External Validation Phases Research Articles

Serum metabolite biomarkers for the early diagnosis and monitoring of age-related macular degeneration

IntroductionAge-related macular degeneration (AMD) is a leading cause of irreversible blindness worldwide, with significant challenges for early diagnosis and treatment. ObjectivesTo identify new biomarkers that are important for the early diagnosis and monitoring of the severity/progression of AMD. MethodsWe investigated the diagnostic and monitoring potential of blood metabolites in a cohort of 547 individuals (167 healthy controls, 240 individuals with other eye diseases as eye disease controls, and 140 individuals with AMD) from 2 centers over three phases: discovery phase 1, discovery phase 2, and an external validation phase. The samples were analyzed via a mass spectrometry-based, widely targeted metabolomic workflow. In discovery phases 1 and 2, we built a machine learning algorithm to predict the probability of AMD. In the external validation phase, we further confirmed the performance of the biomarker panel identified by the algorithm. We subsequently evaluated the performance of the identified biomarker panel in monitoring the progression and severity of AMD. ResultsWe developed a clinically specific three-metabolite panel (hypoxanthine, 2-furoylglycine, and 1-hexadecyl-2-azelaoyl-sn-glycero-3-phosphocholine) via five machine learning models. The random forest model effectively discriminated patients with AMD from patents in the other two groups and showed acceptable calibration (area under the curve (AUC) = 1.0; accuracy = 1.0) in both discovery phases 1 and 2. An independent validation phase confirmed the diagnostic model’s efficacy (AUC = 0.962; accuracy = 0.88). The three-biomarker panel model demonstrated an AUC of 1.0 in differentiating the severity of AMD via RF machine learning, which was consistent across both the discovery and external validation phases. Additionally, the biomarker concentrations remained stable under repeated freeze–thaw cycles (P > 0.05). ConclusionsThis study reveals distinct metabolite variations in the serum of AMD patients, paving the way for the development of the first routine laboratory test for AMD.

Development and external validation of machine learning-based models to predict patients with cellulitis developing sepsis during hospitalisation

ObjectiveCellulitis is the most common cause of skin-related hospitalisations, and the mortality of patients with sepsis remains high. Some stratification models have been developed, but their performance in external validation...

Web-based artificial intelligence to predict cognitive impairment following stroke: A multicenter study

Background and purposePost-stroke cognitive impairment (PSCI) is highly prevalent in modern society. However, there is limited study implying an accurate and explainable machine learning model to predict PSCI. The aim of this study is to develop and validate a web-based artificial intelligence (AI) tool for predicting PSCI. MethodsThe retrospective cohort study design was conducted to develop and validate a web-based prediction model. Adults who experienced a stroke between January 1, 2004, and September 30, 2017, were enrolled, and patients with PSCI were followed up from the stroke index date until their last follow-up. The model's performance metrics, including accuracy, area under the curve (AUC), recall, precision, and F1 score, were compared. ResultsA total of 3209 stroke patients were included in the study. The model demonstrated an accuracy of 0.8793, AUC of 0.9200, recall of 0.6332, precision of 0.9664, and F1 score of 0.7651. In the external validation phase, the accuracy improved to 0.9039, AUC to 0.9094, recall to 0.7457, precision to 0.9168, and F1 score to 0.8224. The final model can be accessed at https://psci-calculator.my.id/. ConclusionOur results are able to produce a user-friendly interface that is useful for health practitioners to perform early prediction on PSCI. These findings also suggest that the provided AI model is reliable and can serve as a roadmap for future studies using AI models in a clinical setting

Updating M6 pregnancy of unknown location risk-prediction model including evaluation of clinical factors.

