Opportunities and challenges of a dynamic consent-based application: personalized options for personal health data sharing and utilization.

BACKGROUND: The principles of dynamic consent are based on the idea of safeguarding the autonomy of individuals by providing them with personalized options to choose from regarding the sharing and utilization of personal health data. To facilitate the widespread introduction of dynamic consent concepts in practice, individuals must perceive these procedures as useful and easy to use. This study examines the user experience of a dynamic consent-based application, in particular focusing on personalized options, and explores whether this approach may be useful in terms of ensuring the autonomy of data subjects in personal health data usage. METHODS: This study investigated the user experience of MyHealthHub, a dynamic consent-based application, among adults aged 18 years or older living in South Korea. Eight tasks exploring the primary aspects of dynamic consent principles-including providing consent, monitoring consent history, and managing personalized options were provided to participants. Feedback on the experiences of testing MyHealthHub was gathered via multiple-choice and open-ended questionnaire items. RESULTS: A total of 30 participants provided dynamic consent through the MyHealthHub application. Most participants successfully completed all the provided tasks without assistance and regarded the personalized options favourably. Concerns about the security and reliability of the digital-based consent system were raised, in contrast to positive responses elicited in other aspects, such as perceived usefulness and ease of use. CONCLUSIONS: Dynamic consent is an ethically advantageous approach for the sharing and utilization of personal health data. Personalized options have the potential to serve as pragmatic safeguards for the autonomy of individuals in the sharing and utilization of personal health data. Incorporating the principles of dynamic consent into real-world scenarios requires remaining issues, such as the need for powerful authentication mechanisms that bolster privacy and security, to be addressed. This would enhance the trustworthiness of dynamic consent-based applications while preserving their ethical advantages.

Integrating Clinical Data and Medical Imaging in Lung Cancer: Feasibility Study Using the Observational Medical Outcomes Partnership Common Data Model Extension.

BACKGROUND: Digital transformation, particularly the integration of medical imaging with clinical data, is vital in personalized medicine. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardizes health data. However, integrating medical imaging remains a challenge. OBJECTIVE: This study proposes a method for combining medical imaging data with the OMOP CDM to improve multimodal research. METHODS: Our approach included the analysis and selection of digital imaging and communications in medicine header tags, validation of data formats, and alignment according to the OMOP CDM framework. The Fast Healthcare Interoperability Resources ImagingStudy profile guided our consistency in column naming and definitions. Imaging Common Data Model (I-CDM), constructed using the entity-attribute-value model, facilitates scalable and efficient medical imaging data management. For patients with lung cancer diagnosed between 2010 and 2017, we introduced 4 new tables-IMAGING_STUDY, IMAGING_SERIES, IMAGING_ANNOTATION, and FILEPATH-to standardize various imaging-related data and link to clinical data. RESULTS: This framework underscores the effectiveness of I-CDM in enhancing our understanding of lung cancer diagnostics and treatment strategies. The implementation of the I-CDM tables enabled the structured organization of a comprehensive data set, including 282,098 IMAGING_STUDY, 5,674,425 IMAGING_SERIES, and 48,536 IMAGING_ANNOTATION records, illustrating the extensive scope and depth of the approach. A scenario-based analysis using actual data from patients with lung cancer underscored the feasibility of our approach. A data quality check applying 44 specific rules confirmed the high integrity of the constructed data set, with all checks successfully passed, underscoring the reliability of our findings. CONCLUSIONS: These findings indicate that I-CDM can improve the integration and analysis of medical imaging and clinical data. By addressing the challenges in data standardization and management, our approach contributes toward enhancing diagnostics and treatment strategies. Future research should expand the application of I-CDM to diverse disease populations and explore its wide-ranging utility for medical conditions.

Discovery of a new long COVID mouse model via systemic histopathological comparison of SARS-CoV-2 intranasal and inhalation infection.

