EFE-8c4, a polyamine from Elaeagnus multiflora, protects neuronal cells by regulating oxidative stress and apoptotic pathways

Neurological disorders, including depression, cognitive impairment, and neurodegenerative diseases, are closely associated with oxidative stress, apoptotic neuronal loss, and impaired neuronal differentiation. Polyamine derivatives from natural products have emerged as potential neuroprotective agents, although their mechanisms remain incompletely understood. In this study, three polyamine compounds isolated from Elaeagnus multiflora fruit were evaluated in SH-SY5Y cell models of oxidative and glucocorticoid-induced stress. Among these, EFE-8c4 exhibited the most pronounced neuroprotective activity, significantly restoring cell viability under corticosterone-induced stress and attenuating oxidative damage induced by hydrogen peroxide. Mechanistically, EFE-8c4 modulated apoptotic signaling by increasing Bcl-2 expression while suppressing Bax, caspase-8, and p53 activation, thereby restoring the balance between pro- and anti-apoptotic pathways. In addition, EFE-8c4 reduced intracellular reactive oxygen species accumulation and enhanced the expression of PCNA and βIII-tubulin, indicating improved cell survival capacity and neuronal phenotype maintenance. Furthermore, EFE-8c4 partially reduced apoptotic cell populations under corticosterone exposure. Collectively, these findings demonstrate that EFE-8c4 exerts multi-target neuroprotective effects through coordinated regulation of apoptosis, oxidative stress, and neuronal differentiation-related pathways, highlighting its potential as a candidate for the treatment of oxidative stress-associated neurological disorders.

Neurochemical-hemodynamic-electrophysiological coupling in the neonatal brain: a multimodal MRS-fMRI-EEG investigation

IntroductionInhibitory and excitatory neurotransmitter levels are linked to fast neuronal oscillations and infra-slow hemodynamic fluctuations, suggesting a shared excitation–inhibition (E/I) regulatory framework across measures. However, these relationships may differ in early development, when both excitatory and inhibitory cortical systems are undergoing substantial functional and structural maturation. Consequently, we hypothesize different functional coupling between neurochemical, electrophysiological, and hemodynamic proxies of E/I signaling in healthy full-term neonates compared to what has been observed in adults.MethodsTwenty-five healthy full-term neonates (mean postmenstrual age at study = 40.1 ± 1.4 weeks) underwent multimodal MRI and electroencephalography (EEG) recordings during natural resting-state to provide proxy measures of neural excitation and inhibition. These included frontal and occipital MRS measures of γ-aminobutyric acid (GABA+) and Glx (glutamate + glutamine) levels, and their ratio; EEG source-reconstructed power spectra decomposed into periodic beta (13–30 Hz) and gamma (30–45 Hz) features (center frequency and peak amplitude), relative to total band power and an aperiodic exponent; and infra-slow fMRI BOLD fluctuations (0.01–0.08 Hz) using amplitude of low-frequency fluctuations (mean and fractional ALFF). Crossmodal relationships were assessed using partial correlations controlling for age.ResultsOccipital GABA+ was negatively correlated with beta relative power (r = −0.64, p = 0.01) and fractional ALFF (r = −0.55, p = 0.048), while mean ALFF was negatively correlated with gamma center frequency (r = −0.99, p = 0.02). These relationships were not observed in the frontal cortex. Instead, frontal Glx positively correlated with beta peak amplitude (r = 0.87, p < 0.01) and negatively correlated with beta (r = −0.78, p = 0.02) and gamma (r = −0.79, p = 0.02) relative power, potentially reflecting the existence of regionally distinct maturational trajectories.DiscussionTogether, these preliminary findings suggest that commonly used neurochemical, oscillatory, and hemodynamic proxy measures of cortical excitatory and inhibitory processes may show only modest correspondence at birth, consistent with ongoing and hierarchal cortical development, leading to complex and asynchronous relationships between these measures.

