Search Results (718)

Paper-based analytical devices (PADs) were developed as low-cost tools for detecting chemical and biological compounds, commonly fabricated from cellulose derived from plant biomass. Bamboo, a fast-growing and abundant plant with high cellulose content (40–50%), was investigated as a substrate source. In this study, the selection of bamboo was based on its rapid growth cycle and the abundance of parenchyma cells that facilitated nanofibrillation compared to cellulose fibers from softwood or hardwood. Cellulose fibers were extracted from black bamboo (30 and 60 mesh) using mechanical and acid hydrolysis methods. The mechanical method employed ultrasonication to obtain nanocellulose, while the acid hydrolysis method used strong acids, i.e., H₂SO₄. The resulting nanocellulose papers exhibited variations in contact angle, porosity, and transmittance that directly affected their permeability and fluid flow behavior. The results indicated that the mechanical method, which extracted nanocellulose from parenchyma cells, yielded more consistent thermophysical and mechanical properties suitable for paper-based biosensors. The fabricated nanocellulose papers were tested as PADs for colorimetric detection of dopamine and hydrogen peroxide. Based on the literature comparison, their sensing performance, including sensitivity, linearity, limit of detection (LOD), and limit of quantification (LOQ), was comparable to other nanocellulose-based papers, indicating the potential of bamboo-derived nanocellulose as a sustainable substrate for PADs. Full article

Hepatitis B virus (HBV) pregenomic RNA (pgRNA), transcribed directly from nuclear covalently closed circular DNA (cccDNA), is an essential component in viral replication. The synthesis and encapsidation of pgRNA depend significantly on the transcriptional activity of cccDNA, making serum pgRNA a recently recognized non-invasive biomarker for evaluating cccDNA activity. However, its clinical application is limited by factors including preanalytical variables, methodological inconsistencies in detection, and a lack of standardization in quantification. This review provides an overview of the biological origins of pgRNA and its critical role in the HBV replication cycle, highlighting the stability challenges encountered during the collection, processing, and storage of plasma/serum samples. Furthermore, it analyzes recent significant advancements in pgRNA detection technologies, encompassing modified reverse transcription quantitative polymerase chain reaction (RT-qPCR), nucleocapsid-captured methodologies, automated testing platforms, multiplex digital PCR, isothermal amplification, and clustered regularly interspaced short palindromic repeats-based assays. A comparison of these technologies revealed that discrepancies in pgRNA quantification arise primarily from variations in sample processing and measurement systems, rather than from inherent biological limitations. Therefore, establishing standardized sample handling procedures, harmonized detection methods, and unified measurement systems is imperative before pgRNA can be reliably applied to monitor treatment, guide cessation decisions, or evaluate cure in chronic hepatitis B. Full article

Reverse engineering (RE) has been increasingly adopted in metal manufacturing to digitize legacy parts, connect “as-is” geometry to mechanical performance, and enable agile repair and remanufacturing. This review consolidates scan-to-simulation workflows that transform 3D measurement data (optical/laser scanning and X-ray computed tomography) into simulation-ready models for structural assessment and manufacturing decisions, with an explicit focus on sustainability. Key steps are reviewed, from acquisition planning and metrological error sources to point-cloud/mesh processing, CAD/feature reconstruction, and geometry preparation for finite-element analysis (watertightness, defeaturing, meshing strategies, and boundary condition transfer). Special attention is given to uncertainty quantification and the propagation of geometric deviations into stress, stiffness, and fatigue predictions, enabling robust accept/reject and repair/replace choices. Sustainability is addressed through a lightweight reporting framework covering material losses, energy use, rework, and lead time across the scan–model–simulate–manufacture chain, clarifying when digitalization reduces scrap and over-processing. Industrial use cases are discussed for high-value metal components (e.g., molds, turbine blades, and marine/energy parts) where scan-informed simulation supports faster and more reliable decision making. Open challenges are summarized, including benchmark datasets, standardized reporting, automation of feature recognition, and integration with repair process simulation (DED/WAAM) and life-cycle metrics. A checklist is proposed to improve reproducibility and comparability across RE studies. Full article

