Abstract: AGI alignment is often evaluated at a snapshot: a system is judged by its current outputs, policy profile, benchmark behavior, or apparent corrigibility. Snapshot evaluation misses a central risk of advanced deployment: a good endpoint can still be reached by a bad journey. Two trajectories may arrive in similar behavioral regions while differing in reversibility, opacity, intervention cost, memory entanglement, institutional dependency, and the quality of human judgment left available for oversight. This paper develops a path-sensitive alternative. It represents AGI development as motion through an augmented state space Z containing model and environment state, world-model structure, policy state, memory and provenance traces, governance affordances, institutional embedding, and human evaluative capacity. Cognitive integrity — the capacity of individuals, teams, or institutions to sustain calibrated attention, trust, contestability, and decision under pressure [1] — is introduced here as an alignment-relevant state variable rather than assumed as a familiar metric. The formal contribution is a scaffold of definitions: controlled transition laws over augmented state, escape cost, path-level alignment functionals, viability floors, forbidden regions, and trajectory classes distinguished by lock-in, basin structure, retargetability, and integrity preservation. The result does not supply a calibrated empirical model of deployed AGI systems. It specifies what such a model must track if alignment evidence is to cover both present behavior and the remaining possibility of legible, reversible, and cognitively intact correction.

Abstract: Introduction: Precision oncology has revolutionized cancer care in high-income countries, but its implementation in Latin American low-resource settings faces profound bioethical dilemmas. This study analyzes these challenges through the lens of social justice and equity. Methods: An integrative review was conducted following the Whittemore and Knafl framework. A systematic search was performed across PubMed, Scopus, SciELO, and LILACS (2015–2025). Thematic synthesis was applied to integrate empirical data with normative bioethical theories. Results: Four major analytical themes were identified: 1) The Innovation Paradox and Financial Toxicity, where prohibitive pricing (exceeding $100,000 USD/year) violates distributive justice and leads to a biological penalty in survival; 2) Infrastructure Deficits and Epistemic Injustice, highlighted by a 9.4% access rate to Next-Generation Sequencing (NGS) and the risks of applying Eurocentric genomic data to admixed LA populations; 3) Research Vulnerability, where clinical trials serve as survival strategies, compromising autonomy and informed consent; and 4) The Judicialization Dilemma, where individual court orders for high-cost drugs threaten systemic sustainability and equity. Conclusions: To prevent a genomic apartheid, Latin America must transition toward genomic sovereignty and frugal precision oncology. Bioethical frameworks in the region must prioritize protection ethics and social justice to ensure that scientific innovation does not exacerbate existing health inequities.

Abstract: Recent studies have investigated whether the rate of global warming has changed since the 1970s, with particular attention to the role of natural variability and its removal from temperature time series. In particular, Foster and Rahmstorf (2026) analyzed global mean surface temperature series, adjusted for natural variability. However, their procedure might produce spurious changepoints, since it does not appropriately handle the autocorrelation present in the residuals of the models considered. In this study, we revisit the same adjusted temperature series using a different methodology (the Quandt likelihood ratio test) while properly accounting for the presence of autocorrelation. We find evidence that global temperature has departed from its previous path since around 2013-2014. Our results provide a robust proof of a clear recent increase in the temperature trend, at a rate of warming that has doubled since that date.