Developing forensic patient-oriented research guidelines: a rapid review using an integrated knowledge translation approach

This paper reports findings from a rapid literature review that informed new guidelines for conducting patient-oriented research in forensic mental health settings. The project adopted an integrated knowledge translation approach at a mental health hospital in Ontario, Canada, engaging a project team that included current forensic patients, hospital staff, and members of an international community of practice. Sources were identified through nine academic databases and targeted grey literature searches, screened independently by two reviewers and extracted using a structured template guided by an a priori framework developed with patients and staff at a knowledge exchange event. Findings were iteratively refined through a patient advisory group, an implementation study, ethnographic observations, and related integrated knowledge translation activities conducted alongside the review. Together, 31 academic and grey literature sources informed a framework organized around five core dimensions: 1) Resourcing, orientation, and training; 2) Confidentiality, consent, and compensation; 3) Relationships, shared understanding, and support; 4) Levels of engagement; and 5) Evaluation and sustainability. Guided by cross-cutting principles common among participatory mental health research, such as dignity, trust, respect, and a commitment to redressing power and attending to forms of epistemic injustice, the guidelines respond to distinctive constraints of forensic environments while highlighting opportunities to promote authentic co-production and sustain patient involvement in research. Recommendations include dedicated resources and capacity-building for patients; relational, ongoing consent practices co-developed with patients; flexible patient researcher roles with fair, paid compensation; and sustained institutional support for participatory practices. We call on forensic hospitals and secure settings to adapt and evaluate these guidelines and to invest in expanding patient leadership to advance the field.

Enhancing psychiatry education: effectiveness of a psychodynamic psychotherapy module for borderline personality disorder for psychiatry residents

BackgroundPsychodynamic psychotherapy is the treatment of choice for borderline personality disorder (BPD); however, psychiatric residents frequently report difficulty in applying it, partly due to the lack of structured training models. This study developed and evaluated the effectiveness of psychodynamic psychotherapy learning modules for BPD among Indonesian psychiatry residents.MethodsA quasi-experimental pre-/post-test control group study using mixed methods was conducted across nine psychiatric residency programs in Indonesia. Thirty-four residents were recruited, of whom 33 completed the study. Learning outcomes were assessed using multiple-choice questions and the Psychodynamic Formulation Competency Assessment Scale (PF-CAS) and Practical Competency Assessment Scale (PC-CAS). The module program was evaluated by the participants using the Indonesian version of the Kirkpatrick Level 1 questionnaire.ResultsThe intervention group showed significantly greater improvement in psychodynamic formulation skills (PF-CAS) than the control group (p < 0.001). The multiple-choice scores improved in both groups, with no significant between-group differences. The intervention group showed a numerically greater improvement in Practical Skills (PC-CAS) than the control group, although the difference was not statistically significant. Participants’ feedback was highly positive, emphasizing the usefulness of psychodynamic formulation training, psychotherapy protocols, and supervision.ConclusionImplementation of the psychodynamic psychotherapy for BPD Learning Module enhanced competencies in the cognitive and affective domains and showed promising trends in practical skills. This positive reception highlights its feasibility and potential benefits as part of the residency training curriculum.

Age-stratified multimodal MRI and machine learning to explore autism-related brain characteristics in youth

PurposeAutism is a common neurodevelopmental condition (NDC) that is characterized by restricted, repetitive behaviors and social communication differences that can impact the daily functioning of individuals. The clinical diagnosis of autism can be challenging, mainly due to its behavioral variability and frequent co-occurrence with other NDCs. This study investigates the ability of machine learning-based classification models trained using multimodal neuroimaging data combined with feature-importance analyses to identify development-specific brain characteristics associated with autism.ApproachA total of 144 participants aged 5 to 18 years with structural MRI (sMRI), diffusion MRI (dMRI), and resting-state functional MRI (rs-fMRI) data available were obtained from the Autism Brain Imaging Data Exchange (ABIDE) database. Radiomic features were extracted from each MRI data modality and used to train support vector machine (SVM) classifiers to identify neuroimaging patterns associated with autism. Single MRI modality classifiers, as well as one combining all three modalities, were trained for comparison purposes. To investigate age-specific effects, the same approach was followed for three age sub-groups: younger children (5–11 years), adolescents (12–18 years), and the entire 5–18 years age cohort. Model performance was evaluated using leave-one-out cross-validation across 30 diagnosis-balanced data splits. Feature-importance analyses were conducted to identify the most important neuroimaging features for classification.ResultsThe classification accuracies of the unimodal models ranged from 68.3% to 75.3% for sMRI, from 69.3% to 77.6% for dMRI, and from 66.3% to 69.9% for rs-fMRI data across age groups. Among all single imaging modalities and age groups, dMRI showed the highest performance with a 77.6% accuracy in younger children (5–11 years). The multimodal approach improved classification performance when compared to the unimodal models in all age groups, achieving accuracies of 78.9%, 76.7%, and 70.5% in the younger, adolescent, and entire age cohorts, respectively. Our findings indicate that multimodal classifiers integrating complementary structural, microstructural, and functional imaging features result in a more comprehensive representation of brain features that strengthens model performance. The most informative brain regions for classification differed between children and adolescents while several diffusion-derived features significantly correlated with social responsiveness scores, emphasizing the clinical importance of studying white and gray matter microstructure in autism.ConclusionsThis study demonstrates the potential of multimodal neuroimaging-based machine learning models to identify development-specific biomarkers associated with autism. The results highlight the value of integrating age-stratified analyses of multimodal neuroimaging to better capture autism-associated developmental brain characteristics. The framework adopted in this study could be extended to explore other NDCs in the future.

