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MDMA-assisted therapy and current treatment options for chronic, treatment-resistant, moderate or higher severity post-traumatic stress disorder: Systematic literature review

Background 3,4-methylenedioxymethamphetamine-assisted therapy (MDMA-AT) is currently being evaluated for treatment of patients with moderate or higher severity post-traumatic stress disorder (PTSD). Objective To provide a comprehensive summary of investigational MDMA-AT and current treatments for PTSD. Methods A search was conducted in PubMed and Embase (December 20, 2023). Populations included adults with chronic, treatment-resistant, moderate or higher severity PTSD. Interventions were MDMA-AT and comparators based on PTSD treatment guidelines. The primary outcome of interest was the Clinician-Administered PTSD Scale (CAPS) score. Other outcomes observed were Beck Depression Inventory (BDI), and loss of diagnosis (LOD). Studies observing chronic, moderate or higher severity treatment-resistant PTSD in adults were included. Only randomized controlled trials published in English were considered. The NICE quality appraisal checklist was used to assess risk of bias in included studies. We provided qualitative synthesis of evidence presented in extraction tables. Results Overall, 77 studies were included. Phase II/III trials consistently reported significantly greater CAPS improvement with MDMA-AT vs. placebo with therapy (PT) after two or three interventional sessions. Durability was observed in a long-term follow-up trial (mean duration, 45.4 months) with a 0.9-point CAPS decrease from post-treatment. FDA-approved and off-label medications used for PTSD treatment did not yield a consistently greater CAPS decrease vs. control arms across trials. Significant CAPS improvement was consistently observed in venlafaxine ER, olanzapine, propranolol (with traumatic memory reactivation), nefazodone, and nabilone placebo-controlled trials. Most psychotherapy trials lacked between-group statistical assessments. Significant CAPS decrease compared to the waitlist was reported for cognitive therapy (CT), cognitive behavioral therapy (CBT), cognitive processing therapy (CPT), prolonged exposure (PE), and group cognitive exposure therapy. CAPS improvement was persistent for CPT and PE in long-term follow up (mean duration 6.2 years). MDMA-AT demonstrated significant improvement in BDI-II score compared to PT (19.7-point vs. 10.8-point decrease, respectively; p = 0.003). The percentage of participants with LOD after two or three active-dose MDMA-AT sessions ranged from 41.7–83.3%. Conclusion This systematic review suggests current treatments for PTSD are associated with heterogeneous evidence and the majority do not demonstrate sustained effects. Results from MDMA-AT showed consistent improvements in CAPS, BDI and LOD.

Self-Stabilized Charge States in a Double-Decker Molecular Magnet on Pb(111)

Incident characterization of health conditions in women suspected of being victims of intimate partner violence

Intimate partner violence (IPV) is a serious public health problem due to its severe health outcomes and its association with significant morbidity and mortality. There are many prevalence studies about this topic but few on incidence. Therefore, an incident health characterization of alleged IPV victims may be relevant to understanding the situation of the person at the moment when the suspicion of violence was recorded. The global aim is to characterize the health conditions of women allegedly victims of IPV. Specifically, the study aims to characterize: (a) health risk behaviours; (b) traumatic injuries and intoxications; (c) mental health disorders; and (d) physical disorders. We conducted a retrospective cohort study based on secondary data analysis of electronic health records and health registry data of patients of the Local Health Unit of Matosinhos (Portugal) between 2001 and 2021. To analyse variables, we selected official codes and regular terms used by physicians to describe violence during any clinical encounter. A total of 16 966 alleged female victims of IPV were identified along with a matched population of 29 980 women. This study highlights the significant increase in incidence rates of various health conditions among those allegedly victimized compared to the matched population. It provides novel insights by comparing incidence rates with previous prevalence estimates, revealing higher incidence ratios across the majority of health conditions among allegedly female IPV victims. This suggests a potential loss of follow-up among these individuals, possibly due to treatment dropout or mortality, highlighting the urgent need for targeted interventions and enhanced healthcare support for IPV victims.

