Search Results (65,385)

Background: In recent years, there has been a growing concern regarding social media driving the decline of mental health, especially among adolescents. However, scientific consensus remains mixed, with many studies reporting only small or inconsistent associations. Aims: This paper aims to present the latest and most influential findings in the field of social media, with a focus on understanding the impact it has on adolescents’ mental health by looking at clinical versus non-clinical populations. Method: We conducted a comprehensive search through Scopus, looking for scientific articles and reviews published from January 2020 to March 2026 that include social media and adolescents with mental health conditions. We examined social media use patterns, affordances, mechanisms of impact, and clinical versus non-clinical populations. Results: There is limited literature comparing clinical versus non-clinical adolescent populations. Adolescents with mental health disorders spend more time online, teens with internalizing conditions report being more prone to social comparison and more sensitive to digital feedback, while those with externalizing conditions report a lack of control over how much time they spend on social media. Screen time alone is not sufficient to determine the impact on mental health. Among the features that might be associated with mental health problems are sharing personal content and scrolling through others’ posts. Conclusions: The impact of social media could be shaped by pre-existing vulnerabilities. There is a need for longitudinal study designs to test temporal associations and more research to cover the gap on clinical populations to develop better policies and interventions. Full article

Feature selection (FS) is a pivotal preprocessing task in data mining aimed at identifying optimal feature subsets to improve model generalization and reduce computational overhead. However, its NP-hard nature poses significant challenges for traditional optimizers in terms of search efficiency and solution quality. The Harris Hawks Optimization (HHO) algorithm is a state-of-the-art population-based metaheuristic method that demonstrates powerful capabilities in various optimization challenges. Despite its advantages, HHO encounters problems such as early stagnation and reduced accuracy. To mitigate these problems, we introduce an advanced algorithm called the Hybrid Strategy Harris Hawks Optimization (HSHHO). The HSHHO combines three key enhancements to support global search diversity and local refinement: (1) an exploration mechanism that utilizes the Self-Parameterized Map (SPM) alongside a dynamic logarithmic spiral to expand search breadth; (2) a nonlinear adjustment to the escape energy parameter for improved phase equilibrium; and (3) an elite perturbation approach that uses Cauchy–Gaussian mutation to strengthen local optimization and solution quality. We assessed HSHHO against eight well-known algorithms on 30 benchmark functions, where it exhibited superior results in the majority of cases. Finally, HSHHO is applied to address 18 feature selection tasks. The results demonstrated that HSHHO achieved highly competitive outcomes in terms of objective values, feature subset size, and classification performance in most datasets, reaching an average accuracy of 94.47%. Full article

Pancreatic cancer is among the most aggressive malignancies, and late diagnosis remains a key challenge. For a systematic review of pancreatic cancer diagnosis and prognosis, Scopus and Web of Science databases were used for the period from 2016 to 2026. The search query included the following keywords and their combinations: pancreatic cancer, diagnosis, early detection, prognosis, biomarkers, metabolomic profiling, CA19-9, microbiome, metagenomic changes, circulating tumor DNA, genomic analysis. Inclusion criteria included only articles published in English. Exclusion criteria included case reports and studies that did not examine pancreatic cancer. Our analysis demonstrates that integrating multi-omics data, particularly combining traditional CA19-9 with circulating tumor DNA (ctDNA) and metabolomic profiles (lipids, amino acids, carbohydrates), significantly improves diagnostic accuracy. Microbiome composition and genomic alterations further refine risk stratification and prognostic assessment. The synergistic use of these biomarkers may facilitate the development of screening, early diagnosis, risk stratification, and treatment optimization. However, the introduction of new diagnostic approaches into clinical practice requires additional verification, standardization and prospective clinical studies. Full article

This study examines the impact of public attention on financial markets during the Israel–Hamas conflict, focusing on the TA35 stock index and the Israeli Shekel (ILS) exchange rate over the period October 2023 to April 2025. By distinguishing between global and domestic Google search activity, the analysis investigates whether the origin of attention differentially affects market performance and currency dynamics. Public attention is treated as a real-time proxy for investor sentiment and perceived risk. Methodologically, the study combines Google Trends data with EGARCH(1,1) models to capture both return effects and asymmetric volatility responses. To enhance robustness, Principal Component Analysis (PCA) is applied separately to global and domestic search datasets, generating latent indices that reflect conflict-related and humanitarian narratives. These indices are subsequently incorporated into the empirical models. The findings reveal that global search intensity related to conflict topics exerts a significant negative effect on stock returns and contributes to currency depreciation, reflecting heightened uncertainty and risk aversion. In contrast, domestic search activity is associated with stabilizing or positive effects, suggesting local resilience and confidence. PCA-based models improve explanatory power and confirm that the geographical origin of attention plays a crucial role in shaping financial outcomes. Additionally, the results indicate that attention-driven shocks influence volatility asymmetrically, amplifying downside risk during periods of intensified global concern. Overall, the study contributes to the literature by integrating behavioral indicators into financial risk modeling and providing a novel, real-time framework for assessing how digital attention transmits geopolitical risk into asset prices. Full article

