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BACKGROUND: Pharmacologic treatments are available to treat insomnia, a common and burdensome sleep disorder, but may be contraindicated in older adults who are prone to side effects from sleep-promoting drugs. These analyses of sleep diary data from Study E2006-G000-303 (Study 303) investigated the benefits of lemborexant 5 mg (LEM5) and 10 mg (LEM10) in the subgroup age ≥ 65 years with insomnia. METHOD: Study 303, a 12-month, double-blind study of LEM5 and LEM10 in adults (age ≥ 18 years) with insomnia disorder (sleep onset and/or maintenance difficulties) assessed subject-reported (subjective) sleep-onset latency (sSOL), sleep efficiency (sSE), wake after sleep onset (sWASO), and total sleep time (sTST). Morning sleepiness/alertness, insomnia severity (Insomnia Severity Index [ISI]), fatigue (Fatigue Severity Scale [FSS]), perceptions of sleep-related medication effects (Patient Global Impression-Insomnia [PGI-I] questionnaire), and safety were also evaluated. RESULTS: In this subgroup of older adults (≥ 65 years; n = 262), there were significantly larger changes from baseline for sSOL, sSE, sTST, and sWASO with LEM5 and LEM10 versus placebo through month 6 (except sWASO month 1), indicating improvement; these improvements were sustained through month 12. Subject-reported increases in morning alertness were significantly greater with one or both LEM doses versus placebo through month 6 and sustained through month 12. There were significantly larger ISI total and daytime functioning score decreases (improvement) from baseline with LEM versus placebo at months 1, 3, and 6 (total score: both doses; daytime functioning: LEM5 month 1 and both doses months 3 and 6) and decreases from baseline FSS at months 1 and 3 (LEM5) and month 6 (both doses), sustained to month 12. Compared with placebo, more subjects reported that LEM (both doses) positively impacted ability to sleep, time to fall asleep, and TST through month 6, sustained to month 12, with no rebound after drug withdrawal. LEM was well tolerated to month 12; mild somnolence was the most common treatment-emergent adverse event. CONCLUSIONS: Improvements in subject-reported efficacy in LEM-treated adults age ≥ 65 years with insomnia were observed as early as the first week of treatment and sustained through end of month 12. LEM was well tolerated. CLINICAL TRIALS REGISTRATION: ClinicalTrials.gov identifier NCT02952820: E2006-G000-303; Study 303; SUNRISE-2 (First posted: October 2016); EudraCT 2015-001463-39 (First posted: November 2016).

Heme-containing enzymes, critical across life's domains and promising for industrial use, face stability challenges. Despite the demand for robust industrial biocatalysts, the mechanisms underlying the thermal stability of heme enzymes remain poorly understood. Addressing this, our research utilizes a ‘keystone cofactor heme-interaction approach’ to enhance ligand binding and improve the stability of lignin peroxidase (LiP). We engineered mutants of the white-rot fungus PcLiP (Phanerochaete chrysosporium) to increase thermal stability by 8.66 °C and extend half-life by 29 times without losing catalytic efficiency at 60 °C, where typically, wild-type enzymes degrade. Molecular dynamics simulations reveal that an interlocked cofactor moiety contributes to enhanced structural stability in LiP variants. Additionally, a stability index developed from these simulations accurately predicts stabilizing mutations in other PcLiP isozymes. Using milled wood lignin, these mutants achieved triple the conversion yields at 40 °C compared to the wild type, offering insights for more sustainable white biotechnology through improved enzyme stability.

Tetramethrin (Tm) is a commonly used pesticide that has been reported to exert estrogen-antagonistic effects selectively on female rats. The present study was undertaken to assess the protective role of lobaric acid (La) on estrous cycle in Tm-treated female Wistar rats. Female rats were exposed to Tm (50 mg/kg b.w/day) only or in combination with La at low (50 mg/kg b.w/day) or high (100 mg/kg b.w/day) dose for 30 days. The results showed that Tm altered the estrous cycle of female rats by decreasing the levels of luteinizing hormone, follicular-stimulating hormone, progesterone, estrone, and estradiol while increasing testosterone level. The morphology of vaginal smears of Tm-treated female rats showed the presence of abnormal cells and/or structures at different phases of estrus cycle. Strikingly, in (Tm + La)-treated rats, all the observed adverse effects of Tm on the hormonal parameters, cell morphology, and the length of each phase of estrous cycle were significantly diminished in a dose-dependent manner. The docking results showed that La competes with Tm for Gonadotropin-Releasing Hormone (GnRH) receptor, thereby reducing the toxicity of Tm but did not cancel the response of GnRH receptor completely. In conclusion, our results designated that La could be used as a potential candidate in the management of insecticide-induced alterations of the reproductive cycle of rodents.

