• Nutrition Research and Practice
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• 제10권1호
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• pp.49-55
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• 2016

BACKGROUND/OBJECTIVES: The purpose of this study is to utilize an extended Theory of Planned Behavior in identifying predictors of exclusive breastfeeding intention and behavior among women in Kelantan, Malaysia. SUBJECTS/METHODS: A prospective cohort study was conducted, recruiting pregnant womenthrough two-stage cluster sampling. Their exclusive breastfeeding intention, attitude, perceived norm, perceived behavioral control and past behavior were obtained at baseline through interviewer-guided questionnaire. At one month after delivery, another interview was conducted to determine the two additional variables in the extended theory, which were their postpartum support and breastfeeding difficulty. The behavior, which was the actual duration of exclusive breastfeeding, was obtained from the second follow-up at six months. Pearson correlation and two hierarchical regression analyses were conducted. RESULTS: A total of 200 women completed the study follow-up. Their median intended exclusive breastfeeding duration was 4.0 (IQR 5) months, and the median actual duration was 1.0 (IQR 4) month. The Theory of Planned Behavior explained 51.0% of the variance in intention, with perceived behavioral control and attitude were the significant predictors. It also explained 10.0% of the variance in behavior, but the addition of postpartum support and breastfeeding difficulty increased the amount of explained variance in behavior by 6.0%. The significant predictors of exclusive breastfeeding behavior were intention, postpartum support and breastfeeding difficulty. CONCLUSION: The extended Theory of Planned Behaviorhad a good predictive ability in explaining exclusive breastfeedingintention and behavior. The women's intention to practice exclusive breastfeeding may be improved by improving their perceived behavioral control and attitude. Providing correct postpartum support and skills to handle breastfeeding difficulties after delivery will improve their exclusive breastfeeding behavior.

• Journal of Korean Neurosurgical Society
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• 제60권1호
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• pp.15-20
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• 2017

Objective : Diagnosing tumor progression and pseudoprogression remains challenging for many clinicians. Accurate recognition of these findings remains paramount given necessity of prompt treatment. However, no consensus has been reached on the optimal technique to discriminate tumor progression. We sought to investigate the role of magnetic resonance perfusion (MRP) to evaluate tumor progression in glioma patients. Methods : An institutional retrospective review of glioma patients undergoing MRP with concurrent clinical follow up visit was performed. MRP was evaluated in its ability to predict tumor progression, defined clinically or radiographically, at concurrent clinical visit and at follow up visit. The data was then analyzed based on glioma grade and subtype. Resusts : A total of 337 scans and associated clinical visits were reviewed from 64 patients. Sensitivity, specificity, positive and negative predictive value were reported for each tumor subtype and grade. The sensitivity and specificity for high-grade glioma were 60.8% and 87.8% respectively, compared to low-grade glioma which were 85.7% and 89.0% respectively. The value of MRP to assess future tumor progression within 90 days was 46.9% (sensitivity) and 85.0% (specificity). Conclusion : Based on our retrospective review, we concluded that adjunct imaging modalities such as MRP are necessary to help diagnose clinical disease progression. However, there is no clear role for stand-alone surveillance MRP imaging in glioma patients especially to predict future tumor progression. It is best used as an adjunctive measure in patients in whom progression is suspected either clinically or radiographically.

• 대한화학회지
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• 제65권4호
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• pp.254-259
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• 2021

The purpose of this work is the development of a method for an effective, less expensive, rapid, and simultaneous determination of three phenolic compounds (caffeic acid, gallic acid, and quercetin) widely present in food resources and known for their antioxidant powers. The method relies on partial least squares (PLS) calibration of UV-visible spectroscopic data. This model was applied to simultaneously determine, the concentrations of caffeic acid (CA), gallic acid (GA), and quercetin (Q) in six herb infusion extracts: basil, chive, laurel, mint, parsley, and thyme. A wavelength range (250-400) nm, and an experimental calibration matrix with 21 samples of ternary mixtures composed of CA (6.0-21.0 mg/L), GA (10.0-35.2 mg/L), and Q (6.4-17.5 mg/L) were chosen. Spectroscopic data were mean-centered before calibration. Two latent variables were determined using the contiguous block cross-validation procedure after calculating the root mean square error cross-validation RMSECV. Other statistic parameters: RMSEP, R², and Recovery (%) were used to determine the predictive ability of the model. The results obtained demonstrated that UV-visible spectrometry and PLS regression were successfully applied to simultaneously quantify the three phenolic compounds in synthetic ternary mixtures. Moreover, the concentrations of CA, GA and Q in herb infusion extracts were easily predicted and found to be 3.918-18.055, 9.014-23.825, and 9.040-13.350 mg/g of dry sample, respectively.

