The last decade has seen major progress in understanding moral judgment, driven in part by advances in Moral Foundations Theory (MFT). Given these developments and their implications for psychology, sociology, and public policy, a review of recent progress is timely. We synthesize developments in four key areas: 1) measurement, 2) genetic underpinnings, 3) neural mechanisms and 4) real-world tests of MFT. Measurement advances include the release of the new MFQ-2 instrument with improved reliability and predictive validity and the division of the fairness foundation into distinct equality and proportionality foundations. Behavioral genetic studies indicate that variation in moral foundations reflects both genetic and environmental influences. Alongside this, neuroimaging studies have revealed dissociable neural areas linked to moral foundations. Finally, global events - especially the COVID-19 pandemic – enabled the evaluation of MFT predictions in real-world contexts, showing both a substantive role for moral foundations in predicting public health behaviour and yielding new insights into MFT.

This study presents a novel numerical framework for simulating glucose-insulin regulatory dynamics using the Caputo-Fabrizio (CF) fractal-fractional operator with both constant and variable fractional orders. The model incorporates an exponential decay kernel to capture memory and hereditary effects in metabolic regulation. A Newton interpolation-based numerical scheme is developed to approximate the CF-FF derivatives, ensuring computational stability and accuracy. For the variable-order formulation, the fractional order dynamically evolves with time, reflecting physiological variability typically observed during intravenous glucose tolerance tests (IVGTT). Numerical experiments reproduce physiologically realistic glucose-insulin oscillations and demonstrate how feedback control stabilizes chaotic metabolic behavior. The results are based entirely on simulation evidence calibrated within clinically reported parameter ranges, providing conceptual validation rather than direct patient-data comparison. The proposed approach bridges mathematical fractional calculus with biomedical applications, offering new insights for personalized diabetes management and adaptive glucose control strategies.•Fractal-fractional model formulation capturing glucose-insulin memory and adaptation•Stable numerical scheme using Newton interpolation for accurate fractional integration•Linear feedback control applied to regulate chaotic glucose-insulin dynamics•Numerical Methodology for glucose-insulin dynamics. Our investigation of the fractal-fractional glucose-insulin system employs the following analytical framework:•Model Development: We formulate a fractal-fractional-order extension of the minimal glucose insulin model, incorporating an exponential decay type kernel to capture the system's memory effects and anomalous diffusion characteristics inherent in metabolic processes. The model accounts for both insulin-dependent and independent glucose utilization dynamics.•Computational Implementation: We develop a novel numerical solver based on Newton's interpolation polynomials, implementing the Atangana-Seda fractal-fractional derivative formulation. This method provides an efficient computational framework for solving the coupled nonlinear fractional differential equations while maintaining numerical stability across different fractional orders.•The purpose of this section is to define a mathematical model to study the dynamic behavior of glucose-insulin physiology.•With the Adams-Bashforth-Moulton numerical scheme, we compute the Lyapunov exponent of the system, which is useful for studying dissipative.•In a generalized numerical method, we simulate the solutions of the system using the time-fractal fractional derivative of Atangana-Seda.

The estrous cycle of female guinea pigs is characterized by a spontaneous ovulation and a subsequent active luteal phase that is physiologically well described. In contrast to other rodents, however, the effects of the cycle on stress responses and behavior are understudied in this species. This study examines the extent to which cortisol concentrations and behavior differ between the estrus and diestrus phases of two consecutive cycles in twelve female guinea pigs. In addition, the parameters were analyzed regarding repeatability to determine how far both physiological parameters and behavior are individually consistent throughout the cycle. Saliva samples to analyze cortisol concentrations were collected before and after a 15-minute dark-light test. The dark-light test and a 15-min sociality test were performed to analyze exploration, anxiety, and social interest. Cortisol concentrations were increased in estrus compared to diestrus. In the dark-light test, the cycle phase had no significant influence on locomotion or anxiety-like behavior. Locomotion and time spent with a conspecific in the sociality test were partly increased in estrus compared to diestrus. All behaviors were repeatable throughout the two investigated cycles. The results show that the estrous cycle of female guinea pigs can influence physiology and behavior to some extent, but that individual differences have a stronger influence on behavior than does the cycle. These findings contribute to the general understanding of physiological and behavioral changes during the estrous cycle, which are understudied not only in guinea pigs but in many mammalian species.

