Multiple Readers Research Articles

Pharmacists’ perceptions of error reporting systems

Background Healthcare-related adverse events directly impact patient safety. Effective reporting of adverse events and workplace factors affecting the quality and quantity of reporting has been a recent focus. In Nova Scotia, pharmacists have been required to report quality-related events (QREs), errors, and near misses since 2010 through the Canadian Pharmacy Incident Reporting (CPhIR) database. This study aims to better understand how healthcare professionals who use the CPhIR system feel about their experience with QREs and the QRE reporting process. Methods A total of 1000 registered pharmacists and staff were contacted through the Nova Scotia College of Pharmacists. Five focus group meetings were conducted from May to October 2018, consisting of 17 community pharmacists, pharmacy technicians, and assistants. Analysis Thematic analysis was used to identify and define emerging themes in the transcripts by multiple readers. The Actor-Network Analytical Theory helped draw a web of connections in producing a safety culture that extends beyond the roles at the dispensing counter. Results It was found that participants were committed to minimizing and reporting errors, but using the CPhIR database system is both time-consuming and onerous. Additionally, there was a lack of continuity in communicating the QRE system protocols and compliance protocols for the system. Conclusions Community-based pharmacy culture needs to shift from a compliance-based culture, where error reporting is based on completion and volume, to a just culture that embraces quality and learning from mistakes, a critical element of safe dispensing.

Evaluation of survivorship and annulus validation in calcein-stained freshwater unionid mussels

Unionid mussels deposit growth rings (annuli) within the shell, which can be used to estimate age and growth. Thin-sectioning is a common technique for counting annuli, wherein a cross-section of a shell valve is taken and evaluated by multiple readers. Correctly identifying annuli can be challenging because ambiguous annuli can bias growth estimates. Staining with calcein, a fluorescent chemical, is a technique that has been used with marine and freshwater species to improve accuracy of growth estimates. This method chelates calcium, causing a permanent mark that fluoresces under ultraviolet light. Calcein has seen limited testing on unionid mussels so it remains unclear if this method has adverse effects on survival and growth. We evaluated calcein against 2 concentrations (125 mg L−1 and 250 mg L−1) at 2 exposure times (12 and 24 h) on Cyclonaias pustulosa, a common North American unionid. Survivorship remained above 80% 6 months post-immersion. Mark quality and retention for 250 mg L−1 were high for both 12- and 24-h immersions, although historical annuli were not highlighted. These findings corroborate studies indicating calcein immersion is generally safe and effective in juveniles and adults and suggest it may be useful in validating new growth.

Performance of tests based on the area under the ROC curve for multireader diagnostic data

One of the main objectives of disease prevention is to lower the healthcare costs and improve the quality of life. To achieve this, reliable diagnostic tools are needed. The diagnostic performance of a tool can be measured by the ROC curve and the AUC. However, some diagnostic tools such as MRI images are not objective, but depend on the interpretation of experts. Therefore, the accuracy of these tools may vary depending on who is interpreting them. To account for possible correlations when multiple readers collect data, Dorfman, Berbaum and Metz (1992) proposed using AUC pseudovalues from the jackknife sampling method and applying them to the mixed model to analyze the diagnostic reagent's accuracy. However, pseudovalues may go beyond the AUC range. Also, the random effect estimate may be negative due to a small number of readers. This paper develops tests based on AUC estimates and gives their asymptotic distribution. Moreover, a two-stage test is suggested to correct for negative random effect estimates. Four tests are created in total and their performance is evaluated by Monte Carlo simulations. The distributional assumption's robustness of these tests is checked, and their applicability is demonstrated by two real data sets.

High-Intensity Focused Ultrasound Enhances Drug Penetration into the Human Skin in the Franz Diffusion Cell.

