Extracellular Vesicle Surface Markers as a Diagnostic Tool in Transient Ischemic Attacks

Background and Purpose: Extracellular vesicles (EVs) are promising biomarkers for cerebral ischemic diseases, but not systematically tested in patients with transient ischemic attacks (TIAs). We aimed at (1) investigating the profile of EV-surface antigens in patients with symptoms suspicious for TIA; (2) developing and validating a predictive model for TIA diagnosis based on a specific EV-surface antigen profile. Methods: We analyzed 40 subjects with symptoms suspicious for TIA and 20 healthy controls from a training cohort. An independent cohort of 28 subjects served as external validation. Patients were stratified according to likelihood of having a real ischemic event using the Precise Diagnostic Score, defined as: unlikely (score 0–1), possible-probable (score 2–3), or very likely (score 4–8). Serum vesicles were quantified by nanoparticle tracking analysis and EV-surface antigen profile characterized by multiplex flow cytometry. Results: EV concentration increased in patients with very likely or possible-probable TIA ( P <0.05) compared with controls. Nanoparticle concentration was directly correlated with the Precise Diagnostic score ( R =0.712; P <0.001). After EV immuno-capturing, CD8, CD2, CD62P, melanoma-associated chondroitin sulfate proteoglycan, CD42a, CD44, CD326, CD142, CD31, and CD14 were identified as discriminants between groups. Receiver operating characteristic curve analysis confirmed a reliable diagnostic performance for each of these markers taken individually and for a compound marker derived from their linear combinations (area under the curve, 0.851). Finally, a random forest model combining the expression levels of selected markers achieved an accuracy of 96% and 78.9% for discriminating patients with a very likely TIA, in the training and external validation cohort, respectively. Conclusions: The EV-surface antigen profile appears to be different in patients with transient symptoms adjudicated to be very likely caused by brain ischemia compared with patients whose symptoms were less likely to due to brain ischemia. We propose an algorithm based on an EV-surface-antigen specific signature that might aid in the recognition of TIA.

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Serum Extracellular Vesicle-Derived circHIPK3 and circSMARCA5 Are Two Novel Diagnostic Biomarkers for Glioblastoma Multiforme

Pharmaceuticals ◽

10.3390/ph14070618 ◽

2021 ◽

Vol 14 (7) ◽

pp. 618

Author(s):

Michele Stella ◽

Luca Falzone ◽

Angela Caponnetto ◽

Giuseppe Gattuso ◽

Cristina Barbagallo ◽

...

Keyword(s):

Glioblastoma Multiforme ◽

Roc Analysis ◽

Characteristic Curve ◽

Area Under The Curve ◽

Digital Pcr ◽

Extracellular Vesicle ◽

Size Exclusion ◽

Circular Rnas ◽

Diagnostic Biomarkers ◽

Exclusion Chromatography

Glioblastoma multiforme (GBM) is the most frequent and deadly human brain cancer. Early diagnosis through non-invasive biomarkers may render GBM more easily treatable, improving the prognosis of this currently incurable disease. We suggest the use of serum extracellular vesicle (sEV)-derived circular RNAs (circRNAs) as highly stable minimally invasive diagnostic biomarkers for GBM diagnosis. EVs were isolated by size exclusion chromatography from sera of 23 GBM and 5 grade 3 glioma (GIII) patients, and 10 unaffected controls (UC). The expression of two candidate circRNAs (circSMARCA5 and circHIPK3) was assayed by droplet digital PCR. CircSMARCA5 and circHIPK3 were significantly less abundant in sEVs from GBM patients with respect to UC (fold-change (FC) of −2.15 and −1.92, respectively) and GIII (FC of −1.75 and −1.4, respectively). Receiver operating characteristic curve (ROC) analysis, based on the expression of sEV-derived circSMARCA5 and circHIPK3, allowed us to distinguish GBM from UC (area under the curve (AUC) 0.823 (0.667–0.979) and 0.855 (0.704 to 1.000), with a 95% confidence interval (CI), respectively). Multivariable ROC analysis, performed by combining the expression of sEV-derived circSMARCA5 and circHIPK3 with preoperative neutrophil to lymphocyte (NLR), platelet to lymphocyte (PLR) and lymphocyte to monocyte (LMR) ratios, three known diagnostic and prognostic GBM markers, allowed an improvement in the GBM diagnostic accuracy (AUC 0.901 (0.7912 to 1.000), 95% CI). Our data suggest sEV-derived circSMARCA5 and circHIPK3 as good diagnostic biomarkers for GBM, especially when associated with preoperative NLR, PLR and LMR.

