Search Results (5,190)

The rapid expansion of molecular point-of-care (POC) diagnostics into decentralized settings, including emergency departments, retail pharmacies, and home environments, has shifted the burden of diagnostic performance from laboratory professionals to heterogeneous, often non-expert users. While traditional evaluation frameworks focus on analytical and clinical validity, they often overlook the impact of human-system interactions on real-world reliability. This review introduces the concept of Operational Validity: the ability of a diagnostic system to preserve its intended performance when operated by intended users within the constraints of real-world workflows and environments. To establish a rigorous foundation for this concept, this study provides a critical comparative analysis contrasting Operational Validity against traditional clinical evaluation dimensions (analytical validity, clinical validity, and clinical utility) and post-market metrics. While existing literature outlines isolated usability principles, the significance of this study lies in its synthesis of these fragmented concepts into a formalized, lifecycle-based “Operational Validity” framework that explicitly maps the causal mechanisms connecting initial user interaction directly to downstream clinical outcomes. By synthesizing international standards (IEC 62366-1) alongside the newly finalized May 2026 U.S. Food and Drug Administration (FDA) guidance on the Content of Human Factors Information in Medical Device Marketing Submissions, we examine how human factors engineering (HFE) and usability engineering serve as the methodological foundation for operational validity. We analyze the specific complexities of molecular workflows, identify key parameters of use-related failure modes in pre-analytical and interpretation stages, and detail the mandatory role of iterative formative and final summative usability testing in mitigating these risks. Finally, we propose a lifecycle-based approach to HFE that integrates design, simulated-use validation, and post-market surveillance. Establishing operational validity is essential to ensure that the high analytical sensitivity of molecular POC platforms translates into consistent clinical utility across the full spectrum of decentralized care. Full article

Simultaneous multi-ion detection is important for interpreting leaching, corrosion, hydration, and solidification processes in cement-based materials, because these processes are controlled by coupled ion migration, binding, and precipitation–dissolution reactions. Conventional methods such as pore-solution extraction, ion chromatography, inductively coupled plasma optical emission spectroscopy, and single-ion potentiometric measurements provide useful chemical information, but they generally rely on discrete sampling or isolated ion channels and therefore have limited ability to capture time-aligned multi-ion evolution. In this study, an IoT-based in situ multi-ion detection system was developed by integrating ion-selective electrodes for Cl⁻, Ca²⁺, F⁻, and H⁺ with an ADS1115 analog-to-digital converter, an ESP32 microcontroller, and a voltage amplification module. The system achieved minimum resolvable concentrations of 10⁻⁵ M for Cl⁻ and F⁻ and 10⁻⁴ M for Ca²⁺, while maintaining pH measurement over the range of 2–12. Ten consecutive measurements at 0.01 M showed relative standard deviations below 0.12%, indicating good short-term repeatability under laboratory calibration conditions. Interference and temperature tests showed that Br⁻ and NO₃⁻ affected the chloride channel at high concentrations, Ca²⁺ reduced free F⁻ activity through Ca–F precipitation equilibrium, and the temperature drift of Cl⁻ and F⁻ electrodes changed direction with concentration, whereas the Ca²⁺ response decreased monotonically with increasing temperature. When applied to phosphogypsum–cement hardened pastes, the system captured rapid Ca²⁺ release, low-level F⁻ fluctuation controlled by Ca–F interaction, non-monotonic Cl⁻ release, and alkaline pH evolution on the same time axis. Compared with existing single-ion or offline methods, the proposed system provides synchronized in situ evidence for interpreting coupled ion leaching in cement-based solid-waste systems.: Full article

Thin-film thermocouples (TFTCs) offer conformal sensing junctions with minimal thermal mass, enabling rapid transient response and direct deposition on curved or moving components, which are difficult to achieve using conventional wire thermocouples in applications such as high-speed machining, electric powertrain thermal management, and fuel-cell monitoring. In practical deployment, the effective accuracy of a TFTC can also be affected by the measurement setup used for calibration and testing, particularly lead-wire material transitions, cold-junction compensation, and wiring-related thermoelectric offsets. This study presents a systematic static calibration and performance evaluation of NiCr-based TFTCs under standardised laboratory conditions, with repeated measurements across the 20–260 °C range using both copper leads and matched compensation wires. The thermoelectric output exhibits excellent linearity; temperature reconstruction against a traceable standard reference yields a maximum deviation of approximately 0.27 °C, with root-mean-square and relative errors within tight bounds. Short-term extended-range verification up to 1000 °C confirms detectable thermoelectric signal generation under the present test conditions. A calibration data packet framework containing the calibrated TFTC sample, wiring configuration, calibration coefficients, validity range, and a GUM-compliant uncertainty budget is proposed to support consistent interpretation of calibration results in future digital integration. The study therefore provides a structured calibration workflow and uncertainty-reporting basis for the tested flexible NiCr-based TFTC configurations, supporting further reliability assessment, material-level characterisation, and digital integration. Full article

