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Drought perturbs water potential in the plants, causing oxidants accumulation and impairing cellular functions. The mineral nutrients are critical for adjusting water potential and modulating antioxidant activity and photosynthesis. This work investigated the impact of sodium nitroprusside (SNP) and chitosan (CS) on six key nutrients (Na, K, P, Ca, Mg, Fe) in leaves of spinach under polyethylene glycol (PEG)-induced drought. The 3-leaved seedlings were irrigated with PEG (5%, 10%, and 15%) and one-day later treated by foliar spray of SNP (25 and 50 µM) and CS (15 and 30 mg/L). The physiological responses were studied by measuring the concentrations of hydrogen peroxide, malondialdehyde, chlorophylls, carotenoids, phenols, flavonoids, anthocyanins, and nutrients using UV/Vis spectrophotometry and inductively coupled plasma optical emission spectrometry. Increasing drought intensity enhanced hydrogen peroxide and malondialdehyde contents. Drought stress increased Na, K, and Mg and decreased Ca and P. Iron remained constant due to its dual function in catalyzing oxidants and in activating antioxidant enzymes and photosynthesis. The SNP and CS applications enhanced photosynthesis and alleviated oxidative stress by enhanced production of phenolics and carotenoids. The elicitors caused higher Ca, P and Fe, and lower Na, K and Mg than those of non-elicited controls. Co-application of both elicitors caused the highest Fe, accompanied by the highest chlorophylls. The intricate interplay between six nutrients were critical to minimize oxidative damage and to improve photosynthetic performance. Overall, the Fe, Mg and Ca interplay were important for photosynthetic performance and antioxidant activity. Moreover, the Na, K and P interplay is essential for osmotic adjustment.

Accurate placement of the endotracheal tube (ETT) is critical for ensuring optimal care for patients requiring mechanical ventilation and preventing potential complications. ETT positioning can be assessed using several methods, with chest X-ray (CXR) being the most precise. Radiologists evaluate whether the ETT requires adjustment by measuring the distance between the distal tip of the ETT and the tracheal carina. This study presents the development of a machine learning model to detect and measure ETT position on adult CXRs and evaluates its performance. Six physicians annotated ETT and trachea locations on a dataset of 3856 CXRs. The U-Net-based model was then trained to generate trachea and ETT segmentations. After post-processing steps, an estimate of the distance between the distal tip of the ETT and the tracheal carina was found. It was demonstrated that the trained model is capable of estimating the position of the ETT and calculating the distance from the tube tip to the tracheal carina. The Dice index for the segmentations on the external validation subset for the trachea and ETT was 89.2% ± 9.0% and 87.8% ± 16.9%, respectively. The estimated absolute error on the external validation subset was 4.72 mm. This model represents a promising tool to support clinicians, particularly in Intensive Care Units, where correct intubation and effective ventilation are critical. It may also be integrated into clinical workflows to facilitate patient management and enhance patient safety.

Waning immunity and reinfection are critical features of many infectious diseases, but epidemiological models often fail to capture the interaction between an individual's immunity history and their current infection status, or do so only simplistically. We develop a dual-age structured model tracking immunity age (time since last recovery) and infection age (time since infection) to analyze epidemic dynamics under waning immunity and reinfection. The model is formulated as a system of age-structured partial differential equations describing susceptible and infected populations stratified by both age variables. The contact rate, mortality and recovery rates, susceptibility, and pathogen load are all treated as parameter functions depending on both immunity and infection age. We derive basic reproduction numbers and numerically solve the system using a second-order Runge-Kutta scheme along characteristic lines. We further extend the model to treat vaccination interventions, specifically booster vaccination strategies targeting individuals by immunity age - interventions that cannot be formulated in standard models. Numerical results reveal that higher contact rates produce larger oscillation amplitudes with longer inter-epidemic periods. However, long-term oscillation amplitude and cumulative infections depend non-monotonically on the initial infected population size, indicating that the relationship between initial infection levels and long-term epidemic outcomes is not straightforward. Vaccination efficiency depends critically on the pathogen load profile, with more concentrated distributions requiring higher vaccination rates for elimination. Most efficient strategies target intermediate immunity ages rather than only fully waned individuals.

