Abstract: The axonal membrane is not the seat of nerve conduction: it is the boundary between two osmotic reservoirs whose asymmetry is the thermodynamic engine of the action potential. Voltage-gated ion channels are not the generators of the nerve signal: they are its osmotic amplifiers, and their spatial distribution along the axon is a geometric necessity, not an arbitrary anatomical feature. The Ionic-Mechano-Hydraulic (IMH) model formalises this principle: intracellular K⁺ adsorbed on the cytoplasmic polyelectrolyte gel triggers an ionic phase transition; extracellular Na⁺ amplifies the resulting hydraulic wave through Nav channels; Kv channels close the osmotic cycle and enforce the refractory period. The conduction velocity is predicted from the elastic modulus of myelin, not from the density of the sodium channel. The model resolves a 75-year-old anomaly that Huxley and Stämpfli themselves described as impossible in a purely electrical system: a positive current enters a node before the membrane potential at the preceding node has reached its maximum. Ten falsifi-able predictions are presented that cover myelin mechanics, mechanoreceptor adaptation, terminal arborisation geometry, velocity-diameter scaling, and axon diameter limits derived from first physical principles. The Hodgkin-Huxley model is not discarded: it is explained.

Abstract: UAV-based crack inspection of port quay walls is promising for efficient infrastructure maintenance, but its practical deployment remains hindered by frequent false positives caused by debris, stains, and irregular surface textures. This study proposes a false-positive reduction framework for a crack inspection system based on aerial images acquired by a small general-purpose UAV. The proposed method introduces anomaly detection after object detection so that detected crack candidate regions are re-evaluated based on their deviation from the learned feature distribution of crack images. A Vision Transformer (ViT)-based anomaly detection model is employed, and both stand-ard-threshold and low-threshold object detection settings are investigated. Experimental validation across five verification areas showed that the combination of standard-threshold object detection and anomaly detection consistently improved F1 and F2 scores over the conventional baseline, demonstrating stable suppression of false positives while main-taining crack detectability. Under the low-threshold setting, Frangi filter-based pre-processing was more effective than grayscale-based preprocessing, achieving a favorable balance between broader crack extraction and false-positive suppression in some 5 m cases. However, this advantage decreased as image resolution deteriorated. Overall, the results indicate that the most robust configuration in the current framework is the combination of standard-threshold object detection and anomaly-based false-positive suppression. In contrast, the benefit of low-threshold operation depends strongly on image resolution. The findings also suggest that practical deployment requires calibration of the anoma-ly-detection threshold based on site conditions and GSD.

Abstract: We present TADI (Tool-Augmented Drilling Intelligence), an agentic AI system that transforms drilling operational data into evidence-based analytical intelligence. Applied to the Equinor Volve Field dataset, TADI integrates 1,759 daily drilling reports, selected WITSML real-time objects, 15,634 production records, formation tops, and perforations into a dual-store architecture: DuckDB for structured queries over 12 tables with 65,447 rows, and ChromaDB for semantic search over 36,709 embedded documents. Twelve domain-specialized tools, orchestrated by a large language model via iterative function calling, support multi-step evidence gathering that cross-references structured drilling measurements with daily report narratives. The system parses all 1,759 DDR XML files with zero errors, handles three incompatible well naming conventions, and is backed by 95 automated tests plus a 130-question stress-question taxonomy spanning six operational categories. We formalize the agent's behavior as a sequential tool-selection problem and propose the Evidence Grounding Score (EGS) as a simple grounding-compliance proxy based on measurements, attributed DDR quotations, and required answer sections. The complete 6,084-line, framework-free implementation is reproducible given the public Volve download and an API key, and the case studies and qualitative ablation analysis suggest that domain-specialized tool design, rather than model scale alone, is the primary driver of analytical quality in technical operations.

