The Effect of Competitive Advantage and Human Advantage on Industrial Competitive Strategy (Case Study: Smis in Gorontalo Province)

Small and Medium Industries (SMIs) have a strategic role in the Indonesian economy, as they earn 61.9 percent of the foreign exchange which goes to make up the nation\u27s Gross Domestic Product, and nationally they are able to absorb 97 percent of the workforce. The Global Competitiveness Report also notes that SMIs serve as the business units that affect every nation\u27s competitiveness. Considering this strategic role, the selection of a competitive strategy for these SMIs is absolutely necessary. Through an in-depth literature review, this study aims to explore what variables influence the competitive strategy of industries, particularly the SMIs. By using a Systematic Literature Review (SLR) with a total of 31 main literature (articles, papers and books), this study has found two dominant factors that influence industrial competitive strategy: Competitive advantage and human advantage, which are subsequently developed into six independent variables (construct variables), i.e. cost, delivery, product quality, product variety, know-how and innovativeness, with a total of 44 indicators. The results of measurements of the sample of SMIs in Gorontalo Province, using Structural Equation Modeling, found that both competitive advantage and human advantage jointly influence 40.2 percent of the industrial competitive strategies. These results indicate that competitive strategies, such as creating products with unique features, on-time delivery, flexibility in production, and employee involvement in the innovations, are indispensable to SMIs in order for them to produce quality products and be able to maintain their advantage

Korean Society of Nephrology 2022 Recommendations on controversial issues in diagnosis and management of hyponatremia

The Korean Society for Electrolyte and Blood Pressure Research, in collaboration with the Korean Society of Nephrology, has published a clinical practice guideline (CPG) document for hyponatremia treatment. The document is based on an extensive evidence-based review of the diagnosis, evaluation, and treatment of hyponatremia with the multidisciplinary participation of representative experts in hyponatremia with methodologist support for guideline development. This CPG consists of 12 recommendations (two for diagnosis, eight for treatment, and two for special situations) based on eight detailed topics and nine key questions. Each recommendation begins with statements graded by the strength of the recommendations and the quality of the evidence. Each statement is followed by rationale supporting the recommendations. The committee issued conditional recommendations in favor of rapid intermittent bolus administration of hypertonic saline in severe hyponatremia, the use of vasopressin receptor antagonists in heart failure with hypervolemic hyponatremia, and syndrome of inappropriate antidiuresis with moderate to severe hyponatremia, the individualization of desmopressin use, and strong recommendation on the administration of isotonic fluids as maintenance fluid therapy in hospitalized pediatric patients. We hope that this CPG will provide useful recommendations in practice, with the aim of providing clinical support for shared decision-making to improve patient outcomes

Short-term blood pressure variability as a potential therapeutic target for kidney disease

Abstract Short-term blood pressure variability (BPV) measured with ambulatory blood pressure (BP) monitoring has been demonstrated to be significant in predicting various clinical outcomes. Short-term BPV is distinguished from long-term BPV based on the time interval in which BP fluctuations are measured. Increased short-term BPV has been linked to detrimental effects on the microvascular structure and contributes to subclinical organ damage in the heart, blood vessels, and kidneys, regardless of the average 24-h BP levels. Short-term BPV can be defined by various measures, including calculated metrics (standard deviation, coefficient of variation, average real variability, weighted standard deviation, variability independent of the mean) or dipping patterns. Nevertheless, the additional role of short-term BPV beyond the predictive value of average 24-h BPs or established risk factors for cardiovascular disease and kidney disease remains unclear. In particular, longitudinal studies that evaluate the association between short-term BPV and kidney function impairment are limited and no conclusive data exist regarding which short-term BPV indicators most accurately reflect the prognosis of kidney disease. The issue of how to treat BPV in clinical practice is another concern that is frequently raised. This paper presents a review of the evidence for the prognostic role of short-term BPV in kidney outcomes. Additionally, this review discusses the remaining concerns about short-term BPV that need to be further investigated as an independent risk modifier

Differential Association of Vitamin D Deficiency With Albuminuria by Sex in the Korean General Population: A Cross-sectional Study of the Korea National Health and Nutrition Examination Survey 2011-2012

Objectives Albuminuria has emerged as a biomarker for several medical conditions, and vitamin D has received attention due to its associations with various disorders. We evaluated the association between low serum vitamin D levels and prevalent albuminuria by sex in the Korean general population. Methods We analyzed 9823 participants (4401 males, 5422 females) from the Korea National Health and Nutrition Examination Survey 2011-2012 (KNHANES V-2), and categorized them as having a normal range of vitamin D levels, vitamin D insufficiency, or vitamin D deficiency. A multivariable logistic regression model was used to compare the risk of albuminuria across these groups. Stratified analyses were conducted by smoking status, obesity, and renal function. Results Albuminuria was found in 325 of the 4401 male participants (7.4%) and in 455 of the 5422 female participants (8.4%). Among the males, vitamin D deficiency was associated with an odds ratio (OR) for albuminuria of 1.78 (95% confidence interval [CI], 1.07 to 2.97, p<0.05). However, such an association was not found in females. The association was stronger in male current smokers (OR, 3.54; 95% CI, 1.47 to 8.50; p=0.005). Conclusions The findings of this study suggest that sex differences exist in the association between serum vitamin D deficiency and albuminuria. Additionally, we observed that the association was stronger in current smokers than in the overall male population, but was not seen in non-smokers. Therefore, different approaches by sex and smoking status might be needed when considering using vitamin D as a biomarker for renal function

Upper Normal Serum Creatinine Concentrations as a Predictor for Chronic Kidney Disease: Analysis of 14 Years’ Korean Genome and Epidemiology Study (KoGES)

