Search Results (87)

Background/Objectives: In the treatment of autism spectrum disorders, families express the need for dedicated clinical spaces to manage emotional overload and to develop effective relational skills. Parent training addresses this need by supporting the parent–child relationship and fostering the child’s development. This study proposes a clinical protocol designed for psychotherapists and behavior analysts, based on the Autism Open Clinical Model (A.-O.C.M.), which integrates the rigor of Applied Behavior Analysis (ABA) with a phenomenological and embodied perspective. The model acknowledges technology—particularly artificial intelligence—as an opportunity to structure adaptive and personalized intervention tools. Methods: A multi-level prompt design system was developed, grounded in the principles of the A.-O.C.M. and integrated with generative AI. The tool employs clinical questions, semantic constraints, and levels of analysis to support the clinician’s reasoning and phenomenologically informed observation of behavior. Results: Recurrent relational patterns emerged in therapist–caregiver dynamics, allowing the identification of structural elements of the intersubjective field that are useful for personalizing interventions. In particular, prompt analysis highlighted how the quality of bodily and emotional attunement influences readiness for change, suggesting that intervention effectiveness increases when the clinician can adapt their style according to emerging phenomenological resonances. Conclusions: The design of clinical prompts rooted in embodied cognition and supported by AI represents a new frontier for psychotherapy that is more attuned to subjectivity. The A.-O.C.M. stands as a theoretical–clinical framework that integrates phenomenology and intelligent systems. Full article

Effective classification and progress monitoring are central to inclusive education, ensuring that students with learning challenges receive timely and appropriate support. However, both international research and Qatari educators’ experiences reveal inconsistencies, limited resources, and a persistent gap between policy and practice. This qualitative study explored the perspectives of 20 stakeholders, including teachers, school leaders, coordinators, and policymakers. Thematic analysis conducted using ATLAS.ti 25 produced six main themes: inconsistent classification; staff and resource shortages; family resistance and collaboration; policy and accommodation gaps; fragmented monitoring; and innovative, inclusive practices. Participants described over-reliance on external diagnostic reports, inconsistent eligibility criteria, limited access to specialists, overcrowded classrooms, and insufficient early screening. Disconnected tools and the lack of a centralized data system hindered monitoring. Despite these barriers, educators showed adaptability through classroom-based interventions, behavioral support, and the emerging use of digital and AI tools. Stake-holders emphasized the need for a unified national framework, systematic early screening, expanded accommodations, integrated Education Management Information System (EMIS) records, and continuous professional development with parent involvement. Findings highlight that classification and monitoring depend on governance, capacity, and data culture, underscoring the need for coordinated policy and practice to achieve equitable, sustainable inclusion in Qatar. Full article

Background: Corporal punishment (CP) remains a common disciplinary practice in many countries, despite evidence of its negative consequences for children’s development. Objective: This study examined the effectiveness of a culturally adapted intervention aimed at reducing parents’ use of CP. Method: Using a 12-month quasi-experimental longitudinal design, the study included an intervention group (n = 21) and a control group (n = 17). We administered standardized instruments at pretest and posttest to assess changes in parenting behavior, emotional regulation, and perceptions of child behavior. Artificial neural networks (ANNs) were used to model nonlinear relationships and classify group membership. Results: The intervention group showed significant improvements in parenting practices and emotion regulation. The ANN model classified participants with 74.6% accuracy. Key predictive variables included emotional suppression, physical punishment, and parental support and acceptance. Conclusions: These findings provide evidence for the effectiveness of the SERES program in reducing harmful parenting behaviors and promoting positive practices. Additionally, the use of AI models proved to be valuable for understanding complex behavioral changes, offering a promising approach for optimizing future interventions aimed at strengthening parenting and preventing family violence. Full article