High-intensity focused ultrasound (HIFU)-assisted drug delivery is a non-invasive tool to deliver drugs to targeted areas, currently used mainly for treating cancer and cardiovascular diseases. However, in terms of transdermal drug delivery, HIFU technology is still poorly understood. Accordingly, this study sought to investigate the effectiveness of HIFU on drug penetration into the skin using human skin tissues. Gel-type drugs whose ingredient is glutathione were labelled with fluorescein isothiocyanate, in turn the drugs were allowed to penetrate to the human skin tissue in the Franz diffusion cell for 24 hours in control and HIFU treatment groups, and their fluorescence intensity was measured using a multiple microplate reader at one, two, six, and 24 hours after drug application. In addition, tissue slice analysis was performed in each tissue slice at 24 hours post-drug application. The % area, fluorescence intensity per area, and penetration depth of the drug were measured using a fluorescence microscope. The fluorescence intensity increased with time in all groups. Specifically, at 24 hours after drug application, the fluorescence intensity (a.u). of the 10-shot HIFU treatment group was significantly enhanced compared to that of the control group (p < 0.05). The tissue slice analysis demonstrated that the % area of fluorescent drug and the fluorescence intensity per area (a.u.) were all significantly increased in both HIFU treatment groups compared to the control group (p < 0.05, p < 0.001). In addition, the penetration depth (μm) also markedly rose in both HIFU treatment groups compared to the control group (p < 0.01, p < 0.05). It was demonstrated for the first time that HIFU significantly facilitated topical drug penetration into the human skin, strongly implying that HIFU can be a useful option for transdermal drug delivery.

Inter-reader agreement for CT/MRI LI-RADS category M imaging features: a systematic review and meta-analysis.

To systematically evaluate inter-reader agreement in the assessment of individual liver imaging reporting and data system (LI-RADS) category M (LR-M) imaging features in computed tomography/magnetic resonance imaging (CT/MRI) LIRADS v2018, and to explore the causes of poor agreement in LR-M assignment. Original studies reporting inter-reader agreement for LR-M features on multiphasic CT or MRI were identified using the MEDLINE, EMBASE, and Cochrane databases. The pooled kappa coefficient (κ) was calculated using the DerSimonian-Laird random-effects model. Heterogeneity was assessed using Cochran's Q test and I2 statistics. Subgroup meta-regression analyses were conducted to explore the study heterogeneity. In total, 24 eligible studies with 5,163 hepatic observations were included. The pooled κ values were 0.72 (95% confidence interval [CI], 0.65-0.78) for rim arterial phase hyperenhancement, 0.52 (95% CI, 0.39-0.65) for peripheral washout, 0.60 (95% CI, 0.50-0.70) for delayed central enhancement, 0.68 (95% CI, 0.57-0.78) for targetoid restriction, 0.74 (95% CI, 0.65-0.83) for targetoid transitional phase/hepatobiliary phase appearance, 0.64 (95% CI, 0.49-0.78) for infiltrative appearance, 0.49 (95% CI, 0.30-0.68) for marked diffusion restriction, and 0.61 (95% CI, 0.48-0.73) for necrosis or severe ischemia. Substantial study heterogeneity was observed for all LR-M features (Cochran's Q test, P<0.01; I2≥89.2%). Studies with a mean observation size of <3 cm, those performed using 1.5-T MRI, and those with multiple image readers, were significantly associated with poor agreement of LR-M features. The agreement for peripheral washout and marked diffusion restriction was limited. The LI-RADS should focus on improving the agreement of LR-M features.

Design of storage benchmark kit framework for supporting the file storage retrieval

An open-source software framework called the storage benchmark kit (SBK) is used to store the system benchmarking performance framework. The SBK is designed to perform any storage client or device using any data type as a payload. SBK simultaneously helps number of readers as well as writes to the storage system of large amounts of data as well as allows end-to-end latency benchmarking for multiple writers and readers. The SBK uses standardized performance measures for comparing and evaluating various storage systems and their combinations. Distributed file systems, distributed database systems, single or local node databases, systems of object storage, platforms of distributed streaming and messaging, and systems of key-value storage are the storage solutions supported by SBK. The SBK supports various storage systems like XFS, Kafka streaming storage systems, and Hadoop distributed file system (HDFS) performance benchmarking. The experimental results show that a proposed method achieves execution time of 65.530 s, 40.826 s and 30.351 s for the 100k, 500k and 1000k files respectively which ensures better improvement than the existing methods such as simple data interface and distributed data protection system.