Glucose-regulated protein 78 (GRP78), a core molecular chaperone governing the endoplasmic reticulum (ER) stress response, exerts dual regulatory effects during the pathogenesis of viral pneumonia. Beyond serving as a critical cofactor that promotes viral invasion and replication, GRP78 functions as an indispensable protective chaperone that preserves pulmonary cellular homeostasis and attenuates lung injury. This review characterizes two distinct subcellular localizations of GRP78 in viral pneumonia, namely ER-resident GRP78 and cell surface GRP78 (csGRP78), and systematically summarizes its dual regulatory mechanisms. Specifically, csGRP78 mediates viral adhesion and internalization, whereas ER-GRP78 promotes viral replication and aggravates pulmonary inflammation by modulating ER stress and the unfolded protein response. Meanwhile, GRP78 alleviates pulmonary tissue damage via suppressing lung cell apoptosis and restraining excessive ERS activation. Moreover, this review provides a comprehensive overview of advances in GRP78-targeted therapeutic strategies. The covered therapeutic modalities include small-molecule inhibitors, biological macromolecular drugs, indirect regulatory compounds, and natural products. This review also elaborates their specific molecular targets, core mechanisms, and preclinical findings. Additionally, the current research trends, existing limitations, and future perspectives of GRP78-related investigations are critically discussed. This review aims to clarify the central regulatory role of GRP78 in viral pneumonia, providing theoretical basis and innovative research directions for the precision targeted therapy of viral pneumonia.

As urban rail transit networks become increasingly dense, new tunnels frequently undercross existing operational lines. The cyclic loads from existing double-track trains pose a threat to the structural safety of the underlying sections. This paper proposes a computationally efficient analytical model for the 'double-track train-soil layer-underpass tunnel section' system. In this model, the double-track train is represented as a moving two-degree-of-freedom (2-DOF) sprung mass system, the soil is modeled as a spring-damper system, and the underpass tunnel roof structure is treated as an Euler-Bernoulli simply supported beam. The system response is solved using the modal superposition method. Key parameters are calibrated via displacement back analysis, and the model's predictive capability is validated against FLAC3D simulations and independent field measurement data. A systematic parametric study investigates the influence of train speed, tunnel beam length, overburden thickness, equivalent stiffness, and soil mechanical parameters. The analysis reveals the underlying mechanisms of system resonance and critical design parameters. For the specific case study, resonance peaks were observed at speeds around 48 km/h and 72 km/h, a dual-peak resonance phenomenon emerged at beam lengths of approximately 25 m and 45 m, and a vibration amplification effect was identified at a soil cover thickness near 5 m. These findings highlight the resonance mechanisms that should be avoided in design, while the specific critical parameter values are case-dependent and should be re-calibrated for other projects. Increasing the equivalent beam stiffness can effectively suppress the vibration response, while grouting reinforcement modulated the system's dynamic response. This study provides a theoretical framework and an efficient preliminary assessment tool for understanding vibration mechanisms and aiding early-stage design in similar projects.

The purpose of this review is to describe the intersection between pediatric hypertension and advancing stages of cardiovascular, kidney metabolic syndrome in children and adolescents. Cardiovascular-kidney-metabolic syndrome is highly prevalent in the pediatric population. The onset of CKM in childhood is influenced by the presence of antenatal risk factors such as maternal hypertensive disorders of pregnancy. Advancing stages of CKM in children and adolescents are strongly influenced by food insecurity and social determinants of health that impact the risk for childhood obesity. Activation of the renin-angiotensin-aldosterone system is a key mediator in the relationship between antenatal risk, early life course exposure and hypertension in children and adolescents. Effective strategies for slowing the rate of advancement of CKM staging in children and adolescents require attention to early course factors that influence the development of hypertension and obesity. Likewise, management strategies and therapeutic interventions that address these factors are critical to mitigating CKM stage advancement. Although hypertension is a component of the CKM framework, the presence of hypertension in children and adolescents drives higher CKM staging. Together, hypertension and higher CKM staging are associated with increased atherosclerotic cardiovascular disease risk. Social factors, including access to healthy foods and attention to early life course nutrition are critical strategies to improving CKM and hypertension related outcomes in children and adolescents.

