Ensure Data Quality Research Articles

Recursive signal denoising method for predictive maintenance of equipment by using deep learning based temporal masking

In the data driven predictive maintenance, high quality data is the premise of high accuracy diagnosis and prediction. In the industrial practice, reducing the noise is of great significance to ensure data quality. This paper proposes a recursive denoising method for manufacturing equipment signals in the data driven predictive maintenance. First, in signal decomposition method, equipment mixed signal is decomposed by temporal masking with dilated convolution neural network to generate a noise mask, which realizes signal decomposition of using recursive operation of temporal masking model. Second, in signal components recognition method, signal component features similarities are calculated, which act on the parameter regulation of signal recognition meta-learning model. The experimental results demonstrated that the proposed method effectively solves the noise reduction problem of the equipment signal. Further engineering tests of a chemical winding machine vibration signal decomposition and recognition show that the proposed method has strong adaptive performance for noise reduction.

Assessing and Improving the Accuracy of Visible Infrared Imaging Radiometer Suite Ocean Color Products in Environments with High Solar Zenith Angles

Utilizing in situ measurement data to assess satellite-derived long-term ocean color products under different observational conditions is crucial for ensuring data quality and integrity. In this study, we conducted an extensive evaluation and analysis of Visible Infrared Imaging Radiometer Suite (VIIRS) remote sensing reflectance (Rrs) products using long-term OC-CCI in situ data from 2012 to 2021. Our research findings indicate that, well beyond its designed operational lifespan, the root mean square difference accuracy of VIIRS Rrs products across most spectral bands remains superior to 0.002 (sr−1). However, VIIRS Rrs products in shorter wavelength bands (e.g., at 412 nm) have exhibited significantly lower accuracy and a long-term bias in recent years. The annual precision of VIIRS Rrs products demonstrated a declining trend, particularly in coastal or eutrophic waters. This degradation in accuracy highlights the imperative for continuous monitoring of VIIRS performance and further advancements in the atmospheric correction algorithm, especially to address satellite records at high solar zenith angles (SZAs) and observation zenith angles (OZAs). Our analysis indicates that, in observation environments with high SZAs (greater than 70°), the accuracy of VIIRS Rrs products has declined by nearly 50% compared to typical solar zenith angle observation conditions. To address the challenge of declining accuracy under large observation geometries, we introduced the neural network atmospheric correction model (NN-V). Developed based on meticulously curated VIIRS products, the NN-V model exhibits outstanding performance in handling VIIRS data in conditions of extensive observation geometries. During the winter season in high-latitude marine regions, the NN-V model demonstrates a remarkable enhancement in ocean color product coverage, achieving an increase of nearly 20 times compared to traditional methods.

An economic evaluation of the expansion of electronic case reporting in an academic healthcare setting.

Determine the economic cost or benefit of expanding electronic case reporting (eCR) for 29 reportable conditions beyond the initial eCR implementation for COVID-19 at an academic health center. The return on investment (ROI) framework was used to quantify the economic impact of the expansion of eCR from the perspective of an academic health system over a 5-year time horizon. Sensitivity analyses were performed to assess key factors such as personnel cost, inflation, and number of expanded conditions. The total implementation costs for the implementation year were estimated to be $5031.46. The 5-year ROI for the expansion of eCR for the 29 conditions is expected to be 142% (net present value of savings: $7166). Based on the annual ROI, estimates suggest that the savings from the expansion of eCR will cover implementation costs in approximately 4.8 years. All sensitivity analyses yielded a strong ROI for the expansion of eCR. Our findings suggest a strong ROI for the expansion of eCR at UHealth, with the most significant cost savings observed implementing eCR for all reportable conditions. An early effort to ensure data quality is recommended to expedite the transition from parallel reporting to production to improve the ROI for healthcare organizations. This study demonstrates a positive ROI for the expansion of eCR to additional reportable conditions beyond COVID-19 in an academic health setting, such as UHealth. While this evaluation focuses on the 5-year time horizon, the potential benefit could extend further.