Energy Efficient Localization Technique Using Multilateration for Reduction of Spatially and Temporally Correlated Data in RFID System

RFID plays a vital role in data communication in multidimensional WSNs as it collects vast amounts of redundant data. The physical phenomena constitute the correlated observations in the space domain and generate spatial correlation. Periodic observations of sensor nodes result in a temporal correlation in the data. Reducing these spatio-temporal correlations in RFID surveillance data is necessary for the smooth functioning of the network. This paper proposes a Voronoi diagram-based spatio-temporal data redundancy elimination approach for RFID systems having multiple readers so only one reader will read every RFID tag depending on the distance between the tag and the center of the Minimum Enclosing Circle of the Voronoi cell to which the reader belongs. This approach eliminates spatial redundancy in the gathered data. Reading the RFID tags at regular time intervals larger than a chosen threshold value minimized temporal redundancy. In contrast to existing methods, the proposed technique is free from any false positive and false negative errors, with no loss of data and every tag being read by only one reader. Simulation of the proposed approach also established its superiority to the existing techniques in terms of these performance parameters.

Location privacy protection of nodes in the perception layer of the Internet of things based on Radio Frequency Identification location aware algorithm

AbstractTo ensure the security of node location privacy in the perception layer of the Internet of Things, this study proposes a location privacy protection method based on RFID location awareness algorithm. This method first introduces an adaptive multi tree anti‐collision algorithm based on collision trees when perceiving and locating the location information of nodes in the perception layer of the Internet of Things, to prevent signal collisions between multiple readers and writers. Then, based on the obtained node location results, a IoT perception layer node location privacy protection scheme based on virtual ring strategy is used to randomly initiate directed co hop routing using the virtual nodes generated after random walking, avoiding the visible area of the source node location, and transmitting data to the aggregation node in the form of the shortest routing path to protect the privacy and security of the node location. Finally, experiments are carried out to prove the progressiveness of the proposed method. The experimental results show that this method can prolong the security period of the source and aggregation nodes in the perception layer of the Internet of Things, protect the privacy and security of node positions, and has great application value.

Generalizations of the Jaccard index and Sørensen index for assessing agreement across multiple readers in object detection and instance segmentation in biomedical imaging

Generalizations of the Jaccard index and Sørensen index for assessing agreement across multiple readers in object detection and instance segmentation in biomedical imaging

Chemical Inhibitors Targeting the Oncogenic m6A Modifying Proteins.

Epigenetics is brought to RNA, introducing a new dimension to gene expression regulation. Among numerous RNA modifications, N6-methyladenosine (m6A) is an abundant internal modification on eukaryote mRNA first identified in the 1970s. However, the significance of m6A modification in mRNA had been long neglected until the fat mass and obesity-associated (FTO) enzyme was identified as the first m6A demethylase almost 40 years later. The m6A modification influences nearly every step of RNA metabolism and thus broadly affects gene expression at multiple levels, playing a critical role in many biological processes, including cancer progression, metastasis, and immune evasion. The m6A level is dynamically regulated by RNA epigenetic machinery comprising methyltransferases such as methyltransferase-like protein 3 (METTL3), demethylases FTO and AlkB human homologue 5 (ALKBH5), and multiple reader proteins. The understanding of the biology of RNA epigenetics and its translational drug discovery is still in its infancy. It is essential to further develop chemical probes and lead compounds for an in-depth investigation into m6A biology and the translational discovery of anticancer drugs targeting m6A modifying oncogenic proteins.In this Account, we present our work on the development of chemical inhibitors to regulate m6A in mRNA by targeting the FTO demethylase, and the elucidation of their mode of action. We reported rhein to be the first substrate competitive FTO inhibitor. Due to rhein's poor selectivity, we identified meclofenamic acid (MA) that selectively inhibits FTO compared with ALKBH5. Based on the structural complex of MA bound with FTO, we designed MA analogs FB23-2 and Dac51, which exhibit significantly improved activities compared with MA. For example, FB23-2 is specific to FTO inhibition in vitro among over 400 other oncogenic proteins, including kinases, proteases, and DNA and histone epigenetic proteins. Mimicking FTO depletion, FB23-2 promotes the differentiation/apoptosis of human acute myeloid leukemia (AML) cells and inhibits the progression of primary cells in xenotransplanted mice. Dac51 treatment impairs the glycolytic activity of tumor cells and restores the function of CD8+ T cells, thereby inhibiting the growth of solid tumors in vivo. These FTO inhibitors were and will continue to be used as probes to promote biological studies of m6A modification and as lead compounds to target FTO in anticancer drug discovery.Toward the end, we also include a brief review of ALKBH5 demethylase inhibitors and METTL3 methyltransferase modulators. Collectively, these small-molecule modulators that selectively target RNA epigenetic proteins will promote in-depth studies on the regulation of gene expression and potentially accelerate anticancer target discovery.

