Interpretation Of Data Research Articles

Why do women choose home births.

In recent years, the US has seen a significant rise in the rate of planned home births, with a 60 % increase from 2016 to 2023, reaching a total of 46,918. This trend positions the US as the leading developed country in terms of home birth prevalence. The American College of Obstetricians and Gynecologists (ACOG) suggests stringent criteria for selecting candidates for home births, but these guidelines have not been adopted by home birth midwives leading to poor outcomes including increased rates of neonatal morbidity and mortality. This paper explores the motivations behind choosing home births in the US despite the known risks. Studies highlight factors such as the desire for a more natural birth experience, previous negative hospital experiences, and the influence of the COVID-19 pandemic on perceptions of hospital safety. We provide new insights into why women choose home births by incorporating insights from Nobel laureate Daniel Kahneman's theories on decision-making, suggesting that cognitive biases may significantly influence these decisions. Kahneman's work provides a framework for understanding how biases and heuristics can lead to the underestimation of risks and overemphasis on personal birth experiences. We also provide recommendations ("nudges according to Richard Thaler") to help ensure women have access to clear, balanced information about home births. The development of this publication was assisted by OpenAI's ChatGPT-4, which facilitated the synthesis of literature, interpretation of data, and manuscript drafting. This collaboration underscores the potential of integrating advanced computational tools in academic research, enhancing the efficiency and depth of our analyses.

Elementary Reaction Steps That Precede or Follow a Unimolecular Reaction Step Can Obfuscate Interpretation of the Driving-Force Dependence to Its Rate Constant.

Assessing the validity of a driving-force-dependent kinetic theory for a unimolecular elementary reaction step is difficult when the observed reaction rate is strongly influenced by properties of the preceding or following elementary reaction step. A well-known example occurs for bimolecular reactions with weak orbital overlap, such as outer-sphere electron transfer, where bimolecular collisional encounters that precede a fast unimolecular electron-transfer step can limit the observed rate. A lesser-appreciated example occurs for bimolecular reactions with stronger orbital overlap, including many proton-transfer reactions, where equilibration of an endergonic unimolecular proton-transfer step results in a relatively small concentration of reaction products, thus slowing the rate of the following step such that it becomes rate limiting. Incomplete consideration of these points has led to discrepancies in interpretation of data from the literature. Our reanalysis of these data suggests that proton-transfer elementary reaction steps have a nonzero intrinsic free energy barrier, implying, in the parlance of Marcus theory, that there is non-negligible nuclear reorganization. Outcomes from our analyses are generalizable to inner-sphere electron-transfer reactions such as those involved in (photo)electrochemical fuel-forming reactions.

Relativistic Quantum Chemical Investigation of Actinide Covalency Measured by Electron Paramagnetic Resonance Spectroscopy.

We investigate actinide covalency effects in two [AnCptt3] (An = Th, U) complexes recently studied with pulsed electron paramagnetic resonance spectroscopy, using the Hyperion package to obtain relativistic hyperfine coupling constants from relativistic multiconfigurational wave functions. 1H and 13C HYSCORE simulations using the computed parameters show excellent agreement with the experimental data, highlighting the accuracy of modern relativistic ab initio methods. The extent of covalency indicated from the calculations on [ThCptt3] is in agreement with the original report based on traditional spectral fitting methods, while the covalency in [UCptt3] is found to be previously overestimated. The latter is due to the paramagnetic spin-orbit effect that arises naturally in a relativistic theory of hyperfine coupling and yet was not accounted for in the original study, thus highlighting the necessity of relativistic approaches for the interpretation of magnetic resonance data pertaining to actinides.

A longitudinal evaluation of personalized intrinsic network topography and cognitive decline in Parkinson's disease.

Resting state functional magnetic resonance imaging (R-fMRI) offers insight into how synchrony within and between brain networks is altered in disease states. Individual and disease-related variability in intrinsic connectivity networks may influence our interpretation of R-fMRI data. We used a personalized approach designed to account for individual variation in the spatial location of correlation maxima to evaluate R-fMRI differences between Parkinson's disease (PD) patients who showed cognitive decline, those who remained cognitively stable and cognitively stable controls. We compared fMRI data from these participant groups, studied at baseline and 18 months later, using both network-based statistics (NBS) and calculations of mean inter- and intra-network connectivity within pre-defined functional networks. The NBS analysis showed that PD participants who remained cognitively stable showed exclusively (at baseline) or predominantly (at follow-up) increased intra-network connectivity, whereas decliners showed exclusively reduced intra-network and inter- (ventral attention and default mode) connectivity, in comparison with the control group. Evaluation of mean connectivity between all regions of interest (ROIs) within a priori networks showed that decliners had consistently reduced inter-network connectivity for ventral attention, somatomotor, visual and striatal networks and reduced intra-network connectivity for ventral attention network to striatum and cerebellum. These findings suggest that specific functional connectivity covariance patterns differentiate PD cognitive subtypes and may predict cognitive decline. Further, increased intra and inter-network synchrony may support cognitive function in the face of PD-related network disruptions.

