Impact of a Nationwide Medication History Sharing Program on the Care Process and End-User Experience in a Tertiary Teaching Hospital: Cohort Study and Cross-Sectional Study.

Background: Timely and comprehensive collection of a patient's medication history in the emergency department (ED) is crucial for optimizing health care delivery. The implementation of a medication history sharing program, titled "Patient's In-home Medications at a Glance," in a tertiary teaching hospital aimed to efficiently collect and display nationwide medication histories for patients' initial hospital visits. Objective: As an evaluation was necessary to provide a balanced picture of the program, we aimed to evaluate both care process outcomes and humanistic outcomes encompassing end-user experience of physicians and pharmacists. Methods: We conducted a cohort study and a cross-sectional study to evaluate both outcomes. To evaluate the care process, we measured the time from the first ED assessment to urgent percutaneous coronary intervention (PCI) initiation from electronic health records. To assess end-user experience, we developed a 22-item questionnaire using a 5-point Likert scale, including 5 domains: information quality, system quality, service quality, user satisfaction, and intention to reuse. This questionnaire was validated and distributed to physicians and pharmacists. The Mann-Whiteny U test was used to analyze the PCI initiation time, and structural equation modeling was used to assess factors affecting end-user experience. Results: The time from the first ED assessment to urgent PCI initiation at the ED was significantly decreased using the patient medication history program (mean rank 42.14 min vs 28.72 min; Mann-Whitney U=346; P=.03). A total of 112 physicians and pharmacists participated in the survey. Among the 5 domains, "intention to reuse" received the highest score (mean 4.77, SD 0.37), followed by "user satisfaction" (mean 4.56, SD 0.49), while "service quality" received the lowest score (mean 3.87, SD 0.79). "User satisfaction" was significantly associated with "information quality" and "intention to reuse." Conclusions: Timely and complete retrieval using a medication history-sharing program led to an improved care process by expediting critical decision-making in the ED, thereby contributing to value-based health care delivery in a real-world setting. The experiences of end users, including physicians and pharmacists, indicated satisfaction with the program regarding information quality and their intention to reuse.

Development of machine-learning models using pharmacy inquiry database for predicting dose-related inquiries in a tertiary teaching hospital.

BACKGROUND: Drug-related problems (DRPs) are a significant concern in healthcare. Pharmacists play a vital role in detecting and resolving DRPs to improve patient safety. A pharmacy inquiry program was established in a tertiary teaching hospital to document inquiries about physicians' orders, aimed at preventing potential DRPs or providing medication information during order reviews. OBJECTIVE: We aimed to develop machine-learning models using a pharmacy inquiry database to predict dose-related inquiries based on prescriptions and patient information. METHODS: This retrospective study analyzed 20,393 pharmacy inquiries collected between January 2018 and February 2023. Data included prescription information (drug ingredient, dose, unit, and frequency), patient characteristics (age, sex, weight, and department), and renal function. The inquiries were categorized into two classes: dose-related inquiries (e.g., wrong dose and inappropriate regimen) and non-dose-related inquiries (e.g., inappropriate drug form and administration route). Six machine-learning models were developed: logistic regression, support vector classifier, decision tree, random forest, extreme gradient boosting, and categorical boosting. To evaluate the performance of the models, the area under the receiver operating characteristic curve and the accuracy were compared. RESULTS: The CatBoost model achieved the highest performance (sensitivity: 0.92; accuracy: 0.79). The SHapley Additive exPlanations values highlighted the importance of features in the model predictions, drug ingredients, units, and renal function, in that order. Notably, lower renal function positively contributed to the prediction of dose-related inquiries. Additionally, the subsequent feature importance among drug ingredients showed that drugs such as acetylsalicylic acid, famotidine, metformin, and spironolactone strongly influenced the prediction of dose-related inquiries. CONCLUSION: Machine-learning models that use pharmacy inquiry data can effectively predict dose-related inquiries. Further external validation and refinement of the models are required for broader applications in healthcare settings. These findings provide valuable guidance for healthcare professionals and highlight the potential of machine learning in pharmacists' decision-making.

Mapping the spatial distribution of primary and secondary PM2.5 in a multi-industrial city by combining monitoring and modeling results.

