Search Results (360)

Objective: Glaucoma is a complex optic neuropathy characterized by the progressive loss of retinal ganglion cells (RGCs). Animal models are crucial tools for deciphering its multidimensional pathogenesis and evaluating novel therapeutic strategies. This review aims to systematically summarize the establishment methods, application advances, and future development trends of various glanimal models. Methods: The literature for this review was identified through systematic searches of electronic databases, including PubMed, Web of Science Core Collection, and Google Scholar. The search strategy utilized a combination of keywords and their variants: “glaucoma”, “animal models”, “retinal ganglion cells”, “intraocular pressure”, “neuroprotection”, “immune inflammation”, “fibrosis”, and “filtration surgery”. The search focused on articles published between 2015 and 2025 to cover the major advances of the last decade. The scope encompassed original research articles, reviews, and meta-analyses. Results: Diverse glanimal models successfully replicate different facets of glaucoma, elucidating multidimensional pathogenesis involving mechanical stress, immune inflammation, excitotoxicity, oxidative stress, and fibrosis. These models have played an indispensable role in screening neuroprotective agents, evaluating anti-fibrotic strategies, and validating the application of advanced imaging and functional assessment technologies. Current research is evolving towards model standardization, multi-factor simulation, and the integration of novel drug delivery systems and immunomodulatory strategies. Conclusions: The diversification of glanimal models provides a powerful platform for in-depth investigation of disease mechanisms and the development of innovative therapies. Future research should focus on establishing standardized models that better mimic the clinical pathological state and deeply integrating multimodal assessment technologies with targeted therapies. This will facilitate the translation of basic research into clinical applications, ultimately achieving personalized precision medicine for glaucoma. Full article

Non-AIDS-defining cancers (NADCs) have emerged as an increasingly prominent cause of non-AIDS-related morbidity and mortality among people living with HIV (PLWH). However, the scarcity of NADC clinical samples, compounded by privacy and security constraints, continues to present formidable obstacles to advancing pathological and clinical investigations. In this study, we adopted a joint analysis strategy and deeply integrated and analyzed transcriptomic data from 12,486 PLWH and cancer patients to systematically identify potential key regulators for 23 NADCs. This effort culminated in NADCdb—a database specifically engineered for NADC pathological exploration, structured around three mechanistic frameworks rooted in the interplay of immunosuppression, chronic inflammation, carcinogenic viral infections, and HIV-derived oncogenic pathways. The “rNADC” module performed risk assessment by prioritizing genes with aberrant expression trajectories, deploying bidirectional stepwise regression coupled with logistic modeling to stratify the risks for 21 NADCs. The “dNADC” module, synergized patients’ dysregulated genes with their regulatory networks, using Random Forest (RF) and Conditional Inference Trees (CITs) to identify pathogenic drivers of NADCs, with an accuracy exceeding 75% (in the external validation cohort, the prediction accuracy of the HIV-associated clear cell renal cell carcinoma model exceeded 90%). Meanwhile, “iPredict” identified 1905 key immune biomarkers for 16 NADCs based on the distinct immune statuses of patients. Importantly, we conducted multi-dimensional profiling of these key determinants, including in-depth functional annotations, phenotype correlations, protein–protein interaction (PPI) networks, TF-miRNA-target regulatory networks, and drug prediction, to deeply dissect their mechanistic roles in NADC pathogenesis. In summary, NADCdb serves as a novel, centralized resource that integrates data and provides analytical frameworks, offering fresh perspectives and a valuable platform for the scientific exploration of NADCs. Full article

