Search Results (3,761)

Background: Osteoporosis is a complex disorder involving bone loss and muscle degeneration. Multi-omics technologies provide novel insights into its molecular mechanisms and may support biomarker discovery, patient stratification, and therapeutic development. Objective: This scoping review aimed to synthesize current evidence on the application of multi-omics approaches in osteoporosis, focusing on molecular insights, methodological diversity, and translational potential. Methods: A literature search of PubMed, Embase, and Scopus retrieved 433 records using the keywords “osteoporosis,” “osteosarcopenia,” and “omics.” After removing duplicates and screening titles, abstracts, and full texts, 30 studies met the inclusion criteria. Data on study populations, biological samples, multi-omics techniques, and integration methods were extracted. Results: Studies employed transcriptomics, proteomics, metabolomics, lipidomics, epigenomics, and metagenomics, often combined in multi-omics analyses with computational modeling. Key pathways included osteoclast differentiation, immune regulation, ferroptosis, and microbiome–metabolite interactions. Multi-omics integration enabled the identification of molecular subtypes, candidate biomarkers, and potential therapeutic targets. Limitations included small or single-center cohorts, heterogeneous designs, and limited validation, restricting generalizability and clinical translation. Conclusions: Multi-omics approaches offer a powerful framework to uncover the molecular mechanisms underlying bone and muscle degeneration and to guide precision diagnostics and interventions. Future studies should prioritize large, multicenter, longitudinal designs integrating multi-omics data with clinical and functional validation to facilitate clinical application. Full article

This study investigats both emerging and existing ethical issues associated with the adoption of artificial intelligence (AI) in Africa, a region characterised by unique socio-economic and cultural complexities. Even though AI adoption is rapidly transforming and delivering substantial benefits in sectors such as healthcare, finance, agriculture, education, industry, and governance, its implementation still raises ethical concerns. These ethical issues include digital colonialism, algorithmic bias, job displacement, limited infrastructure, data scarcity, linguistic diversity, and the risk of imposing foreign values that may undermine indigenous knowledge and social cohesion. Grounded in Afro-communitarianism and stakeholder theory, which emphasises communal values such as Ubuntu and cooperative engagement among stakeholders, this desk-based research identifies these major challenges and introduces a culturally grounded framework for responsible AI adoption in Africa. The framework calls for stronger governance, capacity building, collaboration among stakeholders, and tailored strategies across multiple stakeholders to ensure AI supports Africa’s inclusive and sustainable progress. Full article

Background/Objectives: Dichorionic triamniotic (DCTA) triplet pregnancies are associated with increased rates of placenta-specific complications primarily attributed to vascular anastomoses in the monochorionic (MC) pair. Selective fetal reduction to twins (of one of the MC pair) is a complex and not a widely available procedure. Multifetal reduction (MFR) to singleton pregnancy can reduce adverse pregnancy outcomes but is controversial due to medico-legal and socio-ethical issues. The aim of this study is to identify the rate of miscarriage < 24 weeks or preterm birth < 34 weeks following MFR to singleton pregnancy in DCTA triplets and compare the results with expectant management. Methods: This systematic review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and registered in the Prospective Register of Systematic Reviews System (ID: CRD42023422585). Results: Overall, from 21 citations of relevance, 6 studies with a total of 548 DCTA triplet pregnancies fulfilled the inclusion/exclusion criteria. In comparison with expectant management (n = 336), meta-analysis demonstrated that MFR to singleton pregnancy (n = 212) was associated with a lower rate (9.4% vs. 48.5%) of preterm birth (RR = 0.19, 95%CI 0.07–0.51), whereas the rate of miscarriage (14.6% vs. 9.2%) did not significantly increase (RR = 1.53, 95%CI 0.91–2.55). Conclusions: In DCTA triplet pregnancies, MFR to singleton pregnancy was associated with a reduced preterm birth rate and not associated with an increased miscarriage rate. Given the fact that the MC pair is reduced only to lower the rate of preterm birth, appropriate counselling and justification are important. In the absence of randomized controlled trials, data from systematic reviews are the best available evidence for counseling on the different management options. Full article