Background/Objectives: Infant emotional responses during complementary feeding offer key insights into early developmental processes and feeding behaviors. AI-driven facial emotion analysis presents a novel, objective method to quantify these subtle expressions, potentially informing interventions in early childhood nutrition. We aimed to investigate how maternal and infant traits influence infants’ emotional responses during complementary feeding using an automated facial analysis tool. Methods: This multi-center study involved 117 typically developing infants (6–11 months) and their mothers. Standardized feeding sessions were recorded, and OpenFace software quantified six emotions (surprise, sadness, fear, happiness, anger, disgust). Data were normalized and analyzed via Generalized Estimating Equations to identify associations with maternal BMI, education, work status, and infant age, sex, and complementary feeding initiation. Results: Emotional responses did not differ significantly across five food groups. Infants of mothers with BMI > 30 kg/m² showed greater surprise, while those whose mothers were well-educated and not working displayed more happiness. Older infants and those introduced to complementary feeding before six months exhibited higher levels of anger. Parental or infant food selectivity did not significantly affect responses. Conclusions: The findings indicate that maternal and infant demographic factors exert a more pronounced influence on infant emotional responses during complementary feeding than the type of food provided. These results highlight the importance of integrating broader psychosocial variables into early feeding practices and underscore the potential utility of AI-driven facial emotion analysis in advancing research on infant development. Full article

Background: Supported infant-directed singing (IDS) for parents and their preterm infants has proven beneficial for parents and preterm infants’ health and relationship building. Studying parent–infant contingent interactions through behavioral observations is an established method for assessing the quality of interactions. Very few studies have measured contingency between parent and preterm infants in the neonatal period during supported IDS. Methods: We conducted a feasibility study to assess the possibility of analyzing parent–very preterm infant dyads’ contingency during supported IDS in the NICU. We recruited four mother–infant dyads and video-recorded a single music therapy (MT) session before their discharge from the hospital. Two independent researchers coded three selected segments (beginning, middle, and end) from each video, according to adapted behavioral scales with inter-rater agreement analysis. Contingency between infant and maternal behaviors was analyzed. Results: Twelve video segments were coded. High inter-rater agreements (Cohen’s kappa) were found for infant eye-opening (0.93), hand positions (0.79), and head orientation (0.94), as well as maternal head orientation (0.95) and vocalizations (0.95). During supported IDS, increased infant head orientation toward the mother, eyes closed, as well as maternal head orientation toward the infant (all p < 0.001), were recorded compared to no IDS. Direction of the maternal head toward her infant was contingent on the infant’s closed eyes, extended hands, and head not toward mother. Conclusions: This feasibility study demonstrates contingency between mothers and their preterm infants’ specific behaviors during IDS. These interactions can be analyzed through video segments with high inter-rater agreement. The method described might help in evaluating other modalities that might be related to contingency. Recent advances in AI can make this tool easier to accomplish, with further studies to evaluate the importance of contingency for child development. The findings suggest that supported IDS influences infant attention and regulation. Full article

Kazakhstan cultivates over 12 million hectares of wheat, primarily spring wheat in the northern region. Spring wheat yields are low, ranging from 1.2 to 1.7 t/ha depending on weather conditions. Northern Kazakhstan is served by five spring wheat breeding programs: A.I. Barayev Research and Production Centre for Grain Farming and Agricultural Experimental Stations located in the Aktobe, Karagandy, Kostanay, and North Kazakhstan regions. In 2022, a germplasm set was assembled, including cultivars and breeding lines from the five breeding programs, totaling 84 genotypes. This set was evaluated in field trials during 2022 and 2023 at the breeding programs that contributed to the germplasm (except Aktobe). The material was also screened for molecular markers associated with genes for agronomic traits. The study objective was to compare the diversity and performance of germplasm originating from different breeding programs and identify potential underlying drivers. Breeding sites grouped based on variations in air temperature, precipitation, and grain yield demonstrated both similarities and differences among sites. However, these similarities were not reflected in the agronomic performance of materials originating from different locations. The expectation that germplasm would perform best for grain yield at its “home” location was not always confirmed. Grouping of germplasm based on genetic diversity of 20 molecular markers was not related to similarities in environmental conditions at the places of origin. The performance and diversity of germplasm from each of the five breeding programs is apparently driven by factors beyond environment, including breeding strategy and methodology, parental pool, and, in the absence of modern tools, breeders’ intuition and selection robustness. Kazakh spring wheat breeding programs require improvement to remain competitive in the face of increasing pressure from introduced foreign cultivars. Full article

