No abstract available.
Acupuncture* ; Mycobacterium*

An algific talus slope is composed of broken rocks with vents connected to an ice cave, releasing cool air in summer and relatively warmer air in winter to maintain a more stable microclimate all year round. Such geological features create a very unusual and delicate ecosystem. Although there are around 25 major algific talus slopes in Korea, lichen ecology of these areas had not been investigated to date. In this study, we report the first exploration of lichen diversity and ecology at an algific talus slope, Jangyeol-ri, in Korea. A total of 37 specimens were collected over 2017–2018. Morphological and sequencing analysis revealed 27 species belonging to 18 genera present in the area. Of particular interest among these species was Solorina saccata, as it has previously not been reported in Korea and most members of genus Solorina are known to inhabit alpine regions of the Northern Hemisphere. We provide here a taxonomic key for S. saccata alongside molecular phylogenetic analyses and prediction of potential habitats in South Korea. Furthermore, regions in South Korea potentially suitable for Solorina spp. were predicted based on climatic features of known habitats around the globe. Our results showed that the suitable areas are mostly at high altitudes in mountainous areas where the annual temperature range does not exceed 26.6 °C. Further survey of other environmental conditions determining the suitability of Solorina spp. should lead to a more precise prediction of suitable habitats and trace the origin of Solorina spp. in Korea.

BACKGROUND: Sevoflurane is an inhalational anesthetic that produces rapid induction, emergence and little cardiovascular depression. Elevated sympathetic activity during surgery produces undesirable effects on the cardiovascular system, such as hypertension, tachycardia or arrhythmias. So combined general and epidural anesthesia have been used recently for the operation, especially the abdominal surgery. This study was performed to evaluate the cardiovascular effects of thoracic epidural anesthesia during sevoflurane general anesthesia. METHODS: Forty patients of ASA class 1-2 undergoing elective subtotal gastrectomy were divided into 5 groups. Thoracic epidural bolus injection was administered via an epidural catheter during sevoflurane general anesthesia in a double-blind random manner: Group 1; normal saline (N/S) 10 ml (placebo), Group 2; morphine 0.1 mg/kg mixed with N/S in 10 ml, Group 3; fentanyl 1 mcg/kg mixed with N/S in 10 ml, Group 4; 1% lidocaine 10 ml, and Group 5; 1% lidocaine 10 ml mixed with morphine 0.1 mg/kg and fentanyl 1 mcg/kg. Systolic and diastolic blood pressures, pulse rates, peripheral oxygen saturation levels (SpO2) and end-tidal carbon dioxide partial pressures (ETCO2) were measured every 5 minutes. RESULTS: Systolic and diastolic blood pressures were significantly reduced from 10 minutes after epidural bolus injection in groups 4 and 5, but these decreases in blood pressure were not severe enough to require treatment in either group. Pulse rates were significantly decreased from 10 minutes after injection in groups 3, 4, and 5, but these decreases in pulse rate were not so severe enough to require treatment in 3 groups. SpO2 and ETCO2 were stable, and arrhythmia was not observed. CONCLUSIONS: The thoracic epidural injection of 1% lidocaine mixed with morphine 0.1 mg/kg and fentanyl 1 mcg/kg can be safely used during sevoflurane anesthesia without severe cardiovascular complications during upper abdominal surgery in ASA 1-2 patients.
Anesthesia ; Anesthesia, Epidural ; Anesthesia, General* ; Arrhythmias, Cardiac ; Blood Pressure ; Carbon Dioxide ; Cardiovascular System ; Catheters ; Depression ; Fentanyl* ; Gastrectomy ; Heart Rate ; Humans ; Hypertension ; Injections, Epidural* ; Lidocaine* ; Morphine* ; Oxygen ; Partial Pressure ; Tachycardia