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External Validation and Test-Retest Reliability of Postpartum Bonding Questionnaire in Spanish Mothers

The Spanish Journal of Psychology ◽

10.1017/sjp.2021.44 ◽

2021 ◽

Vol 24 ◽

Author(s):

Anna Torres-Giménez ◽

Alba Roca-Lecumberri ◽

Bàrbara Sureda ◽

Susana Andrés-Perpiña ◽

Bruma Palacios-Hernández ◽

...

Keyword(s):

Mental Health ◽

Maternal Mental Health ◽

Characteristic Curve ◽

External Validation ◽

Area Under The Curve ◽

Retest Reliability ◽

Outpatient Unit ◽

Auc Value ◽

Postpartum Bonding ◽

Test Retest Reliability

Abstract The aim of the present study was to validate the Spanish Postpartum Bonding Questionnaire (PBQ) against external criteria of bonding disorder, as well as to establish its test-retest reliability. One hundred fifty-six postpartum women consecutively recruited from a perinatal mental health outpatient unit completed the PBQ at 4–6 weeks postpartum. Four weeks later, all mothers completed again the PBQ and were interviewed using the Birmingham Interview for Maternal Mental Health to establish the presence of a bonding disorder. Receiver operating characteristic curve analysis revealed an area under the curve (AUC) value for the PBQ total score of 0.93, 95% CI [0.88, 0.98], with the optimal cut-off of 13 for detecting bonding disorders (sensitivity: 92%, specificity: 87%). Optimal cut-off scores for each scale were also obtained. The test-retest reliability coefficients were moderate to good. Our data confirm the validity of PBQ for detecting bonding disorders in Spanish population.

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Proteomic Profile of Urinary Extracellular Vesicles Identifies AGP1 as a Potential Biomarker of Primary Aldosteronism

Endocrinology ◽

10.1210/endocr/bqab032 ◽

2021 ◽

Vol 162 (4) ◽

Author(s):

Eric R Barros ◽

Juan Pablo Rigalli ◽

Alejandra Tapia-Castillo ◽

Andrea Vecchiola ◽

Morag J Young ◽

...

Keyword(s):

Extracellular Vesicles ◽

Primary Aldosteronism ◽

Proteomic Analysis ◽

Characteristic Curve ◽

Area Under The Curve ◽

Extracellular Vesicle ◽

Parent Cell ◽

Spot Urine ◽

Potential Biomarker ◽

Aldosterone To Renin Ratio

Abstract Context Primary aldosteronism (PA) represents 6% to 10% of all essential hypertension patients and is diagnosed using the aldosterone-to-renin ratio (ARR) and confirmatory studies. The complexity of PA diagnosis encourages the identification of novel PA biomarkers. Urinary extracellular vesicles (uEVs) are a potential source of biomarkers, considering that their cargo reflects the content of the parent cell. Objective We aimed to evaluate the proteome of uEVs from PA patients and identify potential biomarker candidates for PA. Methods Second morning spot urine was collected from healthy controls (n = 8) and PA patients (n = 7). The uEVs were isolated by ultracentrifugation and characterized. Proteomic analysis on uEVs was performed using LC-MS Orbitrap. Results Isolated uEVs carried extracellular vesicle markers, showed a round shape and sizes between 50 and 150 nm. The concentration of uEVs showed a direct correlation with urinary creatinine (r = 0.6357; P = 0.0128). The uEV size mean (167 ± 6 vs 183 ± 4nm) and mode (137 ± 7 vs 171 ± 11nm) was significantly smaller in PA patients than in control subjects, but similar in concentration. Proteomic analysis of uEVs from PA patients identified an upregulation of alpha-1-acid glycoprotein 1 (AGP1) in PA uEVs, which was confirmed using immunoblot. A receiver operating characteristic curve analysis showed an area under the curve of 0.92 (0.82 to 1; P = 0.0055). Conclusion Proteomic and further immunoblot analyses of uEVs highlights AGP1 as potential biomarker for PA.