Abstract: This study aims to construct a knowledge system on traditional Chinese medicinal materials centered on large language models and to evaluate the practical feasibility of controlled fine tuning (SFT) techniques within professional Traditional Chinese Medicine (TCM). The study adopts Biancang-Qwen2.5-7B-Instruct as the base model and employs a knowledge internalization strategy to reduce the reliance on external retrieval mechanisms, with supervised fine-tuning conducted using LoRA. The evaluation dataset consists of 1,751 text-only multiple-choice questions related to Chinese medicinal materials from national TCM practitioner examinations conducted between 2005 and 2025. The accuracy of multiple-choice question (MCQ) was used as the primary evaluation metric, and Lingdan-13B-Base was included as a baseline model for comparison. The experimental pipeline covered the pre-processing of the data set and the comparative analysis of the inference results across multiple variants of the model, with the McNemar test applied to examine the statistical significance of the performance differences before and after fine-tuning. The results indicate that, without introducing any external retrieval mechanisms, the accuracy of the model increased from 58.08% to 72.76%, demonstrating substantial improvements in both knowledge comprehension and answer stability regarding Chinese medicinal materials. These findings confirm the effectiveness of the proposed internalized knowledge fine-tuning strategy for the national TCM examination task. Finally, this study delivers an integrated TCM medicinal materials knowledge system that incorporates the fine-tuned model, provides functionalities for national examination practice and TCM knowledge querying, and validates its feasibility and stability in real-world application scenarios.

Abstract: Rapid urbanization and intensifying climate risks are placing unprecedented pressure on cities to transition toward sustainable and resilient models. Achieving Sustainable Development Goals (SDGs) 11 (Sustainable Cities and Communities) and 13 (Climate Action) requires intelligent systems capable of interpreting complex urban dynamics and enabling proactive, adaptive decision-making. This paper presents a PRISMA-guided rapid review examining the role of Agentic Artificial Intelligence (AAI)–autonomous, goal-directed systems with multi-step reasoning, tool use, and multi-agent coordination–in advancing urban sustainability and climate resilience. Studies were required to exhibit at least two attributes: autonomous decision-making, multi-step planning, tool use or environmental interaction, and multi-agent coordination. From 920 records, 70 peer-reviewed studies were synthesized, covering smart mobility, infrastructure planning, waste management, emergency response, climate monitoring, emissions tracking, renewable energy forecasting, and multi-hazard early warning systems. Results show that despite rapid progress, AAI applications remain fragmented and domain-specific. To address this, a unified Agentic AI–Digital Twin framework is proposed, integrating real-time sensing, urban–climate co-simulation, multi-agent coordination, and adaptive decision intelligence. A Pareto-based optimization approach balances competing sustainability goals. Key challenges in interoperability, data governance, ethics, and scalability are identified, alongside a research roadmap for integrated intelligent urban ecosystems.

Abstract: Background/Objectives: Immunoglobin A nephropathy (IgAN) is a type of chronic kidney disease (CKD) and the most common cause of kidney failure in patients <40 years of age. Previous economic models in CKD have generally defined health states solely by the progression of CKD. This manuscript presents an alternative method which also considers the level of proteinuria in a CKD patient. Methods: A cohort-level state transition model was developed comparing the health benefits of sparsentan, a dual endothelin angiotensin receptor antagonist, to irbesartan, an angiotensin receptor blocker, in IgAN. Within four UP/C (proteinuria) states, patients are assigned to three sub-health states according to CKD stage. Patients with end-stage renal disease are grouped together irrespective of UP/C, and are stratified instead by renal replacement therapy modality. Transition matrices are derived from a combination of data from PROTECT, a clinical trial comparing sparsentan to irbesartan, and the UK RaDaR registry. Health-related quality of life data from a general CKD population is used as a proxy. Results: Patients with IgAN who were modelled to receive treatment with sparsentan had estimated total undiscounted life years of 25.5 years, a gain of 0.9 years in comparison with irbesartan. Patients were also more likely to spend more time in earlier CKD stages while pre-ESRD. This translated to significant quality adjusted life year gains for patients treated with sparsentan in comparison with irbesartan. Conclusions: This study presents a new structure for health economic models in IgAN that more comprehensively captures the effect of proteinuria in combination with CKD progression. This new approach ultimately allows for the more robust implementation of clinical trial data in IgAN and estimates of the cost-effectiveness of new treatments.

