Potential False Negatives Research Articles

Clinical utility of receptor status prediction in breast cancer and misdiagnosis identification using deep learning on hematoxylin and eosin-stained slides

BackgroundMolecular profiling of estrogen receptor (ER), progesterone receptor (PR), and ERBB2 (also known as Her2) is essential for breast cancer diagnosis and treatment planning. Nevertheless, current methods rely on the qualitative interpretation of immunohistochemistry and fluorescence in situ hybridization (FISH), which can be costly, time-consuming, and inconsistent. Here we explore the clinical utility of predicting receptor status from digitized hematoxylin and eosin-stained (H&E) slides using machine learning trained and evaluated on a multi-institutional dataset.MethodsWe developed a deep learning system to predict ER, PR, and ERBB2 statuses from digitized H&E slides and evaluated its utility in three clinical applications: identifying hormone receptor-positive patients, serving as a second-read tool for quality assurance, and addressing intratumor heterogeneity. For development and validation, we collected 19,845 slides from 7,950 patients across six independent cohorts representative of diverse clinical settings.ResultsHere we show that the system identifies 30.5% of patients as hormone receptor-positive, achieving a specificity of 0.9982 and a positive predictive value of 0.9992, demonstrating its ability to determine eligibility for hormone therapy without immunohistochemistry. By restaining and reassessing samples flagged as potential false negatives, we discover 31 cases of misdiagnosed ER, PR, and ERBB2 statuses.ConclusionsThese findings demonstrate the utility of the system in diverse clinical settings and its potential to improve breast cancer diagnosis. Given the substantial focus of current guidelines on reducing false negative diagnoses, this study supports the integration of H&E-based machine learning tools into workflows for quality assurance.

Direct quadruplex real-time VPCR for simultaneous rapid detection of Pinelliae Rhizoma, Rhizoma Pinelliae Pedatisectae and Rhizoma Typhonii Flagelliformis

Known for its health benefits, Pinelliae Rhizoma is frequently utilized in many botanical dietary supplements and herbal products. However, the substantial demand has led to the frequent adulteration with morphologically similar species, such as Rhizoma Pinelliae Pedatisectae and Rhizoma Typhonii Flagelliformis. Addressing this challenge, a direct quadruplex real-time VPCR approach has been developed in this study. Firstly, a direct lysis protocol has been implemented to facilitate the rapid release of genomic DNA within only 2 min. Secondly, specific primers and probes were designed for Pinellia ternata (Thunb.) Breit., Pinellia pedatisecta Schott and Typhonium flagelliforme (Lodd.) Blume, respectively. A conserved sequence on the ITS was also included as an internal control to mitigate the potential false negatives arising from DNA extraction or DNA amplification errors. Furthermore, the amplification process has been refined through a VPCR heating model, curtailing the detection to a scant 30 min. After a series of optimizations, the developed direct quadruplex VPCR allows for the simultaneous detection of these three plant materials, with each material having a detection sensitivity as low as 1 pg/µL. At last, the method's applicability was validated by using several commercial samples previously identified by Sanger sequencing.

Predicting the involvement of polyQ- and polyA in protein-protein interactions by their amino acid context

Homorepeats, specifically polyglutamine (polyQ) and polyalanine (polyA), are often implicated in protein-protein interactions (PPIs). So far, a method to predict the participation of homorepeats in protein interactions is lacking. We propose a machine learning approach to identify PPI-involved polyQ and polyA regions within the human proteome based on known interacting regions. Using the dataset of human homorepeats, we identified 157 polyQ and 745 polyA regions potentially involved in PPIs. Machine learning models, trained on amino acid context and homorepeat length, demonstrated high precision (0.90–0.98) but variable recall (0.42–0.85). Random forest outperformed other models (AUC polyQ = 0.686, AUC polyA = 0.732) using the positions surrounding the homorepeat −10 to +10. Integrating paralog information marginally improved predictions but was excluded for model simplicity. Further optimization revealed that for polyQ, using amino acid surrounding positions from −6 to +6 increased AUC to 0.715. For polyA, no improvement was found. Incorporating coiled coil overlap information enhanced polyA predictions (AUC = 0.745) but not polyQ. Finally, we applied these models to predict PPI involvement across all polyQ and polyA regions, identifying potential interactions. Case studies illustrated the method's predictive capacity, highlighting known interacting regions with high scores and elucidating potential false negatives.