Background: Addictive disorders are characterized by the dysregulation of neural circuits involved in reward processing, salience attribution, emotional regulation, and cognitive control. Traditional neuroimaging paradigms based on static or two-dimensional stimuli show limited ecological validity and may fail to capture the contextual complexity of real-world addictive triggers. Immersive virtual reality (VR) offers a novel approach to simulate realistic, multisensory environments capable of eliciting craving and emotional responses. Although several reviews have examined VR in addictive disorders, most combined immersive and non-immersive tools and did not restrict inclusion to studies with brain-based outcomes. Methods: This systematic review with narrative synthesis was conducted in PubMed/MEDLINE and APA PsycINFO for studies published up to 30 December 2025. This systematic review followed PRISMA 2020 and was prospectively registered in PROSPERO; due to heterogeneity, findings were synthesized narratively. Eligible studies included human participants with substance-related or behavioral addictions and employed immersive VR paradigms (e.g., head-mounted display–based environments) combined with neuroimaging or neurophysiological measures (EEG, fMRI, fNIRS, PET, or DTI). Risk of bias was assessed using ROB-2 or ROBINS-I, and overall certainty of evidence was evaluated with the GRADE framework. Results: Ten studies met the inclusion criteria, encompassing over 1450 participants with alcohol, nicotine, methamphetamine, opioid use disorders, and internet gaming disorder. Immersive VR was associated with craving-related neural responses across modalities, involving prefrontal, insular, limbic, and striatal networks. EEG studies reported spectral power changes associated with craving and attentional salience, while fMRI, fNIRS, and PET studies demonstrated activation and modulation of executive control and reward-related circuits. Preliminary longitudinal and interventional studies indicate that repeated VR exposure may induce neurobiological changes consistent with therapeutic modulation. Conclusions: Immersive VR combined with neuroimaging supports the use of immersive VR as an ecologically grounded framework to probe addiction-related brain circuits; however, larger trials and standardized reporting are needed to strengthen clinical translation. Future studies should prioritize adequately powered randomized designs, harmonized VR cue-reactivity paradigms, and transparent neuroimaging reporting to enable reproducibility and cumulative inference. Full article

Student dropout in Higher Education remains a persistent challenge with significant academic, social and economic consequences. Predictive analytics using traditional Machine Learning and Deep Learning have been increasingly explored to support early identification of students at risk. This article presents a structured literature review of studies published between 2018 and 2025 that apply these techniques to predict dropout in Higher Education. Unlike previous reviews, we pay particular attention to model interpretability, practical deployment and ethical considerations when analysing data types, preprocessing strategies and modelling approaches. Results show that transparent traditional models, including Decision Trees, Logistic Regression, and ensemble methods such as Random Forest and Gradient Boosting remain dominant because they perform strongly on structured data and are easier to explain. Deep Learning approaches, although less prevalent, show promise for sequential and behavioural data but face challenges in data availability, explainability, and implementation complexity. Despite frequently high reported performance, most studies rely on single-institution datasets, limiting generalisability, and only a minority address fairness, bias, or real-world integration. This analysis concludes that we must transition from accuracy-focused evaluations to transparent, accountable and actionable predictive systems that facilitate data-driven and inclusive decision-making in Higher Education. Full article

This study examines the structure and use of Axial Parts and Relational Nouns in Italian from both a syntactic and diachronic perspective. In the first part, we argue that these elements function as nouns and establish an elementary predicate relation of inclusion with an adjacent noun. This relation can be analyzed in terms of Ground and Figure: the Axial Part acts as a possessum of the Ground linked, in turn, to a nominal phrase functioning as possessor/Figure. The interpretation of Axial Parts depends on the context, and while the predicative relation is marked by an adpositional relator, its lexical shape varies, precluding a fixed argumental or complemental relation. This Double-Relator Model contrasts with hierarchical functional projections in the PP structure. The second part supports this view with data from early Italian texts. Focusing on common nouns (e.g., front, head, foot, etc.) used as Relational Nouns or Axial Parts, we show that the Double-Relator Model captures the variability in terms of phonological realization and grammatical function of Old Italian complex PPs, at the same time making it possible to clearly analyze each component of these structures from the syntactic point of view. Full article