An Efficient and Secure Method for Simultaneous Ownership Transfer of Multiple Mobile Readers

In recent years, radio frequency identification technology has developed rapidly and has been applied in supply chains. Products in supply chains are varied and may belong to several different owners. Ownership transfer requires the consent of a majority of owners. This article proposed a method suitable for simultaneously transferring the ownership of a large number of goods. The method does not require a trusted third party and can securely and efficiently transfer group ownership for multiple owners with multiple tags. The old and new owner groups can be classified as different authorities. This method can be used in mobile readers to transfer one, some, or all tags in a group. It includes mutual authentication between tags, readers, and backend servers, and can ensure that only assigned owners can obtain the tag ownership. The method proved can resist most known attacks, such as secret disclosure attacks and replay attacks. It can also prevent attacks from the dishonest original owners. Finally, through experiments, we compared the proposed method with other many-to-many ownership transfer methods and demonstrated that the proposed method has better security and fewer transmitted messages than other methods.

RepID represses megakaryocytic differentiation by recruiting CRL4A-JARID1A at DAB2 promoter

BackgroundMegakaryocytes (MKs) are platelet precursors, which arise from hematopoietic stem cells (HSCs). While MK lineage commitment and differentiation are accompanied by changes in gene expression, many factors that modulate megakaryopoiesis remain to be uncovered. Replication initiation determinant protein (RepID) which has multiple histone-code reader including bromodomain, cryptic Tudor domain and WD40 domains and Cullin 4-RING E3 ubiquitin ligase complex (CRL4) recruited to chromatin mediated by RepID have potential roles in gene expression changes via epigenetic regulations. We aimed to investigate whether RepID-CRL4 participates in transcriptional changes required for MK differentiation.MethodsThe PCR array was performed using cDNAs derived from RepID-proficient or RepID-deficient K562 erythroleukemia cell lines. Correlation between RepID and DAB2 expression was examined in the Cancer Cell Line Encyclopedia (CCLE) through the CellMinerCDB portal. The acceleration of MK differentiation in RepID-deficient K562 cells was determined by estimating cell sizes as well as counting multinucleated cells known as MK phenotypes, and by qRT-PCR analysis to validate transcripts of MK markers using phorbol 12-myristate 13-acetate (PMA)-mediated MK differentiation condition. Interaction between CRL4 and histone methylation modifying enzymes were investigated using BioGRID database, immunoprecipitation and proximity ligation assay. Alterations of expression and chromatin binding affinities of RepID, CRL4 and histone methylation modifying enzymes were investigated using subcellular fractionation followed by immunoblotting. RepID-CRL4-JARID1A-based epigenetic changes on DAB2 promoter were analyzed by chromatin-immunoprecipitation and qPCR analysis.ResultsRepID-deficient K562 cells highly expressing MK markers showed accelerated MKs differentiation exhibiting increases in cell size, lobulated nuclei together with reaching maximum levels of MK marker expression earlier than RepID-proficient K562 cells. Recovery of WD40 domain-containing RepID constructs in RepID-deficient background repressed DAB2 expression. CRL4A formed complex with histone H3K4 demethylase JARID1A in soluble nucleus and loaded to the DAB2 promoter in a RepID-dependent manner during proliferation condition. RepID, CRL4A, and JARID1A were dissociated from the chromatin during MK differentiation, leading to euchromatinization of the DAB2 promoter.ConclusionThis study uncovered a role for the RepID-CRL4A-JARID1A pathway in the regulation of gene expression for MK differentiation, which can form the basis for the new therapeutic approaches to induce platelet production.EhctJ7T1-GG5oU5fji2odBVideo