Immersive solutions are becoming a trending technology for decision support across fields such as transportation, healthcare, and urban planning. Despite their role, the mechanism by which they affect decision-making is unclear. Our study examines the design primitives in immersive technology that are manipulated to influence decision-making and synthesizes how they operate to shape decision outcomes. We follow PRISMA guidelines to search. A total of 198 studies were included. Eight primitive families were identified, including perceptual realism, environmental structure, interactivity, temporal simulation, embodiment, social presence, multisensory integration, and other contextual manipulations. Mechanisms through which they impacted decision-making were classified into cognitive, perceptual, affective, motivational, social-influence, and behavioral-heuristic mechanisms. Perceptual realism, environmental structure, and interactivity emerged as the most frequently investigated primitives, while presence, risk perception, spatial cognition, engagement, and social influence were among the most reported mechanisms. Our results suggest that immersive technologies function as decision-shaping systems that alter how users perceive uncertainty, risks, consequences, and alternatives, highlighting the need for theory-driven research and evaluation in high-stakes decision contexts. Full article

The primary control mechanism for synaptic uptake of GABA is through γ-aminobutyric acid transporter 1 (GAT-1, SLC6A1), a known target for anti-epileptic drugs. Although there is a clinically used GAT-1 inhibitor, tiagabine, the development of a new ligand with an advanced pharmacological profile is desirable. For this purpose, a multi-tiered virtual approach to screening has been created, involving pharmacophore-based search; application of the Informational Spectrum Method for Small Molecules, followed by EIIP/AQVN filtering (ISM-SM); molecular docking using an ensemble of several experimentally obtained structures of GAT-1; and ADMET predictions. Pharmacophore-based screening of the ZINC database of natural products, combined with ISM-SM/EIIP filtering, yielded 237 candidate compounds. Structural separation analysis discriminated between the positives and negatives, enabling enrichment-based prioritization. The use of a composite normalized rank score based on docking affinity and structural similarity allowed for the identification of the top candidates: ZINC03643214 and ZINC67840571. Collectively, these refinements establish a more sophisticated computational model for identifying novel GAT-1 inhibitors and highlight promising candidates for future experimental evaluation. Full article

Amyloid PET is increasingly used to confirm cerebral amyloid burden, but standard acquisition may be compromised by head motion, limited patient cooperation, reduced effective counts, premature scan termination, or non-repeatable imaging conditions. This comprehensive narrative review used a structured evidence-mapping approach in accordance with SANRA quality criteria. A structured literature search was performed in PubMed/MEDLINE, Scopus, and Web of Science up to 15 March 2026. Eligible studies included clinical, phantom, or hybrid studies addressing acquisition-time reduction, injected-activity reduction or low-count imaging, motion correction, or artificial intelligence-based image enhancement. Findings were synthesized narratively because of substantial heterogeneity in tracers, scanners, protocols, reconstruction methods, populations, comparators, and endpoints. Sixteen studies were included. Moderate reductions in acquisition time or effective counts generally preserved semiquantitative performance, whereas visual interpretation became more vulnerable under more aggressive reductions, borderline amyloid status, or reduced image quality. Artificial intelligence-based restoration improved image-quality metrics and supported interpretation of short- or low-count acquisitions, but evidence remained model-specific. Motion correction was supported by one amyloid-specific [¹⁸F]flutemetamol PET/CT study and should be interpreted as a potentially useful but under-replicated strategy. Current evidence supports cautious, tracer-, scanner-, reconstruction-, and task-specific optimization under challenging acquisition conditions rather than universal protocol reduction or direct generalization to motion-prone, poorly cooperative, or non-repeatable acquisition scenario. Reduced protocols, artificial intelligence-based restoration, and motion correction should remain locally validated supportive strategies, not substitutes for standard acquisition. Full article