Abstract: Periprosthetic joint infection (PJI) remains a major limitation of joint arthroplasty, driven by the absence of localized, continuous diagnostics and controllable in vivo therapy during the spacer interval. Here, we introduce a theranostic implant platform that integrates sensing, communication, and light-based intervention within a temporary joint spacer. The SmartSpacer enables real-time intra-articular monitoring, combining high-resolution temperature sensing (± 0.1 °C), optical imaging, and spectral detection of bacterial activity down to ~10³ CFU ml⁻¹. We establish a translation-oriented framework that maps wavelength-dependent antimicrobial effects onto implant-level constraints. Within this framework, visible blue light provides continuous suppression of bacterial growth, while ultraviolet radiation enables rapid bactericidal action via pulsed, spatially confined exposure. These modalities operate within implant-compatible energy budgets and without measurable thermal load, enabling sustained and repeatable intervention. Continuous multimodal sensing enables longitudinal tracking of infection dynamics, transforming diagnostics from static assessment to predictive monitoring. By linking localized sensing with controllable therapy, the SmartSpacer converts the spacer interval from a passive waiting phase into an active treatment window. This work defines a system-level strategy for implant-based infection control and establishes a foundation for future feedback-driven, closed-loop therapeutic systems.

Abstract: Cognitive decline represents an increasing public health concern, particularly among older adults with non-communicable disease risk factors such as hypertension, diabetes, and obesity. Social participation has been proposed as a modifiable determinant that may protect cognitive function, although evidence in high-risk community populations remains limited. This study examined the association between social participation and cognitive function and assessed whether social participation moderates the impact of cardiometabolic burden on cognition. A community-based cross-sectional study was conducted among 214 adults aged ≥60 years with at least one non-communicable disease risk factor. Social participation was measured using a structured questionnaire, while cognitive function was assessed using a validated screening instrument. Multivariate regression and moderation analyses were performed adjusting for age, education, depressive symptoms, and physical activity. Higher social participation was associated with significantly better cognitive performance, with mean scores increasing from 22.9 in the low participation group to 26.6 in the high participation group. Social participation remained independently associated with cognitive function (β = 0.38, p &lt; 0.001), whereas cardiometabolic risk showed a negative association (β = −0.41, p = 0.001). A significant interaction indicated that social participation attenuated the adverse effect of disease burden (β = 0.18, p = 0.007). These findings suggest that social participation may buffer cardiometabolic-related cognitive decline. Promoting social engagement within community ageing programs may support cognitive health and healthy ageing strategies.

Abstract: The Apennine wolf (Canis lupus italicus) is a distinct subspecies whose ongoing population recovery in Italy has progressively increased the demand for live capture protocols validated for scientific monitoring and conservation management. Despite the widespread use of mechanical and chemical immobilization in European wolf management, no study has to date systematically evaluated the integrated combina-tion of a humane mechanical restraint system and a structured chemical immobiliza-tion protocol — and specifically the association of the Fremont™ humane foot snare with a medetomidine-ketamine-acepromazine (MKA) protocol, in terms of their joint physiological effects and welfare implications for this subspecies under operational field conditions. Between June 2010 and July 2017, thirteen free-ranging Apennine wolves were captured in Maiella National Park (central Apennines, Italy) using the Fremont™ snare and immobilized with a standardized MKA protocol. Cardiorespira-tory parameters, body temperature, peripheral oxygen saturation, venous blood gas values, and a comprehensive hematological and serum biochemical panel were rec-orded during immobilization. Mean heart rate was 100 ± 15 bpm, respiratory rate 24 ± 13 breaths/min, and body temperature 38.1 ± 1.0°C. No clinically significant hyper-thermia was recorded in the cohort as a whole. Hematological and biochemical values were broadly consistent with published reference ranges for the species, with condi-tion-specific deviations identified in two individuals (one pregnant female and one ju-venile presenting signs of transient capture-related myopathy), both of which resolved without clinical sequelae. No capture-related mortality occurred. All thirteen individ-uals survived the minimum post-capture monitoring period, and preliminary GPS da-ta suggest a transient reduction in movement activity in the immediate post-release period. These findings support the welfare adequacy and operational feasibility of the combined Fremont™ snare–MKA protocol for the Apennine wolf, and provide base-line physiological and hematobiochemical reference data for Canis lupus italicus rele-vant to future capture and conservation management programmes.

