Search Results (13,923)

Background: Accurate identification of pediatric B-cell acute lymphoblastic leukemia (B-ALL) patients at increased risk of relapse remains a major clinical challenge, as relapse occurs in 10–20% of cases, including patients initially classified as low- or intermediate-risk. This study aimed to identify clinical, genomic, and measurable residual disease (MRD) related predictors of relapse in pediatric B-ALL. Methods: 51 pediatric patients with B-ALL were included and followed for a median of 30.5 months (IQR, 16–45.5). Patients were stratified according to relapse status. At diagnosis, all cases underwent comprehensive genomic characterization based on the 2022 WHO and ICC classifications, including SNP-array analysis to identify copy number alterations (CNA) involving recurrent B-ALL genes (IKZF1, CDKN2A/B, PAX5, ETV6, BTG1, EBF1, ERG, RB1, and PAR1) and to determine IKZF1^plus status. Clinical variables, including white blood cell count, cytogenetic risk, and MRD assessed by flow cytometry at day 15, day 33, and at the end of induction, were analyzed. Kaplan–Meier and Firth-penalized Cox regression analyses were performed to identify independent predictors of relapse. Results: 86.3% of patients were classified according to the 2022 WHO/ICC classifications, with high hyperdiploidy being the most frequent subtype. During follow-up, 11 patients relapsed. Relapse was significantly associated with high cytogenetic risk (p = 0.007) and showed a trend toward association with an adverse CNA profile (p = 0.075). Patients with >25% bone marrow blasts at day 15 (p < 0.001) and those with positive MRD at the end of induction (p = 0.017) had a significantly higher risk of relapse. In multivariable analysis, high genetic risk and positive end-of-induction MRD remained independent predictors of relapse, with hazard ratios (HRs) of 9.31 (95% CI, 1.55–56.1), p = 0.010, and 10.9 (95% CI, 2.39–49.9), p = 0.002, respectively. Conclusions: An integrated diagnostic strategy including SNP-array provides a high diagnostic yield. High-risk cytogenetic abnormalities and positive end-of-induction MRD are independent predictors of relapse in pediatric B-ALL. Their combined assessment at diagnosis and early treatment may improve risk stratification and may support personalized therapeutic approaches. Full article

Students in higher education often face challenges related to motivation, self-regulation, and engagement. This article presents TIRIA, a user-centered learning analytics dashboard designed for Moodle and adaptable to other Learning Management Systems (LMSs), and reports a formative evaluation of its high-fidelity prototype. The design followed a Design Thinking methodology informed by a targeted literature review, interviews with 10 instructors and 9 students, and iterative prototyping. TIRIA integrates visual analytics, personalized feedback, and a gamification layer (points, badges, goal-setting, and a virtual assistant), aligned with Self-Regulated Learning (SRL) theory and interpreted through Self-Determination Theory (SDT). Because the evaluation used a Figma prototype populated with mock data rather than a deployed integration, the study reports perceptions rather than learning outcomes. Six undergraduate students completed think-aloud sessions, a semi-structured interview, and a survey combining the System Usability Scale (SUS), a Technology Acceptance Model (TAM) measure, and two five-item measures adapted from learning analytics quality indicators and from gamification research. Participants rated usability highly (SUS = 93.75, PEOU = 4.75) and consistently valued organizational features, while responses to the gamification layer were markedly polarized (individual means ranging from 1.2 to 5.0). The article contributes a documented design case mapping features to SRL phases and SDT constructs, formative evidence supporting an opt-in approach to gamification, and implementation considerations covering privacy and Moodle integration. Findings are exploratory and require confirmation through deployment in an authentic learning environment. Full article

Congenital melanocytic nevi (CMN) are pigmented skin lesions present at birth with a variable risk of developing into melanoma over time. The mitogen-activated protein kinase signaling (MAPK) pathway plays a critical role in the development and progression of CMN, making it a key target for therapeutic interventions. Precision medicine aims to optimize therapeutic efficacy while minimizing adverse effects by tailoring treatments to individuals’ genetic and molecular profiles. This literature review examines the potential of precision medicine in managing CMN, with a specific focus on MEK inhibition within the MAPK pathway, summarizing current preclinical and clinical evidence, mechanisms of action, and therapeutic integration with surgical management strategies. Although early results are promising, the need for personalized treatment strategies, informed by genetic testing and biomarker profiling, is emphasized to improve the management of CMN and reduce the risk of malignant transformation. Further research is necessary to establish the long-term safety and effectiveness of MEK inhibition in CMN therapy, which could offer a more targeted and individualized approach to care. Full article

