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This study presents a comprehensive review of the emerging role of Generative Artificial Intelligence (GenAI) in environmental assessment and sustainability analysis. Positioned within a new paradigm of environmental management, GenAI redefines traditional static models through dynamic, generative, and participatory approaches that integrate data synthesis, scenario modeling, and governance insight. Using a Systematic Literature Review (SLR) guided by the CIMO (Context-Intervention-Mechanism-Outcome) framework, this paper identifies and analyzes 182 scholarly and technical publications published between 2015 and 2025. The review synthesizes developments across key GenAI architectures-Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Transformer-based Large Language Models (LLMs), and Diffusion Models-and evaluates their applications in synthetic data generation, scenario simulation, remote sensing, predictive analytics, and public engagement. The findings reveal that GenAI holds significant potential to address data scarcity, enhance model scalability, and promote participatory and interdisciplinary decision-making, while also presenting challenges related to interpretability, data bias, validation, environmental footprint, and ethical governance. To guide responsible implementation, the study proposes a three-tier framework emphasizing technical fidelity, transparency, and governance alignment. The implications underscore that effective integration of GenAI into environmental management requires hybrid modeling, participatory data governance, and sustainable AI infrastructures to ensure transparency, accountability, and equity. Collectively, this work advances an evidence-based understanding of how GenAI can underpin a data-driven, inclusive, and ethically responsible paradigm in environmental assessment.

Given the inherent complexity of metabolic pathways and disease-associated agents, next-generation healthcare necessitates wearable, non-invasive, and customized approaches to continuously monitor a broad spectrum of physiologically relevant biomarkers for personalized health management. Moreover, existing data-based analytical strategies remain inadequate for delivering quantitative and predictive evaluations of health status in real-life settings. Here, we report an electronic multiplexed microneedle-based biosensor patch (eMPatch) that enables real-time, minimally invasive monitoring of key metabolic biomarkers in interstitial fluid, including glucose, uric acid, cholesterol, sodium, potassium, and pH. By integrating modular microneedle (MN) sensors into a skin-interfaced flexible platform, the eMPatch achieves robust mechanical stability and seamless skin conformity, thereby ensuring reliable and continuous sensing within the dermal space. In vivo validation in animal models under metabolic intervention highlights the strong capability of the eMPatch for real-time physiological tracking across diverse daily activities. Implemented with a machine learning algorithm, the eMPatch enables automatic feature extraction and multi-task health assessment, achieving a classification accuracy of 0.996 in distinguishing normal and diet-induced metabolic disorder for health condition identification and an R² score of 0.977 for the corresponding degree evaluation. This study highlights the potential of the MN-integrated, machine learning-enhanced biosensing platform toward personalized health management.

Two-dimensional/three-dimensional (2D/3D) perovskite heterojunctions at the contact interfaces have been proven to enhance the stability and power conversion efficiency (PCE) of perovskite solar cells (PSCs). The 2D/3D bilayer is typically formed via a solution post-treatment onto the 3D perovskite, where the 2D layer's dimensionality depends on the ligand size and its reactivity. Despite their stability, long-chain ligands typically form 2D perovskites with low dimensionality (n = 1, 2) which feature poor charge conductivity and mobility. Here, we propose an in situ fabrication method incorporating long-chain oleylammonium (OlyA⁺) ligands directly into the perovskite ink. This approach forms 2D perovskite with higher dimensionalities (n ≥ 3) with enhanced (001) crystal facet orientation of the 3D film, improved energetic alignment, charge extraction, and structural stability. The fabricated inverted PSCs with 1.55 eV bandgap achieved a maximum PCE of 26.22% for small area and 24.6% for 1cm² devices, as well as 21.1% for mini-modules (6.8 cm²). Additionally, the PSCs with in situ formed 2D/3D perovskite heterojunctions retained 90% and 80% of their initial PCE after 1200 h photothermal stability and 1050 h outdoor testing, respectively. Our one-step strategy produces uniform and stable 2D/3D perovskite heterojunctions with enhanced passivation capability, overcoming the limitations of conventional sequential methods and offering a promising and effective approach for highly stable and efficient PSCs.