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The Radiographic Union Score for HUmeral fractures (RUSHU) predicts humeral shaft nonunion

The Bone & Joint Journal ◽

10.1302/0301-620x.101b10.bjj-2019-0304.r1 ◽

2019 ◽

Vol 101-B (10) ◽

pp. 1300-1306 ◽

Cited By ~ 7

Author(s):

William M. Oliver ◽

Thomas J. Smith ◽

Jamie A. Nicholson ◽

Sam G. Molyneux ◽

Tim O. White ◽

...

Keyword(s):

Predictive Value ◽

Characteristic Curve ◽

External Validation ◽

Intraclass Correlation ◽

Area Under The Curve ◽

Shaft Fracture ◽

Humeral Shaft Fracture ◽

Humeral Fractures ◽

Humeral Shaft ◽

Radiographic Union

Aims The primary aim of this study was to develop a reliable, effective radiological score to assess the healing of humeral shaft fractures, the Radiographic Union Score for HUmeral fractures (RUSHU). The secondary aim was to assess whether the six-week RUSHU was predictive of nonunion at six months after the injury. Patients and Methods Initially, 20 patients with radiographs six weeks following a humeral shaft fracture were selected at random from a trauma database and scored by three observers, based on the Radiographic Union Scale for Tibial fractures system. After refinement of the RUSHU criteria, a second group of 60 patients with radiographs six weeks after injury, 40 with fractures that united and 20 with fractures that developed nonunion, were scored by two blinded observers. Results After refinement, the interobserver intraclass correlation coefficient (ICC) was 0.79 (95% confidence interval (CI) 0.67 to 0.87), indicating substantial agreement. At six weeks after injury, patients whose fractures united had a significantly higher median score than those who developed nonunion (10 vs 7; p < 0.001). A receiver operating characteristic curve determined that a RUSHU cut-off of < 8 was predictive of nonunion (area under the curve = 0.84, 95% CI 0.74 to 0.94). The sensitivity was 75% and specificity 80% with a positive predictive value (PPV) of 65% and a negative predictive value of 86%. Patients with a RUSHU < 8 (n = 23) were more likely to develop nonunion than those with a RUSHU ≥ 8 (n = 37, odds ratio 12.0, 95% CI 3.4 to 42.9). Based on a PPV of 65%, if all patients with a RUSHU < 8 underwent fixation, the number of procedures needed to avoid one nonunion would be 1.5. Conclusion The RUSHU is reliable and effective in identifying patients at risk of nonunion of a humeral shaft fracture at six weeks after injury. This tool requires external validation but could potentially reduce the morbidity associated with delayed treatment of an established nonunion. Cite this article: Bone Joint J 2019;101-B:1300–1306

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External Validation of Different Scoring Systems for Suspected Choledocholithiasis

Digestive Surgery ◽

10.1159/000495696 ◽

2019 ◽

Vol 36 (6) ◽

pp. 530-538

Author(s):

Nicolò Tamini ◽

Davide Paolo Bernasconi ◽

Luca Gianotti

Keyword(s):

