bluuebunny/crossref_metadata_2025_split huggingface dataset:
Importance of Quality of Medical Record: Differences in Patient Safety Incident Inquiry Results According to Assessment for Quality of Medical Record
Authors: Jeong Hyeran , Choi Eun Young , Lee Won , Jang Seung Gyeong , Pyo Jeehee , Ock Minsu 
Published: March 2024

Background
Medical record review is the gold standard method of identifying adverse events. However, the quality of medical records is a critical factor that can affect the accuracy of adverse event detection. Few studies have examined the impact of medical record quality on the identification of adverse events.

Objectives
In this study, we analyze whether there were differences in screening criteria and characteristics of adverse events according to the quality of medical records evaluated in the patient safety incident inquiry in Korea.

Methods
Patient safety incident inquiry was conducted in 2019 on 7500 patients in Korea to evaluate their screening criteria, adverse events, and preventability. Furthermore, medical records quality judged by reviewers was evaluated on a 4-point scale. The χ2 test was used to examine differences in patient safety incident inquiry results according to medical record quality.

Results
Cases with inadequate medical records had higher rates of identified screening criteria than those with adequate records (88.8% versus 55.7%). Medical records judged inadequate had a higher rate of confirmed adverse events than those judged adequate. “Drugs, fluids, and blood-related events,” “diagnosis-related events,” and “patient care–related events” were more frequently identified in cases with inadequate medical records. There was no statistically significant difference in the preventability of adverse events according to the medical record quality.

Conclusions
Lower medical record quality was associated with higher rates of identified screening criteria and confirmed adverse events. Patient safety incident inquiry should specify medical record quality evaluation questions more accurately to more clearly estimate the impact of medical record quality.
The Relationship Between Anti–SSA-52 and Interstitial Lung Disease
Authors: Saunders Hollie , Baig Hassan , Li Yan , White Launia , Hodge David , Lesser Elizabeth , Stowell Justin T. , Rojas Carlos A. , Mira-Avendano Isabel 
Published: March 2024

Objective
The aims of this study were to assess whether a relationship between anti–SSA-52 and interstitial lung disease (ILD) can be further defined, and to enhance screening, detection, and potentially guide treatment.

Methods
A historical cohort study of 201 patients was conducted at a single tertiary care center between January 1, 2016 and December 31, 2020. All included patients were anti–SSA-52 antibody positive. Chart review was performed for laboratory values, symptoms, pulmonary function tests, treatment, and imaging. Chest computed tomographies were reviewed by chest radiologists.

Results
Among anti–SSA-52 antibody–positive patients, ILD was found in 125 (62.2%) compared with 76 (37.8%) with no ILD (p = 0.001). For those with ILD, 78 (62.4%) were diagnosed with connective tissue disease (CTD)–associated ILD, 28 (22.4%) were diagnosed ILD only, and 19 (15.2%) met the criteria for interstitial pneumonia with autoimmune features. In patients with CTD-ILD, 18 (23.0%) had their ILD diagnosis made over 6 months before a CTD diagnosis, and an additional 43 (55.1%) had their ILD and CTD diagnosed within 6 months of each other (p < 0.001). Common computed tomography patterns were nonspecific interstitial pneumonia/organizing pneumonia overlap in 44 (35.2%), 25 (20.0%) nonspecific interstitial pneumonia, and 15 (12%) usual interstitial pneumonia. Twenty-eight (35.9%) had antisynthetase syndrome, followed by 16 (20.5%) with dermatomyositis, 10 (12.8%) with CTD overlap, and 6 (7.7%) with systemic scleroderma.