This paper proposes a coordinated capacity configuration method for Virtual Power Plant (VPP) distributed resources that considers time-varying power coupling. The method addresses the inadequate economic efficiency and reliability of existing configuration schemes, which stems from insufficient attention to the time-varying power coupling characteristics of Distributed Energy Resources (DERs). Firstly, we define the concepts of direct and indirect power coupling among DERs, derive a Lagrange multiplier-based coupling coefficient model, and realize the quantification of time-varying coupling coefficients through sliding time window correlation analysis (STWCA). Next, a capacity correlation matrix integrating technical and economic synergies is constructed to map coupling characteristics to capacity configuration. Then, a coordinated configuration model with time-varying coupling constraints is established to minimize life-cycle cost and maximize power supply reliability, validated by case simulation. The results demonstrate that the proposed method effectively reduces VPP operation cost and improves resource utilization and reliability, providing theoretical support for the scientific configuration of DERs in VPPs. Full article

Background/Objectives: The aim of this study was to identify anatomical and systemic predictors of early (≤2 months) response to subthreshold micropulse laser (SMPL) in center-involving diabetic macular edema (DME) using automated AI-based OCT biomarker quantification. Methods: Retrospective observational study of 65 eyes. Spectral-domain optical coherence tomography (SD-OCT) volumes were analyzed with a CE-marked software (Ophthal v1.0; Mr. Doc s.r.l., Rome, Italy) to quantify intraretinal fluid (IRF) and subretinal fluid (SRF) volumes and outer retinal integrity (external limiting membrane, ELM; ellipsoid zone, EZ). SMPL (577 nm; 5% duty cycle; 200 ms; 150 µm; 250 mW) was applied in a high-density macular grid, sparing the foveal avascular zone. The primary endpoint was absolute and percentage change in IRF volume from baseline to follow-up; predictors of %IRF reduction were assessed by multivariable linear regression. Results: At 52 days (IQR 41–60), best-corrected visual acuity improved from 0.22 to 0.15 logMAR (p < 0.001). IRF volume decreased (median −0.045 mm³; p = 0.034) despite stable central subfield thickness. All eyes with baseline SRF (n = 5; median 0.026 mm³ [0.020–0.046]) achieved complete SRF resolution. Treatment-naïve eyes had greater %IRF reduction than pretreated eyes (59.6% vs. 11.5%; p = 0.029). High responders showed shorter diabetes duration than low responders (14.5 vs. 17 years; p = 0.025); however, treatment-naïve status was the strongest independent predictor of %IRF reduction (p = 0.028). Conclusions: AI-derived fluid volumetrics capture early SMPL response despite unchanged thickness. Treatment-naïve status and shorter diabetes duration may define a metabolic window for optimal early response in DME. Full article

Isocitrate dehydrogenase (IDH) variants can lead to the development and/or progression of various solid tumors and hematological malignancies. IDH testing can guide diagnosis, prognosis, and therapeutic choice and typically relies on NGS, IHC, or PCR-based assays. Here, we evaluated the analytical performance of the Idylla IDH1-2 mutation assay for IDH variant detection using 70 fixed samples from patients with solid tumors and 36 DNA extracts from patients with acute myeloid leukemias previously characterized by NGS +/− IHC. Idylla IDH1-2 mutation assay gave 98.1% of valid results with an overall agreement, sensitivity, and specificity of 97.1%, 96.2%, and 98.1%, respectively, compared to NGS. Using commercial DNA standards, the limit of detection of the assay was 1.6% and 0.5% for IDH1 R132H and IDH2 R172K variants, respectively. Based on these data, the Idylla IDH1-2 mutation assay represents a fast and reliable alternative to detect IDH hotspot variants in solid tumors and hematological malignancies using either fixed tissue sections or DNA extracts. Particular attention, however, is needed for the interpretation of cases with cycle of quantification values of the internal controls over 35, for which a variant with low allelic frequency could be missed due to low DNA quantity or quality. Full article

Higher education institutions face a critical methodological challenge in pursuing net-zero commitments: Within the amount ofhe emissions related to Scope 3, including indirect emissions from water consumption, waste disposal, business travel, and mobility, employees commuting represents 50–92% of campus carbon footprints, yet reliable quantification remains elusive due to fragmented data collection and governance silos. The present research investigates how purposeful integration of the Home-to-Work Commuting Plan (HtWCP)—mandatory under Italian Decree 179/2021—into the Climate Neutrality Plan (CNP) could constitute an innovative strategy to enhance emissions accounting rigor while strengthening institutional governance. Stemming from the University of Genoa case study, we show how leveraging mandatory HtWCP survey infrastructure to collect granular mobility behavioral data (transportation mode, commuting distance, and travel frequency) directly addresses the GHG Protocol-specified distance-based methodology for Scope 3 accounting. In turn, the CNP could support the HtWCP in framing mobility actions into a wider long-term perspective, as well as suggesting a compensation mechanism and paradigm for mobility actions that are currently not included. We therefore establish a replicable model that simultaneously advances three institutional dimensions, through the operationalization of the Avoid–Shift–Improve framework within an integrated workflow: (1) methodological rigor—replacing proxy methodologies with actual behavioral data to eliminate the notorious Scope 3 data gap; (2) governance coherence—aligning voluntary and regulatory instruments to reduce fragmentation and enhance cross-functional collaboration; and (3) adaptive management—embedding biennial feedback cycles that enable continuous validation and iterative refinement of emissions reduction strategies. This framework positions universities as institutional innovators capable of modeling integrated governance approaches with potential transferability to municipal, corporate, and public administration contexts. The findings contribute novel evidence to scholarly literature on institutional sustainability, policy integration, and climate governance, whilst establishing methodological standards relevant to international harmonization efforts in carbon accounting. Full article