Background: Obesity is a chronic, multifactorial disease that demands personalized and sustainable management approaches. Digital health technologies, such as artificial intelligence, wearable devices, mobile health apps, and virtual reality (VR), may support obesity care by providing enhanced behavioral monitoring, personalized feedback, and patient engagement. Objective: This critical narrative review discusses the current evidence on artificial intelligence, wearable technologies, and VR in the context of precision nutrition and obesity management and their possible clinical applications and limitations. Method: A critical narrative review was conducted using peer-reviewed literature published between 2019 and 2026 and identified through PubMed and Google Scholar. Search terms included combinations of “precision nutrition,” “personalized nutrition,” “obesity,” “weight management,” “metabolic health,” “digital health,” “artificial intelligence,” “machine learning,” “mobile health,” “wearable devices,” and “omics” using Boolean operators. Evidence from randomized controlled trials, systematic reviews, meta-analyses, and key conceptual studies was critically synthesized due to substantial heterogeneity in interventions and outcomes. Result: Wearables and mobile applications can enable continuous self-monitoring of physical activity, dietary intake, sleep, and physiological measures. Artificial intelligence may improve dietary personalization, risk prediction, glycemic control, and adaptive feedback. VR offers an immersive way to tackle behavioral and cognitive mechanisms related to overeating such as cravings, food cue reactivity, and inhibitory control. However, the evidence is heterogeneous, with many studies limited by short follow-up periods, small samples, variable adherence, and insufficient clinical validation. Conclusions: Artificial intelligence, wearable technologies, and VR are promising tools for precision obesity management, but their long-term clinical effectiveness remains uncertain. Future research should prioritize adequately powered trials, longer follow-up, standardized outcomes, transparent algorithms, ethical data governance, and integration with multidisciplinary nutrition and obesity care. Full article

Target-speaker automatic speech recognition (TS-ASR) enables transcription of a specific speaker in multi-talker environments, yet remains largely unexplored for multilingual, low-resource languages. Existing TS-ASR systems predominantly target monolingual English using diarization-based or speaker-embedding approaches, leaving a critical gap for languages such as Kazakh, where code-switching with Russian and English is commonplace. We propose Persona-ASR, a modular two-stage architecture. The first stage is an explicit target-presence gate that verifies whether the enrolled speaker appears in the mixture and emits a <no_target> token to suppress transcription when the speaker is absent, directly addressing the acoustic-hallucination failure mode of prior systems. The second stage performs enrollment-conditioned recognition: a 192-dimensional ECAPA-TDNN speaker embedding modulates a WavLM-Base-Plus encoder through feature-wise linear modulation (FiLM), while language-specific CTC heads enable joint Kazakh and English decoding without forcing Latin and Cyrillic symbols to compete in a single output space. To evaluate the system, we introduce KazMix3, a Kazakh overlap dataset for TS-ASR training, and PersonaMix, a controlled bilingual benchmark spanning same- and cross-language enrollment across varying interferer counts (1–3) and signal-to-noise ratios ($-3$ to $+3$ dB). Persona-ASR outperforms a strong off-the-shelf cascade baseline by 13.3 WER points on English and 24.6 on Kazakh, and matches a published monolingual English baseline. On PersonaMix, speaker conditioning reduces relative word error rate by 40.7% on English and 59.3% on Kazakh mixtures over an unconditioned variant of the same model, and cross-language enrollment (unseen during training) remains effective, increasing average raw WER by only 4.1 points (English) and 2.2 points (Kazakh) relative to same-language enrollment. To our knowledge, Persona-ASR is the first TS-ASR system for the Kazakh language, establishing a foundation for multilingual personalized ASR in low-resource settings. Full article

Psoriasis vulgaris is a chronic immune-mediated inflammatory disease in which treatment response is assessed using clinical indices such as the Psoriasis Area and Severity Index (PASI) and Dermatology Life Quality Index (DLQI), despite their limited ability to capture systemic inflammation and underlying immunological activity. This narrative review aims to summarize current evidence on inflammatory biomarkers for monitoring treatment response and to evaluate their potential clinical utility. A structured, non-systematic literature search was performed in April–May 2026 across PubMed, Cochrane Library, Scopus, and ClinicalTrials.gov, focusing primarily on literature published during the preceding 10 years, with selected earlier studies being retained when directly relevant. Emerging data indicate that multiple biomarker domains may reflect therapeutic outcomes, including cytokines and chemokines, acute-phase proteins, complete-blood-count (CBC)-derived inflammatory indices, genetic markers, micro(mi)RNAs, metabolomic and lipidomic profiles, and tissue-based markers. These biomarkers may serve as severity-associated, baseline-predictive, pharmacodynamic, or prognostic markers, and these roles should not be interpreted interchangeably. Nevertheless, discrepancies between biomarker dynamics and clinical improvement occur, reflecting the partial dissociation between local and systemic inflammation, disease heterogeneity, and differences in response kinetics. Although inflammatory biomarkers provide a biologically grounded framework for assessing treatment response, their clinical implementation remains limited. Further large-scale, standardized studies are required to validate candidate markers and support the development of integrated, multi-omics approaches for personalized management. Full article