Ionic Hydrophobic Gates on Metal–Organic Frameworks Enable High-Purity CO₂ Separation from Humid Flue Gas

Physician perspectives on reducing harm and supporting emergency department patients who use drugs

Background People who use drugs (PWUD) frequently seek care in the emergency department (ED). Little is known about ED physician perspectives and experiences integrating a harm reduction approach into care, including interventions that reduce the health, social and legal consequences of drug use without requiring a reduction in drug use. Objective This study aimed to describe the experiences of Canadian emergency physicians caring for PWUD, and facilitators and barriers to implementing harm reduction interventions in the ED. Methods Purposive sampling, using an existing national network, and snowball sampling techniques were used to recruit practicing emergency physicians. Semi-structured, one-on-one telephone interviews were conducted until theoretical data saturation was achieved. Interview recordings were transcribed and analyzed using latent content analysis. Interviews took place between June 2019 and February 2020. This work is a secondary analysis specifically focused on harm reduction approaches to care. Results 32 physician interviews were included. Participants had a median of 10 years of experience (range 1–33) and most (29/32) worked in urban EDs. Participants highlighted the complexities of caring for PWUD, including the intersection of structural vulnerability with substance use. The ED environment varied across Canada and either facilitated or hindered the adoption of harm reduction interventions. Additional barriers included a lack of training and experience; lack of community follow-up care; insufficient ED funding and staffing resources; and, tensions over the appropriate scope of emergency medicine practice. Facilitators included tailored education and training; specialized multidisciplinary teams; ED harm reduction champions; and standardized protocols. Conclusions Though variability existed in the adoption and practice of harm reduction in Canadian EDs, most interviewed physicians supported a harm reduction approach to care. To facilitate widespread ED adoption of harm reduction interventions, there is a need for standardized guidance, supplemental resources, facilitated culture change, and sufficient community-based services.

Monolayered Metal–Organic Framework Unlocks Integration of Shaped Nanoparticles for Synergistic Photocatalysis

Development of a risk prediction model for sepsis-related delirium based on multiple machine learning approaches and an online calculator

Background Sepsis-associated delirium (SAD) occurs due to disruptions in neurotransmission linked to inflammatory responses from infections. It poses significant challenges in clinical management and is associated with poor outcomes. Survivors often experience long-term cognitive and behavioral issues that impact their quality of life and place a burden on their families. This study aimed to develop and validate an interpretable machine learning model for early prediction of SAD in critically ill patients. Additionally, we constructed an online risk calculator to facilitate real-time clinical assessment. Methods This study is a retrospective analysis utilizing data from 16,120 patients in the Medical Information Mart for Intensive Care IV database. To manage imbalanced data, we applied the Synthetic Minority Over-sampling Technique (SMOTE) method. Feature selection was conducted using Multivariate Logistic Regression, LASSO regression, and the Boruta algorithm. We developed predictive models using eight machine learning algorithms and selected the best one for validation. The SHapley Additive exPlanations (SHAP) method was used for visualization and interpretation, enhancing the clinical understanding of the model, alongside the creation of an online web calculator. Results We combined three feature selection methods to identify 17 key features for our machine learning prediction model. The Gradient Boosting Machine (GBM) model demonstrated excellent calibration and strong predictive accuracy in the validation cohort. The SHAP feature importance ranking revealed five critical risk factors for predicting outcomes: Glasgow Coma Scale (GCS), ICU stay duration, chloride, sodium, and Sequential Organ Failure Assessment (SOFA). Based on this optimal model, we successfully developed an online web calculator. Conclusion We developed and validated a machine learning model capable of accurately predicting SAD with high clinical applicability. The integration of interpretable machine learning and an online calculator offers a practical tool to support early identification and timely management of SAD in critically ill patients.

Stereodivergent Synthesis of Perhydrobenz[<i>e</i>]indene Terpenoids

F-TransR: A sports event revenue prediction model integrating multi-modal and time-series data

Sports event revenue prediction is a complex, multimodal task that requires effective integration of diverse data sources. Traditional models struggle to combine real-time data streams with historical time-series data, resulting in limited prediction accuracy. To address this challenge, we propose F-TransR, a Transformer-based multimodal revenue prediction model. F-TransR introduces key innovations, including a real-time data stream processing module, a historical time-series modeling module, a novel multimodal fusion mechanism, and a cross-modal interaction modeling module. These modules enable the model to effectively integrate and capture dynamic interactions between multimodal features and temporal dependencies, which previous models fail to handle efficiently. Experimental results demonstrate that F-TransR significantly outperforms state-of-the-art models, including Informer, Autoformer, FEDformer, MTNet, and CrossFormer, on the Kaggle Sports Analytics and Reddit Comments datasets. On the Kaggle dataset, MSE and MAPE are reduced by 6.4% and 2.9%, respectively, while R2 increases to 0.938. On the Reddit dataset, MSE and MAPE decrease by 6.6% and 5.3%, respectively, and R2 improves to 0.854. Compared to existing methods, F-TransR not only improves the interaction efficiency of multimodal features but also demonstrates strong robustness and scalability, providing substantial support for multimodal revenue prediction in real-world applications.