МЕТОДИ ТА МОДЕЛІ DATA SCIENCE В СУЧАСНІЙ ЕКОНОМІЦІ

In contemporary economics, data science models play a crucial role in analyzing complex relationships, predicting economic trends, and informing policy decisions. This article reviews the most commonly used data science models in economics, including econometric models like linear and logistic regression, Probit and Tobit models, time series analysis models such as ARIMA and Vector Autoregression (VAR), and panel data analysis methods like fixed and random effects models and Difference-in-Differences (DiD). Additionally, it explores machine learning algorithms, clustering and classification techniques, dimensionality reduction methods, Bayesian methods, and natural language processing (NLP) applications. The article highlights their purposes, applications, and relevant works, emphasizing the strengths and limitations of each model. It also discusses the impact of these models across various sectors, including finance, retail, energy, and healthcare. This comprehensive overview underscores the importance of aligning data science models with business objectives, ensuring data quality, investing in scalable technologies, fostering a data-driven culture, and addressing ethical considerations. The article concludes with future research directions, such as advanced neural network architectures, large language models, generative AI models, hybrid models, and the need for interpretable and ethical AI applications in economics. The importance of this topic lies in the transformative potential of data science models to enhance economic analysis and decision-making. By leveraging advanced data science techniques, economists can gain deeper insights into complex economic phenomena, improve forecasting accuracy, and develop more effective policies. As data-driven approaches continue to evolve, they provide powerful tools for addressing critical economic challenges, driving innovation, and fostering sustainable growth across various sectors.

Antimicrobial agent usage patterns and their role in preventing surgical site infections at a tertiary care teaching hospital: A comprehensive study

Background: Surgical site infections (SSIs) present a formidable challenge in contemporary healthcare, impacting patient well-being and contributing to escalating healthcare costs. The judicious utilization of antimicrobial agents is paramount in effectively combating SSIs. This study, conducted at a Tertiary Care Teaching Hospital in Kerala, seeks to unravel the intricate patterns of antimicrobial agent usage within the General Surgery Department over 10 months. Aims and Objectives: The primary aim is to thoroughly examine antimicrobial agent patterns within the General Surgery Department, focusing specifically on patients undergoing surgical procedures. The study aims to assess the appropriateness of antimicrobial agent usage, encompassing the choice for prophylactic purposes, timing of pre-operative dosing, the occurrence of intraoperative dosing, and the selection appropriateness for post-operative prophylaxis. Materials and Methods: Crafted for robust analysis, the study included 150 cases, implementing specific inclusion and exclusion criteria to ensure data quality. The emphasis was placed on patients undergoing surgical procedures, focusing on prophylactic antimicrobial use. Data collection involved meticulously evaluating appropriateness across critical parameters, including the choice of prophylactic antimicrobial agent, pre-operative dosing timing, intraoperative dosing, and selection appropriateness for post-operative prophylaxis. Results: Analysis of 150 cases provided valuable insights into the age and gender distribution, predominantly within the 41–60 age groups. Prevalent surgical procedures, such as hernia and appendicitis, underscored the necessity for tailored antimicrobial protocols. Cephalosporins emerged as the dominant class, raising concerns about potential overuse and resistance. Pre-exposure prophylactic antibiotics, predominantly cephalosporins, were administered in 88 cases. Post-exposure prophylaxis mirrored this trend, necessitating a critical evaluation of antibiotic selection and duration. Detailed analysis of cephalosporin pre- and post-exposure prophylaxis identified prevalent choices such as Cefotaxime and Cefoperazone + Sulbactam, emphasizing the importance of scrutinizing cephalosporin selection. Conclusion: This study furnishes valuable insights into the appropriateness of antimicrobial agent usage for preventing SSIs, serving as a model for analogous healthcare settings. It underscores the significance of local antibiotic stewardship, guideline adherence, and the ongoing commitment to optimizing prophylactic antimicrobial usage for ensuring patient safety and advancing healthcare quality amidst evolving challenges.

