mdpi-ai/citelens huggingface dataset:
The use of video feedback to promote developmentally supportive parent–child interactions with young children with ASD or at risk: study protocol for a randomized controlled trial (VIFEPOPA-RCT)
Field: PSYCHOLOGY

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by difficulties in social communication and interaction, and repetitive and restrictive behaviors and interests from an early age. ASD often negatively affects caregiver-child interactions, caregiver emotional well-being and self-efficacy, and quality of family life. Positive caregiver–child interactions are crucial for good developmental outcomes, leading to the development of Parent-Mediated Interventions (PMIs). PMIs tend to follow an expert model where professionals provide direct instruction on treatment techniques and parental behaviors. However, research supports a shift towards a more collaborative and reflective approach, using coaching strategies that highlight caregiver strengths and encourage self-reflection. This study tests a video-feedback intervention (VFI) with parents of young children at risk of ASD. A randomized controlled trial (RCT) with 60 families, recruited from Early Intervention Centers in Spain, meeting inclusion criteria: adequate use of internet, child aged 24–36 months with a high risk of ASD (M-CHAT-R score ≥ 8), and participant primary caregiver (mother or father) with high anxiety, depression, or parental stress (score ≥ 1 SD above M), and low or medium–low developmentally supportive parental behaviors (PICCOLO score ≤ 40). Families will be randomly assigned to an intervention group (receiving usual services plus VFI) or a control group (usual services). The intervention includes twelve bi-weekly 90-min sessions over six months, with the caregiver. Outcome measures include parenting behaviors, emotional state, self-efficacy, family quality of life, and child development collected at pre-intervention, post- intervention and six-month follow-up. The study will assess whether the intervention enhances developmentally supportive parental behaviors, emotional well-being, self-efficacy, and family quality of life, with a secondary positive impact on child development. If proven effective, it could be a cost-effective intervention with both short and long-term benefits. ClinicalTrials.gov Identifier NCT06604988. Registered on September 17, 2024. Retrospectively registered.
Basic psychological needs satisfaction as a mediator of the effects of a formative assessment practice on behavioral engagement and autonomous motivation
Field: EDUCATION

Formative assessment has been suggested as a means of supporting student motivation. However, empirical studies have shown mixed effects of formative assessment interventions on students’ motivation, making it necessary to understand the mechanisms underlying these effects. We analyzed a formative classroom practice implemented by a 10th-grade first-language teacher during 7 months. Teacher logs, classroom observations and a teacher interview were used to collect data for characterizing the formative assessment practice. Changes in students’ satisfaction regarding the basic psychological needs of perceived autonomy, competence and relatedness, as well as changes in student motivation manifesting as engagement in learning activities and autonomous types of motivation, were measured by pre- and post-questionnaires in the intervention class and four comparison classes. Since the intraclass correlation values ICC(1) and ICC(2) were low, we treated the comparison classes as one group and t-tests were used in the significance testing of the differences in changes in psychological needs satisfaction and motivation between the intervention class and the comparison classes. Path analysis was conducted to investigate whether a possible influence of the intervention on autonomous motivation and behavioral engagement would be mediated by basic psychological needs satisfaction. The analysis of the classroom practice in the intervention class identifies that both teacher and students were proactive agents in formative assessment processes. The analysis of the quantitative data shows that students’ psychological needs satisfaction increased more in the intervention class than in the comparison classes, and that this needs satisfaction mediated an effect on students’ behavioral engagement and autonomous motivation.
Impact of interdisciplinary tumor boards (ITB) and personalized treatment on survival outcomes in metastatic castration-resistant prostate cancer
Field: ONCOLOGY

Purpose: Interdisciplinary tumor boards (ITB) are essential in optimizing treatment recommendations for metastatic castration-resistant prostate cancer (mCRPC) by incorporating oncology guidelines, clinical trials, and patient-specific factors to ensure individualized care. This study examines clinical parameters that influence ITB recommendations, evaluates their adherence to guidelines, and assesses their impact on patient survival. Methods: In a retrospective analysis, data from 187 mCRPC patients discussed at an ITB in a tertiary care center in 2018 were evaluated. Patient- and disease-specific factors were correlated with adherence to National Comprehensive Cancer Network® (NCCN®) guidelines and overall survival (OS). The impact of clinical parameters on survival outcomes was assessed through univariate and multivariate analyses. Results: The median patient age was 72.8 years, with a median prostate-specific antigen (PSA) level of 65.0 ng/ml. Guideline-compliant recommendations were given in 42.9% of cases, while 57.1% received individualized recommendations. Clinical trial eligibility was noted in 24.8% of patients. Individualized ITB recommendations were associated with significantly longer OS (38.3 vs. 21.2 months, p = 0.03). Shorter OS correlated with renal impairment (p = 0.007), symptomatic metastases (p < 0.0001), and visceral metastases (p < 0.0001). Limitations include the retrospective design, lack of follow-up on therapy adherence, and absence of progression-free survival (PFS) data. Conclusion: ITB discussions improve survival in mCRPC patients, mainly due to personalized approaches and better access to clinical trials. Visceral and symptomatic metastases as well as renal impairment are risk factors for reduced OS, emphasizing the need for careful management of these high-risk patients. The results support the expanded use of ITB to improve mCRPC treatment outcomes.
An encounter with death: a comparative thematic and content analysis of naturalistic DMT experiences and the near-death experience
Field: PSYCHOLOGY