Electrodermal activity (EDA) is a powerful anchor for assessing human sympathetic nervous system (SNS) arousal. However, EDA alone is only one facet of physiological response. Researchers have increasingly moved away from single-sensor analysis to multimodal wearable systems, integrating EDA with other signals such as heart rate variability (HRV), photoplethysmography (PPG), skin temperature (SKT), blood oxygen (SpO₂) and more. This critical shift in methodology is not yet reflected in current reviews of the literature. Existing surveys thoroughly cover EDA as a standalone measure, but the combination of sensor technologies has been largely unexamined. In this context, multimodal refers to integrating EDA with complementary biosignals (HRV, PPG, SKT, SpO₂, etc.) commonly captured by modern wearable platforms. This review provides a comprehensive analysis focused on multimodal systems for assessing SNS arousal. A total of 58 studies met the inclusion criteria. We map the landscape, from single signal methods to complex sensor-fusion, and highlight advances in multimodal sensor models, physiological modeling, and context-aware sensing. We also examine recent advances in signal processing and machine learning that enhance multimodal SNS arousal inference, outlining current capabilities and identifying open directions for future work. By providing a framework of this emerging field, this paper serves as a resource for all researchers aiming to build and deploy the next generation of context-aware SNS arousal-sensing technology. Full article

Visual pollution in urban environments has a significant impact on aesthetic quality, level of environmental complexity, coherence, and emotional well-being. Due to that, it needs to be analysed considering not only physical environment features and indicators but also aspects of environmental psychology and human emotional needs towards the urban environment. Taking into account this approach, in this research, it is studied applying a genotype-based framework using space syntax analysis and emotional mapping. Spatial analysis tools, such as space syntax and visibility graph analysis (VGA) provide reliable tools for statistically analysing this phenomenon. This method evaluates visual exposure and connectedness to polluting components across the map, resulting in locations with the most obvious pollution (The research examines spatial metrics such as integration, connectivity, and visibility, as well as emotional responses, to reveal significant links between urban spatial configurations and the visual pollution index (VPI). Zones with great accessibility and reachable by people, such as parks and public spaces, have positive emotional responses and low VPI scores, suggesting accessibility and visual harmony. On the contrary, low-integrated and fragmented areas have high VPI ratings, suggesting visual clutter, poor maintenance, and user dissatisfaction. Visual pollution affects the quality of urban surroundings by filling the visual space with contrasting and varied elements, resulting in visual dissonance. Common sources of visual pollution include architectural forms, billboards, advertising boards, signage, and poorly maintained building façades, particularly in modernist neighbourhoods. The Dainava neighbourhood in Kaunas city is used as a case study to apply this integrated methodology, revealing spatial and emotional aspects of the neighbourhood relevant to the VPI assessment. The findings highlight the relevance of a complex methodological approach that integrates spatial and emotional qualities of the environment and the importance of targeted actions, such as improving visibility, creating visual relations, and reducing visual clutter, in establishing inclusive, legible, and visually harmonious urban spaces. This methodological framework provides urban planners with a practical tool for the evaluation of visual pollution that integrates egzogenous (physical) and endogenous (emotional) factors and has predictive capacities to indicate the environment that is the most sensitive to visual pollution. Full article

Open-air food markets have long functioned as key sites of food provision, social interaction, and local economic exchange in European cities. In recent decades, many of these markets have undergone significant transformation as part of modernization-oriented urban regeneration. This study examines the transformation of Palilula Market in Belgrade, Serbia’s capital, from a traditional open-air market to a large, enclosed market complex, situating the analysis within the post-socialist urban context. Utilizing historical analysis, semi-structured interviews with vendors, and on-site observations, the research examines the impact of spatial reconfiguration on vendor livelihoods, economic practices, and social relations. The results demonstrate that, although the new indoor market has enhanced infrastructure, hygiene, and year-round usability, it has also led to higher rents, reduced stall capacity, increased competition, and stricter regulations. These developments have constrained small-scale vendors and diminished informal social interactions. This study expands the understanding of urban regeneration processes in post-socialist neoliberal contexts by showing how market modernization shapes the inclusivity and socio-cultural significance of traditional urban markets. Full article

