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PPIxGPN: plasma proteomic profiling of neurodegenerative biomarkers with protein-protein interaction-based eXplainable graph propagational network
No full textNeurodegenerative diseases involve progressive neuronal dysfunction, requiring the identification of specific pathological features for accurate diagnosis. While cerebrospinal fluid analysis and neuroimaging are commonly used, their invasive nature and high costs limit clinical applicability. Recently advances in plasma proteomics offer a less invasive and cost-effective alternative, further enhanced by machine learning (ML). However, most ML-based studies overlook synergetic effects from protein-protein interactions (PPIs), which play a key role in disease mechanisms. Although graph convolutional network and its extensions can utilize PPIs, they rely on locality-based feature aggregation, overlooking essential components and emphasizing noisy interactions. Moreover, expanding those methods to cover broader PPIs results in complex model architectures that reduce explainability, which is crucial in medical ML models for clinical decision-making. To address these challenges, we propose Protein-Protein Interaction-based eXplainable Graph Propagational Network (PPIxGPN), a novel ML model designed for plasma proteomic profiling of neurodegenerative biomarkers. PPIxGPN captures synergetic effects between proteins by integrating PPIs with independent effects of proteins, leveraging globality-based feature aggregation to represent comprehensive PPI properties. This process is implemented using a single graph propagational layer, enabling PPIxGPN to be configured by shallow architecture, thereby PPIxGPN ensures high model explainability, enhancing clinical applicability by providing interpretable outputs. Experimental validation on the UK Biobank dataset demonstrated the superior performance of PPIxGPN in neurodegenerative risk prediction, outperforming comparison methods. Furthermore, the explainability of PPIxGPN facilitated detailed analyses of the discriminative significance of synergistic effects, the predictive importance of proteins, and the longitudinal changes in biomarker profiles, highlighting its clinical relevance
Impact of Renal Impairment on the Pharmacodynamic and Pharmacokinetic Profiles of Epaminurad, a Novel Uric Acid Transporter 1 Inhibitor
No full textEpaminurad, a novel uricosuric agent, exhibits potent inhibitory activity against the human uric acid transporter. This study aimed to investigate the effects of renal function and food intake on the pharmacokinetic, pharmacodynamic, and safety characteristics of 9 mg epaminurad. This study was designed as a phase 1, partially randomized, open-label, oral administration, partial crossover trial. Participants were assigned to three groups based on renal function: normal (Group 1), moderate renal impairment classified as Stage 3a (Group 2) and Stage 3b (Group 3). Each group aimed to enroll 6-10 participants. Blood and urine samples were collected to evaluate the pharmacokinetics and pharmacodynamics of epaminurad. Safety assessments were also conducted throughout the study. A total of 27 participants completed the study, including 12 with normal renal function (Group 1) and 9 and 6 participants with moderate renal impairment (Groups 2 and 3), respectively. When a single 9 mg dose of epaminurad was administered under fasted conditions, the pharmacokinetic, pharmacodynamic, and safety profiles did not show clear differences among the renal function groups. Furthermore, no notable differences were observed in these profiles between the fasted and fed states. Patients with moderate renal impairment can receive (eGFR of 30-59 mL/min/1.73 m(2)) 9 mg epaminurad without dose adjustment, and the drug may be administered regardless of food intake
Powered Tool for the Removal of a Well-Fixed Acetabular Cup: A Comparative Experimental Study
No full textBACKGROUD: The removal of a well-fixed acetabular cup is a challenging, labor-intensive, and time-consuming step during revision hip arthroplasty. Although the advent of the manual osteotome, Explant, has simplified the procedure, it is still a stressful process as it dissipates the surgeon's strength and time and risks an iatrogenic pelvic fracture. Recently, EZX, a powered tool for extraction of well-fixed acetabular cups with semicircular blade was invented. This study aimed to compare Explant and EZX in an experimental condition for their efficacy and safety. METHODS: Cementless acetabular cups were press-fitted to 20 hemipelvic polyurethane models using foam adhesives. Ten cups were removed with each tool for comparison of the elapsed time, loads on the entire hemipelvis, periacetabular strain and temperature, volume of periacetabular bone