Analysis of Green Building Certification System for Developing G-SEED

G-SEED (Green Standard for Energy and Environmental Design) is operating in Korea as a certification system to evaluate the environmental friendliness of buildings. Buildings are generating loads such as carbon dioxide emission, energy consumption, the consumption of resources, destruction of natural ecosystem, change of global climate, pollution of indoor environment, and waste generation. For this purpose, a green building certification system has been introduced. In the green building certification system, evaluation of buildings through methods such as utilization of natural energy, adoption of energy saving system, rainwater management, etc., is induced to reduce environmental load. These certification systems are operated by each country. The level of the certification system is different according to the characteristics such as climate environment. BREEAM in the UK and LEED in the US are operating a version for international certification as well as domestic certification. In this study, analyze the certification system which operates the global version such as BREEAM, LEED. And to investigate the differences between domestic and international versions of each version of the certification system. Certification items classified as reflected by climate characteristics, national characteristics reflected items, general items, such as attempts to analyze the rating system. And certification items were grouped into general “Global certification items” having no specificity for locality and “Local certification items” that differ by each locality according to social, environmental, and political factors. Using the analysis results that comparison of LEED and BREEAM, the study then aims to ascertain the possibility of whether the G-SEED can be used to certify international buildings. And explore the possibility of developing international version of the G-SEED and indicate the direction of G-SEED amendments that may arise in the future. The study results would be available as basic data at the development of G-SEED for international version

A Pharmacometric Model to Predict Chemotherapy-Induced Myelosuppression and Associated Risk Factors in Non-Small Cell Lung Cancer

Chemotherapy often induces severe neutropenia due to the myelosuppressive effect. While predictive pharmacokinetic (PK)/pharmacodynamic (PD) models of absolute neutrophil count (ANC) after anticancer drug administrations have been developed, their deployments to routine clinics have been limited due to the unavailability of PK data and sparseness of PD (or ANC) data. Here, we sought to develop a model describing temporal changes of ANC in non-small cell lung cancer patients receiving (i) combined chemotherapy of paclitaxel and cisplatin and (ii) granulocyte colony stimulating factor (G-CSF) treatment when needed, under such limited circumstances. Maturation of myelocytes into blood neutrophils was described by transit compartments with negative feedback. The K-PD model was employed for drug effects with drug concentration unavailable and the constant model for G-CSF effects. The fitted model exhibited reasonable goodness of fit and parameter estimates. Covariate analyses revealed that ANC decreased in those without diabetes mellitus and female patients. Using the final model obtained, an R Shiny web-based application was developed, which can visualize predicted ANC profiles and associated risk of severe neutropenia for a new patient. Our model and application can be used as a supportive tool to identify patients at the risk of grade 4 neutropenia early and suggest dose reduction

Pharmacokinetic Comparison of 2 Fixed-Dose Combination Tablets of Amlodipine and Valsartan in Healthy Male Korean Volunteers: A Randomized, Open-Label, 2-Period, Single-Dose, Crossover Study