Ectopic pregnancy (EP) is a major high-risk outcome following a pregnancy of unknown location (PUL) classification. Biochemical markers are used to triage PUL as high vs low risk to guide appropriate follow-up. The M6 model is currently the best risk-prediction model. We aimed to update the M6 model and evaluate whether performance can be improved by including clinical factors. This prospective cohort study recruited consecutive PUL between January 2015 and January 2017 at eight units (Phase 1), with two centers continuing recruitment between January 2017 and March 2021 (Phase 2). Serum samples were collected routinely and sent for β-human chorionic gonadotropin (β-hCG) and progesterone measurement. Clinical factors recorded were maternal age, pain score, bleeding score and history of EP. Based on transvaginal ultrasonography and/or biochemical confirmation during follow-up, PUL were classified subsequently as failed PUL (FPUL), intrauterine pregnancy (IUP) or EP (including persistent PUL (PPUL)). The M6 models with (M6P ) and without (M6NP ) progesterone were refitted and extended with clinical factors. Model validation was performed using internal-external cross-validation (IECV) (Phase 1) and temporal external validation (EV) (Phase 2). Missing values were handled using multiple imputation. Overall, 5473 PUL were recruited over both phases. A total of 709 PUL were excluded because maternal age was < 16 years or initial β-hCG was ≤ 25 IU/L, leaving 4764 (87%) PUL for analysis (2894 in Phase 1 and 1870 in Phase 2). For the refitted M6P model, the area under the receiver-operating-characteristics curve (AUC) for EP/PPUL vs IUP/FPUL was 0.89 for IECV and 0.84-0.88 for EV, with respective sensitivities of 94% and 92-93%. For the refitted M6NP model, the AUCs were 0.85 for IECV and 0.82-0.86 for EV, with respective sensitivities of 92% and 93-94%. Calibration performance was good overall, but with heterogeneity between centers. Net Benefit confirmed clinical utility. The change in AUC when M6P was extended to include maternal age, bleeding score and history of EP was between -0.02 and 0.01, depending on center and phase. The corresponding change in AUC when M6NP was extended was between -0.01 and 0.03. At the 5% threshold to define high risk of EP/PPUL, extending M6P altered sensitivity by -0.02 to -0.01, specificity by0.03 to 0.04 and Net Benefit by -0.005 to 0.006. Extending M6NP altered sensitivity by -0.03 to -0.01, specificity by 0.05 to 0.07 and Net Benefit by -0.005 to 0.006. The updated M6 model offers accurate diagnostic performance, with excellent sensitivity for EP. Adding clinical factors to the model improved performance in some centers, especially when progesterone levels were not suitable or unavailable. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Application of Hybrid DeepLearning Architectures for Identification of Individuals with Obsessive Compulsive Disorder Based on EEG Data.

Objective: Obsessive-compulsive disorder (OCD) is a highly common psychiatric disorder. The symptoms of this condition overlap and co-occur with those of other psychiatric illnesses, making diagnosis difficult. The availability of biomarkers could be useful for aiding in diagnosis, although prior neuroimaging studies were unable to provide such biomarkers. Method: In this study, patients with OCD were classified from healthy controls using 2 different hybrid deep learning models: one-dimensional convolutional neural networks (1DCNN) together with long-short term memory (LSTM) and gradient recurrent units (GRU), respectively. Results: Both models exhibited exceptional classification accuracies in cross-validation and external validation phases. The mean classification accuracies in the cross-validation stage were 90.88% and 85.91% for the 1DCNN-LSTM and 1DCNN-GRU models, respectively. The inferior frontal, temporal, and occipital electrodes were predominant in providing discriminative features. Conclusion: Our findings underscore the potential of hybrid deep learning architectures utilizing EEG data to effectively differentiate patients with OCD from healthy controls. This promising approach holds implications for advancing clinical decision-making by offering valuable insights into diagnostic markers for OCD.

External and internal validation of healthcare-associated infection data collected by the Korean National healthcare-associated Infections Surveillance System (KONIS)

This study analyzed the validity of healthcare-associated infection (HAI) data of the Korean National healthcare-associated Infections Surveillance System. The validation process consisted of external (EV) and internal (IV) validation phases. Of the 193 hospitals that participated from July 2016 through June 2017, EV was performed for 10 hospitals that were selected based on the HAI rate percentile. The EV team reviewed 295 medical records for 60 HAIs and 235 non-HAI control patients. IV was performed for both the 10 EV hospitals and 11 other participating hospitals that did not report any HAIs. In the EV, the diagnosis of urinary tract infections had a sensitivity of 72.0% and a specificity of 99.3%. The respective sensitivities of bloodstream infection and pneumonia were 63.2% and 70.6%; the respective specificities were 98.8% and 99.6%. The agreement (ĸ) between the EV and IV for 10 hospitals was 0.754 for urinary tract infections and 0.674 for bloodstream infections (P<.001, respectively). Additionally, IV found additional cases among 11 zero-report hospitals. This study demonstrates the need for ongoing validation and continuous training to maintain the accuracy of nationwide surveillance data. IV should be considered a validation method to supplement EV.