Both serum creatinine (sCr) and estimated glomerular filtration rate (eGFR) have been used to assess kidney function in public health check-ups. However, when the sCr is within the normal levels but the eGFR is &lt;60 mL/min/1.73 m2, a dilemma arises, as the patients might progress to chronic kidney disease (CKD) after several years. We aimed to evaluate the association between normal sCr and the risk of incident CKD in the general population. For this, 9445 subjects from the Korean Genome and Epidemiology Study, with normal sCr and eGFR of &gt;60 mL/min/1.73 m2 were analyzed. The subjects were classified into quartiles based on sCr levels. The primary outcome was the development of eGFR &lt;60 mL/min/1.73 m2 on two consecutive measures. During a mean follow-up of 8.4 &#177; 4.3 years, 779 (8.2%) subjects developed eGFR &lt;60 mL/min/1.73 m2. The incidence of the development of eGFR &lt;60 mL/min/1.73 m2 was higher in the higher quartiles than in the lowest quartile. In multivariable Cox analysis, the highest quartile was associated with an increased risk for the development of eGFR &lt;60 mL/min/1.73 m2 (hazard ratio (HR), 4.71; 95% confidence interval (CI), 3.29&#8315;6.74 in females; HR, 12.77; 95% CI, 7.69&#8315;21.23 in males). In the receiver operating characteristic curve analysis, adding sCr to the traditional risk factors for CKD improved the accuracy of predicting the development of eGFR &lt;60 mL/min/1.73 m2 (area under the curve, 0.83 vs. 0.80 in females and 0.85 vs. 0.78 in males), and the cutoff value of sCr was 0.75 mg/dL and 0.78 mg/dL in females and males. Cautious interpretation is necessary when sCr is within the normal range, considering that the upper normal range of sCr has a higher risk of CKD development

High Triglyceride-Glucose Index with Renal Hyperfiltration and Albuminuria in Young Adults: The Korea National Health and Nutrition Examination Survey (KNHANES V, VI, and VIII)

Background: High triglyceride-glucose (TyG) index, a surrogate marker of insulin resistance, is associated with an increased risk of albuminuria in adults. However, the relationship between high TyG index associated with renal hyperfiltration (RHF) and albuminuria among young adults is unclear. Methods: A total of 5420 participants aged 19–39 years were enrolled from the Korean National Health and Nutrition Examination Survey (2011–2014 and 2019) and their TyG index levels were analyzed. RHF was defined as eGFR with residuals > 90th percentile after adjusting for age, sex, weight, and height. Albuminuria was defined as urinary albumin-to-creatinine ratio ≥ 30 mg/g Cr. Logistic regression analyses were used to evaluate the association between TyG index, RHF, and albuminuria. Results: The mean age was 30.7 ± 6.0 years and 46.4% were male. The prevalence of albuminuria and RHF was higher in the higher tertiles of TyG index. In our multivariable model, high TyG index showed higher risk of albuminuria (odds ratio (OR) per 1.0 increase in TyG index, 1.56; 95% confidence interval (CI), 1.24–1.95 and OR in the highest tertile, 1.65; 95% CI, 1.08–2.52). High TyG index was associated with higher risk of RHF (OR per 1.0 increase in TyG index, 1.56; 95% CI, 1.32–1.84 and OR in the highest tertile, 1.73; 95% CI, 1.31–2.30). When participants were divided into with or without RHF, high-TyG index-associated high risk of albuminuria was only observed in those with RHF. Participants with concurrent high TyG index and RHF showed the highest risk of albuminuria. Mediation analysis showed that 54.2% of the relation between TyG index and albuminuria was mediated by RHF (95% CI of indirect effect, 0.27–0.76). Finally, incorporating TyG index into our basic model improved the predictive value for albuminuria only in participants with RHF. Conclusion: High TyG index associated with RHF was the strongest risk factor for albuminuria in this study. Early identification of high TyG index with RHF may prevent future development of CKD in relatively healthy and young adults

Prediction model development of late-onset preeclampsia using machine learning-based methods.

Preeclampsia is one of the leading causes of maternal and fetal morbidity and mortality. Due to the lack of effective preventive measures, its prediction is essential to its prompt management. This study aimed to develop models using machine learning to predict late-onset preeclampsia using hospital electronic medical record data. The performance of the machine learning based models and models using conventional statistical methods were also compared. A total of 11,006 pregnant women who received antenatal care at Yonsei University Hospital were included. Maternal data were retrieved from electronic medical records during the early second trimester to 34 weeks. The prediction outcome was late-onset preeclampsia occurrence after 34 weeks' gestation. Pattern recognition and cluster analysis were used to select the parameters included in the prediction models. Logistic regression, decision tree model, naïve Bayes classification, support vector machine, random forest algorithm, and stochastic gradient boosting method were used to construct the prediction models. C-statistics was used to assess the performance of each model. The overall preeclampsia development rate was 4.7% (474 patients). Systolic blood pressure, serum blood urea nitrogen and creatinine levels, platelet counts, serum potassium level, white blood cell count, serum calcium level, and urinary protein were the most influential variables included in the prediction models. C-statistics for the decision tree model, naïve Bayes classification, support vector machine, random forest algorithm, stochastic gradient boosting method, and logistic regression models were 0.857, 0.776, 0.573, 0.894, 0.924, and 0.806, respectively. The stochastic gradient boosting model had the best prediction performance with an accuracy and false positive rate of 0.973 and 0.009, respectively. The combined use of maternal factors and common antenatal laboratory data of the early second trimester through early third trimester could effectively predict late-onset preeclampsia using machine learning algorithms. Future prospective studies are needed to verify the clinical applicability algorithms