Organic solvents are routinely used to dissolve poorly water-soluble chemicals in zebrafish-based assays. However, their intrinsic biological activity may confound behavioural endpoints and compromise data interpretation. This study systematically evaluated the effects of seven commonly used laboratory solvents, dimethyl sulfoxide (DMSO), ethanol (EtOH), methanol (MeOH), acetone (Ace), acetonitrile (ACN), isopropanol (IPA), and ethyl acetate (EtOAc), at four concentrations (1, 0.5, 0.1, 0.05%; v/v), on locomotor behaviour of zebrafish Danio rerio larvae at 120 h post fertilization. Larval activity was quantified using automated video tracking under alternating light-dark conditions, assessing distance moved, velocity, acceleration, mobility states, and turning behaviour. Behavioural responses were strongly solvent- and concentration-dependent. EtOH, MeOH, Ace, and IPA induced hyperactivity at ≥ 0.5%, while DMSO showed biphasic effects, stimulating activity at 0.5% but suppressing it at 1%. ACN demonstrated a significant inhibitory response even at 0.5%, while EtOAc was tolerated only at ≤ 0.1%. Principal component analysis (PCA) and hierarchical clustering on principal components (HCPC) further revealed coherent behavioural signatures shaped not only by solvent identity and concentration but also strongly by light-dark transitions, underscoring the critical importance of consistent illumination conditions in zebrafish behavioural assays. Overall, ≤ 0.1% of Ace and EtOAc, and ≤ 0.05% of DMSO, EtOH, MeOH, ACN, and IPA did not significantly alter locomotion, suggesting their suitability for zebrafish behavioural assays. These findings establish reference points for solvent selection and concentration thresholds in zebrafish behavioural assays under the specific experimental conditions tested, thereby supporting reproducibility and reliability in scientific research.

Emerging evidence indicates that ion channels and transporters are critical for normal embryonic development. They are essential for establishing membrane potential (Vm), a property of all cells. However, it is uncertain if embryos have germ-layer specific regional differences in electrical behavior that play a role in defining local cell fates. In our study, by manipulating ion concentrations and applying a wide spectrum of inhibitors, we demonstrate that various ion channels and transporters differentially determine Vm in specific germ layers of the Xenopus tropicalis embryo. Altering the electrical properties of the embryo leads to localized changes in cell fate and alterations in left-right patterning, which can lead to severe diseases, including heterotaxy and congenital heart disease. Our results indicate that the electrical properties of early embryonic cells are regionally specified and affect local cell fates.

The road safety and traffic efficiency is enhanced by providing communication between Vehicles in Vehicle Ad hoc networks (VANETs). Position falsification attacks represent a significant threat in VANETs, where the accuracy and integrity of location-based information are critical for safe and efficient transportation. The misbehavior detection frameworks can effectively identify position falsification attacks. The frameworks employ machine learning classifiers trained on features derived from inter-vehicular communication data. Optimization can enhance a stacked ensemble model for misbehaviour detection by hyperparameter tuning of classifiers. The proposed methodology involves constructing a stacked ensemble model composed of five diverse base classifiers, such as K Nearest Neighbours Classifier (KNN), Ada Boost Classifier (ADA), Extra Trees Classifier (ETC), Random Forest (RF), and Extreme Gradient Boosting Classifier (XGBC). Meta Classifier is used to combine predictions from the individual classifiers with logistic regression. To achieve the highest accuracy, Artificial Bee Colony (ABC) optimization is used to enhance the hyperparameters for the base classifiers. The optimized stacked ensemble model shows that our model provides the best results when compared with the existing methods.

The present work explores corrosion response of Selective Laser Melting (SLM) prepared NiTi shape memory alloys with an aim to study processing-induced defects and surface-specific microstructural features inherent to SLM, influence on alloy corrosion response. The study systematically investigated corrosion behavior within the defect-minimized region of established SLM printability maps by comparing representative low- and high-power/scan speed conditions (P-80: 80 W, 330 mm s⁻¹; P-200: 200 W, 1080 mm s⁻¹) and by varying hatch spacing (64 μm and 80 μm) under constant laser power and scan speed. Corrosion behavior analysis was carried out on four distinct surfaces following a 72-h immersion in Hank's Balanced Salt Solution (HBSS): the internal and external surface of P-80 and P-200 specimens sectioned parallel to build direction (BD), and top and bottom surface of h-64 and h-80 specimens sectioned perpendicular to BD. Unlike prior studies that compared SLM NiTi against wrought material or varied parameters broadly, this work systematically isolates surface-specific defect populations within the defect-minimized region of an established printability map at matched volumetric energy density. The parallel to BD, P-80 specimens, showed the highest porosity of 5.4% and a corrosion current density of 140 nA/cm², with localized pits reaching up to 110 μm in depth, initiating primarily at porosity defects thereby disrupting the passive film formation. In contrast, perpendicular to BD specimens, particularly h-80 with larger hatch spacings, showed enhanced corrosion resistance on a single-specimen basis, attributed to a more effective passive film formation. Surfaces with higher defect density developed effective thicker but defect-rich passive films with poor protective performance, whereas Ti-enriched, low-defect surfaces formed effective thinner yet compact and highly resistive films. These results indicate that within the defect-minimized region of the printability map, surface-specific defect populations and surface chemistry are the dominant factors governing corrosion response, rather than nominal process parameters. The findings imply that build-orientation selection and surface-specific post-processing-beyond bulk-density optimization-are required for corrosion-critical SLM NiTi components. Direct passive-film characterization, replicate testing, and Ni-ion-release quantification are identified as essential follow-on work.

