Large Datasets Research Articles

Machine-Learning Analysis of Streptomyces coelicolor Transcriptomes Reveals a Transcription Regulatory Network Encompassing Biosynthetic Gene Clusters.

Streptomyces produces diverse secondary metabolites of biopharmaceutical importance, yet the rate of biosynthesis of these metabolites is often hampered by complex transcriptional regulation. Therefore, a fundamental understanding of transcriptional regulation in Streptomyces is key to fully harness its genetic potential. Here, independent component analysis (ICA) of 454 high-quality gene expression profiles of the model species Streptomyces coelicolor is performed, of which 249 profiles are newly generated for S. coelicolor cultivated on 20 different carbon sources and 64 engineered strains with overexpressed sigma factors. ICA of the transcriptome dataset reveals 117 independently modulated groups of genes (iModulons), which account for 81.6% of the variance in the dataset. The genes in each iModulon are involved in specific cellular responses, which are often transcriptionally controlled by specific regulators. Also, iModulons accurately predict 25 secondary metabolite biosynthetic gene clusters encoded in the genome. This systemic analysis leads to reveal the functions of previously uncharacterized genes, putative regulons for 40 transcriptional regulators, including 30 sigma factors, and regulation of secondary metabolism via phosphate- and iron-dependent mechanisms in S. coelicolor. ICA of large transcriptomic datasets thus enlightens a new and fundamental understanding of transcriptional regulation of secondary metabolite synthesis along with interconnected metabolic processes in Streptomyces.

Explainable Machine Learning Model to Accurately Predict Protein-Binding Peptides

Enzymes play key roles in the biological functions of living organisms, which serve as catalysts to and regulate biochemical reaction pathways. Recent studies suggest that peptides are promising molecules for modulating enzyme function due to their advantages in large chemical diversity and well-established methods for library synthesis. Experimental approaches to identify protein-binding peptides are time-consuming and costly. Hence, there is a demand to develop a fast and accurate computational approach to tackle this problem. Another challenge in developing a computational approach is the lack of a large and reliable dataset. In this study, we develop a new machine learning approach called PepBind-SVM to predict protein-binding peptides. To build this model, we extract different sequential and physicochemical features from peptides and use a Support Vector Machine (SVM) as the classification technique. We train this model on the dataset that we also introduce in this study. PepBind-SVM achieves 92.1% prediction accuracy, outperforming other classifiers at predicting protein-binding peptides.

Empirical Bayesian Kriging, a Robust Method for Spatial Data Interpolation of a Large Groundwater Quality Dataset from the Western Netherlands

No single spatial interpolation method reigns supreme for modelling the precise spatial distribution of groundwater quality data. This study addresses this challenge by evaluating and comparing several commonly used geostatistical methods: Local Polynomial Interpolation (LPI), Ordinary Kriging (OK), Simple Kriging (SK), Universal Kriging (UK), and Empirical Bayesian Kriging (EBK). We applied these methods to a vast dataset of 3033 groundwater records encompassing a substantial area (11,100 km2) in the coastal lowlands of the western Netherlands. To our knowledge, no prior research has investigated these interpolation methods in this specific hydrogeological setting, exhibiting a range of groundwater qualities, from fresh to saline, often anoxic, with high natural concentrations of PO4 and NH4. The prediction performance of the interpolation methods was assessed through statistical indicators such as root means square error. The findings indicated that EBK outperforms the other geostatistical methods in forecasting groundwater quality for the five variables considered: Cl, SO4, Fe, PO4, and NH4. In contrast, SK performed worst for the species except for SO4. We recommend not using SK to interpolate groundwater quality species unless the data exhibit low spatial variation, high sample density, or evenly distributed sampling.

Antibody sequence determinants of viral antigen specificity.