Hidden Impurities Generate False Positives in Single Atom Catalyst Imaging

Single‐atom catalysts (SACs) are an emerging class of materials, leveraging maximum atom utilization and distinctive structural and electronic properties to bridge heterogeneous and homogeneous catalysis. Direct imaging methods, such as aberration‐corrected high‐angle annular dark‐field scanning transmission electron microscopy, are commonly applied to confirm the atomic dispersion of active sites. However, interpretations of data from these techniques can be challenging due to simultaneous contributions to intensity from impurities introduced during synthesis processes, as well as any variation in position relative to the focal plane of the electron beam. To address this matter, this paper presents a comprehensive study on two representative SACs containing isolated nickel or copper atoms. Spectroscopic techniques, including X‐ray absorption spectroscopy, were employed to prove the high metal dispersion of the catalytic atoms. Employing scanning transmission electron microscopy imaging combined with single‐atom‐sensitive electron energy loss spectroscopy, we scrutinized thin specimens of the catalysts to provide an unambiguous chemical identification of the observed single‐atom species and thereby distinguish impurities from active sites at the single‐atom level. Overall, the study underscores the complexity of SACs characterization and establishes the importance of the use of spectroscopy in tandem with imaging at atomic resolution to fully and reliably characterize single‐atom catalysts.

Hidden Impurities Generate False Positives in Single Atom Catalyst Imaging.

Single-atom catalysts (SACs) are an emerging class of materials, leveraging maximum atom utilization and distinctive structural and electronic properties to bridge heterogeneous and homogeneous catalysis. Direct imaging methods, such as aberration-corrected high-angle annular dark-field scanning transmission electron microscopy, are commonly applied to confirm the atomic dispersion of active sites. However, interpretations of data from these techniques can be challenging due to simultaneous contributions to intensity from impurities introduced during synthesis processes, as well as any variation in position relative to the focal plane of the electron beam. To address this matter, this paper presents a comprehensive study on two representative SACs containing isolated nickel or copper atoms. Spectroscopic techniques, including X-ray absorption spectroscopy, were employed to prove the high metal dispersion of the catalytic atoms. Employing scanning transmission electron microscopy imaging combined with single-atom-sensitive electron energy loss spectroscopy, we scrutinized thin specimens of the catalysts to provide an unambiguous chemical identification of the observed single-atom species and thereby distinguish impurities from active sites at the single-atom level. Overall, the study underscores the complexity of SACs characterization and establishes the importance of the use of spectroscopy in tandem with imaging at atomic resolution to fully and reliably characterize single-atom catalysts.

Examining the Effectiveness of Interactive Webtoons for Premature Birth Prevention: Protocol for a Randomized Controlled Trial.