Background: Renal vasculitis encompasses a heterogeneous spectrum of disorders where vascular inflammation leads to organ dysfunction. Given that renal involvement is the primary determinant of long-term morbidity, timely diagnosis and intervention are paramount. This review aims to synthesize recent pathogenic insights and evaluate how these mechanistic breakthroughs are reshaping current diagnostic and therapeutic paradigms. Methods: A narrative review of the literature was performed to analyze the pathophysiology, diagnosis, and management of pediatric renal vasculitis. The analysis synthesizes current clinical guidelines and recent trial data, highlighting the transition toward biomarker-driven precision medicine for refined disease assessment and management. Results: Diagnosis remains multimodal, necessitating the integration of clinical, laboratory, and histopathological data. In ANCA-associated vasculitis (AAV), recent evidence has challenged the traditional “pauci-immune” concept. Management of pediatric IgA vasculitis utilizes a risk-stratified approach, whereas cryoglobulinemic vasculitis requires targeted trigger elimination. Across all pediatric syndromes, there is a shift toward minimizing corticosteroid exposure and utilizing individualized frameworks. Conclusions: Despite substantial progress in targeted biological therapies and reduced corticosteroid burden, the long-term morbidity of pediatric renal vasculitis remains substantial. Outcomes are dictated by a synergy of disease-specific and patient-specific factors. Addressing persistent unmet needs in the field requires further refinement of individualized management protocols and the continued validation of dynamic biomarkers, alongside the implementation of pediatric-specific guidelines and age-appropriate outcome measures. Full article

Thermal overload in internal combustion engines may progressively destabilize lubricant-film integrity and promote severe tribological deterioration within highly stressed contact interfaces. This study investigates the thermo-tribological degradation sequence of a high-mileage gasoline engine subjected to prolonged idle operation under impaired cooling conditions, ultimately resulting in engine seizure. The investigated engine had accumulated 356,724 km, while the lubricant had remained in service for approximately 26,724 km prior to the experiment. The post-failure investigation combined teardown inspection, geometrical camshaft assessment, reverse gravimetric reconstruction, hydraulic tappet surface profiling, XRF surface characterization, laboratory oil analysis, and SEM/EDS evaluation of wear debris. The results demonstrated strongly localized degradation concentrated primarily within the cam–tappet interfaces. Severe non-uniform camshaft wear was accompanied by pronounced hydraulic tappet surface damage and evidence of unstable boundary-lubrication conditions. Laboratory oil analysis revealed elevated wear-metal concentrations, depletion of the alkaline reserve, increased oxidation indicators, and a final Class D oil condition assessment. SEM/EDS characterization identified Fe-bearing wear debris associated with sustained material removal and debris recirculation during the final degradation stage. The combined evidence supports a coupled thermo-tribological degradation mechanism involving lubricant deterioration, boundary-lubrication instability, adhesive wear acceleration, oxidative surface degradation, and debris-assisted surface damage preceding final engine seizure. The present case study provides experimentally documented evidence of lubrication collapse under real-engine thermal runaway conditions and highlights the critical role of lubricant condition in maintaining tribological stability under severe thermal loading. Full article

The growing demand for sustainable energy carriers has intensified interest in hydrogen production from renewable resources and waste-derived substrates. In this context, microbial electrolysis cells (MECs) have emerged as a promising technology for the simultaneous treatment of organic waste and biohydrogen generation. This review provides an overview of recent advances in MEC systems, focusing on reactor configurations, performance indicators such as hydrogen production rate, coulombic efficiency, and chemical oxygen demand removal. Attention is given to the valorization of real waste streams, including municipal and agro-industrial effluents, highlighting the differences between laboratory- and pilot-scale applications. While numerous studies have demonstrated the technical feasibility of MECs, several bottlenecks still limit their large-scale implementation, including challenges associated with the use of complex substrates. In particular, untreated wastewater often leads to reduced process efficiency due to its variable composition and the occurrence of competing microbial pathways. To overcome these limitations, integrated approaches are also discussed, with emphasis on the coupling of dark fermentation, capable of enhancing substrate biodegradability through the production of volatile fatty acids, with MEC systems. Overall, MEC technology represents a promising pathway for sustainable hydrogen production within circular waste management frameworks, although further advancements are required to enable its practical application. Full article