In vivo confocal microscopy (IVCM) is a critical ophthalmic examination that provides in vivo cytological and neurological information essential for diagnosing corneal and certain systemic diseases, but its clinical utility is limited by time-consuming interpretation and the need for subspecialty expertise. We developed IVCM-Insight, an artificial intelligence (AI) system integrating image-text contrastive learning with large language models (LLMs) for automated report generation and interactive question answering (QA). Based on 30,368 IVCM images and 4155 paired clinical reports, the model was trained with contrastive alignment, image-conditioned language modeling, and multi-image consistency loss to produce structured diagnostic reports while a domain-adapted LLM supported patient-centered QA. Automated evaluation showed strong agreement with the reference reports: Bilingual Evaluation Understudy (BLEU)-1 to BLEU-4 scores were 0.69, 0.58, 0.47, and 0.41, Recall-Oriented Understudy for Gisting Evaluation (ROUGE-L) was 0.67, Consensus-based Image Description Evaluation (CIDEr) was 1.85, and Metric for Evaluation of Translation with Explicit Ordering (METEOR) was 0.66. In addition, the multi-label classification achieved an accuracy of 0.96 and an F1 score of 0.80. Manual assessment by corneal specialists rated report accuracy (4.17), completeness (4.19), coherence (4.70), and diagnostic support (4.06), with excellent inter-rater reliability; QA outputs achieved high accuracy (4.33), relevance (4.54), and non-harmfulness (4.81). Representative cases, including cytomegalovirus, fungal, and Acanthamoeba keratitis, demonstrated accurate detection of key findings and clinically safe explanations. To our knowledge, IVCM-Insight is the first dedicated AI system for comprehensive IVCM interpretation, with potential to enhance diagnostic efficiency, strengthen physician-patient communication, and broaden access to advanced corneal imaging across care settings.

The management of postoperative pain is a critical ethical concern and a challenge in laboratory animal research, particularly for mice. Surgical procedures are routinely conducted in mice, but the use of analgesic drugs is underreported and the assessment of their efficacy is limited. This systematic review assesses the efficacy of analgesic drugs for postoperative pain in mice, addressing the influence of various factors, including sex, surgical invasiveness, pain modalities and analgesic classes. A systematic literature search resulted in 48 eligible studies included in the qualitative analysis and 43 in the quantitative analysis. The overall pooled standardized mean difference (SMD) for analgesic drugs was 0.46 (95% confidence interval 0.31 to 0.60), indicating a positive pain-reducing effect. Subgroup analysis revealed that analgesic treatment was significantly more effective in male (SMD 0.84; 0.60 to 1.08) than in female mice, in mild (SMD 0.57; 0.38 to 0.75) than in severe surgical procedures and in reducing evoked pain (SMD 1.12; 0.83 to 1.41) than in reducing spontaneous pain. In addition, almost all analgesic drug classes tested, including opioids, nonsteroidal anti-inflammatory drugs, acetaminophen and local anesthetics were effective in reducing evoked pain but not spontaneous pain (SMD 0.12; -0.03 to 0.27). However, most studies present limitations that could produce a high risk of bias. Taken together, our results indicate that, although analgesic drugs can reduce postoperative pain in mice, their efficacy is reduced in females, severely invasive surgeries and spontaneous pain. Thus, high-quality studies are still needed to answer ethical concerns and to guarantee full analgesia in laboratory mice submitted to surgical procedures.