Background/Objectives: Special populations, including pediatric, geriatric, and organ-impaired patients, are consistently underrepresented in randomized controlled trials (RCTs), resulting in limited evidence for safe and effective dosing. Off-label use is common, and variability in drug exposure and response increases the risk of adverse drug reactions (ADRs). This review aims to examine how integrating pharmacometrics (PMX) with real-world data (RWD) can address evidence gaps by supporting dose optimization, population expansion, and safety evaluation in these vulnerable groups. Methods: A narrative literature review was conducted using PubMed, Embase, and Web of Science (January 2000–November 2025). Using Boolean combinations of PMX and RWD-related search terms, approximately 200–300 records were identified across the three databases; approximately 30 full-text articles were reviewed, and representative case studies were selected based on population diversity, methodological variation, and regulatory or clinical impact. Results: RWD–PMX integration has been applied across three domains: (i) dosing optimization through therapeutic drug monitoring (TDM)-informed PopPK modeling and model external validation in pediatric and neonatal populations; (ii) population expansion supporting dose extrapolation and regulatory decision-making for unapproved groups; and (iii) safety evaluation enabling identification of exposure–toxicity risk factors in vulnerable cohorts. Conclusions: Integrating PMX with RWD provides a practical and mechanistically grounded framework for evaluating dosing, treatment eligibility, and safety in populations insufficiently represented in clinical trials. Accumulating evidence indicates that RWD–PMX methodologies can complement traditional clinical research and inform regulatory decision-making. Continued refinement of data quality standards, validation practices, and guidance frameworks will be essential for broader adoption. Full article

To support offline process-parameter screening for Czochralski (CZ) silicon single crystal growth, this paper proposes a surrogate-assisted optimization framework based on an improved Langevin evolutionary algorithm. First, a multi-variable constrained optimization model is established, with the LSA-Transformer-predicted solid–liquid interface deformation used as the objective evaluation and with process-smoothness and physical-feasibility constraints considered. Six key process parameters–heater power, pulling rate, argon flow rate, crystal rotation speed, crucible rotation speed, and magnetic field strength–are selected as decision variables. Second, building on the classical Langevin algorithm, an adaptive inertia weight mechanism, a diversity promoter (DP) operator, and a local escaping operator (LEO) are introduced to improve global exploration and local optima escape in complex search spaces. Verification on 23 classical benchmark functions indicates that the ILEE algorithm shows competitive overall performance and achieves better or comparable results on many functions when compared with particle swarm optimization (PSO), grey wolf optimization (GWO), the original Langevin evolutionary algorithm (LEE), and other baseline algorithms. The proposed framework is then used for offline candidate recipe generation during the crystal equal-diameter growth stage (200 mm, 400 mm, 600 mm, 800 mm, and 1000 mm). The optimized candidate parameter combinations yield lower surrogate-predicted interface deformation under the given LSA-Transformer model and physical constraints. Because these values are not independent CFD or experimental measurements, the results should be interpreted as process-parameter guidance for future physical validation. This work provides a feasible surrogate-assisted offline screening framework for CZ silicon single crystal growth. Full article

Background: Lung cancer remains the leading cause of cancer-related mortality worldwide, and early diagnosis and accurate disease stratification are still major clinical challenges. Radiomics has emerged as a quantitative imaging approach that extracts high-dimensional features from radiological imaging, with applications in diagnosis, prognosis, radio genomics, and assessment of treatment response. However, its clinical translation is still limited by methodological heterogeneity and a lack of standardization. Aim: This narrative review synthesizes evidence from systematic reviews and meta-analyses on radiomics in thoracic imaging for lung cancer, focusing on clinical applications, methodological limitations, and translational challenges. Methods: A structured search was conducted in PubMed and Scopus using predefined keywords related to radiomics, lung cancer, and imaging modalities. Only peer-reviewed systematic reviews and meta-analyses published in English were included. In total, 27 studies were selected and synthesized using a structured narrative approach guided by the ANDJ checklist. A differential integrative framework was adopted to connect evidence from systematic reviews and meta-analyses with primary empirical studies and policy documents through an intermediate layer of translational recommendations, ensuring a multi-level and interpretation-driven synthesis. Results: Radiomics demonstrated consistent potential across multiple clinical domains, including lesion classification, histological differentiation, molecular profiling, prognostic stratification, and prediction of treatment response. Machine learning and deep learning approaches frequently improved predictive performance. However, key limitations were identified, including heterogeneity in imaging protocols, lack of external validation, small single-centre datasets, and limited reproducibility of radiomic features. Conclusions: Radiomics in lung cancer imaging shows strong clinical potential but remains constrained by methodological and translational barriers. Future progress will depend on standardization, external validation, multimodal data integration, and improved interpretability, alongside alignment with regulatory and clinical implementation frameworks. Full article