Background/Objectives: Although they differ in terms of epidemiological incidence, melanoma and sarcoma are rare, aggressive cancers with a substantial impact on patients’ quality of life and that of their caregivers. Caregivers, who are often family members, experience high levels of stress, anxiety, and caregiving burden, leading to significant psychological, social, and economic repercussions. The aim of this scoping review was to identify the assessment tools used to study the quality of life and psychological well-being of caregivers of patients with melanoma and/or sarcoma, and to highlight the main areas of difficulty. Methods: A systematic search of PubMed, Embase, and PsycINFO databases identified 325 studies, of which 16 met the inclusion criteria. Eligible studies were English language publications focusing on caregivers of adult patients (>18 years) with melanoma or sarcoma. Reviews, case reports, animal studies, or clinical trials, abstracts, books or book chapters, and studies without clear information on assessment tools or involving the pediatric population were excluded. Results: The studies included (2007–2024) involve 3464 caregivers in all, 211 of whom were caring for patients with melanoma or sarcoma. Caregivers were predominantly female (women-to-men ratio of 2:1) with an average age of 50 years. Caregivers presented severely impaired quality of life, high psychological distress, economic and work difficulties, and limited access to emotional support. Conclusions: Cancer caregiving emerges as a complex process influenced by individual, relational, and contextual factors that affect psychological well-being. Despite limitations related to the small number of studies, the predominance of melanoma-focused research, limited sample diversity, and heterogeneous designs and assessment tools, this review highlights the need for a theoretically grounded and clinically integrated approach that recognizes caregivers as active yet vulnerable participants within the care system. Full article

The growing demand for clean-label bakery products requires a deeper understanding of how functional ingredients and physicochemical properties shape consumer perception. This study characterized nine commercial clean-label breads formulated with alternative flours, oilseeds, and functional ingredients by integrating instrumental analyses (color, porosity, free amino acids, total phenolic content, antioxidant activity) with consumer evaluation using hedonic testing and Check-All-That-Apply (CATA). Sixty-five consumers evaluated the breads under blind conditions. Results showed that flour type and seed inclusion significantly affected color, structure, and bioactive compound levels. Breads with higher phenolic content and antioxidant activity (GB-B, GB-C, GB-D, PB-I) exhibited more complex aroma profiles, whereas breads with higher porosity (GB-A, PB-G) were perceived as softer. Taste and texture showed the strongest correlation with overall liking (r > 0.84). CATA and penalty analysis identified soft, easy to chew, sweet, and umami as key drivers of liking, while dry, adhesive, bran odor, and bitter negatively impacted acceptance. Data revealed that consumer preference depends on the balance between structural attributes, flavor development, and nutritional composition. These findings provide actionable insights for the formulation of clean-label breads that balance health benefits and sensory acceptance. Full article

Transportation infrastructure is often viewed as a driver of regional convergence, yet its distributional consequences remain empirically unsettled. This study examines the direct and spatial spillover effects of road network density on urban–rural income inequality across 44 counties in the Lanxi (Lanzhou–Xining) Urban Agglomeration (2013–2022), a key development cluster in the upper reaches of the Yellow River Basin in Northwest China. By employing a Spatial Durbin Model with two-way fixed effects and three alternative spatial weight matrices (inverse geographic distance, economic distance, and an economic–geographic nested specification), we decompose total effects into direct and indirect components. The results indicate that the inequality effect of road density is specification-dependent: under the baseline geographic matrix, road density shows no robust inequality-reducing effect, while its spillover effect becomes significantly negative when spatial dependence is defined by economic similarity (p < 0.05). In contrast, local government health expenditure—a fiscal proxy for public service provision—exhibits a consistently negative association with urban–rural income inequality across all specifications, with statistically significant direct and total effects. These findings suggest that physical connectivity is a necessary but insufficient condition for inclusive growth; fiscal commitment to public services—particularly healthcare—appears to represent a key constraint for urban–rural convergence in topographically complex, ecologically sensitive regions. Full article

Ionic liquid (IL) N-methyl-N′-1-(4-t-butylphenylphosphinyl)butylimidazolium bis(trifluoromethylsulphonyl) imide was used for the first time as an ion carrier in membrane systems to selectively transport Au(III), Pt(IV), and Pd(II) ions. Au(III), Pd(II), and Pt(IV) were transported from HCl solutions utilizing a polymer inclusion membrane (PIM) with cellulose triacetate as the support, o-nitrophenyl pentyl ether as the plasticizer, and ionic liquid as the mentioned ion carrier. The modifications of source and receiving aqueous phase compositions are examined. High selectivity for Au(III) using the ionic liquid in the membrane was achieved at elevated HCl concentrations (≥0.5 M). When a 0.010 M KI solution was used as the receiving phase and a membrane with the optimal composition was applied, the extraction of Au(III) ions reached a maximum recovery rate of 93%. Moreover, PIM studies showed that carrier molecules doped in the membrane creates complexes with the Au(III) ion with a molar ratio of 1:1. The extractability of Au(III) through PIMs exceeded that of other metal ions, with the selectivity of transported metal ions ranked as follows: Au(III) >> Pt(IV), Pd(II). The recovery factors for gold, platinum, and palladium ions after 6 h of transport were 94%, 8%, and 1%, respectively. Full article

