• Journal of Digital Policy
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• v.1 no.2
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• pp.61-69
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• 2022

The paper is a study to develop and verify a blood glucose level prediction model based on biosignals obtained from photoplethysmography (PPG) sensors, ICT technology and data. Blood glucose prediction used the MLP architecture of machine learning. The input layer of the machine learning model consists of 10 input nodes and 5 hidden layers: heart rate, heart rate variability, age, gender, VLF, LF, HF, SDNN, RMSSD, and PNN50. The results of the predictive model are MSE=0.0724, MAE=1.1022 and RMSE=1.0285, and the coefficient of determination (R²) is 0.9985. A blood glucose prediction model using bio-signal data collected from digital devices and machine learning was established and verified. If research to standardize and increase accuracy of machine learning datasets for various digital devices continues, it could be an alternative method for individual blood glucose management.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.257-269
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• 2021

The purpose of this study is to collect biosignal data in a non-invasive and non-restrictive manner using a BCG (Ballistocardiogram) sensor, and utilize artificial intelligence machine learning algorithms in ICT and high-performance computing environments. And it is to present and study a method for developing and validating a data-based blood glucose prediction model. In the blood glucose level prediction model, the input nodes in the MLP architecture are data of heart rate, respiration rate, stroke volume, heart rate variability, SDNN, RMSSD, PNN50, age, and gender, and the hidden layer 7 were used. As a result of the experiment, the average MSE, MAE, and RMSE values of the learning data tested 5 times were 0.5226, 0.6328, and 0.7692, respectively, and the average values of the validation data were 0.5408, 0.6776, and 0.7968, respectively, and the coefficient of determination (R²) was 0.9997. If research to standardize a model for predicting blood sugar levels based on data and to verify data set collection and prediction accuracy continues, it is expected that it can be used for non-invasive blood sugar level management.

• Journal of The Korean Society of Clinical Toxicology
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• v.13 no.2
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• pp.55-61
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• 2015

Purpose: Many studies have examined the mechanisms of impaired glucose homeostasis after organophosphate (OP) exposure, however no study has evaluated the clinical utility of blood glucose measurements in patients with OP poisoning. The current study was conducted to evaluate the initial glucose level at presentation and the glycemic variables during the first 3 days after admission as a predictor of mortality. Methods: This retrospective observational case series included 228 patients with a history of OP poisoning. Among other clinical data, information on the initial glucose level at presentation and mean glucose level, delta glucose level, and the presence of a hypoglycemic event during the first 3 days of admission, was collected. Results: Survivors had lower initial glucose levels at presentation and glucose variability during the first 3 days of admission compared to non-survivors. The frequency of hypoglycemic events was higher in non-survivors. In multivariate analysis, the initial glucose level (> 233 mg/dl) was an independent predictor of mortality, along with age. Conclusion: The initial glucose level at presentation can be helpful in prediction of mortality in cases of OP intoxication at bedside. The physician should pay attention to patients with a glucose level >233 mg/dl at presentation after ingestion of OP.

• Journal of Digital Convergence
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• v.21 no.3
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• pp.33-39
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• 2023

This study is a performance evaluation of a blood sugar monitoring system that combines a PPG sensor, which is an evaluation device for blood glucose monitoring, and a DNN algorithm when monitoring capillary blood glucose. The study is a researcher-led clinical trial conducted on participants from September 2023 to November 2023. PPG-BGMS compared predicted blood sugar levels for evaluation using 1-minute heart rate and heart rate variability information and the DNN prediction algorithm with capillary blood glucose levels measured with a blood glucose meter of the standard personal blood sugar management system. Of the 100 participants, 50 had type 2 diabetes (T2DM), and the average age was 67 years (range, 28 to 89 years). It was found that 100% of the predicted blood sugar level of PPG-BGMS was distributed in the A+B area of the Clarke error grid and Parker(Consensus) error grid. The MARD value of PPG-BGMS predicted blood glucose is 5.3 ± 4.0%. Consequentially, the non-blood-based PPG-BGMS was found to be non-inferior to the instantaneous blood sugar level of the clinical standard blood-based personal blood glucose measurement system.