Background: Cholangiocarcinoma (CCA) is a highly heterogeneous malignancy with limited therapeutic options and poor prognosis. The increasing complexity of molecular stratification and treatment selection has stimulated interest in computational and biological modeling approaches for precision oncology. Objective. This narrative review aims to provide a comprehensive overview of digital twins (DTs), DT-enabling computational models, and biological twins (BTs) in CCA, discussing their applications, limitations, and potential integration within hybrid precision medicine frameworks. Methods: A narrative literature review was conducted. To inform the twin-focused sections, a structured PubMed search was performed using predefined keywords related to CCA and twin-related technologies, including organoids, xenografts, organ-on-chip systems. Particular attention was devoted to recent studies addressing computational modeling, patient-derived experimental systems, and translational applications. Results: DT development in CCA is supported by an ecosystem of DT-enabling technologies, including radiomics, artificial intelligence, multi-omics integration, and simulation-based models. However, fully realized medical DTs remain unavailable. BTs, including patient-derived organoids, xenografts, and microfluidic platforms, enable functional validation of therapeutic hypotheses but face challenges related to scalability, standardization, and clinical feasibility. Emerging hybrid DT-BT frameworks seek to combine computational prediction with biological validation through iterative feedback loops, potentially improving patient stratification and treatment personalization. Conclusions: DTs and BTs represent complementary components of an evolving precision oncology ecosystem in CCA. Although technical, biological, regulatory, and implementation challenges remain, the convergence of computational models, longitudinal molecular monitoring, and patient-derived systems may facilitate clinically actionable hybrid twin frameworks. Successful translation will require both technological innovation and healthcare-system improvements to precision medicine access. Full article

There is a rapidly rising demand for cosmeceutical solutions for skin lightening/brightening due to the growing need for dermatologic applications and consumers’ preference to enhance the brightness of skin. Traditionally, the main ingredients for developing dermatological products were the potentially harmful and controversial hydroquinone compounds; however, in recent years, the research field experienced a transition towards safer antioxidant-based agents. The present review analyzes the application and use of antioxidants in modern cosmetics as an innovative approach, and the chemical and biological mechanisms of their functions that could revolutionize the industrial applications. The most effective ones include competitive inhibition of tyrosinase enzyme, pheomelanin switch through the help of glutathione, protection from reactive oxygen species (ROS) in order to inhibit ultraviolet (UV)-stimulated production of melanin, as well as prevention of the migration of melanosomes using niacinamide. The review identifies and analyzes several components used as ingredients, such as vitamins C, Niacinamide, vitamin E, licorice root, epigallocatechin-3-gallate (EGCG), or resveratrol, among many more. In spite of their high efficiency, there are numerous problems associated with the instability, dermal irritation, and absorption of pure antioxidants. Finally, the legal framework developed by the Food and Drug Administration (FDA) and European Union (EU), potential risks like pro-oxidation damage, and future perspectives such as personalized skincare via machine learning and artificial intelligence (AI) are discussed. Full article

Cross-modal person re-identification between visible and infrared domains remains a challenging problem due to significant modality gaps. This paper presents a novel approach termed Intermediate Shared Feature Network (ISFNet) that explicitly addresses this issue by exploiting intermediate feature representations within a dual-stream backbone. Unlike conventional methods that primarily focus on final-layer features, ISFNet introduces two complementary components: a Multi-layer Feature Cascade Module (MFCM) that aggregates discriminative features across different network stages, and a Dual Feature Generation Module (DFGM) that creates diverse intermediate representations through Instance-Batch Normalization variants. By integrating these modules, ISFNet effectively bridges the cross-modal gap and improves matching accuracy. Comprehensive experiments on the SYSU-MM01 and RegDB datasets demonstrate that the proposed method achieves competitive performance against state-of-the-art approaches, with noticeable improvements in both Rank-1 accuracy and mean average precision. Full article