Industrial cities are strongly influenced by primary emissions of PM2.5 from local industries. In addition, gaseous precursors, such as sulfur oxides (SOX), nitrogen oxides (NOX), and volatile organic compounds (VOCs), emitted from industrial sources, undergo conversion into secondary inorganic and organic aerosols (SIAs and SOAs). In this study, the spatial distributions of primary and secondary PM2.5 in Ulsan, the largest industrial city in South Korea, were visualized. PM2.5 components (ions, carbons, and metals) and PM2.5 precursors (SO2, NO2, NH3, and VOCs) were measured to estimate the concentrations of secondary inorganic ions (SO42-, NO3-, and NH4+) and secondary organic aerosol formation potential (SOAFP). The spatial distributions of SIAs and SOAs were then plotted by combining atmospheric dispersion modeling, receptor modeling, and monitoring data. Spatial distribution maps of primary and secondary PM2.5 provide fundamental insights for formulating management policies in different districts of Ulsan. For instance, among the five districts in Ulsan, Nam-gu exhibited the highest levels of primary PM2.5 and secondary nitrate. Consequently, controlling both PM2.5 and NO2 emissions becomes essential in this district. The methodology developed in this study successfully identified areas with dominant contributions from both primary emissions and secondary formation. This approach can be further applied to prioritize control measures during periods of elevated PM levels in other industrial cities.

Empowering SARS-CoV-2 variant neutralization with a bifunctional antibody engineered with tandem heptad repeat 2 peptides.

With the global pandemic and the continuous mutations of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the need for effective and broadly neutralizing treatments has become increasingly urgent. This study introduces a novel strategy that targets two aspects simultaneously, using bifunctional antibodies to inhibit both the attachment of SARS-CoV-2 to host cell membranes and viral fusion. We developed pioneering IgG4-(HR2)4 bifunctional antibodies by creating immunoglobulin G4-based and phage display-derived human monoclonal antibodies (mAbs) that specifically bind to the SARS-CoV-2 receptor-binding domain, engineered with four heptad repeat 2 (HR2) peptides. Our in vitro experiments demonstrate the superior neutralization efficacy of these engineered antibodies against various SARS-CoV-2 variants, ranging from original SARS-CoV-2 strain to the recently emerged Omicron variants, as well as SARS-CoV, outperforming the parental mAb. Notably, intravenous monotherapy with the bifunctional antibody neutralizes a SARS-CoV-2 variant in a murine model without causing significant toxicity. In summary, this study unveils the significant potential of HR2 peptide-driven bifunctional antibodies as a potent and versatile strategy for mitigating SARS-CoV-2 infections. This approach offers a promising avenue for rapid development and management in the face of the continuously evolving SARS-CoV-2 variants, holding substantial promise for pandemic control.

Distributions of PCDD/Fs, PCBs, and PCNs in coastal sediments collected from major industrial bays in South Korea.

Polychlorinated dibenzo-p-dioxins/furans (PCDD/Fs), polychlorinated biphenyls (PCBs), and polychlorinated naphthalenes (PCNs) were assessed in coastal sediments from industrial bays in South Korea to evaluate the pollution levels and their environmental impact. The mean sediment concentrations of Σ17 PCDD/Fs, Σ18 PCBs, and Σ15 PCNs were 198 ± 140, 3427 ± 7037, and 85 ± 336 pg/g dw, respectively. Generally, pollutant concentrations in the inner bay were higher than those in the outer bay, indicating the influence of industrial emissions and harbor activities. The primary sources were identified as steel manufacturing and wastewater treatment plants for PCDD/Fs, harbor and shipbuilding activities for PCBs, and combustion-related sources for PCNs. Notably, PCDD/F concentrations exceeded sediment guideline values. The combined effects of PCDD/Fs and PCBs demonstrated adverse impacts on aquatic organisms. Hence, the release of toxic pollutants into the marine environment could have potential biological effects due to the combined impact of these various compounds.

Protein S-palmitoylation enhances profibrotic signaling in response to cadmium.