Validating fatty acid binding protein 3 as a diagnostic andprognostic biomarker for peripheral arterial disease: Athree-year prospective follow-up study.

Patients with peripheral arterial disease (PAD) often remain undiagnosed and therefore suboptimally managed. Here, we investigated the diagnostic and prognostic potential of fatty acid binding protein 3 (FABP3) in patients with PAD. In the discovery phase, 374 PAD and 184 non-PAD patients were recruited from vascular surgery ambulatory clinics at St. Michael's Hospital (Toronto, Ontario, Canada) between October 4, 2017 to October 29, 2018. The diagnostic ability of baseline FABP3 level was investigated through receiver operator characteristic (ROC) curves to determine two cutoff points: 1) an exclusionary "rule out" cutoff point, and 2) a confirmatory "rule in" cutoff point. Next, these cutoff points were confirmed in the external validation phase using a separate cohort of 312 patients (180 PAD and 132 non-PAD) recruited from ambulatory vascular surgery clinics at St.Michael's Hospital (Canada) between November 6, 2018-July 30, 2019. Cox regression analyses were used to explore the independent association between FABP3 and major adverse limb events (MALE - defined as need for arterial revascularization or major amputation) and decrease in ankle-brachial index (ABI -defined as drop ≥0.15) during 3 years of follow-up. In the discovery phase, FABP3 levels were significantly elevated in patients with PAD compared to non-PAD patients. ROC analysis demonstrated that FABP3 had an AUC of 0.83 (95% CI: 0.81-0.86, p-value < 0.001). FABP3 exclusionary cutoff was <1.55ng/ml (sensitivity=96%; specificity=40%), whereas FABP3 confirmatory cutoff was >3.55ng/ml (sensitivity=43%; specificity=95%) - values that were confirmed in the external validation phase. Cox regression analysis demonstrated FABP3 to be an independent predictor of increase in MALE [HR=1.14 (1.03-1.29); p-value=0.010] and worsening PAD status (drop in ABI >0.15 [HR=1.11 (1.02-1.19); p-value=0.009]). Our findings suggested that FABP3 levels can be used as both a diagnostic and prognostic biomarker for PAD, and may facilitate risk stratification in select individuals for purposes of vascular evaluation or intensive medical management. Funding for this study was provided by the Bill and Vicky Blair Foundation.

Development and external validation of automated ICD-10 coding from discharge summaries using deep learning approaches

ObjectivesTo develop an automated international classification of diseases (ICD) coding tool using natural language processing (NLP) and discharge summary texts from Thailand. Materials and methodsThe development phase included 15,329 discharge summaries from Ramathibodi Hospital from January 2015 to December 2020. The external validation phase included Medical Information Mart for Intensive Care III (MIMIC-III) data. Three algorithms were developed: naïve Bayes with term frequency-inverse document frequency (NB-TF-IDF), convolutional neural network with neural word embedding (CNN-NWE), and CNN with PubMedBERT (CNN-PubMedBERT). In addition, two state-of-the-art models were also considered; convolutional attention for multi-label classification (CAML) and pretrained language models for automatic ICD coding (PLM-ICD). ResultsThe CNN-PubMedBERT model provided average micro- and macro-area under precision-recall curve (AUPRC) of 0.6605 and 0.5538, which outperformed CNN-NWE (0.6528 and 0.5564), NB-TF-IDF (0.4441 and 0.3562), and CAML (0.6257 and 0.4964), with corresponding differences of (0.0077 and −0.0026), (0.2164 and 0.1976), and (0.0348 and 0.0574), respectively. However, CNN-PubMedBERT performed less well relative to PLM-ICD, with corresponding AUPRCs of 0.7202 and 0.5865. The CNN-PubMedBERT model was externally validated using two subsets of MIMIC-III; MIMIC-ICD-10, and MIMIC-ICD-9 datasets, which contained 40,923 and 31,196 discharge summaries. The average micro-AUPRCs were 0.3745, 0.6878, and 0.6699, corresponding to directly predictive MIMIC-ICD-10, MIMIC-ICD-10 fine-tuning, and MIMIC-ICD-9 fine-tuning approaches; the average macro-AUPRCs for the corresponding models were 0.2819, 0.4219 and 0.5377, respectively. DiscussionCNN-PubMedBERT performed second-best to PLM-ICD, with considerable variation observed between average micro- and macro-AUPRC, especially for external validation, generally indicating good overall prediction but limited predictive value for small sample sizes. External validation in a US cohort demonstrated a higher level of model prediction performance. ConclusionBoth PLM-ICD and CNN-PubMedBERT models may provide useful tools for automated ICD-10 coding. Nevertheless, further evaluation and validation within Thai and Asian healthcare systems may prove more informative for clinical application.