Effects of Balint group combined with mindfulness-based stress reduction on humanistic care ability and psychological resilience among obstetric nurses

BackgroundHumanistic care competence and psychological resilience are essential for improving nursing quality, particularly in high-stress specialties such as obstetrics. However, effective interventions that simultaneously enhance both interpersonal and intrapersonal capacities among nurses remain limited.MethodsA total of 87 obstetric nurses from a tertiary hospital in Hebei Province, China, were enrolled and allocated into three groups: a combined Balint group and mindfulness-based stress reduction (MBSR) intervention group, a Balint group, and a control group (n = 29 each). The intervention was conducted over 8 weeks. Outcomes, including humanistic care competence, empathy, emotional intelligence, and psychological resilience, were measured at baseline, post-intervention, and 6-week follow-up using validated Chinese versions of standardized scales. Data were analyzed using repeated-measures analysis.ResultsThe combined intervention group showed significantly greater associations with improvements in all outcomes compared with the Balint and control groups (all P < 0.001). Empathy, humanistic care competence, emotional intelligence, and psychological resilience were significantly higher after the intervention and continued to show positive trends at follow-up. Although the Balint group alone also demonstrated moderate improvements, the combined intervention consistently produced stronger and more sustained associations.ConclusionThe integration of Balint group and MBSR interventions eff is associated with enhanced psychological resilience and humanistic care competence among obstetric nurses. This study builds on previous research by examining the combined effect of reflective and mindfulness-based approaches in a specific clinical population, providing evidence for a feasible strategy to improve nurses’ professional quality and mental well-being.

Awareness, Educational Needs, and Curriculum Preferences Regarding AI and Medical Big Data Education Among Clinical Medicine Undergraduates: Cross-Sectional Survey Study

Background: The rapid integration of artificial intelligence (AI) and medical big data into health care is transforming diagnosis, treatment planning, and research. However, formal education in these areas remains limited in undergraduate medical curricula, particularly in China. Objective: This study aimed to investigate clinical medicine undergraduates’ familiarity with AI and medical big data, their perceived need for related courses, and their preferred curriculum design and assessment methods. Methods: A cross-sectional, web-based survey was conducted at Zunyi Medical University, Guizhou, China, from January 10 to 17, 2025. In the institutional context of this study, “clinical medicine” included related clinical-track specialties such as pediatrics and psychiatry. All eligible students (N=1094) were invited, and 871 (79.6%) were included in the final analysis. The self-administered questionnaire was developed based on a literature review and expert consultation, with content validity quantified using the content validity index. Descriptive statistics were used to summarize response distributions. For ordinal outcomes (items 1-14), adjusted ordinal logistic regression models were applied, with gender and grade as predictors and major as a covariate. Given the small number of third- and fourth-year students, grade was modeled as an ordered trend variable. For nominal outcomes (items 15-16), group differences were assessed using chi-square tests or Fisher exact tests, as appropriate. Results: A total of 871 students were analyzed, of whom 62.6% (n=545) were women. Overall familiarity with AI and medical big data was limited: 34.8% (303/871) agreed or strongly agreed that they were familiar with the topic, and only 33% (287/871) reported having at least some prior learning experience. In contrast, the perceived educational need was high: 94% (819/871) considered such a course at least somewhat necessary, 57% (497/871) reported that the course was needed or very needed, 75.5% (658/871) indicated that they would likely or definitely enroll, and 56.5% (492/871) reported that they would likely or definitely engage in self-directed learning. Personalized teaching based on textbooks (566/871, 65%) or open-book examinations (633/871, 72.7%) was the most preferred instructional and assessment format. Preferences for course materials and assessment methods differed by grade but not by gender. Conclusions: Early-stage clinical medicine undergraduates demonstrated limited familiarity with AI and medical big data but expressed a strong demand for related education. Students preferred structured yet flexible instructional formats and open-book assessments. Although the findings are based predominantly on first- and second-year students, they support the development of staged, practice-oriented AI and medical big data curricula tailored to the needs of early-stage clinical medicine undergraduates.
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Why California’s carbon manure math doesn’t add up

Something stinks in California’s climate policies.