Abstract: Chagas disease, caused by Trypanosoma cruzi, remains a major public health problem in Latin America and an emerging global health concern due to population mobility. Alt-hough benznidazole and nifurtimox remain the only approved antiparasitic drugs, their limited efficacy in chronic infection, prolonged treatment regimens, frequent adverse ef-fects, and variable activity across parasite strains highlight the need for new therapeutic strategies. In addition, the pathogenesis of chronic Chagas disease is driven not only by parasite persistence but also by immune-mediated tissue damage, particularly in chronic Chagas cardiomyopathy. In this review, we examine emerging therapeutic approaches that extend beyond conventional trypanocidal chemotherapy, with emphasis on plant-derived extracts, essential oils, antimicrobial peptides, and cell-based immuno-modulatory strategies. Plant compounds and essential oils have shown antiparasitic ac-tivity through mechanisms including oxidative stress induction, membrane disruption, interference with sterol biosynthesis, and mitochondrial dysfunction, while some extracts also modulate host immune responses. Antimicrobial peptides display dual potential by directly damaging parasite membranes and organelles or by reshaping infec-tion-associated inflammatory responses. In parallel, cell-based therapies such as mesen-chymal stromal cells, tolerogenic dendritic cells, and bone marrow-derived cells have demonstrated promising cardioprotective and immunoregulatory effects in experimental chronic Chagas disease. Collectively, these approaches support a multitarget therapeutic framework in which parasite-directed and host-directed interventions may complement each other. Further mechanistic studies, standardization, and translational validation will be essential to advance these candidates toward clinically useful therapies for Chagas disease.

Abstract: Generative artificial intelligence (GAI) is increasingly embedded in personal financial, yet little is known about how models make recommendations using financial information and demographic cues. This study audits three frontier GAI models, GPT 5.5, Gemini 3.1 Pro, and Claude Opus 4.7, using a full-profile conjoint experiment in which each model evaluated the same 1,000 hypothetical investor profiles and selected among standardized conservative, balanced, and aggressive portfolios. Investor profiles systematically varied attributes, including risk tolerance, time horizon, goal type, income, and age, gender, ethnicity, marital status, and employment type. Ordered logistic regressions and matched-profile comparisons show that all three models base recommendations primarily on legitimate financial inputs, especially risk tolerance and time horizon. Gender and ethnicity do not significantly influence recommendations, although age affects all models and marital status affects ChatGPT. However, the models are not interchangeable: they differ significantly in overall risk appetite and in how they translate risk tolerance, time horizon, goal type, and age into portfolio choices, with economically meaningful differences in predicted recommendations for identical clients. These findings suggest that contemporary GAI investment advice exhibits limited evidence of conventional demographic bias but introduces a distinct form of platform risk arising from model-specific advisory logic.

Abstract: Contrastive learning–based models such as DrugCLIP have recently emerged as scalable tools for structure-based virtual screening by embedding protein structures and small molecules into a shared representation space. While these approaches demonstrate high throughput and competitive screening performance in ligand retrieval tasks, their ability to correctly identify biologically relevant ligand-binding pockets has not been systematically evaluated. Here, we construct a benchmarking dataset comprising 42 pharmacologically diverse human protein targets with experimentally validated drug-bound structures spanning multiple target families. Using this dataset, we evaluate the pocket recognition capability of DrugCLIP and compare its performance with a traditional structure-based workflow (Fpocket combined with ESSA) and a machine learning-based method (P2Rank). DrugCLIP shows robust performance for well-characterized target classes, including kinases (10/10) and nuclear receptors (5/5), but exhibits markedly reduced accuracy for ion channels (1/4), GPCRs (3/5) and transporters (3/5). Notably, pocket prediction accuracy does not strongly correlate with structural data availability, suggesting that intrinsic pocket characteristics rather than training data abundance primarily affect model performance. Across the benchmark, DrugCLIP achieves an overall success rate of 71% (95% CI: 56-83%), compared with 79% (95% CI: 64-88%) for Fpocket+ESSA and 93% (95% CI: 81-98%) for P2Rank. McNemar’s test showed no significant difference between DrugCLIP and Fpocket+ESSA (p=0.508), whereas P2Rank significantly outperformed DrugCLIP (p=0.012). Together, these results provide a quantitative evaluation of pocket recognition by contrastive learning–based models and highlight key limitations of embedding-based approaches for pocket localization.

