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The effect of melatonin supplementation on the plasma levels of 2-arachidonoylglycerol, ghrelin and hedonic eating intensity in overweight/obese females: A study protocol for a pilot randomized controlled trial
Introduction Hedonic eating, reward-driven eating rather than out of biological needs, has been proposed as one of the important causes of overweight and obesity in recent years. Dopamine, endocannabinoids, opioids, and ghrelin are among the physiological factors associated with hedonic eating. Since the results of some previous animal studies have indicated the effectiveness of melatonin supplementation on the levels of endocannabinoids, and ghrelin, therefore this pilot study will investigate the effect of melatonin supplementation on plasma levels of endocannabinoid 2-arachidonylglycerol, ghrelin, and the intensity of hedonic eating in overweight/obese females. Methods In a randomized, double-blinded, placebo-controlled study, forty-six women with overweight/obesity and high hedonic eating intensity (total score of power of food scale > 2.5) will be recruited. They will receive either a 5 mg/day melatonin supplement (n = 23) or a placebo (n = 23) for 8 weeks. The primary outcomes, including the plasma levels of 2-arachidonylglycerol and ghrelin, and the intensity of hedonic eating will be assessed at the baseline and end of the study. Additionally, the secondary outcomes (dietary intake, and body weight) will be evaluated at the study’s onset, after four weeks, and upon completion of the intervention. A one-way analysis of covariance (ANCOVA) will be used to detect the effect of melatonin supplementation on outcome variables. Discussion Considering the positive effects of melatonin supplementation in reducing endocannabinoid levels, the expression of the ghrelin hormone gene, the level of ghrelin, and the cannabinoid receptor type 1 gene expression in animal studies, it is possible that in human subjects, it could impact the intensity of hedonic eating by lowering endocannabinoid and ghrelin levels. Trial registration The trial was registered with the Iranian Registry of Clinical Trials in June 2023 under the ID number IRCT20080904001197N22.
Boosting hydrogel conductivity via water-dispersible conducting polymers for injectable bioelectronics
Abstract Bioelectronic devices hold transformative potential for healthcare diagnostics and therapeutics. Yet, traditional electronic implants often require invasive surgeries and are mechanically incompatible with biological tissues. Injectable hydrogel bioelectronics offer a minimally invasive alternative that interfaces with soft tissue seamlessly. A major challenge is the low conductivity of bioelectronic systems, stemming from poor dispersibility of conductive additives in hydrogel mixtures. We address this issue by engineering doping conditions with hydrophilic biomacromolecules, enhancing the dispersibility of conductive polymers in aqueous systems. This approach achieves a 5-fold increase in dispersibility and a 20-fold boost in conductivity compared to conventional methods. The resulting conductive polymers are molecularly and in vivo degradable, making them suitable for transient bioelectronics applications. These additives are compatible with various hydrogel systems, such as alginate, forming ionically cross-linkable conductive inks for 3D-printed wearable electronics toward high-performance physiological monitoring. Furthermore, integrating conductive fillers with gelatin-based bioadhesive hydrogels substantially enhances conductivity for injectable sealants, achieving 250% greater sensitivity in pH sensing for chronic wound monitoring. Our findings indicate that hydrophilic dopants effectively tailor conducting polymers for hydrogel fillers, enhancing their biodegradability and expanding applications in transient implantable biomonitoring.