Diagnostic Accuracy of the Desmopressin Stimulation Test in the Comprehensive Assessment of ACTH-Dependent Cushing’s Syndrome: A Comparative Analysis with BIPSS and TSS

ABSTRACT Background Cushing’s syndrome (CS) poses diagnostic challenges, particularly in distinguishing pituitary-dependent Cushing’s syndrome, Cushing’s disease (CD), from the ectopic ACTH syndrome (EAS). This study evaluated the diagnostic value of the desmopressin stimulation test (DST) in patients with ACTH-dependent CS in helping this discrimination. Methods Twenty-three ACTH-dependent CS patients underwent sequential DST, bilateral inferior petrosal sinus sampling (BIPSS), and transsphenoidal surgery (TSS). Two definitions of a positive DST results were applied. Diagnostic performance was assessed using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and likelihood ratios. To avoid bias from predetermined criteria, we generated univariate receiver-operating characteristic (ROC) curves, plotting sensitivity against 1-specificity at various percentage cortisol and ACTH response levels. Results Against BIPSS, DST demonstrated robust sensitivity (Definition 1: 90.0%, Definition 2: 76.2%) and overall accuracy (Definition 1: 87.0%, Definition 2: 73.9%). PPV was high (Definition 1: 95.0%, Definition 2: 94.1%), but NPV indicated potential false negatives. Compared to TSS, DST showed good sensitivity (Definition 1: 90.9–77.3%) and PPV (100.0%) but limited NPV (16.7%). The likelihood ratios emphasized the diagnostic value of the test. Notably, against TSS, DST showed perfect discriminatory power (AUC 1.000 for percent ACTH, 0.983 for percent cortisol). Conclusion The desmopressin test shows promise in accurately identifying the underlying cause of ACTH-dependent CS, potentially reducing the reliance on invasive procedures and providing a practical solution for managing complex cases. Further research with larger cohorts is required to validate the utility of the DST in routine clinical practice.

Detection of explosives residue using a novel subsampling technique for DART-HRMS analysis

In the ensuing investigation after an explosion, determining the explosive used is of prime importance to establish investigative leads. Post-blast samples have many interferences and considerations that make quick, reliable identification a challenge. The use of a novel subsampling technique with DART-HRMS provides the ability to quickly detect and identify explosive residue after detonation. Simulated improvised explosive devices were constructed with a variety of materials and detonated with the help of the Montgomery County Fire Marshal’s Office (TX). Post-blast debris was subsequently collected and swabbed with a novel subsampling technique, utilizing filter paper. This filter paper was then introduced into the DART gas stream, with an internal standard to minimize potential false negatives. After introducing explosive residue from swabbed post-blast substrates, characteristic ions of selected constituents of smokeless powder including diphenylamine, ethyl centralite, di-n-butyl phthalate, and nitroglycerin were detected and confirmed through comparison of accurate mass measurements to theoretical exact masses. Additionally, characteristic ions of 2,4,6-trinitrotoluene (TNT) and Royal Demolition eXplosive (RDX) were also detected using this technique. Overall, the detection of characteristic ions was more successful when recovering residue from plastic compared to wood or metal, with success rates routinely at 100%. Implementing this screening technique enables rapid detection and reliable identification of explosive residue in a detonation incident. The developed subsampling technique provides practitioners with a practical method of screening post-blast debris in a laboratory setting, requiring minimal sample preparation.

Generic qPCR assays for quality control in environmental DNA research

AbstractEnvironmental DNA (eDNA) has been widely used for species surveillance. However, the lack of adequate quality control in many eDNA research projects and applications can lead to false‐negative results, greatly affecting biosecurity surveillance and conservation efforts. Exogenous DNA is routinely added to eDNA samples and used as a positive control, typically after DNA extraction. However, this type of positive control is only able to identify false negatives due to errors at the amplification stage. Therefore, errors in upstream processes, such as sample collection will not be identified by an exogenous control. We designed two independent sets of generic quality control qPCR assays (QCqPCR) targeting abundant endogenous DNA that is obtained during sample collection. Our QCqPCR assays target the chloroplast 16S and 23S ribosomal RNA sequences. In silico analyses indicated these regions were highly conserved among plants, algae and bacteria commonly found in freshwater, marine, or terrestrial environments. These QCqPCR assays were purposely mismatched against the human genome to avoid false positives resulting from human DNA contamination. Both assays remained highly efficient and sensitive under annealing temperatures between 58 and 62°C, allowing them to be multiplexed with most qPCR analyses. We validated our assays by multiplexing with a species‐specific Murray cod (Maccullochella peelii) assay on field‐collected environmental water samples. Potential false‐negative reactions can be identified by the failed or suppressed QCqPCR assay and the negative species‐specific assay. We recommend incorporating either one of the QCqPCR assays in qPCR‐based eDNA analysis to identify potential false negatives and improve the reliability of eDNA surveys.