Intranasal infection is commonly used to establish a SARS-CoV-2 mouse model due to its non-invasive procedures and a minimal effect from the operation itself. However, mice intranasally infected with SARS-CoV-2 have a high mortality rate, which limits the utility of this model for exploring therapeutic strategies and the sequelae of non-fatal COVID-19 cases. To resolve these limitations, an aerosolised viral administration method has been suggested. However, an in-depth pathological analysis comparing the two models is lacking. Here, we show that inhalation and intranasal SARS-CoV-2 (106 PFU) infection models established in K18-hACE2 mice develop unique pathological features in both the respiratory and central nervous systems, which could be directly attributed to the infection method. While the inhalation-infection model exhibited relatively milder pathological parameters, it closely mimicked the prevalent chest CT pattern observed in COVID-19 patients with focal, peripheral lesions and fibrotic scarring in the recuperating lung. We also found the evidence of direct neuron-invasion from the olfactory receptor neurons to the olfactory bulb in the intranasal model and showed the trigeminal nerve as an alternative route of transmission to the brain in inhalation infected mice. Even after viral clearance confirmed at 14 days post-infection, mild lesions were still found in the brain of inhalation-infected mice. These findings suggest that the inhalation-infection model has advantages over the intranasal-infection model in closely mimicking the pathological features of non-fatal symptoms of COVID-19, demonstrating its potential to study the sequelae and possible interventions for long COVID.

Characterization of lung function impairment and pathological changes induced by chronic lead and cadmium inhalation: Insights from a mouse model study.

Exposure to environmental heavy metals such as lead (Pb) and cadmium (Cd) is a global concern due to their widespread presence. However, the specific pulmonary effects of inhaled exposure, especially related to long-term effects, remain poorly understood. In this study, we developed a novel mouse model of Pb and Cd inhalation to mimic real-world conditions and investigate pulmonary effects. Mice were exposed to Pb and Cd inhalation for 6 months using a whole-body exposure system, resulting in decreased lung compliance and progression from emphysematous changes to fibrosis. In addition, the blood Pb/Cd levels of mice exposed to Pb/Cd for 6 months are like those of humans occupationally exposed to heavy metals. Histology revealed inflammation and collagen deposition. Transcriptomic analysis highlighted immune responses and macrophage activity in developing fibrosis. These results confirm an association between Pb/Cd exposure and emphysema and fibrosis, reflecting clinical findings. The study highlights the importance of long-term exposure assessment and time-course analysis for understanding Pb/Cd-induced lung disease. The relevance of the mouse model in replicating human exposure scenarios underscores its value in studying fibrosis and emphysema simultaneously. These findings provide a basis for targeted therapeutic interventions against heavy metal-induced lung injury.

Chiral-Induced Surface-Enhanced Raman Optical Activity on a Single-Particle Substrate.

Surface-enhanced Raman optical activity (SEROA) is a promising method for analyzing chiral molecules' molecular chirality and structural changes. However, conventional SEROA measurements face challenges related to substrate stability, signal uniformity, and interference from electronic circular dichroism (ECD). Therefore, in this study, we present a uniform and stable substrate for SEROA measurements by utilizing Au nanoparticles on the Au nanofilm structure to confine hotspots to the film-particle junctions and minimize ECD interference. This method also uses the induction of chirality from chiral molecules to achiral molecules to overcome the limitation of chiral molecules in SEROA measurements, specifically their lower signal efficiency. Successful chirality transfer is demonstrated through distinguishable SEROA signals when the l/d-alanine mixture is present. Enantiomeric discrimination of different l/d-alanine ratios was achieved with linear responses in the circular intensity difference (CID). Altogether, the proposed chiral-induced SEROA on the AuNP_on_AuNF substrate shows promising potential for detecting and characterizing structural changes in biomolecules, thus making it a valuable tool for various research applications.

Tobacco-induced hyperglycemia promotes lung cancer progression via cancer cell-macrophage interaction through paracrine IGF2/IR/NPM1-driven PD-L1 expression.

Tobacco smoking (TS) is implicated in lung cancer (LC) progression through the development of metabolic syndrome. However, direct evidence linking metabolic syndrome to TS-mediated LC progression remains to be established. Our findings demonstrate that 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone and benzo[a]pyrene (NNK and BaP; NB), components of tobacco smoke, induce metabolic syndrome characteristics, particularly hyperglycemia, promoting lung cancer progression in male C57BL/6 J mice. NB enhances glucose uptake in tumor-associated macrophages by increasing the expression and surface localization of glucose transporter (GLUT) 1 and 3, thereby leading to transcriptional upregulation of insulin-like growth factor 2 (IGF2), which subsequently activates insulin receptor (IR) in LC cells in a paracrine manner, promoting its nuclear import. Nuclear IR binds to nucleophosmin (NPM1), resulting in IR/NPM1-mediated activation of the CD274 promoter and expression of programmed death ligand-1 (PD-L1). Restricting glycolysis, depleting macrophages, or blocking PD-L1 inhibits NB-mediated LC progression. Analysis of patient tissues and public databases reveals elevated levels of IGF2 and GLUT1 in tumor-associated macrophages, as well as tumoral PD-L1 and phosphorylated insulin-like growth factor 1 receptor/insulin receptor (pIGF-1R/IR) expression, suggesting potential poor prognostic biomarkers for LC patients. Our data indicate that paracrine IGF2/IR/NPM1/PD-L1 signaling, facilitated by NB-induced dysregulation of glucose levels and metabolic reprogramming of macrophages, contributes to TS-mediated LC progression.