Throughout life, humans experience repeated exposure to viral antigens through infection and vaccination, resulting in the generation of diverse, antigen-specific antibody repertoires. A paramount feature of antibodies that enables their critical contributions in counteracting recurrent and novel pathogens, and consequently fostering their utility as valuable targets for therapeutic and vaccine development, is the exquisite specificity displayed against their target antigens. Yet, there is still limited understanding of the determinants of antibody-antigen specificity, particularly as a function of antibody sequence. In recent years, experimental characterization of antibody repertoires has led to novel insights into fundamental properties of antibody sequences but has been largely decoupled from at-scale antigen specificity analysis. Here, using the LIBRA-seq technology, we generated a large data set mapping antibody sequence to antigen specificity for thousands of B cells, by screening the repertoires of a set of healthy individuals against 20 viral antigens representing diverse pathogens of biomedical significance. Analysis uncovered virus-specific patterns in variable gene usage, gene pairing, somatic hypermutation, as well as the presence of convergent antiviral signatures across multiple individuals, including the presence of public antibody clonotypes. Notably, our results showed that, for B-cell receptors originating from different individuals but leveraging an identical combination of heavy and light chain variable genes, there is a specific CDRH3 identity threshold above which B cells appear to exclusively share the same antigen specificity. This finding provides a quantifiable measure of the relationship between antibody sequence and antigen specificity and further defines experimentally grounded criteria for defining public antibody clonality.IMPORTANCEThe B-cell compartment of the humoral immune system plays a critical role in the generation of antibodies upon new and repeated pathogen exposure. This study provides an unprecedented level of detail on the molecular characteristics of antibody repertoires that are specific to each of the different target pathogens studied here and provides empirical evidence in support of a 70% CDRH3 amino acid identity threshold in pairs of B cells encoded by identical IGHV:IGL(K)V genes, as a means of defining public clonality and therefore predicting B-cell antigen specificity in different individuals. This is of exceptional importance when leveraging public clonality as a method to annotate B-cell receptor data otherwise lacking antigen specificity information. Understanding the fundamental rules of antibody-antigen interactions can lead to transformative new approaches for the development of antibody therapeutics and vaccines against current and emerging viruses.

Determination of a normal orogenic palaeo-geothermal gradient with clay mineral and organic matter indices: a review

A collection of large data sets from different orogenic belts was compiled for a correlation between organic matter (OM) versus clay mineral (CM) indices calibrated with the vitrinite reflectance, (VR) vs Kübler-Indices (KI) method. Data selection was based on a normal geothermal gradient (25 to 35 °C/km) as determined in previous studies, e.g. by maturity modelling and clay mineral reaction progress calculations. In the Lower Austroalpine (Eastern Switzerland, European Alps) a 20 myr lasting metamorphic overprint caused an OM–CM thermal equilibrium among the indices used. The observed correlation enables to determine gradual changes in metamorphic factors such as pressure, temperature and time causing sensitive shifts of the gradient slope in the range of normal gradients. For New Caledonia, an identical correlation has been determined. Prior to re-equilibration of the VR/KI indices, sediments in New Caledonia of diagenetic to incipient metamorphic grade underwent a high-pressure subduction event. VR/KI indices are in or close to equilibrium, while slight differences in OM vs CM indices allow for a better understanding of polyphase conditions, especially with respect to pressure. Temperature estimations are identical despite of their poly-phase metamorphic history, which was mainly controlled by the last orogenic thermal event lasting > 5 to < 10 myr. In the eastern Helvetic Alps and Northern Calcareous Alps similar correlations were found with slightly different slopes. Comparison between different regions is possible when using KI standardization and same data discrimination. In both parts of the Alps a complex thermal history of short durations (< 5.0 myr for the Northern Calcareous Alps to 10 myr for the Helvetic Alps) caused similar VR/KI trends, but disequilibrium is suggested by weaker regression parameters. The following correlation is calculated for a moderate geotherm (55 to 74 mWm2, mean = 61 mWm2) and normal temperature gradient conditions (25 to 35 °Ckm−1): KI = 1.134e−0.305VR, (R2 = 0.880, n = 462) with VR given as %Rmax, KI as Δ°2θ (limited to values between 0.2 to 1.0 Δ°2θ). With increasing depth (z) a VR gradient of 1.4 ± 0.2%Rmaxkm−1 is determined and a KI gradient of 0.09 ± 0.002 Δ°2θ km−1 is observed. The study illustrates that a normal geotherm can be described by VR/KI correlation, even if different heating episodes may occur. For the detection of a poly-phase or plurifacial thermal history, several indices of clay minerals and organic matter with very different kinetics should be used, as e.g. demonstrated by strong differences in smectite content at equal VR/KI values versus structural depth. A specific interest is given to the correlation of vitrinite like solid bitumen reflectance as an alternative method to VR, the persistent preservation of liptinite macerals and the stability range of clay minerals and sub-greenschist facies critical minerals compared with VR/KI data. Until now, despite the Alps in this study, systematic liptinite maceral studies have not been published in other orogenic settings.