Abstract: Introduction: Conventional oncological pathology practice faces critical challenges stemming from interobserver variability and an ever-growing clinical workload. This review evaluates the technological maturity and clinical utility of artificial intelligence (AI) as a diagnostic and predictive support tool in histopathology. Methods: An integrative review of the literature (2019–2026) was conducted in PubMed, Scopus, and IEEE Xplore, following the methodology of Whittemore and Knafl. Studies on the diagnostic accuracy of deep learning algorithms in neoplasia histopathology were selected, with methodological quality assessed using QUADAS-2. Results: The evidence confirms that convolutional neural networks (CNNs) achieve diagnostic accuracy comparable to or exceeding that of pathologists in binary classification tasks, consistently reporting areas under the curve (AUC) > 0.98 in lung, breast, and prostate cancer. A disruptive finding is the validation of predictive computational histology, capable of inferring genotypic alterations—such as EGFR mutations or microsatellite instability—directly from standard hematoxylin and eosin (H&E) images, offering a cost-effective alternative for molecular screening. The evidence strongly supports the “augmented intelligence” model, in which the pathologist–AI synergy surpasses individual performance and mitigates visual fatigue. Conclusions: AI has transcended the experimental phase to become a robust technology for triage and digital phenotyping. Its definitive clinical adoption requires prioritizing multicenter external validation and the development of explainable AI (XAI) interfaces to overcome the “black box” barrier.

Abstract: Foundation-model agents increasingly rely on reusable skills to support tool use, long-horizon planning, and cross-task adaptation. Yet the term remains ambiguous in the literature, where it may refer to prompt packages, executable workflows, learned routines, or repository artifacts. This ambiguity makes it difficult to compare methods, evaluate progress, and reason clearly about security and governance.We study agent skills as reusable and adaptive units of competence that sit between model capability and situated task execution. The survey first separates skills from nearby constructs such as prompts, tools, memory, and policies. It then organizes the literature around representation, lifecycle and orchestration, evaluation, security and governance, and application domains. The evidence suggests that skill quality alone is not enough: useful skills also depend on abstraction choices, retrieval and composition mechanisms, ecosystem structure, and infrastructure security. We therefore treat agent skills as a research object in their own right and identify open problems in automatic induction, cross-environment transfer, longitudinal evaluation, and trustworthy sharing in open agent ecosystems.

Abstract: Background and Objectives: Human immunodeficiency virus (HIV) infection is associated with immune dysregulation, which may influence the development of autoimmune diseases. However, population-based evidence on the prevalence of autoimmune diseases in individuals living with HIV remains limited, particularly in Asian populations. This study aimed to evaluate the prevalence of autoimmune diseases in individuals living with HIV in Korea using nationwide population-based data. Materials and Methods: We conducted a cross-sectional analysis using the Health Insurance Review and Assessment Service National Patient Samples from 2012 to 2015, including 4,851,064 individuals aged ≥15 years. HIV infection and autoimmune diseases were identified using ICD-10 codes. The prevalence of autoimmune diseases in individuals with HIV infection was compared with that in the general population. Antiretroviral therapy (ART) status was determined based on prescription records. Results: A total of 1,023 individuals were identified with HIV infection, all of whom were receiving antiretroviral therapy. The overall prevalence of autoimmune diseases was 4.37% in males and 2.38% in females with HIV, without significant differences compared to controls. However, the prevalence of Behçet’s disease, ulcerative colitis, and primary biliary cholangitis was significantly higher in males with HIV (P &lt; 0.05), while dermatomyositis was significantly more prevalent in females with HIV (P &lt; 0.001). Conclusions: Although the overall prevalence of autoimmune diseases was not significantly increased in individuals living with HIV, specific autoimmune diseases showed higher prevalence in this population. These findings suggest that clinicians should consider autoimmune dis-eases in the differential diagnosis of patients with HIV and highlight the need for further research on underlying immunological mechanisms.

Abstract: The present study assessed heavy metal and microbial contamination in soil and groundwater around a municipal solid waste dumpsite in Zomba, Malawi. The potential ecological and health risks to communities were also examined. The results revealed that wet season groundwater had elevated total coliforms (20900 CFU/100 mL), Escherichia coli (3300 CFU/100 mL), Staphylococcus aureus (2500 CFU/100 mL), and Vibrio cholerae (5900 CFU/100 mL), which were significantly higher than the permissible limits of the Malawi Standards. In water samples, heavy metals, in-cluding Chromium (0.011–0.14 mg/L) and Cadmium (0. 07 – 041 mg/L), raise concern. In the soil samples, the Lead concentration ranged from 0.16 to 224.05 mg/kg, the Copper ranged from 3.03 to 94.86 mg/kg, the Cadmium concentration varied between the BDL and 0.89 mg/kg, Arsenic ranged from the BDL to 1.88 mg/kg, and the Cr varied between 0.07 and 0.91 mg/kg. Further-more, the cancer risk assessment indicated that all sampling points had CR levels greater than 1 × 10^-3 for adults, with 40% of the sampling points showing elevated CR levels for infants and chil-dren, highlighting the cancer risk from Cd exposure, especially among vulnerable populations.