Incorporation of Eye Tracking and Gaze Feedback to Characterize and Improve Radiologist Search Patterns of Chest X-Rays: A Randomized Controlled Clinical Trial

Diagnostic errors in radiology often result from incomplete visual assessments attributed to insufficient training in search patterns. Radiologists lack consistent feedback, relying on ad-hoc strategies, leading to suboptimal patterns and potential false negatives. This study used eye tracking to analyze search patterns, quantify performance metrics, and evaluate the impact of an automated continuous improvement feedback-driven framework on detection accuracy. In a controlled trial with ten residents, the intervention group (received feedback) demonstrated a significant 38.9% absolute improvement in detecting suspicious pulmonary nodules compared to the control group (5.6%, p=0.006). While improvements were more rapid over sessions (p=0.0001), other metrics showed no significant changes. The findings highlight the potential of automated feedback systems to enhance radiologist accuracy and reduce errors, emphasizing the need for further research and broader implementation in radiology training.

Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results

Metabolite genome-wide association studies (mGWAS) have advanced our understanding of the genetic control of metabolite levels. However, interpreting these associations remains challenging due to a lack of tools to annotate gene-metabolite pairs beyond the use of conservative statistical significance threshold. Here, we introduce the shortest reactional distance (SRD) metric, drawing from the comprehensive KEGG database, to enhance the biological interpretation of mGWAS results. We applied this approach to three independent mGWAS, including a case study on sickle cell disease patients. Our analysis reveals an enrichment of small SRD values in reported mGWAS pairs, with SRD values significantly correlating with mGWAS p values, even beyond the standard conservative thresholds. We demonstrate the utility of SRD annotation in identifying potential false negatives and inaccuracies within current metabolic pathway databases. Our findings highlight the SRD metric as an objective, quantitative and easy-to-compute annotation for gene-metabolite pairs, suitable to integrate statistical evidence to biological networks.

Assessment of gunshot residue detection on a large variety of surfaces by portable LIBS system for crime scene application

The application of Laser-Induced Breakdown Spectroscopy (LIBS) in forensic science has garnered increasing attention. The ability to perform real-time, on-site analysis of Gunshot Residue (GSR) particles and potential elements originating from bullets or projectile cores on various surfaces holds the potential to assist in resolving firearms-related cases. This includes facilitating trajectory determination by locating distinct impact points and identifying the types of ammunition used. This study evaluates the utilization of a portable LIBS device for ballistic forensic purposes. Additionally, it focuses on the assessment of potential false positives and false negatives arising from the different materials where the shots have been fired. Since the system performs laser ablation of both surface particles and the substrate, it emphasizes the importance of conducting preliminary screening in an area with the same composition as the impact zone to minimize potential false positives during direct surface analysis. Furthermore, the results demonstrate the capability to detect the constituent elements of characteristic gunshot residue particles (GSR particles): lead (Pb), antimony (Sb), and barium (Ba) adhering to bullets, as well as the principal elements composing the jacket or core of the projectile: lead (Pb), copper (Cu), and zinc (Zn) through direct analysis, without the need for a sampling kit, on different surfaces such as walls, furniture, or fabrics. Analyses conducted a month after the shots were fired indicate the potential for finding residues in the vicinity of the bullet hole. Analyses conducted a month after the shots were fired indicate the possibility of finding residues in the area around the bullet hole.

Autism spectrum disorder prevalence in Italy: a nationwide study promoted by the Ministry of Health

