Caffeine had been shown to have an ergogenic effect on trained individuals; however, studies investigating the physiological effects of caffeine on the sedentary population are limited. The aim of this study was to examine the effect of caffeine on oxygen consumption and rating of perceived exertion during moderate intensity exercise among sedentary young adult females. Subjects comprised 16 female undergraduates aged between 22 to 24 years studying at Universiti Kebangsaan Malaysia. Eligibility criteria were based on low physical activity level and daily caffeine intake of less than 50 mg a day, which was screened using the International Physical Activity Questionnaire (short version) and caffeine consumption questionnaire, respectively. The design of this study is single-blind, crossover, placebo-controlled with all subjects serving as their own controls. Subjects were required to report to the physical activity laboratory for two experimental sessions after either ingesting placebo or caffeine capsule with an interval of 3 days between these two experimental sessions. Sixty minutes after ingesting placebo capsule (Glucolin, glucose) or 100 mg caffeine (Pro-plus, United Kingdom), subjects were required to run on a treadmill for 30 minutes at a standardized power output equivalent to 60% of maximal heart rate. Oxygen consumption, heart rate, and rating of perceived exertion were recorded at 20th, 25th and 30th minutes, while blood pressure was recorded immediately after subjects completed their 30 minutes run. Mean body fat percentage was 28.4 ± 5.4. Differences were recorded after every subject completed both the placebo and caffeine experiments. Paired t-tests showed no significant difference between placebo vs caffeine trials for oxygen consumption (13.99 ± 2.47 vs 14.49 ± 1.73, p = 0.440), rating of perceived exertion (12.3 ± 2.5 vs 12.3 ± 2.1, p = 1.000), systolic blood pressure (113 ± 10 vs 117 ± 11, p = 0.129), diastolic blood pressure (67 ± 8 vs 69 ± 10, p = 0.408) and heart rate (127.3 ± 11.0 vs 127.1 ± 11.6, p = 0.912). There was strong significant negative correlation between body fat percentage and oxygen consumption (r = –0.568, p < 0.05) and strong significant positive correlation between body fat percentage and rating of perceived exertion (r = 0.515, p < 0.05). The non-significance in the results obtained could be due to the small effect size of the study (d = 0.24). Hence, future studies with a larger number of participants should be carried out to examine the effects of caffeine during exercise in a sedentary population

Bipolar disorder (BD) is a major psychiatric disorder that is easily misdiagnosed. Patient adherence to a treatment regimen is of utmost importance for successful outcomes in BD. Several trials of antipsychotics suggested that depot antipsychotics, including long-acting first- and second-generation agents, are effective in preventing non-adherence, partial adherence, and in reducing relapse in BD. Various long-acting injectable (LAI) antipsychotics are available, including fluphenazine decanoate, haloperidol decanoate, olanzapine pamoate, risperidone microspheres, paliperidone palmitate, and aripiprazole monohydrate. Due to the increasing number of BD patients receiving LAI antipsychotics, treatment guidelines have been developed. However, the clinical applicability of LAI antipsychotics remains a global cause for concern, particularly in Asian countries. Expert physicians from Taiwan participated in a consensus meeting, which was held to review key areas based on both current literature and clinical practice. The purpose of this meeting was to generate a practical and implementable set of recommendations for LAI antipsychotic use to treat BD; target patient groups, dosage, administration, and adverse effects were considered. Experts recommended using LAI antipsychotics in patients with schizophrenia, rapid cycling BD, BD I, and bipolar-type schizoaffective disorder. LAI antipsychotic use was recommended in BD patients with the following characteristics: multiple episodes and low adherence; seldom yet serious episodes; low adherence potential per a physician's clinical judgment; preference for injectable agents over oral agents; and multiple oral agent users still experiencing residual symptoms.
Antipsychotic Agents* ; Asian Continental Ancestry Group ; Bipolar Disorder* ; Consensus* ; Fluphenazine ; Haloperidol ; Humans ; Judgment ; Microspheres ; Patient Compliance ; Psychotic Disorders ; Recurrence ; Risperidone ; Schizophrenia ; Taiwan ; Aripiprazole ; Paliperidone Palmitate

Background/Aims: Direct‐acting antivirals (DAAs) have been approved for hepatitis C virus (HCV) treatment in patients with end-stage renal disease (ESRD) on hemodialysis. Nevertheless, the complicated comedications and their potential drug-drug interactions (DDIs) with DAAs might limit clinical practice in this special population. Methods: The number, class, and characteristics of comedications and their potential DDIs with five DAA regimens were analyzed among HCV-viremic patients from 23 hemodialysis centers in Taiwan. Results: Of 2,015 hemodialysis patients screened in 2019, 169 patients seropositive for HCV RNA were enrolled (mean age, 65.6 years; median duration of hemodialysis, 5.8 years). All patients received at least one comedication (median number, 6; mean class number, 3.4). The most common comedication classes were ESRD-associated medications (94.1%), cardiovascular drugs (69.8%) and antidiabetic drugs (43.2%). ESRD-associated medications were excluded from DDI analysis. Sofosbuvir/velpatasvir/voxilaprevir had the highest frequency of potential contraindicated DDIs (red, 5.6%), followed by glecaprevir/pibrentasvir (4.0%), sofosbuvir/ledipasvir (1.3%), sofosbuvir/velpatasvir (1.3%), and elbasvir/grazoprevir (0.3%). For potentially significant DDIs (orange, requiring close monitoring or dose adjustments), sofosbuvir/velpatasvir/voxilaprevir had the highest frequency (19.9%), followed by sofosbuvir/ledipasvir (18.2%), glecaprevir/pibrentasvir (12.6%), sofosbuvir/velpatasvir (12.6%), and elbasvir/grazoprevir (7.3%). Overall, lipid-lowering agents were the most common comedication class with red-category DDIs to all DAA regimens (n=62), followed by cardiovascular agents (n=15), and central nervous system agents (n=10). Conclusions HCV-viremic patients on hemodialysis had a very high prevalence of comedications with a broad spectrum, which had varied DDIs with currently available DAA regimens. Elbasvir/grazoprevir had the fewest potential DDIs, and sofosbuvir/velpatasvir/voxilaprevir had the most potential DDIs.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.