Retraction: Effect of varying thickness properties of the slow release fertilizer films on morphology, biodegradability, urea release, soil health, and plant growth

Mixed Amide Paracyclophane Assemblies Emulating Supramolecular Copolymers

Adherence to Iron and Folic Acid Supplementation and its determinants among pregnant women in East Africa: Analysis of Demographic and Health Surveys data from nine East African countries

Introduction Anemia due to deficiency of nutrients like iron and folic acid (IFA) is one of the global public health issues that has contributed to an unacceptably high proportion of maternal and childhood morbidity and mortality. IFA supplementation (IFAS) during pregnancy is critical for reducing anemia and related undesired outcomes. However, comprehensive evidence on the magnitude of adherence to IFA supplementation and its associated factors in East Africa remains limited. Objective The objective of this study was to assess the level of adherence to IFAS during pregnancy and to identify the factors associated with adherence among pregnant women in East African countries. Methods In this study, we analyzed demographic and health survey (DHS) data from nine countries in East Africa, yielding a sample of 57,283 pregnant women. The study used multilevel mixed effects cross-sectional design. We applied four models, and we compared the models using the Akaike information criterion (AIC) and the Bayes information criterion (BIC). The model with smaller AIC and BIC was the best to fit the data, and the interpretation of the fixed effects was based on this model. To measure cluster variation, we used the intra-cluster correlation coefficient (ICC) standard deviation. Finally, we utilized fixed effects to estimate the association between adherence to IFAS and the independent variables, and we reported the results as an odds ratio with a 95% confidence range. Results The overall prevalence of adherence to IFAS among pregnant women in East Africa was 35.8% (95% CI: 35.4–36.2). This prevalence ranges from 3.8% in Burundi to 83.7% in Zambia, with significant differences between countries. Adherence to IFAS among women with secondary and more than secondary level of education was higher by 29% (AOR = 1.29, 95% CI: 1.19, 1.41) and 92% (AOR = 1.92, 95% CI: 1.68, 2.20), respectively, compared to women with no formal education. Adherence to IFAS among women who had the first ANC visit on the second and third trimesters was lower by 16% (AOR = 0.84, 95% CI: 0.80, 0.89) and 74% (AOR = 0.26, 95% CI: 0.23, 0.30), respectively, compared to those with the first ANC visit on the first trimester. Furthermore, four or more ANC visits during pregnancy and a lower distance to health facilities were significantly associated with adherence to IFAS in East Africa. Conclusions Only one-third of pregnant women in East Africa adhered to IFAS. Adherence was significantly associated with higher education, early ANC booking, more frequent ANC visits, proximity to health facilities, and country of residence. Interventions should prioritize improving women’s education, promoting timely and frequent ANC attendance, and addressing country-specific barriers to improve IFAS uptake and maternal outcomes.

DACH-ZYC-Phos/Pd-Catalyzed Enantioselective Allenylation of Secondary Phosphine Oxides via Ligand Relay

The role of contributing factors, triggers, and prodromal symptoms in the etiological classification of out-of-hospital cardiac arrest; A scoping review

