Evaluating the validity of the nursing statements algorithmically generated based on the International Classifications of Nursing Practice for respiratory nursing care using large language models.

OBJECTIVE: This study aims to facilitate the creation of quality standardized nursing statements in South Korea's hospitals using algorithmic generation based on the International Classifications of Nursing Practice (ICNP) and evaluation through Large Language Models. MATERIALS AND METHODS: We algorithmically generated 15 972 statements related to acute respiratory care using 117 concepts and concept composition models of ICNP. Human reviewers, Generative Pre-trained Transformers 4.0 (GPT-4.0), and Bio_Clinical Bidirectional Encoder Representations from Transformers (BERT) evaluated the generated statements for validity. The evaluation by GPT-4.0 and Bio_ClinicalBERT was conducted with and without contextual information and training. RESULTS: Of the generated statements, 2207 were deemed valid by expert reviewers. GPT-4.0 showed a zero-shot  AUC of 0.857, which aggravated with contextual information. Bio_ClinicalBERT, after training, significantly improved, reaching an AUC of 0.998. CONCLUSION: Bio_ClinicalBERT effectively validates auto-generated nursing statements, offering a promising solution to enhance and streamline healthcare documentation processes.

Efficacy and Safety of Candidate Biosimilar CT-P43 Versus Originator Ustekinumab in Moderate to Severe Plaque Psoriasis: 28-Week Results of a Randomised, Active-Controlled, Double-Blind, Phase III Study.

BACKGROUND: CT-P43 is a candidate ustekinumab biosimilar in clinical development. OBJECTIVES: This paper aims to demonstrate equivalent efficacy of CT-P43 to originator ustekinumab in adults with moderate to severe plaque psoriasis. METHODS: This double-blind, phase III trial randomised patients (1:1) to receive subcutaneous CT-P43 or originator ustekinumab (45/90 mg for patients with baseline body weight ≤ 100 kg/> 100 kg) at week 0 and week 4 in Treatment Period I. Prior to week 16 dosing in Treatment Period II, patients receiving originator ustekinumab were re-randomised (1:1) to continue originator ustekinumab or switch to CT-P43; patients initially randomised to CT-P43 continued receiving CT-P43 (at weeks 16, 28 and 40). The primary endpoint of the trial was mean per cent improvement from baseline in Psoriasis Area Severity Index (PASI) score at week 12. Equivalence was concluded if confidence intervals (CIs) for the estimate of treatment difference were within pre-defined equivalence margins: ± 10% [90% CI; modified intent-to-treat set; Food and Drug Administration (FDA) approach] or ± 15% [95% CI; full analysis set for patients only receiving 45 mg doses in Treatment Period I; European Medicines Agency (EMA) approach]. Additional efficacy, pharmacokinetic, safety and immunogenicity endpoints were evaluated through week 52. Results to week 28 are reported here. RESULTS: In Treatment Period I, 509 patients were randomised (CT-P43: N = 256; originator ustekinumab: N = 253). The mean per cent improvement in PASI score at week12 was 77.93% and 75.89% for CT-P43 and originator ustekinumab, respectively (FDA approach); per the EMA approach, corresponding values were 78.26% and 77.33%. Estimated treatment differences were 2.05 (90% CI -0.23, 4.32) and 0.94 (95% CI -2.29, 4.16); equivalence was achieved for both sets of assumptions. Further efficacy parameters and pharmacokinetic, safety and immunogenicity outcomes were comparable between treatment groups, including after switching from originator ustekinumab to CT-P43. CONCLUSIONS: CT-P43 demonstrated equivalent efficacy to originator ustekinumab in patients with moderate to severe plaque psoriasis, with comparable pharmacokinetic, safety and immunogenicity profiles. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04673786; date of registration: 17 December, 2020.

Attitudes and perspectives of nurses and physicians in South Korea towards the clinical use of person-generated health data.