With the rapid development of general artificial intelligence, large models have gradually become the key force driving the digital transformation of the field. Agriculture has distinct domain characteristics, and traditional deep learning models are difficult to meet its cross-regional and cross-task requirements. Large models specifically designed for the agricultural field can integrate multi-source data and prior knowledge to break through this bottleneck. Therefore, tracking the development trend of large agricultural models is an important prerequisite for building new, quality productive forces in smart agriculture and promoting the digital transformation of agriculture. This article conducts a literature search and review around the research on large agricultural models, following the PRISMA guidelines. It combines the keywords of large models, crops, livestock breeding, etc., and only includes journal papers from 2022 to 2026, totaling 713 articles. Then, it performs topic modeling to deeply clarify the current research and application status, and summarizes the challenges faced and makes future research prospects. Existing evidence indicates that current large agricultural models are gradually developing towards agents and embodied intelligence, and are widely applied in scenarios such as agricultural knowledge services, pest and disease diagnosis and prevention, livestock and fishery breeding, and smart agricultural machinery control. However, they still face many key challenges, and further exploration is needed in theoretical methods and practical applications. In the future, research can be further deepened and expanded in areas such as the construction of high-quality data sets, the construction of domain evaluation systems, strengthening model reliability, building multi-agent systems, and lightweight deployment of large models and embodied intelligence. Full article

Background/Objectives: Apathy is among the most common neuropsychiatric features of late-life neurocognitive disorders and predicts functional decline and greater caregiver burden. As no treatment is formally established, identifying effective interventions is a priority. We systematically reviewed non-pharmacological randomized controlled trials (RCTs) targeting apathy in older adults with neurocognitive disorders. Methods: We searched PubMed/MEDLINE, PsycInfo, the Cochrane Library, and Google Scholar (final search 23 March 2026). Eligible studies were non-pharmacological RCTs reporting an apathy outcome. Evidence levels were graded with OCEBM and quality with PEDro; two reviewers mapped PEDro items onto Cochrane risk-of-bias domains. Reporting followed PRISMA 2020. Results: Sixty-two RCTs were included. Physical exercise and music-based interventions showed the most consistent benefit, whereas technology-based and brain stimulation approaches remained experimental. Only 30 trials (48%) showed a significant between-group effect on apathy—most were null, within-group, or had apathy as a secondary outcome. Marked heterogeneity precluded meta-analysis. Most trials were of moderate to high quality, though near-universal performance bias arose from the inability to blind participants and providers. Conclusions: Managing apathy in these populations remains challenging, and the certainty of the evidence is limited. Purpose-built, apathy-focused trials reporting effect sizes and durability are needed before disease-specific recommendations can be made. Full article

Background: Glioblastoma (used to be called glioblastoma multiforme—GBM) is the most common and aggressive brain tumor, having the lowest overall survival rate. Initial focal neurological deficits are primarily attributable to surrounding edema; however, as tumor invasion progresses, these deficits become more pronounced and permanent. The standard treatment for newly diagnosed glioblastoma is represented by cytoreductive neurosurgery followed by the Stupp Protocol. Postoperative recovery of the patient with glioblastoma is a long-term process that should include, for overall more acceptable outcomes, neurorehabilitation. This review aims to bring together evidence from neuro-oncology, neurosurgery, and neurorehabilitation in order to better understand the factors associated with recovery, functional status, and quality of life (QoL) after glioblastoma surgery. Our work also aimed to update the related knowledge base and to attempt to optimize the related protocols in patients with operated cerebral glioblastoma. Methods: For these purposes, we conducted a systematic literature review to assess the current state of research referring to the above-mentioned topic. We have used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA—widely recognized internationally) methodology. We used, in this respect, specific keyword combinations/“syntaxes” for searching literature in the domain, in four international databases. Results: Following PRISMA screening, 14 studies met the predefined eligibility criteria. Additional manual reference screening and complementary searches identified further relevant publications, resulting in a total of 22 included articles. Together, the reviewed work addressed a diverse range of topics relevant to postoperative glioblastoma management, including the potential role of multidisciplinary rehabilitation, cognitive interventions, neuromodulation approaches, and functional assessment strategies in improving postoperative outcomes and QoL in glioblastoma patients, while emphasizing that this interdisciplinary domain warrants more extended approaches. Discussion and Conclusions: Despite the relatively limited and largely exploratory available information, neurorehabilitation may contribute to improved functional outcomes and QoL in patients with glioblastoma. Full article