Background: Increasing evidence suggests that schizophrenia may be associated with peripheral immune–inflammatory alterations, although the distributional characteristics and heterogeneity of routinely available complete blood count (CBC)-derived inflammatory indices in real-world psychiatric inpatient settings remain insufficiently characterized. The present study aimed to descriptively evaluate the distributional properties of CBC-derived inflammatory markers in hospitalized patients with schizophrenia using an exploratory panel-based analytical framework. Methods: We conducted a retrospective cross-sectional analysis using anonymized CBC laboratory panels obtained from hospitalized patients with schizophrenia at a tertiary psychiatric center. Following panel reconstruction and quality control procedures, 858 structurally valid CBC panels were included in the analyses. Primary inflammatory indices included neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), and systemic immune–inflammation index (SII). Descriptive distributional analyses, threshold-based prevalence estimation, Spearman correlation analyses, and exploratory unsupervised clustering procedures were performed to evaluate inflammatory variability and internal distributional patterns within the dataset. Results: Median NLR was 2.51 (IQR: 1.95–3.55), median MLR was 0.25 (IQR: 0.19–0.31), median PLR was 124.10 (IQR: 100.40–163.94), and median SII was 686.96 (IQR: 484.81–1045.85). Threshold-based analyses demonstrated substantial variability in inflammatory burden distributions, with 35.9% of panels showing NLR > 3 and 27.0% demonstrating SII > 1000. Correlation analyses revealed strong positive associations among NLR, PLR, and SII, whereas RDW-CV and MPV demonstrated weaker and more heterogeneous relationships with the principal inflammatory indices. Exploratory clustering analyses generated two distributional clusters, including a smaller cluster exhibiting relatively higher NLR, MLR, PLR, SII, WBC, and platelet values than the remaining panels. Female panels demonstrated significantly higher PLR and SII distributions following false discovery rate (FDR) correction. Conclusions: The present findings suggest that CBC-derived inflammatory indices demonstrate substantial distributional variability within this panel-based schizophrenia dataset. Although the exploratory design, absence of patient-level linkage, and lack of clinical confounder adjustment substantially limit biological interpretation, routinely available hematological inflammatory markers may still provide a pragmatic framework for descriptive characterization of inflammatory variability patterns in real-world psychiatric populations. Future patient-level longitudinal studies integrating clinical, pharmacological, and molecular variables will be necessary to determine the potential clinical relevance of inflammatory heterogeneity in schizophrenia. Full article

Transferosomes are liposome-derived ultradeformable vesicles designed to improve drug delivery across restrictive biological barriers, particularly in non-invasive administration routes. Their structure is based on phospholipid bilayers modified with edge activators, usually surfactants or bile salts, which increase membrane flexibility while preserving vesicular organization. This balance between deformability and stability distinguishes transferosomes from conventional liposomes and has supported their use in dermal, transdermal, ocular, nasal, buccal, and other mucosal delivery systems. However, despite extensive experimental interest, the field remains limited by inconsistent terminology, heterogeneous formulation strategies, non-harmonized deformability assays, and incomplete translation from laboratory formulations to clinically relevant products. This review critically examines transferosomes from a formulation-development perspective, focusing on the relationship between lipid composition, edge-activator selection, vesicle properties, deformability, drug release, and biological performance. Particular attention is given to critical quality attributes, analytical characterization, mechanistic interpretations of barrier interaction, and the unresolved debate between intact vesicle penetration, drug-release-dominated delivery, and barrier perturbation. Transferosomes are also positioned in comparison with conventional liposomes, ethosomes, and transethosomes. Finally, the review identifies key unmet needs related to standardization, reproducibility, scalability, storage stability, and regulatory uncertainty. By integrating formulation design with mechanistic and translational analysis, this review aims to clarify when transferosomes offer a genuine delivery advantage and which parameters must be controlled to support their further pharmaceutical development. Full article