Cadmium (Cd) is a naturally occurring, toxic environmental metal found in foods. Humans do not have an efficient mechanism for Cd elimination; thus, Cd burden in humans increases with age. Cell and mouse studies show that Cd burden from low environmental levels of exposure impacts lung cell metabolism, proliferation signaling and cell growth as part of disease-promoting profibrotic responses in the lungs. Prior integrative analysis of metabolomics and transcriptomics identified the zDHHC11 transcript as a central functional hub in response to Cd dose. zDHHC11 encodes a protein S-palmitoyltransferase, but no evidence is available for effects of Cd on protein S-palmitoylation. In the present research, we studied palmitoylation changes in response to Cd and found increased protein S-palmitoylation in human lung fibroblasts that was inhibited by 2-bromopalmitate (2-BP), an irreversible palmitoyltransferase inhibitor. Mass spectrometry-based proteomics showed palmitoylation of proteins involved in divalent metal transport and in fibrotic signaling. Mechanistic studies showed that 2-BP inhibited palmitoylation of divalent metal ion transporter ZIP14 and also inhibited cellular Cd uptake. Transcription analyses showed that Cd stimulated transforming growth factor (TGF)-ß1 and ß3 expression within 8 h and lung fibrotic markers α-smooth muscle actin, matrix metalloproteinase-2, and collagen 1α1 gene expression and that these effects were blocked by 2-BP. Because 2-BP also blocked palmitoylation of proteins controlled by TGFß1, these results show that palmitoylation impacts Cd-dependent fibrotic signaling both by enhancing cellular Cd accumulation and by supporting post-translational processing of TGFß1-dependent proteins.

Perception of Long-Term Oxygen Therapy for Chronic Lung Diseases May Affect Poor Adherence in Korea.

BACKGROUND: Long-term oxygen therapy (LTOT) improves the survival of patients with hypoxemia due to chronic respiratory diseases. The clinical outcomes of LTOT are strongly associated with patient adherence. To improve the adherence of patients, physicians have focused on the efficacy of LTOT. However, poor adherence may stem from patients' perceptions of LTOT. Herein we evaluated patients' perceptions of LTOT affecting adherence. METHODS: We conducted a cross-sectional survey study using descriptive, open, and closed-ended questionnaire. Patients using oxygen therapy (OT) or requiring it but avoiding OT responded to the questionnaires at three university hospitals. RESULTS: Seventy-nine patients responded to the questionnaires. The number of patients using home and portable OT was 69 (93%) and 37 (46.3%), respectively. Patients with good adherence were 22 (30.1%). Among patients with good adherence, 90.9% used oxygen according to physicians' prescriptions whereas only 37.3% of those with poor adherence followed physicians' prescriptions (p<0.01). The reasons for avoiding using home OT were fear of permanent use (50%), unwanted attention (40%), and lack of symptoms (40%). They avoided portable OT because of unwanted attention (39%), heaviness (31.7%), and lack of symptoms (21.6%). CONCLUSION: Patients on LTOT had the perception of the misunderstanding the effects of OT and of psychosocial barriers to initiate or use LTOT. Considering these findings, health professionals need to provide effective education on the purpose of LTOT to improve patient adherence to OT and provide sufficient support for the management of psychosocial barriers in patients using LTOT.

Preclinical and dose-ranging assessment of hESC-derived dopaminergic progenitors for a clinical trial on Parkinson's disease.

Human embryonic stem cell (hESC)-derived midbrain dopaminergic (mDA) cell transplantation is a promising therapeutic strategy for Parkinson's disease (PD). Here, we present the derivation of high-purity mDA progenitors from clinical-grade hESCs on a large scale under rigorous good manufacturing practice (GMP) conditions. We also assessed the toxicity, biodistribution, and tumorigenicity of these cells in immunodeficient rats in good laboratory practice (GLP)-compliant facilities. Various doses of mDA progenitors were transplanted into hemi-parkinsonian rats, and a significant dose-dependent behavioral improvement was observed with a minimal effective dose range of 5,000-10,000 mDA progenitor cells. These results provided insights into determining a low cell dosage (3.15 million cells) for human clinical trials. Based on these results, approval for a phase 1/2a clinical trial for PD cell therapy was obtained from the Ministry of Food and Drug Safety in Korea, and a clinical trial for treating patients with PD has commenced.

A longitudinal molecular and cellular lung atlas of lethal SARS-CoV-2 infection in K18-hACE2 transgenic mice.