Prediction and Analysis of Corporate Financial Distress Based on Random Forest Model and GBDT

Predicting financial trouble effectively is now crucial as businesses face an increasing variety of financial threats. This research utilizes a dataset to predict a company's financial difficulties using GBDT and Random Forest models. The objective is to assess how well these models handle nonlinear interactions, capture data properties, and prevent overfitting. Firstly, data preprocessing ensures data quality, and then random forest and GBDT models are applied for analysis. Random forests perform outstandingly in feature selection and avoiding overfitting, while GBDT has significant advantages in capturing nonlinear relationships. The evaluation results show that the single model has limitations. Therefore, this article proposes to integrate the random forest and GBDT model to comprehensively leverage their respective advantages. The experimental results of the integrated model show a significant improvement in predictive performance. In summary, the model integration strategy effectively improves the accuracy of financial distress prediction, provides a more reliable tool for enterprise risk management, and also offers new directions for future research.

A Holistic Approach Towards Digitized Audit Procedures in Manufacturing for Data Quality Assessment

Digitized assessment of data quality in smart manufacturing facilitates the transformation towards efficient decision making in production environments. Inference can be drawn from the need of manufacturers, implementing robust and effective data analytics tools which emphasizes the demand for holistic data quality standards. Therefore, an interactive chatbot containing a comprehensive audit procedure incorporating a guideline aligned to ISO standards was developed. Using this tool, an applicable and structured audit process is offered to ensure data quality. This concludes in specific recommendations for manufacturers improving smart applications.

Checking Data Quality of Longitudinal Household Travel Survey Data

Ensuring data quality of household travel survey data is often tedious and, thus, time-consuming. To speed up the process of data-checking and to gain an in-depth understanding of the data, data visualization is a practical, fundamental tool. Since 1994, data visualization has been used in the German Mobility Panel (MOP) data-checking process. This paper presents two graphical visualization tools developed for the MOP. Both tools speed up the data checks and ensure high consistency in identifying erroneous data. This paper describes and discusses how the tools provide a continuous data quality assessment.

The application value of behavior analysis based on deep learning in the evaluation of depression in art students

Abstract This study discusses the application value of behavior analysis based on deep learning in the evaluation of depression in art students. Because of the professional characteristics and creative pressure, art college students are at high risk of mental health, among which the incidence of depression is increasing year by year, which has a serious impact on their studies and quality of life. With the rapid development of AI technology, deep learning algorithms show significant advantages in processing complex data and pattern recognition. In this study, by collecting the daily behavior data of art college students and combining it with a deep learning algorithm, an efficient depression evaluation model was constructed. The model aims to realize the early identification and evaluation of depressive symptoms of art college students and provide new methods and means for mental health management. The study collected data using various methods such as questionnaire surveys, mobile application tracking, and social media data crawling, and went through detailed data preprocessing steps, including missing value processing, outlier detection, data standardization, and feature selection, to ensure data quality and model training effectiveness. Subsequently, this study designed a deep learning model (CNN-LSTM) based on the combination of Convolutional Neural Network (CNN) and Long Short Term Memory Network (LSTM), which can capture temporal dependencies and spatial relationships between features in the data, thereby improving the accuracy of depression assessment. The empirical findings demonstrate that the CNN-LSTM integrated model has attained remarkable accuracy in assessing the depressive tendencies of art students, underscoring the efficacy of deep learning techniques in behavioral analysis. This research further scrutinizes the impact of various attributes on the predictive outcomes, highlighting the significance of social interaction frequency, academic stress, and artistic engagement levels in depression assessment.