Diagnostic role of plasma ORM2 in differentiating prostate cancer from benign prostatic hyperplasia.

Markers are needed to increase the diagnostic accuracy of prostate-specific antigen (PSA) in prostate cancer (PCa) screening. Mounting evidence has shown that plasma proteins can be hopeful biomarkers for cancer diagnosis. Tandem mass tag (TMT)-based proteomics and parallel reaction monitoring (PRM) analysis were used to screen the differential proteins and further validated in other independent studies (n = 539). Receiver-operating characteristic (ROC), decision curves and nomograms were applied to assess the diagnostic accuracy of biomarkers. Three candidate proteins (DBP, LCAT and ORM2) were preliminarily screened. Subsequent validation studies revealed significant upregulation of ORM2 in PCa patients across other independent cohorts. ORM2 yielded excellent discriminative power for PCa from benign prostatic hyperplasia (BPH) patients (AUC = 0.861 and 0.814 in validation phases 2a and 2b, respectively). Importantly, the combination of ORM2 and PSA gave better predictive accuracy than PSA alone. We incorporated age, PSA and ORM2 into a nomogram, which yielded C-index of 0.883 in validation phase 2a. A similar C-index of 0.879 was obtained in external validation phase 2b. In summary, our study suggests that ORM2 could be treated as a complementary biomarker for PSA in distinguishing PCa from BPH.

Identification of key predictors of hospital mortality in critically ill patients with embolic stroke using machine learning.

Embolic stroke (ES) is characterized by high morbidity and mortality. Its mortality predictors remain unclear. The present study aimed to use machine learning (ML) to identify the key predictors of mortality for ES patients in the intensive care unit (ICU). Data were extracted from two large ICU databases: Medical Information Mart for Intensive Care (MIMIC)-IV for training and internal validation, and eICU Collaborative Research Database (eICU-CRD) for external validation. We developed predictive models of ES mortality based on 15 ML algorithms. We relied on the synthetic minority oversampling technique (SMOTE) to address class imbalance. Our main performance metric was area under the receiver operating characteristic (AUROC). We adopted recursive feature elimination (RFE) for feature selection. We assessed model performance using three disease-severity scoring systems as benchmarks. Of the 1566 and 207 ES patients enrolled in the two databases, there were 173 (15.70%), 73 (15.57%), and 36 (17.39%) hospital mortality in the training, internal validation, and external validation cohort, respectively. The random forest (RF) model had the largest AUROC (0.806) in the internal validation phase and was chosen as the best model. The AUROC of the RF compact (RF-COM) model containing the top six features identified by RFE was 0.795. In the external validation phase, the AUROC of the RF model was 0.838, and the RF-COM model was 0.830, outperforming other models. Our findings suggest that the RF model was the best model and the top six predictors of ES hospital mortality were Glasgow Coma Scale, white blood cell, blood urea nitrogen, bicarbonate, age, and mechanical ventilation.