Operating Characteristic ◽

Bile Duct Stone ◽

Characteristic Curve ◽

External Validation ◽

Area Under The Curve ◽

Scoring Systems ◽

Youden Index ◽

Predictive Values ◽

Sensitivity Specificity ◽

Symptoms And Signs

Aim of the Study: The diagnosis of choledocholithiasis is challenging. Previously published scoring systems designed to calculate the risk of choledocholithiasis were evaluated to appraise the diagnostic performance. Patients and Methods: Data of patients who were admitted between 2013 and 2015 with the following characteristics were retrieved: bile stone-related symptoms and signs, and indication to laparoscopic cholecystectomy. To validate and appraise the performance of the 6 scoring systems, the acknowledged domains of each metrics were applied to the present cohort. Sensitivity, specificity, positive, negative predictive, Youden index, and receiver operating characteristic curve with the area under the curve (AUC) values of the scores were calculated. Results: Two-hundred patients were analyzed. The highest sensitivity and specificity were obtained from the Menezes’ (96.6%) and Telem’s (99.3%) metrics respectively. The Telem’s and Menezes’ scores had the best positive (75.0%) and negative (96.4%) predictive values respectively. The best accuracy, as computed by the Youden index and AUC, was found for the Soltan’s scoring system (0.628 and 0.88, respectively). Conclusion: The available scoring systems are precise only in identifying patients with a negligible risk of common bile duct stone, but overall insufficiently accurate to suggest the routine use in clinical practice.

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Preoperative CT-Based Radiomics Combined With Nodule Type to Predict the Micropapillary Pattern in Lung Adenocarcinoma of Size 2 cm or Less: A Multicenter Study

Frontiers in Oncology ◽

10.3389/fonc.2021.788424 ◽

2021 ◽

Vol 11 ◽

Author(s):

Meirong Li ◽

Yachao Ruan ◽

Zhan Feng ◽

Fangyu Sun ◽

Minhong Wang ◽

...

Keyword(s):

Characteristic Curve ◽

External Validation ◽

Area Under The Curve ◽

Prediction Performance ◽

High Resolution Computed Tomography ◽

Preoperative Prediction ◽

Number Of Patients ◽

Significant Difference ◽

Validation Set ◽

Micropapillary Pattern

PurposeTo construct an optimal radiomics model for preoperative prediction micropapillary pattern (MPP) in adenocarcinoma (ADC) of size ≤ 2 cm, nodule type was used for stratification to construct two radiomics models based on high-resolution computed tomography (HRCT) images.Materials and MethodsWe retrospectively analyzed patients with pathologically confirmed ADC of size ≤ 2 cm who presented to three hospitals. Patients presenting to the hospital with the greater number of patients were included in the training set (n = 2386) and those presenting to the other two hospitals were included in the external validation set (n = 119). HRCT images were used for delineation of region of interest of tumor and extraction of radiomics features; dimensionality reduction was performed for the features. Nodule type was used to stratify the data and the random forest method was used to construct two models for preoperative prediction MPP in ADC of size ≤ 2 cm. Model 1 included all nodule types and model 2 included only solid nodules. The receiver operating characteristic curve was used to assess the prediction performance of the two models and independent validation was used to assess its generalizability.ResultsBoth models predicted ADC with MPP preoperatively. The area under the curve (AUC) of prediction performance of models 1 and 2 were 0.91 and 0.78, respectively. The prediction performance of model 2 was lower than that of model 1. The AUCs in the external validation set were 0.81 and 0.72, respectively. The DeLong test showed statistically significant differences between the training and validation sets in model 1 (p = 0.0296) with weak generalizability. There was no statistically significant difference between the training and validation sets in model 2 (p = 0.2865) with some generalizability.ConclusionNodule type is an important factor that affects the performance of radiomics predictor model for MPP with ADC of size ≤ 2 cm. The radiomics prediction model constructed based on solid nodules alone, can be used to evaluate MPP and may contribute to proper surgical planning in patients with ADC of size ≤ 2 cm.

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Plasma extracellular vesicle long RNA profiling identifies a diagnostic signature for the detection of pancreatic ductal adenocarcinoma

Gut ◽

10.1136/gutjnl-2019-318860 ◽

2019 ◽

Vol 69 (3) ◽

pp. 540-550 ◽

Cited By ~ 13

Author(s):

Shulin Yu ◽

Yuchen Li ◽

Zhuan Liao ◽

Zheng Wang ◽

Zhen Wang ◽

...