Affective touch reduces histamine evoked itch experience
Itch is a commonly experienced symptom of skin diseases such as eczema. Topical corticosteroid medications are widely used in chronic itch conditions but can lead to skin thinning, and in certain cases, topical corticosteroid withdrawal. As such, non-pharmaceutical alternatives are being researched. The present research explored affective touch (slow stroking, gentle touch signalled by C-tactile afferents) as a strategy to reduce histamine induced itch. Whilst experiencing histamine induced itch on the volar side of the forearms/wrist, participants (n = 60) were subjected to 3 experimental conditions of modulatory somatosensation applied to the volar aspect of the same forearm relative to the site of itch induction (18 trials of each); 1) affective touch (stroking the forearm with a soft brush at 3 cm/s), 2) non-affective touch (stroking the forearm with a soft brush at 18 cm/s) and 3) active control (static brush tapping on the forearm at 1Hz). Participants were asked to rate the severity of itch, and pleasantness of touch, after each trial. We also investigated whether changes in itch severity scores during the affective touch condition were moderated by individual differences in somatosensory experiences and attitudes as measured on the Touch Experiences and Attitudes Questionnaire (TEAQ), and the Pain Vigilance and Awareness Questionnaire (PVAQ). A linear mixed effects model indicated a main effect of condition on itch severity, whereby affective touch significantly reduced itch severity compared to non-affective touch (p < .001) and active control (p < .001). The TEAQ and PVAQ scores did not correlate significantly with itch scores in the affective touch condition. These results suggest that affective touch has a relieving effect on histamine-induced itch. Our findings lend further credibility to the idea that affective touch might be able to serve as an effective non-pharmaceutical treatment of itch conditions complementing established approaches.
Correction to Supporting Information for Moreno-Spiegelberg et al., How spatiotemporal dynamics can enhance ecosystem resilience
Etched BiVO4 photocatalyst with charge separation efficiency exceeding 90%
RETRACTED: A deep learning-based ensemble for autism spectrum disorder diagnosis using facial images
The Sex Inclusive Research Framework to address sex bias in preclinical research proposals
Chameleon sequences—Structural effects
The predisposition of amino acids towards accepting the appropriate secondary structure form is ambiguous. The identified sequences (6–12 aa in length – ChSeq data base) of the chameleon type (the same sequence accepting different secondary structures) constitute a puzzle that makes it difficult to indicate the initial conformation in a chain with a given amino acid sequence. The analysis of proteins presented in this paper uses the hydrophobicity distribution in protein body as the criterion for comaparable analysis of the status of helica/Beta-structural chameleon fragments in pairs of proteins. The sub-base is the object of analysis containg the proteins representing the organisation of hydrophobicity in one protein of the pair as ordered according to micelle-like organisation (hydrophobic core with polar surface) and the second one in pair with disordered hydrophobicity organisation. The status of chameleon sections appears to represent local organisation of hydrophobicity highly accordant in both proteins in chameleon pair independently on the status of the structural unit they belong to. The fuzzy oil drop model (FOD) in its modified form (FOD-M) is applied for analysis. This work aims to verify the hypothesis assuming the subordination of the form of secondary structure to the superior goal of obtaining a hydrophobicity distribution suitable for the given biological activity of the protein, ensuring biological functionality. Secondary structure is not an aim by itself. It is shown, that the main goal is to reach the structure representing specific activity. Secondary structure is a means to achieve this goal.