Country of origin label monitoring of musky and common octopuses (Eledone spp. and Octopus vulgaris) by means of a portable near-infrared spectroscopic device

Modern analytical techniques using miniaturized and portable near infrared (NIR) spectroscopy instruments are particularly suited for assessing the authenticity of fishery products since meeting the requirements of rapidity, eco-friendliness, cost-effectiveness, and easiness of application. The objective of the present study was to verify the suitability of use of a portable and ultra-compact NIR spectrometer combined with machine learning to characterize the geographic origin of two octopus species. Replicate NIR spectra (908.1–1676.2 nm) of 118 musky and 29 common octopus specimens (Eledone spp. and Octopus vulgaris) from Portuguese Atlantic or Spanish Mediterranean fishing areas were recorded, pre-processed and elaborated via the following classification algorithms: orthogonal partial least square discriminant analysis (OPLS-DA), logistic regression (LR), random forest (RF), support vector machine (SVM), and multilayer perceptron-artificial neural network (MLP-ANN). When 7-fold cross validation was performed on 75% of data, the results showed that linear tools (OPLS-DA and LR) were the most powerful and stable techniques in recognizing the origin of both octopus species (mean sensitivity, specificity, accuracy, and precision values above 98%). During the external validation phase OPLS-DA, SVM, and MLP-ANN performed better for common octopuses, while LR and MLP-ANN for musky octopuses. The achieved outcomes suggest the combination of portable NIR spectroscopy and machine learning as a promising plan of action to be adopted for the creation of an integrated analytical platform with capabilities for automated data recording, processing, and reporting, which may be helpful for on-site and in-line monitoring of fishery products.

Three plasma-based microRNAs as potent diagnostic biomarkers for endometrial cancer.

MicroRNAs (miRNAs), with noticeable stability and unique expression pattern in plasma of patients with various diseases, are powerful non-invasive biomarkers for cancer detection including endometrial cancer (EC). The objective of this study was to identify promising miRNA biomarkers in plasma to assist the clinical screening of EC. A total of 93 EC and 79 normal control (NC) plasma samples were analyzed using Quantitative Real-time Polymerase Chain Reaction (qRT-PCR) in this four-stage experiment. The receiver operating characteristic curve (ROC) analysis was conducted to evaluate the diagnostic value. Additionally, the expression features of the identified miRNAs were further explored in tissues and plasma exosomes samples. The expression of miR-142-3p, miR-146a-5p, and miR-151a-5p was significantly overexpressed in the plasma of EC patients compared with NCs. Areas under the ROC curve of the 3-miRNA signature were 0.729, 0.751, and 0.789 for the training, testing, and external validation phases, respectively. The diagnostic performance of the identified signature proved to be stable in the three public datasets and superior to the other miRNA biomarkers in EC diagnosis. Moreover, the expression of miR-151a-5p was significantly elevated in EC plasma exosomes. A signature consisting of 3 plasma miRNAs was identified and showed potential for the non-invasive diagnosis of EC.

MicroRNA expression profile in serum reveals novel diagnostic biomarkers for endometrial cancer.

Purpose: Circulating microRNAs (miRNAs) prove to be promising diagnostic biomarkers for various cancers, including endometrial cancer (EC). The present study aims to identify serum microRNAs that can serve as potential biomarkers for EC diagnosis.Patients and methods: A total of 92 EC and 102 normal control (NC) serum samples were analyzed using quantitative real-time polymerase chain reaction (qRT-PCR) in this four-phase experiment. The logistic regression method was used to construct a diagnostic model based on the differentially expressed miRNAs in serum. The receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic value. To further validate the diagnostic capacity of the identified signature, the 6-miRNA marker was compared with previously reported biomarkers and verified in three public datasets. In addition, the expression characteristics of the identified miRNAs were further explored in tissue and serum exosomes samples.Results: Six miRNAs (miR-143-3p, miR-195-5p, miR-20b-5p, miR-204-5p, miR-423-3p, and miR-484) were significantly overexpressed in the serum of EC compared with NCs. Areas under the ROC of the 6-miRNA signatures were 0.748, 0.833, and 0.967 for the training, testing, and the external validation phases, respectively. The identified signature has a very stable diagnostic performance in the large cohorts of three public datasets. Compared with previously identified miRNA biomarkers, the 6-miRNA signature in the present study has superior performance in diagnosing EC. Moreover, the expression of miR-143-3p and miR-195-5p in tissues and the expression of miR-20b-5p in serum exosomes were consistent with those in serum.Conclusions: We established a 6-miRNA signature in serum and they could function as potential non-invasive biomarker for EC diagnosis.

