Abstract: Most audit fee studies treat the relationship between fees and client risk as symmetric. A unit increase and a unit decrease in client risk are assumed to produce equal but opposite fee responses. We examine whether that assumption holds in the U.S. audit market using 4,090 firm-year observations of U.S. listed companies from 2010 to 2022 and a first-difference specification with firm and year fixed effects. The data show that audit fees rise by about 1.06 percent for each one-unit increase in the Audit Analytics Risky Client Score (p < 0.001). The response of fees to risk decreases is not statistically different from zero (coefficient = 0.001, p = 0.708). The implied stickiness differential is 0.0093 (p = 0.058). The stickiness ratio is approximately 0.13. Fees adjust downward at about 13 percent of the rate at which they adjust upward following an equivalent risk movement in the opposite direction. The pattern is robust to a strict definition of risk decreases, holds in both early (2010–2016) and late (2017–2022) sub-samples, and is corroborated by an alternative risk proxy based on loss-status transitions, where fees rise 4.3 percent on entry to loss status and do not adjust on exit. The result has implications for audit pricing models, audit committee oversight, and the way fee dynamics are interpreted by users of audit fee data.

Abstract: Background: Lung cancer is a highly heterogeneous disease in which molecular characte-rization has become essential for guiding personalized therapies. The implementation of next-generation sequencing (NGS) allows the simultaneous detection of multiple genomic alterations, improving tumor profiling and therapeutic decision-making. This study aimed to characterize the molecular landscape of lung cancer using NGS and to evaluate its as-sociation with histological subtypes and programmed death-ligand 1 (PD-L1) expression. Methods: A retrospective observational study was conducted on 96 patients diagnosed with lung cancer between 2023 and 2025. Molecular profiling was performed using the Action OncoKitDx panel. Associations between genetic alterations, histological subtypes, and PD-L1 expression were analyzed using Fisher’s exact test, with p < 0.05 considered statistically significant. Results: Adenocarcinoma was the most common histological subtype (67.7%), followed by squamous cell carcinoma (26%). The most common mutations were KRAS (34.4%), TP53 (29.2%), and EGFR (11.5%). KRAS mutations were significantly associated with adenocar-cinoma (p = 0.001), while the absence of detectable mutations was associated with squa-mous cell carcinoma (p = 0.002). Co-mutations were identified in 22.9% of cases, with KRAS–TP53 being the most common combination. Tumors harboring EGFR mutations showed a significantly lower frequency of co-mutations (p = 0.012). No significant asso-ciations were found between PD-L1 expression and either histological subtypes or the analyzed genetic alterations. Conclusions: Lung cancer exhibits marked molecular heterogeneity, with a predominance of KRAS mutations in adenocarcinoma. The low frequency of co-mutations in EGFR-mutated tumors supports their role as dominant driver alterations. The lack of asso-ciation between PD-L1 expression and genomic alterations highlights the complexity of its regulation and suggests the involvement of multiple biological factors. These findings reinforce the clinical value of NGS in comprehensive tumor profiling and in the develop-ment of precision medicine strategies.

Abstract: Despite the large amount of successful existing methods and frameworks for planning sets of multiple Unmanned Aerial Systems (UAS), there are still lack of coordination frameworks capable of coping with real-world operational conditions. This paper presents U-Plan, an integrated management framework for the coordination of multi-UAS missions. U-Plan is designed to plan, schedule, monitor, and replan a heterogeneous set of UAS to complete Points of Interest (PoIs) visiting missions while ensuring that all generated trajectories are safe, feasible, and compliant with the required PoIs’ arrival times, UAS kinematics and energetic constraints, and the existing 3D No-FLy Zones (NFZs). U-Plan is designed as a practical tool for strongly dynamic missions, and is built upon three core components: 1) an NFZ-aware route computation method that explicitly accounts for NFZs prior to the Vehicle Routing Problem (VRP) optimization, resulting in shorter NFZ-safe routes; 2) a trajectory planning module that ensures the generation of kinematically-feasible trajectories for fixed-wing UAS; and 3) a mission supervision module for real-time monitoring and replanning in case of changes in UAS, mission, wind speed, or airspace restrictions. It was implemented and validated by interfacing with professional-grade Visionair Ground Control Station Software and the VECTOR-SIL Software-in-the-Loop simulator, which realistically replicates the behavior of certified fixed-wing autopilots under various weather conditions. The validation shows U-Plan’s capacity to efficiently satisfy complex mission requirements with strong scalability. Due to its high computational efficiency, U-Plan enables online mission replanning, allowing UAS fleets to seamlessly adapt to changes typical of real-world operational scenarios.