Autocrine insulin-like growth factor 2 signaling as a potential target in the associated development of pulmonary emphysema and cancer in smokers.

BACKGROUND: Tobacco smoking causes pulmonary inflammation, resulting in emphysema, an independent risk factor for lung cancer. Induction of insulin-like growth factor 2 (IGF2) in response to lung injury by tobacco carcinogens, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol and polycyclic aromatic hydrocarbon benzo[a]pyrene in combination (NB), is critical for the proliferation of alveolar type 2 cells (AT2s) for lung repair. However, persistent IGF2 overexpression during NB-induced severe injury results in hyperproliferation of AT2s without coordinated AT2-to-AT1 differentiation, disrupting alveolar repair, which leads to the concurrent development of emphysema and lung cancer. The current study aims to verify the role of IGF2 signaling in the associated development of emphysema and cancer and develop effective pharmaceuticals for the diseases using animal models that recapitulate the characteristics of these chronic diseases. METHODS: The pathogenesis of pulmonary emphysema and cancer was analyzed by lung function testing, histological evaluation, in situ zymography, dihydroethidium staining, and immunofluorescence and immunohistochemistry analyses utilizing mouse models of emphysema and cancer established by moderate exposure to NB for up to seven months. RESULTS: Moderate NB exposure induced IGF2 expression in AT2s during the development of pulmonary emphysema and lung cancer in mice. Using AT2-specific insulin receptor knockout mice, we verified the causative role of sustained IGF2 signaling activation in AT2s in emphysema development. IGF2-targeting strategies, including voltage-dependent calcium channel blocker (CCB) and a neutralizing antibody, significantly suppressed the NB-induced development of emphysema and lung cancer. A publicly available database revealed an inverse correlation between the use of calcium channel blockers and a COPD diagnosis. CONCLUSIONS: Our work confirms sustained IGF2 signaling activation in AT2s couples impaired lung repair to the concurrent development of emphysema and cancer in mice. Additionally, CCB and IGF2-specific neutralizing antibodies are effective pharmaceuticals for the two diseases.

Current Health Data Standardization Project and Future Directions to Ensure Interoperability in Korea.

OBJECTIVES: The need for interoperability at the national level was highlighted in Korea, leading to a consensus on the importance of establishing national standards that align with international technological standards and reflect contemporary needs. This article aims to share insights into the background of the recent national health data standardization policy, the activities of the Health Data Standardization Taskforce, and the future direction of health data standardization in Korea. METHODS: To ensure health data interoperability, the Health Data Standardization Taskforce was jointly organized by the public and private sectors in December 2022. The taskforce operated three working groups. It reviewed international trends in interoperability standardization, assessed the current status of health data standardization, discussed its vision, mission, and strategies, engaged in short-term standardization activities, and established a governance system for standardization. RESULTS: On September 15, 2023, the notice of "Health Data Terminology and Transmission Standards" in Korea was thoroughly revised to improve the exchange of health information between information systems and ensure interoperability. This notice includes the Korea Core Data for Interoperability (KR CDI) and the Korea Core Data Transmission Standard (HL7 FHIR KR Core), which are outcomes of the taskforce's efforts. Additionally, to reinforce the standardized governance system, the Health-Data Standardization Promotion Committee was established. CONCLUSIONS: Active interest and support from medical informatics experts are needed for the development and widespread adoption of health data standards in Korea.

Geographic information system-based determination of priority monitoring areas for hazardous air pollutants in an industrial city.