This study aimed to explore the adoption of person-generated health data in clinical settings and discern the factors influencing clinicians' willingness to use it. A web-based survey containing 48 questions was developed based on prior research and the Unified Theory of Acceptance and Use of Technology 2 model. The survey was administered to a convenience sample of 486 nurses and physicians in South Korea recruited through an online community and snowball sampling. Of these, 70.7% were physicians. While 65% had used mobile health apps and devices, only 12.8% were familiar with person-generated health data. Still, a promising 73.3% expressed interest in incorporating person-generated health data into patient care, particularly data on blood glucose and vital signs. The findings of the study also indicated that clinicians specializing in internal medicine (OR: 1.9, CI: 1.16-3.19), familiar with person-generated health data (OR: 2.6, CI: 1.58-4.29), with a positive view of information and communication technology adoption (OR: 2.6, CI: 1.65-4.13), and who see the value in person-generated health data (OR: 3.9, CI: 2.55-6.09) showed higher inclination to utilize it. However, those in outpatient settings (OR: 0.4, CI: 0.19-0.73) showed less enthusiasm. The findings of this study suggest that despite the willingness of clinicians to use person-generated health data, various barriers must be addressed first, including a lack of knowledge regarding its use, concerns about data reliability and quality, and a lack of provider incentives. Overcoming these challenges demands concerted organizational or policy support. This research underscores person-generated health data's untapped potential in healthcare and the pressing need for strategies that facilitate its clinical integration.

Essential properties and explanation effectiveness of explainable artificial intelligence in healthcare: A systematic review.

Background: Significant advancements in the field of information technology have influenced the creation of trustworthy explainable artificial intelligence (XAI) in healthcare. Despite improved performance of XAI, XAI techniques have not yet been integrated into real-time patient care. Objective: The aim of this systematic review is to understand the trends and gaps in research on XAI through an assessment of the essential properties of XAI and an evaluation of explanation effectiveness in the healthcare field. Methods: A search of PubMed and Embase databases for relevant peer-reviewed articles on development of an XAI model using clinical data and evaluating explanation effectiveness published between January 1, 2011, and April 30, 2022, was conducted. All retrieved papers were screened independently by the two authors. Relevant papers were also reviewed for identification of the essential properties of XAI (e.g., stakeholders and objectives of XAI, quality of personalized explanations) and the measures of explanation effectiveness (e.g., mental model, user satisfaction, trust assessment, task performance, and correctability). Results: Six out of 882 articles met the criteria for eligibility. Artificial Intelligence (AI) users were the most frequently described stakeholders. XAI served various purposes, including evaluation, justification, improvement, and learning from AI. Evaluation of the quality of personalized explanations was based on fidelity, explanatory power, interpretability, and plausibility. User satisfaction was the most frequently used measure of explanation effectiveness, followed by trust assessment, correctability, and task performance. The methods of assessing these measures also varied. Conclusion: XAI research should address the lack of a comprehensive and agreed-upon framework for explaining XAI and standardized approaches for evaluating the effectiveness of the explanation that XAI provides to diverse AI stakeholders.

Developing a Dietary Lifestyle Ontology to Improve the Interoperability of Dietary Data: Proof-of-Concept Study.

BACKGROUND: Dietary habits offer crucial information on one's health and form a considerable part of the patient-generated health data. Dietary data are collected through various channels and formats; thus, interoperability is a significant challenge to reusing this type of data. The vast scope of dietary concepts and the colloquial expression style add difficulty to standardizing the data. The interoperability issues of dietary data can be addressed through Common Data Elements with metadata annotation to some extent. However, making culture-specific dietary habits and questionnaire-based dietary assessment data interoperable still requires substantial efforts. OBJECTIVE: The main goal of this study was to address the interoperability challenge of questionnaire-based dietary data from different cultural backgrounds by combining ontological curation and metadata annotation of dietary concepts. Specifically, this study aimed to develop a Dietary Lifestyle Ontology (DILON) and demonstrate the improved interoperability of questionnaire-based dietary data by annotating its main semantics with DILON. METHODS: By analyzing 1158 dietary assessment data elements (367 in Korean and 791 in English), 515 dietary concepts were extracted and used to construct DILON. To demonstrate the utility of DILON in addressing the interoperability challenges of questionnaire-based multicultural dietary data, we developed 10 competency questions that asked to identify data elements sharing the same dietary topics and assessment properties. We instantiated 68 data elements on dietary habits selected from Korean and English questionnaires and annotated them with DILON to answer the competency questions. We translated the competency questions into Semantic Query-Enhanced Web Rule Language and reviewed the query results for accuracy. RESULTS: DILON was built with 262 concept classes and validated with ontology validation tools. A small overlap (72 concepts) in the concepts extracted from the questionnaires in 2 languages indicates that we need to pay closer attention to representing culture-specific dietary concepts. The Semantic Query-Enhanced Web Rule Language queries reflecting the 10 competency questions yielded correct results. CONCLUSIONS: Ensuring the interoperability of dietary lifestyle data is a demanding task due to its vast scope and variations in expression. This study demonstrated that we could improve the interoperability of dietary data generated in different cultural contexts and expressed in various styles by annotating their core semantics with DILON.