Nanotechnology is rapidly transforming the perspective on pediatric neurology, enabling diagnostic, therapeutic, and monitoring strategies tailored to the unique features of neurological illnesses in children. This review acknowledges the problems caused by delays in diagnosis, the limitations of conventional procedures, and the need for new, focused approaches, highlighting recent advances in nanoscale materials and smart nanocarriers. Specifically, this paper summarizes advances in nanomaterials that can overcome physiological barriers, such as the developing blood–brain barrier (BBB) and age-dependent pharmacokinetics. We discuss innovations in stimuli-responsive delivery systems, theranostic platforms, and multimodal nanohybrids designed for precise targeting and real-time treatment monitoring. Special emphasis is placed on pediatric-specific considerations, including developmental differences in immune and metabolic responses, the necessity for age-adjusted dosing, and the potential long-term safety implications of nanoparticle exposure. Transformative applications are explored in various pediatric neurological conditions, including brain tumors, epilepsy, neurodevelopmental disorders, and rare degenerative diseases, emphasizing both achievements and challenges in translation. This paper evaluates various regulatory, ethical, and societal factors, alongside the integration of converging technologies such as AI-driven nanoparticle optimization, brain organoids, and digital twins to accelerate personalized therapy development. Conclusively, this paper emphasizes the importance of interdisciplinary collaboration, pediatric-focused clinical trial designs, and sustained investment to fully realize the potential of nanotechnology in improving neurological outcomes for children. Full article

Introduction: Improving adult pneumococcal vaccination is a public-health priority in Romania, and evidence regarding patient-level outcomes following different approaches to physician training remains limited. Materials and methods: This non-randomised, multicentre, quasi-experimental implementation study examined patient-level changes across three geographically distinct Romanian settings between August and November 2025. Physicians in Iași and Dolj counties received in-person training combining medical content with communication techniques based on motivational interviewing, whereas physicians in the webinar group received online medical training without a dedicated communication module. Eligible adult patients completed questionnaires before and after counselling and reported whether they had been vaccinated or had scheduled a vaccination appointment. Results: The analysis included 295 questionnaire pairs from patients nested within 29 physicians. Baseline attitudes differed markedly across settings (composite score of 5.64 in the webinar group, 5.29 in Dolj County, and 4.09 in Iași County; p < 0.001), as did strong baseline vaccination intention (75.6%, 48.1%, and 27.2%, respectively). Using a composite score comprising all seven attitude items, with item 7 reverse-coded and vaccination intention analysed separately, the cluster-robust group-by-time model indicated different mean changes between settings (global p = 0.001): +0.28 in the webinar group, +1.02 in Iași, and +0.60 in Dolj. Strong vaccination intention increased within each setting, but the magnitude of change did not differ between settings (group-by-time p = 0.929). The combined self-reported vaccination-or-scheduling outcome was observed in 81.5% of patients in the webinar group, 49.4% in Iași, and 73.3% in Dolj; after adjustment for baseline intention, there was no longer evidence of an overall group difference (p = 0.651). Discussion and conclusions: Because training approach, delivery modality, geographical setting, physician cohort, and patient population were not independently allocated, and because baseline levels differed markedly between settings, the findings should be interpreted as exploratory implementation patterns rather than causal comparative effects. Full article

Drawing on interviews with ten preschool principals who recruit, mentor, and evaluate preschool teachers, this study examines how playfulness is conceptualized as a professional competence within early childhood education and care (ECEC) practice. In Swedish ECEC, play and teaching are conceptualized as intertwined processes, and co-play is positioned as a central pedagogical approach that requires both personal dispositions and ongoing professional development. The study highlights the importance of integrating playfulness into ECEC teacher education in order to strengthen future teachers’ capacity to co-play with children and engage in play-based teaching practices on children’s terms. The findings also suggest that professional playfulness emerges as a multifaceted and context-dependent competence, shaped by institutional conditions as well as individual orientations towards play and teaching. By foregrounding leadership perspectives, the study contributes to ongoing discussions on the role of play in ECEC and emphasizes the need for deliberate efforts to support the development of professional playfulness in both practice and teacher education. Full article