Abstract: The development of healthy mangroves strongly depends on several factors including water physiochemical characteristics, soil composition and tidal inundation regimes. This paper presents a characterization of tidal inundation regimes for mangroves in Abu Dhabi, based on a field measurement campaign combined with hydrodynamic modelling. Water-level measurements were collected over a 9-month period at a site where Avicennia marina is present and widespread, capturing spring-neap cycles and seasonal variability. The results provide a detailed quantification of tidal inundation characteristics. Mangroves at the study site were inundated for approximately 33-56% of the time, depending on the season, with higher inundation durations during summer months associated with seasonal mean sea level variability. Mean inundation durations averaged 371 min per event and 620 min per day, with an average of 1.7 inundation events per day. A hydrodynamic numerical model was developed and validated against in situ measurements. Model outputs were used to spatially extend site-specific observations and derive estimates of suitable ground elevation for mangrove development, corresponding to values between +0.12 m and +0.14 m relative to local mean sea level. These findings provide a physically based framework to support mangrove restoration and conservation efforts in Abu Dhabi, where improper tidal exposure remains a key factor limiting restoration success.

Abstract: Generation Z, a cohort defined by digital connectivity, sensitivity to social influence, and environmental awareness, has attracted considerable scholarly attention in sustainable consumption research. Yet a persistent gap between their expressed pro-sustainability attitudes and actual purchasing decisions remains well-documented. This study examines whether Gen Z characteristics help bridge that gap by directly influencing sustainable purchase behavior and by moderating the role of purchase intention in that process. A quantitative design was employed using survey responses from 302 Gen Z consumers. The findings suggest that while Gen Z characteristics significantly predicted actual sustainable purchasing and purchase intention exerted a positive direct effect, the interaction between the two was negative and statistically significant. Conditional effects analysis further revealed that the influence of generational characteristics on purchasing behavior is stronger at lower levels of purchase intention and progressively weaker as intention increases. These results suggest that traits such as digital responsiveness, social embeddedness, and environmental orientation do not merely reinforce existing intentions but appear to compensate for their absence, activating sustainability-aligned behavior even when motivational commitment is limited. The study repositions the intention-behavior gap among Gen Z as something modulated by generational characteristics that drive purchasing behavior when intention alone falls short.

Abstract: Purpose: This research intends to explore the relationships between digital risk management practices and the successful implementation of innovative banking services with moderation effect of digital capabilities and moderation effect of digital culture. Methodology Approach: In this study, the data was gathered using a quantitative approach and the cross-sectional survey method with responses from participants who were chosen as the unit of analysis of being investigated for the study. Islamic finance institutions in Jordan were used as the unit of analysis in this study. Responses of different Islamic finance institutions were surveyed in a structured manner to collect data. The current study then used a structural equation modeling using SmartPLS to investigate the relationship between the variables. Findings: The results show that utilizing digital risk management advanced analytics artificial intelligence and automated compliance systems is essential to fostering innovation while upholding Shariah compliance. The study also shows that efficient digital risk management boosts users confidence increases service effectiveness and facilitates the launch of cutting-edge Shariah-compliant products. The findings reveal meditation and moderation significant effect of digital capabilities and digital culture respectively among between digital risk management and innovative banking services. Originality: By investigating digital risk management in the particular context of Islamic innovative banking services, this study provides novel insight. In contrast to earlier research that focuses on innovation in Islamic finance or digital risk management in conventional banking independently this paper examines how digital risk management frameworks impact the creation governance and sustainability of innovative banking services that adhere to Shariah.

Abstract: The purpose of this paper is to investigate, collect, and analyze the different technologies that are being integrated into vehicle automation systems. These technologies can range from LIDAR/RADAR sensors, voice recognition, and AI models. With the continued push for the development of AI and au- tonomous vehicles in both the economy and among the populace, designers and engineers are more incentivized than ever to break new ground. As technology in the industry changes, so must the priorities of its developers. First, data and analysis on the safety of autonomous vehicles will be provided, providing context for the importance of the topic. Second, an overview of the research and development of the technology used to address the previous concerns is provided. Third, an examination of the successes and failures of the technology in regard to those concerns will be made. Lastly, this paper will explore the emerging breakthroughs and future advancements that will drive the mass adoption of autonomous vehicles, specifically those that can be scaled up to civilian automobiles.