Knockdown of the potential cancer stem-like cell marker Rex-1 improves chemotherapeutic effects in gliomas.

In the present study, we show that Rex-1 mRNA and protein are found at high levels in both 1,3-bis(2-chloroethyl)-1-nitrosourea (BCNU)-resistant glioma cell subpopulations and malignant glioblastoma multiforme (GBM) tissue. We used a combination therapy of small interfering RNA (siRNA) against Rex-1 (siRex-1) and BCNU to target GBM cells. Rex-1 siRNA/BCNU treatment resulted in growth inhibition and a diminished S phase. The treatment efficiently induced P38/JNK and Akt/PI3K/GSK3ß signaling and led to apoptosis both in vitro and in vivo. We also show that Rex-1/ABCG2 (ATP binding cassette transporter G2)-coexpressing subpopulations were chemoresistant; however, BCNU was not a substrate for ABCG2. siRex-1 treatment led to cell death in GBM subpopulations by promoting apoptosis. Moreover, siRex-1/BCNU combination therapy targeted both the major population and cancer stem cell-like subpopulations. Our findings are important for the development of clinical applications to treat GBM.

Wnt5a-mediating neurogenesis of human adipose tissue-derived stem cells in a 3D microfluidic cell culture system.

In stem cell biology, cell plasticity refers to the ability of stem cells to differentiate into a variety of cell lineages. Recently, cell plasticity has been used to refer to the ability of a given cell type to reversibly de-differentiate, re-differentiate, or transdifferentiate in response to specific stimuli. These processes are regulated by multiple intracellular and extracellular growth and differentiation factors, including low oxygen. Our recent study showed that 3D microfluidic cell culture induces activation of the Wnt5A/ß-catenin signaling pathway in hATSCs (human Adipose Tissue-derived Stem Cells). This resulted in self renewal and transdifferentiation of hATSCs into neurons. To improve neurogenic potency of hATSCs in response to low oxygen and other unknown physical factors, we developed a gel-free 3D microfluidic cell culture system (3D-µFCCS). The functional structure was developed for the immobilization of 3D multi-cellular aggregates in a microfluidic channel without the use of a matrix on the chip. Growth of hATSCs neurosphere grown on a chip was higher than the growth of control cells grown in a culture dish. Induction of differentiation in the Chip system resulted in a significant increase in the induction of neuronal-like cell structures and the presentation of TuJ or NF160 positive long neuritis compared to control cells after active migration from the center of the microfluidic channel layer to the outside of the microfluidic channel layer. We also observed that the chip neurogenesis system induced a significantly higher level of GABA secreting neurons and, in addition, almost 60% of cells were GABA + cells. Finally, we observed that 1 month of after the transplantation of each cell type in a mouse SCI lesion, chip cultured and neuronal differentiated hATSCs exhibited the ability to effectively transdifferentiate into NF160 + motor neurons at a high ratio. Interestingly, our CHIP/PCR analysis revealed that HIF1α-induced hATSCs neurogenesis on the chip. This induction was a result of the direct binding of HIF1α to the regulatory regions of the Oct4 and ß-catenin genes in nucleus. In the Chip culture of hATSCs that we developed, a low oxygen microenvironment was induced. The low oxygen level induced HIF1α expression, which resulted in increased expression of Wnt5A/ß-catenin and Oct4 via the direct binding of HIF1α to the regulatory regions of ß-catenin and Oct4.