Cultivated-meat production relies on robust animal cell-line engineering, scalable tissue-engineering strategies, and clearly defined regulatory standards. This review examines the developmental pipeline from primary tissue biopsy to large-scale expansion and regulatory evaluation, focusing on stable and safe immortalized cell platforms. We compare muscle satellite cells, mesenchymal stromal/adipogenic progenitors and induced pluripotent stem cells, highlighting trade-offs among proliferative capacity, lineage commitment, genomic stability, and food-safety considerations. We then analyze immortalization strategies, including spontaneous senescence bypass, telomerase reactivation and CRISPR-based checkpoint modulation, highlighting their impact on genomic stability and food-safety risks. Recent advances in serum-free media, extracellular matrix-mimetic biomaterials and staged co-culture protocols have enabled centimeter-scale tissues with improved texture and marbling; however, cost, reproducibility and scalability remain bottlenecks. Integrating multi-omics surveillance with life-cycle assessment reveals that environmental benefits (land, water and antibiotic reduction) are attainable only when energy inputs and growth-factor sourcing are optimized. Finally, we examine regulatory frameworks that distinguish food-grade immortalized cells from pharmaceutical substrates and genetically modified crops. By integrating cell biology, animal biotechnology, and bioprocess engineering, this review identifies technical priorities for advancing cultivated meat from laboratory development to industrial implementation, positioning genomic monitoring as an essential framework for assessing biological stability, functional predictability, and food-production suitability. Full article

Growing demand for greener and more sustainable materials in cultural heritage conservation has prompted the investigation of bio-based alternatives for cleaning applications. This study presents a preliminary evaluation of bacterial nanocellulose (BNC) membranes for the removal of acrylic resins from mural paintings, comparing commercial medical-grade and laboratory-produced BNC with conventional gel systems under simulated application conditions. Both BNC types were characterized in terms of composition, pH, electrical conductivity, Water Holding Capacity and Water Retention Rate. Acetone loading via solvent exchange was assessed by thermogravimetric analysis (TGA), while mechanical behavior before and after solvent loading was evaluated through tensile testing and optical density measurements of the immersion media. The performance of BNCs and reference delivery systems was comparatively assessed in terms of solvent retention, solvent penetration depth into the substrate and residue release. Cleaning performance was investigated through FTIR spectroscopy and semi-quantitative image analysis as indirect indicators of residual resin content, on both mock-up samples and in situ applications. Under the tested conditions, both BNC membranes were compatible with acetone loading and maintained mechanical integrity after solvent exposure. FTIR analysis showed a reduction in the acrylic carbonyl band after treatment with acetone-loaded BNC, which exhibited greater solvent diffusion depth; the underlying removal mechanism, including the possible contribution of solvent-driven redistribution phenomena, remains to be clarified. Differences in reproducibility were observed between medical-grade and laboratory-produced BNC. Overall, the study provides experimental data contributing to the assessment of BNC membranes as bio-based solvent delivery systems for conservation practice. Full article

Partial discharge (PD) source-type classification is essential for condition-based maintenance of high-voltage apparatus. Existing approaches based on grid discretizations of phase-resolved partial discharge (PRPD) patterns suffer from performance degradation under stochastic interference and multi-source conditions. This paper proposes a graph-based framework that integrates the morphological characterization of raw high-frequency PD waveforms with the phase-amplitude position of individual discharge events to enable multi-label classification, identifying multiple PD sources coexisting within a single test. The framework operates through three stages: a multi-task neural network extracts per-pulse embeddings and confidence scores; a construction procedure establishes selective graph connectivity based on spatial proximity and morphological similarity; and an edge-conditioned graph neural network performs classification via message passing weighted by multimodal edge attributes. Experimental evaluation on PD measurements acquired in accordance with IEC 60270 shows that the proposed framework achieves a Matthews correlation coefficient (MCC) of 0.98 and an exact match ratio of 0.97 across single-source, noisy, and multi-source conditions, substantially outperforming histogram- and set-based baselines. The framework maintains an MCC of 0.97 in multi-source scenarios, where its advantage over existing methods is most pronounced. Cross-domain evaluation on an independent dataset acquired with different laboratory equipment confirms the approach’s robustness, achieving an MCC of 0.93 without retraining. Finally, an ablation study demonstrates that the joint removal of morphological similarity filtering and confidence-based node filtering and edge gating reduces the MCC by 0.25, confirming the critical role of the waveform-informed relational structure. Full article

Aquatic ecosystems are under severe stress from a diverse combination of contaminants, including heavy metals, pesticides, pharmaceuticals, and microplastics, driven by rapid industrialization, intensive agriculture, and urbanization. Globally, 80% of wastewater remains untreated, and conventional systems often fail to address emerging contaminants. Consequently, toxic heavy metals like lead and mercury can persist in water sources for decades. In response, phytoremediation has emerged as a scalable, eco-friendly, nature-based alternative. Among phytoremediation agents, duckweeds are increasingly recognized for their rapid growth, simple morphology, and continuous water-column contact. This review outlines the landscape of duckweed-based remediation, detailing molecular detoxification pathways and the synergistic role of associated microbiomes in enhancing environmental cleanup. Evidence indicates that contaminant removal is often supported by plant-microbe interactions. Despite extensive laboratory validation, field-scale implementation remains constrained by environmental complexity, pollutant mixtures, and variable climatic conditions. Furthermore, while duckweed systems hold promise within circular bioeconomy frameworks, converting wastewater into nutrient-rich biomass, contaminant accumulation in plant tissues raises concerns about biomass utilization and contaminant carryover. Addressing these challenges requires an integrative approach that links molecular detoxification, ecological interactions, and engineered system design to realize the full potential of duckweeds for sustainable aquatic pollution management. Full article

