Publications by authors named "B S Ku"

This study assessed the therapeutic effectiveness of a single-pill combination (SPC) of olmesartan/amlodipine plus rosuvastatin for blood pressure (BP) and low-density lipoprotein cholesterol (LDL-C) in patients with hypertension and dyslipidemia. Adult patients with hypertension and dyslipidemia who were decided to be treated with the study drug were eligible. The primary endpoint was the proportion of patients who achieved BP, LDL-C and both BP and LDL-C treatment goals at weeks 24-48.

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Background: Amino acid supplements are crucial for animal health and productivity. Traditional analysis methods face limitations like complexity, long testing times and toxic reagents. Therefore, a more efficient and reliable method is needed.

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Background: Three dimensional (3D) cell cultures can be effectively used for drug discovery and development but there are still challenges in their general application to high-throughput screening. In this study, we developed a novel high-throughput chemotherapeutic 3D drug screening system for gastric cancer, named 'Cure-GA', to discover clinically applicable anticancer drugs and predict therapeutic responses.

Methods: Primary cancer cells were isolated from 143 fresh surgical specimens by enzymatic treatment.

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Introduction: We investigated the efficacy of a multidomain intervention (MI) via face-to-face and video communication platforms using a tablet personal computer application in patients with mild cognitive impairment (MCI).

Methods: Three hundred participants with MCI and ≥ 1 modifiable dementia risk factor, aged 60-85 years, were randomly assigned to either the MI group, who underwent a 24-week intervention, or the control group, who received usual care.

Results: The overall adherence rate to MI was 84.

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Lung cancer is a malignant tumor with high incidence and mortality rates in both men and women worldwide. Although anticancer drugs are prescribed to treat lung cancer patients, individual responses to these drugs vary, making it crucial to identify the most suitable treatment for each patient. Therefore, it is necessary to develop an anticancer drug efficacy prediction model that can analyze drug efficacy before patient treatment and establish personalized treatment strategies.

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