• Annals of Clinical Neurophysiology
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• v.16 no.2
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• pp.55-61
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• 2014

Background: Viruses can cause either meningitis or encephalitis. It is unclear why some people suffer from aseptic meningitis, and others acquire aseptic encephalitis when infected with the same viral pathogens. The aim of this study was to compare demographic and laboratory factors between patients with aseptic meningitis and encephalitis. Methods: The demographic and laboratory differences were analyzed according to age, sex, diabetes, hypertension, C-reactive protein in the blood, white blood cell and protein in the cerebrospinal fluid, and glucose ratio (cerebrospinal fluid/blood). Additionally, we analyzed the nation-wide differencesin age between the patients with aseptic meningitis and those with encephalitis in Korea. Results: The patients with aseptic encephalitis were older, more likely to have hypertension, and had higher levels of C-reactive protein than did the patients with aseptic meningitis. However, the numbers of white blood cells in the cerebrospinal fluid were significantly higher in the patients with meningitis than in the patients with encephalitis. Multivariable analysis revealed that age >49 years, hypertension and a C-reactive protein level >5.81 mg/dL were independent and significant variables in the prediction of aseptic encephalitis. Additionally, the patients with aseptic encephalitis were older than those with aseptic meningitis in the nation-wide Korean database. Conclusions: Older age, hypertension, and higher levels of C-reactive protein are useful factors for the prediction of aseptic encephalitis.

• Korean Journal of Veterinary Service
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• v.38 no.3
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• pp.155-162
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• 2015

The incidence of second-grade milk production in 9 dairy farms of South Korea was investigated from May 2011 to March 2012, and the serum composition of cows producing first- and second-grade milk in 14 farms including the 9 farms was analyzed. The incidence rate of second-grade milk production of 402 cows in nine dairy farms located in the central and southwestern regions of Korea was 15.4% with the highest rate being 34.4%. Seasonal morbidity was higher during late winter (February) and early summer (June) with the highest rate observed in February (32.6%) followed by November (33.3%). Second-grade milk was most frequently found within one month postpartum (34.1%) while only 3.5% was found during the first 60~90 days of lactating period (n=785, 5 herds). The morbidity increased thereafter (P<0.05) with the highest observed between 270~300 days of lactation (36.1%). The acidity was not significantly different between second-grade ($0.159{\pm}0.026%$) and first-grade milk ($0.158{\pm}0.027%$). Blood serum analysis of 371 cows in the 14 dairy farms indicated that aspartate aminotransferase (AST) level was significantly higher (P<0.001) in cows producing second-grade milk while albumin was significantly lower (P<0.001) than cows producing first-grade milk. Total protein and triglyceride was also significantly low along with glucose, non-esterified fatty acid and blood urea nitrogen in cows producing second-grade milk. Statistical analysis including sensitivity, specificity and positive/negative prediction values showed that lactating cows with high AST, low albumin, total protein and triglyceride levels in the serum tended to produce second-grade milk. It was concluded that serological parameters, especially live functional and metabolic-related serum compositions (AST, albumin, total protein and triglyceride), were significantly influenced in cows producing second-grade milk.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.6
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• pp.870-875
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• 2015

The purpose of this study was to investigate the alternative splicing in equine cordon-bleu WH2 repeat protein-like 1 (COBLL1) gene that was identified in horse muscle and blood leukocytes, and to predict functional consequences of alternative splicing by bioinformatics analysis. In a previous study, RNA-seq analysis predicted the presence of alternative spliced isoforms of equine COBLL1, namely COBLL1a as a long form and COBLL1b as a short form. In this study, we validated two isoforms of COBLL1 transcripts in horse tissues by the real-time polymerase chain reaction, and cloned them for Sanger sequencing. The sequencing results showed that the alternative splicing occurs at exon 9. Prediction of protein structure of these isoforms revealed three putative phosphorylation sites at the amino acid sequences encoded in exon 9, which is deleted in COBLL1b. In expression analysis, it was found that COBLL1b was expressed ubiquitously and equivalently in all the analyzed tissues, whereas COBLL1a showed strong expression in kidney, spinal cord and lung, moderate expression in heart and skeletal muscle, and low expression in thyroid and colon. In muscle, both COBLL1a and COBLL1b expression decreased after exercise. It is assumed that the regulation of COBLL1 expression may be important for regulating glucose level or switching of energy source, possibly through an insulin signaling pathway, in muscle after exercise. Further study is warranted to reveal the functional importance of COBLL1 on athletic performance in race horses.