MicroRNA panel in serum reveals novel diagnostic biomarkers for prostate cancer.

PurposeMicroRNAs (miRNAs), which could be stably preserved and detected in serum or plasma, could act as biomarkers in cancer diagnosis. Prostate cancer is the second cancer in males for incidence. This study aimed to establish a miRNA panel in peripheral serum which could act as a non-invasive biomarker helping diagnosing PC.MethodsA total of 86 PC patients and 86 normal control serum samples were analyzed through a four-stage experimental process using quantitative real-time polymerase chain reaction. Logistic regression method was used to construct a diagnostic model based on the differentially expressed miRNAs in serum. Receiver operating characteristic curves were constructed to evaluate the diagnostic accuracy. We also compared the 3-miRNA panel with previously reported biomarkers and verified in four public datasets. In addition, the expression characteristics of the identified miRNAs were further explored in tissue and serum exosomes samples.ResultsWe identified a 3-miRNA signature including up-regulated miR-146a-5p, miR-24-3p and miR-93-5p for PC detection. Areas under the receiver operating characteristic curve of the 3-miRNA panel for the training, testing and external validation phase were 0.819, 0.831 and 0.814, respectively. The identified signature has a very stable diagnostic performance in the large cohorts of four public datasets. Compared with previously identified miRNA biomarkers, the 3-miRNA signature in this study has superior performance in diagnosing PC. What’s more, the expression level of miR-93-5p was also elevated in exosomes from PC samples. However, in PC tissues, none of the three miRNAs showed significantly dysregulated expression.ConclusionsWe established a three-miRNA panel (miR-146a-5p, miR-24-3p and miR-93-5p) in peripheral serum which could act as a non-invasive biomarker helping diagnosing PC.

Evaluation of Near Infrared Spectroscopy (NIRS) for Estimating Soil Organic Matter and Phosphorus in Mediterranean Montado Ecosystem

The Montado is an agro-silvo-pastoral ecosystem characteristic of the Mediterranean region. Pasture productivity and, consequently, the possibilities for intensifying livestock production depend on soil fertility. Soil organic matter (SOM) and phosphorus (P2O5) are two indicators of the evolution of soil fertility in this ecosystem. However, their conventional analytical determination by reference laboratory methods is costly, time consuming, and laborious and, thus, does not meet the needs of current production systems. The aim of this study was to evaluate an alternative approach to estimate SOM and soil P2O5 based on near infrared spectroscopy (NIRS) combined with multivariate data analysis. For this purpose, 242 topsoil samples were collected in 2019 in eleven fields. These samples were subjected to reference laboratory analysis and NIRS analysis. For NIRS, 165 samples were used during the calibration phase and 77 samples were used during the external validation phase. The results of this study showed significant correlation between NIRS calibration models and reference methods for quantification of these soil parameters. The coefficient of determination (R2, 0.85 for SOM and 0.76 for P2O5) and the residual predictive deviation (RPD, 2.7 for SOM and 2.2 for P2O5) obtained in external validation indicated the potential of NIRS to estimate SOM and P2O5, which can facilitate farm managers’ decision making in terms of dynamic management of animal grazing and differential fertilizer application.

Plasma tRNA Fragments Derived from 5′ Ends as Novel Diagnostic Biomarkers for Early-Stage Breast Cancer

Small RNAs derived from tRNAs are attracting considerable attention; however, the effects of tRNA-derived fragments (tRFs) and tRNA halves (tiRNAs) as biomarkers have not been investigated in early-stage breast cancer (EBC). The study aimed to explore whether tRFs and tiRNAs could be detected in plasma and whether they could serve as diagnostic biomarkers. The study was conducted in four phases. Thirty tRFs and tiRNAs were selected by high-throughput sequencing in screening phase and then assessed in training, testing, and external validation phases by qRT-PCR. Six tRFs (tRF-Glu-CTC-003, tRF-Gly-CCC-007, tRF-Gly-CCC-008, tRF-Leu-CAA-003, tRF-Ser-TGA-001, and tRF-Ser-TGA-002) were found significantly downregulated in plasma samples of patients with EBC compared with normal controls, and all were derived from 5′ ends of tRNAs. Patients with HER2+ EBC with low expression levels of tRF-Glu-CTC-003 were related to worse disease-free survival and overall survival. The identified tRFs were further examined in cell supernatants, exosomes isolated from plasma, and tissues. In conclusion, our study identified six tRFs from the 5′ ends of tRNAs as novel diagnostic biomarkers for EBC, providing additional evidence for, and a better understanding of, circulating tRFs and EBC.