Background Current Utstein etiological classifications for out-of-hospital cardiac arrest (OHCA) are heterogenous and inaccurate when compared with robust sources. This heterogeneity may influence reporting incidence and outcomes and patient enrollment in observational studies and clinical trials. Circumstance-related factors may contribute to cardiac arrest; however, the role of these factors in improving the etiological classification of OHCA is unknown. Objective This scoping review was proposed to explore current evidence to identify the role of contributing factors, triggers, and prodromal symptoms of out-of-hospital cardiac arrest in the reported etiology of cardiac arrest based on emergency medical services data, medical records, or autopsy reports. Method We searched Medline, Embase, and EMB review-Cochrane databases from 1946 to 2024. Studies were selected if the included population was adults with OHCA for whom the initial etiology was assigned, and any contributing factors, triggers, or prodromal symptoms of OHCA were reported. A descriptive review of the included studies was conducted. Result The search yielded 24,833 citations. Seventy studies met the inclusion criteria. These studies were published predominantly in Europe and Asia between 2010 and 2024 and classified as contributing factors (n = 24), exercise (n = 13), environmental triggers (n = 24), and prodromal symptoms (n = 9). The etiology of cardiac arrest initially assigned to cardiac or obvious non-cardiac classification may be precipitated by seizures (n = 8), trauma (n = 7), alcohol or drug intoxication (n = 6), Covid-19 infection (n = 5), myocardial infarction (n = 4), suicide (n = 4), antipsychotic medications (n = 4), and illicit drug use (n = 3). Exercise and environmental factors (e.g., particulate matter (PM) 2.5µ and ambient temperature) may trigger cardiac arrest predominantly due to cardiac etiologies. Based on EMS data, approximately 50% of patients with OHCA experienced symptoms prior to cardiac arrest which suggested cardiac and non-cardiac etiologies. Conclusion Many circumstance-related factors may directly or indirectly contribute to cardiac arrest etiology classification. Listing these factors in the reporting template may help prehospital personnel and data abstractors gather enough information to identify more accurately the etiology of OHCA.

Formation of Sandwich Complexes between Lanthanides and Chlorophylls Recovers Photosynthetic Activity and Imparts Crop Resistance to UV Stress via Single-Nanodose Seed Treatment

Hybrid feature selection framework for enhanced credit card fraud detection using machine learning models

Electronic payment methods are increasingly prevalent worldwide, facilitating both in-person and online transactions. As credit card usage for online payments grows, fraud and payment defaults have also risen, resulting in significant financial losses. Detecting fraudulent transactions is challenging due to the highly imbalanced nature of transaction datasets, where fraudulent activities constitute only a small fraction of the data. To address this, we propose a novel hybrid feature selection framework designed to enhance the performance of machine learning models in credit card fraud detection. Our framework integrates three complementary feature selection techniques: Pearson correlation, information gain (IG), and random forest importance (RFI), each optimized for the dataset‘s characteristics. Pearson Correlation eliminates redundancy by removing highly correlated features, while IG and RFI evaluate the relevance of the remaining features. A union operation combines the most informative features from these methods, ensuring comprehensive and efficient feature selection. To validate the proposed approach, we test it on five diverse datasets with varying characteristics and imbalance levels, employing five state-of-the-art machine learning algorithms: Random Forest (RF), Extra Trees (ET), XGBoost (XGBC), AdaBoost, and CatBoost. We primarily propose this work for PCA-transformed datasets, but for the validation of our research, we also apply it to a real-world dataset. The results demonstrate that our methodology outperforms existing baseline approaches, achieving superior fraud detection performance across all datasets. Our findings highlight the robustness and adaptability of the proposed framework, offering a practical solution for real-world fraud detection systems. Additionally, we believe that our proposed framework can serve as a decision support system for the detection of fraudulent transactions in real-time credit cards, with the potential to make a substantial contribution to the business industry.

Maternity care professionals’ preparedness for and experiences of screening and responding to disclosures of domestic violence in the peripartum period: A protocol for a qualitative evidence synthesis

Background Maternity care professionals, such as midwives, public health nurses, doctors, and social workers, are in the unique position of having regular contact with women in the peripartum period. They are well-placed to recognise and respond to disclosures of domestic violence, however many lack confidence and feel unprepared for this in practice. While there are screening tools used for enquiry about domestic violence in pregnancy, there are variations in the tools used, the frequency and timing of enquiry, and the response/referral pathways across professions. Research exists on the role of health care professionals such as midwives, doctors, and nurses with regards to domestic violence, however little is understood about the collective and shared experience of maternity care professionals who screen for and respond to domestic violence in the peripartum period. Methods A qualitative evidence synthesis of maternity care professionals’ preparedness for and experiences of screening and responding to disclosures of domestic violence in the peripartum period will be conducted. Qualitative studies of any design, and mixed method and other design studies where qualitative data can be extracted will be considered for inclusion. A systematic search of the following electronic bibliographic databases will be conducted: ASSIA, CINAHL, EMBASE, Maternity and Infant Care, MEDLINE, APA PsycINFO and SocINDEX. The Critical Skills Appraisal Programme (CASP) qualitative studies tool will be used to assess methodological quality of included studies. Data synthesis will involve three sequential stages, coding, development of descriptive themes and generation of analytical themes. Confidence in findings will be assessed using the Grading of Recommendations Assessment, Development and Evaluation-Confidence in the Evidence from Reviews of Qualitative research (GRADE-CERQual) tool. Discussion This QES will provide a deeper understanding of maternity care professionals’ preparedness for and experiences of screening and responding to domestic violence disclosures in the peripartum period. The findings will, potentially, identify what aspects of education and preparedness work well and what might be improved.