Premature birth poses significant health challenges globally, impacting infants, families, and society. Despite recognition of its contributing factors, efforts to reduce its incidence have seen limited success. A notable gap exists in the awareness among women of childbearing age (WCA) regarding both the risks of premature birth and the preventative measures they can take. Research suggests that enhancing health beliefs and self-management efficacy in WCA could foster preventive health behaviors. Interactive webtoons offer an innovative, cost-effective avenue for delivering engaging, accessible health education aimed at preventing premature birth. This protocol describes a randomized controlled trial to assess the effectiveness and feasibility of a novel, self-guided, web-based intervention-Pregnancy Story I Didn't Know in Interactive Webtoon Series (PSIDK-iWebtoons)-designed to enhance self-management efficacy and promote behaviors preventing premature birth in WCA. Using an explanatory sequential mixed methods design, this study first conducts a quantitative analysis followed by a qualitative inquiry to evaluate outcomes and feasibility. Participants are randomly assigned to 2 groups: one accessing the PSIDK-iWebtoons and the other receiving Pregnancy Story I Didn't Know in Text-Based Information (PSIDK-Texts) over 3 weeks. We measure primary efficacy through the self-management self-efficacy scale for premature birth prevention (PBP), alongside secondary outcomes including perceptions of susceptibility, severity, benefits, and barriers based on the health belief model for PBP and PBP intention. Additional participant-reported outcomes are assessed at baseline, the postintervention time point, and the 4-week follow-up. The feasibility of the intervention is assessed after the end of the 3-week intervention period. Outcome analysis uses repeated measures ANOVA for quantitative data, while qualitative data are explored through content analysis of interviews with 30 participants. The study received funding in June 2021 and institutional review board approval in October 2023. Both the PSIDK-iWebtoons and PSIDK-Texts interventions have been developed and pilot-tested from July to November 2023, with the main phase of quantitative data collection running from November 2023 to March 2024. Qualitative data collection commenced in February 2024 and will conclude in May 2024. Ongoing analyses include process evaluation and data interpretation. This trial will lay foundational insights into the nexus of interactive web-based interventions and the improvement of knowledge and practices related to PBP among WCA. By demonstrating the efficacy and feasibility of a web-based, interactive educational tool, this study will contribute essential evidence to the discourse on accessible and scientifically robust digital platforms. Positive findings will underscore the importance of such interventions in fostering preventive health behaviors, thereby supporting community-wide efforts to mitigate the risk of premature births through informed self-management practices. Korea Disease Control and Prevention Agency (KDCA) KCT0008931; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=25857. DERR1-10.2196/58326.

Image analysis of acoustic data and interpretation of rock stress orientations for geothermal exploration in Gothenburg borehole GE-1, SW Sweden

Abstract This study contributes to geothermal exploration in 1660–1520 Ma old, reworked bedrock in Sweden. Our primary objectives are to constrain the orientation of horizontal stresses, and to discuss implications for geothermal exploration. High-resolution acoustic televiewer image data reveal the downhole distribution of stress indicators (borehole breakouts, drilling-induced fractures and petal centreline fractures) and pre-existing structures (natural fractures, foliation). About 135 m of stress indicators are measured from 0.2–1.0 km. The results suggest a uniform NNW–SSE mean maximum horizontal stress orientation. A total of 1525 pre-existing structures (natural fractures, foliation) are mapped in borehole GE-1. The prevailing stress regime controls whether natural fractures and foliation are well-oriented for stimulation. For strike-slip and normal faulting stress regimes, well-oriented fractures steeply dip towards the WSW. For a reverse faulting stress regime, shallow dipping fractures are well-oriented for stimulation. The downhole distribution of stress indicators and other stress measurements in the region and other parts of Fennoscandia tentatively suggest a strike-slip stress regime, but additional studies are needed to constrain the complete stress field at study depth and towards engineered geothermal systems reservoir target depths. Our secondary objective is to highlight that interpretation of high-resolution acoustic data, particularly in metamorphic crystalline rocks, is subjective and that more guidelines for data interpretation are needed. The interactive interpretation of the images is based on visual analyses of complex pre-existing structures and stress indicators with highly variable shapes. The application of three methods for data analyses in the GE-1 borehole proposes that drilling-induced fractures are little influenced by the method applied. Interpretations on individual borehole breakout azimuths may, however, result in over 10° differences in orientation.

Leveraging Digital Advertising Platforms for Consumer Research

Abstract Digital advertising platforms have emerged as a widely utilized data source in consumer research; yet, the interpretation of such data remains a source of confusion for many researchers. This article aims to address this issue by offering a comprehensive and accessible review of four prominent data collection methods proposed in the marketing literature: “informal studies,” “multiple-ad studies without holdout,” “single-ad studies with holdout,” and “multiple-ad studies with holdout.” By outlining the strengths and limitations of each method, we aim to enhance understanding regarding the inferences that can and cannot be drawn from the collected data. Furthermore, we present seven recommendations to effectively leverage these tools for programmatic consumer research. These recommendations provide guidance on how to use these tools to obtain causal and non-causal evidence for the effects of marketing interventions, and the associated psychological processes, in a digital environment regulated by targeting algorithms. We also give recommendations for how to describe the testing tools and the data they generate and urge platforms to be more transparent on how these tools work.