Conclusions
There was a significant association between anti–SSA-52 antibodies and ILD across a wide spectrum of rheumatological diagnoses. A significant portion of patients were diagnosed with ILD either at the same time or before their CTD diagnosis. Further study will be needed to assess effective treatment and response.
Translational analysis of data science and causal learning in real-world clinical evaluation of traditional Chinese medicine
Authors: Yang Wei , Yi Danhui , Zhou XiaoHua , Leng Yuanming 
Published: March 2024

Abstract
Real-world clinical evaluation of traditional Chinese medicine (RWCE-TCM) is a method for comprehensively evaluating the clinical effects of TCM, with the aim of delving into the causality between TCM intervention and clinical outcomes. The study explored data science and causal learning methods to transform RWD into reliable real-world evidence, aiming to provide an innovative approach for RWCE-TCM. This study proposes a 10-step data science methodology to address the challenges posed by diverse and complex data in RWCE-TCM. The methodology involves several key steps, including data integration and warehouse building, high-dimensional feature selection, the use of interpretable statistical machine learning algorithms, complex networks, and graph network analysis, knowledge mining techniques such as natural language processing and machine learning, observational study design, and the application of artificial intelligence tools to build an intelligent engine for translational analysis. The goal is to establish a method for clinical positioning, applicable population screening, and mining the structural association of TCM characteristic therapies. In addition, the study adopts the principle of real-world research and a causal learning method for TCM clinical data. We constructed a multidimensional clinical knowledge map of “disease-syndrome-symptom-prescription-medicine” to enhance our understanding of the diagnosis and treatment laws of TCM, clarify the unique therapies, and explore information conducive to individualized treatment. The causal inference process of observational data can address confounding bias and reduce individual heterogeneity, promoting the transformation of TCM RWD into reliable clinical evidence. Intelligent data science improves efficiency and accuracy for implementing RWCE-TCM. The proposed data science methodology for TCM can handle complex data, ensure high-quality RWD acquisition and analysis, and provide in-depth insights into clinical benefits of TCM. This method supports the intelligent translation and demonstration of RWD in TCM, leads the data-driven translational analysis of causal learning, and innovates the path of RWCE-TCM.
Leveraging Single-Cell Sequencing to Classify and Characterize Tumor Subgroups in Bulk RNA-Sequencing Data
Authors: Shetty Arya , Wang Su , Khan A. Basit , English Collin W. , Nouri Shervin Hosseingholi , Magill Stephen T. , Raleigh David R. , Klisch Tiemo J. , Harmanci Arif O. , Patel Akash J. , Harmanci Akdes Serin 
Published: March 2024

AbstractAccurate classification of cancer subgroups is essential for precision medicine, tailoring treatments to individual patients based on their cancer subtypes. In recent years, advances in high-throughput sequencing technologies have enabled the generation of large-scale transcriptomic data from cancer samples. These data have provided opportunities for developing computational methods that can improve cancer subtyping and enable better personalized treatment strategies. Here in this study, we evaluated different feature selection schemes in the context of meningioma classification. While the scheme relying solely on bulk transcriptomic data showed good classification accuracy, it exhibited confusion between malignant and benign molecular classes in approximately ~8% of meningioma samples. In contrast, models trained on features learned from meningioma single-cell data accurately resolved the sub-groups confused by bulk-transcriptomic data but showed limited overall accuracy. To integrate interpretable features from the bulk (n=78 samples) and single-cell profiling (~10K cells), we developed an algorithm named CLIPPR which combines the top-performing single-cell models with RNA-inferred copy number variation (CNV) signals and the initial bulk model to create a meta-model, which exhibited the strongest performance in meningioma classification. CLIPPR showed superior overall accuracy and resolved benign-malignant confusion as validated on n=792 bulk meningioma samples gathered from multiple institutions. Finally, we showed the generalizability of our algorithm using our in-house single-cell (~200K cells) and bulk TCGA glioma data (n=711 samples). Overall, our algorithm CLIPPR synergizes the resolution of single-cell data with the depth of bulk sequencing and enables improved cancer sub-group diagnoses and insights into their biology.
Multi-trait Analysis of GWAS for circulating FGF23 Identifies Novel Network Interactions Between HRG-HMGB1 and Cardiac Disease in CKD
Authors: Perwad Farzana , Akwo Elvis A. , Vartanian Nicholas , Suva Larrry J , Friedman Peter A , Robinson-Cohen Cassianne 
Published: March 2024