BackgroundThis nationwide study aimed to estimate Autism Spectrum Disorder (ASD) prevalence in 7–9-year-old Italian children. Promoted by Italy's Ministry of Health and coordinated by the National Observatory for Autism at the National Institute of Health, it covered schools in northern (Lecco and Monza-Brianza), central (Rome and its province), and southern (Palermo and its province) regions from February 24, 2016, to February 23, 2018, using a multi-stage approach defined by the European Union's ASD network.MethodsPhase one identified ASD-diagnosed children in mainstream schools through local Ministry of Education (MoE) disability registries. Phase two had a subset of schools screen 7–9-year-olds using the Social Communication Questionnaire-Life version (SCQ-L). Those with SCQ-L scores of 15 + underwent clinical consultation for ASD symptoms, cognitive abilities, and life skills. To counter potential false negatives, 20% scoring 11–14 were randomly assessed via Autism Diagnostic Interview-Revised (ADI-R).ResultsMoE data revealed 9.8 per 1000 certified ASD children in the north, 12.2 in the central, and 10.3 in the south. In phase two, 35,823 SCQ-L questionnaires were distributed across 198 schools (northern: 11,190 in 49 schools, central: 13,628 in 87 schools, southern: 11,005 in 62 schools). Of SCQ-L respondents, 2.4% (n = 390) scored above the 15 cutoff. Among these, 100 had ASD diagnoses, and 50 had other diagnoses. Among 115 families assessed, 16.5% (n = 19) received ASD diagnoses.ConclusionsThe estimated prevalence of ASD in Italy was 13.4 (11.3–16.0) per 1,000 children aged 7–9 years, with a male-to-female ratio of 4.4:1. It will guide national policies in enhancing services tailored to the specific needs of autistic children.

Joint unsupervised contrastive learning and robust GMM for text clustering

Text clustering aims to organize a vast collection of documents into meaningful and coherent clusters, thereby facilitating the extraction of valuable insights. While current frameworks for text clustering try to minimize the anisotropy of pre-trained language models through contrastive learning of text embeddings, the approach of treating in-batch samples as negatives is suboptimal. The K-means algorithm offers a way to sample both hard negatives and false negatives. However, relying solely on a single measure of semantic similarity between distributions and using coarse-grained weighting for negative pairs may potentially limit performance. Furthermore, considering the very similar distribution in text clusters due to rich semantics, the Mahalanobis distance-based Gaussian Mixture Model (GMM) is prone to falling into local optima due to one Gaussian model, having a smaller weight, may gradually merging into another during the parameter evaluation by the EM algorithm. To tackle these challenges, we propose a model named JourTC: Joint unsupervised contrastive learning and robust GMM for Text Clustering. In the contrastive learning phase, hard negatives, potential false negatives, and their corresponding global similarity-aware weights are determined through posterior probabilities derived from a Robust GMM (RGMM). This RGMM utilizes the entropy of each individual Gaussian model as a metric and adaptively adjusts the posterior probabilities of samples based on the Gaussian models with both maximum and minimum entropy to diminish the influence of low-entropy Gaussian models. Extensive experiments have shown that JourTC can be seamlessly integrated into existing text clustering frameworks, leading to a notable improvement in accuracy. Our code is publicly available.11http://github.com/nickhcx/JourTC.

Contrastive sentence representation learning with adaptive false negative cancellation

Contrastive sentence representation learning has made great progress thanks to a range of text augmentation strategies and hard negative sampling techniques. However, most studies directly employ in-batch samples as negative samples, ignoring the semantic relationship between negative samples and anchors, which may lead to negative sampling bias. To address this issue, we propose similarity and relative-similarity strategies for identifying potential false negatives. Moreover, we introduce adaptive false negative elimination and attraction methods to mitigate their adverse effects. Our proposed approaches can also be considered semi-supervised contrastive learning, as the identified false negatives can be viewed as either negative or positive samples for contrastive learning in adaptive false negative elimination and attraction methods. By fusing information from positive and negative pairs, contrastive learning learns rich and discriminative representations that capture the intrinsic characteristics of the sentence. Experimental results indicate that our proposed strategies and methods can bring further significant performance improvements. Specifically, the combination of similarity strategy and adaptive false negative elimination method achieves the best results, yielding an average performance gain of 2.1% compared to SimCSE in semantic textual similarity (STS) tasks. Furthermore, our approach is generalizable and can be applied to different text data augmentation strategies and certain existing contrastive sentence representation learning models. Our experimental code and data are publicly available at the link: https://github.com/Linda230/AFNC.

SCL-ST: Supervised Contrastive Learning With Semantic Transformations for Multiple Lead ECG Arrhythmia Classification.