AI-based detection of contrast-enhancing MRI lesions in patients with multiple sclerosis

BackgroundContrast-enhancing (CE) lesions are an important finding on brain magnetic resonance imaging (MRI) in patients with multiple sclerosis (MS) but can be missed easily. Automated solutions for reliable CE lesion detection are emerging; however, independent validation of artificial intelligence (AI) tools in the clinical routine is still rare.MethodsA three-dimensional convolutional neural network for CE lesion segmentation was trained externally on 1488 datasets of 934 MS patients from 81 scanners using concatenated information from FLAIR and T1-weighted post-contrast imaging. This externally trained model was tested on an independent dataset comprising 504 T1-weighted post-contrast and FLAIR image datasets of MS patients from clinical routine. Two neuroradiologists (R1, R2) labeled CE lesions for gold standard definition in the clinical test dataset. The algorithmic output was evaluated on both patient- and lesion-level.ResultsOn a patient-level, recall, specificity, precision, and accuracy of the AI tool to predict patients with CE lesions were 0.75, 0.99, 0.91, and 0.96. The agreement between the AI tool and both readers was within the range of inter-rater agreement (Cohen’s kappa; AI vs. R1: 0.69; AI vs. R2: 0.76; R1 vs. R2: 0.76). On a lesion-level, false negative lesions were predominately found in infratentorial location, significantly smaller, and at lower contrast than true positive lesions (p < 0.05).ConclusionsAI-based identification of CE lesions on brain MRI is feasible, approaching human reader performance in independent clinical data and might be of help as a second reader in the neuroradiological assessment of active inflammation in MS patients.Critical relevance statementAl-based detection of contrast-enhancing multiple sclerosis lesions approaches human reader performance, but careful visual inspection is still needed, especially for infratentorial, small and low-contrast lesions.Graphical

Genre as Reception: A Multidimensional Network Approach

Abstract A reception-oriented approach to genre will challenge the “objective” genre categories applied to the Bible by analyzing their culturally contingent place in the history of interpretation. It then gathers alternative genre groupings for the biblical texts from that history in order to comprehend the features of the texts more fully through the multiple ways readers have responded to their various capacities. This article incorporates conceptual blending and network theory with a growing reception-consciousness in biblical studies to develop a new three-dimensional version of the common comparison of genres to constellations. This new version communicates the value of a reception-oriented approach for appreciating the complexity of texts, biblical and otherwise, along with their readers’ cultural perspectives. The new multidimensional approach to genre that results is applied to the “Wisdom Literature” genre category as an example.

Prostate Imaging after Focal Ablation (PI-FAB): A Proposal for a Scoring System for Multiparametric MRI of the Prostate After Focal Therapy

At present there is no standardised system for scoring the appearance of the prostate on multiparametric magnetic resonance imaging (MRI) after focal ablation for localised prostate cancer. We propose a novel scoring system, the Prostate Imaging after Focal Ablation (PI-FAB) score, to fill this gap. PI-FAB involves a 3-point scale for rating MRI sequences in sequential order: (1) dynamic contrast-enhanced sequences; (2) diffusion-weighted imaging, split into assessment of the high-b-value sequence first and then the apparent diffusion coefficient map; and (3) T2-weighted imaging. It is essential that the pretreatment scan is also available to help with this assessment. We designed PI-FAB using our experience of reading postablation scans over the past 15 years and include details for four representative patients initially treated with high-intensity focus ultrasound at our institution to demonstrate the scoring system. We propose PI-FAB as a standardised method for evaluating prostate MRI scans after treatment with focal ablation. The next step is to evaluate its performance across multiple experienced readers of MRI after focal therapy in a clinical data set. Patient summaryWe propose a scoring system called PI-FAB for assessing the appearance of magnetic resonance imaging scans of the prostate after focal treatment for localised prostate cancer. This will help clinicians in deciding on further follow-up.

Region-specific deep learning models for accurate segmentation of rectal structures on post-chemoradiation T2w MRI: a multi-institutional, multi-reader study.

