sclerosis-associated intermediate uveitis included unilateral or bilateral intermediate uveitis and multiple sclerosis diagnosed by the McDonald criteria. Key exclusions included syphilis and sarcoidosis. The misclassification rates for multiple sclerosis-associated intermediate uveitis were 0 % in the training set and 0% in the validation set. CONCLUSIONS: The criteria for multiple sclerosis-associated intermediate uveitis had a low misclassification rate and appeared to perform sufficiently well enough for use in clinical and translational research

Classification Criteria For Pars Planitis

PURPOSE: To determine classification criteria for pars planitis DESIGN: Machine learning of cases with pars planitis and 4 other intermediate uveitides. METHODS: Cases of intermediate uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the intermediate uveitides. The resulting criteria were evaluated on the validation set. RESULTS: Five hundred eighty-nine cases of intermediate uveitides, including 226 cases of pars planitis, were evaluated by machine learning. The overall accuracy for intermediate uveitides was 99.8% in the training set and 99.3% in the validation set (95% confidence interval 96.1, 99.9). Key criteria for pars planitis included unilateral or bilateral intermediate uveitis with either 1) snowballs in the vitreous or 2) snowbanks on the pars plana. Key exclusions included: 1) multiple sclerosis, 2) sarcoidosis, and 3) syphilis. The misclassification rates for pars planitis were 0 % in the training set and 1.7% in the validation set, respectively. CONCLUSIONS: The criteria for pars planitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research

Classification Criteria For Multiple Evanescent White Dot Syndrome

Purpose: To determine classification criteria for multiple evanescent white dot syndrome (MEWDS). / Design: Machine learning of cases with MEWDS and 8 other posterior uveitides. / Methods: Cases of posterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the infectious posterior/panuveitides. The resulting criteria were evaluated on the validation set. / Results: One thousand sixty-eight cases of posterior uveitides, including 51 cases of MEWDS, were evaluated by machine learning. Key criteria for MEWDS included: 1) multifocal gray white chorioretinal spots with foveal granularity; 2) characteristic imaging on fluorescein angiography (“wreath-like” hyperfluorescent lesions) and/or optical coherence tomography (hyper-reflective lesions extending from retinal pigment epithelium through ellipsoid zone into the retinal outer nuclear layer); and 3) absent to mild anterior chamber and vitreous inflammation. Overall accuracy for posterior uveitides was 93.9% in the training set and 98.0% (95% confidence interval 94.3, 99.3) in the validation set. The misclassification rates for MEWDS were 7% in the training set and 0% in the validation set. / Conclusions: The criteria for MEWDS had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research