Income convergence? Evidence of non-linearity in the East Asian Economies: A comment

This study demonstrates the usefulness of Kapetanois et al. (2003) test in differentiating the two stages of income convergence—long run convergence and catching up. A re-examination of the “Four Asian Dragons” economies, in which their income differentials with respect to Japan have been identified as non-linear stationary in Liew and Lim (2005), reveals that the economy of Hong Kong, Korea and Singapore are catching up, while Taiwan has yet to catch up, with the Japan economy

Income convergence? Evidence of non-linearity in the East Asian Economies: A comment

This study demonstrates the usefulness of Kapetanois et al. (2003) test in differentiating the two stages of income convergence—long run convergence and catching up. A re-examination of the “Four Asian Dragons” economies, in which their income differentials with respect to Japan have been identified as non-linear stationary in Liew and Lim (2005), reveals that the economy of Hong Kong, Korea and Singapore are catching up, while Taiwan has yet to catch up, with the Japan economy

Income convergence? Evidence of Non-linearity in the East Asian Economies : A Comment

This study demonstrates the usefulness of Kapetanois et al. (2003) test in differentiating the two stages of income convergence—long run convergence and catching up. A reexamination of the “Four Asian Dragons” economies, in which their income differentials with respect to Japan have been identified as non-linear stationary in Liew and Lim (2005), reveals that the economy of Hong Kong, Korea and Singapore are catching up, while Taiwan has yet to catch up, with the Japan economy

Income convergence: fresh evidence from the Nordic countries

Using the recently developed linearity test and non-linear unit root test, this study shows that the income gaps of Finland, Norway and Sweden with respect to Denmark are non-linear but stationary with no significant trend effect, implying the Nordic countries have already attained steady state of income equality, a state beyond catching up as identified earlier in Oxley and Greasley (1999). This fresh evidence strengthens the finding of Nordic Convergence Club by these authors

Confirmation of PSMA expression measured on [68Ga]Ga-PSMA PET/CT by immunohistochemistry in prostate adenocarcinoma

Background: Our aim is to determine the accuracy of [68Ga]Ga-PSMA PET/CT in showing PSMA expression in primary prostate cancer and to investigate the relationship between SUVmax and immunohistochemical PSMA expression, Gleason score, and PSA value. Material and methods: We retrospectively analyzed 66 male patients who were diagnosed with primary prostate adenocarcinoma, underwent pre-treatment [68Ga]Ga-PSMA PET/CT examination for staging, and performed radical prostatectomy between March 2018–August 2020. Immunohistochemical staining was applied to the radical prostatectomy specimens of all patients to detect PSMA expression. The results were evaluated as an immunoreactive score (IRS) and a modified IRS was obtained. Gleason score groups and prostate-specific antigen (PSA) serum values of the patients were obtained from the patient files. Results: The high SUVmax of primary prostate tumors was significantly correlated with a high modified IRS score (score 2; 3), high PSA value, high Gleason score, and metastasis. In correlation analysis, a positive correlation was found between SUVmax and PSA value and modified IRS score (r = 0.69, p = 0.001; r = 0.39, p = 0.001). In addition, there was a statistically significant weak correlation between PSA serum concentration and modified IRS scores (r = 0.267; p = 0.03). In regression analysis, the percentage of positive cells had a statistically significant and increasing effect on SUVmax (p = 0.031; std beta = 0.268; 95% CI = 0.231–4.596). Conclusions: In prostate adenocarcinoma, SUVmax of the primary tumor in [68Ga]Ga-PSMA PET/CT correlates with immunohistochemical PSMA expression. In addition, high SUVmax is associated with markers of poor prognoses, such as high PSMA expression, PSA value, and Gleason score

Quantum Computational Investigation of (E)-1-(4-methoxyphenyl)-5-methyl-N?-(3-phenoxybenzylidene)-1H-1,2,3-triazole-4-carbohydrazide