Abstract: Background: Marfan syndrome (MFS) is a connective tissue disorder caused by FBN1 mutations, leading to elastic fiber disarray and early thoracic aortic aneurysm (TAA) formation. Currently, pharmacological treatments lack specificity and only delay progression. We previously reported a specific TGFβ-driven miR-632 up-regulation in MFS TAA tissues and blood causing smooth muscle cell dedifferentiation and aortic wall degeneration. This study evaluated the effects of three conventional antihypertensive drugs (β-blocker, ACE inhibitor and sartan) on parietal remodeling comparing them with a miR-632 inhibitor in an ex vivo TGFβ –induced model of MFS TAA. Methods and Results: Gene expression and western blot analyses demonstrated that only losartan significantly reduced miR-632 and vascular degeneration markers. Notably, combined treatment with ramipril and carvediol compromised losartan’s efficacy, highlighting the need for careful therapeutic selection. miR-632 inhibitor was the most effective strategy in this ex vivo setting, although further preclinical validation is needed to confirm its therapeutic potential in vivo. Conclusions: Our data emphasize that choosing the right treatment in MFS aortopathy requires understanding its specific impact on cellular pathways. Our findings identify losartan as the most effective standard drug while suggesting miR-632 as a promising future target to stabilize the aortic wall and delay surgery.

Abstract: Alzheimer's disease (AD), a leading cause of dementia worldwide, is a neurological disorder characterized by progressive cognitive decline. AD is also considered a significant socioeconomic burden. While definitive diagnostic tools such as positron emission tomography (PET) imaging and cerebrospinal fluid (CSF) biomarker analysis offer high sensitivity and specificity, they are limited by high cost, invasiveness, and limited accessibility. Consequently, these gold standard approaches hinder their applicability for large-scale screening and longitudinal follow-up. Recent advances in blood-based biomarkers hold promise in capturing systemic molecular changes associated with AD. In particular, transcriptomic signatures derived from RNA sequencing (RNA-seq) are promising in capturing systemic molecular changes associated with AD. Gene expression profiles in peripheral blood reveal underlying pathological processes. These pathological processes can be listed as synaptic dysfunction, neuroinflammation, and metabolic dysregulation. Together with the high-dimensional datasets and AI approaches enable the identification of robust predictive models which has the assistance of estimating AD-related biomarker status. We further discussed the integration of multiple omics data, including genomics, proteomics, and metabolomics to improve biomarker robustness. We also addressed key challenges related to reproducibility, repeatibility, cohort heterogeneity, and clinical application. And we outline future directions of standardized, scalable, and clinically applicable diagnostic machineries.

Abstract: Background: Psychosocial assessment is a critical component of transplant candidate evaluation, yet its clinical utility is often limited by the descriptive nature of existing tools such as the Stanford Integrated Psychosocial Assessment for Transplantation (SIPAT). Translating multidimensional assessment data into actionable clinical insights remains a challenge in routine practice. Methods: We developed a clinical decision support application that integrates SIPAT item-level data with probabilistic risk estimation, visualization, and cohort-referenced interpretation. The application was based on a retrospective dataset of 496 lung transplant candidates evaluated at a single tertiary transplant center. Random forest–based models were used to transform SIPAT item-level data into probabilistic risk representations to estimate domain-specific risks, including depression, anxiety, nicotine-related risk, alcohol-related risk, illicit drug use, social support deficits, and non-adherence. Risk estimates were expressed as calibrated probabilities and categorized into clinically interpretable levels. Additional components included domain-level burden scoring and unsupervised clustering of multidomain risk profiles. Results: Estimated risks were predominantly low across the cohort, with high-risk subgroups identified for depression (6.5%), anxiety (2.2%), nicotine-related risk (11.3%), alcohol-related risk (4.4%), illicit drug use (2.2%), social support deficits (8.1%), and non-adherence (1.4%). Clustering analysis revealed three distinct profiles: a low-risk majority group, a subgroup characterized by elevated nicotine-related risk, and a small high-burden group with substantially elevated psychological distress, reduced social support, and increased non-adherence risk. Risk estimates showed strong and domain-consistent correlations with SIPAT scores (Spearman rho up to 0.80, p < 0.001). Feature importance analyses confirmed that risk estimation was primarily driven by clinically relevant SIPAT items. The application generated structured outputs integrating risk estimates, visualization, and intervention prioritization. Conclusions: The proposed application translates SIPAT-based psychosocial assessment into structured, multidomain risk profiles that enhance clinical interpretability and support targeted psychosocial prehabilitation. This approach provides a practical framework for translating psychosocial assessment into individualized intervention planning in lung transplant settings.