The automatic classification of electrocardiogram (ECG) signals has played an important role in cardiovascular diseases diagnosis and prediction. With recent advancements in deep neural networks (DNNs), particularly Convolutional Neural Networks (CNNs), learning deep features automatically from the original data is becoming an effective and widespread approach in a variety of intelligent tasks including biomedical and health informatics. However, most of the existing approaches are trained on either 1D CNNs or 2D CNNs, and they suffer from the limitations of random phenomena (i.e. random initial weights). Furthermore, the ability to train such DNNs in a supervised manner in healthcare is often limited due to the scarcity of labeled training data. To address the problems of weight initialization and limited annotated data, in this work, we leverage recent self-supervised learning technique, namely, contrastive learning, and present supervised contrastive learning (sCL). Different from existing self-supervised contrastive learning approaches, which often generate false negatives because of random selection of negative anchors, our contrastive learning makes use of labeled data to pull the same class closer together and push different classes far apart to avoid potential false negatives. Furthermore, unlike other kinds of signals (e.g. speech, image, video), ECG signal is sensitive to changes, and inappropriate transformation could directly affect diagnosis results. To deal with this issue, we present two semantic transformations, i.e. semantic split-join and semantic weighted peaks noise smoothing. The proposed deep neural network sCL-ST with supervised contrastive learning and semantic transformations is trained as an end-to-end framework for the multi-label classification of 12-lead ECGs. Our sCL-ST network contains two sub-networks i.e. pre-text task and down-stream task. Our experimental results have been evaluated on 12-lead PhysioNet 2020 dataset and shown that our proposed network outperforms the state-of-the-art existing approaches.

Wearable ultrasound bioelectronics for healthcare monitoring

Wearable ultrasound bioelectronics for healthcare monitoring

Localizing Post-Admixture Adaptive Variants with Object Detection on Ancestry-Painted Chromosomes.

Gene flow between previously differentiated populations during the founding of an admixed or hybrid population has the potential to introduce adaptive alleles into the new population. If the adaptive allele is common in one source population, but not the other, then as the adaptive allele rises in frequency in the admixed population, genetic ancestry from the source containing the adaptive allele will increase nearby as well. Patterns of genetic ancestry have therefore been used to identify post-admixture positive selection in humans and other animals, including examples in immunity, metabolism, and animal coloration. A common method identifies regions of the genome that have local ancestry "outliers" compared with the distribution across the rest of the genome, considering each locus independently. However, we lack theoretical models for expected distributions of ancestry under various demographic scenarios, resulting in potential false positives and false negatives. Further, ancestry patterns between distant sites are often not independent. As a result, current methods tend to infer wide genomic regions containing many genes as under selection, limiting biological interpretation. Instead, we develop a deep learning object detection method applied to images generated from local ancestry-painted genomes. This approach preserves information from the surrounding genomic context and avoids potential pitfalls of user-defined summary statistics. We find the method is robust to a variety of demographic misspecifications using simulated data. Applied to human genotype data from Cabo Verde, we localize a known adaptive locus to a single narrow region compared with multiple or long windows obtained using two other ancestry-based methods.

Pluto: Exposing Vulnerabilities in Inter-Contract Scenarios

Attacks on smart contracts have caused considerable losses to digital assets. Many techniques based on symbolic execution, fuzzing, and static analysis are used to detect contract vulnerabilities. Most of the current analyzers only consider vulnerability detection intra-contract scenarios. However, Ethereum contracts usually interact with others by calling their functions. A bug hidden in a path that depends on information from external contract calls is defined as an inter-contract vulnerability. Failure to deal with this kind of bug can result in potential false negatives and false positives. In this work, we propose Pluto, which supports vulnerability detection in inter-contract scenarios. It first builds an Inter-contract Control Flow Graph (ICFG) to extract semantic information among contract calls. Afterward, it symbolically explores the ICFG and deduces Inter-Contract Path Constraints (ICPC) to check the reachability of execution paths more accurately. Finally, Pluto detects whether there is a vulnerability based on some predefined rules. For evaluation, we compare Pluto with five state-of-the-art tools, including Oyente, Mythril, Securify, ILF, and Clairvoyance on a labeled benchmark and 39,443 real-world Ethereum smart contracts. The result shows that other tools can only detect 10% of the inter-contract vulnerabilities, while Pluto can detect 80% of them on the labeled dataset. Beyond that, Pluto has detected 451 confirmed vulnerabilities on real-world contracts, including 36 vulnerabilities in inter-contract scenarios. Two bugs have been assigned with unique CVE identifiers by the US National Vulnerability Database (NVD). On average, Pluto costs 16.9 seconds to analyze a contract, which is as fast as the state-of-the-art tools.