ABSTRACTBackgroundGenome-wide association studies (GWAS) have identified numerous genetic loci associated with mineral metabolism (MM) markers but have exclusively focused on single-trait analysis. In this study, we performed a multi-trait analysis of GWAS (MTAG) of MM, exploring overlapping genetic architecture between traits, to identify novel genetic associations for fibroblast growth factor 23 (FGF23).MethodsWe applied MTAG to genetic variants common to GWAS of 5 genetically correlated MM markers (calcium, phosphorus, FGF23, 25-hydroxyvitamin D (25(OH)D) and parathyroid hormone (PTH)) in European-ancestry subjects. We integrated information from UKBioBank GWAS for blood levels for phosphate, 25(OH)D and calcium (n=366,484), and CHARGE GWAS for PTH (n=29,155) and FGF23 (n=16,624). We then used functional genomics to model interactive and dynamic networks to identify novel associations between genetic traits and circulating FGF23.ResultsMTAG increased the effective sample size for all MM markers to n=50,325 for FGF23. After clumping, MTAG identified independent genome-wide significant SNPs for all traits, including 62 loci for FGF23. Many of these loci have not been previously reported in single-trait analyses. Through functional genomics we identified Histidine-rich glycoprotein (HRG) and high mobility group box 1(HMGB1) genes as master regulators of downstream canonical pathways associated with FGF23. HRG-HMGB1 network interactions were also highly enriched in left ventricular heart tissue of a cohort of deceased hemodialysis patients.ConclusionOur findings highlight the importance of MTAG analysis of MM markers to boost the number of genome-wide significant loci for FGF23 to identify novel genetic traits. Functional genomics revealed novel networks that inform unique cellular functions and identifiedHRG-HMGB1as key master regulators of FGF23 and cardiovascular disease in CKD. Future studies will provide a deeper understanding of genetic signatures associated with FGF23 and its role in health and disease.
Engineering a wolf spider A-family toxin towards increased antimicrobial activity but low toxicity
Authors: Dersch Ludwig , Stahlhut Antonia , Eichberg Johanna , Paas Anne , Hardes Kornelia , Vilcinskas Andreas , Lüddecke Tim 
Published: March 2024

AbstractPeptides with insecticidal, antimicrobial and/or cytolytic activities, also known as spider venom antimicrobial peptides (AMPs), can be found in the venoms of RTA-clade spiders. They show translational potential as therapeutic leads. A set of 52 AMPs has been described in the Chinese wolf spider (Lycosa shansia), and many have been shown to exhibit antibacterial effects. Here we explored the potential to enhance their antimicrobial activity using bioengineering. We generated a panel of artificial derivatives of an A-family peptide and screened their activity against selected microbial pathogens, vertebrate cells and insects. In several cases, we increased the antimicrobial activity of the derivatives while retaining the low cytotoxicity of the parental molecule. Furthermore, we injected the peptides into adultDrosophila suzukiiand found no evidence of insecticidal effects, confirming the low levels of toxicity. Our data therefore suggest that spider venom linear peptides can be modified into more potent antimicrobial agents that could help to battle infectious diseases in the future.
Biological modifications of the immune response to COVID-19 vaccine in patients treated with rituximab and immune-checkpoint inhibitors
Authors: Ravera Francesco , Dameri Martina , Lombardo Isabella , Stabile Mario , Fallani Neri , Scarsi Camilla , Cigolini Benedetta , Gentilcore Giusy , Domnich Alexander , Zullo Lodovica , Cella Eugenia , Francia Giulia , Montanari Eugenia , Orsi Andrea , Bellodi Andrea , Ferrando Fabio , Rinchai Darawan , Ballerini Filippo , Bruzzone Bianca , Chaussabel Damien , Grivel Jean-Charles , Genova Carlo , Lemoli Roberto Massimo , Bedognetti Davide , Ballestrero Alberto , Ferrando Lorenzo , Zoppoli Gabriele 
Published: March 2024