Industrial cities are hotspots for many hazardous air pollutants (HAPs), which are detrimental to human health. We devised an identification method to determine priority HAP monitoring areas using a comprehensive approach involving monitoring, modeling, and demographics. The methodology to identify the priority HAP monitoring area consists of two parts: (1) mapping the spatial distribution of selected categories relevant to the target pollutant and (2) integrating the distribution maps of various categories and subsequent scoring. The identification method was applied in Ulsan, the largest industrial city in South Korea, to identify priority HAP monitoring areas. Four categories related to HAPs were used in the method: (1) concentrations of HAPs, (2) amount of HAP emissions, (3) the contribution of industrial activities, and (4) population density in the city. This method can be used to select priority HAP monitoring areas for intensive monitoring campaigns, cohort studies, and epidemiological studies.

Ginseng-derived compounds as potential anticancer agents targeting cancer stem cells.

Cancer stem cells (CSCs) are a rare subpopulation of cancer cells that exhibit stem cell-like characteristics, including self-renewal and differentiation in a multi-stage lineage state via symmetric or asymmetric division, causing tumor initiation, heterogeneity, progression, and recurrence and posing a major challenge to current anticancer therapy. Despite the importance of CSCs in carcinogenesis and cancer progression, currently available anticancer therapeutics have limitations for eradicating CSCs. Moreover, the efficacy and therapeutic windows of currently available anti-CSC agents are limited, suggesting the necessity to optimize and develop a novel anticancer agent targeting CSCs. Ginseng has been traditionally used for enhancing immunity and relieving fatigue. As ginseng's long history of use has demonstrated its safety, it has gained attention for its potential pharmacological properties, including anticancer effects. Several studies have identified the bioactive principles of ginseng, such as ginseng saponin (ginsenosides) and non-saponin compounds (e.g., polysaccharides, polyacetylenes, and phenolic compounds), and their pharmacological activities, including antioxidant, anticancer, antidiabetic, antifatigue, and neuroprotective effects. Notably, recent reports have shown the potential of ginseng-derived compounds as anti-CSC agents. This review investigates the biology of CSCs and efforts to utilize ginseng-derived components for cancer treatment targeting CSCs, highlighting their role in overcoming current therapeutic limitations.

Immune Cells Are Differentially Affected by SARS-CoV-2 Viral Loads in K18-hACE2 Mice.

Viral load and the duration of viral shedding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are important determinants of the transmission of coronavirus disease 2019. In this study, we examined the effects of viral doses on the lung and spleen of K18-hACE2 transgenic mice by temporal histological and transcriptional analyses. Approximately, 1×105 plaque-forming units (PFU) of SARS-CoV-2 induced strong host responses in the lungs from 2 days post inoculation (dpi) which did not recover until the mice died, whereas responses to the virus were obvious at 5 days, recovering to the basal state by 14 dpi at 1×102 PFU. Further, flow cytometry showed that number of CD8+ T cells continuously increased in 1×102 PFU-virus-infected lungs from 2 dpi, but not in 1×105 PFU-virus-infected lungs. In spleens, responses to the virus were prominent from 2 dpi, and number of B cells was significantly decreased at 1×105 PFU; however, 1×102 PFU of virus induced very weak responses from 2 dpi which recovered by 10 dpi. Although the defense responses returned to normal and the mice survived, lung histology showed evidence of fibrosis, suggesting sequelae of SARS-CoV-2 infection. Our findings indicate that specific effectors of the immune response in the lung and spleen were either increased or depleted in response to doses of SARS-CoV-2. This study demonstrated that the response of local and systemic immune effectors to a viral infection varies with viral dose, which either exacerbates the severity of the infection or accelerates its elimination.

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.

IL-15 in T-Cell Responses and Immunopathogenesis.

IL-15 belongs to the common gamma chain cytokine family and has pleiotropic immunological functions. IL-15 is a homeostatic cytokine essential for the development and maintenance of NK cells and memory CD8+ T cells. In addition, IL-15 plays a critical role in the activation, effector functions, tissue residency, and senescence of CD8+ T cells. IL-15 also activates virtual memory T cells, mucosal-associated invariant T cells and γδ T cells. Recently, IL-15 has been highlighted as a major trigger of TCR-independent activation of T cells. This mechanism is involved in T cell-mediated immunopathogenesis in diverse diseases, including viral infections and chronic inflammatory diseases. Deeper understanding of IL-15-mediated T-cell responses and their underlying mechanisms could optimize therapeutic strategies to ameliorate host injury by T cell-mediated immunopathogenesis. This review highlights recent advancements in comprehending the role of IL-15 in relation to T cell responses and immunopathogenesis under various host conditions.

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.

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.

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.

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.

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.

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.