Abstract: Aiming at the core limitations of single large language models in complex task solving, including coarse task decomposition, cumulative long-chain reasoning errors, and the lack of explicit cross-agent collaboration, this paper proposes a large language model-driven multi-agent collaborative method. A hierarchical and role-based agent architecture is designed to separate task decomposition, specialized reasoning, result verification, and decision fusion, thereby enabling modular task solving and closed-loop orchestration over the full execution process. In addition, an efficient semantic communication mechanism is introduced to transmit compressed reasoning states across agents without breaking intermediate logical dependencies. A dynamic feedback iteration module is further employed to adjust routing strategy, collaboration intensity, and reasoning paths in real time according to subtask progress and verification outcomes. Comparative experiments on mathematical reasoning, multi-step planning, and complex information integration show that, relative to a single large language model, the proposed method improves the average completion rate by 21.3%, reduces the long-chain reasoning error rate by 18.7%, and reaches 92.6% cross-agent decision consistency. These results demonstrate that structured collaboration substantially improves robustness and accuracy for complex task solving and provides a practical technical path for diverse intelligent systems.

Abstract: Drug-related criminal activities on social media increasingly employ dynamic coded language—such as fruit substitutions, numeric homophones, and dialectal metaphors—to evade detection. This linguistic obfuscation poses significant challenges to conventional keyword-based monitoring systems. Furthermore, the scarcity of open-source datasets capturing these specific evasive expressions severely impedes automated detection research. To address these limitations, we construct a dedicated dataset of 10000 samples of drug-related coded texts sourced from mainstream Chinese social media platforms. Concurrently, we propose an optimized, TextCNN-based deep learning framework tailored for the automated identification of such illicit content. By leveraging multi-scale convolutional feature extraction, our model effectively captures intricate local semantic patterns and morphological variations inherent in short, highly noisy social media texts. Experimental results demonstrate that the proposed method achieves an F1-score of 99.3%, significantly outperforming established baseline approaches in the semantic representation of coded language. These findings indicate that our framework provides an efficient, robust, and scalable computational solution for intelligent drug-related content monitoring in complex online environments.

Abstract: Autonomous driving systems rely on a sophisticated pipeline of artificial intelligence models to perceive, predict, and plan in dynamic environments. This review presents a systematic analysis of the machine learning and deep learning models underpinning vehicle autonomy, spanning classical convolutional neural networks (CNNs) for object detection and semantic segmentation, to recurrent and Transformer-based architectures for trajectory prediction and motion planning. In this review, a critical examination of the autonomous vehicle sensor stack—including cameras, LiDAR, radar, ultrasonics, and GNSS/IMU as data acquisition systems, highlighting modality-specific AI challenges such as monocular depth estimation, 3D point cloud processing, and radar Doppler interpretation. The evolution of perception and decision-making pipelines is reviewed, contrasting modular architectures with end-to-end learning paradigms that directly map raw sensor data to control commands, and discussing their trade-offs in interpretability, safety assurance, and robustness to rare edge cases. We further survey specialized hardware accelerators and heterogeneous automotive SoCs designed to meet stringent real-time and power constraints. Industrial strategies are compared, including multi-modal sensor fusion and vision-centric approaches based on large-scale imitation learning. Finally, we identify open challenges related to robustness under adverse conditions, domain shift, causal ambiguity, and the need for interpretable and certifiable AI in safety-critical autonomous driving systems.