This systematic review examines chronic kidney disease-associated pruritus (CKD-aP) as a complex clinical manifestation in patients undergoing hemodialysis. Traditionally considered a secondary symptom of end-stage renal disease, emerging evidence now positions CKD-aP as a multidimensional disorder with substantial pathogenic influence on patient outcomes. Using the PRISMA 2020 methodology, we critically evaluated 54 peer-reviewed studies published between 2020 and 2025. Our synthesis highlights a convergence of five mechanistic frameworks underpinning CKD-aP: elevated levels of uremic toxins originating from gut microbial dysbiosis, immune activation driven by IL-31 and other pro-inflammatory cytokines, heightened peripheral and central neural sensitization, dysregulation of endogenous opioid receptor pathways favoring μ-receptor activation, and xerosis-related epidermal barrier dysfunction. These mechanisms contribute to a systemic cycle of microinflammation, pruritogenic signaling, and neural hyperexcitability. We also identified and compared validated assessment tools—including the NRS, VAS, Skindex-10, and the UP-Dial scale—that facilitate standardized quantification of disease burden. While available treatments such as gabapentinoids and phototherapy offer partial relief, targeted therapies—including κ-opioid receptor agonists—represent a major advancement, although long-term effectiveness and accessibility remain under investigation. Growing scientific consensus establishes CKD-aP as a priority therapeutic target in hemodialysis care, underscoring the need for integrated, mechanism-based management strategies to improve quality of life and clinical outcomes. This work represents a narrative systematic review, integrating evidence from mechanistic, translational, and clinical studies to critically examine the biological pathways underlying CKD-associated pruritus. Full article

Accurate prognosis of the remaining useful life (RUL) for lithium-ion batteries is critical for mitigating range anxiety and ensuring the operational safety of electric vehicles. However, existing data-driven methods often struggle to maintain robustness when transferring from controlled laboratory conditions to complex, sensor-limited, real-world environments. To bridge this gap, this study presents U-H-Mamba, a novel uncertainty-aware hierarchical framework trained on a massive hybrid repository comprising over 146,000 charge–discharge cycles from both laboratory benchmarks and operational electric vehicle datasets. The proposed architecture employs a two-level design to decouple degradation dynamics, where a Multi-scale Temporal Convolutional Network functions as the base encoder to extract fine-grained electrochemical fingerprints, including derived virtual impedance proxies, from high-frequency intra-cycle measurements. Subsequently, an enhanced Pressure-Aware Multi-Head Mamba decoder models the long-range inter-cycle degradation trajectories with linear computational complexity. To guarantee reliability in safety-critical applications, a hybrid uncertainty quantification mechanism integrating Monte Carlo Dropout with Inductive Conformal Prediction is implemented to generate calibrated confidence intervals. Extensive empirical evaluations demonstrate the framework’s superior performance, achieving a RMSE of 3.2 cycles on the NASA dataset and 5.4 cycles on the highly variable NDANEV dataset, thereby outperforming state-of-the-art baselines by 20–40%. Furthermore, SHAP-based interpretability analysis confirms that the model correctly identifies physics-informed pressure dynamics as critical degradation drivers, validating its zero-shot generalization capabilities. With high accuracy and linear scalability, the U-H-Mamba model offers a viable and physically interpretable solution for cloud-based prognostics in large-scale electric vehicle fleets. Full article