BACKGROUND: The global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to approximately 500 million cases and 6 million deaths worldwide. Previous investigations into the pathophysiology of SARS-CoV-2 primarily focused on peripheral blood mononuclear cells from patients, lacking detailed mechanistic insights into the virus's impact on inflamed tissue. Existing animal models, such as hamster and ferret, do not faithfully replicate the severe SARS-CoV-2 infection seen in patients, underscoring the need for more relevant animal system-based research. METHODS: In this study, we employed single-cell RNA sequencing (scRNA-seq) with lung tissues from K18-hACE2 transgenic (TG) mice during SARS-CoV-2 infection. This approach allowed for a comprehensive examination of the molecular and cellular responses to the virus in lung tissue. FINDINGS: Upon SARS-CoV-2 infection, K18-hACE2 TG mice exhibited severe lung pathologies, including acute pneumonia, alveolar collapse, and immune cell infiltration. Through scRNA-seq, we identified 36 different types of cells dynamically orchestrating SARS-CoV-2-induced pathologies. Notably, SPP1+ macrophages in the myeloid compartment emerged as key drivers of severe lung inflammation and fibrosis in K18-hACE2 TG mice. Dynamic receptor-ligand interactions, involving various cell types such as immunological and bronchial cells, defined an enhanced TGFß signaling pathway linked to delayed tissue regeneration, severe lung injury, and fibrotic processes. INTERPRETATION: Our study provides a comprehensive understanding of SARS-CoV-2 pathogenesis in lung tissue, surpassing previous limitations in investigating inflamed tissues. The identified SPP1+ macrophages and the dysregulated TGFß signaling pathway offer potential targets for therapeutic intervention. Insights from this research may contribute to the development of innovative diagnostics and therapies for COVID-19. FUNDING: This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2020M3A9I2109027, 2021R1A2C2004501).

Development of machine learning algorithms for scaling-up antibiotic stewardship.

BACKGROUND: Antibiotic stewardship programs (ASP) aim to reduce inappropriate use of antibiotics, but their labor-intensive nature impedes their wide adoption. The present study introduces explainable machine learning (ML) models designed to prioritize inpatients who would benefit most from stewardship interventions. METHODS: A cohort of inpatients who received systemic antibiotics and were monitored by a multidisciplinary ASP team at a tertiary hospital in the Republic of Korea was assembled. Data encompassing over 130,000 patient-days and comprising more than 160 features from multiple domains, including prescription records, laboratory, microbiology results, and patient conditions was collected.Outcome labels were generated using medication administration history: discontinuation, switching from intravenous to oral medication (IV to PO), and early or late de-escalation. The models were trained using Extreme Gradient Boosting (XGB) and light Gradient Boosting Machine (LGBM), with SHapley Additive exPlanations (SHAP) analysis used to explain the model's predictions. RESULTS: The models demonstrated strong discrimination when evaluated on a hold-out test set(AUROC - IV to PO: 0.81, Early de-escalation: 0.78, Late de-escalation: 0.72, Discontinue: 0.80). The models identified 41%, 16%, 22%, and 17% more cases requiring discontinuation, IV to PO, early and late de-escalation, respectively, compared to the conventional length of therapy strategy, given that the same number of patients were reviewed by the ASP team. The SHAP results explain how each model makes their predictions, highlighting a unique set of important features that are well-aligned with the clinical intuitions of the ASP team. CONCLUSIONS: The models are expected to improve the efficiency of ASP activities by prioritizing cases that would benefit from different types of ASP interventions along with detailed explanations.

Particulate matter-induced metabolic recoding of epigenetics in macrophages drives pathogenesis of chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a group of illnesses associated with unresolved inflammation in response to toxic environmental stimuli. Persistent exposure to PM is a major risk factor for COPD, but the underlying mechanism remains unclear. Using our established mouse model of PM-induced COPD, we find that repeated PM exposure provokes macrophage-centered chronic inflammation and COPD development. Mechanistically, chronic PM exposure induces transcriptional downregulation of HAAO, KMO, KYNU, and QPRT in macrophages, which are the enzymes of de novo NAD+ synthesis pathway (kynurenine pathway; KP), via elevated chromatin binding of the CCCTC-binding factor (CTCF) near the transcriptional regulatory regions of the enzymes. Subsequent reduction of NAD+ and SIRT1 function increases histone acetylation, resulting in elevated expression of pro-inflammatory genes in PM-exposed macrophages. Activation of SIRT1 by nutraceutical resveratrol mitigated PM-induced chronic inflammation and COPD development. In agreement, increased levels of histone acetylation and decreased expression of KP enzymes were observed in pulmonary macrophages of COPD patients. We newly provide an evidence that dysregulated NAD+ metabolism and consecutive SIRT1 deficiency significantly contribute to the pathological activation of macrophages during PM-mediated COPD pathogenesis. Additionally, targeting PM-induced intertwined metabolic and epigenetic reprogramming in macrophages is an effective strategy for COPD treatment.