Introduction: Classical near-death experiences (NDEs) refer to states of disconnected consciousness characterised by a range of features occurring in the context of being close to death. Various psychedelic substances, such as N,N-dimethyltryptamine (DMT), consistently replicate NDE features and may be considered ‘near-death-like experiences.’ However, a systematic qualitative analysis comparing the specifics of content with the broader themes of both psychedelic and NDEs has yet to be conducted.Methods: We report the third thematic and content analysis of the DMT experience from a naturalistic field study, focusing on themes related to death and dying. Based on 36 semi-structured interviews, this analysis is then directly compared, qualitatively and in terms of content frequency, with a novel extension of a previous thematic analysis of 34 written NDE narratives.Results: The ‘canonical NDE themes’ identified across the DMT experiences included Translocation, Bright Light(s), Sense of Dying, The Void, Disembodiment, Tunnel-like Structures, Light Being-esque Entities, Deceased Family, Life Review-like, and Hyper-empathic Experiences. A total of 95% of participants reported at least one of these. Twelve ‘less typical NDE motifs’ were also noted. Five classical NDE features were entirely absent from DMT, while DMT exhibited an even broader array of experience features that were absent from NDEs. DMT clearly shares a more basic phenomenological structure with NDEs but shows differences in the prevalence of certain features. Furthermore, DMT did not present any immediately recognisable linear sequencing of themes. Overall, DMT is distinctly unique in its qualitative content, characterised by its more prodigious and stereotypical nature, which includes kaleidoscopic, extraterrestrial, transcultural, fluctuating, and overwhelming elements.Discussion: When examining the comparability between DMT and NDEs at a fundamentally more nuanced level of qualitative content (as opposed to broad themes or questionnaire items), the two experiences clearly diverge. However, a minority of NDEs, which are themselves unique, do share significant content with DMT. Taken together, DMT could be considered an ‘NDE-mimetic.’ The weaker comparability is likely due not only to differences in context but also to the complex neural processes occurring near death, in which endogenous DMT may only play a small role. In light of this level of parallelism with NDEs, some potential clinical applications of DMT are also discussed.
The relevance of lead prioritization: a B2B lead scoring model based on machine learning
Field: AI

In business-to-business (B2B) companies, marketing and sales teams face significant challenges in identifying, qualifying, and prioritizing a large number of leads. Lead prioritization is a critical task for B2B organizations because it allows them to allocate resources more effectively, focus their sales force on the most viable and valuable opportunities, optimize their time spent qualifying leads, and maximize their B2B digital marketing strategies. This article addresses the topic by presenting a case study of a B2B software company's development of a lead scoring model based on data analytics and machine learning under the consumer theory approach. The model was developed using real lead data generated between January 2020 and April 2024, extracted from the company's CRM, which were analyzed and evaluated by fifteen classification algorithms, where the results in terms of accuracy and ROC AUC showed a superior performance of the Gradient Boosting Classifier over the other classifiers. At the same time, the feature importance analysis allowed the identification of features such as “source” and “lead status,” which increased the accuracy of the conversion prediction. The developed model significantly improved the company's ability to identify high quality leads compared to the traditional methods used. This research confirms and complements existing theories related to understanding the application of consumer behavior theory and the application of machine learning in the development of B2B lead scoring models. This study also contributes to bridging the gap between marketers and data scientists in jointly understanding lead scoring as a critical activity because of its impact on overall marketing strategy performance and sales revenue performance in B2B organizations.
Semaglutide, a glucagon-like peptide-1 receptor agonist, inhibits oral squamous cell carcinoma growth through P38 MAPK signaling pathway
Field: ONCOLOGY