Lactate, a key byproduct of tumor metabolic reprogramming, accumulates in the tumor microenvironment (TME) and profoundly shapes T cell-mediated anti-tumor immunity. As research into TME metabolism advances, lactate has emerged as a critical regulator with broad effects on immune function. In many cancers-including gastric cancer, hepatocellular carcinoma, lung cancer, melanoma, and pancreatic cancer-lactate suppresses or remodels anti-tumor immunity by acting on CD8⁺ T cells, regulatory T cells (Tregs), dendritic cells (DCs), and immune checkpoint molecules. The underlying mechanisms are becoming increasingly well-defined. However, major knowledge gaps remain, especially regarding how lactate-associated enzymes (e.g., LDHA), lactate transporters (e.g., MCT4), and signaling pathways impact T cell function. This review summarizes how lactate regulates anti-tumor immune responses and explores emerging immunotherapies targeting lactate metabolism, with a focus on metabolic enzymes and transporters. We cover preclinical and clinical progress on LDHA inhibitors and lactate transporter inhibitors. By comprehensively analyzing lactate's function in the TME, we aim to build a theoretical framework for precision tumor immunotherapy and propose future directions centered on modulating the immune microenvironment through lactate-targeted strategies.

Precise cancer lesion analysis in medical imaging critically depends on the accurate definition of regions of interest (ROIs), which directly influence diagnostic and clinical outcomes. While peritumoral features are known to enhance lesion characterization, efficiently defining meaningful peritumoral ROIs remains a challenge. We propose an adaptive peritumoral area selection approach (APASA) that systematically identifies the most informative ROI surrounding a lesion, enabling the extraction of meaningful radiomic features for improved diagnostic performance. Unlike conventional heuristic or morphology-based methods, APASA leverages the minimum coverage graph algorithm, using the tumor ROI as a reference to construct a graph encompassing both the tumor and its peritumoral microenvironment. The effectiveness of the proposed approach was evaluated within AI-based frameworks for automated lesion differentiation in breast and thyroid cancers. Extensive experiments employing five widely used machine learning models demonstrated that APASA-selected peritumoral features consistently outperformed conventional morphological dilation. Performance improvements reached up to 30.75% in AUC and 29.00% in F1-score compared with the tumor ROI baseline. Moreover, the optimal model was found to vary depending on the ROI type, shape, and cancer type, offering new insights into the interaction between ROI selection and model choice. These results highlight APASA as a principled and efficient strategy for adaptive ROI definition in ultrasound-based cancer lesion analysis, demonstrating effectiveness across two ultrasound datasets, with potential extension to other imaging modalities and clinical settings pending further validation.

Intensive care unit-acquired weakness (ICU-AW) represents a critical complication that severely impairs the prognostic outcomes of patients with sepsis. This study aimed to elucidate whether the activation of the adenosine 5'-monophosphate-activated protein kinase (AMPK)/silent information regulator 1 (SIRT1) signaling pathway ameliorates sepsis-acquired weakness (SAW) via modulating the peroxisome proliferator-activated receptor γ coactivator-1α (PGC-1α)/peroxisome proliferator-activated receptor γ (PPARγ) axis. A PGC-1α knockout mouse model was established using the cecal ligation and puncture (CLP) method to induce sepsis and subsequent SAW. Multiple histological analyses were performed to evaluate tissue pathological lesions, mitochondrial morphological and structural abnormalities, myelin injury, and skeletal muscle alterations. ELISA was utilized to quantify the expression levels of pro-inflammatory factors and neurotransmitters. Western blotting and RT-qPCR were further employed to detect the expression of mitochondrial functional proteins and downstream molecules associated with the AMPK/SIRT1 signaling pathway. Successful construction of the PGC-1α knockout CLP mouse model was confirmed via systematic verification. Functional experiments validated that AMPK/SIRT1 pathway activation exerts protective effects against SAW in CLP mice through targeting the PGC-1α/PPARγ signaling cascade. Mechanistically, AMPK/SIRT1 activation further modulates the PGC-1α/PPARγ pathway, thereby alleviating the progression of SAW. Collectively, targeted regulation of PGC-1α may serve as a promising and actionable therapeutic strategy for the clinical prevention and treatment of ICU-AW in septic patients.