Superadditive communication with the green machine as a practical demonstration of nonlocality without entanglement
Data-driven survival modeling for breast cancer prognostics: A comparative study with machine learning and traditional survival modeling methods
Background This investigation delves into the potential application of data-driven survival modeling approaches for prognostic assessments of breast cancer survival. The primary objective is to evaluate and compare the ability of machine learning (ML) models and conventional survival analysis techniques, to identify consistent key predictors of breast cancer survival outcomes. Methods This study employs data-driven survival modeling approaches to predict breast cancer survival, including survival-specific methods such as the Cox Proportional Hazards (CPH) model, Random Survival Forests (RSF), and Cox Proportional Deep Neural Networks (DeepSurv), as well as machine learning models like Random Forests (RF), XGBoost, Support Vector Machines (SVM) with an RBF Kernel, and LightGBM. The dataset, sourced from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) program, comprises 4,024 women diagnosed with infiltrating duct and lobular carcinoma breast cancer between 2006 and 2010. To ensure interpretability across all models, the Shapley Additive Explanation (SHAP) method was applied to RSF, DeepSurv, Random Forests (RF), and XGBoost. This enabled the identification of key predictors influencing breast cancer survival, highlighting consistent factors across models while uncovering unique insights specific to each approach. Results The performance of survival-specific and ML models were evaluated using the Concordance index (C-index), Integrated Brier Score (IBS), mean accuracy, and mean AUC. The CPH model achieved a C-index of 0 . 71 ± 0 . 015 and an IBS of 0 . 08 ± 0 . 006, while RSF demonstrated slightly better discriminatory power with a C-index of 0 . 72 ± 0 . 0117. DeepSurv performed comparably, with a C-index of 0 . 71 ± 0 . 0095 and an IBS of 0 . 09 ± 0 . 0008. Both Cox and RSF models achieved the lowest IBS (0 . 08), indicating accurate survival probability predictions over time. For ML models, RF achieved a mean AUC of 0 . 74 ± 0 . 0021, and XGBoost with a mean AUC 0 . 69 ± 0 . 0183, reflecting fair discriminatory ability but not accounting for censoring in survival data. SHAP analysis for the top-performing models highlighted the extent of lymph node involvement, Regional Node-Positive (number of affected lymph nodes), tumor grade (cell abnormality and growth rate), progesterone status, and age as key predictors of breast cancer survival outcomes. Conclusions While ML models like XGBoost and RF can effectively identify important predictors and patterns in breast cancer outcomes, survival-specific methods such as the Cox model, RSF, and DeepSurv provide essential capabilities for handling time-to-event data and censoring, making them more suitable for accurate survival predictions. The primary objective of including ML models in this analysis was to leverage their interpretability in identifying key variables alongside survival-specific models, rather than to directly compare their performance against survival models. By examining both ML and survival models, this research highlights the complementary strengths of each approach. This study contributes to the integration of artificial intelligence in healthcare, emphasizing the value of data-driven survival modeling techniques in supporting healthcare professionals with accurate, personalized, and actionable insights for high-risk patients. Together, these approaches enhance the precision of survival predictions, paving the way for more informed clinical decision-making and improved patient care.
Broken symmetries associated with a Kagome chiral charge order
Digital transformation and the choice of management control modes in enterprise groups
Digital transformation has a significant impact on the choice of management control modes within enterprise groups. This study uses data from publicly listed companies in China from 2010 to 2022 to empirically examine the effect of digital transformation on the management control modes of enterprise groups. It further explores the mechanism and moderating effects of digital transformation in influencing the selection of management control modes. The findings indicate that, under the impact of digital transformation, enterprise groups are more inclined to adopt decentralized management control modes. The mechanism analysis suggests that digital transformation can mitigate principal-agent problems between parent and subsidiary companies by improving internal control quality, thus promoting a decentralized management control mode. The moderating effects reveal that the facilitative impact of digital transformation on choosing a decentralized control mode is more pronounced in state-owned enterprise groups and those operating in environments with higher uncertainty. Moreover, as the level of digital transformation or corporate governance improves, enterprise groups are more likely to adopt decentralized management control modes. This study extends the measurement indicators for the choice of management control modes in enterprise groups across four dimensions: personnel authority, operational authority, investment authority, and financial authority, and constructs a research framework to reveal the practical effects of China’s digital transformation strategy.
Injectable extracellular vesicle hydrogels with tunable viscoelasticity for depot vaccine
Benefits of a non-traditional science communication and internship experience based on research from the National Science Foundation Research Traineeship at a Research Intensive University
Effective science communication and stakeholder engagement are crucial skills for climate scientists, yet formal training in these areas remains limited in graduate education. The National Science Foundation Research Traineeship (NRT) at Auburn University (AU) addresses this gap through an innovative program combining science communication training with co-production approaches to enhance climate resiliency of built, natural, and social systems within the Southeastern United States (US). This paper evaluates the effectiveness of two novel graduate-level courses: one focused on science communication for non-technical audiences and another combining co-production methods with practical internship experience. Our research employed a mixed-methods approach, including a comprehensive analysis of course catalogs from 146 research-intensive universities and qualitative assessment of student experiences through surveys and descriptive exemplars. Analysis revealed that AU’s NRT program is unique among peer institutions in offering both specialized science communication training and co-production internship opportunities to graduate students across departments. Survey data from 11 program participants and detailed case studies of three program graduates demonstrated significant professional development benefits. Key outcomes included enhanced stakeholder engagement capabilities, improved science communication skills, and better preparation for both academic and non-academic careers. These findings suggest that integrating structured science communication training with hands-on co-production experience provides valuable preparation for climate scientists. The success of AU’s program model indicates that similar curriculum structures could benefit graduate programs nationwide, particularly in preparing students to effectively communicate complex scientific concepts to diverse audiences and engage with stakeholders in climate resilience efforts.