The title compound was synthesized and structurally characterized. Theoretical IR, NMR (with the GIAO technique), UV, and nonlinear optical properties (NLO) in four different solvents were calculated for the compound. The calculated HOMO–LUMO energies using time-dependent (TD) DFT revealed that charge transfer occurs within the molecule, and probable transitions in the four solvents were identified. The in silico absorption, distribution, metabolism, and excretion (ADME) analysis was performed in order to determine some physicochemical, lipophilicity, water solubility, pharmacokinetics, drug-likeness, and medicinal properties of the molecule. Finally, molecular docking calculation was performed, and the results were evaluated in detai

Younger age of escalation of cardiovascular risk factors in Asian Indian subjects

<p>Abstract</p> <p>Background</p> <p>Cardiovascular risk factors start early, track through the young age and manifest in middle age in most societies. We conducted epidemiological studies to determine prevalence and age-specific trends in cardiovascular risk factors among adolescent and young urban Asian Indians.</p> <p>Methods</p> <p>Population based epidemiological studies to identify cardiovascular risk factors were performed in North India in 1999–2002. We evaluated major risk factors-smoking or tobacco use, obesity, truncal obesity, hypertension, dysglycemia and dyslipidemia using pre-specified definitions in 2051 subjects (male 1009, female 1042) aged 15–39 years of age. Age-stratified analyses were performed and significance of trends determined using regression analyses for numerical variables and Χ²test for trend for categorical variables. Logistic regression was used to identify univariate and multivariate odds ratios (OR) for correlation of age and risk factors.</p> <p>Results</p> <p>In males and females respectively, smoking or tobacco use was observed in 200 (11.8%) and 18 (1.4%), overweight or obesity (body mass index, BMI ≥ 25 kg/m²) in 12.4% and 14.3%, high waist-hip ratio, WHR (males > 0.9, females > 0.8) in 15% and 32.3%, hypertension in 5.6% and 3.1%, high LDL cholesterol (≥ 130 mg/dl) in 9.4% and 8.9%, low HDL cholesterol (<40 mg/dl males, <50 mg/dl females) in 16.2% and 49.7%, hypertriglyceridemia (≥ 150 mg/dl) in 9.7% and 6%, diabetes in 1.0% and 0.4% and the metabolic syndrome in 3.4% and 3.6%. Significantly increasing trends with age for indices of obesity (BMI, waist, WHR), glycemia (fasting glucose, metabolic syndrome) and lipids (cholesterol, LDL cholesterol, HDL cholesterol) were observed (p for trend < 0.01). At age 15–19 years the prevalence (%) of risk factors in males and females, respectively, was overweight/obesity in 7.6, 8.8; high WHR 4.9, 14.4; hypertension 2.3, 0.3; high LDL cholesterol 2.4, 3.2; high triglycerides 3.0, 3.2; low HDL cholesterol 8.0, 45.3; high total:HDL ratio 3.7, 4.7, diabetes 0.0 and metabolic syndrome in 0.0, 0.2 percent. At age groups 20–29 years in males and females, ORs were, for smoking 5.3, 1.0; obesity 1.6, 0.8; truncal obesity 4.5, 3.1; hypertension 2.6, 4.8; high LDL cholesterol 6.4, 1.8; high triglycerides 3.7, 0.9; low HDL cholesterol 2.4, 0.8; high total:HDL cholesterol 1.6, 1.0; diabetes 4.0, 1.0; and metabolic syndrome 37.7, 5.7 (p < 0.05 for some). At age 30–39, ORs were- smoking 16.0, 6.3; overweight 7.1, 11.3; truncal obesity 21.1, 17.2; hypertension 13.0, 64.0; high LDL cholesterol 27.4, 19.5; high triglycerides 24.2, 10.0; low HDL cholesterol 15.8, 14.1; high total:HDL cholesterol 37.9, 6.10; diabetes 50.7, 17.4; and metabolic syndrome 168.5, 146.2 (p < 0.01 for all parameters). Multivariate adjustment for BMI, waist size and WHR in men and women aged 30–39 years resulted in attenuation of ORs for hypertension and dyslipidemias.</p> <p>Conclusion</p> <p>Low prevalence of multiple cardiovascular risk factors (smoking, hypertension, dyslipidemias, diabetes and metabolic syndrome) in adolescents and rapid escalation of these risk factors by age of 30–39 years is noted in urban Asian Indians. Interventions should focus on these individuals.</p