AbstractBackgroundAmlodipine and valsartan have different mechanisms of action, and it is known that the combination therapy with the 2 drugs increases treatment effects compared with the monotherapy with each drug. A fixed-dose combination (FDC) drug is a formulation including fixed amounts of active drug ingredients combined in a single dosage form that is expected to improve medication compliance.ObjectiveThe goal of this study was to compare the pharmacokinetic profiles of single administration of a newly developed FDC tablet containing amlodipine orotate 10 mg and valsartan 160 mg (test formulation) with the conventional FDC tablet of amlodipine besylate 10 mg and valsartan 160 mg (reference formulation) in healthy male Korean volunteers.MethodsThis was a randomized, open-label, single-dose, 2-way crossover study. Eligible subjects were between the ages of 20 and 50 years and within 20% of their ideal weight. Each subject received a single dose of the reference and the test formulations, with a 14-day washout period between formulations. Blood samples were collected up to 144 hours after the dose, and pharmacokinetic parameters were determined for amlodipine and valsartan. Adverse events were evaluated based on subject interviews and physical examinations.ResultsForty-eight of the 50 enrolled subjects completed the study. For both amlodipine and valsartan, the primary pharmacokinetic parameters were included in the range for assumed bioequivalence, yielding 90% CI ratios of 0.9277 to 0.9903 for AUC0–last and 0.9357 to 1.0068 for Cmax in amlodipine, and 0.9784 to 1.1817 for AUC0–last and 0.9738 to 1.2145 for Cmax in valsartan. Dizziness was the most frequently noted adverse event, occurring in 4 subjects with the test formulation, followed by oropharyngeal pain occurring in 1 subject with the test formulation and 3 subjects with the reference formulation. All other adverse events occurred in <3 subjects.ConclusionsThese findings suggest that the pharmacokinetics of the newly developed FDC tablet of amlodipine and valsartan did not differ significantly from the conventional FDC tablet in these healthy Korean male subjects. Both formulations were well tolerated, with no serious adverse events observed. ClinicalTrials.gov identifier: NCT01823913

Predictive models for chronic kidney disease after radical or partial nephrectomy in renal cell cancer using early postoperative serum creatinine levels

BackgroundSeveral predictive factors for chronic kidney disease (CKD) following radical nephrectomy (RN) or partial nephrectomy (PN) have been identified. However, early postoperative laboratory values were infrequently considered as potential predictors. Therefore, this study aimed to develop predictive models for CKD 1 year after RN or PN using early postoperative laboratory values, including serum creatinine (SCr) levels, in addition to preoperative and intraoperative factors. Moreover, the optimal SCr sampling time point for the best prediction of CKD was determined.MethodsData were retrospectively collected from patients with renal cell cancer who underwent laparoscopic or robotic RN (n=557) or PN (n=999). Preoperative, intraoperative, and postoperative factors, including laboratory values, were incorporated during model development. We developed 8 final models using information collected at different time points (preoperative, postoperative day [POD] 0 to 5, and postoperative 1 month). Lastly, we combined all possible subsets of the developed models to generate 120 meta-models. Furthermore, we built a web application to facilitate the implementation of the model.ResultsThe magnitude of postoperative elevation of SCr and history of CKD were the most important predictors for CKD at 1 year, followed by RN (compared to PN) and older age. Among the final models, the model using features of POD 4 showed the best performance for correctly predicting the stages of CKD at 1 year compared to other models (accuracy: 79% of POD 4 model versus 75% of POD 0 model, 76% of POD 1 model, 77% of POD 2 model, 78% of POD 3 model, 76% of POD 5 model, and 73% in postoperative 1 month model). Therefore, POD 4 may be the optimal sampling time point for postoperative SCr. A web application is hosted at https://dongy.shinyapps.io/aki_ckd.ConclusionsOur predictive model, which incorporated postoperative laboratory values, especially SCr levels, in addition to preoperative and intraoperative factors, effectively predicted the occurrence of CKD 1 year after RN or PN and may be helpful for comprehensive management planning

Development of Machine Learning Models Predicting Estimated Blood Loss during Liver Transplant Surgery

The incidence of major hemorrhage and transfusion during liver transplantation has decreased significantly over the past decade, but major bleeding remains a common expectation. Massive intraoperative hemorrhage during liver transplantation can lead to mortality or reoperation. This study aimed to develop machine learning models for the prediction of massive hemorrhage and a scoring system which is applicable to new patients. Data were retrospectively collected from patients aged &gt;18 years who had undergone liver transplantation. These data included emergency information, donor information, demographic data, preoperative laboratory data, the etiology of hepatic failure, the Model for End-stage Liver Disease (MELD) score, surgical history, antiplatelet therapy, continuous renal replacement therapy (CRRT), the preoperative dose of vasopressor, and the estimated blood loss (EBL) during surgery. The logistic regression model was one of the best-performing machine learning models. The most important factors for the prediction of massive hemorrhage were the disease etiology, activated partial thromboplastin time (aPTT), operation duration, body temperature, MELD score, mean arterial pressure, serum creatinine, and pulse pressure. The risk-scoring system was developed using the odds ratios of these factors from the logistic model. The risk-scoring system showed good prediction performance and calibration (AUROC: 0.775, AUPR: 0.753)