Discovery of Potential Serum Protein Biomarkers in Ankylosing Spondylitis Using Tandem Mass Tag-Based Quantitative Proteomics.

Ankylosing spondylitis (AS) is a systemic, chronic, and inflammatory rheumatic disease that affects 0.2% of the population. Current diagnostic criteria for disease activity rely on subjective Bath Ankylosing Spondylitis Disease Activity Index scores. Here, we aimed to discover a panel of serum protein biomarkers. First, tandem mass tag (TMT)-based quantitative proteomics was applied to identify differential proteins between 15 pooled active AS and 60 pooled healthy subjects. Second, cohort 1 of 328 humans, including 138 active AS and 190 healthy subjects from two independent centers, was used for biomarker discovery and validation. Finally, biomarker panels were applied to differentiate among active AS, stable AS, and healthy subjects from cohort 2, which enrolled 28 patients with stable AS, 26 with active AS, and 28 healthy subjects. From the proteomics study, a total of 762 proteins were identified and 46 proteins were up-regulated and 59 proteins were down-regulated in active AS patients compared to those in healthy persons. Among them, C-reactive protein (CRP), complement factor H-related protein 3 (CFHR3), α-1-acid glycoprotein 2 (ORM2), serum amyloid A1 (SAA1), fibrinogen γ (FG-γ), and fibrinogen β (FG-β) were the most significantly up-regulated inflammation-related proteins and S100A8, fatty acid-binding protein 5 (FABP5), and thrombospondin 1 (THBS1) were the most significantly down-regulated inflammation-related proteins. From the cohort 1 study, the best panel for the diagnosis of active AS vs healthy subjects is the combination of CRP and SAA1. The area under the receiver operating characteristic (ROC) curve was nearly 0.900, the sensitivity was 0.970%, and the specificity was 0.805% at a 95% confidence interval from 0.811 to 0.977. Using 0.387 as the cutoff value, the predictive values reached 92.00% in the internal validation set (62 with active AS vs 114 healthy subjects) and 97.50% in the external validation phase (40 with active AS vs 40 healthy subjects). From the cohort 2 study, a panel of CRP and SAA1 can differentiate well among active AS, stable AS, and healthy subjects. For active AS vs stable AS, the area under the ROC curve was 0.951, the sensitivity was 96.43%, the specificity was 88.46% at a 95% confidence interval from 0.891 to 1, and the coincidence rate was 92.30%. For stable AS vs healthy humans, the area under the ROC curve was 0.908, the sensitivity was 89.29%, the specificity was 78.57% at a 95% confidence interval from 0.836 to 0.980, and the coincidence rate was 83.93%. For active AS vs healthy subjects, the predictive value was 94.44%. The results indicated that the CRP and SAA1 combination can potentially diagnose disease status, especially for active or stable AS, which will be conducive to treatment recommendation for patients with AS.

MicroRNA profiling in serum: Potential signatures for breast cancer diagnosis.