Semi-Supervised Training for (Pre-Stack) Seismic Data Analysis

A lack of labeled examples is a problem in different domains, such as text and image processing, medicine, and static reservoir characterization, because supervised learning relies on vast volumes of these data to perform successfully, but this is quite expensive. However, large amounts of unlabeled data exist in these domains. The deep semi-supervised learning (DSSL) approach leverages unlabeled data to improve supervised learning performance using deep neural networks. This approach has succeeded in image recognition, text classification, and speech recognition. Nevertheless, there have been few works on pre-stack seismic reservoir characterization, in which knowledge of rock and fluid properties is fundamental for oil exploration. This paper proposes a methodology to estimate acoustic impedance using pre-stack seismic data and DSSL with a recurrent neural network. The few labeled datasets for training were pre-processed from raw seismic and acoustic impedance data from five borehole logs. The results showed that the acoustic impedance estimation at the well location and outside it was better predicted by the DSSL compared to the supervised version of the same neural network. Therefore, employing a large amount of unlabeled data can be helpful in the development of seismic data interpretation systems.

Blade fouling fault detection based on shaft orbit generative adversarial network

To address the challenges of accuracy and interpretability in mechanical fault detection models, this study proposes a shaft orbit generative adversarial network (SOGAN) and applies it to detect blade fouling faults. Variational autoencoder (VAE) is used as the foundational network architecture for extracting high-dimensional latent features from the shaft orbit images. Concurrently, the invariant moments of the shaft orbit images are extracted and embedded in a bypass within the generator, thereby enhancing the accuracy of fault detection. Two sets of real-world blade fouling fault data are collected and meticulously analyzed. The proposed SOGAN model demonstrates significant performance improvements, with average increases of 18.91%, 10.20%, and 26.79% in accuracy compared to the autoencoder, VAE, and GANomaly algorithms, respectively. The F1 scores for both the groups exceed 0.98. The data generated by the proposed SOGAN model exhibit a trend-wise correspondence with the finite element modeling data. In addition, the use of gradient information for the localization and visual analysis of anomalies dynamically tracks the spatial evolution of the rotor shaft orbit throughout its lifecycle. The data generation capability and interpretability of the proposed model can effectively support digital twin modeling and health management of rotating machinery.

Analysis of measurable residual disease by IG/TR gene rearrangements: quality assurance and updated EuroMRD guidelines.

Minimal/measurable residual disease (MRD) diagnostics using real-time quantitative PCR analysis of rearranged immunoglobulin and T-cell receptor gene rearrangements are nowadays implemented in most treatment protocols for patients with acute lymphoblastic leukemia (ALL). Within the EuroMRD Consortium, we aim to provide comparable, high-quality MRD diagnostics, allowing appropriate risk-group classification for patients and inter-protocol comparisons. To this end, we set up a quality assessment scheme, that was gradually optimized and updated over the last 20 years, and that now includes participants from around 70 laboratories worldwide. We here describe the design and analysis of our quality assessment scheme. In addition, we here report revised data interpretation guidelines, based on our newly generated data and extensive discussions between experts. The main novelty is the partial re-definition of the "positive below quantitative range" category by two new categories, "MRD low positive, below quantitative range" and "MRD of uncertain significance". The quality assessment program and revised guidelines will ensure reproducible and accurate MRD data for ALL patients. Within the Consortium, similar programs and guidelines have been introduced for other lymphoid diseases (e.g., B-cell lymphoma), for new technological platforms (e.g., digital droplet PCR or Next-Generation Sequencing), and for other patient-specific MRD PCR-based targets (e.g., fusion genes).

Squark production with R-symmetry beyond NLO at the LHC

The Minimal R-symmetric Supersymmetric Standard Model (MRSSM) provides a realisation of supersymmetry in which the parameter space is less constrained by the current LHC data than in the simplest supersymmetric scenarios. In the present paper, we obtain the most precise theoretical predictions in the MRSSM for squark production at the LHC, enabling accurate interpretations of LHC data in terms of the MRSSM. We perform threshold resummation of soft gluon corrections to the total cross sections for the production of squark-(anti)squark pairs at the LHC in the MRSSM framework. The resummation is carried out using the direct QCD method and reaches the next-to-next-to-leading-logarithmic (NNLL) accuracy, which requires calculating the one-loop matching coefficients in the relevant production channels. The resummed cross sections are then matched to the available NLO results and evaluated for S\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\sqrt{S} $$\\end{document} = 13.6 TeV. Compared with the Minimal Supersymmetric Standard Model (MSSM), the cross sections in the MRSSM can be significantly reduced, implying less stringent limits on squark and gluino masses. Our results carry significant implications for exploring the viability of supersymmetry at the LHC. The results of our calculation are publicly available as a numerical package.