• 대한종양외과학회지
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• 제14권2호
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• pp.135-141
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• 2018

Purpose: Axillary lymph node dissection (ALND) and sentinel lymph node biopsy (SLNB) are important for staging of patients with node-positive breast cancer. However, these can be avoided in select micrometastatic diseases, preventing postoperative complications. The present study evaluated the ability of axillary lymph node maximum standardized uptake value (SUVmax) on positron emission tomography-computed tomography (PET-CT) to predict axillary metastasis of breast cancer. Methods: The records of invasive breast cancer patients who underwent pretreatment (surgery and/or chemotherapy) PET-CT between January 2006 and December 2014 were reviewed. ALNs were preoperatively evaluated by PET-CT. Lymph nodes were dissected by SLNB or ALND. SUVmax was measured in both the axillary lymph node and primary tumor. Student t-test and chi-square test were used to analyze sensitivity and specificity. Receiver operating characteristic (ROC) and area under the ROC curve (AUC) analyses were performed. Results: SUV-tumor (SUV-T) and SUV-lymph node (SUV-LN) were significantly higher in the triple-negative breast cancer (TNBC) group than in other groups (SUV-T: 5.99, P<0.01; SUV-LN: 1.29, P=0.014). The sensitivity (0.881) and accuracy (0.804) for initial ALN staging were higher in fine needle aspiration+PET-CT than in other methods. For PET-CT alone, the subtype with the highest sensitivity (0.870) and negative predictive value (0.917) was TNBC. The AUC for SUV-LN was greatest in TNBC (0.797). Conclusion: The characteristics of SUV-T and SUV-LN differed according to immunohistochemistry subtype. Compared to other subtypes, the true positivity of axillary metastasis on PET-CT was highest in TNBC. These findings could help tailor management for therapeutic and diagnostic purposes.

• Neurospine
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• 제15권4호
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• pp.329-337
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• 2018

Objective: Machine learning algorithms excel at leveraging big data to identify complex patterns that can be used to aid in clinical decision-making. The objective of this study is to demonstrate the performance of machine learning models in predicting postoperative complications following anterior cervical discectomy and fusion (ACDF). Methods: Artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), and random forest decision tree (RF) models were trained on a multicenter data set of patients undergoing ACDF to predict surgical complications based on readily available patient data. Following training, these models were compared to the predictive capability of American Society of Anesthesiologists (ASA) physical status classification. Results: A total of 20,879 patients were identified as having undergone ACDF. Following exclusion criteria, patients were divided into 14,615 patients for training and 6,264 for testing data sets. ANN and LR consistently outperformed ASA physical status classification in predicting every complication (p < 0.05). The ANN outperformed LR in predicting venous thromboembolism, wound complication, and mortality (p < 0.05). The SVM and RF models were no better than random chance at predicting any of the postoperative complications (p < 0.05). Conclusion: ANN and LR algorithms outperform ASA physical status classification for predicting individual postoperative complications. Additionally, neural networks have greater sensitivity than LR when predicting mortality and wound complications. With the growing size of medical data, the training of machine learning on these large datasets promises to improve risk prognostication, with the ability of continuously learning making them excellent tools in complex clinical scenarios.

• 대한임상독성학회지
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• 제16권2호
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• pp.149-156
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• 2018

Purpose: The purpose of this study was to determine whether hepatotoxicity could be predicted early using biochemical markers in patients with acetaminophen (AAP) poisoning and to assess the usefulness of predictive factors for acute liver injury or hepatotoxicity. Methods: This study was a retrospective observational study involving a medical records review. The participants were patients who were admitted to the emergency department (ED) with AAP overdose at two hospitals over a 10-year period. Demographic data, age, time from ingestion to visit, initial AAP level, initial hepatic aminotransferases, and initial prothrombin time were recorded. Acute liver injury was defined as a peak serum ALT >50 U/L or double the admission value, and hepatotoxicity was defined as a peak ALT >1,000 U/L. Receiver operating characteristic curve analyses were performed to compare the prognostic performance among variables. Results: A total of 97 patients were admitted to the ED with AAP overdose, of whom 26 had acute liver injury and 6 had hepatotoxicity. Acute liver injury was associated with the time interval after taking the drug, and hepatotoxicity was associated with the initial PT and the ALT level. The scoring system proposed by the authors has a significant ability to predict both acute liver injury and hepatotoxicity. Conclusion: To predict the prognosis of AAP poisoning patients, the time interval after taking AAP was important, and initial prothrombin time and ALT level were useful tests. Also a scoring system combining variables may be useful.

• 한국융합학회논문지
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• 제11권12호
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• pp.283-290
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• 2020

본 연구는 한국 성인을 대상으로 고요산혈증 위험을 예측하기 위한 중성지방-혈당 지수(triglyceride and glucose index, TyG index)의 유용성을 평가하였다. 서울지역 종합병원에서 2017년부터 2019년까지 건강진단을 실시한 20세 이상 남성 14,266명, 여성 9,033명을 대상으로 하였다. TyG 지수에 따른 고요산혈증 발생 위험도는 로지스틱 회귀분석을 실시하였으며, TyG 지수의 고요산혈증 위험 예측능력을 확인하기 위해 ROC 곡선을 구하였다. 고요산혈증을 예측하기 위한 TyG 지수의 정확도는 0.68이며, 남성 0.61, 여성 0.67이었다(각각 p<0.001). TyG 지수의 고요산혈증 발생 위험은 1사분위수보다 4사분위수에서 1.69배 높았으며, 남성은 2.03배, 여성은 2.07배 높았다(각각 p<0.05). 따라서 TyG 지수는 고요산혈증의 선별검사로서 진단적 유용성은 높지 않았으나, TyG 지수와 고요산혈증간에는 관련이 있었다.