Abstract: The energy consumed by thermal management systems strongly affects the driving range of battery electric vehicles. This study develops an integrated control strategy that couples the Sparrow Search Algorithm (SSA) with Nonlinear Model Predictive Control (NMPC) to simultaneously reduce energy consumption and satisfy cabin comfort and battery safety requirements. A multi-loop coupled, heat pump based integrated thermal management model is constructed, including a compressor, heat exchangers, expansion valves, and an electro thermal battery sub model. Bench and vehicle level tests confirm that the model predicts refrigerant mass flow rate and heating capacity with mean relative errors of 4.76 % and 4.30 %, respectively. The SSA is used to tune the NMPC weighting parameters offline, minimizing the mean absolute errors of the cabin temperature, battery temperature, and total system energy consumption. The resulting SSA NMPC strategy is evaluated under NEDC and CLTC P driving cycles. Under the NEDC cycle, the strategy limits cabin temperature overshoot to 0.35°C and battery temperature fluctuation to 0.26°C, while achieving a 6.31 % energy saving under high speed cruising. The proposed framework focuses on cabin and battery thermal regulation and considers motor waste heat recovery. These results demonstrate that the SSA NMPC approach can improve thermal management performance under the investigated operating conditions.

Abstract: Background: Drug-coated balloons (DCBs) have emerged as a "leave-nothing-behind" strategy in percutaneous coronary intervention (PCI), with potential advantages over drug-eluting stents (DES) in selected patients with acute coronary syndrome (ACS).Methods: We performed a narrative review of randomized controlled trials, registries, and meta-analyses evaluating DCB therapy in ACS, including PEPCAD NSTEMI, REVELATION, BASKET-SMALL 2, AGENT IDE, REC-CAGEFREE I/II, and the ongoing TRANSFORM II trial. Articles were identified through searches of PubMed/MEDLINE, Embase, Scopus, Web of Science, and Cochrane CENTRAL covering January 2005 to February 2026.Results: Across published studies, DCBs have shown outcomes that are non-inferior to those of DES in selected ACS subsets, together with a lower risk of major bleeding attributable to shorter dual antiplatelet therapy (DAPT) requirements. Advances in intravascular imaging and lesion preparation, alongside emerging applications of artificial intelligence (AI) and robotic-assisted PCI, may further improve DCB performance, although evidence specific to DCB use in ACS remains limited for these adjunctive technologies. Conclusions: DCBs are a reasonable alternative to DES in selected patients with ACS, particularly those at high bleeding risk or with lesion subsets in which DES perform less well (small vessels, in-stent restenosis, bifurcations, diffuse disease). Adequately powered randomized trials with long-term follow-up are required before broader recommendations can be made.