Circulating microRNAs (miRNAs) prove to be potential non-invasive indicators of cancers. The purpose of this study is to profile serum miRNA expression in breast cancer (BC) patients to find potential biomarkers for BC diagnosis. The miRNA expression patterns of serum samples from 216 BC patients and 214 normal control subjects were compared. A four-phase validation was conducted for biomarker identification. In the screening phase, the Exiqon miRNA qPCR panel was employed to select candidates, which were further analyzed by quantitative reverse transcriptase PCR in the following training, testing, and external validation phases. A 12-miRNA (let-7b-5p, miR-106a-5p, miR-19a-3p, miR-19b-3p, miR-20a-5p, miR-223-3p, miR-25-3p, miR-425-5p, miR-451a, miR-92a-3p, miR-93-5p, and miR-16-5p) panel in serum was constructed. The diagnostic performance of the panel was assessed using ROC curve analyses. The area under the curves (AUCs) were 0.952, 0.956, 0.941 and 0.950 for the four separate phases, respectively. Additionally, the expression features of the 12 miRNAs were further explored in 32 pairs of BC tumor and para-tumor tissues, and 32 pairs of serum exosomes samples from patients and healthy subjects. miR-16-5p, miR-106a-5p, miR-25-3p, miR-425-5p, and miR-93-5p were highly overexpressed and let-7b-5p was conversely downregulated in tumor tissues. Excluding miR-20a-5p and miR-223-3p, the 10 other miRNAs were all significantly upregulated in BC serum-derived exosomes. A signature consisting of 12 serum miRNAs was identified and showed potential for use in non-invasive diagnosis of BC.

Circulating plasma microRNA signature for the diagnosis of cervical cancer.

To determine the utility of plasma microRNAs (miRNAs) as biomarkers in cervical cancer (CC). Some studies were conducted about the specific expression of plasma miRNAs in the diagnosis of CC. Plasma samples of 97 CC patients and 87 normal controls (NCs) were used to identify dysregulation of miRNAs in the training, testing, and external validation phases. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) were used to evaluate the sensitivity and specificity of identified individual miRNAs and miRNA panels for the diagnosis of CC. Expression levels of specific miRNAs were also examined in plasma exosomes and tissue samples of CC patients. Four plasma miRNAs (miR-146a-5p, miR-151a-3p, miR-2110 and miR-21-5p) which showed up-regulation were identified and validated in CC patients. A panel of the four miRNAs were constructed as potential diagnostic markers for CC. The AUCs of the panel of these four-miRNAs for the training, testing, and external validation phases were 0.911, 0.774, and 0.786, respectively. miR-146a-5p and miR-21-5p levels were all up-regulated in CC tissue specimens, whereas miR-146a-5p, miR-151a-3p, and miR-2110 levels were up-regulated in plasma exosomes. The signature of the four-miRNAs identified in peripheral plasma is a promising novel biomarker for the diagnosis of CC.

Identifying Opioid Use Disorder in the Emergency Department: Multi-System Electronic Health Record-Based Computable Phenotype Derivation and Validation Study.

BackgroundDeploying accurate computable phenotypes in pragmatic trials requires a trade-off between precise and clinically sensical variable selection. In particular, evaluating the medical encounter to assess a pattern leading to clinically significant impairment or distress indicative of disease is a difficult modeling challenge for the emergency department.ObjectiveThis study aimed to derive and validate an electronic health record–based computable phenotype to identify emergency department patients with opioid use disorder using physician chart review as a reference standard.MethodsA two-algorithm computable phenotype was developed and evaluated using structured clinical data across 13 emergency departments in two large health care systems. Algorithm 1 combined clinician and billing codes. Algorithm 2 used chief complaint structured data suggestive of opioid use disorder. To evaluate the algorithms in both internal and external validation phases, two emergency medicine physicians, with a third acting as adjudicator, reviewed a pragmatic sample of 231 charts: 125 internal validation (75 positive and 50 negative), 106 external validation (56 positive and 50 negative).ResultsCohen kappa, measuring agreement between reviewers, for the internal and external validation cohorts was 0.95 and 0.93, respectively. In the internal validation phase, Algorithm 1 had a positive predictive value (PPV) of 0.96 (95% CI 0.863-0.995) and a negative predictive value (NPV) of 0.98 (95% CI 0.893-0.999), and Algorithm 2 had a PPV of 0.8 (95% CI 0.593-0.932) and an NPV of 1.0 (one-sided 97.5% CI 0.863-1). In the external validation phase, the phenotype had a PPV of 0.95 (95% CI 0.851-0.989) and an NPV of 0.92 (95% CI 0.807-0.978).ConclusionsThis phenotype detected emergency department patients with opioid use disorder with high predictive values and reliability. Its algorithms were transportable across health care systems and have potential value for both clinical and research purposes.