Detecting floating litter in freshwater bodies with semi-supervised deep learning

Researchers and practitioners have extensively utilized supervised Deep Learning methods to quantify floating litter in rivers and canals. These methods require the availability of large amount of labeled data for training. The labeling work is expensive and laborious, resulting in small open datasets available in the field compared to the comprehensive datasets for computer vision, e.g., ImageNet. Fine-tuning models pre-trained on these larger datasets helps improve litter detection performances and reduces data requirements. Yet, the effectiveness of using features learned from generic datasets is limited in large-scale monitoring, where automated detection must adapt across different locations, environmental conditions, and sensor settings. To address this issue, we propose a two-stage semi-supervised learning method to detect floating litter based on the Swapping Assignments between multiple Views of the same image (SwAV). SwAV is a self-supervised learning approach that learns the underlying feature representation from unlabeled data. In the first stage, we used SwAV to pre-train a ResNet50 backbone architecture on about 100k unlabeled images. In the second stage, we added new layers to the pre-trained ResNet50 to create a Faster R-CNN architecture, and fine-tuned it with a limited number of labeled images (≈1.8k images with 2.6k annotated litter items). We developed and validated our semi-supervised floating litter detection methodology for images collected in canals and waterways of Delft (the Netherlands) and Jakarta (Indonesia). We tested for out-of-domain generalization performances in a zero-shot fashion using additional data from Ho Chi Minh City (Vietnam), Amsterdam and Groningen (the Netherlands). We benchmarked our results against the same Faster R-CNN architecture trained via supervised learning alone by fine-tuning ImageNet pre-trained weights. The findings indicate that the semi-supervised learning method matches or surpasses the supervised learning benchmark when tested on new images from the same training locations. We measured better performances when little data (≈200 images with about 300 annotated litter items) is available for fine-tuning and with respect to reducing false positive predictions. More importantly, the proposed approach demonstrates clear superiority for generalization on the unseen locations, with improvements in average precision of up to 12.7%. We attribute this superior performance to the more effective high-level feature extraction from SwAV pre-training from relevant unlabeled images. Our findings highlight a promising direction to leverage semi-supervised learning for developing foundational models, which have revolutionized artificial intelligence applications in most fields. By scaling our proposed approach with more data and compute, we can make significant strides in monitoring to address the global challenge of litter pollution in water bodies.

Postoperative epidural analgesia and outcomes following pediatric bilateral lung and heart-lung transplantation: a retrospective observational study

BackgroundThe value of epidural analgesia in pediatric patients having heart and lung transplant surgery is unknown. We aimed to characterize various quality outcomes in patients who did and did not...

The opposite aging effect to single cell transcriptome profile among cell subsets.

Comparing transcriptome profiling between younger and older samples reveals genes related to aging and provides insight into the biological functions affected by aging. Recent research has identified sex, tissue, and cell type-specific age-related changes in gene expression. This study reports the overall picture of the opposite aging effect, in which aging increases gene expression in one cell subset and decreases it in another cell subset. Using the Tabula Muris Senis dataset, a large public single-cell RNA sequencing dataset from mice, we compared the effects of aging in different cell subsets. As a result, the opposite aging effect was observed widely in the genes, particularly enriched in genes related to ribosomal function and translation. The opposite aging effect was observed in the known aging-related genes. Furthermore, the opposite aging effect was observed in the transcriptome diversity quantified by the number of expressed genes and the Shannon entropy. This study highlights the importance of considering the cell subset when intervening with aging-related genes.

Phoenix criteria for sepsis: are these enough to guide a clinician?