Background: Artificial intelligence (AI) is transforming medical practice, enhancing diagnostic accuracy, personalisation, and clinical efficiency. However, this transition raises complex ethical challenges related to transparency, accountability, fairness, and human oversight. This study examines how the literature conceptualises and distributes ethical responsibility in AI-assisted healthcare. Methods: This semi-systematic, theory-informed thematic review was conducted in accordance with the PRISMA 2020 guidelines. Publications from 2020 to 2025 were retrieved from PubMed, ScienceDirect, IEEE Xplore databases, and MDPI journals. A semi-quantitative keyword-based scoring model was applied to titles and abstracts to determine their relevance. High-relevance studies (n = 187) were analysed using an eight-category ethical framework: transparency and explainability, regulatory challenges, accountability, justice and equity, patient autonomy, beneficence–non-maleficence, data privacy, and the impact on the medical profession. Results: The analysis revealed a fragmented ethical landscape in which technological innovation frequently outperforms regulatory harmonisation and shared accountability structures. Transparency and explainability were the dominant concerns (34.8%). Significant gaps in organisational responsibility, equitable data practices, patient autonomy, and professional redefinition were reported. A multilevel ethical responsibility model was developed, integrating micro (clinical), meso (institutional), and macro (regulatory) dimensions, articulated through both ex ante and ex post perspectives. Conclusions: AI requires governance frameworks that integrate ethical principles, regulatory alignment, and epistemic justice in medicine. This review proposes a multidimensional model that bridges normative ethics and operational governance. Future research should explore empirical, longitudinal, and interdisciplinary approaches to assess the real impact of AI on clinical practice, equity, and trust. Full article

Background/Objectives: Blood thinners (anticoagulants) remain the first line pharmacotherapy for the management of cardiovascular and thromboembolic disorders. The increased utilization of polypharmacy, likely driven by the greater burden of comorbidities, elevates the risk of potential drug–drug interactions (pDDIs) and creates a significant challenge in anticoagulant management. The aim of the study was to assess the prescribing trend and impact of polypharmacy and pDDIs in patients receiving anticoagulant drug therapy in a public hospital providing secondary care. Methods: A cross-sectional observational study was undertaken between January–June 2023. Data from electronic medical records of prescriptions for anticoagulants were collected, analyzed for prescribing patterns, and checked for pDDIs using Micromedex database 2.0^®. Utilizing binary logistic regression, the relationship between polypharmacy and sociodemographic factors was assessed. Multivariate logistic regression analysis served to uncover determinants linked to pDDIs. Results: Of the total 130 patients, females were predominant (58.46%), with a higher prevalence among those aged 61–90 years. Atrial fibrillation emerged as the main clinical reason and apixaban (51.53%) ranked as the top prescribed anticoagulant in our cohort. Among the 766 pDDIs identified, the majority [401 (52.34%)] were categorized as moderate in severity. Polypharmacy was strongly linked to age (p = 0.001), the Charlson comorbidity index (CCI) (p = 0.040), and comorbidities (p = 0.005) in the binary logistic regression analysis. In the multivariable analysis, the number of medications remain a strong predictor of pDDIs (adjusted OR: 30.514, p = 0.001). Conclusions: Polypharmacy and pDDIs were exhibited in a significant segment of cohort receiving anticoagulant therapy, with strong correlations to age, CCI, comorbidities, and the number of medications. A multidimensional approach involving collaboration among healthcare providers assisted by clinical decision support systems can help optimize the management of polypharmacy, minimize the risks of pDDIs, and ultimately enhance health outcomes. Full article

Background: Short tandem repeats (STRs) are highly variable sequences present along the human genome, including the Y-chromosome. Y-STRs are exclusive to males, and the haplotypes they define are informative. Objectives: Twenty-six Y-STR loci were genotyped in 252 males from Northern Portugal to characterise Y-chromosome genetic variation using the Investigator Argus Y28 QS Kit. Methods: The kit mentioned was used to amplify male DNA samples, and capillary electrophoresis was used to analyze the fragments. Forensic parameters and haplotype diversity were computed, and samples’ haplogroups were predicted. A multidimensional scaling (MDS) plot was used to graphically represent the R_ST genetic distances, including reference populations. Results: A total of 250 different haplotypes were observed, including 248 unique ones, yielding a very high haplotype diversity (HD = 0.999) and discriminatory power (DP = 0.992). Haplogroup analysis indicated a predominance of R1b (58.7%), followed by E1b1b, I and J, pointing to a population history shaped by Mediterranean and North African gene flow. Comparative analysis between Portugal and 5 other populations showed greater genetic affinity with Spain and Italy, while revealing marked differentiation from Greece, Morocco, and former Portuguese colonies. Conclusions: The results confirm that the Northern Portuguese Population exhibits high Y-STR variability and robust forensic resolution. The dataset was submitted to the YHRD database, enhancing the representation of the Portuguese population and underscoring the value of the 26 locus panel for applications in forensic science, genealogy, and population genetics. Full article