removed, and diameter of the remaining acetabular rim. Strains and loads were quantitatively assessed using strain gauges and load cells for precise and reliable measurements. RESULTS: The mean duration required to remove a well-fixed cup with EZX was 38.5 seconds (range, 25-55), whereas that with Explant was 543.7 seconds (range, 214-1,051) (p < 0.001). The load on the entire hemipelvis with EZX (mean, 9.1 kgf; range, 6.4-11.3) was 33% lower than that with Explant (mean, 13.6 kgf; range, 9.2-17.1) (p < 0.001). The periacetabular peak strains at the 3 positions with EZX were significantly lower than those with Explant (p < 0.001). The temperature during the removal did not differ significantly between the 2 tools. Although the mean volume of bone loss with Explant was 2.4 mL more than that with EZX (p < 0.001), the mean diameters of the remaining acetabular rim were not significantly different, measuring 54.1 mm with both tools. CONCLUSIONS: The present experiment revealed that a well-fixed cup could be removed using a powered tool with less strength and time and less load on the entire pelvis. Although the powered tool removed a larger volume of bone, the diameters of the remaining acetabular rims were equivalent. This tool may help surgeons remove well-fixed cups in a short time and reduce the deforming load on the bone around the cup without increasing the size of the subsequent reconstruction cup
Clinical Relevance of Immunologic Diagnosis of Shrimp Allergy in Adults
No full textPURPOSE: Shrimp is a predominant allergic food in adults and adolescents. This study aims to evaluate the clinical efficacy of immunologic diagnosis in differentiating the clinical phenotypes of shrimp allergy. METHODS: We enrolled 85 adults diagnosed with shrimp allergy based on clinical symptoms that occurred at least twice after shrimp ingestion and who had specific immunoglobulin E (IgE) results for shrimp extract, were enrolled in the study. Patients were classified into 2 groups: anaphylaxis (ANA) and non-ANA. Serum-specific IgEs to shrimp and recombinant tropomyosin from both house dust mite (HDM) and shrimp were measured using ImmunoCAP. RESULTS: Among the patients (mean age 38 years; 51.8% female), 32 were diagnosed with shrimp-induced ANA. The remaining 53 were classified into the non-ANA group, including 46 acute urticaria/angioedema and 7 isolated oropharyngeal manifestations. There were no significant differences in shrimp-specific IgE positivity (78.1% vs. 60.4%) or skin prick test (SPT) positivity (16.7% vs. 25.9%) between groups. However, specific IgE to shrimp extract was significantly higher in the ANA group. Receiver operating characteristic analysis indicated that a shrimp-specific IgE level > 0.7 kU/L was an appropriate cutoff for identifying ANA among patients with shrimp allergy (area under the curve 0.643, P = 0.028). No significant differences were observed in specific IgEs to recombinant shrimp and the HDM tropomyosin between the groups. The ANA group had a greater prevalence of nonsteroidal anti-inflammatory drug hypersensitivity (31.3% vs. 7.5%, P = 0.006) and chronic urticaria (35.5% vs. 15.4%, P = 0.035). CONCLUSIONS: Patients with shrimp-induced ANA presented higher levels of specific IgE to shrimp extract compared to those with acute urticaria or localized oropharyngeal symptoms. Neither SPTs nor specific IgE tests for recombinant tropomyosin effectively differentiate ANA among shrimp allergy patients
Advancements and challenges in neonatal resuscitation: embracing laryngeal mask airways for improved outcomes
No full textComparison of 2 Paclitaxel-Coated Balloons with Different Excipients for the Treatment of Femoropopliteal Artery Disease: A Randomized Prospective Trial
No full textPURPOSE: To evaluate the effectiveness and safety of a novel drug-coated balloon (DCB), Genoss DCB (Genoss), using shellac plus vitamin E as an excipient, compared with a reference DCB using urea. MATERIALS AND METHODS: Patients with femoropopliteal arterial disease under Rutherford Classes 2-5 were enrolled in this prospective, multicenter, noninferiority clinical trial and randomly assigned 1:1 to Genoss DCB and IN.PACT Admiral (Medtronic, Dublin, Ireland). The primary endpoint was late lumen loss at 6 months, which was evaluated using computed tomography (CT) angiography by an independent investigator blinded to the treatment assignment. RESULTS: A total of 119 patients from 10 institutions in the Republic of Korea were assigned to the Genoss DCB (n = 59) and IN.PACT Admiral (n = 60) groups. The late lumen losses were -0.08 mm (SD +/- 0.59) in the Genoss DCB group and 0.02 mm (SD +/- 0.72) in the IN.PACT Admiral group (P = .469). The upper limit of the 1-sided 97.5% confidence interval for differences in late lumen loss was 0.17 mm, lower than the noninferiority limit of 0.50 mm, demonstrating the noninferiority of Genoss DCB compared with IN.PACT Admiral. In addition, the 2 groups showed no significant differences in clinically-driven target lesion revascularization, major amputation, and all-cause mortality. CONCLUSIONS: The safety and 6-month late lumen loss of a new DCB using shellac plus vitamin E as excipients were noninferior compared with those of the reference DCB using urea as the excipient
Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study
No full textBACKGROUND: Delirium in intensive care unit (ICU) patients poses a significant challenge, affecting patient outcomes and health care efficiency. Developing an accurate, real-time prediction model for delirium represents an advancement in critical care, addressing needs for timely intervention and resource optimization in ICUs. OBJECTIVE: We aimed to create a novel machine learning model for delirium prediction in ICU patients using only continuous physiological data. METHODS: We developed models integrating routinely available clinical data, such as age, sex, and patient monitoring device outputs, to ensure practicality and adaptability in diverse clinical settings. To confirm the reliability of delirium determination records, we prospectively collected results of Confusion Assessment Method for the ICU (CAM-ICU) evaluations performed by qualified investigators from May 17, 2021, to December 23, 2022, determining Cohen kappa coefficients. Participants were included in the study if they were aged >/=18 years at ICU admission, had delirium evaluations using the CAM-ICU, and had data collected for at least 4 hours before delirium diagnosis or nondiagnosis. The development cohort from Yongin Severance Hospital (March 1, 2020, to January 12, 2022) comprised 5478 records: 5129 (93.62%) records from 651 patients for training and 349 (6.37%) records from 163 patients for internal validation. For temporal validation, we used 4438 records from the same hospital (January 28, 2022, to December 31, 2022) to reflect potential seasonal variations. External validation was performed using data from 670 patients at Ajou University Hospital (March 2022 to September 2022). We evaluated machine learning algorithms (random forest [RF], extra-trees classifier, and light gradient boosting machine) and selected the RF model as the final model based on its performance. To confirm clinical utility, a decision curve analysis and temporal pattern for model prediction during the ICU stay were performed. RESULTS: The kappa coefficient between labels generated by ICU nurses and prospectively verified by qualified researchers was 0.81, indicating reliable CAM-ICU results. Our final model showed robust performance in internal validation (area under the receiver operating characteristic curve [AUROC]: 0.82; area under the precision-recall curve [AUPRC]: 0.62) and maintained its accuracy in temporal validation (AUROC: 0.73; AUPRC: 0.85). External validation supported its effectiveness (AUROC: 0.84; AUPRC: 0.77). Decision curve analysis showed a positive net benefit at all thresholds, and the temporal pattern analysis showed a gradual increase in the model scores as the actual delirium diagnosis time approached. CONCLUSIONS: We developed a machine learning model for delirium prediction in ICU patients using routinely measured variables, including physiological waveforms. Our study demonstrates the potential of the RF model in predicting delirium, with consistent performance across various validation scenarios. The model uses noninvasive variables, making it applicable to a wide range of ICU patients, with minimal additional risk
Imeglimin Inhibits Macrophage Foam Cell Formation and Atherosclerosis in Streptozotocin-Induced Diabetic ApoE-Deficient Mice
No full textAtherosclerotic cardiovascular disease is a major complication of diabetes, whose progression is significantly accelerated by hyperglycemia. Imeglimin, a novel oral antidiabetic agent, has demonstrated efficacy in glucose control; however, its role in diabetes-related cardiovascular complications has not yet been fully explored. This study aimed to investigate the effects of imeglimin on foam cell formation and atherosclerosis in the context of diabetes. THP-1 macrophages were treated with oxidized low-density lipoprotein (LDL) and high glucose to induce foam cell formation in vitro. Additionally, ApoE(-/-) mice with streptozotocin-induced diabetes were used to determine the effects of imeglimin in vivo by analyzing metabolic parameters and atherosclerotic plaque formation. Imeglimin inhibited macrophage-derived foam cell formation by promoting the expression of ATP-binding cassette transporters (ABC) A1 and ABCG1 and downregulating the expression of CD36. The effects of imeglimin on ABCG1 and CD36 expression regulation was mediated by AMPK. In diabetic ApoE(-/-) mice, imeglimin reduced the atherosclerotic plaque area, decreased fasting glucose and LDL cholesterol levels, and upregulated ABCG1 expression in the liver and aorta. These findings suggest that imeglimin may have a preventive effect on foam cell formation and a therapeutic role in atherosclerosis progression in diabetic conditions