The physicochemical controls on gold precipitation in orogenic gold deposits remain poorly constrained, with traditional fluid inclusion and isotopic studies often yielding ambiguous results due to overprinting or incomplete records. This study addresses this challenge using chlorite—a sensitive mineral proxy for fluid conditions—as a quantitative sensor in the Jinshan orogenic gold deposit (>200 t Au) of the Jiangnan orogenic belt, South China. Hosted in Neoproterozoic phyllite within NE–NNE-trending ductile–brittle shear zones, Jinshan features auriferous quartz–polymetallic sulfide veins with prominent chlorite alteration. Integrating high-resolution SEM-EPMA analyses of multi-generational chlorite with thermodynamic modeling, we reconstruct the temporal evolution of temperature, oxygen fugacity (fO₂), pH and sulfur fugacity (fS₂) during ore formation. Four paragenetic stages are identified: Stage 1 (ankerite–quartz), Stage 2 (pyrite–arsenopyrite–quartz), Stage 3 (quartz–gold–polymetallic sulfide), and Stage 4 (chlorite–carbonate–quartz). Electron microprobe analysis reveals that the chlorite composition changes from Fe-rich chamosite (Stage 2) to Mg-rich clinochlore (Stage 3) and then to Fe-rich chamosite (Stage 4). Chlorite from Stage 2 (Chl-1) formed metasomatically at low fluid/rock ratios, while Stage 3 and 4 chlorites (Chl-2 and Chl-3) precipitated directly from higher fluid/rock ratio fluids. Chlorite compositions record a critical Stage 2–3 transition involving cooling from ~320 °C to ~260 °C, reduction (log fO₂ from −33.6 to −39.7), and alkalinization, and sulfur fugacity remained stable within a narrow range (log fS₂ = −13.6 to −8.0), followed in Stage 4 by minor reheating to ~280 °C, re-acidification, and a slight rebound in oxygen fugacity. Thermodynamic simulations reveal that the destabilization of Au(HS)₂⁻ complexes, primarily driven by the synergistic effects of cooling, pH increase, and decreasing oxygen fugacity, triggered gold precipitation during the main ore stage. Results demonstrate that abrupt cooling coupled with fluid alkalinization and reduction exerted the dominant control on gold precipitation in Jinshan, resolving long-standing debates on ore-forming mechanisms and highlighting chlorite as a robust quantitative sensor for fluid evolution. Full article

In recent years, Latin America has experienced a growing incorporation of Artificial Intelligence (AI) into business and organizational environments, driven by digital transformation, data availability, and competitive pressures. Across multiple sectors, AI-based tools are increasingly used to support complex decision-making processes, raising both opportunities and challenges related to efficiency, ethics, and organizational readiness. Within this context, this systematic review examines the scientific evidence on the implementation of AI in business decision-making in Latin America. Following PRISMA 2020 guidelines, a systematic search was conducted in the Scopus database for articles published between 2021 and 2025. The search strategy combined Boolean operators related to AI and decision-making. Inclusion criteria comprised original, open-access research articles conducted in Latin American countries and published in Spanish or Portuguese. After screening for temporality, geographic focus, language, document type, accessibility, duplication, and relevance, 27 studies were selected from an initial pool of 276,302 records. The studies originated mainly from Peru, Colombia, Chile, and Ecuador. The findings show that AI is applied across sectors such as industry, agriculture, finance, education, and public services, primarily to enhance predictive capacity, automate processes, and support data-driven decisions. While AI adoption improves efficiency, cost reduction, and strategic innovation, its effectiveness depends on staff training, ethical governance, and strategic alignment. Persistent challenges include resistance to change, data quality limitations, and concerns regarding transparency and algorithmic bias. Overall, AI emerges as a transformative but context-dependent tool for business decision-making in Latin America. Full article