In conclusion, Phoenix criteria are a step in the right direction to define life-threatening organ dysfunction in sepsis, but clinicians need to be mindful that diagnosis/treatment of less severe sepsis should not be delayed if these criteria are not met. Therefore, local early detection and management tools for sepsis should be followed. • There has always been a quest for a definition for pediatric sepsis. There are limitations to the previous pediatric sepsis criteria which were published in 2005 by the International Pediatric Sepsis Consensus Conference (IPSCC). IPSCC defines sepsis as a suspected or confirmed infection in the presence of systemic inflammatory response syndrome (SIRS). These new Phoenix Pediatric Sepsis (PPS) criteria for sepsis and septic shock are intended to identify children with life-threatening organ dysfunction due to infection, and the score was developed based on a very large pediatric dataset. • Though the intention of Phoenix criteria is to help identify children with life threatening organ dysfunction, unfortunately the crietria will miss signs of early sepis. In this manuscript, we point out some of the drawbacks of these criteria which need to be borne in mind while applying these criteria.

AMP: Total Variation Reduction for Lossless Compression via Approximate Median-based Preconditioning

With the increasing scale of cloud computing applications of next-generation embedded systems, a major challenge that domain scientists are facing is how to efficiently store and analyze the vast volume of output data. Compression can reduce the amount of data that needs to be transferred and stored. However, most of the large datasets are in floating-point format, which exhibits high entropy. As a result, existing lossless compressors cannot provide enough performance for such applications. To address this problem, we propose a total variation reduction method for improving the compression ratio of lossless compressors (namely, FPC + and FPZIP + ), which employs a median-based hyperplane to precondition the data. In particular, we first try to exploit the space-filling curve (SFC), a well-known technique to preserve data locality for a multi-dimensional dataset. We show and explain why a raw SFC, such as Hilbert and Z-order curves, cannot improve the compression ratio. Then, we explore the opportunity and theoretical feasibility of the proposed total variation reduction-based algorithm. The experiment results show the effectiveness of the proposed method. The compression ratios are improved up to 48.2% (20.6% on average) for FPZIP and 42.4% (18.4% on average) for FPC. Moreover, through observing the time composition of the proposed method, it is found that the median finding holds a high percentage of the execution time. Hence, we further introduce an approximate median finding algorithm, providing a linear-time overhead reduction scheme. The experiment results clearly demonstrate that this algorithm reduces execution time by an average of 56.7% and 40.7% compared to FPC + and FPZIP + , respectively.

Determinants of survival after first relapse of acute lymphoblastic leukemia: a Children's Oncology Group study.

Limited prognostic factors have been associated with overall survival (OS) post-relapse in childhood Acute Lymphoblastic Leukemia (ALL). Patients enrolled on 12 Children's Oncology Group frontline ALL trials (1996-2014) were analyzed to assess for additional prognostic factors associated with OS post-relapse. Among 16,115 patients, 2053 (12.7%) relapsed. Relapse rates were similar for B-ALL (12.5%) and T-ALL (11.2%) while higher for infants (34.2%). Approximately 50% of B-ALL relapses occurred late (≥36 months) and 72.5% involved the marrow. Conversely, 64.8% of T-ALL relapses occurred early (<18 months) and 47.1% involved the central nervous system. The 5-year OS post-relapse for the entire cohort was 48.9 ± 1.2%; B-ALL:52.5 ± 1.3%, T-ALL:35.5 ± 3.3%, and infant ALL:21.5 ± 3.9%. OS varied by early, intermediate and late time-to-relapse; 25.8 ± 2.4%, 49.5 ± 2.2%, and 66.4 ± 1.8% respectively for B-ALL and 29.8 ± 3.9%, 33.3 ± 7.6%, 58 ± 9.8% for T-ALL. Patients with ETV6::RUNX1 or Trisomy 4 + 10 had median time-to-relapse of 43 months and higher OS post-relapse 74.4 ± 3.1% and 70.2 ± 3.6%, respectively. Patients with hypodiploidy, KMT2A-rearrangement, and TCF3::PBX1 had short median time-to-relapse (12.5-18 months) and poor OS post-relapse (14.2 ± 6.1%, 31.9 ± 7.7%, 36.8 ± 6.6%). Site-of-relapse varied by cytogenetic subtype. This large dataset provided the opportunity to identify risk factors for OS post-relapse to inform trial design and highlight populations with dismal outcomes post-relapse.