The treatment of adolescent idiopathic scoliosis (AIS) requires the use of orthopedic braces. However, few current designs provide real-time monitoring or inform clinicians about the precise adjustment of therapeutic pressure. The objective of this study is to develop a low-cost open-system prototype capable of providing future researchers with objective information regarding brace adherence and adjustment. For adherence evaluation, a market study was conducted to identify temperature-measuring devices and a custom system was developed to measure adjustment. Cor-Esc-25 was developed to monitor brace adherence using a non-invasive temperature sensor which connects via Bluetooth to the parents’ smartphone, which runs an app that uploads the data to an online platform accessible to clinicians. In addition, a custom-designed pressure sensing device was created. This system uses three patches connected to an acquisition board and are installed on the brace each time the patient visits the clinic. It connects to a customized application where clinicians can view all the information. Cor-Esc-25 represents a first step toward the creation of personalized consultations, where AIS treatment monitoring is based on objective criteria that consider both adherence and brace adjustment. Its design allows for easy integration into clinical settings, thereby improving the ability of researchers and clinicians to assess the effectiveness of brace treatment. Full article

The absence of parental presence has a direct impact on the emotional stability and social routines of children, especially during extended periods of separation from their family environment, as in the case of daycare centers, hospitals, or when they remain alone at home. At the same time, the technology currently available to provide emotional support in these contexts remains limited. In response to the growing need for emotional support and companionship in child care, this project proposes the development of a multi-platform software architecture based on artificial intelligence (AI), designed to be integrated into humanoid robots that assist children between the ages of 6 and 14. The system enables daily verbal and non-verbal interactions intended to foster a sense of presence and personalized connection through conversations, games, and empathetic gestures. Built on the Robot Operating System (ROS), the software incorporates modular components for voice command processing, real-time facial expression generation, and joint movement control. These modules allow the robot to hold natural conversations, display dynamic facial expressions on its LCD (Liquid Crystal Display) screen, and synchronize gestures with spoken responses. Additionally, a graphical interface enhances the coherence between dialogue and movement, thereby improving the quality of human–robot interaction. Initial evaluations conducted in controlled environments assessed the system’s fluency, responsiveness, and expressive behavior. Subsequently, it was implemented in a pediatric hospital in Guayaquil, Ecuador, where it accompanied children during their recovery. It was observed that this type of artificial intelligence-based software, can significantly enhance the experience of children, opening promising opportunities for its application in clinical, educational, recreational, and other child-centered settings. Full article

Artificial Intelligence (AI) profoundly shapes adolescents’ digital experiences, presenting both developmental opportunities and risks related to privacy and psychological well-being. This study investigates first the possible generational gap between adolescents and their parents in AI use and trust, and then the associations between the Trait Emotional Intelligence (trait EI), parenting styles, perceived social support, and parental involvement on adolescents’ use and trust in AI-based technologies. Participants were 170 adolescents (aged 13–17) and 175 parents from southern Italy, who completed standardized questionnaires assessing parenting styles, Trait Emotional Intelligence (Trait EI), social support, digital literacy, and use and trust in AI. Adolescents used AI more frequently than parents, especially for school- or work-related support and were more likely to seek behavioral advice from AI. They also showed higher trust in AI data security and the quality of behavioral advice than parents. Moreover, greater trait EI and more authoritative (vs. authoritarian) parenting were associated with less frequent AI use and lower use and trust in AI. In 47 matched parent–adolescent dyads, cluster analysis identified Balanced Users (higher trait EI, authoritative parenting, stronger support, cautious AI use) and At-Risk Users (lower trait EI, authoritarian parenting, lower support, heavier and more trusting AI use) Despite no causal inferences can be drawn due to the correlational nature of the data, the results suggested the importance of considering adolescents’ trait EI and authoritative parenting practices in supporting balanced and critical digital engagement, highlighting the concept of a “digital secure base” as essential for navigating the evolving digital landscape. Full article