Sequence Programmable Order–Disorder Transitions in Supramolecular Assembly of Peptide Nanofibers

Global, regional, and national burden of early-onset OA attributable to high BMI: 1990–2021 estimates and 2036 projections from the global burden of disease study

Objective High BMI is a critical risk factor for early-onset OA (diagnosed before age 55). This study aimed to analyze global trends in the age-standardized disability-adjusted life years rates (ASDR) attributable to high BMI from 1990 to 2021. Methods Data from the Global Burden of Disease 2021 (GBD 2021) study were analyzed to assess early-onset OA attributable to high BMI across 204 countries, 21 GBD regions, and 5 Socio-Demographic Index (SDI) tiers. Temporal trends in ASDR were quantified using the estimated annual percentage change (EAPC) and Joinpoint regression. Age-period-cohort models and decomposition analysis identified drivers of burden, while inequality was assessed using the Slope Index of Inequality (SII) and Concentration Index (CI). ARIMA models projected trends to 2036. Results The disease burden of early-onset OA attributable to high BMI increased significantly between 1990 and 2021. The DALYs for early-onset knee and hip OA rose by 203.01% and 170.12%, respectively, with average annual percentage changes (AAPC) of 1.39% and 1.40%. The age-period-cohort analysis indicated that DALYs risk increased with age and period, and later birth cohorts experienced a higher burden of early-onset OA attributable to high BMI. Decomposition analysis revealed that population growth was the primary driver of the rising disease burden. There were significant absolute and relative inequalities in the burden of early-onset OA attributable to high BMI, as measured by the SDI. Countries with higher SDI bore a greater burden. The SII demonstrated that the ASDR gap between countries with the highest and lowest SDI widened steadily from 1990 to 2021. Predictive analysis suggested that the burden of early-onset OA attributable to high BMI will continue to increase over the next 15 years. Conclusion From 1990 to 2021, the global burden of early-onset OA attributable to high BMI showed a consistent upward trend, with significant inequalities across countries. The disease burden is projected to grow further in the future.

Psychometric validation of the Turkish expanded mindful eating scale

Background/Objectives The Expanded Mindful Eating Scale (EMES) assesses mindfulness by considering both individual health and environmental sustainability. A distinguishing feature of the EMES is its inclusion of sustainability, setting it apart from other eating awareness scales. This study was carried out to determine the validity and reliability of the Turkish culture-adapted version of the EMES in adults. Materials/Methods In this cross-sectional study, 662 Turkish adults (mean age=29.8, SD=12.18; 18.4% male) were recruited via an online questionnaire. In this study, participants were recruited via an online survey that included their sociodemographic characteristics, the EMES, Mindful Eating Questionnaire (MEQ), and sick, control, one, fat, food (SCOFF) Eating Disorders Scale. Confirmatory factor analysis (CFA) was used to test the validity of the EMES. Analyses were conducted using SPSS version 25.0 and SPSS AMOS version 25.0. The 5-factor structure identified by Explanatory Factor Analysis (EFA) was validated using CFA. Results As a result of confirmatory factor analysis (RMSEA: 0.053, CFI: 0.89, GFI: 0.94, and AGFI: 0.92), model fit indices were obtained within acceptable limits. In the reliability analysis, it was observed that the EMES scale had a good level of reliability with the Spearman-Brown coefficient. A significant correlation was observed between the EMES and MEQ. Conclusion The results showed that the use of the Turkish version of the EMES is valid and reliable. It is thought that the validation of the Expanded Mindful Eating Scale reflects a comprehensive approach that recognizes the interconnectedness of individual health and environmental well-being.