The article discusses the correlation of the social emotional learning (SEL) components perceived by secondary school students in Ho Chi Minh City, Vietnam. The study results show that there is a close correlation among SEL components. Based on the findings, it is implied that the development of SEL competences for secondary school students is an integrated process requiring interactions among different components of the SEL model and forming the model itself via the changes of each component. Moreover, when one component is affected, the influence does not merely happen on that specific component, yet on the entire SEL model as it was a case for the participating secondary school students in this study.

The self-awareness capacity has long been no stranger in the foreign education, but in Vietnam, self-awareness capacity has only been developed in education in recent years. The self-awareness capacity related problems among Vietnamese students have been exacerbated such as gender-based violence, gender bias, wrong career choices, disorientation and school counseling activities currently could not support this difficulty for students. Based on the theoretical framework of self-awareness capacity from previous studies in Vietnam and further developing the aspects of self-awareness by age, the questionnaire had been done to survey of 726 lower-secondary school students and 474 upper-secondary school students to find the situation of their self-awareness capacity problems, as well as the need for self-awareness capacity problems counseling. The results demonstrated a positive correlation between self-awareness capacity problems and students' need for self-awareness capacity problems counseling. In addition, identifying self-awareness capacity problems encountered by students helps us to develop a new school counseling model, as well as a process to counsel self-awareness capacity problems for students to enhance the self-awareness capacity and support school counseling activities effectively, also meeting the requirements of renewing the general curriculum in the students' capacity development orientation in Vietnam.

BackgroundPatients with severe dengue who develop severe respiratory failure requiring mechanical ventilation (MV) support have significantly increased mortality rates. This study aimed to develop a robust machine learning-based risk score to predict the need for MV in children with dengue shock syndrome (DSS) who developed acute respiratory failure.MethodsThis single-institution retrospective study was conducted at a tertiary pediatric hospital in Vietnam between 2013 and 2022. The primary outcome was severe respiratory failure requiring MV in the children with DSS. Key covariables were predetermined by the LASSO method, literature review, and clinical expertise, including age (< 5 years), female patients, early onset day of DSS (≤ day 4), large cumulative fluid infusion, higher colloid-to-crystalloid fluid infusion ratio, severe bleeding, severe transaminitis, low platelet counts (< 20 x 109/L), elevated hematocrit, and high vasoactive-inotropic score. These covariables were analyzed using supervised models, including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbor (KNN), and eXtreme Gradient Boosting (XGBoost). Shapley Additive Explanations (SHAP) analysis was used to assess feature contribution.ResultsA total of 1,278 patients were included, with a median patient age of 8.1 years (IQR: 5.4-10.7). Among them, 170 patients (13.3%) with DSS required mechanical ventilation. A significantly higher fatality rate was observed in the MV group than that in the non-MV group (22.4% vs. 0.1%). The RF and SVM models showed the highest model discrimination. The SHAP model explained the significant predictors. Internal validation of the predictive model showed high consistency between the predicted and observed data, with a good slope calibration in training (test) sets 1.0 (0.934), and a low Brier score of 0.04. Complete-case analysis was used to construct the risk score.ConclusionsWe developed a robust machine learning-based risk score to estimate the need for MV in hospitalized children with DSS.