This research compares speech discrimination abilities between 17 children who are hard-of-hearing (CHH) and 13 children with normal hearing (CNH), aged 9 to 36 months, using either a conditioned head turn (CHT) or condition play paradigm, for two phoneme pairs /ba-da/ and /sa-ʃa/. As CHH were tested in the aided and unaided conditions, CNH were also tested on each phoneme contrast twice to control for learning effects. When speech discrimination abilities were compared between CHH, with hearing aids (HAs), and CNH, there were no statistical differences observed in performance on stop consonant discrimination, but a significant statistical difference was observed for fricative discrimination performance. Among CHH, significant benefits were observed for /ba-da/ speech discrimination while wearing HAs, compared to the no HA condition. All CHH were early-identified, early amplified, and were enrolled in parent-centered early intervention services. Under these conditions, CHH demonstrated the ability to discriminate speech comparable to CNH. Additionally, repeated testing within 1-month did not result in a change in speech discrimination scores, indicating good test–retest reliability of speech discrimination scores. Finally, this research explored the question of infant/toddler listening fatigue in the behavioral speech discrimination task. The CHT paradigm included returning to a contrast (i.e., /a-i/) previously shown to be easier for both CHH and CNH to discriminate to examine if failure to discriminate /ba-da/ or /sa-ʃa/ was due to listening fatigue or off-task behavior. Full article

Exosomes are nanoscale extracellular vesicles (EVs) that carry biomolecular signatures reflective of their parent cells, making them powerful tools for non-invasive diagnostics and therapeutic monitoring. Despite their potential, clinical application is hindered by challenges such as low abundance, heterogeneity, and the complexity of biological samples. To address these limitations, plasmonic biosensing technologies—particularly propagating surface plasmon resonance (PSPR), localized surface plasmon resonance (LSPR), and surface-enhanced Raman scattering (SERS)—have been developed to enable label-free, highly sensitive, and multiplexed detection at the single-vesicle level. This review outlines recent advancements in nanoplasmonic platforms for exosome detection and profiling, emphasizing innovations in nanostructure engineering, microfluidic integration, and signal enhancement. Representative applications in oncology, neurology, and immunology are discussed, along with the increasingly critical role of artificial intelligence (AI) in spectral interpretation and diagnostic classification. Key technical and translational challenges—such as assay standardization, substrate reproducibility, and clinical validation—are also addressed. Overall, this review highlights the synergy between exosome biology and plasmonic nanotechnology, offering a path toward real-time, precision diagnostics via sub-femtomolar detection of exosomal miRNAs through next-generation biosensing strategies. Full article

This study investigates the impact of family engagement on student reading achievement in the United Arab Emirates (UAE) using counterfactual explainable artificial intelligence (CXAI) analysis. Drawing data from 24,600 students in the UAE PISA dataset, the analysis employed Gradient Boosting, SHAP (SHapley Additive exPlanations), and counterfactual simulations to model and interpret the influence of ten parental involvement variables. The results identified time spent talking with parents, frequency of family meals, and encouragement to achieve good marks as the strongest predictors of reading performance. Counterfactual analysis revealed that increasing the time spent talking with parents and frequency of family meals from their minimum (1) to maximum (5) levels, while holding other variables constant at their medians, could increase the predicted reading score from the baseline of 358.93 to as high as 448.68, marking an improvement of nearly 90 points. These findings emphasize the educational value of culturally compatible parental behaviors. The study also contributes to methodological advancement by integrating interpretable machine learning with prescriptive insights, demonstrating the potential of XAI for educational policy and intervention design. Implications for educators, policymakers, and families highlight the importance of promoting high-impact family practices to support literacy development. The approach offers a replicable model for leveraging AI to understand and enhance student learning outcomes across diverse contexts. Full article