The objective of this study was to analyze social perception, trust, and vaccine hesitancy through a combined approach of bibliometric analysis and qualitative synthesis, based on the most cited articles in the recent scientific literature. A systematic search was conducted in indexed databases, identifying patterns of production, collaboration, citation, thematic networks, and conceptual trends associated with the study of public trust in vaccines. The results reveal a marked geographic concentration of scientific production, dominated by the United States and the United Kingdom, as well as a strong articulation of thematic clusters linked to digital disinformation, health communication, risk perception, and psychosocial determinants of vaccine acceptance. The qualitative synthesis of the most influential studies reveals that vaccine hesitancy is a multidimensional phenomenon, determined by sociocultural, cognitive, emotional, and structural factors that interact dynamically according to each context. Disinformation, institutional trust, community narratives, and the credibility of sources emerge as central components in individual decision-making. Together, the integrated results enable a deeper understanding of vaccine hesitancy beyond traditional cognitive models, highlighting the need for contextualized communication strategies, intercultural approaches, and health policies based on trust and social participation. This study provides an integral view of the scientific landscape and establishes priority lines for future research and the design of effective public health interventions. Full article

In recent years, with the rapid advancement of science and technology, generative artificial intelligence has increasingly entered the public eye. Primarily through intelligent algorithms that simulate human logic and integrate vast amounts of network data, it provides designers with solutions that transcend traditional thinking, enhancing both design efficiency and quality. Compared to traditional design methods reliant on human experience, generative design possesses robust data processing capabilities and the ability to refine design proposals, significantly reducing preliminary design time. This study employs the CiteSpace visualization tool to systematically organize and conduct knowledge map analysis of research literature related to generative AI in architectural design within the Web of Science database from 2005 to 2025. Findings reveal the following: (1) International research exhibits a trend toward interdisciplinary convergence. In recent years, research in this field has grown rapidly across nations, with continuously increasing academic influence; (2) Research primarily focuses on technological applications within architectural design, aiming to drive innovation and development by providing superior, more efficient technical support; (3) Generative AI in architectural design has emerged as a prominent international research focus, reflecting a shift from isolated design to industry-wide integration; (4) Generative AI has become a core global architectural design topic, with future research advancing toward full-process intelligent collaboration. High-quality knowledge graphs tailored for the architecture industry should be constructed to overcome data silos. Concurrently, a multidimensional evaluation system for generative quality must be established to deepen the symbiotic design paradigm of human–machine collaboration. This significantly enhances efficiency while reducing the iterative nature of traditional methods. This study aims to provide empirical support for theoretical and practical advancements, offering crucial references for practitioners to identify business opportunities and policymakers to optimize relevant strategies. Full article

With the acceleration of digital transformation, open-source software has become a fundamental component of modern smart grids and other critical infrastructures. However, the complex dependency structures of open-source ecosystems and the continuous emergence of vulnerabilities pose substantial challenges to software supply chain security. In power information networks and cyber–physical control systems, vulnerabilities in open-source components integrated into Supervisory Control and Data Acquisition (SCADA), Energy Management System (EMS), and Distribution Management System (DMS) platforms and distributed energy controllers may propagate along the supply chain, threatening system security and operational stability. In such application scenarios, large language models (LLMs) often suffer from limited semantic accuracy when handling domain-specific security terminology, as well as deployment inefficiencies that hinder their practical adoption in critical infrastructure environments. To address these issues, this paper proposes KD-SecBERT, a domain-specific semantic bidirectional encoder optimized through multi-level knowledge distillation for open-source software supply chain security in smart grid applications. The proposed framework constructs a hierarchical multi-teacher ensemble that integrates general language understanding, cybersecurity-domain knowledge, and code semantic analysis, together with a lightweight student architecture based on depthwise separable convolutions and multi-head self-attention. In addition, a dynamic, multi-dimensional distillation strategy is introduced to jointly perform layer-wise representation alignment, ensemble knowledge fusion, and task-oriented optimization under a progressive curriculum learning scheme. Extensive experiments conducted on a multi-source dataset comprising National Vulnerability Database (NVD) and Common Vulnerabilities and Exposures (CVE) entries, security-related GitHub code, and Open Web Application Security Project (OWASP) test cases show that KD-SecBERT achieves an accuracy of 91.3%, a recall of 90.6%, and an F1-score of 89.2% on vulnerability classification tasks, indicating strong robustness in recognizing both common and low-frequency security semantics. These results demonstrate that KD-SecBERT provides an effective and practical solution for semantic analysis and software supply chain risk assessment in smart grids and other critical-infrastructure environments. Full article