Accurate quantification of aircraft emissions and their uncertainties is essential for well-informed policy-making, air quality management, and the development of sustainable airport strategies. This study addresses uncertainties in aircraft emission estimates implemented for local air pollutants with hourly resolution at six European airports. Publicly available flight-tracking data were used to determine aircraft movements and types, but they typically lack detailed information on aircraft engine models, thus contributing to uncertainties in emission factors. Times-in-mode for take-off, climb-out, and approach modes followed International Civil Aviation Organization (ICAO) recommendations, while taxi times, known to vary between airports, were modeled using statistical distributions derived from Eurocontrol, and the contribution of taxi time to overall uncertainty in emission estimates was investigated. Monte Carlo simulation combined with Sobol sensitivity analysis identified the relative contribution of each uncertainty source. On average, the results indicate an uncertainty of 23% for CO, 34% for HC, 7% for NO_x, and 21% for PM across the airports analyzed. Overall, the proposed methodology introduces a novel framework utilizing publicly available, hourly resolved flight-tracking data with robust uncertainty analysis to estimate airport-level emissions with enhanced reliability, providing crucial information for local air quality assessment and policy development. Full article

The in-vial microextraction technique is emerging as an alternative sample treatment, as it integrates sorbent preparation, adsorption, and desorption of analytes in a single device before instrumental analysis. In this work, the applicability of polycaprolactone polymeric film, recently used for the in-vial microextraction of sex hormones from environmental waters, is studied in a low-capacity format for unconjugated sex hormones determination in biological samples by HPLC-MS/MS. Its performance was evaluated in urine and serum, achieving extraction in a short time (10 and 30 min, in turn) and satisfactory elution with ethanol, with recovery in the range of 65–111% in urine, 55–122% in bovine serum albumin (BSA) solution, and 66–121% in fetal bovine serum (FBS). In the case of protein matrices, a dilution to 20 g L⁻¹ protein content and washing step (3 × 1 mL ultrapure water) afore the elution are required to achieve clean extract, as verified by a Bradford assay. Matrix-matched calibration was used for quantification, obtaining correlation coefficients greater than 0.9929; limits of detection and quantification were in the range of 0.01–0.65 and 0.03–1.96 ng mL⁻¹ in urine, 0.02–0.8 and 0.05–2.5 ng mL⁻¹ in BSA, and 0.02–1.0 and 0.06–3.0 g mL⁻¹ in FBS, respectively. The in-vial polycaprolactone film proved to be reusable for several cycles (up to ten), and the greenness assessment revealed a good adhesion to green sample preparation principles. All these achievements further strengthen its feasibility for efficient extraction/clean-up of trace sex hormones in complex biological samples. Full article

Microplastic quantification in marine vegetated ecosystems remains analytically demanding, yet little is known about the environmental footprint of the laboratory procedures required to isolate and measure these particles. This study applies Life Cycle Assessment (LCA) to laboratory analytical workflows for microplastics quantification, focusing exclusively on sample preparation and analytical procedures rather than natural environmental absorption or fate processes, in two ecologically relevant matrices: (i) pelagic algae (Sargassum) and (ii) seagrass biomass. Using the openLCA 2.5 and the ReCiPe Midpoint (H) v1.13 methods, the analysis integrates foreground inventories of digestion, filtration, drying, and spectroscopic identification, combined with background datasets from OzLCI2019, ELCD 3.2 and USDA. Results show substantially higher impacts for the algae scenario, particularly for climate change, human toxicity, ionising radiation and particulate matter formation, largely driven by longer digestion times, increased reagent use and higher energy demand during sample pre-treatment. Conversely, the seagrass scenario exhibited lower burdens per functional unit due to reduced organic complexity and shorter laboratory processing requirements. These findings highlight the importance of matrix-specific methodological choices and the influence of background datasets on impact profiles. This study provides the first benchmark for the environmental performance of microplastic analytical workflows and underscores the need for harmonised, low-impact laboratory protocols to support sustainable monitoring of microplastic pollution in marine ecosystems. Full article