EFFECTIVE MACHINE LEARNING TECHNIQUES FOR STOCK PRICE FORECASTING

The paper explores the dynamic intersection of financial markets and advanced data analytics. In a world where markets evolve swiftly, informed decision-making is imperative for investors, traders, and financial analysts. The paper addresses this need by developing a machine learning model employing a random forest classifier to forecast the direction of the S&P 500 index. It unfolds through a systematic process, commencing with data retrieval from Yahoo Finance, data preprocessing to ensure data quality, attribute selection, and model training. We rigorously evaluate the model using precision as the primary metric, which measures its accuracy in predicting stock market trends. We integrate data visualization too ls to enhance interpretability and user -friendliness, allowing users to intuitively grasp the model's performance and outcomes. Beyond its predictive capabilities, the project offers an educational tool for learners interested in machine learning and its applications in finance. The system's architecture prioritizes modularity and scalability, establishing the foundation for potential future improvements.

Integrating longitudinal mental health data into a staging database: harnessing DDI-lifecycle and OMOP vocabularies within the INSPIRE Network Datahub.

Longitudinal studies are essential for understanding the progression of mental health disorders over time, but combining data collected through different methods to assess conditions like depression, anxiety, and psychosis presents significant challenges. This study presents a mapping technique allowing for the conversion of diverse longitudinal data into a standardized staging database, leveraging the Data Documentation Initiative (DDI) Lifecycle and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standards to ensure consistency and compatibility across datasets. The "INSPIRE" project integrates longitudinal data from African studies into a staging database using metadata documentation standards structured with a snowflake schema. This facilitates the development of Extraction, Transformation, and Loading (ETL) scripts for integrating data into OMOP CDM. The staging database schema is designed to capture the dynamic nature of longitudinal studies, including changes in research protocols and the use of different instruments across data collection waves. Utilizing this mapping method, we streamlined the data migration process to the staging database, enabling subsequent integration into the OMOP CDM. Adherence to metadata standards ensures data quality, promotes interoperability, and expands opportunities for data sharing in mental health research. The staging database serves as an innovative tool in managing longitudinal mental health data, going beyond simple data hosting to act as a comprehensive study descriptor. It provides detailed insights into each study stage and establishes a data science foundation for standardizing and integrating the data into OMOP CDM.

Design and Development of an Automated Product Categorization Software: AI-Driven Solutions for E-Commerce Platforms

This article outlines the design and development process of an automatic product categorization software intended for use in e-commerce and online marketplace platforms. The project aims to tackle the urgent issue of effectively classifying a wide range of products within digital markets. By utilizing artificial intelligence (AI) and machine learning methodologies, the software effectively analyzes product descriptions, enabling users to seamlessly incorporate products through automated categorization. The significance of the project is rooted in its ability to guarantee the accuracy and consistency of category hierarchies, automate the process of categorizing, and improve the overall user experience. The main goals involve the process of project planning, data collecting, and preparation. Machine learning models have been built and subsequently incorporated to facilitate the study of product descriptions. Through strict evaluation and optimization processes, a high level of accuracy and efficiency is achieved, resulting in several anticipated benefits. These benefits cover automated product categorization, enhanced user experience, and the potential for online platforms to gain a competitive edge. The key elements of innovation involve AI-driven textual analysis, learning methodologies grounded in data, and the ability to adapt to diverse industry contexts. Precautions and backup strategies are implemented to tackle technical issues, including the selection of machine learning libraries and algorithms, ensuring data quality, and integrating with various platforms. The success criteria encompass the objective of achieving a minimum prediction accuracy rate of 90%, optimizing business efficiency, enhancing user pleasure, and ensuring smooth system functioning. This project is a significant contribution to the field of product categorization inside the digital marketplace, as it provides automation, accuracy, and efficiency, ultimately resulting in an enhanced user experience.