AbstractInvestigating the impact of immune-modulating therapies on mRNA vaccine efficacy transcends the immediate context of the COVID-19 pandemic. This study focuses on the differential immune responses to the third dose of COVID-19 mRNA vaccine among healthy volunteers, cancer patients treated with immune-checkpoint inhibitors (ICIs), and those treated with the anti-CD20 antibody rituximab. Utilizing RNA sequencing, serology, and interferon-γ release assessment, we charted the temporal dynamics of the immune response in such cohorts. Our findings indicate that ICIs maintain an immune profile similar to that of healthy individuals, whereas treatment with rituximab is associated with impairment of type I interferon response and the upregulation of transcripts pertaining to regulatory T cells, with a global dysfunction of both humoral and cellular immunity. This research deepens our understanding of the sophisticated interplay within the immune system in health and disease states, potentially informing therapeutic strategies across a spectrum of immunological conditions.Significance statementOur study examines how cancer treatments that modify the immune system affect transcriptional, serological, and cellular responses to a model for repeated antigenic stimulation in humans, represented by the SARS-CoV-2 booster vaccine. Specifically, we investigated patients treated with rituximab (RTX), which impairs antibody production, and immune checkpoint inhibitors (ICI), which can trigger autoimmune disorders. We discovered that RTX-treated patients not only exhibit a reduced antibody response but actually show a diminished interferon-mediated immune response, indicating a broader immune disruption than anticipated. Conversely, ICI-treated patients responded to the vaccine similarly to healthy individuals, suggesting that fears of adverse vaccine reactions in these patients may be unfounded. This research highlights important considerations for the clinical management of cancer patients receiving these treatments.
Mapping the knowledge structure of artificial neural network research in the stock market: a bibliometric analysis and future research pathways
Authors: Kaur Manpreet , Kumar Amit , Mittal Anil Kumar 
Published: March 2024

PurposeIn past decades, artificial neural network (ANN) models have revolutionised various stock market operations due to their superior ability to deal with nonlinear data and garnered considerable attention from researchers worldwide. The present study aims to synthesize the research field concerning ANN applications in the stock market to a) systematically map the research trends, key contributors, scientific collaborations, and knowledge structure, and b) uncover the challenges and future research areas in the field.Design/methodology/approachTo provide a comprehensive appraisal of the extant literature, the study adopted the mixed approach of quantitative (bibliometric analysis) and qualitative (intensive review of influential articles) assessment to analyse 1,483 articles published in the Scopus and Web of Science indexed journals during 1992–2022. The bibliographic data was processed and analysed using VOSviewer and R software.FindingsThe results revealed the proliferation of articles since 2018, with China as the dominant country, Wang J as the most prolific author, “Expert Systems with Applications” as the leading journal, “computer science” as the dominant subject area, and “stock price forecasting” as the predominantly explored research theme in the field. Furthermore, “portfolio optimization”, “sentiment analysis”, “algorithmic trading”, and “crisis prediction” are found as recently emerged research areas.Originality/valueTo the best of the authors’ knowledge, the current study is a novel attempt that holistically assesses the existing literature on ANN applications throughout the entire domain of stock market. The main contribution of the current study lies in discussing the challenges along with the viable methodological solutions and providing application area-wise knowledge gaps for future studies.
Fostering sustainability performance in the Malaysian manufacturing companies: the role of green technology innovation and innovation capabilities
Authors: Mukhtar Bilal , Shad Muhammad Kashif , Lai Fong Woon 
Published: March 2024

PurposeThe purpose of this study is to examine the influence of green technology innovation on sustainability performance with the moderating effect of innovation capabilities in the Malaysian manufacturing listed companies.Design/methodology/approachThis was a quantitative study and carried out by applying a research survey. The questionnaire was used to collect the data from 204 Malaysian manufacturing companies of the “consumer products and services” sector listed at Bursa Malaysia, incorporating a five-point Likert scale. All the hypothesized relationships were tested by using the partial least square structural equation modeling (PLS-SEM).FindingsThe empirical results showed that the comprehensive adoption of green technology innovation significantly promotes sustainability performance including economic, environmental and social performance. In addition, innovation capabilities significantly and positively moderate the relationship between green technology innovation and sustainability performance.Research limitations/implicationsThe scope of this study is specifically confined to the Malaysian manufacturing listed companies, operating within the consumer products and services sector listed at Bursa Malaysia. Consequently, the findings of this study may not be generalized to manufacturing companies of the different geographical contexts.Practical implicationsThe findings of this study may help the top management and policymakers of the Malaysian manufacturing listed companies to scrutinize green technology innovation and innovation capabilities to achieve higher sustainability performance.Originality/valueThis study magnifies and provides new insights into the extant literature by developing a comprehensive research model that concurrently tests the direct and moderation effects between green technology innovation, innovation capabilities and sustainability performance. Additionally, this is the first study to examine the influence of green technology innovation on sustainability performance with the moderating effect of innovation capabilities in the Malaysian manufacturing listed companies. This distinct approach significantly bolsters the originality of this study.
Synergistic lubrication effects of Sn–Ag–Cu and MXene–Ti3C2 to improve tribological properties of M50 bearing steel with microporous channels
Authors: Huang Yuchun , Ma Haishu , Meng Yubo , Mao Yazhou 
Published: March 2024