Abstract: Global land degradation affects approximately 2 billion hectares, threatening food security, biodiversity, and climate stability while undermining the United Nations Sustainable Development Goals (SDGs). The concurrent urgency to decarbonize the energy system and mobilize green finance for sustainable transitions has created a rare policy window in which AI-optimized biofuel production on degraded lands can simultaneously serve multiple imperatives. This study presents a comprehensive secondary data analysis of AI-based optimization frameworks for deploying biofuel production systems on degraded lands, integrating an explicit green finance dimension that has been largely absent from prior synthesis literature. Drawing on 152 peer-reviewed studies and authoritative datasets from FAO, IEA, IRENA, UNCCD, the Green Climate Fund (GCF), and the World Bank, we analyze machine learning, deep learning, reinforcement learning, and hybrid AI architectures applied to feedstock selection, soil remediation, yield prediction, supply-chain logistics, and green finance risk-return optimization. Our findings reveal that AI-optimized biofuel systems on degraded lands recover 75-94% of prime-land bioenergy yields, sequester 8.3-10.5 t CO2e ha-1 over 30 years, reduce lifecycle GHG emissions by 55-88%, and generate internal rates of return of 9-22% when green finance instruments are systematically integrated. Green bonds, Article 6 carbon credits, GCF concessional finance, and blended finance structures are identified as the most impactful instruments, collectively capable of reducing project risk scores by 30-45% and expanding the investable universe of degraded-land biofuel projects by an estimated 340%. We develop the AI-Biofuel-Land Restoration (ABLR) conceptual framework with explicit green finance routing pathways and identify critical policy enablers for global deployment. This study advances the evidence base for policy-makers, investors, researchers, and development practitioners working at the intersection of artificial intelligence, bioenergy, green finance, and sustainable land management.

Abstract: Background/Objectives: Long-term clinical data on direct posterior composite restorations are scarce. This study evaluated their performance after almost three decades using selected FDI criteria. Temporal changes were tracked across follow-up exams, including within a predefined subcohort. Methods: This observational follow-up involved 21 patients with 57 posterior composite restorations placed in 1995–1996. The 2025 follow-up used FDI criteria to assess functional, esthetic, and biological properties, classifying outcomes as clinically acceptable, intervention needed, or failure. Descriptive analyses were applied to the entire cohort. Longitudinal analyses were conducted on a subcohort of 14 patients with 27 restorations at three time points. Exploratory analyses assessed associations with restoration factors, caries experience, and gingival health. Results: In 2025, 54.4% of restorations were clinically acceptable, 28.1% required intervention, and 17.5% were failures. Functional criteria remained mostly acceptable, though form and contour showed the highest mean values. In the longitudinal subcohort, significant changes over time were observed in anatomical form and occlusal wear. Retention, marginal adaptation, proximal contact, and surface luster did not change significantly. Biologically, restorations available for direct assessment had low incidences of secondary caries, hard-tissue defects, and postoperative sensitivity or pulpal issues. Conclusions: Posterior composite restorations can function for nearly three decades but gradually deteriorate in certain aspects. Long-term changes mainly involve cumulative functional aging of the anatomical form and occlusal wear, rather than widespread biological failure. These findings underline the importance of differentiated long-term assessment and support conservative management approaches where clinically feasible before replacement is undertaken.

Abstract: Chronic migraine is increasingly understood as a network-level disorder in which trigeminovascular nociception is sustained by metabolic, inflammatory, and macro-network dysfunction rather than by an isolated headache mechanism. Glucagon-like peptide-1 receptor agonists (GLP-1RAs), originally developed for type 2 diabetes and obesity, engage receptors expressed in choroid plexus, cortex, hippocampus, thalamus, and hypothalamus. Evidence converges on four mechanistic intersections between GLP-1 signalling and the chronic-migraine cascade: restoration of cerebral insulin signalling and cortical energy supply; modulation of cerebrospinal-fluid secretion and intracranial pressure with downstream relevance for glymphatic dynamics (most clearly validated in idiopathic intracranial hypertension, with explicit caveats for normotensive migraine); suppression of microglial activation in pain-relevant circuits (established preclinically, with one pilot human imaging study); and direct trigeminal-nociceptor effects via TRPV1 inhibition demonstrated for exendin-derived peptides. A 2026 real-world cohort of approximately 11,000 chronic-migraine adults provides a preliminary clinical signal. We propose, and operationalise as falsifiable, the hypothesis that GLP-1RAs are disease-modifying rather than purely symptomatic in chronic migraine, and we describe a placebo-controlled trial with parallel multiple mediation analysis on weight change AND HOMA-IR/TyG delta that can refute the claim within five years. A biochemical scheiding between exendin-based agonists (exenatide, lixisenatide) and human GLP-1 analogues (semaglutide, liraglutide, dulaglutide) generates a falsifiable sub-hypothesis on peripheral TRPV1 inhibition that is testable retrospectively on data that already exist.