Adapting to the Digital Age: An Evaluation of Online Learning Strategies in Public Health and Social Care Education

Introduction. The global shift towards digital education in the wake of the COVID-19 pandemic has reshaped the landscape of higher education. Public health and social care programmes have adapted to this transformation, embracing online learning strategies to engage students. This study focuses on the assessment of online learning experiences in public health and social care education, particularly during the post-COVID-19 period. Materials and Methods. This qualitative study employed semi-structured interviews and focus groups with 49 university students in England. These phases, conducted both in person and via video, were audio and video recorded and transcribed verbatim. A purposive sampling technique was employed to gather data, and framework analysis facilitated data management and interpretation. Results. The presentation highlights three main themes and 12 accompanying sub-themes that capture the clear preferences of students for both in-person and online learning methods. The findings elucidate the merits and demerits of online learning strategies in contrast to traditional face-to-face classes. Online classes offer distinct advantages in terms of flexibility, cost-effectiveness, time efficiency, and technological proficiency. However, they also bring to the fore concerns related to social isolation, mental health challenges, technical challenges, and distractions within the home environment. Conclusion. Considering the global transition to digital education, this study contributes to the dynamic discourse surrounding online learning in the post-COVID-19 era. The diverse preferences expressed by students highlight the importance of personalised and flexible teaching methods in online education, underlining the need for adaptability and inclusivity to meet the diverse needs of today’s learners.

Introducing regression discontinuity design to applied linguistics

Abstract This report presents regression discontinuity design (RDD) as a powerful analytical tool for use in applied linguistics showcased through our study of the impact of Japanese government study-abroad (SA) scholarships. RDD enables the estimation of causal effects in scenarios where a true experiment is not feasible by exploiting a naturally occurring cutoff point for treatment assignment. Because RDD may be novel to most readers in applied linguistics, this report provides a detailed step-by-step explanation of the standard RDD procedures our study exemplifies. The results section is crafted to reflect the conventional presentation style of RDD findings. Furthermore, the report’s concluding segment suggests scenarios within SA-related language learning research that could benefit from RDD application by enhancing the nuanced and precise interpretation of data in applied linguistics contexts.

Application of Natural Language Processing in Electronic Health Record Data Extraction for Navigating Prostate Cancer Care: A Narrative Review.

Introduction: Natural language processing (NLP)-based data extraction from electronic health records (EHRs) holds significant potential to simplify clinical management and aid research. This review aims to evaluate the current landscape of NLP-based data extraction in prostate cancer (PCa) management. Materials and Methods: We conducted a literature search of PubMed and Google Scholar databases using the keywords: "Natural Language Processing," "Prostate Cancer," "data extraction," and "EHR" with variations of each. No language or time limits were imposed. All results were collected in a standardized manner, including country of origin, sample size, algorithm, objective of outcome, and model performance. The precision, recall, and the F1 score of studies were collected as a metric of model performance. Results: Of the 14 studies included in the review, 2 articles focused on documenting digital rectal examinations, 1 on identifying and quantifying pain secondary to PCa, 8 on extracting staging/grading information from clinical reports, with an emphasis on TNM-classification, risk stratification, and identifying metastasis, 2 articles focused on patient-centered post-treatment outcomes such as incontinence, erectile, and bowel dysfunction, and 1 on loneliness/social isolation following PCa diagnosis. All models showed moderate to high data annotation/extraction accuracy compared with the gold standard method of manual data extraction by chart review. Despite their potential, NLPs face challenges in handling ambiguous, institution-specific language and context nuances, leading to occasional inaccuracies in clinical data interpretation. Conclusion: NLP-based data extraction has effectively extracted various outcomes from PCa patients' EHRs. It holds the potential for automating outcome monitoring and data collection, resulting in time and labor savings.

THE MONGOL EMPIRE AND KOREA: FROM WAR TO VASSALAGE

The purpose of the article is an in-depth analysis of the complex relationship between the Mongolian Empire and the Korean state from the period of military conflicts to the establishment of vassalage. The research is aimed at identifying the key factors determining the course and historical significance of these relations. The novelty of the study lies in an in-depth analysis of the numerous Mongol campaigns against Korea and the resistance of Korea. The main direction of the research is to study the impact of these events on the historical dynamics of the region and the development of society as a whole. Special attention is paid to the analysis of diplomatic and trade relations between the nomads of the Far East and Korea, as well as China's role in the fate of Korea as a traditional overlord. Despite the difficult times, the article emphasizes that Korea was able to maintain its autonomy and become a key hub on the Great Silk Road, which contributed to the development of handicraft production and increased its authority. The article represents an important contribution to historical science, enriching our understanding of the complex relationships in the Northeast Asian region. The practical significance of the article lies in the possibility of applying its results to a deeper analysis of modern geopolitical processes. The research methodology includes an analytical approach to data interpretation and a comparative analysis of historical events. The authors analyze the impact of the collapse of the clan system and the crisis of the central government on the military system of the Korean state, paying attention to changes in the mobilization system, which turned out to be one of the reasons for the deterioration of the political and social situation in the country. As a result of the research, the key factors determining the course of relations between the Mongolian Empire and the Korean state have been identified. The analysis allows us to conclude that these events are important for shaping the historical trajectory of both peoples and the region as a whole.