Abstract: Ultraweak photon emission (UPE) refers to spontaneous, low-intensity photon release from biological systems, generated largely through oxidative metabolic reactions involving reactive oxygen species, lipid peroxidation, mitochondrial activity, and electronically excited molecular intermediates. Because the nervous system is highly metabolically active and vulnerable to oxidative stress, hypoxia, excitotoxicity, inflammation, and mitochondrial dysfunction, UPE may offer a noninvasive optical window into neural physiology and disease. In this narrative review, we examine experimental and translational evidence linking UPE to nervous system function, with emphasis on neuronal excitation, glutamate-mediated activity, ischemia-reperfusion injury, stroke, neurodegeneration, mental-state and anesthesia paradigms, photobiomodulation, demyelinating disease, Parkinson disease, amyotrophic lateral sclerosis, and neuro-oncology. Across these domains, UPE appears most consistently associated with redox metabolism, mitochondrial function, oxidative stress, and excitation–metabolism coupling, whereas evidence that endogenous photons mediate functional neural signaling remains preliminary. Current data suggest that UPE may be most promising as a preclinical biomarker of tissue metabolic state, delayed post-ischemic dysfunction, and early neurodegenerative change, particularly when integrated with electrophysiology, perfusion imaging, molecular assays, and other physiologic measures. However, clinical translation is limited by low photon flux, limited temporal and spectral resolution, difficulty localizing signals from deep tissue, heterogeneous experimental protocols, and incomplete source attribution. Overall, UPE represents a promising but still early-stage framework for studying nervous system metabolism and disease, with future progress dependent on standardized methods, multimodal validation, and disease-specific investigation.

Abstract: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder for which early and accurate diagnosis remains a critical challenge. In this work, we propose a Multi-ROI Multimodal 3D Vision Transformer for AD classification that integrates structural MRI data with clinical and volumetric biomarkers within a unified attention-based framework. The proposed approach leverages anatomically guided multi-region-of-interest (ROI) decomposition to focus on disease-relevant brain structures, including the hippocampus, entorhinal cortex, fornix, and major cortical lobes. Each ROI is encoded using 3D tubelet embeddings, while clinical and volumetric features are transformed into feature-wise tokens, enabling seamless multimodal fusion through self-attention mechanisms. A hemisphere-aware selection strategy is introduced to identify the most discriminative ROI representations, enhancing both performance and interpretability. The model is evaluated on a merged multi-cohort dataset combining ADNI, AIBL, and OASIS, using a 7-fold cross-validation protocol. Experimental results demonstrate that the proposed method achieves high classification performance, reaching an accuracy of 97.62% and an AUC of 0.9940, outperforming single-modality and whole-brain baselines. Furthermore, attention-based analysis provides interpretable insights into the relative importance of clinical and neuroanatomical features, revealing consistency with established AD biomarkers. These findings highlight the effectiveness of multimodal integration and ROI-based representation for robust and explainable AD classification.