Model-informed precision dosing in vancomycin treatment

Introduction: While vancomycin remains a widely prescribed antibiotic, it can cause ototoxicity and nephrotoxicity, both of which are concentration-associated. Overtreatment can occur when the treatment lasts for an unnecessarily long time. Using a model-informed precision dosing scheme, this study aims to develop a population pharmacokinetic (PK) and pharmacodynamic (PD) model for vancomycin to determine the optimal dosage regimen and treatment duration in order to avoid drug-induced toxicity.Methods: The data were obtained from electronic medical records of 542 patients, including 40 children, and were analyzed using NONMEM software. For PK, vancomycin concentrations were described with a two-compartment model incorporating allometry scaling.Results and discussion: This revealed that systemic clearance decreased with creatinine and blood urea nitrogen levels, history of diabetes and renal diseases, and further decreased in women. On the other hand, the central volume of distribution increased with age. For PD, C-reactive protein (CRP) plasma concentrations were described by transit compartments and were found to decrease with the presence of pneumonia. Simulations demonstrated that, given the model informed optimal doses, peak and trough concentrations as well as the area under the concentration-time curve remained within the therapeutic range, even at doses smaller than routine doses, for most patients. Additionally, CRP levels decreased more rapidly with the higher dose starting from 10 days after treatment initiation. The developed R Shiny application efficiently visualized the time courses of vancomycin and CRP concentrations, indicating its applicability in designing optimal treatment schemes simply based on visual inspection

Investigation of radiative cooling performance of random microparticle-polymer composite coating structure

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Changes in Fan Energy Consumption According to Filters Installed in Residential Heat Recovery Ventilators in Korea

In recent years, because of outdoor ultrafine particles, residential heat recovery ventilators (HRVs) have been used with high efficiency filters by residents in Korea. However, as pre-filters are primarily used in residential HRVs, when a high-efficiency particulate air (HEPA) filter is installed, the filter pressure drop increases, reducing the airflow rate, which requires the fan to draw more power to maintain the airflow rate. Therefore, in this study, the change in power usage of HRVs installed in residential apartments in Korea with various air volumes and filters were analyzed. The results show that HEPA filters consumed 13.5–17.5% (16.1% on an average), 11.8–16.0% (13.8% on an average), and 16.8–41.3% (30.1% on an average) more power at 0.5, 1.0, and 1.5 air changes/h, respectively, than the pre-filter. These results indicate that unexpected power consumption increase could be caused if a pre-filter is replaced with a HEPA filter in residential small air-volume HRVs. This may lead to noise or failure due to fan overload. Thus, it is necessary to operate residential HRVs at the optimum air volume according to the fan performance

Predicting the longitudinal changes of levodopa dose requirements in Parkinson’s disease using item response theory assessment of real‐world Unified Parkinson's Disease Rating Scale

Abstract Item response theory (IRT) has been recently adopted to successfully characterize the progression of Parkinson's disease using serial Unified Parkinson's Disease Rating Scale (UPDRS) measurements. However, it has yet to be applied in predicting the longitudinal changes of levodopa dose requirements in the real‐world setting. Here we use IRT to extract two latent variables that represent tremor and non‐tremor‐related symptoms from baseline assessments of UPDRS Part III scores. We show that relative magnitudes of the two latent variables are strong predictors of the progressive increase of levodopa equivalent dose (LED). Retrospectively collected item‐level UPDRS Part III scores and longitudinal records of prescribed medication doses of 128 patients with de novo PD extracted from the electronic medical records were used for model building. Supplementary analysis based on a subset of 36 patients with at least three serial assessments of UPDRS Part III scores suggested that the two latent variables progress at significantly different rates. A web application was developed to facilitate the use of our model in making individualized predictions of future LED and disease progression