This study develops a Pore-Prior and Progressive-Sampling Transformer architecture, termed PPFormer, for the laboratory-scale analysis of microscopic remaining-oil images acquired from photolithographic glass-micromodel displacement experiments. The architecture integrates pore-prior embedding, progressive sampling of morphology-sensitive tokens, multi-scale self-attention encoding, relative position encoding, and boundary-enhanced decoding. PPFormer identifies five microscopic remaining-oil morphologies: cluster-like remaining oil, columnar remaining oil, droplet-like remaining oil, film-like remaining oil, and blind-end remaining oil. Under the investigated experimental conditions, the model achieved an overall pixel accuracy of 93.6%. The resulting morphology identification maps were used for pore-space-normalized area characterization and displacement-efficiency analysis under three permeability conditions and four displacement strategies. Relative to conventional waterflooding, the area-reduction ranges of cluster-like remaining oil, columnar remaining oil, and droplet-like remaining oil were from 2.29% to 12.66%, from −0.46% to 21.86%, and from 0.09% to 10.75%, respectively. Film-like remaining oil and blind-end remaining oil exhibited smaller changes, ranging from −0.50% to 8.19% and from −0.59% to 5.39%, respectively. Uncertainty was evaluated across independent replicate runs and by comparing predicted masks with consensus ground-truth masks. Full article

Arthropathies are a major global health challenge because of their high prevalence, chronic progression, and significant impact on quality of life and health systems. Therefore, prompt and accurate diagnosis is critical for slowing disease progression and improving outcomes. Traditional imaging modalities, such as ultrasound and magnetic resonance imaging, suffer from significant limitations, including operator dependence, limited accessibility, high cost, and limited reproducibility. Infrared thermography has become a promising non-invasive imaging technique for identifying thermal variations linked to inflammatory and metabolic processes. Advances in quantitative thermography, automated segmentation, and artificial intelligence have greatly enhanced its clinical applicability. This review summarizes recent advances in thermography-based biomarkers, including region-of-interest-derived metrics, asymmetry indices, hotspot burden, spatial and texture descriptors, and composite thermographic scores. It discusses the role of machine learning and deep learning in prediction, phenotyping, and multimodal integration with clinical, laboratory, and imaging data. Heterogeneity of protocols, variability in measurements, domain shift, validation design, overfitting, and reporting quality are also addressed. Overall, thermography combined with AI is highly promising as an adjunct to early diagnosis, assessment of disease activity, and follow-up in arthropathies. However, clinical application at a large scale requires strict standardization, external validation, transparent reporting, and well-elucidated, reproducible analytical processes. Full article

Solar-driven interfacial evaporation (SIE) has emerged as a transformative, off-grid technology that confines heat at the air–liquid interface, enabling high-efficiency vapor generation for decentralized water purification. Here, we present a comprehensive and critical review of the field, charting its evolution from fundamental photothermal principles to integrated multifunctional systems. We first elucidate the thermodynamics of interfacial heat localization and the resultant enhancement in evaporation efficiency. We then systematically analyze material innovation strategies—including broadband-absorbing photothermal agents and tailored evaporator architectures—designed to overcome persistent challenges such as salt crystallization, fouling, and thermal losses. Moving beyond freshwater production, we highlight emerging pathways for extending SIE platforms toward water–energy cogeneration, selective resource recovery, and zero-liquid-discharge wastewater treatment. We further identify and objectively assess the key bottlenecks that currently hinder the transition from laboratory-scale prototypes to real-world deployment, with a focus on long-term material robustness under harsh environments, adaptability to fluctuating water chemistries, and techno-economic viability. Finally, we outline forward-looking research directions, including stimulus-responsive smart evaporators, elucidation of multi-field coupling mechanisms, and the establishment of standardized performance evaluation protocols. This review aims to provide both a tutorial for newcomers and a critical assessment for experienced researchers, offering a balanced perspective on the current state-of-the-art and a roadmap for translating SIE from academic research into sustainable, impactful technologies. Full article