Insomnia treatment among individuals with comorbid insomnia and obstructive sleep apnea is suboptimal. In a pilot randomized controlled trial, 19 individuals with comorbid insomnia and obstructive sleep apnea were allocated to one of two arms: EX + EX, consisting of two 8-week phases of exercise training (EX), or RE + CBTiEX, encompassing 8 weeks of relaxation training (RE) followed by 8 weeks of combined cognitive-behavioral therapy and exercise (CBTiEX). Outcomes included Insomnia Severity Index (ISI), polysomnography, and cardiorespiratory fitness measures. A mixed-model analysis of variance revealed a Group × Time interaction on peak oxygen consumption change, F(1, 14) = 10.1, p =.007, and EX increased peak oxygen consumption (p =.03, g′ = −0.41) and reduced ISI (p =.001, g′ = 0.82) compared with RE (p =.49, g = 0.16) post-8 weeks. Post-16 weeks, there was a significant Group × Time interaction (p =.014) driven by RE + CBTiEX yielding a larger improvement in ISI (p =.023, g′ = 1.48) than EX + EX (p =.88, g′ < 0.1). Objective sleep was unchanged. This study showed promising effects of regular EX alone and combined with cognitive-behavioral therapy for insomnia on ISI in comorbid insomnia and obstructive sleep apnea. [ABSTRACT FROM AUTHOR]

IntroductionSince 2015, the World Health Organization (WHO) has recommended that all people living with HIV (PLHIV) initiate antiretroviral treatment (ART), irrespective of CD4+ count or clinical stage. National adoption of universal treatment has accelerated since WHO's 2015 "Treat All" recommendation; however, little is known about the translation of this guidance into practice. This study aimed to assess the status of Treat All implementation across regions, countries, and levels of the health care delivery system.MethodsBetween June and December 2017, 201/221 (91%) adult HIV treatment sites that participate in the global IeDEA research consortium completed a survey on capacity and practices related to HIV care. Located in 41 countries across seven geographic regions, sites provided information on the status and timing of site-level introduction of Treat All, as well as site-level practices related to ART initiation.ResultsAlmost all sites (93%) reported that they had begun implementing Treat All, and there were no statistically significant differences in site-level Treat All introduction by health facility type, urban/rural location, sector (public/private) or country income level. The median time between national policy adoption and site-level introduction was one month. In countries where Treat All was not yet adopted in national guidelines, 69% of sites reported initiating all patients on ART, regardless of clinical criteria, and these sites had been implementing Treat All for a median period of seven months at the time of the survey. The majority of sites (77%) reported typically initiating patients on ART within 14 days of confirming diagnosis, with 60% to 62% of sites implementing Treat All in East, Southern and West Africa reporting same-day ART initiation for most patients.ConclusionsBy mid- to late-2017, the Treat All strategy was the standard of care at almost all IeDEA sites, including rural, primary-level health facilities in low-resource settings. While further assessments of site-level capacity to provide high-quality HIV care under Treat All and to support sustained viral suppression after ART initiation are needed, the widespread introduction of Treat All at the service delivery level is a critical step towards global targets for ending the HIV epidemic as a public health threat.

While light-driven water splitting offers a means to produce renewable H2 fuel, water oxidation limits performances and yields low-value products. Here, authors demonstrate a photoelectrochemical cell that converts lignocellulosic biomass into valuable products alongside H2.

Background: Depressive disorder, particularly major depressive disorder (MDD), significantly impact individuals and society. Traditional analysis methods often suffer from subjectivity and may not capture complex, non-linear relationships between risk factors. Machine learning (ML) offers a data-driven approach to predict and diagnose depression more accurately by analyzing large and complex datasets. Methods: This study utilized data from the National Health and Nutrition Examination Survey (NHANES) 2013–2014 to predict depression using six supervised ML models: Logistic Regression, Random Forest, Naive Bayes, Support Vector Machine (SVM), Extreme Gradient Boost (XGBoost), and Light Gradient Boosting Machine (LightGBM). Depression was assessed using the Patient Health Questionnaire (PHQ-9), with a score of 10 or higher indicating moderate to severe depression. The dataset was split into training and testing sets (80% and 20%, respectively), and model performance was evaluated using accuracy, sensitivity, specificity, precision, AUC, and F1 score. SHAP (SHapley Additive exPlanations) values were used to identify the critical risk factors and interpret the contributions of each feature to the prediction. Results: XGBoost was identified as the best-performing model, achieving the highest accuracy, sensitivity, specificity, precision, AUC, and F1 score. SHAP analysis highlighted the most significant predictors of depression: the ratio family income to poverty (PIR), sex, hypertension, serum cotinine and hydroxycotine, BMI, education level, glucose levels, age, marital status, and renal function (eGFR). Conclusion: We developed ML models to predict depression and utilized SHAP for interpretation. This approach identifies key factors associated with depression, encompassing socioeconomic, demographic, and health-related aspects. [ABSTRACT FROM AUTHOR]