Years ago, the state set up a system that pays cattle farmers across the country to turn the methane emitted from cattle manure into natural gas, encouraging the dairy sector to produce a gas we burn instead of one that just pollutes the air.

It’s become wildly popular because the subsidies are extremely lucrative. But a growing body of research suggests the program is a case study in the shortcomings of our preferred approaches to climate action. Instead of simply forcing industries to directly cut their pollution or pay for it as a cost of doing business, legislators have repeatedly opted to set up convoluted incentive systems that swap climate responsibilities between parties and regions. As studies have shown again and again, these carbon offsetting and trading schemes often dramatically overstate the emissions reductions actually achieved in the one place that matters: the atmosphere.

The dairy program illustrates a particular version of this problem, muddling the impacts of different types of greenhouse gases in a way that researchers argue will lock in more warming in the future.

Despite this and other concerns, California regulators decided in 2024 to extend parts of the program beyond 2050. And a recent proposal by the state’s air resources board could send millions of additional dollars to dairy farmers as part of a plan that would ease restrictions on major greenhouse-gas producers.

Here’s how the system works: The state’s climate regulations require the transportation fuels industry to lower the carbon dioxide levels in its products over time—or purchase credits from other parties that cut fuel emissions, including cattle farmers.

Dairies generally spray cattle manure into giant open lagoons, where microbes gobble up organic matter and produce methane as a by-product. But if farmers set up what are known as anaerobic digesters, the sludge is redirected into covered vessels that capture the biogas, which can be converted into natural gas and injected into a pipeline. It can then be used to fuel certain vehicles or generate electricity in a power plant. Either way, petroleum companies can pay those farmers for Low Carbon Fuel Standard (LCFS) credits, to meet regulatory requirements in lieu of reducing the emissions from their own fuels.

Burning biogas in a bus or turbine still releases carbon dioxide, but the idea is that this process reduces market demand to extract natural gas from the ground and avoids the release of methane, which is a far more powerful greenhouse gas (at least initially). In fact, methane is so much more powerful that under California’s program, “adding one average biogas-powered vehicle to the fleet would produce enough LCFS credits to cover the deficits incurred by 26 similar gasoline-powered vehicles,” according to Aaron Smith, a UC Berkeley economist.

But there’s a problem with this carbon math. California assumes that methane exerts about 25 times the warming effect of carbon dioxide over a 100-year period. That’s not how it really works in the atmosphere, though.

Methane is very powerful, but it also breaks down quickly, generally within a couple of decades. Meanwhile, carbon dioxide builds up cumulatively in the atmosphere—and much of whatever we emit will continue heating up the planet for hundreds to thousands of years.

So, in effect, the state has created a system that reduces short-term warming at the cost of increasing all-but-permanent warming. Any methane that digesters capture today would have caused extra-powerful warning if released, but by 2050 that effect would have mostly faded away. Meanwhile, that additional carbon dioxide we permitted in its place could continue warming the world for millennia.

It is a good idea to cut methane emissions, and dairy digesters achieve this (though not always as effectively as hoped). But we can’t swap a decrease in short-lived greenhouse gases for an increase in long-lived ones if we hope to keep global temperatures within relatively safe levels in the coming century, as researchers have long warned. We have to slash both.

The problem I keep returning to, after years of covering carbon markets and offsets, is this: We need to clean up every sector, completely, over the next few decades. It’s increasingly untenable for so many of our climate ambitions to turn on getting one industry to make progress on paper by paying another one to reduce emissions, at a point when every business in every industry needs to be racing toward net zero.

It’s time to move past the idea that we need to reward sectors for doing us the favor of not polluting the atmosphere, and simply require them to stop unloading the huge environmental burden of their business onto society.

This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

Accelerated iTBS for PTSD and Depression

Conditions: Post Traumatic Stress Disorder PTSD; Major Depressive Disorder (MDD)

Interventions: Device: Accelerated intermittent theta burst stimulation

Sponsors: Lawson Research Institute of St. Joseph’s

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