Identifying facilitators of and barriers to the adoption of dynamic consent in digital health ecosystems: a scoping review.

BACKGROUND: Conventional consent practices face ethical challenges in continuously evolving digital health environments due to their static, one-time nature. Dynamic consent offers a promising solution, providing adaptability and flexibility to address these ethical concerns. However, due to the immaturity of the concept and accompanying technology, dynamic consent has not yet been widely used in practice. This study aims to identify the facilitators of and barriers to adopting dynamic consent in real-world scenarios. METHODS: This scoping review, conducted in December 2022, adhered to the PRISMA Extension for Scoping Reviews guidelines, focusing on dynamic consent within the health domain. A comprehensive search across Web of Science, PubMed, and Scopus yielded 22 selected articles based on predefined inclusion and exclusion criteria. RESULTS: The facilitators for the adoption of dynamic consent in digital health ecosystems were the provision of multiple consent modalities, personalized alternatives, continuous communication, and the dissemination of up-to-date information. Nevertheless, several barriers, such as consent fatigue, the digital divide, complexities in system implementation, and privacy and security concerns, needed to be addressed. This study also investigated current technological advancements and suggested considerations for further research aimed at resolving the remaining challenges surrounding dynamic consent. CONCLUSIONS: Dynamic consent emerges as an ethically advantageous method for digital health ecosystems, driven by its adaptability and support for continuous, two-way communication between data subjects and consumers. Ethical implementation in real-world settings requires the development of a robust technical framework capable of accommodating the diverse needs of stakeholders, thereby ensuring ethical integrity and data privacy in the evolving digital health landscape.

Development of a neutralization monoclonal antibody with a broad neutralizing effect against SARS-CoV-2 variants.

BACKGROUND: The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants has challenged the effectiveness of current therapeutic regimens. Here, we aimed to develop a potent SARS-CoV-2 antibody with broad neutralizing effect by screening a scFv library with the spike protein receptor-binding domain (RBD) via phage display. METHODS: SKAI-DS84 was identified through phage display, and we performed pseudovirus neutralization assays, authentic virus neutralization assays, and in vivo neutralization efficacy evaluations. Furthermore, surface plasmon resonance (SPR) analysis was conducted to assess the physical characteristics of the antibody, including binding kinetics and measure its affinity for variant RBDs. RESULTS: The selected clones were converted to human IgG, and among them, SKAI-DS84 was selected for further analyses based on its binding affinity with the variant RBDs. Using pseudoviruses, we confirmed that SKAI-DS84 was strongly neutralizing against wild-type, B.1.617.2, B.1.1.529, and subvariants of SARS-CoV-2. We also tested the neutralizing effect of SKAI-DS84 on authentic viruses, in vivo and observed a reduction in viral replication and improved lung pathology. We performed binding and epitope mapping experiments to understand the mechanisms underlying neutralization and identified quaternary epitopes formed by the interaction between RBDs as the target of SKAI-DS84. CONCLUSIONS: We identified, produced, and tested the neutralizing effect of SKAI-DS84 antibody. Our results highlight that SKAI-DS84 could be a potential neutralizing antibody against SARS-CoV-2 and its variants.

SARS-CoV-2 infection engenders heterogeneous ribonucleoprotein interactions to impede translation elongation in the lungs.