Enhanced deep learning based decision support system for kidney tumour detection

This study presents a high-accuracy deep learning-based decision support system for kidney cancer detection. The research utilizes a relatively large dataset of 10,000 CT images, including both healthy and tumour-detected kidney scans. After data preprocessing and optimization, various deep learning models were evaluated, with DenseNet-201 emerging as the top performer, achieving an accuracy of 99.75 %. The study compares multiple deep learning architectures, including AlexNet, EfficientNet, Darknet-53, Xception, and DenseNet-201, across different learning rates. Performance metrics such as accuracy, precision, sensitivity, F1-score, and specificity are analysed using confusion matrices. The proposed system outperforms different deep learning networks, demonstrating superior accuracy in kidney cancer detection. The improvement is attributed to effective data engineering and hyperparameter optimization of the deep learning networks. This research contributes to the field of medical image analysis by providing a robust decision support tool for early and rapid diagnosis of kidney cancer. The high accuracy and efficiency of the proposed system make it a promising aid for healthcare professionals in clinical settings.

Sensitive Data Flagging within Data Quality reports: R and Regex Integration for Effective Text Flagging in Large Datasets

ObjectiveWe’re introducing a comprehensive approach to enhance sensitive data flagging within our Data Quality (DQ) tool. Within de-identified health records, sensitive data such as user ids, post codes, phone numbers, and addresses pose significant privacy risks if exposed in their raw form. To address this challenge, we propose using all necessary regex patterns and free text field checks for sensitive data flagging with the objective of efficient detection within large datasets and ultimately reporting such data. ApproachA regex map has been developed along free text field thresholds, distribution counts and percentage checks to allow a generic approach. This ensures that any new regexes can be added to the flagging process without significant code changes. Once identified, sensitive data instances, including fields that contain free text are flagged and displayed in an html report that is human readable by DQ reviewers. ResultsThrough a combination of SQL and R regex based text processing, our approach allows a seamless identification and flagging of sensitive data within datasets. Sensitive data for large tables of +25 million records with over 50 columns are getting flagged in less than 150 seconds (approx. 2 minutes). ConclusionOur development offers a practical solution for sensitive data flagging in complex datasets where team members can implement robust sensitive data flagging by adding more regex formulas in the codebase. ImplicationsDQ reviewers can inspect the sensitive flagged fields in a human readable way, providing an extra measure of confidence when determining the quality of de-identified health records.

A Unified Pipeline for Simultaneous Brain Tumor Classification and Segmentation Using Fine-Tuned CNN and Residual UNet Architecture

In this paper, I present a comprehensive pipeline integrating a Fine-Tuned Convolutional Neural Network (FT-CNN) and a Residual-UNet (RUNet) architecture for the automated analysis of MRI brain scans. The proposed system addresses the dual challenges of brain tumor classification and segmentation, which are crucial tasks in medical image analysis for precise diagnosis and treatment planning. Initially, the pipeline preprocesses the FigShare brain MRI image dataset, comprising 3064 images, by normalizing and resizing them to achieve uniformity and compatibility with the model. The FT-CNN model then classifies the preprocessed images into distinct tumor types: glioma, meningioma, and pituitary tumor. Following classification, the RUNet model performs pixel-level segmentation to delineate tumor regions within the MRI scans. The FT-CNN leverages the VGG19 architecture, pre-trained on large datasets and fine-tuned for specific tumor classification tasks. Features extracted from MRI images are used to train the FT-CNN, demonstrating robust performance in discriminating between tumor types. Subsequently, the RUNet model, inspired by the U-Net design and enhanced with residual blocks, effectively segments tumors by combining high-resolution spatial information from the encoding path with context-rich features from the bottleneck. My experimental results indicate that the integrated pipeline achieves high accuracy in both classification (96%) and segmentation tasks (98%), showcasing its potential for clinical applications in brain tumor diagnosis. For the classification task, the metrics involved are loss, accuracy, confusion matrix, and classification report, while for the segmentation task, the metrics used are loss, accuracy, Dice coefficient, intersection over union, and Jaccard distance. To further validate the generalizability and robustness of the integrated pipeline, I evaluated the model on two additional datasets. The first dataset consists of 7023 images for classification tasks, expanding to a four-class dataset. The second dataset contains approximately 3929 images for both classification and segmentation tasks, including a binary classification scenario. The model demonstrated robust performance, achieving 95% accuracy on the four-class task and high accuracy (96%) in the binary classification and segmentation tasks, with a Dice coefficient of 95%.

