Drug Attitude and Medication Adherence of Patients with Early Psychosis in South Korea: Mediating Effect of Medication Adherence Self-Efficacy.

Purpose: This study examined the mediating effect of medication adherence self-efficacy (MASE) on the relationship between drug attitude (DA) and medication adherence (MA) in patients with early psychosis. Patients and Methods: A total of 166 patients, aged 20 years or older, and who had received treatment within 5 years of their initial psychotic episode at a University Hospital outpatient center, participated in the study. Data were analyzed using descriptive statistics, t-tests, one-way analysis of variance, Pearson's correlation coefficients, and multiple linear regression. Additionally, a bootstrapping test was conducted to determine the statistical significance of the mediating effect. All study procedures adhered to Strengthening the reporting of observational studies in epidemiology (STROBE) guidelines. Results: This study found a significant correlation between MA and DA (r=0.393, p<0.001), and between MA and MASE (r=0.697, p<0.001). MASE had a partial mediating effect on the association between DA and MA. The model that integrated both DA and MASE accounted for 53.4% of the variation in MA. Bootstrapping analysis indicated that MASE was a significant partial parameter (lower limit confidence interval [CI] 0.114; upper limit CI 0.356). Further, 64.5% of the study participants were either currently enrolled in college or had higher levels of education. Conclusion: These findings could potentially lead to a more personalized approach to medication education and adherence, considering the unique DA and MASE of each patient. By identifying the mediating effect of MASE on the relationship between DA and MA, healthcare providers could tailor interventions to enhance the ability of patients with early psychosis to adhere to prescribed medication regimens.

E3 ligase AtAIRP5/GARU regulates drought stress response by stimulating SERINE CARBOXYPEPTIDASE-LIKE1 turnover.

Ubiquitination is a major mechanism of eukaryotic posttranslational protein turnover that has been implicated in abscisic acid (ABA)-mediated drought stress response. Here, we isolated T-DNA insertion mutant lines in which ABA-insensitive RING protein 5 (AtAIRP5) was suppressed, resulting in hyposensitive ABA-mediated germination compared to wild-type Arabidopsis (Arabidopsis thaliana) plants. A homology search revealed that AtAIRP5 is identical to gibberellin (GA) receptor RING E3 ubiquitin (Ub) ligase (GARU), which downregulates GA signaling by degrading the GA receptor GID1, and thus AtAIRP5 was renamed AtAIRP5/GARU. The atairp5/garu knockout progeny were impaired in ABA-dependent stomatal closure and were markedly more susceptible to drought stress than wild-type plants, indicating a positive role for AtAIRP5/GARU in the ABA-mediated drought stress response. Yeast two-hybrid, pull-down, target ubiquitination, and in vitro and in planta degradation assays identified serine carboxypeptidase-like1 (AtSCPL1), which belongs to the clade 1A AtSCPL family, as a ubiquitinated target protein of AtAIRP5/GARU. atscpl1 single and atairp5/garu-1 atscpl1-2 double mutant plants were more tolerant to drought stress than wild-type plants in an ABA-dependent manner, suggesting that AtSCPL1 is genetically downstream of AtAIRP5/GARU. After drought treatment, the endogenous ABA levels in atscpl1 and atairp5/garu-1 atscpl1-2 mutant leaves were higher than those in wild-type and atairp5/garu leaves. Overall, our results suggest that AtAIRP5/GARU RING E3 Ub ligase functions as a positive regulator of the ABA-mediated drought response by promoting the degradation of AtSCPL1.

Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals.

The emergence of an aging society is inevitable due to the continued increases in life expectancy and decreases in birth rate. These social changes require new smart healthcare services for use in daily life, and COVID-19 has also led to a contactless trend necessitating more non-face-to-face health services. Due to the improvements that have been achieved in healthcare technologies, an increasing number of studies have attempted to predict and analyze certain diseases in advance. Research on stroke diseases is actively underway, particularly with the aging population. Stroke, which is fatal to the elderly, is a disease that requires continuous medical observation and monitoring, as its recurrence rate and mortality rate are very high. Most studies examining stroke disease to date have used MRI or CT images for simple classification. This clinical approach (imaging) is expensive and time-consuming while requiring bulky equipment. Recently, there has been increasing interest in using non-invasive measurable EEGs to compensate for these shortcomings. However, the prediction algorithms and processing procedures are both time-consuming because the raw data needs to be separated before the specific attributes can be obtained. Therefore, in this paper, we propose a new methodology that allows for the immediate application of deep learning models on raw EEG data without using the frequency properties of EEG. This proposed deep learning-based stroke disease prediction model was developed and trained with data collected from real-time EEG sensors. We implemented and compared different deep-learning models (LSTM, Bidirectional LSTM, CNN-LSTM, and CNN-Bidirectional LSTM) that are specialized in time series data classification and prediction. The experimental results confirmed that the raw EEG data, when wielded by the CNN-bidirectional LSTM model, can predict stroke with 94.0% accuracy with low FPR (6.0%) and FNR (5.7%), thus showing high confidence in our system. These experimental results demonstrate the feasibility of non-invasive methods that can easily measure brain waves alone to predict and monitor stroke diseases in real time during daily life. These findings are expected to lead to significant improvements for early stroke detection with reduced cost and discomfort compared to other measuring techniques.