• 디지털융복합연구
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• 제18권12호
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• pp.585-594
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• 2020

현재 노화와 관련된 퇴행성 근육 질환은 심각한 문제로 간주되고 있으나 근육 감소증의 치료 및 예방에 대한 약물 효과는 충분히 연구되지 않다. 이에 중국 전통차인 보이차의 추출물을 근육 감소증의 증상을 완화하기 위한 치료제로서 가치를 평가하고자 하였다. 본 실험에서는 이를 평가하기 위하여 ABTS 분석, MTS 분석, 면역 블롯 분석, 면역 형광 현미경법을 수행하였다. 보이차 추출물은 우수한 라디칼 소거 능을 나타냈으며, 또한 근관을 형성하는 Myh3의 발현이 촉진시켰고, 근관 형성을 증진시켰다. 따라서 보이차는 근육생성을 촉진하는 천연 물질이며 근감소증을 포함한 다양한 근육 질환의 예방 및 치료에 대한 제약 연구의 재료로 가치가 있음을 시사하며, 보이차의 구체적인 지표물질을 확인하고 이에 대한 추가연구가 필요할 것으로 보인다.

• Journal of Preventive Medicine and Public Health
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• 제56권1호
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• pp.41-49
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• 2023

Objectives: Rwanda reported a stunting rate of 33% in 2020, decreasing from 38% in 2015; however, stunting remains an issue. Globally, child deaths from malnutrition stand at 45%. The best options for the early detection and treatment of stunting should be made a community policy priority, and health services remain an issue. Hence, this research aimed to develop a model for predicting stunting in Rwandan children. Methods: The Rwanda Demographic and Health Survey 2019-2020 was used as secondary data. Stratified 10-fold cross-validation was used, and different machine learning classifiers were trained to predict stunting status. The prediction models were compared using different metrics, and the best model was chosen. Results: The best model was developed with the gradient boosting classifier algorithm, with a training accuracy of 80.49% based on the performance indicators of several models. Based on a confusion matrix, the test accuracy, sensitivity, specificity, and F1 were calculated, yielding the model's ability to classify stunting cases correctly at 79.33%, identify stunted children accurately at 72.51%, and categorize non-stunted children correctly at 94.49%, with an area under the curve of 0.89. The model found that the mother's height, television, the child's age, province, mother's education, birth weight, and childbirth size were the most important predictors of stunting status. Conclusions: Therefore, machine-learning techniques may be used in Rwanda to construct an accurate model that can detect the early stages of stunting and offer the best predictive attributes to help prevent and control stunting in under five Rwandan children.

• Animal Bioscience
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• 제36권7호
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• pp.1003-1009
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• 2023

Objective: The objective was to compare (pedigree-based) best linear unbiased prediction (BLUP), genomic BLUP (GBLUP), and single-step GBLUP (ssGBLUP) methods for genomic evaluation of growth traits in a Mexican Braunvieh cattle population. Methods: Birth (BW), weaning (WW), and yearling weight (YW) data of a Mexican Braunvieh cattle population were analyzed with BLUP, GBLUP, and ssGBLUP methods. These methods are differentiated by the additive genetic relationship matrix included in the model and the animals under evaluation. The predictive ability of the model was evaluated using random partitions of the data in training and testing sets, consistently predicting about 20% of genotyped animals on all occasions. For each partition, the Pearson correlation coefficient between adjusted phenotypes for fixed effects and non-genetic random effects and the estimated breeding values (EBV) were computed. Results: The random contemporary group (CG) effect explained about 50%, 45%, and 35% of the phenotypic variance in BW, WW, and YW, respectively. For the three methods, the CG effect explained the highest proportion of the phenotypic variances (except for YW-GBLUP). The heritability estimate obtained with GBLUP was the lowest for BW, while the highest heritability was obtained with BLUP. For WW, the highest heritability estimate was obtained with BLUP, the estimates obtained with GBLUP and ssGBLUP were similar. For YW, the heritability estimates obtained with GBLUP and BLUP were similar, and the lowest heritability was obtained with ssGBLUP. Pearson correlation coefficients between adjusted phenotypes for non-genetic effects and EBVs were the highest for BLUP, followed by ssBLUP and GBLUP. Conclusion: The successful implementation of genetic evaluations that include genotyped and non-genotyped animals in our study indicate a promising method for use in genetic improvement programs of Braunvieh cattle. Our findings showed that simultaneous evaluation of genotyped and non-genotyped animals improved prediction accuracy for growth traits even with a limited number of genotyped animals.