The high concentration of ammonia from deteriorated aquaculture environments and intensive culture system increases the susceptibility to white spot syndrome virus (WSSV) and causes high mortality in Pacific white shrimp Litopenaeus vannamei. However, its molecular mechanism remains to be elucidated. In the present study, small RNA sequencing was performed for understanding the molecular mechanism of acute ammonia toxicity increasing susceptibility to WSSV in Pacific white shrimp with the individuals under normal conditions (LC group), ammonia stress (LA group), WSSV infection (LV group), and WSSV infection with ammonia stress (LAV group). Compared with other groups, more significantly upregulated differentially expressed miRNAs (DEMs) were identified in the LAV group, which targeted to the immune-, energy metabolism-, and ROS scavenging-related genes. The KEGG enrichment analysis showed that the target genes of the DEMs in LA and LAV groups (both with ammonia stress) were specially involved in viral carcinogenesis, and those in LAV group were also specially involved in hippo signaling pathway and chemokine signaling pathway. The copper zinc superoxide dismutase (Cu/Zn-SOD) was predicted as the target gene of the specific DEMs of novel_59 and novel_71 in the LAV group. The mRNA expression and enzyme activity of Cu/Zn-SOD was significantly lower in the LAV group than other groups, but the ROS production rate was the highest in the LAV. This study provides new insights for understanding the molecular mechanism of high WSSV-infection under ammonia toxicity in shrimps from the post-transcriptional perspective.

Chronic post-surgical pain (CPSP) in rats is characterized by persistent mechanical allodynia and spinal neuronal hypersensitivity. Astrocyte-derived L-lactate, a key modulator of neuronal excitability and synaptic plasticity, was herein investigated for its role in CPSP development following skin/muscle incision and retraction (SMIR). SMIR triggered long-lasting mechanical allodynia, concomitantly with astrocyte activation and elevated L-lactate levels in the spinal dorsal horn. Blockage of glycogenolysis by 4-dideoxy-1,4-imino-D-arabinitol (DAB), inhibition of carbonic anhydrase (CA) by acetazolamide or inhibition of soluble adenylyl cyclase (sAC) by bithionol prevented SMIR-induced mechanical allodynia and reduced spinal dorsal horn L-lactate levels, implicating a critical role of astrocyte-derived lactate in CPSP development and maintenance. Chemogenetic inhibition of spinal astrocyte suppressed mechanical allodynia and decreased L-lactate accumulation in the dorsal horn. Notably, exogenous L-lactate enhanced the firing rate of spinal lamina Ⅰ-II neurons but failed to alter excitatory synaptic transmission, suggesting a selective role for L-lactate in modulating spinal neuronal intrinsic excitability. Mechanistically, SMIR elevated plasma glucocorticoid levels, while adrenalectomy (ADX) abolished both SMIR- induced mechanical allodynia and spinal lactate elevation. Collectively, these findings indicate that glucocorticoid receptor signaling drives astrocytic L-lactate release in spinal dorsal horn following SMIR, which promotes spinal neuronal hyperexcitability and contributes to CPSP pathogenesis.

The multi-energy complementary power system achieves comprehensive and synergistic utilization of diverse energy sources, generating large-scale and distributed operational data. This introduces challenges in leveraging operational data for accurate and efficient carbon emission prediction. To effectively process the large-scale distributed operational data of power systems, identify key influencing factors, and achieve high-precision carbon emission prediction, this study investigates a carbon emission prediction method for multi-energy complementary power systems based on a multiple linear regression model. The structure of the multi-energy complementary power system is analyzed, and its carbon emission intensity is calculated. Based on the analysis results, preliminary selection of carbon emission influencing factors is conducted. A multiple linear regression model is constructed with the selected factors as independent variables and carbon emissions as the dependent variable. By performing significance tests on each independent variable, key influencing factors are identified, yielding an optimized multiple linear regression model. The model is integrated into the MapReduce parallel framework to expand computational scalability, enabling parallel processing of large-scale distributed power system data while ensuring prediction efficiency. The results demonstrate that the selected factor variables are reasonable, and the constructed prediction model exhibits a high goodness-of-fit. The prediction error ranges between 0.00516% and 0.00818%, confirming high accuracy and efficiency. The prediction results indicate that the experimental multi-energy complementary energy center's carbon emissions increase annually from 2025 to 2031 and gradually decline from 2031 to 2034. These findings provide a scientific basis for formulating carbon emission reduction policies in multi-energy complementary power systems.

Background: Infection imaging plays a crucial role in clinical decision making, guiding antibiotic therapy and surgical management. Nuclear medicine offers molecular level approaches using three main techniques: [¹⁸F]FDG PET/CT, radiolabelled white blood cell (WBC) scintigraphy, and anti-granulocyte monoclonal antibody scintigraphy with [^99mTc]Tc-besilesomab. Among these, [^99mTc]Tc-besilesomab was developed to simplify infection imaging compared with in vitro WBC labelling, improving accessibility for hospitals without cell-labelling facilities. However, its use is restricted by the need for prior testing for human anti-mouse antibodies (HAMA) to prevent hypersensitivity reactions.