Abstract: The study investigates overconfidence bias in the Bangladesh equity market through the relationship between the market returns, and the trading volume in a nonlinear, information-theoretic model. Building upon the traditional literature on returns and volume, the study differentiates between the total market returns and unexpected market returns, the latter being the unexpected information shocks represented under the Market Index Model. Transfer Entropy with bootstrap inference is used to determine directional and asymmetric causality across various market states, including bullish, bearish, crisis, extended crisis, and COVID-19. The findings indicate that the total market returns give weak and inconsistent evidence of overconfidence, which is bi-directional but limited information flow. Conversely, unexpected market returns have a statistically significant directional effect on trading volume, which represents strong evidence of overconfidence. The results also reveal that overconfidence is conditional as it is stronger in normal and bullish market contexts, and weaker during times of crisis. Asymmetric analysis reveals that the overreaction of investors is more pronounced when the market trends are negative, implying that unexpected losses stimulate an amplified trading effect due to the feeling of mispricing and recovery hopes. The results have significant implications on market efficiency, investor behavior and regulatory policies to improve market stability and facilitate informed financial decision-making.

Abstract: The composition of low-polarity extracts obtained by sequential extraction of the aerial parts of Rhododendron adamsii Rehd. with hexane and methyl tert-butyl ether (MTBE) was investigated using GC-MS. The hexane extract was dominated by non-polar components: squalene, n-alkanes (nonacosane, hentriacontane), sesquiterpenes (trans-nerolidol, spathulenol, β-farnesene), and β-sitosterol. The subsequent MTBE extract was enriched in more polar lipids, primarily free triterpenic acids (ursolic and oleanolic acids). A critical finding was the complete absence of diterpene grayanotoxins in all tested extracts, confirming the safety of the non-polar extraction approach. In bioactivity assays, the total hexane extract demonstrated potent inhibitory activity against the SARS-CoV-2 main protease (3CLpro) with IC₅₀ values of 0.0125–0.025 mg/mL, only one order of magnitude higher than the reference inhibitor disulfiram. Fractionation revealed that the activity was distributed among free acids, bound acids, and the unsaponifiable residue, indicating a multicomponent mechanism. Importantly, none of the samples inhibited HIV-1 protease (IC₅₀ > 0.1 mg/mL), demonstrating selectivity for the cysteine protease 3CLpro over the aspartyl protease of HIV-1. These results highlight that sequential non-polar extraction of R. adamsii provides a grayanotoxin-free lipophilic complex with selective anti-SARS-CoV-2 protease activity, paving the way for bioactivity-guided identification of individual inhibitors.

Abstract: Waerebo Village, a UNESCO World Cultural Heritage site in Indonesia, represents a profound harmony between the Manggarai people, nature, and spirituality, yet the technical functional role of its traditional zoning remains under-researched. This study examines the Waerebo landscape model by integrating horizontal and vertical spatial patterns through literature reviews, field observations, interviews, and GIS-based analyses, including Digital Elevation Models (DEM) and multi-temporal NDVI from 2015 to 2025. Find-ings indicate that Waerebo’s landscape is organized into three concentric zone—core, uti-lization, and sacred zones—mirroring a tripartite spiritual framework of God, ancestors, and nature spirits. Geospatial data reveals a sophisticated indigenous landscape engineering system where the settlement is strategically positioned on a stable 16° terrace, while sacred forests are maintained on extreme 85° slopes to protect watersheds and mitigate landslides. Multi-temporal NDVI analysis confirms an increase in forest density from 0.47 in 2015 to 0.52 in 2025, validating the effectiveness of customary laws in maintaining ecological integrity despite tourism pressures. The study concludes that Waerebo's cosmic spiral model achieves a vital balance between culture, socio-economic survival, and environmental conservation, offering a functional blueprint for resilient cultural heritage management in challenging topographies.