Background/Objectives: Therapeutic drug monitoring (TDM) of β-lactams (BL), BL/β-lactamase inhibitor (BLI) combinations (BL/BLIc), and of fosfomycin may play a key role in optimizing antimicrobial therapy and in preventing resistance development, especially when used by continuous infusion in critically ill or immunocompromised patients. Unfortunately, analytical methods for simultaneously quantifying multiple BL/BLIc in plasma are still lacking. Methods: The aim of this study was to develop and validate two rapid, sensitive, and accurate UPLC–qTOF–MS/MS methods for the simultaneous quantification of five novel β-lactam or β-lactam/β-lactamase inhibitor combinations (ceftolozane/tazobactam, ceftazidime/avibactam, meropenem/vaborbactam, cefiderocol, and ceftobiprole) along with fosfomycin. Methods: Human plasma samples were prepared by protein precipitation using methanol containing isotopically labeled internal standards. Chromatographic separation was achieved within 10–12 min using two Agilent Poroshell columns (EC-C18 and PFP) under positive and negative electrospray ionization modes. The method was validated according to the EMA guidelines by assessing selectivity, linearity, precision, accuracy, matrix effects, extraction recovery, and stability. Results: The methods exhibited excellent linearity (R² ≥ 0.998) across the calibration ranges for all of the analytes (1.56–500 µg/mL), with limits of quantification ranging from 1.56 to 15.62 µg/mL. Intra- and inter-day precision and accuracy were always within ±15%. Extraction recovery always exceeded 92%, and the matrix effects were effectively corrected through isotopic internal standards. No carry-over or isobaric interferences were observed. All the analytes were stable for up to five days at 4 °C, but the BL and BL/BLIc stability was affected by multiple freeze–thaw cycles. Conclusions: These UPLC-qTOF-MS/MS multi-analyte methods enabled a simultaneous, reliable quantification in plasma of five novel beta-lactams and of fosfomycin. Robustness, high throughput, and sensitivity make these multi-methods feasible for real-time TDM, supporting personalized antimicrobial dosing and improved therapeutic outcomes in patients with severe or multidrug-resistant infections. Full article

Lakes on the Qinghai–Tibet Plateau are important indicators of global climate change, and variations in their water storage strongly influence regional hydrological cycles and ecosystems. However, existing studies have largely focused on relative changes in lake volume, while the precise quantification of absolute water storage remains insufficient, largely due to the lack of long-term, high-accuracy water storage time series. Constrained by harsh natural conditions and limited in situ observations, conventional approaches struggle to achieve the accurate long-term monitoring of lake water storage across the Plateau. To address this challenge, we propose a DEM-based underwater topography extrapolation method. Under the assumption of continuity between surrounding onshore terrain and submerged lakebed morphology, nearshore DEM data are extrapolated to reconstruct lake bathymetry. By integrating multi-source remote sensing observations of lake area and water level, we estimate and reconstruct 30-year absolute water storage time series for 120 Plateau lakes larger than 50 km². This method does not require measured water depth data and is particularly suitable for data-scarce, topographically complex, high-altitude lake regions, effectively overcoming key limitations of conventional methods used for absolute water storage monitoring. Validation shows strong agreement between our estimates and an independent validation dataset, with an overall correlation coefficient of 0.95; the reconstructed time series are highly reliable, with correlation coefficients exceeding 0.6. During the study period, the total lake water storage of the Qinghai–Tibet Plateau exhibited a significant increasing trend, with a cumulative growth of approximately 137.297 billion m³, representing a 20.73% increase, and showing notable spatial heterogeneity. The water storage dataset constructed in this study provides reliable data support for research on water cycles, climate change assessment, and regional water resource management on the Qinghai–Tibet Plateau. Full article

In complex energy storage operating scenarios, batteries seldom undergo complete charge–discharge cycles required for periodic capacity calibration. Methods based on accelerated aging experiments can indicate possible aging paths; however, due to uncertainties like changing operating conditions, environmental variations, and manufacturing inconsistencies, the degradation information obtained from such experiments may not be applicable to the entire lifecycle. To address this, we developed a stage-wise state-of-health (SOH) prediction approach that combined offline training with online updating. During the offline training phase, multiple single-cell experiments were conducted under various combinations of depth of discharge (DOD) and C-rate. Multi-dimensional health features (HFs) were extracted, and an accelerated aging probability $p_{AA}$ was defined. Based on the correlation statistics between HFs, $k_{HF}$, the SOH, and $p_{AA}$, all cells in the dataset were divided into general early, middle, and late aging stages. For each stage, cells were further classified by their longevity (long, medium, and short), and multiple models were trained offline for each category. The results show that models trained on cells following similar aging paths achieve significantly better performance than a model trained on all data combined. Meanwhile, HF optimization was performed via a three-step process: an initial screening based on expert knowledge, a second screening using Spearman correlation coefficients, and an automatic feature importance ranking using a random forest regression (RFR) model. The proposed method is innovative in the following ways: (1) The stage-wise multi-model strategy significantly improves the SOH prediction accuracy across the entire lifecycle, maintaining the mean absolute percentage error (MAPE) within 1%. (2) The improved model provides uncertainty quantification, issuing a warning signal at least 50 cycles before the onset of accelerated aging. (3) The analysis of feature importance from the model outputs allows the indirect identification of the primary aging mechanisms at different stages. (4) The model is robust against missing or low-quality HFs. If certain features cannot be obtained or are of poor quality, the prediction process does not fail. Full article