The size of critical secondary nuclei of polymer crystals does not depend on supersaturation
A modification of technology acceptance model for investigating driver-vehicle interaction systems usage
This descriptive study investigated respondents’ acceptance of driver-vehicle interaction systems by modifying the technology acceptance model using user experience concepts. A questionnaire survey was conducted, which including 15 variables and a Likert scale (1–5) range from ‘strongly agree’ to ‘strongly disagree’ was adopted for all variables. The questionnaire examined six constructs proposed in the modified technology acceptance model: perceived usefulness, perceived enjoyment, satisfaction, attitude, interactive media, and user interface. 495 samples aged from 21 to 82 years old (48.7 ± 12.5 years) including 279 males and 216 females were collected for data analysis. Confirmatory factor analysis demonstrated the reliability and validity of the model using several indices. Correlations between constructs were proven using path analysis (p < 0.05). Then, the influences of drivers’ gender (male = 1 and female = 2) and age on the constructs were analyzed using linear regression analysis. Male drivers had higher perceived usefulness (Beta = 0.235, p < 0.01) and more positive attitudes (Beta = 0.087, p < 0.01) than female drivers. However, their perceived enjoyment of the system was lower than that of females (Beta = -0.135, p < 0.01). Older age led drivers to prefer a negative attitude toward driver-vehicle interaction systems (Beta = 0.072, p < 0.01) and dislike utilizing the user interface (Beta = 0.139, p < 0.01). Finally, four implications based on the analysis were proposed to guide the design of driver-vehicle interaction systems. This study clarified the feasibility of the modified technology acceptance model for investigating Japanese driver’s opinions on driver-vehicle interaction systems and provided insights for designing vehicle human-machine interfaces to improve driver acceptance.
SAMD9 senses cytosolic double-stranded nucleic acids in epithelial and mesenchymal cells to induce antiviral immunity
Detection of micro-pinhole defects on surface of metallized ceramic ring combining improved DETR network with morphological operations
Metallized Ceramic Ring is a novel electronic apparatus widely applied in communication, new energy, aerospace and other fields. Due to its complicated technique, there would be inevitably various defects on its surface; among which, the tiny pinhole defects with complex texture are the most difficult to detect, and there is no reliable method of automatic detection. This Paper proposes a method of detecting micro-pinhole defects on surface of metallized ceramic ring combining Improved Detection Transformer (DETR) Network with morphological operations, utilizing two modules, namely, deep learning-based and morphology-based pinhole defect detection to detect the pinholes, and finally combining the detection results of such two modules, so as to obtain a more accurate result. In order to improve the detection performance of DETR Network in aforesaid module of deep learning, EfficientNet-B2 is used to improve ResNet-50 of standard DETR network, the parameter-free attention mechanism (SimAM) 3-D weight attention mechanism is used to improve Sequeeze-and-Excitation (SE) attention mechanism in EfficientNet-B2 network, and linear combination loss function of Smooth L1 and Complete Intersection over Union (CIoU) is used to improve regressive loss function of training network. The experiment indicates that the recall and the precision of the proposed method are 83.5% and 86.0% respectively, much better than current mainstream methods of micro defect detection, meeting requirements of detection at industrial site.