Abstract: Background and Objectives: Vitamin D deficiency is a pervasive public health issue in high-latitude regions, yet large-scale population data for the Baltic states remain sparse. This study aimed to determine the prevalence of vitamin D status and identify its primary determinants within a primary care setting in Lithuania. Materials and Methods: We conducted a retrospective cross-sectional analysis of serum 25-hydroxyvitamin D [25(OH)D] concentrations from 14,330 unique patients (aged 1–101 years) collected during 2025 at a major clinic in Vilnius. Vitamin D status was categorized according to the Central and Eastern European Expert Consensus thresholds. Results: The overall median 25(OH)D concentration was 68.3 nmol/L (Mean: 74.7 nmol/L; SD: 35.1), placing the population average in the "insufficiency" range (50–75 nmol/L). Seasonality emerged as the most significant predictor of deficiency; multivariable logistic regression showed a maximal risk reduction in September (OR 0.33; 95% CI: 0.27–0.41) and August (OR 0.34) compared to January, while June and November provided no significant protection. Age-specific analysis revealed a non-linear "U-shaped" distribution: children aged 0–6 years had the highest levels (mean ~100 nmol/L), likely due to rickets prophylaxis, whereas adolescents (12–18 years) exhibited the highest vulnerability, with approximately 80% suffering from deficiency or insufficiency. Males faced a 13.9% higher likelihood of deficiency than females (OR 1.14; p = 0.0036), potentially due to lower rates of elective supplementation. Conclusions: These findings suggest that current supplementation strategies successfully protect infants but fail to sustain adequacy through adolescence and adulthood, particularly during the "vitamin D winter." Targeted public health interventions for adolescents and year-round monitoring are recommended to mitigate the high prevalence of suboptimal vitamin D status in Lithuania.

Abstract: The association of small molecules with lipid membranes plays a central role in drug delivery, pharmacokinetics, toxicity, and membrane biophysics, also being of fundamental importance in drug pharmacodynamics given that most drug targets are membrane-associated proteins. Accurate determination of solute–membrane association affinities, however, remains challenging due to the diversity of experimental systems, the complexity of membrane environments, and the intrinsic limitations of individual methodologies. This review provides a comprehensive overview of the experimental and computational approaches currently used to quantify small molecules association with lipid membranes. Standard experimental techniques, including spectroscopy-based methods, calorimetry, electrophoretic measurements, and surface-sensitive approaches, are discussed alongside established computational strategies ranging from continuum models to atomistic molecular dynamics simulations. Particular emphasis is placed on the formalisms required for data analysis, including partitioning models and thermodynamic frameworks, as well as on the assumptions underlying each method. The validity limits, sources of uncertainty, and common experimental and interpretative pitfalls are critically examined. By providing a unified and comparative perspective, this work establishes a structured framework for the quantitative study of solute–membrane interactions, guiding new researchers in the selection of appropriate methodologies and in the rigorous analysis of experimental and computational results. Moreover, it enables the consistent and quantitative rationalization of affinity parameters reported across the literature, supporting the development of curated datasets and predictive relationships that can inform the design of new and more effective drugs.

Abstract: Aluminium powder, an energetic material, is prone to thermal runaway upon water exposure under local heat sources, yet the nonadiabatic mechanisms of micron sized accumulated aluminium powder under localized heating remain unclear. This study employs a proprietary characterization platform to investigate the effects of particle size, water content, and local heat source power on heat transfer in the dry state and on parameters including induction time, onset temperature, peak heat release rate, and reaction heat during the induction and main reaction phases. In the dry state, decreasing particle size enhances effective thermal conductivity and accelerates temperature rise, whereas elevated local heat source power exacerbates thermal inertia. Under local heating upon water exposure, reduced particle size significantly enhances reactivity; the reaction heat of 2 μm powder reaches 983 J/g, approximately fourfoldAs shown in Figure9 that of 106 μm powder. Water content exhibits nonmonotonic regulation, with onset temperature minimizing at 25% water content and 66.4 °C and reaction heat peaking at 33%. Paradoxically, elevated local heat source power suppresses reaction intensity, and reaction heat at 10 W is one sixth of that at 2.5 W, attributed to rapid product layer densification and the steam film barrier effect shifting the controlling mechanism from chemical to diffusion control. A coupled multifactorial predictive model incorporating the three factors was established with R2 of 0.92, providing data and guidance for aluminium powder storage hazard prevention.