Appropriate cardiopulmonary resuscitation duration and predictors of return of spontaneous circulation in traumatic cardiac arrest
No full textBACKGROUND: Despite advances in trauma care, traumatic cardiac arrest (TCA) shows significantly poorer outcomes compared to non-traumatic cardiac arrest, with mortality rates exceeding 96%. However, no standardized protocol exists for appropriate cardiopulmonary resuscitation (CPR) duration in TCA. This study aimed to establish evidence-based CPR duration thresholds and identify factors associated with return of spontaneous circulation (ROSC) in TCA patients. METHODS: We conducted a retrospective observational study using a single-centre trauma registry of adult patients with TCA between January 2021 and December 2023. Univariate analysis was used to identify differences in the baseline and outcome variables between the ROSC and no-ROSC groups. We performed multivariable logistic regression analysis to identify factors independently associated with ROSC. We also investigated the appropriate cutoff time of pre-hospital and total CPR duration for ROSC (the CPR duration that has maximum sensitivity and specificity for ROSC). RESULTS: In total, 422 patients with TCA were included, of whom 250 were eligible for analysis. The proportion of patients with ROSC was 22.4% (n = 56), and trauma bay/emergency department mortality and in-hospital mortality rates were 80.8% (n = 202) and 97.2% (n = 243), respectively. Factors associated with ROSC included alert mental status in the field, as indicated by verbal response (adjusted odds ratio [OR], 0.07; 95% confidence interval [CI], 0.01-1.12; p = 0.06), pain response (OR, 0.03; 95% CI, 0.01-0.43; p = 0.009), and unresponsiveness (OR, 0.04; 95% CI, 0.01-0.44; p = 0.009) and non-asystolic initial rhythms, such as pulseless electrical activity (OR, 4.26; 95% CI, 1.92-9.46; p < 0.001), shockable rhythm (OR, 14.26; 95% CI, 1.44-141.54; p = 0.023), pre-hospital CPR duration (OR, 0.90; 95% CI, 0.85-0.95), and total CPR duration (OR, 0.88; 95% CI, 0.84-0.92; p < 0.001). The upper limits of pre-hospital and total CPR durations for achieving a probability of ROSC < 1% were 23 and 30 min, respectively, whereas those for a cumulative portion of ROSC > 99% were 27 and 38 min, respectively. Among the survivors (n = 7), six had favourable functional outcomes at discharge. CONCLUSIONS: This study provides evidence-based CPR duration thresholds in TCA, demonstrating that resuscitation efforts beyond 27 min in prehospital settings and 38 min in total were futile. Additionally, an alert mental status in the field and non-asystolic initial rhythm were identified as positive predictors of ROSC. These findings may help guide appropriate duration of resuscitation efforts in TCA
Predicting Nottingham grade in breast cancer digital pathology using a foundation model
No full textBACKGROUND: The Nottingham histologic grade is crucial for assessing severity and predicting prognosis in breast cancer, a prevalent cancer worldwide. Traditional grading systems rely on subjective expert judgment and require extensive pathological expertise, are time-consuming, and often lead to inter-observer variability. METHODS: To address these limitations, we develop an AI-based model to predict Nottingham grade from whole-slide images of hematoxylin and eosin (H&E)-stained breast cancer tissue using a pathology foundation model. From TCGA database, we trained and evaluated using 521 H&E breast cancer slide images with available Nottingham scores through internal split validation, and further validated its clinical utility using an additional set of 597 cases without Nottingham scores. The model leveraged deep features extracted from a pathology foundation model (UNI) and incorporated 14 distinct multiple instance learning (MIL) algorithms. RESULTS: The best-performing model achieved an F1 score of 0.731 and a multiclass average AUC of 0.835. The top 300 genes correlated with model predictions were significantly enriched in pathways related to cell division and chromosome segregation, supporting the model's biological relevance. The predicted grades demonstrated statistically significant association with 5-year overall survival (p < 0.05). CONCLUSION: Our AI-based automated Nottingham grading system provides an efficient and reproducible tool for breast cancer assessment, offering potential for standardization of histologic grade in clinical practice