Background/Objectives: Obstructive sleep apnea (OSA) is characterized by recurrent episodes of complete or partial upper airway collapse during sleep, leading to apnea or hypopnea and recurrent oxygen desaturation. Intermittent hypoxia (IH) and sleep fragmentation have been proposed as key mechanisms contributing to the adverse cardiovascular consequences observed in OSA. The present study aimed to identify clinical variables independently associated with IH in patients with OSA and to examine their relationships with common comorbid conditions. Methods: This retrospective study included 569 adult patients diagnosed with obstructive sleep apnea (OSA) by overnight polysomnography (apnea–hypopnea index [AHI] ≥ 5 events/hour) between February 2020 and January 2025 at the Sleep Laboratory of Trakya University Hospital. Demographic characteristics, body mass index (BMI), AHI values, comorbid medical conditions, average nocturnal oxygen saturation, and the duration of intermittent hypoxia (time below 90% SpO₂ [T90]) were retrieved from the laboratory database. Normality of distribution was assessed using the Kolmogorov–Smirnov test. Group differences were evaluated using the Mann–Whitney U test and the Kruskal–Wallis test with Dunn–Bonferroni post hoc analysis. Correlations were examined using Spearman’s correlation analysis, and variables independently associated with average nocturnal oxygen saturation and intermittent T90 were assessed using multivariable linear regression analysis. Results: The presence of hypertension, diabetes mellitus, and comorbid conditions was associated with significant differences in T90 among patients with OSA. T90 also differed significantly across AHI severity grades. Significant negative correlations were observed between nocturnal oxygen saturation and BMI, hypertension, diabetes, comorbidities, and age. Nocturnal oxygen saturation values likewise differed significantly across BMI-defined obesity groups. In the multivariable regression analysis, BMI, AHI, and age were independently associated with lower nocturnal oxygen saturation and longer T90. Conclusions: This study provides important insight into the complex relationships among OSA severity, patient demographics, comorbidities, and intermittent hypoxia. In multivariable analysis, BMI, AHI, and age showed independent associations with reduced nocturnal oxygen saturation and prolonged T90. These findings highlight the importance of a multidimensional clinical assessment in OSA and support the use of intermittent hypoxia metrics as additional indicators of disease burden and potential clinical impact. Full article

Objectives: To systematically analyze the expression profiles of long non-coding RNAs (lncRNAs) in serum-derived exosomes from patients with age-related hearing loss (ARHL), and to further identify key regulatory lncRNAs involved in the pathogenesis and progression of ARHL. Methods: Peripheral blood samples were collected from patients with ARHL and age-matched normal-hearing controls. Serum was separated and exosomes were extracted. The exosomes were identified by nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), and Western blot. Subsequently, total RNA was extracted from the purified exosomes for lncRNA transcriptome sequencing. Based on the sequencing results, we identified differentially expressed lncRNAs and mRNAs and conducted multi-dimensional functional analysis, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome pathway database (Reactome), and Disease Ontology (DO). Finally, four key mRNAs (THAP2, ZNF225, MED12, and RNF141) and four differentially expressed lncRNAs (DE-lncRNAs), namely MSTRG.150961.7, ENSG00000273015, MSTRG.336598.1, and ENSG00000273493, were experimentally verified by quantitative real-time polymerase chain reaction (RT-qPCR) technology. Results: Exosomes were successfully isolated from serum and confirmed by particle size, morphological examination, and the expression of exosome-labeled proteins. A total of 2874 DE-lncRNAs were identified, among which 988 were downregulated and 1886 were upregulated. Similarly, 2132 DE-mRNAs were detected, among which 882 were downregulated and 1250 were upregulated. GO analysis revealed significant enrichment in biological processes such as “phospholipid binding”, “phosphatidylinositol binding”, “phosphatase binding”, “phosphatidylinositol bisphosphate binding”, “phosphatidylinositol-4,5-bisphosphate binding”, “phosphatidylinositol-3,5-bisphosphate phosphatase activity”. KEGG is significantly enriched in signaling pathways including “Wnt signaling pathway”, “Hippo signaling pathway”, “Cushing syndrome”, and “Nucleocytoplasmic transport”. The functional annotations of Reactome were significantly enriched in biomolecular pathways including “tRNA processing”, “Cellular response to heat stress”, “Extra-nuclear estrogen signaling”, “Metabolism of non-coding RNA”, and “CTNNB1 T41 mutants aren’t phosphorylated”. DO is significantly enriched in diseases or pathological conditions such as “hepatitis”, “bacterial infectious disease”, “cystic fibrosis”, and “vasculitis”. Conclusions:THAP2, ZNF225, MED12, and RNF141 may serve as potential candidate biomarker for ARHL. Additionally, lncRNA MSTRG.150961.7, lncRNA MSTRG.336598.1, and lncRNA ENSG00000273493 may play significant roles in the pathogenesis of this condition. Full article