Keyword(s):

Pancreatic Ductal Adenocarcinoma ◽

Validation Cohort ◽

Characteristic Curve ◽

External Validation ◽

Extracellular Vesicle ◽

Carbohydrate Antigen ◽

Superior Performance ◽

Ductal Adenocarcinoma ◽

Support Vector ◽

Internal Validation

ObjectivePancreatic ductal adenocarcinoma (PDAC) is difficult to diagnose at resectable stage. Recent studies have suggested that extracellular vesicles (EVs) contain long RNAs. The aim of this study was to develop a diagnostic (d-)signature for the detection of PDAC based on EV long RNA (exLR) profiling.DesignWe conducted a case-control study with 501 participants, including 284 patients with PDAC, 100 patients with chronic pancreatitis (CP) and 117 healthy subjects. The exLR profile of plasma samples was analysed by exLR sequencing. The d-signature was identified using a support vector machine algorithm and a training cohort (n=188) and was validated using an internal validation cohort (n=135) and an external validation cohort (n=178).ResultsWe developed a d-signature that comprised eight exLRs, including FGA, KRT19, HIST1H2BK, ITIH2, MARCH2, CLDN1, MAL2 and TIMP1, for PDAC detection. The d-signature showed high accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.960, 0.950 and 0.936 in the training, internal validation and external validation cohort, respectively. The d-signature was able to identify resectable stage I/II cancer with an AUC of 0.949 in the combined three cohorts. In addition, the d-signature showed superior performance to carbohydrate antigen 19-9 in distinguishing PDAC from CP (AUC 0.931 vs 0.873, p=0.028).ConclusionThis study is the first to characterise the plasma exLR profile in PDAC and to report an exLR signature for the detection of pancreatic cancer. This signature may improve the prognosis of patients who would have otherwise missed the curative treatment window.

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Abstract TP13: External Validation of Stroke Prognostication Scales - A Retrospective Cohort Study

Stroke ◽

10.1161/str.51.suppl_1.tp13 ◽

2020 ◽

Vol 51 (Suppl_1) ◽

Author(s):

Nauman Jahangir ◽

Nicholas Lanzotti ◽

Kyle Gollon ◽

Mehwish Farooqi ◽

Michael Buhnerkempe ◽

...

Keyword(s):

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Characteristic Curve ◽

External Validation ◽

Area Under The Curve ◽

Predictive Ability ◽

Scoring Systems ◽

Predictive Performance ◽

Discriminatory Ability ◽

Single Center Study

Introduction: In recent years, many scoring models have been proposed to predict clinical outcomes after acute ischemic stroke. Aim of our study was to perform a comparative analysis of these scoring systems to assess predictive reliability. Method: This retrospective single center study included 166 community-based patients presenting with an acute ischemic stroke between 2015 and 2018 who had undergone mechanical thrombectomy with or without IV r-tPA administration prior to the procedure. Patients with unknown 90 day Modified Ranking Scale (mRS) were excluded from the study. We included SPAN-100, THRIVE, HIAT2, iScore , TPI, DRAGON, ASTRAL and HAT predictive models to our study. To predict MRS at 90 days, we first dichotomize mRS into two groups: scores of 0 and 1 and scores 2 and above. We then used logistic regression to find the association between a stroke score and the probability of having a 90-day mRS of 2 or above. Separate univariate logistic regressions were fit for each stroke score. We assessed the ability of each stroke score to predict 90-day mRS using the area-under-the-curve (AUC) of the receiver operating characteristic curve (ROC - plot of sensitivity against 1-specificity). AUC values range from 0.5 to 1 with values above 0.7 showing good discriminatory ability. Results: SPAN-100, HIAT2, iScore, and ASTRAL scores have similar predictive ability with AUC values over 0.7 (Table 1). The ASTRAL score had the highest predictive ability with a score above 31.5 indicating a high likelihood of a 90-day MRS ≥ 2 (Table 1). The TPI, DRAGON, and HAT scores all had AUCs below 0.65 indicating poor predictive performance in our data. Conclusion: The SPAN-100, HIAT2, iScore, and ASTRAL scores reliably predicts 90-day mRS of 2 or greater in patients with acute ischemic stroke.

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Discriminating Malignancy in Thyroid Nodules: The Nomogram Versus the Kwak and ACR TI-RADS

Otolaryngology ◽

10.1177/0194599820939071 ◽

2020 ◽

Vol 163 (6) ◽

pp. 1156-1165

Author(s):

Juan Xiao ◽

Qiang Xiao ◽

Wei Cong ◽

Ting Li ◽

Shouluan Ding ◽

...