Operando Scanning Small-/Wide-Angle X-ray Scattering for Polymer Electrolyte Fuel Cells: Investigation of Catalyst Layer Saturation and Membrane Hydration- Capabilities and Challenges.

Polymer electrolyte fuel cells are an essential technology for future local emission-free mobility. One of the critical challenges for thriving commercialization is water management in the cells. We propose small- and wide-angle X-ray scattering as a suitable diagnostic tool to quantify the liquid saturation in the catalyst layer and determine the hydration of the ion-conducting membrane in real operating conditions. The challenges that may occur in operando data collection are described in detail─separation of the anode and cathode, cell alignment to the beam, X-ray radiation damage, and the possibility of membrane swelling. A synergistic development of experimental setup, data acquisition, and data interpretation circumvents the major challenges and leads to practical and reliable insights.

Music Information Retrieval using Deep Learning Techniques

Music Information Retrieval (MIR) is gaining attention due to the surge in digital music and the need for efficient search and recommendation systems. Traditional MIR methods rely on hand-crafted features and rule-based systems, limiting their adaptability. Deep Learning (DL) shows promise in automatically extracting complex patterns from raw data. This paper offers an extensive overview of MIR tasks like classification, genre recognition, similarity search, and recommendation, along with DL models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), including LSTM and Transformer architectures, tailored for MIR. Challenges such as data scarcity, computational complexity, and interpretability persist, with proposed solutions like data augmentation, transfer learning, and attention mechanisms. Experimental results on benchmark datasets demonstrate DL's superiority in accuracy, scalability, and robustness over traditional methods. Practical examples highlight DL's potential to revolutionize music search, recommendation, and analysis. Emphasizing the importance of large annotated datasets for training high-quality DL models, strategies for data collection, labeling, and preprocessing are outlined. DL offers promising prospects for advancing MIR by addressing its inherent challenges and establishing new performance benchmarks. Further DL development is expected to drive innovation and enhance digital music consumption experiences through MIR systems. Keywords: Deep Learning, Convolution Neural Network (CNNs), Recurrent Neural Network (RNNs), Digital Music Consumption, Genre recognition.

HiPSC-CM Electrophysiology: Impact of Temporal Changes and Study Parameters on Experimental Reproducibility.

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are frequently used for preclinical cardiotoxicity testing and remain an important tool for confirming model-based predictions of drug effects in accordance with Comprehensive in Vitro Proarrhythmia Assay (CiPA). Despite the considerable benefits hiPSC-CMs provide, concerns surrounding experimental reproducibility have emerged. We investigated the effects of temporal changes and experimental parameters on hiPSC-CM electrophysiology. iCell cardiomyocytes2 were cultured and biosignals were acquired using a microelectrode array (MEA) system (1-14 days). Continuous recordings revealed a 22.6% increase in the beating rate and 7.7% decrease in the field potential duration (FPD) during a 20-minute equilibration period. Location specific differences across a multiwell plate were also observed, with iCell cardiomyocytes2 in the outer rows beating 8.8 beats per minute (BPM) faster than the inner rows. Cardiac endpoints were also impacted by cell culture duration; from 2-14 days the beating rate decreased (-12.7 BPM), FPD lengthened (+257 ms), and spike amplitude increased (+3.3 mV). Cell culture duration (4-10 days) also impacted cardiomyocyte drug responsiveness (E-4031, nifedipine, isoproterenol). qRT-PCR results suggest that daily variations in cardiac metrics may be linked to the continued maturation of hiPSC-CMs in culture (2-30 days). Daily experiments were also repeated using a second cell line (Cor.4U). Collectively, our study highlights multiple sources of variability to consider and address when performing hiPSC-CM MEA studies. To improve reproducibility and data interpretation, MEA-based studies should establish a standardized protocol and report key experimental conditions (e.g., cell line, culture time, equilibration time, electrical stimulation settings, raw data values).