Background/Objectives: Family members are the principal providers of home-based care for migrant older adults. Linguistic, cultural, and structural barriers within health systems exacerbate the caregiver burden across emotional, physical and financial domains. Although home healthcare services may alleviate this burden, variability in access, cultural safety, and care coordination can also intensify it. This scoping review maps the evidence on the burden experienced by family caregivers who deliver home-based healthcare to migrant older adults and examines how these arrangements affect caregivers’ health and well-being. It synthesizes the literature on facilitators and barriers—including access, cultural-linguistic fit, coordination with formal services, and legal/immigration constraints—and distills implications for policy and practice to strengthen equitable, culturally responsive home care. Method: The Joanna Briggs Institute (JBI) scoping review framework was used to conduct the review. A comprehensive search was performed across six databases (CINAHL, Scopus, Web of Science, PsycINFO, MEDLINE and Sociological Abstracts) for articles published between 2000 and 2025. Studies were selected based on predefined inclusion criteria focusing on the family caregiver burden in providing home healthcare for migrant older adults. Data extraction and thematic analysis were conducted to identify key themes. Results: The review identified 20 studies across various geographical regions, highlighting four key themes: (1) Multidimensional Caregiver Burden, (2) The Influence of Gender, Family Hierarchy, and Migratory Trajectories on Caregiving, (3) Limited Access to Formal and Culturally Appropriate Support, and (4) Health Outcomes, Coping, and the Need for Community-Based Solutions. Conclusions: System-level reforms are required to advance equity in home healthcare for aging migrants. Priorities include establishing accountable cultural-safety training for providers; expanding multilingual access across intake, assessment, and follow-up; and formally recognizing and resourcing family caregivers (e.g., navigation support, respite, training, and financial relief). Investment in community-driven programs, frameworks and targeted outreach—co-designed with migrant communities—can mitigate isolation and improve uptake. While home healthcare is pivotal, structural inequities and cultural barriers continue to constrain equitable access. Addressing these gaps demands coordinated policy action, enhanced provider preparation, and culturally responsive care models. Future research should evaluate innovative frameworks that integrate community partnerships and culturally responsive practices to reduce the caregiver burden and improve outcomes for migrant families. Full article

This paper is dedicated to analysing sustainability and digitalisation in the transport systems of the European Union (EU) and Ukraine, with a particular focus on three representative subsectors: freight rail, urban public transport and last-mile postal logistics. It explores how technological innovation, operational efficiency and environmental responsibility interact within these sectors under distinct institutional and economic conditions: mature, market-based systems in the EU and resilience-driven systems in wartime Ukraine. This study applies a comparative, descriptive–analytical methodology using secondary data drawn from corporate sustainability reports, official statistics and sectoral databases for 2022. Quantitative KPls were complemented with a qualitative assessment of digitalisation maturity to ensure cross-country comparability. Through a comparative analysis of KPIs, such as freight volumes, emissions intensity, revenue efficiency and digital maturity, this study identifies structural and policy gaps that hinder progress toward sustainable mobility. This study develops a multi-dimensional framework combining operational, financial, environmental and digital indicators. In this paper, digital integration refers to the degree to which transport operators embed digital tools such as tracking, data management and automation into their core processes, while environmental efficiency denotes the ability to deliver transport services with minimal resource consumption and carbon emissions per operational unit. Institutional resilience is understood here as the capacity of transport organisations and governing institutions to maintain functionality, adapt and recover under crisis or systemic stress, which is particularly relevant for Ukraine’s wartime context. The findings demonstrate that while EU operators lead in transparency, digital integration and environmental performance, Ukrainian actors exhibit rapid adaptive innovation and significant potential for technological leapfrogging during reconstruction. This paper concludes that the EU must overcome regulatory inertia and infrastructure fatigue, while Ukraine should institutionalise resilience and transparency. Full article