Learning multi-view visual correspondences with self-supervision

Stereo-based 3D reconstruction requires to match features across images captured from slightly different viewing angles to recover 3D coordinates of the image pixels. Despite the workload of collecting data, annotating matched pixels requires also heavy labor. As recent researches for self-supervised representation learning has gained great progress, learning multi-view visual correspondences from large scale raw videos serves as an alternative. However, existing methods which benefit from contrastive learning tend to neglect false negative samples when matching between adjacent frames in a video, leading to sub-optimal optimization for visual features. In this paper, we propose a contrastive learning framework that construct self-supervision by semi-global visual correspondence to alleviate learning degradation when false negatives are involved in training. Our learning framework consists of pixel-level contrastive learning via patch reconstruction and patch-level contrastive learning cross videos. We also introduce saliency guidance to extract salient regions from video frames to further reduce potential false negatives. By optimizing the model with the proposed semi-global contrastive learning method, learned representations are forced to be discriminative and robust. Experiments demonstrate that our proposed method outperforms previous self-supervised methods on video object segmentation tasks. Moreover, when compared to fully-supervised algorithms designed for specific tasks, our proposed method also achieves competitive results.

Raman Spectroscopy for Forensic Identification of Body Fluid Traces: Method Validation for Potential False Negatives Caused by Blood-Affecting Diseases

Two critical issues in forensic science are identifying body fluid traces found at crime scenes and preserving them for DNA analysis. However, the majority of current biochemical tests for body fluid identification, which are applicable at the crime scene, are presumptive and destructive to the sample. Raman Spectroscopy provides a suitable alternative to current methods as a nondestructive, confirmatory, and potentially in field method. Our laboratory has developed a chemometric model for the identification of five main body fluids using Raman spectroscopy. This model was developed using samples obtained from healthy donors. Thus, it is of most importance for the forensic application of the method to validate its performance for donors with diseases that might affect the biochemical composition of body fluids. In this study, the developed method was validated using peripheral blood samples acquired from donors with Celiac Disease, Sickle Cell Anemia, and Type 2 Diabetes. It was shown that the method correctly identified all samples as peripheral blood indicating that no false positives could occur because the blood traces were originated from donors suffering from the diseases.

Comparison of Video-Identified Head Contacts and Sensor-Recorded Events in High School Soccer.

Field studies have evaluated the accuracy of sensors to measure head impact exposure using video analysis, but few have studied false negatives. Therefore, the aim of the current study was to investigate the proportion of potential false negatives in high school soccer head impact data. High school athletes (23 females and 31 males) wore headband-mounted Smart Impact Monitor-G impact sensors during competitive soccer games. Video footage from 41 varsity games was analyzed by 2 independent reviewers to identify head contact events, which were defined as visually observed contact to the head. Of the 1991 video-identified head contact events for which sensors were functioning and worn by the players, 1094 (55%) were recorded by the sensors. For female players, 45% of video-identified head contact events were recorded by the sensor compared with 59% for male players. For both females and males, sensitivity varied by impact mechanism. By quantifying the proportion of potential false negatives, the sensitivity of a sensor can be characterized, which can inform the interpretation of previous studies and the design of future studies using head impact sensors. Owing to the difficulty in obtaining ground truth labels of head impacts, video review should be considered a complementary tool to head impact sensors.

Validation of a redesigned pan-poliovirus assay and real-time PCR platforms for the global poliovirus laboratory network.

Surveillance and detection of polioviruses (PV) remain crucial to monitoring eradication progress. Intratypic differentiation (ITD) using the real-time RT-PCR kit is key to the surveillance workflow, where viruses are screened after cell culture isolation before a subset are verified by sequencing. The ITD kit is a series of real-time RT-PCR assays that screens cytopathic effect (CPE)-positive cell cultures using the standard WHO method for virus isolation. Because ITD screening is a critical procedure in the poliovirus identification workflow, validation of performance of real-time PCR platforms is a core requirement for the detection of poliovirus using the ITD kit. In addition, the continual update and improvement of the ITD assays to simplify interpretation in all platforms is necessary to ensure that all real-time machines are capable of detecting positive real-time signals. Four platforms (ABI7500 real-time systems, Bio-Rad CFX96, Stratagene MX3000P, and the Qiagen Rotor-Gene Q) were validated with the ITD kit and a redesigned poliovirus probe. The poliovirus probe in the real-time RT-PCR pan-poliovirus (PanPV) assay was re-designed with a double-quencher (Zen™) to reduce background fluorescence and potential false negatives. The updated PanPV probe was evaluated with a panel consisting of 184 polioviruses and non-polio enteroviruses. To further validate the updated PanPV probe, the new assay was pilot tested in five Global Polio Laboratory Network (GPLN) laboratories (Madagascar, India, Philippines, Pakistan, and Democratic Republic of Congo). The updated PanPV probe performance was shown to reduce background fluorescence and decrease the number of false positives compared to the standard PanPV probe.