Keyword(s):

Thyroid Nodules ◽

Characteristic Curve ◽

Area Under The Curve ◽

Diagnostic Study ◽

Diagnostic Efficiency ◽

Training Set ◽

Multivariable Logistic Regression Model ◽

Predictive Values ◽

Validation Set ◽

Sensitivity Specificity

Objective To develop an easy-to-use nomogram for discrimination of malignant thyroid nodules and to compare diagnostic efficiency with the Kwak and American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TI-RADS). Study Design Retrospective diagnostic study. Setting The Second Hospital of Shandong University. Subjects and Methods From March 2017 to April 2019, 792 patients with 1940 thyroid nodules were included into the training set; from May 2019 to December 2019, 174 patients with 389 nodules were included into the validation set. Multivariable logistic regression model was used to develop a nomogram for discriminating malignant nodules. To compare the diagnostic performance of the nomogram with the Kwak and ACR TI-RADS, the area under the receiver operating characteristic curve, sensitivity, specificity, and positive and negative predictive values were calculated. Results The nomogram consisted of 7 factors: composition, orientation, echogenicity, border, margin, extrathyroidal extension, and calcification. In the training set, for all nodules, the area under the curve (AUC) for the nomogram was 0.844, which was higher than the Kwak TI-RADS (0.826, P = .008) and the ACR TI-RADS (0.810, P < .001). For the 822 nodules >1 cm, the AUC of the nomogram was 0.891, which was higher than the Kwak TI-RADS (0.852, P < .001) and the ACR TI-RADS (0.853, P < .001). In the validation set, the AUC of the nomogram was also higher than the Kwak and ACR TI-RADS ( P < .05), each in the whole series and separately for nodules >1 or ≤1 cm. Conclusions When compared with the Kwak and ACR TI-RADS, the nomogram had a better performance in discriminating malignant thyroid nodules.

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Development of a practical prediction score for acute renal injury after surgery for Stanford type A aortic dissection

Interactive CardioVascular and Thoracic Surgery ◽

10.1093/icvts/ivaa011 ◽

2020 ◽

Vol 30 (5) ◽

pp. 746-753

Author(s):

Ning Dong ◽

Hulin Piao ◽

Yu Du ◽

Bo Li ◽

Jian Xu ◽

...

Keyword(s):

Aortic Dissection ◽

Risk Score ◽

Scoring System ◽

Characteristic Curve ◽

Area Under The Curve ◽

Kidney Injury ◽

Type A ◽

Postoperative Management ◽

Prediction Score ◽

Improve Patient

Abstract OBJECTIVES Acute kidney injury (AKI) is a common complication of cardiovascular surgery that is associated with increased mortality, especially after surgeries involving the aorta. Early detection and prevention of AKI in patients with aortic dissection may help improve outcomes. The objective of this study was to develop a practical prediction score for AKI after surgery for Stanford type A acute aortic dissection (TAAAD). METHODS This was a retrospective cohort study that included 2 independent hospitals. A larger cohort of 326 patients from The Second Hospital of Jilin University was used to identify the risk factors for AKI and to develop a risk score. The derived risk score was externally validated in a separate cohort of 102 patients from the other hospital. RESULTS The scoring system included the following variables: (i) age >45 years; (ii) body mass index >25 kg/m2; (iii) white blood cell count >13.5 × 109/l; and (iv) lowest perioperative haemoglobin <100 g/l, cardiopulmonary bypass duration >150 min and renal malperfusion. On receiver operating characteristic curve analysis, the score predicted AKI with fair accuracy in both the derivation [area under the curve 0.778, 95% confidence interval (CI) 0.726–0.83] and the validation (area under the curve 0.747, 95% CI 0.657–0.838) cohorts. CONCLUSIONS We developed a convenient scoring system to identify patients at high risk of developing AKI after surgery for TAAAD. This scoring system may help identify patients who require more intensive postoperative management and facilitate appropriate interventions to prevent AKI and improve patient outcomes.

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