Gamification in the STEM Domain Subject: The Prospective Method to Strengthen Teaching and Learning

Several meta-analysis studies related to game-based learning or Gamification have been carried out by various researchers. However, there is still a scarcity of studies specifically elucidating the extent of Gamification's impact on STEM domain subjects. Therefore, this research aims to summarize various research results related to the influence of game-based learning in improving student learning outcomes in STEM domain subjects. This research was a meta-analysis using the standardized group contrast design with a random model. Research data were from articles published in Scopus-indexed journals or proceedings. The inclusion criteria in this research were articles published between 2014 – 2023 in English, quantitative type data with a contrast group design containing control and experimental groups, complete data (n, mean, SD), and research focused on STEM domain subjects. The data collection process used the PRISMA method. To ensure data quality, researchers conducted publication bias analysis using the fail-safe N method. The moderator variables were continent, stem domain, era, developing competency, game type, and education level. The results find that Gamification has a positive impact on student learning outcomes, as indicated by an effect size value of 0.5492 [0.3943; 0.7041] with a confidence interval of 95%. Gamification does not provide a significant positive difference in the moderator variables continent, era, developing competence, and game type. For STEM domain subjects, Gamification is highly recommended to be applied. For the educational level, Gamification is most recommended to be implemented in elementary school.

Machine Learning Model for Multiomics Biomarkers Identification for Menopause Status in Breast Cancer

Identifying menopause-related breast cancer biomarkers is crucial for enhancing diagnosis, prognosis, and personalized treatment at that stage of the patient’s life. In this paper, we present a comprehensive framework for extracting multiomics biomarkers specifically related to breast cancer incidence before and after menopause. Our approach integrates DNA methylation, gene expression, and copy number alteration data using a systematic pipeline encompassing data preprocessing and handling class imbalance, dimensionality reduction, and classification. The framework starts with MutSigCV for data preprocessing and ensuring data quality. The Synthetic Minority Over-sampling Technique (SMOTE) up-sampling technique is applied to address the class imbalance representation. Then, Principal Component Analysis (PCA) transforms the DNA methylation, gene expression, and copy number alteration data into a latent space. The purpose is to discard irrelevant variations and extract relevant information. Finally, a classification model is built based on the transformed multiomics data into a unified representation. The framework contributes to understanding the complex interplay between menopause and breast cancer, thereby revealing more precise diagnostic and therapeutic strategies in the future. The explainable artificial intelligence model Shapley based on the XGBoost regressor showed the power of the selected gene expressions for predicting the menopause status, and the potential biomarkers included RUNX1, PTEN, MAP3K1, and CDH1. The literature confirmed the findings.

Challenges and Strategies in Teaching English Speaking Skills to Young Learners: Perspectives of Teachers in Indonesia

This research explores the challenges and strategies associated with teaching English speaking skills to young learners in Indonesia, focusing on students learning English as a second or foreign language. The study aims to understand the specific obstacles faced by teachers in this context and to identify effective methods for overcoming these challenges. Employing a qualitative case study approach, the research involved five elementary English teachers selected through purposive sampling. Data collection was conducted through interviews and observations, followed by thematic analysis to interpret the findings. The study also emphasized ensuring data quality through the establishment of trustworthiness. The findings revealed five key issues impacting the teaching of English-speaking skills to young learners in Indonesia. Challenges stemming from the students included inhibition, negative attitudes towards learning English, and low participation rates. From the teachers' perspective, difficulties in classroom management and the lack of a supportive English learning environment were identified as significant obstacles. To address these challenges, the teachers employed various strategies, including individualized instruction, use of translation, repetition techniques, and integrating games into the learning process. These approaches aimed to enhance student engagement and facilitate a more effective learning environment for developing English speaking abilities in young learners. The study's insights contribute to a better understanding of the complexities involved in teaching English as a second language in the Indonesian educational setting and offer practical solutions for educators facing similar challenges.

Leveraging Interpretable Feature Representations for Advanced Differential Diagnosis in Computational Medicine.