Aims Researches have shown that diabetes mellitus (DM) can promote the risk and progression of oral squamous cell carcinoma (OSCC). Semaglutide, a glucagon-like peptide-1 receptor agonist, is currently employed to treat type 2 diabetes mellitus (T2DM) and obesity. This study intends to explore the potential effects and mechanism of Semaglutide on OSCC. Methods The expression of GLP-1R in OSCC cells and tissues was evaluated by qRT-PCR, western blot and immunohistochemistry assays. Cell proliferation, invasion, migration and apoptosis abilities were determined by relevant experiments. Western blot was employed to verify the expression of relevant proteins and examine the effect of Semaglutide on the MAPK signaling pathway. The xenograft transplantation model of OSCC was established to examine the anti-cancer effects of Semaglutide in vivo and immunohistochemistry assays were performed on tumor tissues. Results GLP-1R expression was elevated in OSCC cells and tissues as compared with that in normal. Semaglutide effectively inhibited the proliferation, migration and invasion of OSCC cells while concurrently promoting apoptosis. Moreover, Semaglutide specifically activated the P38 MAPK signaling pathway without significant influence on ERK1/2 or SAPK/JNK, and its pro-apoptotic effects in OSCC cells was related to P38 pathway activation. Animal experiments verified the inhibitory effect of Semaglutide on OSCC tumors in mice. Conclusions Semaglutide exerts inhibitory actions on OSCC and may induce apoptosis in OSCC cells via the P38 MAPK signaling pathway. This study has significant implications for the treatment of patients with diabetes who are also afflicted by OSCC.
Trade-Offs in Navigation Problems Using Value-Based Methods
Field: AI

Deep Q-Networks (DQNs) have shown remarkable results over the last decade in scenarios ranging from simple 2D fully observable short episodes to partially observable, graphically intensive, and complex tasks. However, the base architecture of a vanilla DQN presents several shortcomings, some of which were mitigated by new variants focusing on increased stability, faster convergence, and time dependencies. These additions, on the other hand, bring increased costs in terms of the required memory and lengthier training times. In this paper, we analyze the performance of state-of-the-art DQN families in a simple partially observable mission created in Minecraft and try to determine the optimal architecture for such problem classes in terms of the cost and accuracy. To the best of our knowledge, the analyzed methods have not been tested on the same scenario before, and hence a more in-depth comparison is required to understand the real performance improvement they provide better. This manuscript also offers a detailed overview of state-of-the-art DQN methods, together with the training heuristics and performance metrics registered during the proposed mission, allowing researchers to select better-suited models to solving future problems. Our experiments show that Double DQN networks are capable of handling partially observable scenarios gracefully while maintaining a low hardware footprint, Recurrent Double DQNs can be a good candidate even when the resources must be restricted, and double-dueling DQNs are a well-performing middle ground in terms of their cost and performance.
Procedural Learning in Mixed Reality: Assessing Cognitive Load and Performance
Field: EDUCATION

Immersive technologies offer promising advancements in medical education, particularly in procedural skill acquisition. However, their implementation often lacks a foundation in learning theories. This study investigates the application of the split-attention principle, a multimedia learning guideline, in the design of knot-tying procedural content using a mixed reality (MR) technology, specifically Microsoft HoloLens 2. A total of 26 participants took part in a between-group design experiment comparing integrated and split-source formats for learning arthroscopic knots, with the performance and the cognitive load assessed. The initial hypotheses were not confirmed, as results did not show significant differences in performance during recall, nor in extraneous and germane cognitive load. However, the findings on intrinsic cognitive load highlight the complexity of participant engagement and the cognitive demands of procedural learning. To better capture the split-attention effect, future research should address the high element interactivity in MR representations. The study provides some foundation for designing procedural simulation training that considers both learners’ needs and cognitive processes in highly immersive environments. It contributes to the ongoing exploration of instructional design in MR-based medical education, emphasizing both the potential and challenges of multimedia learning principles in advanced technological contexts.
Sex, Resilience and Psychological Well-Being in Mexican University Students
Field: PSYCHOLOGY

Mental health is currently highly relevant in society and one of the factors that could contribute to its improvement is psychological well-being, hence the importance of conducting studies that focus on analyzing variables that predict psychological well-being. Therefore, the goal of this research is to use models of structural equations to analyze the relationships among the variables of sex and resilience for psychological well-being. The total sample was 1190 Mexican university students, with an average age of 20.66 years (SD = 1.89). The results indicate that the resilience factors (strength and confidence, family support, and social support) are the variables with the greatest explanatory power on psychological well-being. It also highlights the mediating capacity of the strength and confidence factor between the other two resilience factors (family support, social support) and perceived psychological well-being. The implications of the study are that sex and two of the dimensions of resilience (family support and social support) show an indirect and positive effect on the perception of psychological well-being through the strength and confidence factor. Therefore, when implementing interventions to improve psychological well-being, these factors should be considered in order to have a greater positive impact on the population that is being studied. Future research should replicate these findings in larger samples.
Code generation system based on MDA and convolutional neural networks
Field: AI