In recent decades, rapid socioeconomic development and population growth have led to the degradation of river and lake health worldwide, posing severe challenges to watershed ecological management. The growing intensity of land-use has significantly contributed to the accelerated deterioration of aquatic ecosystems. River and lake health assessment has evolved from single-parameter metrics (e.g., water quality) to multidimensional frameworks integrating hydrological, biological, and anthropogenic factors. This research conducted a bibliometric analysis of 1302 publications from 1996 to 2023 in the Web of Science database to identify research trends and hotspots. Results showed that publications exhibited a three-phase growth incubation (1996–2000), expansion (2001–2012), and acceleration (2013–2023), with the U.S., China, and Australia as leading contributors characterized by regionally clustered international collaborations. Research themes have shifted from single water quality parameters to integrated assessments. “Land-use”, “water quality”, and “biotic integrity” have emerged as core hotspots, forming a synergistic assessment framework that combines physicochemical, biological, and socioeconomic factors. The research scale underwent a spatial refinement process from the whole watershed to the buffer zone of rivers and lakes, and land-use effects on aquatic ecosystems vary significantly across spatial scales (entire watershed and riparian zones). Fine-scale studies better capture localized pollution pathways, supporting targeted conservation strategies. This review systematically outlines research status, hotspots, and development directions for river and lake health studies, highlighting the need for integrated watershed management, emphasizing conservation through fine-scale land-use monitoring, and providing scientific support for integrated refined governance of watershed ecology. Full article

Background: Nearshoring has risen after shocks and policy shifts. We synthesize evidence in a compact loop linking triggers (trade frictions, supply-chain risk, new agreements) to location choices mediated by multidimensional proximity (geographic, institutional, organizational, social, cognitive, functional) to components (manufacturing footprint, Foreign Direct Investment (FDI), employment) and outcomes (spillovers, productivity, innovation) conditioned by absorptive capacity and institutions. Methods: We conducted a literature review using major bibliographic databases. A staged screening pipeline (deduplication, pre-eligibility, and title–abstract screening) preceded full-text coding aligned with the review framework (triggers, proximity, components, outcomes, mediators). Studies were appraised with a five-criterion checklist, and themes were consolidated with basic bibliometric checks. Results: Evidence is North Atlantic and manufacturing-centric. Supply-chain disruptions dominate triggers; non-geographic proximity strongly moderates relocation. FDI anchors ecosystems, while employment effects are lagged and compositional. Strong capability and policy mixes yield broader spillovers; otherwise, benefits remain enclave-like. Sustainability and transformative outcomes are rarely assessed. Conclusions: The loop clarifies feedback from outcomes to future siting. Firms should build proximity beyond geography and pair early FDI with supplier and skills upgrading; policymakers should align instruments to governance, capability formation, and logistics. Research should expand Global South coverage and integrate environmental and inclusion metrics. Full article

The electric vehicle (EV) battery supply chain plays a critical role in promoting sustainable transportation and tackling scarce resources, environmental costs, and supply chain vulnerabilities. The current study aims to conduct an extensive literature review of the EV battery supply chain given its importance for developing sustainable and efficient EVs. Using keyword co-occurrence and article co-citation analyses, this study analyses more than 681 publications from 2005 to 2024 and sourced from the Scopus database. Findings show that the number of articles increased considerably after 2020, which can be attributed to the global focus on decarbonization, electromobility, and circular economy practices. The review identifies important themes such as sustainability challenges, critical materials management, reverse logistics, and policy-driven frameworks for closed-loop supply chains. The findings from this study highlight a multidimensional approach where the integration of technologies, innovative policies, and collaborative actions can contribute to the resilience and sustainability of EV battery supply chains. It offers practical insights for stakeholders, strategic directions to maximize EV battery lifecycle management, and outlines the pathways to reach carbon neutrality in the transportation sector. By identifying the intellectual structure of this emerging field, the study contributes to academic discourse and informs the formulation of practical strategies to advance sustainable mobility. Full article