The growing influence of technology in the healthcare industry has led to the creation of innovative applications that improve convenience, accessibility, and diagnostic accuracy. However, health applications face significant challenges concerning user privacy and data security, as they handle extremely sensitive personal and medical information. Privacy-Enhancing Technologies (PETs), such as Privacy-Attribute-based Credentials, Differential Privacy, and Federated Learning, have emerged as crucial tools to tackle these challenges. Despite their potential, PETs are not widely utilized due to technical and implementation obstacles. This research introduces a comprehensive framework for protecting health applications from privacy and security threats, with a specific emphasis on gamified mental health apps designed to manage Attention Deficit Hyperactivity Disorder (ADHD) in children. Acknowledging the heightened sensitivity of mental health data, especially in applications for children, our framework prioritizes user-centered design and strong privacy measures. We suggest an identity management system based on blockchain technology to ensure secure and transparent credential management and incorporate Federated Learning to enable privacy-preserving AI-driven predictions. These advancements ensure compliance with data protection regulations, like GDPR, while meeting the needs of various stakeholders, including children, parents, educators, and healthcare professionals. Full article

Background/Objectives: Adolescent Idiopathic Scoliosis (AIS) is a lateral curvature of the spine combined with rotation and associated postural changes. Curves are classified according to direction and the spinal region, with right thoracic curves being a common presentation. Curve magnitude is measured using Cobb angles on radiographs and is used to monitor curve progression, with one of the main aims of treatment being prevention of progression to surgical levels. Treatment options may include observation, physiotherapeutic scoliosis-specific exercises (PSSE), thoracolumbosacral orthotic (TLSO) bracing, or surgery and are dependent on curve magnitude, risk of progression, and patient goals. Methods: This case series includes five patients (four female and one male, mean age of 14.8 y) who received previous non-surgical treatment without success and had severe right thoracic AIS with an average Cobb angle measurement of 53.4°, involving spinal curve magnitudes that warrant surgical recommendation. Results: These patients’ curves were successfully reduced to nonsurgical levels utilizing a non-surgical, multimodal treatment approach combining 3D corrective TLSO bracing using the ScoliBrace^®, PSSEs, and spinal rehabilitation over an average of 37.0 months. The average Cobb angle reduced from 53.4° to 29.6° (44.6% reduction) after being weaned off treatment. Conclusions: This series has shown successful, clinically significant improvement in Cobb angle and trunk symmetry in five patients with severe AIS using a non-surgical, multimodal approach combining 3D corrective TLSO bracing using the ScoliBrace^® and spinal rehabilitation procedures. Further investigation into this multimodal non-surgical approach for children, parents, and healthcare providers and policymakers seeking an alternative to surgical intervention for AIS is warranted. Full article

Context: Respiratory morbidity is a leading cause of children’s consultations with general practitioners. Auscultation, the act of listening to breath sounds, is a crucial diagnostic method for respiratory system diseases. Problem: Parents and caregivers often lack the necessary knowledge and experience to identify subtle differences in children’s breath sounds. Furthermore, obtaining reliable feedback from young children about their physical condition is challenging. Methods: The use of a human–artificial intelligence (AI) tool is an essential component for screening and monitoring young children’s respiratory diseases. Using clinical data to design and validate the proposed approaches, we propose novel methods for recognizing and classifying children’s breath sounds. Different breath sound signals were analyzed in the time domain, frequency domain, and using spectrogram representations. Breath sound detection and segmentation were performed using digital signal processing techniques. Multiple features—including Mel–Frequency Cepstral Coefficients (MFCCs), Linear Prediction Coefficients (LPCs), Linear Prediction Cepstral Coefficients (LPCCs), spectral entropy, and Dynamic Linear Prediction Coefficients (DLPCs)—were extracted to capture both time and frequency characteristics. These features were then fed into various classifiers, including K-Nearest Neighbor (KNN), artificial neural networks (ANNs), hidden Markov models (HMMs), logistic regression, and decision trees, for recognition and classification. Main Findings: Experimental results from across 120 infants and preschoolers (2 months to 6 years) with respiratory disease (30 asthma, 30 croup, 30 pneumonia, and 30 normal) verified the performance of the proposed approaches. Conclusions: The proposed AI system provides a real-time diagnostic platform to improve clinical respiratory management and outcomes in young children, thereby reducing healthcare costs. Future work exploring additional respiratory diseases is warranted. Full article