Protein-protein interactions (PPIs) are dynamic and critical to adaptive homeostasis. While there have been massive efforts to catalogue proteome-wide PPIs, global quantification of changes remains a challenge. Here, we integrate dynamic protein correlation profiling - mass spectrometry (PCP-MS) and quantitative cross linking-mass spectrometry (qXL-MS) using multiplexed stable isotope labelling to characterise global PPI remodelling following the development of chronic skeletal muscle insulin resistance (IR) with or without acute insulin stimulation. We quantify >7,000 unique PPIs amongst 5,346 proteins and show changes in the interactome network dominate the proteome response. Our data show the dysregulation of protein processing in the endoplasmic/sarcoplasmic reticulum involving changes in PPIs with protein chaperones and disulfide isomerases is a major hallmark of skeletal muscle IR. Mechanistically, we show the dysregulation of PPIs with Protein-Disulfide Isomerase 6 (PDIA6) regulates cysteine oxidation and insulin sensitivity. Taken together, we show in vivo quantitative interactome mapping is a powerful approach to understand disease mechanisms and provide new insights into protein network re-organisations with IR.

Lanthanides (Ln), a group of 15 rare earth elements (REEs), are critical for advanced technologies, although their conventional extraction and processing are environmentally unsustainable. Here, we present a microbial platform based on Pseudomonas putida KT2440 for the eco-friendly recovery and transformation of Ln, introducing a key methodological innovation: the use of a resting cell system to circumvent the pervasive issue of abiotic lanthanide-phosphate precipitation. This approach enables controlled investigation of Ln biomineralization under mild conditions. Mechanistically, the results showed that Ln recovery proceeds via rapid cell-surface adsorption, followed by surface-templated nucleation and extracellular mineralization. This process leads to the formation of well-defined biogenic nanoparticles primary identified as CePO₄ and GdPO₄. Structural analyses reveal nanorod morphologies, while functional characterization shows that CePO₄ nanoparticles retain photoluminescent properties and GdPO₄ nanoparticles preserve paramagnetic behavior. Compared to conventional chemical synthesis, this biosynthetic strategy eliminates toxic reagents and energy-intensive steps, yielding biocompatible materials with controlled size and morphology. Our findings establish P. putida KT2440 as an efficient and sustainable platform for Ln recovery and functional nanoparticle production, providing a foundation for scalable green alternatives to traditional Ln processing.

Urban agglomerations serve as a critical vehicle for advancing high-quality regional urbanization, with the Lanzhou-Xining (Lanxi) urban agglomeration representing an advanced stage of urbanization in western China. As a nationally significant urban agglomeration in the western region, the Lanxi urban agglomeration also functions as a vital ecological barrier. Studying the supply and demand dynamics of ecotourism in this area is therefore important for the development of ecotourism and for promoting sustainable regional tourism practices. Based on this premise, this study focuses on the supply and demand conditions of new urbanization and ecotourism across 19 cities (districts and counties) within the Lanxi urban agglomeration. An evaluation index system is developed to assess the development of regional new-type urbanization and ecotourism supply and demand. The study employs methods including the coupling coordination model, the obstacle degree model, and the geographical detector to examine the comprehensive development levels of the regional systems, their spatial and temporal distribution characteristics, and the associated obstacle factors. The results indicate the following: (1) During the study period, the average comprehensive development level of the three major systems within the Lanxi urban agglomeration exhibited a steady upward trend overall. (2) Over the study period, the degree of coupling coordination among the three systems shifted from moderate imbalance (with coordination values between 0.2 and 0.3) to mild imbalance (with coordination values between 0.3 and 0.4). Significant regional differences and spatial clustering were observed in the coupling coordination levels. The overall spatial distribution of new-type urbanization and ecotourism supply and demand in the Lanxi urban agglomeration generally presented a pattern characterized by higher values in the central area (Lanzhou and Xining) and lower values in the peripheral areas. (3) The four dimensions represented by eight influencing factors-namely tourism scale, tourism benefits, tourism resources, and infrastructure-are identified as key determinants influencing the coupled and coordinated development of regional new urbanization and ecotourism supply and demand.

The NLRP3 inflammasome has been shown to assemble on multiple organelles, including the mitochondria, endoplasmic reticulum, trans-Golgi network, and endosomes. Yet, the precise site of assembly remains unresolved. Emerging evidence suggests that membrane lipid composition may play a critical role with phosphoinositides, cholesterol, and cardiolipin, alongside membrane biophysical properties and cellular metabolic state, collectively shaping membranes where inflammasome assembles. Here, we review how lipid-dependent mechanisms regulate NLRP3 assembly across membranes and consider whether a defined membrane lipid signature governs inflammasome assembly. This understanding may have broader implications for therapeutic targeting in inflammatory and metabolic diseases.