For locally advanced rectal cancers, in vivo radiological evaluation of tumor extent and regression after neoadjuvant therapy involves implicit visual identification of rectal structures on magnetic resonance imaging (MRI). Additionally, newer image-based, computational approaches (e.g., radiomics) require more detailed and precise annotations of regions such as the outer rectal wall, lumen, and perirectal fat. Manual annotations of these regions, however, are highly laborious and time-consuming as well as subject to inter-reader variability due to tissue boundaries being obscured by treatment-related changes (e.g., fibrosis, edema). This study presents the application of U-Net deep learning models that have been uniquely developed with region-specific context to automatically segment each of the outer rectal wall, lumen, and perirectal fat regions on post-treatment, T2-weighted MRI scans. In multi-institutional evaluation, region-specific U-Nets (wall Dice = 0.920, lumen Dice = 0.895) were found to perform comparably to multiple readers (wall inter-reader Dice = 0.946, lumen inter-reader Dice = 0.873). Additionally, when compared to a multi-class U-Net, region-specific U-Nets yielded an average 20% improvement in Dice scores for segmenting each of the wall, lumen, and fat; even when tested on T2-weighted MRI scans that exhibited poorer image quality, or from a different plane, or were accrued from an external institution. Developing deep learning segmentation models with region-specific context may thus enable highly accurate, detailed annotations for multiple rectal structures on post-chemoradiation T2-weighted MRI scans, which is critical for improving evaluation of tumor extent in vivo and building accurate image-based analytic tools for rectal cancers.

Reliability of MRI Interpretation of Discoid Lateral Meniscus: A Multicenter Study.

Discoid lateral meniscus (DLM) has a varied and complex morphology that can be challenging to assess and treat. Preoperative magnetic resonance imaging (MRI) is frequently used for diagnosis and surgical planning; however, it is not known whether surgeons are reliable and accurate in their interpretation of MRI findings when defining the pathomorphology of DLM. Surgeons experienced in treating DLM are able to reliably interpret DLM pathology using MRI. Cohort study (diagnosis); Level of evidence, 3. Knee MRI scans from 44 patients (45 knees) were selected from a pool of surgically treated patients with DLM. Five reviewers (fellowship-trained pediatric sports medicine surgeons) performed independent review of each MRI scan using the PRiSM Discoid Meniscus Classification. Inter- and intraobserver reliability of the rating factors-primary (width, height, presence of peripheral instability or tear) and secondary (location of instability or tear, tear type)-was assessed using the Fleiss κ coefficient, designed for multiple readers with nominal variables (fair reliability, 0.21-0.40; moderate, 0.41-0.60; substantial, 0.61-0.80; excellent, 0.81-1.00). Reliability is reported as κ (95% CI). Interobserver reliability in assessing most primary and secondary characteristics ranged from substantial (meniscal width) to moderate (peripheral instability, anterior instability, posterior instability, and posterior tear). Intraobserver reliability for most characteristics ranged from substantial (peripheral instability, presence of tear, anterior instability, posterior instability, and posterior tear) to moderate (meniscal width, anterior tear, and tear type). Notable exceptions were presence of tear, anterior tear, and tear type-all with fair interobserver reliability. Height had poor interobserver reliability and fair intraobserver reliability. Orthopaedic surgeons reliably interpret MRI scans using the PRiSM Discoid Meniscus Classification for the majority of DLM characteristics but vary in their assessment of height and presence and type of tear. MRI evaluation may be helpful to diagnose discoid by width and identify the presence of instability: 2 major factors in the decision to proceed with surgery. Arthroscopic evaluation should be used in conjunction with MRI findings for complete DLM diagnosis.

Can Machine Learning Be Better than Biased Readers?