Introduction: The software industry has rapidly evolved with high performance. This is owing to the implementation of good programming practices and architectures that make it scalable and adaptable. Therefore, a strong incentive is required to develop the processes that initiate this project.Method: We aimed to provide a platform that streamlines the development process and connects planning, structuring, and development. Specifically, we developed a system that employs computer vision, deep learning, and MDA to generate source code from the diagrams describing the system and the respective study cases, thereby providing solutions to the proposed problems.Results and discussion: The results demonstrate the effectiveness of employing computer vision and deep learning techniques to process images and extract relevant information. The infrastructure is designed based on a modular approach employing Celery and Redis, enabling the system to manage asynchronous tasks efficiently. The implementation of image recognition, text analysis, and neural network construction yields promising outcomes in generating source code from diagrams. Despite some challenges related to hardware limitations during the training of the neural network, the system successfully interprets the diagrams and produces artifacts using the MDA approach. Plugins and DSLs enhance flexibility by supporting various programming languages and automating code deployment on platforms such as GitHub and Heroku.
A Bibliometric Analysis on Artificial Intelligence in the Production Process of Small and Medium Enterprises
Field: AI

Industry 4.0 represents the main paradigm currently bringing great innovation in the field of automation and data exchange among production technologies, according to the principles of interoperability, virtualization, decentralization and production flexibility. The Fourth Industrial Revolution is driven by structural changes in the manufacturing sector, such as the demand for customized products, market volatility and sustainability goals, and the integration of artificial intelligence and Big Data. This work aims to analyze, from a bibliometric point of view of journal papers on Scopus, with no time limitation, the existing literature on the application of AI in SMEs, which are crucial elements in the industrial and economic fabric of many countries. However, the adoption of modern technologies, particularly AI, can be challenging for them, due to the intrinsic structure of this type of enterprise, despite the positive effects obtained in large organizations.
Integrating Pose Features and Cross-Relationship Learning for Human–Object Interaction Detection
Field: AI

Background: The main challenge in human–object interaction detection (HOI) is how to accurately reason about ambiguous, complex, and difficult to recognize interactions. The model structure of the existing methods is relatively single, and the image input may be occluded and cannot be accurately recognized. Methods: In this paper, we design a Pose-Aware Interaction Network (PAIN) based on transformer architecture and human posture to address these issues through two innovations: A new feature fusion method is proposed, which fuses human pose features and image features early before the encoder to improve the feature expression ability, and the individual motion-related features are additionally strengthened by adding to the human branch; the Cross-Attention Relationship fusion Module (CARM) better fuses the three-branch output and captures the detailed relationship information of HOI. Results: The proposed method achieves 64.51%AProle#1, 66.42%AProle#2 on the public dataset V-COCO and 30.83% AP on HICO-DET, which can recognize HOI instances more accurately.
Emotion-Aware Embedding Fusion in Large Language Models (Flan-T5, Llama 2, DeepSeek-R1, and ChatGPT 4) for Intelligent Response Generation
Field: AI

Empathetic and coherent responses are critical in automated chatbot-facilitated psychotherapy. This study addresses the challenge of enhancing the emotional and contextual understanding of large language models (LLMs) in psychiatric applications. We introduce Emotion-Aware Embedding Fusion, a novel framework integrating hierarchical fusion and attention mechanisms to prioritize semantic and emotional features in therapy transcripts. Our approach combines multiple emotion lexicons, including NRC Emotion Lexicon, VADER, WordNet, and SentiWordNet, with state-of-the-art LLMs such as Flan-T5, Llama 2, DeepSeek-R1, and ChatGPT 4. Therapy session transcripts, comprising over 2000 samples, are segmented into hierarchical levels (word, sentence, and session) using neural networks, while hierarchical fusion combines these features with pooling techniques to refine emotional representations. Attention mechanisms, including multi-head self-attention and cross-attention, further prioritize emotional and contextual features, enabling the temporal modeling of emotional shifts across sessions. The processed embeddings, computed using BERT, GPT-3, and RoBERTa, are stored in the Facebook AI similarity search vector database, which enables efficient similarity search and clustering across dense vector spaces. Upon user queries, relevant segments are retrieved and provided as context to LLMs, enhancing their ability to generate empathetic and contextually relevant responses. The proposed framework is evaluated across multiple practical use cases to demonstrate real-world applicability, including AI-driven therapy chatbots. The system can be integrated into existing mental health platforms to generate personalized responses based on retrieved therapy session data. The experimental results show that our framework enhances empathy, coherence, informativeness, and fluency, surpassing baseline models while improving LLMs’ emotional intelligence and contextual adaptability for psychotherapy.