The mammalian mesocortex (MCx) was redefined recently as a complete ring intercalated between the allo- and iso-cortex, attending to the consistent expression of 46 selective gene markers. The MCx exhibits various other characteristics such as low myelin content of its fibers, a property directly related to its function and susceptibility to degenerative disorders. Irrespective of its shared molecular profile, the MCx ring differentiates into different sectors with singular molecular and cytoarchitectonic characteristics, such as the posterior orbitary cortex, the cingulate cortex, and the insula. In the present study, using anterograde connectivity experiments published in an Allen Institute for Brain Science public database ( https://brain-map.org/our-research/connectivity ), we analyzed the internal connectivity of the mesocortical ring. We observed that its different sectors are multiply interconnected, possibly achieving integrative functions. The medial posterior orbitary cortex stands out; it appears to play a higher hierarchical role within the MCx ring by sending and receiving projections from all mesocortical sectors.

Large-area and high-spatial-resolution photodetectors with the ability of light direction recognition have promising application prospects in smart sensors, human-machine interaction, the Internet of Things, and other fields. However, current photodetectors often fail to achieve accurate direction recognition of light and require a trade-off between high spatial resolution and device size. Here, we show a printed microscale perovskite dual-line structure designed for resonant, directionally selective absorption, which creates differentiated photocurrents under visible light from different directions. After investigating the change of reflected light from the eyeball, a single dual-line microstructure can be integrated as a wearable photodetector for monitoring eye movement abnormalities. The frequency and amplitude of eye movement can be recorded for early warning of neurological disorders and monitoring nervous system diseases. This strategy provides a new approach for creating high-performance optoelectronic devices using printed photonic resonant structures, which extends the application of optoelectronic devices in brain science.

Towards a strategic assessment of brain sciences in Latin America: current status, strengths and structural needs.

This study introduces the Heart-Based Resonant Field (HBRF) framework as a unifying, multi scale hypothesis linking planetary electromagnetic structure, biological systems, and long-term sociopolitical organization. Integrating evidence from developmental biology, geophysics, cardiovascular physiology, and statistical spatial analysis, the work examines whether large scale environmental gradients, particularly geomagnetic and geomorphic features, may act as persistent contextual modulators of human physiology and collective behavior. A central empirical case is the Algeria–Libya boundary, which exhibits sustained alignment with a narrow lithologic transition over ~100 km. Under conservative probabilistic modeling, this alignment deviates from random expectation by orders of magnitude, reaching effectively zero probability under null assumptions of spatial independence. This statistical rejection of randomness motivates the exploration of structured environmental constraints as contributing factors in geopolitical stabilization. The framework advances a non deterministic, physiologically mediated pathway whereby geomagnetic variability influences autonomic regulation and heart rate variability, potentially biasing population level stress responsiveness, decision making, and adaptive behavior over long timescales. These cumulative effects, interacting with geomorphic and ecological constraints, may contribute to the emergence and persistence of geopolitical boundaries. Importantly, the HBRF model does not replace established historical or sociopolitical explanations but situates them within a physically structured planetary context. The theory generates explicit, testable predictions across disciplines, including correlations between geomagnetic environments and physiological markers, and preferential boundary persistence along geophysical gradients. Within a Medical Hypotheses framework, this work establishes a quantitatively grounded and biologically plausible direction for investigating crossscale coupling between Earth’s physical fields and human organizational systems.

A useful distinction within cognitive and brain sciences is that between fluid and crystallized ability. Although fluid ability is widely studied, crystalized ability, which draws on acquired, declarative semantic knowledge and the mental lexicon, has been less well studied. Partly this is due to the culture and language specificity of assessment methods. We developed and assessed the psychometric properties of a simple 42-item lexical decision task that could be used with Thai speakers to assess the breadth of their crystalized vocabulary knowledge. A large sample of responses from 662 Thai-speaking participants, collected online, was used to refine the scale through exploratory factor analysis, and establish its internal consistency. A smaller sample of 90 participants was interviewed to establish validity of the task as a measure of Thai vocabulary. Large positive correlations (i.e., > .3) were found between the Thai Lexical Decision Task and measures of verbal fluency, particularly in the first 30 seconds of responding, and with other measures of Thai language skill. Temporal stability of the scale was assessed in a subsample of 27 participants. This confirmed that the Thai Lexical Decision Task has little obvious practice effect and excellent test-retest reliability. We also observed the expected positive associations with age, educational level, and self-reported proficiency in the language, supporting the ability of the task to measure acquired, crystalized knowledge. This new task could be used to estimate cognitive ability in Thai adults and shows potential as a measure of premorbid cognitive function for use in neuropsychological assessments.