Main body: In recent years, significant regulatory changes in the European framework for in vitro diagnostics (IVDR 2017/746) have coincided with the withdrawal of the quantitative HAMA enzyme-linked immunosorbent assay from the market and its replacement with a qualitative rapid test. Although this shift aimed to streamline in vitro testing procedures, it has complicated the interpretation of HAMA results and raised concerns about diagnostic accessibility. In some centres, an increase in positive results has been observed with rapid tests, suggesting that patients may be inappropriately excluded from [^99mTc]Tc-besilesomab imaging. This situation highlights a paradox: a radiopharmaceutical designed to improve accessibility is now constrained by regulatory and methodological factors. The issue also reflects a broader challenge in nuclear medicine, ensuring patient safety and compliance without limiting access to essential diagnostic tools.

Conclusion: This debate argues that restoring accessibility to [^99mTc]Tc-besilesomab immunoscintigraphy requires both technological and regulatory innovation. Developing quantitative point-of-care HAMA assays, promoting humanised or nanobody-based tracers, and establishing harmonised European guidelines could help balance patient safety with diagnostic availability. The [^99mTc]Tc-besilesomab case exemplifies how well-intentioned regulatory transitions may have unintended consequences, underscoring the need for a pragmatic equilibrium between innovation, safety, and accessibility in infection imaging.

As the environmental problems caused by the greenhouse effect become more and more serious, and the forest as the largest carbon pool can effectively slow down the greenhouse effect, it is particularly important to accurately predict the carbon storage of the forest. In order to accurately estimate the biomass and carbon storage of Quercus mongolica in Northeast China, the biomass allocation pattern of Q. mongolica was analyzed. In this study, data of 175 Q. mongolica trees in Heilongjiang, Jilin, Liaoning and eastern Inner Mongolia were collected, including aboveground organ biomass, DBH, tree height, age and climatic factors, as well as published carbon content data of different organs. In this study, the biomass allocation pattern of individual Q. mongolica was analyzed. An additively compatible aboveground biomass and carbon storage model and an algebraically controlled aggregation model were established using nonlinear simultaneous equations. After selecting the aggregate biomass compatibility model, climate factors were added to establish a compatibility model containing climate factors. In addition, the root-stem ratio model was used to construct the underground compatible biomass and carbon storage model. The adjusted R²_adj values of the final established aboveground components and aboveground total biomass and carbon storage models were between 0.7048 and 0.9618, the total relative error ( TRE ) was within ± 1%, and the average prediction error ( MPE ) was below 10%, which met the modeling accuracy standard. The belowground biomass models showed adjusted R²_adj values between 0.7702 and 0.7801, TRE ≤ 1%, and MPE < 15%. This study elucidated the biomass allocation pattern of individual Q. mongolica. All the developed models meet the accuracy requirements and can be applied to predict the biomass and carbon storage of Q. mongolica in Northeast China. In the compatibility model with climate factors, the accuracy of leaf and branch models has been greatly improved, indicating that the addition of climate factors in the independent model can greatly improve the accuracy of each component model, which can provide a theoretical basis for the establishment of each component model in the compatibility model of other tree species.

To examine inflammatory biomarkers as potential mediators in the association between urinary metal exposure and advanced Cardiovascular-Kidney-Metabolic Syndrome (CKM) risk in US adults, and to evaluate urinary metals' association with risk and their predictive value. Analysis included 6249 NHANES participants. Restricted Cubic Spline (RCS) explored dose-response relationships. Multivariable and piecewise logistic regression assessed associations between specific metals and advanced CKM risk at different exposure levels. Receiver Operating Characteristic (ROC) curves evaluated predictive performance. Mediation analysis tested the role of inflammatory biomarkers. Generalized Weighted Quantile Sum (gWQS) regression and machine learning (ML) models further assessed metal mixture effects and mechanisms. Restricted cubic spline analysis indicated linear associations between all urinary metal levels and advanced CKM risk. Elevated levels of the Multi-Metal Inflammatory Index (MMII), cadmium (Cd), and cobalt (Co) were significantly associated with increased risk of advanced CKM: each 1-unit increase was associated with a 122%, 28%, and 14% higher risk, respectively. This association was significant only at higher exposure levels. ROC analysis showed good predictive performance. Inflammatory biomarkers, including WBC, NENO, SIRI, AISI, MHR, and NHR, mediated the associations between MMII/Cd/Co and advanced CKM risk. gWQS and ML analyses confirmed the adverse associations of MMII, Cd, and Co, ranking their importance as MMII > Cd > Co. Higher levels of MMII, Cd, and Co are significantly associated with increased advanced CKM risk among US adults, with inflammatory biomarkers playing a key mediating role. These findings highlight a notable public health consideration.