Background: Training machine learning (ML) models in medical imaging requires large amounts of labeled data. To minimize labeling workload, it is common to divide training data among multiple readers for separate annotation without consensus and then combine the labeled data for training a ML model. This can lead to a biased training dataset and poor ML algorithm prediction performance. The purpose of this study is to determine if ML algorithms can overcome biases caused by multiple readers' labeling without consensus. Methods: This study used a publicly available chest X-ray dataset of pediatric pneumonia. As an analogy to a practical dataset without labeling consensus among multiple readers, random and systematic errors were artificially added to the dataset to generate biased data for a binary-class classification task. The Resnet18-based convolutional neural network (CNN) was used as a baseline model. A Resnet18 model with a regularization term added as a loss function was utilized to examine for improvement in the baseline model. Results: The effects of false positive labels, false negative labels, and random errors (5-25%) resulted in a loss of AUC (0-14%) when training a binary CNN classifier. The model with a regularized loss function improved the AUC (75-84%) over that of the baseline model (65-79%). Conclusion: This study indicated that it is possible for ML algorithms to overcome individual readers' biases when consensus is not available. It is recommended to use regularized loss functions when allocating annotation tasks to multiple readers as they are easy to implement and effective in mitigating biased labels.

Conformational and Thermodynamic Differences Underlying Wild-Type and Mutant Eleven-Nineteen-Leukemia YEATS Domain Specificity for Epigenetic Marks.

Histone post-translational modifications (PTMs) are interpreted by multiple reader domains and proteins to regulate gene expression. The eleven-nineteen-leukemia (ENL) YEATS domain is a prototypical PTM reader that recognizes multiple lysine acetylation marks on the histone H3 tails as a way of recruiting chromatin remodellers. Two ENL YEATS mutations have been identified which have been linked with leukemia, Wilms tumor, and other forms of cancer and result in either an insertion or deletion of residues in the loop connecting beta sheets distant from the protein active site. In vitro experiments have shown that these mutations modulate the selectivities of YEATS domains for various lysine acetylation marks, although different experiments have provided contrasting views on the abilities of the insertion and deletion mutants to discern specific PTMs. Here, we have performed multiple molecular dynamics simulations of wild-type and insertion and deletion mutant YEATS domains free from and in complex with two PTM peptides: one that is acetylated at K9 of H3 and the other that is acetylated at residue K27 of H3. Results show that these two peptides have distinct flexibilities and binding energetics when bound to YEATS domains and that these properties are affected by interactions with residues within and outside of the peptide consensus motif. Furthermore, these properties are modulated by the YEATS insertion and deletion mutants, which results in disparate binding effects in these systems. Together, these results suggest that only the partial exposure of histone tails is sufficient in the context of nucleosomes for YEATS-mediated recognition of acetylation marks on histone tails. They also caution against the overinterpretation of results obtained from experiments on reader domain-histone peptide binding in isolation and not in the full-length nucleosome context.

Clean mucosal area detection of gastroenterologists versus artificial intelligence in small bowel capsule endoscopy.

Studies comparing the detection of clean mucosal areas in capsule endoscopy (CE) using human judgment versus artificial intelligence (AI) are rare. This study statistically analyzed gastroenterologist judgments and AI results. Three hundred CE video clips (100 patients) were prepared. Five gastroenterologists classified the video clips into 3 groups (≥75% [high], 50%-75% [middle], and < 50% [low]) according to their subjective judgment of cleanliness. Visualization scores were calculated using an AI algorithm based on the predicted visible area, and the 5 gastroenterologists' judgments and AI results were compared. The 5 gastroenterologists evaluated CE clip video quality as "high" in 10.7% to 36.7% and as "low" in 28.7% to 60.3% and 29.7% of cases, respectively. The AI evaluated CE clip video quality as "high" in 27.7% and as "low" in 29.7% of cases. Repeated-measures analysis of variance (ANOVA) revealed significant differences in the 6 evaluation indicators (5 gastroenterologists and 1 AI) (P < .001). Among the 300 judgments, 90 (30%) were consistent with 5 gastroenterologists' judgments, and 82 (91.1%) agreed with the AI judgments. The "high" and "low" judgments of the gastroenterologists and AI agreed in 95.0% and 94.9% of cases, respectively. Bonferroni's multiple comparison test showed no significant difference between 3 gastroenterologists and AI (P = .0961, P = 1.0000, and P = .0676, respectively) but a significant difference between the other 2 with AI (P < .0001). When evaluating CE images for cleanliness, the judgments of 5 gastroenterologists were relatively diverse. The AI produced a relatively universal judgment that was consistent with the gastroenterologists' judgements.