Abstract: Binary sequences (binary codes), where the elements are −1 or +1, are useful in many fields, including communications, radar, sonar, mathematics, physics, and cryptography. This paper considers binary sequences with low aperiodic autocorrelations and focuses on the small peak sidelobe levels alongside the merit factor. Two families of binary sequences are considered, namely Rudin-Shapiro and Legendre sequences. For both families, we applied a heuristic algorithm to minimize the peak sidelobe levels for sequences of lengths up to 2^16 and 220−1, respectively. The main contribution of the article is two conjectures associated with Legendre sequences: (1) The obtained binary sequences with the best-known peak sidelobe levels have merit factor ≈5.0, (2) The number of elements that differ between the resulting binary sequences and the initial Legendre sequences follows a linear dependence on the sequence length (n), namely ≈0.01n. The Rudin-Shapiro sequences do not exhibit these properties, as worse peak sidelobe level and merit factor values were obtained. The number of elements that differ between the resulting binary sequences and the initial Rudin-Shapiro sequences is also much higher compared to that of the Legendre sequences.

Abstract: Existence of global smooth solutions to the three-dimensional (3D) Navier-Stokes equations is disproved for pressure-driven plane Poiseuille flow with no-slip boundary conditions. This study is rigorously grounded in Sobolev space analysis. We show that the solution breakdown arises from the regularity degeneration instead of velocity blow-up. For disturbed laminar plane Poiseuille flow, the instantaneous velocity field is decomposed into a time-averaged flow and a disturbance flow, both characterized by their regularity in Sobolev spaces. When the Reynolds number is larger than the critical Reynolds number, the nonlinear interaction modifies the mean flow profile, and the disturbance amplitude grows significantly. This amplification leads to a local cancellation between viscous terms of the mean flow and the disturbance flow, resulting in the total viscous term (i.e., the Laplacian term) vanishing locally at the critical point $(\boldsymbol{x}^*, t^*)$. The local vanishing viscous term leads to zero velocity according to the Energy-Velocity Monotonicity Principle (EVMP), which contradicts the non-vanishing incoming velocity, leading to formation of a singularity. This singularity induces a velocity discontinuity, which causes the $L^\infty$ -norm of the velocity gradient to diverge, violating the definition of a global smooth solution in Sobolev spaces. The analysis is strictly grounded in partial differential equations (PDE) theory, with all key steps validated by Sobolev space properties and a priori estimates.

Abstract: Persistent post-concussion symptoms (PPCS) following mild traumatic brain injury (mTBI) are common and frequently disabling. However, symptom persistence is often poorly correlated with injury severity or structural brain abnormalities. Increasing clinical and research evidence suggests substantial overlap between PPCS and functional neurological disorder (FND), yet this interface remains poorly synthesised and conceptually unresolved. To systematically review and synthesise the evidence linking mTBI with functional neurological symptoms, and to refine existing conceptual models by proposing a clinically useful framework for differentiating functional and organic contributions to persistent post-concussion presentations. A scoping review with narrative synthesis were conducted. Database searches yielded 120 records; after duplicate removal and abstract screening, 32 studies underwent full-text review. Included studies comprised systematic reviews, narrative and conceptual reviews, mechanistic hypothesis papers, primary observational studies, case series, case reports, and early interventional and neu-roimaging investigations examining functional neurological symptoms in the context of mTBI. The literature demonstrates substantial phenomenological overlap between PPCS and FND across cognitive, motor, sensory, visual, and seizure-related domains. Functional neurological symptoms can emerge after concussion and may closely resemble PPCS, often in association with psychiatric comorbidity, dissociation, trauma exposure, and maladaptive attentional or illness-belief processes. Objective neurological impairment and injury severity show weak and inconsistent associations with symptom persistence. The evidence base is dominated by clinic-derived observational studies, with no population-level incidence estimates identified. Functional neurological symptoms represent a significant and under-recognised contributor to persistent symptoms after mTBI. Existing evidence supports moving beyond binary organic–psychogenic models toward a functional–organic differentiation framework that acknowledges dynamic interactions between injury-related and functional mechanisms. Improved screening, diagnostic communication, and stratified management are likely to enhance outcomes for patients with persistent post-concussion symptoms.