Introduction: Periodontitis is a chronic inflammatory disease that leads to irreversible damage to the gums, periodontal ligament, and alveolar bone. Its progression is driven by a complex interaction of microbial imbalance, host immune responses, and patient-specific risk factors like smoking and diabetes. Digital Twin (DT) technology- virtual models that mimic anatomical and physiological behaviours- has shown promise in orthodontics and implantology, but its use in periodontal disease remains unexplored. Hereby, authors introduce an agent-based, multimodal periodontal disease simulator that generates synthetic patient groups, models inflammation and bone loss over time, and produces synthetic radiographic images. Aim: To develop and evaluate an agent-based, multimodal in-silico simulator to model periodontal disease progression by simulating inflammatory dynamics, alveolar bone loss, and synthetic radiographic features, and to assess the performance of machine learning models trained on the generated data. Materials and Methods: Three interacting agents were created: Data agent generated a cohort of 600 patients with demographic data, risk factors, and baseline imaging. The computational simulation used synthetic data and agentbased modeling without involving human participants, clinical facilities, or time-bound data collection, so Institutional location and study duration are not applicable. Inflammation agent simulated month-by-month inflammatory trajectories based on risk factors and graph-based neighbour interactions, and bone loss agent translated inflammation into bone loss scores and updated lesion masks. Results: Machine learning models trained on this synthetic data achieved high accuracy in classifying severity {accuracy ≈ 0.98, macro F1≈0.97, Receiver Operation Characteristic-Area Under Curve (ROC-AUC)≈0.997}, although continuous bone loss regression performed poorly (R²≈0.10). Simple adaptive thresholding for lesion segmentation yielded moderate mean Intersection over Union (IoU) (≈ approximately 0.67) and Dice (≈approximately 0.75) scores. An ablation study of the network indicated that even small interactions among neighbours increased average bone loss, suggesting population-level effects. Statistical evaluation employed a Random Forest classifier and regressor for severity classification and bone loss prediction, with segmentation assessed via IoU, dice, precision, and recall. Conclusion: Although the dataset is synthetic and not validated, this work demonstrates the potential of combining agent-based modeling, graph theory, synthetic imaging, and machine learning to explore the dynamics of periodontal disease.

Prevalence and associated factors of non-suicidal self-injury among Chinese adults: a national, population-based cross-sectional study.

Clinical safety of a low-modification hyaluronic acid filler (MoD 2%) for facial rejuvenation.

Proteome unravels mechanism differences in embryogenesis between honey bee drone and worker (Apis mellifera L.).

Hydrocephalus is a debilitating neurological condition characterized by abnormal cerebrospinal fluid (CSF) accumulation, frequently linked to inflammation and barrier dysfunction in the choroid plexus epithelium (CPE) as Blood-CSF-Barrier (BCSFB). Prior studies in a genetic rat model demonstrated that inhibiting transient receptor potential vanilloid 4 (TRPV4), an osmo-, shear-, temperature-, pressure-, and inflammation-sensitive non-selective cation channel expressed in the CPE, can ameliorate ventriculomegaly, but the extent to which inflammatory mediators directly modulate TRPV4 activity in human CPE cells remains unknown. Lysophosphatidic acid (LPS) and hemoglobin are key drivers of inflammation in post-infectious and post-hemorrhagic hydrocephalus respectively. We hypothesize that these molecules could modulate TRPV4 channel activity and disrupt CPE barrier function, contributing to CSF dysregulation. To investigate this, we employed the human choroid plexus papilloma cell line (HIBCPP), a well-characterized in vitro model that closely resembles the morphology, transporter expression, and physiological behavior of native CPE cells. HIBCPP cells were treated with physiologically relevant concentrations of LPS (10 ng/mL) or bovine hemoglobin (1.4 mg/mL) applied to the apical, basolateral, or both sides of the epithelium, to simulate the inflammatory conditions. Using Ussing-style electrophysiology, we quantified transepithelial conductance (a measure of transepithelial permeability) and transepithelial ion flux before and following the activation of TRPV4 by using the selective agonist GSK1016790A. Our findings suggest that LPS treatment modifies the ion transport across the CPE both within a short time (10 minutes) and long-term treatment (24 hours), with the most robust effects following the basolateral application. This included a significant increase in transepithelial conductance after 24 hours. Hemoglobin treatment for 10 minutes did not cause significant changes in ion flux or conductance. Therefore, the data indicate that selective inflammatory mediators may modulate both TRPV4 activity and barrier function in the CPE and suggest a potential mechanism contributing to the dysregulation of CSF and identify TRPV4 as a possible therapeutic target to reduce the inflammatory-driven changes in hydrocephalus. As a result of this observation, we further speculate that exposure to inflammatory mediators could induce production of cytokines by the CPE, thus leading to propagation of inflammatory signals into distant parts of the brain and contributing to neuroinflammation and CSF dysregulation in hydrocephalus. This study was supported by DoD Congressionally Directed Medical Research Program Grants #HT94252310296 and #HT94252310401 (BBY) This abstract was presented at the American Physiology Summit 2026 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.