Diagnostic errors represent a critical issue in clinical diagnosis and treatment. In China, the rate of misdiagnosis in clinical diagnostics is approximately 27.8%. By comparison, in the United States, which boasts the most developed medical resources globally, the average rate of misdiagnosis is estimated to be 11.1%. It is estimated that annually, approximately 795,000 Americans die or suffer permanent disabilities due to diagnostic errors, a significant portion of which can be attributed to physicians' failure to make accurate clinical diagnoses based on patients' clinical presentations. Differential diagnosis, as an indispensable step in the clinical diagnostic process, plays a crucial role. Accurately excluding differential diagnoses that are similar to the patient's clinical manifestations is key to ensuring correct diagnosis and treatment. Most current research focuses on assigning accurate diagnoses for specific diseases, but studies providing reasonable differential diagnostic assistance to physicians are scarce. This study introduces a novel solution specifically designed for this scenario, employing machine learning techniques distinct from conventional approaches. We develop a differential diagnosis recommendation computation method for clinical evidence-based medicine, based on interpretable representations and a visualized computational workflow. This method allows for the utilization of historical data in modeling and recommends differential diagnoses to be considered alongside the primary diagnosis for clinicians. This is achieved by inputting the patient's clinical manifestations and presenting the analysis results through an intuitive visualization. It can assist less experienced doctors and those in areas with limited medical resources during the clinical diagnostic process. Researchers discuss the effective experimental results obtained from a subset of general medical records collected at Shengjing Hospital under the premise of ensuring data quality, security, and privacy. This discussion highlights the importance of addressing these issues for successful implementation of data-driven differential diagnosis recommendations in clinical practice. This study is of significant value to researchers and practitioners seeking to improve the efficiency and accuracy of differential diagnoses in clinical diagnostics using data analysis.

Ensemble Active Learning by Contextual Bandits for AI Incubation in Manufacturing

An Industrial Cyber-physical System (ICPS) provides a digital foundation for data-driven decision-making by artificial intelligence (AI) models. However, the poor data quality (e.g., inconsistent distribution, imbalanced classes) of high-speed, large-volume data streams poses significant challenges to the online deployment of offline-trained AI models. As an alternative, updating AI models online based on streaming data enables continuous improvement and resilient modeling performance. However, for a supervised learning model (i.e., a base learner), it is labor-intensive to annotate all streaming samples to update the model. Hence, a data acquisition method is needed to select the data for annotation to ensure data quality while saving annotation efforts. In the literature, active learning methods have been proposed to acquire informative samples. Different acquisition criteria were developed for exploration of under-represented regions in the input variable space or exploitation of the well-represented regions for optimal estimation of base learners. However, it remains a challenge to balance the exploration-exploitation trade-off under different online annotation scenarios. On the other hand, an acquisition criterion learned by AI adapts itself to a scenario dynamically, but the ambiguous consideration of the trade-off limits its performance in frequently changing manufacturing contexts. To overcome these limitations, we propose an ensemble active learning method by contextual bandits ( CbeAL ). CbeAL incorporates a set of active learning agents (i.e., acquisition criteria) explicitly designed for exploration or exploitation by a weighted combination of their acquisition decisions. The weight of each agent will be dynamically adjusted based on the usefulness of its decisions to improve the performance of the base learner. With adaptive and explicit consideration of both objectives, CbeAL efficiently guides the data acquisition process by selecting informative samples to reduce the human annotation efforts. Furthermore, we characterize the exploration and exploitation capability of the proposed agents theoretically. The evaluation results in a numerical simulation study and a real case study demonstrates the effectiveness and efficiency of CbeAL in manufacturing process modeling of the ICPS.