Because bioequivalence studies are performed using a crossover design, information on the intra-subject coefficient of variation (intra-CV) for pharmacokinetic measures is needed when determining the sample size. However, calculated intra-CVs based on bioequivalence results of identical generic drugs produce different estimates. In this study, we collected bioequivalence results using public resources from the Ministry of Food and Drug Safety (MFDS) and calculated the intra-CVs of various generics. For the generics with multiple bioequivalence results, pooled intra-CVs were calculated. The estimated intra-CVs of 142 bioequivalence studies were 14.7±8.2% for AUC and 21.7±8.8% for C(max). Intra-CVs of C(max) were larger than those of area under the concentration-time curve (AUC) in 129 studies (90.8%). For the 26 generics with multiple bioequivalence results, the coefficients of variation of intra-CVs between identical generics (mean±sd (min ~ max)) were 38.0±24.4% (1.9 ~ 105.3%) for AUC and 27.9±18.2 % (4.0 ~ 70.1%) for C(max). These results suggest that substantial variation exists among the bioequivalence results of identical generics. In this study, we presented the intra-CVs of various generics with their pooled intra-CVs. The estimated intra-CVs calculated in this study will provide useful information for planning future bioequivalence studies.
Area Under Curve ; Cross-Over Studies ; Drugs, Generic ; Sample Size* ; Therapeutic Equivalency*

For the treatment of hypertension, fixed-dose combinations (FDCs) of antihypertensive drugs can provide complementary benefits from improved compliance and cost-effectiveness compared with loose combinations of corresponding drugs. A new FDC of fimasartan/ amlodipine/hydrochlorothiazide 60/10/25 mg is undergoing clinical development. A randomized, open-label, single-dose, 3-period, 3-sequence, partially replicated crossover phase 1 study was conducted to compare the pharmacokinetics (PKs) between the FDC of fimasartan/amlodipine/hydrochlorothiazide 60/10/25 mg and a loose combination of a dual-combination FDC (fimasartan/amlodipine 60/10 mg) and hydrochlorothiazide 25 mg. Sixty healthy subjects were randomized, and 55 subjects completed the study. Serial blood samples were collected, and plasma concentrations of fimasartan, amlodipine and hydrochlorothiazide were measured to analyze PK parameters. The PK profiles of the FDC were similar to those of the loose combinations. The geometric mean ratios (GMRs) and 90% confidence intervals (CIs) of the FDC to loose combinations for the maximum plasma concentration (Cmax ) and area under the curve until the last measurable time point (AUClast ) were within the conventional bioequivalent range of 0.80 to 1.25. The GMRs and 90% CIs of fimasartan, amlodipine and hydrochlorothiazide were 1.0163 (0.8681–1.1898), 0.9595 (0.9256–0.9946), and 1.1294 (1.0791–1.1821) for Cmax and 1.0167 (0.9347–1.1059), 0.9575 (0.9317–0.9841), and 1.0561 (1.0170–1.0967) for AUClast , respectively. Both the FDC and loose combinations were well tolerated. In conclusion, the FDC of fimasartan/amlodipine/ hydrochlorothiazide 60/10/25 mg showed similar PK profiles to those of the corresponding loose combination, and both treatments were well tolerated.

The retraction has been agreed upon due to critical typographical errors throughout the contents from accidents at the manuscript editing step.

Because bioequivalence studies are performed using a crossover design, information on the intra-subject coefficient of variation (intra-CV) for pharmacokinetic measures is needed when determining the sample size. However, calculated intra-CVs based on bioequivalence results of identical generic drugs produce different estimates. In this study, we collected bioequivalence results using public resources from the Ministry of Food and Drug Safety (MFDS) and calculated the intra-CVs of various generics. For the generics with multiple bioequivalence results, pooled intra-CVs were calculated. The estimated intra-CVs of 142 bioequivalence studies were 14.7±8.2% for AUC and 21.7±8.8% for Cmax. Intra-CVs of Cmax were larger than those of area under the concentration-time curve (AUC) in 129 studies (90.8%). For the 26 generics with multiple bioequivalence results, the coefficients of variation of intra-CVs between identical generics (mean±sd (min ~ max)) were 38.0±24.4% (1.9 ~ 105.3%) for AUC and 27.9±18.2% (4.0 ~ 70.1%) for Cmax. These results suggest that substantial variation exists among the bioequivalence results of identical generics. In this study, we presented the intra-CVs of various generics with their pooled intra-CVs. The estimated intra-CVs calculated in this study will provide useful information for planning future bioequivalence studies. (This is republication of the article 'Transl Clin Pharmacol 2017;25:179-182' retracted from critical typographic errors. See the 'Retraction and Republication section of this issue for further information)
Area Under Curve ; Cross-Over Studies ; Drugs, Generic ; Sample Size ; Therapeutic Equivalency