Introduction: The recognition of Complex Post-Traumatic Stress Disorder (CPTSD) as a distinct diagnosis in ICD-11 has intensified the need to clarify its boundaries with Borderline Personality Disorder (BPD), given their symptom overlap and shared traumatic origins. Therefore, this systematic review aimed to examine whether CPTSD is distinct from BPD, assess their comorbidity and symptom overlap, identify key criteria for differential diagnosis, and explore therapeutic implications. Method: A systematic search was conducted in PubMed, Scopus, and Web of Science following PRISMA 2020 guidelines. Seven empirical studies (2015–2025) were included, comprising 2574 adults (mean age 40.4 years; 73.3% women). Methods included latent class analysis, structural equation modeling, and network analysis. The methodological quality of the included studies was evaluated independently by two reviewers using the JBI Critical Appraisal Checklist for Analytical Cross-Sectional Studies. Results: Findings consistently supported that CPTSD and BPD are empirically distinguishable, though substantially correlated, particularly within the ICD-11 framework. High symptom co-occurrence was observed, with affective dysregulation identified as the symptom most centrally connecting the two symptom networks. Self-concept emerged as the most robust differentiator: stable and persistently negative in CPTSD versus unstable and fragmented in BPD. Shame was a key affective marker of more severe presentations, and trauma severity, rather than diagnostic category, was associated with symptom variation across studies. Conclusions: CPTSD and BPD are distinct yet frequently co-occurring conditions. Differential diagnosis should focus on self-concept stability, patterns of behavioral dysregulation, and shame. Treatment requires individualized, trauma-informed, and shame-sensitive approaches, with transdiagnostic emotion regulation strategies across presentations. These conclusions should be interpreted with caution, given the predominantly cross-sectional design and methodological heterogeneity of the available evidence. Full article

Objective: While advancements in radiotherapy and systemic agents have significantly improved survival rates in head and neck squamous cell carcinoma (HNSCC), managing long-term, treatment-induced toxicities remains a critical clinical challenge. This study aimed to develop a personalized, supervised machine learning-driven predictive model for multiple chronic toxicities by integrating clinical, dosimetric, and genetic data specifically evaluating the impact of the NBN gene rs1805794 (c.553G>C) polymorphism. Methods: This study enrolled 125 patients with HNSCC who received curative-intent radiotherapy and remained disease-free during follow-up with a median of 98 months. Comprehensive clinical and dosimetric data were collected, and chronic toxicities were recorded. Peripheral blood samples were analyzed for the NBN rs1805794 polymorphism using allele-specific PCR (AS-PCR). Following feature selection, four supervised machine learning classifiers were trained and evaluated to identify the optimal model for predicting multiple chronic toxicities. Results: The XGBoost algorithm emerged as the highest performing model. Baseline clinico-dosimetric predictors of multiple chronic toxicities included PTV70 volume, the addition of concurrent chemotherapy, advanced T and N stages, and continued smoking. Integrating the NBN rs1805794 genotype into the XGBoost architecture enhances its predictive capability. The final model accurately identified patients at high risk for multiple chronic toxicities, achieving an area under the curve (AUC) of 0.78, an accuracy of 0.77, a sensitivity of 0.74, and a specificity of 0.79. Conclusions: Integrating clinical, dosimetric, and genetic data within a machine learning framework effectively predicts multiple chronic toxicities in HNSCC. This approach enabled early risk stratification, providing the potential for personalized therapy. Full article

Trajectory data may suffer from missing locations due to environmental and equipment constraints, which impact the performance of downstream tasks. Trajectory recovery aims to enhance the utility of mobility data by reconstructing high-quality trajectories. However, existing methods face two significant limitations. First, most existing methods rely on individual historical trajectories, making it difficult to exploit global spatial dependencies across the population. Second, current approaches ignore the role of local multi-factor semantics in personalized trajectory recovery. To address the above challenges, we propose a joint trajectory recovery method named GLTrajRec, which integrates global spatial dependencies and local multi-factor semantics. Specifically, global trajectory flow graph modeling is proposed to capture shared mobility patterns from all users’ trajectories, which can provide effective spatial transition constraints even when individual historical data is insufficient. Then, multi-factor local semantics embedding is designed to comprehensively encode multi-dimensional personalized mobility information from multiple aspects, enabling more accurate personalized recovery results. In addition, a Transformer-based encoder–decoder framework is developed to bidirectionally encode the embedded global dependencies and local semantics and to decode multi-class information. Finally, we construct a transition matrix based on the global trajectory flow graph to improve the accuracy of the recovery output. Extensive experiments conducted on two real-world datasets demonstrate that the proposed method achieves mean average precision (MAP) scores of 0.7181 and 0.7345, respectively, representing an improvement of approximately 5% to 7% over traditional trajectory recovery methods. Full article