The high mortality and recurrence rates associated with coronary heart disease (CHD) impose substantial health care costs and economic burdens globally. Identifying effective interventions to improve patient outcomes is paramount. Digital health technologies (DHTs) offer novel solutions to overcome the challenge of low participation rates in traditional cardiac rehabilitation (CR). This review aims to systematically map the scope of application, intervention objectives, and evaluation metrics of DHTs in CR for patients with CHD, thereby providing a structured evidence base for future research and practice. This scoping review adheres to the Joanna Briggs Institute's methodology and is reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A systematic search was conducted across 5 major databases, PubMed, Web of Science, Embase, Cochrane Library, and EBSCO, covering the period from inception to February 2026. Inclusion criteria were developed based on the participants, concept, and context framework. Studies focused on the application of various DHTs within CR settings for patients with CHD. Eligible literature comprised randomized controlled trials, quasi-randomized controlled trials, and longitudinal before-and-after studies published in peer-reviewed journals. Two researchers (XZ and ZL) independently conducted literature screening and data extraction. Findings were presented through a comprehensive narrative synthesis and evidence gap maps. A total of 43 studies were included, predominantly randomized controlled trials (n=40). Findings revealed (1) diverse technological formats, categorized into 3 main types: digital health tools, real-time remote support, and asynchronous communication. Multitechnology combined interventions have become the mainstream model (36/43, 83.7%). (2) Intervention objectives were multifaceted, consolidating into 4 dimensions: motivation and guidance, knowledge and skills, monitoring and security, and social and group dynamics. (3) Evaluation metrics were multidimensional, encompassing clinical physiological indicators, health behaviors, patient-reported outcomes, service use rates, and technological feasibility. DHTs demonstrated positive effects in improving short-term physiological function and health behaviors; however, evidence remains insufficient regarding their impact on long-term clinical outcomes such as reducing adverse events. The innovation of this scoping review lies in integrating highly heterogeneous evidence to reveal the field's evolution from isolated tools toward systematic, integrated solutions. Research confirms that DHTs effectively overcome temporal and spatial constraints, enhancing rehabilitation accessibility and engagement. They serve as crucial strategic tools for bridging geographical disparities in health care resources and advancing equity in cardiovascular health services. However, the evidence base remains limited, including insufficient long-term efficacy data and inadequate exploration of vulnerable populations such as older people and those with low digital literacy. Future research urgently requires large-scale, long-term follow-up clinical trials, alongside enhanced studies on adaptability for specific populations and considerations of health equity. This will propel digital CR toward greater scientific rigor, universal applicability, and precision.

Physiological and biochemical behavior of mini-cuttings of Ocotea odorifera (Vell.) Rohwer in seedling production.

Evolution of neuropeptides: From diffusing molecules to modulators of synaptic transmission.