Background: Non-contact anterior cruciate ligament (ACL) injury is common and disabling, often requiring reconstruction and predisposing individuals to early post-traumatic osteoarthritis, making scalable, exercise-based prevention a clinical and public health priority. Excessive peak vertical ground reaction force (vGRF) during landing is a modifiable biomechanical risk marker for ACL injury, although whether reducing it lowers injury incidence is unproven. We evaluated the effect of movement retraining on peak vGRF during landing in pivot-sport athletes and general athletic populations. Methods: MEDLINE (PubMed), Embase, and the Cochrane Central Register of Controlled Trials were searched from inception through to 25 May 2026. Two reviewers independently screened records and extracted data. Random-effects meta-analyses (DerSimonian–Laird) used Hedges’ g; risk of bias was assessed with RoB 2 and certainty with GRADE. The protocol was registered in PROSPERO (CRD42025116119). Results: Nine comparisons from eight randomised controlled trials (292 participants) were included. Movement retraining significantly reduced peak vGRF (Hedges’ g = −0.94, 95% CI −1.34 to −0.54; I² = 63%), with larger effects in general athletic populations (g = −1.50) than in pivot-sport athletes (g = −0.66; subgroup difference p = 0.005). Knee flexion angle at initial contact showed a non-significant increasing trend (g = 0.48; p = 0.18). Certainty of evidence (GRADE) was low. Conclusions: Movement retraining was associated with a reduction in peak vGRF during landing, a surrogate biomechanical marker for ACL injury, on the basis of low-certainty evidence with substantial heterogeneity (I² = 63%). A subgroup difference favouring general over pivot-sport athletes was observed but is exploratory, resting on only three general-athletic comparisons. Because no included trial measured injury incidence, whether these biomechanical changes reduce ACL injury is unknown, and the findings should be regarded as hypothesis-generating. Full article

Background: Early rehabilitation after pediatric ischemic stroke may support neuroplasticity and improve long-term functional outcomes. However, rehabilitation practices remain heterogeneous, and evidence-based recommendations regarding the optimal timing and intensity of intervention are limited. Objectives: This scoping review aimed to evaluate the available evidence regarding early rehabilitation after pediatric ischemic stroke, identify prognostic factors associated with functional recovery, summarize current therapeutic approaches, and highlight gaps in the existing literature. Eligibility Criteria: Eligible studies included children and adolescents aged 0–18 years diagnosed with ischemic stroke and receiving rehabilitation or therapeutic intervention. Studies addressing the timing, intensity, and effects of physiotherapy, occupational therapy, speech and language therapy, neuropsychological intervention, neuromodulation, or multidisciplinary rehabilitation were considered for inclusion. Sources of Evidence: A structured literature search was conducted in PubMed/MEDLINE, Scopus, Web of Science, the Cochrane Library, and Google Scholar for studies published between 2000 and January 2025. Charting Methods: Data were extracted using a standardized charting form and synthesized narratively because of substantial heterogeneity in study design, populations, interventions, and outcome measures. Results: Twenty-one sources met the inclusion criteria. Direct evidence specifically addressing early rehabilitation after pediatric ischemic stroke was limited and consisted primarily of observational studies. A substantial proportion of the available evidence was indirect, originating from studies of perinatal stroke, unilateral brain injury, cerebral palsy, and related pediatric neurorehabilitation populations, as well as clinical guidelines and expert consensus documents. The available evidence suggests potential benefits across motor, cognitive, communication, and functional domains, although the strength and directness of evidence varied substantially. Several studies identified the early post-stroke period as a potentially important window for neuroplasticity, while family involvement, individualized treatment planning, and interdisciplinary care were consistently highlighted as important components of rehabilitation. Evidence supporting neuromodulation techniques remained preliminary and was largely limited to selected pediatric populations. Conclusions: The available evidence, although heterogeneous and largely indirect, suggests that early coordinated and multidisciplinary rehabilitation may be beneficial in pediatric ischemic stroke care. However, the current evidence base remains limited, and high-quality prospective studies are needed to establish standardized rehabilitation protocols and determine the optimal timing and intensity of therapeutic interventions. Full article