The Book Review Column

Graph Theory in America: The First Hundred Years (Princeton University Press, 2023) by Robin Wilson, John J. Watkins, and David J. Parks chronicles the first hundred years of graph theory in North America, starting with the arrival of James Joseph Sylvester at Johns Hopkins University in 1876 and ending with the proof of the four color theorem by Kenneth Appel and Wolfgang Haken in 1976. Computability and Complexity: Foundations and Tools for Pursuing Scientific Applications (Springer, 2024) by decorated author Rod Downey is a new undergraduate textbook that covers advanced topics such as parameterized complexity, generic case complexity, and smoothed analysis. How to Think about Algorithms, 2nd ed. (Cambridge University Press, 2024) by Jeff Edmonds aims to teach students to think like a designer of algorithms. This new edition includes a short chapter on machine learning algorithms and more than 150 new exercises. A review of the first edition of this book appeared in this column in 2009. Differential Privacy (The MIT Press, 2025) by Simson L. Garfinkel, a senior data scientist at the US Department of Homeland Security, explains the algorithmic technology used by Google, Apple, and the US Census Bureau to protect the privacy of individuals in large datasets.

When we talk about Big Data, What do we really mean? Toward a more precise definition of Big Data

Despite the lack of consensus on an official definition of Big Data, research and studies have continued to progress based on this “no consensus” stance over the years. However, the lack of a clear definition and scope for Big Data results in scientific research and communication lacking a common ground. Even with the popular “V” characteristics, Big Data remains elusive. The term is broad and is used differently in research, often referring to entirely different concepts, which is rarely stated explicitly in papers. While many studies and reviews attempt to draw a comprehensive understanding of Big Data, there has been little systematic research on the position and practical implications of the term Big Data in research environments. To address this gap, this paper presents a Systematic Literature Review (SLR) on secondary studies to provide a comprehensive overview of how Big Data is used and understood across different scientific domains. Our objective was to monitor the application of the Big Data concept in science, identify which technologies are prevalent in which fields, and investigate the discrepancies between the theoretical understanding and practical usage of the term. Our study found that various Big Data technologies are being used in different scientific fields, including machine learning algorithms, distributed computing frameworks, and other tools. These manifestations of Big Data can be classified into four major categories: abstract concepts, large datasets, machine learning techniques, and the Big Data ecosystem. This study revealed that despite the general agreement on the “V” characteristics, researchers in different scientific fields have varied implicit understandings of Big Data. These implicit understandings significantly influence the content and discussions of studies involving Big Data, although they are often not explicitly stated. We call for a clearer articulation of the meaning of Big Data in research to facilitate smoother scientific communication.

PCGmix: A Data-Augmentation Method For Heart-Sound Classification.

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, responsible for 32% of all deaths, with the annual death toll projected to reach 23.3 million by 2030. The early identification of individuals at high risk of CVD is crucial for the effectiveness of preventive strategies. In the field of deep learning, automated CVD-detection methods have gained traction, with phonocardiogram (PCG) data emerging as a valuable resource. However, deep-learning models rely on large datasets, which are often challenging to obtain. In recent years, data augmentation has become a viable solution to the problem of scarce data. In this paper, we propose a novel data-augmentation technique named PCGmix, specifically engineered for the augmentation of PCG data. The PCGmix algorithm employs a process of segmenting and reassembling PCG recordings, incorporating meticulous interpolation to ensure the preservation of the cardinal diagnostic features pertinent to CVD detection. The empirical assessment of the PCGmix method was utilized on a publicly available database of normal and abnormal heart-sound recordings. To evaluate the impact of data augmentation across a range of dataset sizes, we conducted experiments encompassing both limited and extensive amounts of training data. The experimental results demonstrate that the novel method is superior to the compared state-of-the-art, time-series augmentation. Notably, on limited data, our method achieves comparable accuracy to the no-augmentation approach when trained on 31% to 69% larger datasets. This study suggests that PCGmix can enhance the accuracy of deep-learning models for CVD detection, especially in data-constrained environments.