Background/Objectives: Schizophrenia is one of the most extensively studied yet conceptually unstable disorders in the history of medicine and brain sciences. Since its formalization at the turn of the twentieth century, the disorder has been repeatedly redefined, reflecting changes in clinical observation, diagnostic philosophy, and neuroscientific models of brain function. The objective of this review is to critically examine the historical evolution of schizophrenia as a medical construct and to analyze how shifts in diagnostic systems have shaped the search for biological and molecular biomarkers. Methods: A narrative-historical review of the literature was conducted, integrating classical psychiatric texts, diagnostic manuals, and contemporary neuroscientific studies. Key milestones in the conceptualization of schizophrenia were analyzed alongside the development of biological hypotheses, including neurochemical, electrophysiological, neuroimaging, genetic, immunological, omics-based, and digital approaches. Emphasis was placed on identifying conceptual continuities, ruptures, and methodological limitations across historical periods. Results: The analysis reveals that the evolution of schizophrenia has been characterized by increasing diagnostic standardization accompanied by growing biological heterogeneity. While successive biological models have provided valuable insights into specific aspects of the disorder, none have yielded single, robust diagnostic biomarkers. Instead, findings consistently reflect partial overlaps between clinical phenotypes and biological signals, strongly influenced by historically derived diagnostic categories. Conclusions: The persistent absence of definitive diagnostic biomarkers for schizophrenia reflects not only technical limitations but also the historical construction of the disorder as a heterogeneous clinical category. Understanding this historical context is essential for interpreting current findings in brain sciences. Future research is likely to benefit from stratification-based, dimensional, and integrative frameworks that move beyond categorical diagnosis while preserving clinical relevance.

Obesity is a risk factor for compromised health and a driver for non-communicable diseases. Effects of various fats on health, behavior, and other parameters have been studied using different model organisms, including fruit flies Drosophila melanogaster, by exposing them to dietary fats (saturated and trans fatty acids). However, the long-term and short-term effects of dietary unsaturated fatty acids (USFA) on physiology and sleep-activity behavior are relatively less explored. Hence, the present study hypothesizes that exposure to a USFA-rich diet differentially influences early-life behavioral traits and long-term fitness in fruit flies. The results of our study reveal that the flies exhibit attraction to USFA, with comparable responses to control and the higher doses. We further observed sexual dimorphism in lifespan, with males fed with low-dose (2.5%) and intermediate-dose (10%) USFA outliving females. Further, upon first-time examining the interaction between behavioral changes and fitness of flies fed with USFA, our results reveal that the females under MUFA increased late-night activity, and the female flies fed highest dose of MUFA (20%) exhibited sleep reorganization, by reduced sleep in nighttime and increased in daytime.Further, MUFA-fed flies enhance sleep fragmentation at an early age. In line at late age, flies fed with an intermediate dose of MUFA showed loose gut integrity (Smurf positive). Overall, this study suggests that USFA-fed flies have shorter survival associated with sleep fragmentation in early life and reduced gut integrity of flies at a late age in a sex and dose-dependent manner. Highlighting sleep fragmentation and gut dysfunction may contribute dietary USFA quality to aging-associated fitness.

The BrainNet imperative: why global brain science needs its own ImageNet moment.

The catecholamines octopamine and tyramine undoubtedly have a major impact on the life of an insect. A wide range of physiological processes and behaviors are regulated by these neurotransmitters/hormones. Octopamine and tyramine act homologous to the adrenergic system of vertebrates, primarily adapting the organism to the given situation, by switching between the states of alertness and rest. Interestingly, higher brain functions like learning and memory are also regulated by octopamine and tyramine. About 30 y ago, initial work in Drosophila demonstrated that dopaminergic neurons signal punishment, while octopaminergic neurons signal reward during olfactory associative learning and memory. In the meantime, however, it has become clear that distinct types of dopaminergic neurons convey both reward and punishment signals to the mushroom bodies, a central brain region responsible for the formation and storage of associative memories. Although some conflicting data remain, these findings challenge the previously established model of functional segregation and may limit the proposed role of octopamine neurons in mediating reinforcing information during memory formation. We have therefore re-examined the role of octopamine in learning and memory in Drosophila larvae. Our findings suggest that optogenetic activation of octopaminergic neurons is sufficient to drive appetitive and aversive memory formation. Further, through a combination of Ca2+ imaging, anatomical studies, and loss-of-function behavioral approaches, we demonstrate that octopamine signaling plays a crucial role in larval learning by modulating dopaminergic neurons across distinct cell clusters to orchestrate memory processes.