Construction and application of nasopharyngeal carcinoma-specific big data platform based on electronic health records

ObjectiveTo establish a nasopharyngeal carcinoma-specific big data platform based on electronic health records (EHRs) to provide data support for real-world study of nasopharyngeal carcinoma. MethodsA multidisciplinary expert team was established for this project. Based on industry standards and practical feasibility, the team designed the nasopharyngeal carcinoma data element standards including 14 modules and 640 fields. Data from patients diagnosed with nasopharyngeal carcinoma who visited Southern Hospital after 1999 were extracted from 15 EHRs systems and were cleaned, structured, and standardized using information technologies such as machine learning and natural language processing. In addition, a series of measures such as quality control and data encryption were taken to ensure data quality and patient privacy. At the platform application level, 10 functional modules were designed according to the needs of nasopharyngeal carcinoma research. ResultsAs of 1 October 2022, the Big Data platform has included 11,617patients, of whom 8228 (70.83 %) were male and 3389 (29.17 %) were female, with a median age of 48 years (interquartile range, 40 years). The data in the platform were validated to have a high level of completeness and accuracy, especially for key variables such as social demographics, laboratory tests and vital signs. Currently, six projects involving risk factors, early diagnosis, treatment efficacy and prevention of treatment-related toxic reactions have been conducted on the platform. ConclusionsWe have established a high-quality NPC-specific big data platform by integrating heterogeneous data from multiple sources in the EHR. The platform provides an effective tool and strong data support for real-world studies of nasopharyngeal carcinoma, which helps to improve research efficiency, reduce costs, and improve the quality of research results. We expect to promote multicenter nasopharyngeal carcinoma data sharing in the future to facilitate the generation of high-quality real-world evidence in nasopharyngeal carcinoma. This article may provide some reference value for other comprehensive hospitals to establish a big data platform for nasopharyngeal carcinoma.

Optimised liquid chromatography tandem mass spectrometry method for the simultaneous quantification of serum vitamin D analogues while also accounting for epimers and isobars

The accurate quantification of multiple vitamin D analogues simultaneously is challenging. This study set out to use liquid chromatography-tandem mass spectrometry (LC-MS/MS) to develop a method capable of measuring a comprehensive vitamin D profile, encompassing twelve vitamin D analogues (vitamin D2, D3, 25(OH)D2, 25(OH)D3, 1,25(OH)2D2, 1,25(OH)2D3, 24,25(OH)2D2, 24,25(OH)2D3, 3-epi-25(OH)D2, 3-epi-25(OH)D3, 7αC4 and1α(OH)D3) in a single run. Serum samples were prepared using double liquid–liquid extraction and analysed on an Agilent 6460 QQQ LC-MS/MS equipped with a Pursuit 3 Pentafluorophenyl (4.6 x 100 mm, 3 μm) column. Recovery rates for all analytes were above 95 % with a coefficient of variation (CV) below 10 %. The method exhibited good linearity (r > 0.995) and had a range of detection limits between 0.01 and 0.35 ng/mL and quantification limits between 0.15 and 0.96 ng/mL. Repeatability and within-lab precision were acceptable, with CV values below 10 % and 15 %, respectively. Method accuracy was excellent, with a systematic error below 6.60 %. additionally, all analytes-maintained stability for 48 h following sample preparation, and no interferences were observed among co-eluting analytes. Lastly, this method achieved “world-class” status according to the Sigma metric scale specifications, requiring minimal quality control to ensure data quality. This successfully validated method has the potential not only for improving vitamin D profiling procedures but also for aiding in the diagnosis of other genetic disorders where measuring beyond 25(OH)D is crucial.

Exploration on Standardization of Clinical Research Data in a Clinical Trial Institute of Traditional Chinese Medicine

Traditional Chinese Medicine(TCM) products and practices are widely used worldwide with increasing popularity. The number of TCM clinical trials has increased significantly in the past two decades. There is an urgent need to standardize TCM data and terminology for use in clinical studies. It will ensure data quality, improve the efficiency and expedite scientific communications in TCM. This paper illustrates our exploration processes in the application and development of the data standards in TCM clinical trials. We hope these explorations will help TCM data exchange, sharing, and aggregation which will ultimately accelerate the launch of TCM products and bring benefits to people.