Evaluating Prediction Models with Hearing Handicap Inventory for the Elderly in Chronic Otitis Media Patients

Background: This retrospective, cross-sectional study aimed to assess the functional hearing capacity of individuals with Chronic Otitis Media (COM) using prediction modeling techniques and the Hearing Handicap Inventory for the Elderly (HHIE) questionnaire. This study investigated the potential of predictive models to identify hearing levels in patients with COM. Methods: We comprehensively examined 289 individuals diagnosed with COM, of whom 136 reported tinnitus and 143 did not. This study involved a detailed analysis of various patient characteristics and HHIE questionnaire results. Logistic and Random Forest models were employed and compared based on key performance metrics. Results: The logistic model demonstrated a slightly higher accuracy (73.56%), area under the curve (AUC; 0.73), Kappa value (0.45), and F1 score (0.78) than the Random Forest model. These findings suggest the superior predictive performance of the logistic model in identifying hearing levels in patients with COM. Conclusions: Although the AUC for the logistic regression did not meet the benchmark, this study highlights the potential for enhanced reliability and improved performance metrics using a larger dataset. The integration of prediction modeling techniques and the HHIE questionnaire shows promise for achieving greater diagnostic accuracy and refining intervention strategies for individuals with COM.

Enhancing the usability of health data for inequalities research: a UK quality improvement project and code list curation.

Objective and ApproachPrimary care datasets provide a rich source of information for inequalities research; however, the value derived from these depends largely on the comprehensiveness of the code lists used by researchers. To date, no standardised code list for inequalities research has been developed. The aim of this project was to develop a comprehensive code list for groups of interest to inequalities research, namely: people with learning disabilities (LDs), people with severe mental illness (SMI), people from ethnic minority groups, and people who are transgender. Existing code lists were extracted from the Clinical Practice Research Datalink (CPRD) Bibliography (the largest research dataset in the UK), and four UK code list repositories: OpenCodelists, Health Data Research UK, University of Cambridge, and London School of Hygiene and Tropical Medicine. Comprehensive code lists were then curated through collation and removal of duplicates. Results16 code lists were identified for LDs, 18 for SMI, 16 for ethnicity, and 2 for transgender. From these, 661, 733, 346 and 68 unique codes were identified, respectively. Preliminary testing of the curated code lists, in a CPRD dataset, indicated that the number of individuals belonging to these groups was increased by 90% (compared to the original code list). Conclusions and ImplicationsOur findings suggest that the curated code lists improved data capture. These lists now need to be validated by healthcare specialists, before being made publicly available. The inclusion of code-specific definitions will enable international researchers to adapt the code lists to their medical systems.

Calcium kinetic rates in Caucasian males and females from birth to adulthood

Calcium plays an important role in bone physiology and its kinetics change over lifetime. The analysis of calcium deposition and release through stable isotope techniques has guided recommendations on nutritional uptake for overall health. In addition, calcium kinetics have great relevance for toxicokinetic studies of bone-seeking elements (e.g, aluminium and lead) since these elements use common uptake and release pathways. While the impact of many factors on calcium kinetics have been investigated individually, a consolidated age- and sex-dependent kinetic description amenable for toxicokinetic modeling, however, is still lacking. Motivated by this need, we systematically reviewed the existing literature on calcium kinetics and assembled a large and consistent dataset. Then, building on the work of O'Flaherty in the 1990s, we formulated age- and sex-dependent functions describing calcium deposition, release, net retention, and mass. This description represents the current knowledge on calcium kinetics in a reference individual of Caucasians as most data was from this population.