Translational regulation in tissue environments during in vivo viral pathogenesis has rarely been studied due to the lack of translatomes from virus-infected tissues, although a series of translatome studies using in vitro cultured cells with viral infection have been reported. In this study, we exploited tissue-optimized ribosome profiling (Ribo-seq) and severe-COVID-19 model mice to establish the first temporal translation profiles of virus and host genes in the lungs during SARS-CoV-2 pathogenesis. Our datasets revealed not only previously unknown targets of translation regulation in infected tissues but also hitherto unreported molecular signatures that contribute to tissue pathology after SARS-CoV-2 infection. Specifically, we observed gradual increases in pseudoribosomal ribonucleoprotein (RNP) interactions that partially overlapped the trails of ribosomes, being likely involved in impeding translation elongation. Contemporaneously developed ribosome heterogeneity with predominantly dysregulated 5 S rRNP association supported the malfunction of elongating ribosomes. Analyses of canonical Ribo-seq reads (ribosome footprints) highlighted two obstructive characteristics to host gene expression: ribosome stalling on codons within transmembrane domain-coding regions and compromised translation of immunity- and metabolism-related genes with upregulated transcription. Our findings collectively demonstrate that the abrogation of translation integrity may be one of the most critical factors contributing to pathogenesis after SARS-CoV-2 infection of tissues.

Passive air sampling of VOCs, O3, NO2, and SO2 in the large industrial city of Ulsan, South Korea: spatial-temporal variations, source identification, and ozone formation potential.

Concerns about volatile organic compounds (VOCs) have increased due to their toxicity and secondary reaction with nitrogen oxides (NOX) to form ozone (O3). In this study, passive air sampling of VOCs, O3, NO2, and SO2 was conducted in summer, fall, winter, and spring from 2019 to 2020 at six industrial and ten urban sites in Ulsan, the largest industrial city in South Korea. Over the entire sampling period, the concentration of toluene (mean: 8.75 µg/m3) was the highest of the 50 target VOCs, followed by m,p-xylenes (4.52 µg/m3), ethylbenzene (4.48 µg/m3), 3-methylpentane (4.40 µg/m3), and n-octane (4.26 µg/m3). Total (Σ50) VOC levels did not statistically differ between seasons, indicating that large amounts of VOCs are emitted into the atmosphere throughout the year. On the other hand, O3, NO2, and SO2 exhibited strong seasonal variation depending on the meteorological conditions and emission sources. The spatial distribution of Σ50 VOCs, NO2, and SO2 indicated that industrial complexes were major sources in Ulsan, while O3 had the opposite spatial distribution. Using a positive matrix factorization model, five major sources were identified, with industrial effects dominant. Aromatic compounds, such as m,p,o-xylenes, toluene, and 1,2,4-trimethylbenzene, significantly contributed to O3 formation. The VOC/NO2 ratio and O3 concentrations suggested that reducing VOC emissions is more effective than reducing NO2 emissions in terms of preventing the secondary formation of O3. The findings of this study allow for a better understanding of the relationship between VOCs, O3, NO2, and SO2 in industrial cities.

Efficacy of Pirfenidone According to Dose in Patients with Idiopathic Pulmonary Fibrosis: A Prospective, Observational, Single-Center Cohort Study.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic lung disease with a poor prognosis. Pirfenidone is approved and widely used for the treatment of IPF and reduces lung function decline. The aim of this study was to evaluate the efficacy of different doses of pirfenidone for the prevention of disease progression in patients with IPF. METHODS: This was a prospective, observational, single-center cohort study conducted in Haeundae Paik Hospital, Republic of Korea, from April 2021 to March 2023. IPF patients were assigned to three groups according to the dose of pirfenidone (600 mg, 1200 mg, 1800 mg). Disease progression was defined as an absolute decline to ≥5% of forced vital capacity (FVC) (% predicted value) or an absolute decline to ≥10% of diffusing capacity of the lung for carbon monoxide (DLco) (% predicted value) over 12 months. The primary endpoint was to evaluate the clinical effects of pirfenidone of each dosage on disease progression in IPF patients by comparing the FVC (% predicted value) and DLco (% predicted value) values over 12 months. The secondary endpoint was to evaluate the prognostic value of Krebs von den Lungen-6 (KL-6) in the disease progression in IPF patients using the baseline KL-6 value and the change in KL-6 values between the baseline and 12 months. RESULTS: A total of 44 patients were enrolled, of whom 39 completed the study, with 13 patients assigned to each of the three groups. The median age was 71.7 years, and 79.5% of patients were men. The baseline characteristics were similar across groups, except the 600 mg group was older (75.9 vs. 69.2 vs. 68.2 years, p = 0.016). The overall median change in FVC and DLco over 12 months was -2.7% (IQR: -9.1%, -1.2%) and -3.8% (IQR: -13.6%, -3.7%), respectively. There was no difference in the decline in FVC (change in FVC, % predicted value: -3.23 vs. -4.08 vs. -1.54, p = 0.621) and DLco (change in DLco, % predicted value: 0.00 vs. -3.62 vs. -3.15, p = 0.437) among the three groups. Fourteen patients (35.9%) suffered disease progression. The rate of disease progression did not differ according to the dose of pirfenidone (38.5 vs. 38.5 vs. 30.8%, p = 1.000). In multivariable logistic regression analysis, KL-6 was not a statistically significant predictor of disease progression. CONCLUSIONS: In our study, regardless of dose, consistent pirfenidone use for 12 months resulted in similar efficacy for the prevention of disease progression in patients with IPF. Large-scale, randomized, double-blind, placebo-controlled clinical trials are needed.