Purpose
This paper aims to study the synergistic lubrication effects of Sn–Ag–Cu and MXene–Ti3C2 to improve the tribological properties of M50 bearing steel with microporous channels.

Design/methodology/approach
M50 matrix self-lubricating composites (MMSC) were designed and prepared by filling Sn–Ag–Cu and MXene–Ti3C2 in the microporous channels of M50 bearing steel. The tribology performance testing of as-prepared samples was executed with a multifunction tribometer. The optimum hole size and lubricant content, as well as self-lubricating mechanism of MMSC, were studied.

Findings
The tribological properties of MMSC are strongly dependent on the synergistic lubrication effect of MXene–Ti3C2 and Sn–Ag–Cu. When the hole size of microchannel is 1 mm and the content of MXene–Ti3C2 in mixed lubricant is 4 wt.%, MMSC shows the lowest friction coefficient and wear rate. The Sn–Ag–Cu and MXene–Ti3C2 are extruded from the microporous channels and spread to the friction interface, and a relatively complete lubricating film is formed at the friction interface. Meanwhile, the synergistic lubrication of Sn–Ag–Cu and MXene–Ti3C2 can improve the stability of the lubricating film, thus the excellent tribological property of MMSC is obtained.

Originality/value
The results help in deep understanding of the synergistic lubrication effects of Sn–Ag–Cu and MXene–Ti3C2 on the tribological properties of M50 bearing steel. This work also provides a useful reference for the tribological design of mechanical components by combining surface texture with solid lubrication.

Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2023-0381/
‘Work locally but think globally’: The Alliance Against Women's Oppression and transnational multiracial grassroots activism in the 1980s
Authors: Toscano Bruno Walter Renato 
Published: March 2024

AbstractThis article examines the transnational history of the Alliance Against Women's Oppression (AAWO), a multiracial and Marxist US women's organisation founded in California in 1979. By focusing on the political connection between the AAWO, the so‐called ‘Third World’ and other international organisations such as the Women International Democratic Federation, the article intends to delve into the relationship between global far‐left, non‐White women's organisations in the United States and the Global South. Using extensive archival sources internal to the organisation, this work highlights the 1980s as a central period for anti‐imperialist women's activism against the Reagan administration and for the formation of international grassroots connections.
Unveiling the criticality of digitalization, eco‐innovation, carbon tax, and environmental regulation in G7 quest for carbon footprint mitigation: Insights for sustainable development
Authors: Wang Yu , Chen Xudong , Ibrahim Ridwan Lanre , Al‐Faryan Mamdouh Abdulaziz Saleh 
Published: March 2024

AbstractA great deal of empirical research has been conducted to find effective solutions to global warming, which is widely recognized as a major cause of environmental degradation and overall decline in well‐being. It should be noted that international coalitions such as the G7 countries (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United) are not left of the ravaging adverse effects of environmental pollution. Consequently, this study contributes to the literature by examining the role of digitalization on carbon footprint amidst environmental‐related technologies, renewable energy, environmental policy stringency, carbon tax, and financial development in G7 countries from 1996 to 2019. The study relies on cross‐sectional autoregressive distributed lag, common correlated effects mean group, augmented mean group, and method of moment quantile regression (MMQR). Results from the analyses show that digitalization is an essential mitigating tool for the surging carbon footprint in G7 countries. Besides, the imperatives of other covariates in subduing the adverse environmental effects of carbon footprint are empirically supported except for financial development. Remarkably, the distributional effects of the exogenous variables on carbon footprint based on MMQR are found robust for the primary analyses. The direction of cause standing between bidirectional and unidirectional heightens the novelties of this study. Based on the findings, sustainable footprint policies in G7 economies are suggested.