Abstract: Robust age measurements for isolated neutron stars (NSs) are not easily available. That is why, often the characteristic age τ_ch=P/2Ṗ is used as a proxy. Here P is the spin period of the NS and Ṗ is the time derivative of P. Additional assumptions related to the initial properties and spin-down evolution are made to derive τ_ch. As a result, it is expected that τ_ch is an upper limit for the real age τ_real. Recently, Chrimes et al. presented measurements of kinematic ages τ_kin for several magnetars. Surprisingly, for the majority of these sources τ_kin>τ_ch. We present a simple model including a realistic approximation for the magnetic field decay in magnetars and a simple phenomenological description of the field re-emergence after an episode of fallback after the birth of a NS. We demonstrate that this simple model can explain the observed relation τ_kin>τ_ch for realistic sets of parameters.

Abstract: To elucidate the characteristics of fatty acid composition in different tissues of Anguilla japonica before and after their seaward spawning migration, this study measured the fatty acid contents in the muscle, liver, and ovary of eels collected from the Yangtze River Estuary and offshore waters, and analyzed the distribution characteristics and transformation patterns of various fatty acids in different tissues. The results showed that the fatty acid composition in different tissues of eels from both the Yangtze River Estuary and offshore waters was essentially similar. Among all fatty acids, C18:1n9c had the highest proportion, accounting for over 31% of the total fatty acids in each tissue. Comparing different tissues, in eels from the Yangtze River Estuary, the muscle had the highest content of EPA, the liver had the highest content of DHA and EPA+DHA, and the ovary had the highest contents of ARA, n6-PUFA and SFA. In offshore eels, the muscle had the highest contents of C16:1, C18:1n9c and MUFA; the liver had the highest content of C16:0; and the ovary had the highest contents of C18:0, DPA, HUFA, n3-PUFA and PUFA. Comparing eels before and after seaward migration, the contents of C16:0, ARA, n6-PUFA, SFA, and the DHA/EPA ratio in the ovary of Yangtze River Estuary eels were higher than those in offshore eels. Conversely, the contents of C18:0, C16:1, C18:1n9c, EPA, DPA, DHA, EPA+DHA, HUFA, n3-PUFA, MUFA, PUFA, as well as the EPA/ARA, n3/n6 PUFA and PUFA/SFA ratios in the ovary of offshore eels were higher than those in Yangtze River Estuary eels. The ovary of Yangtze River Estuary eels mainly contained fatty acids for energy provision and precursors for long-chain fatty acid synthesis, whereas the ovary of offshore eels had preliminarily accumulated PUFA nutrients required for egg and embryonic development. Thus, the distribution patterns of different fatty acids among tissues are closely related to the seaward spawning migration process of A. japonica. Before migration, eels in the Yangtze River Estuary primarily focus on energy accumulation and liver metabolism; after entering the sea, eels gradually accumulate PUFA such as EPA, DPA+DHA and n3-PUFA as gonads develop. The selective reservation of different fatty acids in the ovary represents a physiological regulation in response to the nutritional demands of gonadal development.