Impact of a Decision Support System on Fall-Prevention Nursing Practices.

OBJECTIVES: The aim of this study was to develop a computerized decision support system (CDSS) that could automatically calculate the risk of falls using electronic medical record data and provide evidence-based fall-prevention recommendations based on risk factors. Furthermore, we analyzed the usability and effect of the system on fall-prevention nursing practices. METHODS: A computerized fall-prevention system was developed according to the system development life cycle, and implemented between March and August 2019 in a single medical unit with a high prevalence of falls. The usability was evaluated 1 month after CDSS implementation. In terms of time and frequency, changes in fall-prevention nursing practices were analyzed using survey data and nursing documentation, respectively. Finally, the incidence of falls before and after system implementation was compared to examine the clinical effectiveness of the CDSS. RESULTS: According to the usability test, the average ease of learning score (5.083 of 7) was the highest among 4 dimensions. The time spent engaged in fall-prevention nursing care per patient per shift increased, particularly for nursing diagnoses and planning. Moreover, the mean frequency of daily documented fall-prevention interventions per patient also increased. Particularly, nursing statements related to nonspecific interventions such as environmental modifications increased. However, the incidence of falls did not decrease after implementation of the CDSS. CONCLUSIONS: Although adoption of the computerized system increased the time spent and number of records created in terms of fall-prevention practices in nurses, no improvement in clinical outcomes was observed, particularly in terms of fall rate reduction.

Comparative Study on Indium Precursors for Plasma-Enhanced Atomic Layer Deposition of In2O3 and Application to High-Performance Field-Effect Transistors.

Indium oxide (In2O3) is a transparent wide-bandgap semiconductor suitable for use in the back-end-of-line-compatible channel layers of heterogeneous monolithic three-dimensional (M3D) devices. The structural, chemical, and electrical properties of In2O3 films deposited by plasma-enhanced atomic layer deposition (PEALD) were examined using two different liquid-based precursors: (3-(dimethylamino)propyl)-dimethyl indium (DADI) and (N,N-dimethylbutylamine)trimethylindium (DATI). DATI-derived In2O3 films had higher growth per cycle (GPC), superior crystallinity, and low defect density compared with DADI-derived In2O3 films. Density functional theory calculations revealed that the structure of DATI can exhibit less steric hindrance compared with that of DADI, explaining the superior physical and electrical properties of the DATI-derived In2O3 film. DATI-derived In2O3 field-effect transistors (FETs) exhibited unprecedented performance, showcasing a high field-effect mobility of 115.8 cm2/(V s), a threshold voltage of -0.12 V, and a low subthreshold gate swing value of <70 mV/decade. These results were achieved by employing a 10-nm-thick HfO2 gate dielectric layer with an effective oxide thickness of 3.9 nm. Both DADI and DATI-derived In2O3 FET devices exhibited remarkable stability under bias stress conditions due to a high-quality In2O3 channel layer, good gate dielectric/channel interface matching, and a suitable passivation layer. These findings underscore the potential of ALD In2O3 films as promising materials for upper-layer channels in the next generation of M3D devices.