Objectives: Digital therapeutics (DTx) are software-based therapeutic interventions based on clinical evidence. Randomized clinical trials (RCTs) are often the source of clinical evidence, similar to conventional drugs or medical devices. However, novel approaches such as the use of real-world data or digital biomarkers are also utilized. This article aimed to review how DTx products have been clinically evaluated. Methods: DTx products approved by the US Food and Drug Administration as of 2020 were reviewed and products with sufficient published information were selected. Pivotal clinical trials were analyzed according to the elements of the Consolidated Standards of Reporting Trials (CONSORT) guideline. Case reviews were presented for other clinical evaluation strategies, considering the small number of publications. Results: Most approved DTx products used RCTs for clinical evaluations. Similar to conventional RCTs, parallel-group designs with statistical hypothesis testing were adopted. However, DTx trials were often not blinded due to practical issues and involved various comparator groups. In addition, DTx products could be readily evaluated in home-based settings and delivered through the internet. Other evaluation approaches included retrospective analyses using insurance claims data or usage data, which enabled long-term evaluations of effectiveness. Digital biomarkers obtained from real-time and continuous log data were also used to improve the objectiveness of endpoints. Conclusions RCTs accounted for the majority of DTx evaluations. The designs of DTx trials were comparable to those of drug or device trials, but blinding and comparator elements were often different. Furthermore, the use of real-world data and digital biomarkers are also being tried.

Obesity has been a growing worldwide concern, and surgical intervention including bariatric surgery is considered as one of the options for treatment. However, there still is controversy over the change in pharmacokinetics (PKs) of drugs after the surgery. To investigate the potential covariates that can influence the area under the curve (AUC) and maximum plasma concentration (Cmax), the design of previous studies was reviewed based on pre-determined eligibility criteria. Each study calculated the ratios of the AUC and Cmax before and after bariatric surgery. These studies investigated whether the PK parameters were affected by the time after the surgery or by the type of control group. The ratio of the AUC calculated in the early and late follow-up period was similar across Roux-en Y gastric bypass patients. No significant difference in the PK parameters was found between the pre-surgical patients and matched healthy subjects. However, certain control groups could be preferable depending on the purpose of the clinical trial. Although Cmax was inconsistent compared to the AUC, insufficient sampling of the time points may have caused such an inconsistency. This is the first article exploring the appropriate methodology in designing clinical studies for changes in the PK characteristics of orally administered drugs in patients with bariatric surgery.

Objective: Diagnosis and assessment of depression rely on scoring systems based on questionnaires, either self-reported by patients or administered by clinicians, and observation of patient facial expressions during the interviews plays a crucial role in making impressions in clinical settings. Deep learning driven approaches can assist clinicians in the course of diagnosis of depression by recognizing subtle facial expressions and emotions in depression patients. Methods: Seventeen simulated patients who acted as depressed patients participated in this study. A trained psychiatrist structurally interviewed each participant with moderate depression in accordance with a prepared scenario and without depressive features. Interviews were video-recorded, and a facial emotion recognition algorithm was used to classify emotions of each frame. Results: Among seven emotions (anger, disgust, fear, happiness, neutral, sadness, and surprise), sadness was expressed in a higher proportion on average in the depression-simulated group compared to the normal group. Neutral and fear were expressed in higher proportions on average in the normal group compared to the normal group. The overall distribution of emotions between the two groups was significantly different (p ＜ 0.001). Variance in emotion was significantly less in the depression-simulated group (p ＜ 0.05). Conclusion This study suggests a novel and practical approach to understand the emotional expression of depression patients based on deep learning techniques. Further research would allow us to obtain more perspectives on the emotional profiles of clinical patients, potentially providing helpful insights in making diagnosis of depression patients.