Abstract: Hypoxia is a prevalent characteristic of neurological diseases, including ischemic injury, neurodegeneration and infectious disease complications. Concurrently, hypoxia shapes both protective and pathological responses within the central nervous system (CNS). Central to this process is hypoxia-inducible factor 1α (HIF1α), a transcription factor that regu-lates cellular adaptation to reduced oxygen availability through coordinated glycolytic, inflammatory and cell survival pathways. Under hypoxic conditions, HIF1α transcriptional activity influences microglial activation, mitochondrial quality control, and cytokine production, thereby modulating neuroinflammation and neuroprotection. Preclinical evidence points toward hypoxia preconditioning being neuroprotective through HIF1α-dependent mechanisms in a context-dependent matter. This review synthesizes the current understanding of the role of HIF1α across neurological disease contexts, highlighting the intersection of hypoxia, neuroinflammation and neuronal survival. Ultimately, defining the cell-specific and context-dependent involvement of HIF1α will be critical for targeted therapeutic approaches to alleviate neuronal death and slow disease progression.

Abstract: Let R be a commutative ring with a nonzero identity and M a nonzero unital R-module. We introduce the concepts of weakly n-submodules and weakly (1, n)-submodules as module-theoretic generalisations of the weakly n-ideal and weakly (1, n)-ideal. A proper submodule N of M is called a weakly n-submoduleif whenever 0 ̸= am ∈N for some a ∈R and m ∈M , then a ∈(N :R M )or m ∈Nil(M )·M , where Nil(M ) = annR(M ). Similarly, N is called a weakly (1, n)-submodule if whenever 0 ̸= abm ∈N for some nonunit elementsa, b ∈R and m ∈M , then ab ∈(N :R M ) or m ∈Nil(M )·M . Every weaklyn-submodule is a weakly (1, n)-submodule, and every weakly (1, n)-submoduleis weakly 1-absorbing primary. We provide a six-fold characterisation, provestructure theorems classifying the rings and modules over which every propersubmodule belongs to these classes in particular, we show that for faithfulnitely generated multiplication modules, every proper submodule is weakly (1, n) if and only if R is a UN-ring or a product of two elds and investigatebehaviour under homomorphisms, localisations, and quotient modules.

Abstract: Zinc deficiency is increasingly recognized as a risk factor for neurodegenerative diseases, yet the underlying molecular mechanisms remain incompletely understood. In this study, we investigated the impact of intracellular zinc depletion on oxidative stress and in-flammasome activation in microglial (SIM-A9) and neuronal (SH-SY5Y) cell models, and evaluated the protective effects of polyphenolic compounds. Intracellular zinc chelation with the membrane-permeable chelator TPEN markedly increased reactive oxygen species (ROS) production, reduced cell viability, and upregulated the mRNA expression of NLRP3 inflammasome–related genes and pro-inflammatory cytokines. In contrast, extracellular zinc chelation had no effect, highlighting the critical role of intracellular zinc homeostasis in maintaining redox balance. Zinc supplementation significantly attenuated these responses. Among 32 polyphenols screened by DPPH radical scavenging assay, caffeic acid derivatives—chicoric acid (ChA), rosmarinic acid (RA), and caffeic acid phenethyl ester (CAPE)—exhibited the most potent antioxidant activity, surpassing that of edaravone. These compounds suppressed ROS production and differentially protected against zinc deficiency–induced cellular damage. ChA showed the strongest ROS in-hibitory activity (IC50: 1.9 µM in SIM-A9), RA provided robust cytoprotection even at low concentrations, and CAPE most effectively suppressed inflammasome-related gene ex-pression and inhibited aggregation of both Aβ1–42 and the highly neurotoxic py-roglutamate-modified variant pEAβ3–42. These findings demonstrate that intracellular zinc deficiency drives ROS-dependent NLRP3 inflammasome activation, and suggest that caffeic acid derivative polyphenols may serve as complementary agents for mitigating neuroinflammatory and amyloidogenic processes relevant to Alzheimer's disease.