Abstract: Introduction: Systemic inflammation is a key contributor to the pathophysiology of carotid plaque burden (CpB). Increasing evidence supports a link between periodontitis and systemic conditions, including endothelial dysfunction, and CpB. This study aimed to explore the relationship between periodontal status, inflammatory biomarkers, and CpB, as well as the potential impact of periodontal therapy. Methods: A pilot study was conducted on subjects presenting with both periodontitis and CpB. Of 87 initially screened participants, 10 met the inclusion criteria and completed the study. Periodontal parameters—probing pocket depth (PPD), clinical attachment loss (CAL), and bleeding on probing (BOP)—were recorded. Systemic inflammatory biomarkers, including matrix metalloproteinase-8 (MMP-8), myeloperoxidase (MPO), lipoprotein-associated phospholipase A2 (Lp-PLA2), and soluble CD40 ligand (sCD40L), were analyzed. Results: Participants demonstrated severe periodontal disease, with mean PPD of 5.5 mm (maximum 8 mm), mean CAL of 6.46 mm (maximum 12 mm), and BOP of 67%. High serum Lp-PLA2 levels were associated with increased periodontal tissue destruction and inflammatory burden, supporting its role in both periodontitis and CpB pathophysiology. MMP-8 and MPO showed positive correlations with periodontal parameters, although these did not consistently reach statistical significance. Following periodontal therapy, a significant reduction in MMP-8 and Lp-PLA2 levels was observed, while MPO and sCD40L exhibited a decreasing trend without statistical significance. Conclusion: Inflammatory biomarkers may represent important mechanistic links between periodontitis and carotid artery disease. Within the limitations of this pilot study, non-surgical periodontal therapy was associated with reductions in selected systemic inflammatory biomarkers, supporting the feasibility of investigating the periodontitis–carotid plaque axis in larger translational cohorts. Larger studies are needed to validate these findings.

Abstract: Psoriasis and psoriatic arthritis (PsA) are systemic immune-mediated diseases, but the features that distinguish cutaneous-dominant psoriasis from musculoskeletal involvement remain unclear. We analyzed four core public cross-sectional datasets spanning whole-blood methylation, PBMC single-cell RNA sequencing summarized at the subject level, skin RNA sequencing, and purified CD4+ T-cell methylation, and used two additional public skin cohorts for external contextual checks to define an inflammatory disease axis (DIR) and a contrast-resolved systemic-state coordinate (CRS) representing additional systemic immune-state variation associated with PsA. In whole-blood methylation, DIR primarily separated healthy controls from psoriasis, whereas CRS separated psoriasis from PsA with minimal correlation to DIR. In PBMC single-cell data, CRS was higher in PsA and in the source-defined PSX subgroup (joint pain without CASPAR-classified PsA) than in PsO. Cell-type-resolved analyses localized CRS-related shifts to CD8 naive T cells, NK cells, CD14 monocytes, and regulatory T cells and identified multicompartment pathway-state remodeling along the CRS continuum. In contrast, skin RNA sequencing mainly captured lesional inflammatory burden and showed only limited additional PsA-related separation within the same tissue state. These findings support a model in which PsA is distinguished from psoriasis by an additional systemic immune-state axis rather than by skin inflammatory burden alone.

Abstract: Objective: To investigate the correlations of procalcitonin (PCT), interleukin-6 (IL-6), and lactate (Lac) with disease severity (assessed by APACHE II score) and organ dysfunction in patients with septic shock, and to compare their predictive values. Methods: We prospectively enrolled 320 patients with septic shock across four clinical centers from June 2023 to March 2025. Patients were divided into a Survival group (n = 248) and a Death group (n = 72) based on 28-day outcomes. Spearman correlation analysis was used to evaluate the relationships of PCT, IL-6, and Lac with APACHE II scores and organ function indicators (creatinine, platelet count, ALT). Results: Lactate showed the strongest correlation with APACHE II scores and was significantly associated with renal dysfunction and coagulopathy. Lactate (Lac) remained the most potent independent predictor of mortality (AUC = 0.884, 95% CI: 0.832-0.936). However, a tri-marker combined model (Lac + IL-6 + NGAL) achieved a superior AUC of 0.942, significantly outperforming any single biomarker (p < 0.001). Conclusion: lactate should be considered a core biomarker for assessing critical illness and prognosis in septic shock.