Abstract: Robots deployed in disaster environments—such as collapsed buildings, flooded tunnels, and conflict-damaged urban areas—must navigate without GPS, operate under degraded sensing conditions including dust, smoke, and darkness, and adapt rapidly to changing mission conditions. Most existing learning-based navigation approaches rely on a single policy, which often fails when the environment shifts in unexpected ways. This paper presents UC-MESL (Uncertainty-Conditioned MAP-Elites Skill Library), a framework that learns a diverse library of specialized navigation behaviors and dynamically switches between them in real time based on environmental uncertainty. Each skill is optimized for a specific operating condition and characterized by three interpretable traits: risk tolerance, exploration preference, and movement style. A lightweight selector uses live uncertainty estimates from the robot’s onboard map to choose the most appropriate skill during deployment. We evaluate UC-MESL across three simulated rescue scenarios—collapsed rubble, flooded tunnels, and war-damaged urban blocks—under four levels of sensor degradation and realistic communication outages. Compared with the strongest single-policy baseline, UC-MESL finds 18.4% more victims within the mission time budget, reaches the first victim 31.2% faster, reduces hazard exposure by 24.6%, and loses only 8.3% of performance under severe sensor noise, compared with 29.7% for a single-policy NEAT baseline. These results demonstrate that maintaining a diverse repertoire of specialized navigation skills, combined with uncertainty-aware skill selection, provides more robust and reliable autonomy for disaster-response robotics than optimizing a single general-purpose policy.

Abstract: The YOLO (You Only Look Once) object detection models have undergone rapid evolution, with each version introducing architectural enhancements aiming to improve speed, accuracy, and deployment. Simultaneously, Single-Board Computers (SBCs) have advanced to support increasingly complex AI models in edge environments. This study presents a comprehensive benchmarking of YOLO versions 8 through 12 across a range of SBCs, including Raspberry Pi4/5, NVIDIA Jetson Nano, Jetson Orin, and LattePanda, under different power modes. Key performance metrics, including inference speed (FPS), detection accuracy (mAP), RAM usage, and computational complexity (FLOPs), are evaluated. These findings offer practical insights for developers and researchers to select optimal YOLO variants and SBC configurations for real-time edge deployment.

Abstract: Background: Hantavirus pulmonary syndrome (HPS), also designated hantavirus cardiopulmonary syndrome, is caused by New World hantaviruses, principally Sin Nombre virus in North America and Andes virus in South America. The syndrome is characterized by rapidly progressive noncardiogenic pulmonary edema and myocardial depression, with case fatality rates of 30% to 50%. Methods: This review synthesizes peer-reviewed literature on the virological, pathophysiological, clinical, and therapeutic aspects of HPS, with emphasis on cardiopulmonary mechanisms. Sources were identified through PubMed, prioritizing original research, clinical series, and controlled trials published through 2025. Results: Pathogenic hantaviruses enter endothelial cells and platelets via αvβ3 integrins, disrupting the VEGF-VEGFR2 signaling axis and rendering endothelial cells hypersensitive to physiological VEGF concentrations. Expansion of CD8+ T cells and activated macrophages releases TNF-alpha, IFN-gamma, and nitric oxide, amplifying microvascular permeability and contributing to myocardial depression. Autopsy studies demonstrate direct hantaviral myocarditis with viral antigen in cardiac endothelium and interstitial macrophages. Transpulmonary thermodilution confirms simultaneous hypovolemia, reduced global ejection fraction, and elevated extravascular lung water. VA-ECMO initiated at the first signs of cardiopulmonary decompensation has reported survival rates approaching 80% in selected experienced centers. No antiviral has demonstrated efficacy in controlled trials during the cardiopulmonary phase. Conclusions: HPS produces a mixed shock state through increased microvascular permeability, T cell-mediated immunopathology, and direct myocarditis. Management follows a stepwise algorithm: suspected HPS triggers immediate complete blood count with peripheral blood smear and hantavirus IgM serology or RT-PCR, followed by ICU admission, conservative fluid resuscitation guided by transpulmonary thermodilution, and early contact with an ECMO-capable center at the first sign of rising lactate, falling cardiac index, refractory shock, arrhythmia, or rapid oxygenation failure.

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.