Search Results (2,739)

In recent decades, plastic consumption has risen across various industries and everyday products, leading to greater plastic use and the generation of waste, which results in the leaching of micro- and nanoplastics into the environment. This review summarizes recent analytical methods for the detection of nanoplastics (NPs) in several marine matrices, divided into three main stages: extraction, separation, and identification. The literature reviewed indicates that chemical and enzymatic digestion are the most commonly used procedures for the extraction step. For the separation step, flotation, filtration, and centrifugation are the most used techniques. Finally, two groups of techniques may be used for the identification step. The first category consists of methods used for qualitative identification, with spectroscopic methods such as Raman and FTIR being the most frequently used. The second category comprises those used for the quantitative analysis of NPs, where fluorescence-based methods and nanoparticle tracking analysis are increasingly used for this assessment. Despite these advances, significant challenges remain, such as matrix interferences caused by salinity and organic matter, low environmental concentrations of NPs, and the lack of standardized protocols. This review highlights the need for standardized protocols, validated reference materials, and integrated multi-technique approaches to improve the comparability of nanoplastics measurements in marine environments. Full article

Invasive cervical cancer is a very common cause of cancer death in women worldwide, primarily due to late detection of this cancer. The clinical manifestations of cervical cancer vary significantly and are difficult to predict. Finding new effective biomarkers for the early detection of cervical cancer is essential to reducing mortality. Small microRNA molecules have also recently emerged as potential biomarker candidates in the diagnosis of cervical cancer. Despite analytical limitations in microRNA assays and the lack of automated and standardized tests, validated and prospective systematic evaluation of this new parameter in cervical cancer deserves further development. This review describes the importance and potential usefulness of microRNAs in detecting cervical cancer at an early stage, monitoring the course of the disease, and assessing the effectiveness of treatment. The diagnostic importance of microRNAs is well documented in many publications, suggesting that, as microRNA research progresses, they may become a useful diagnostic tool for cervical cancer. Full article

Cardiovascular remodeling, encompassing vascular remodeling, myocardial remodeling, and fibrosis-associated tissue remodeling, underlies atherosclerosis, pulmonary hypertension, myocardial infarction, myocardial fibrosis, and other cardiovascular diseases. Its regulation has traditionally been studied through transcriptional, inflammatory, metabolic, mechanical, and intercellular signaling mechanisms. Recent advances in epitranscriptomics have identified N6-methyladenosine (m⁶A) RNA methylation as an additional post-transcriptional layer that interacts with microRNA (miRNA) pathways during cardiovascular disease progression. This review summarizes current evidence for m⁶A-miRNA crosstalk in cardiovascular remodeling, focusing on epitranscriptomic checkpoints that regulate miRNA fate, feedback-like regulatory circuits involving miRNAs and the m⁶A machinery, and cell-type-specific programs across endothelial cells, vascular smooth muscle cells, fibroblasts, and cardiomyocytes. We further discuss emerging analytical technologies and translational implications of this regulatory axis. Future studies should clarify causal mechanisms, cell-type and disease-stage specificity, and translational feasibility. Together, this multilayered framework provides a systems-level perspective on how RNA regulatory networks may shape pathological remodeling in cardiovascular disease. Full article

Digital platforms are increasingly presented as instruments for sustainable tourism governance, yet destinations often remain data-rich and governance-poor: digital traces are dispersed across actors, indicators are weakly standardised and communities frequently lack meaningful access to the information that shapes destination decisions. This article addresses this problem through the conceptual design and preliminary formative evaluation of ORVE (Optimisation of Resources and Valorisation of Experiences), a destination-level platform designed to connect tourists and residents, companies and institutions and Destination Management Organisations (DMOs) through a circular data ecosystem, understood as feedback loops across stakeholder levels. Methodologically, the study adopts Design Science Research (DSR). It operationalises problem identification, definition of solution objectives, artefact design and development, preliminary demonstration and formative evaluation, while recognising that full-scale causal evaluation remains a future research stage. The empirical component draws on a real-world pre-test with 12 tourism companies mediated by Biosphere Portugal, two Biosphere-administered pilot-company surveys involving 58 and 52 companies and scenario-based testing by 14 student groups involving more than 60 final-year students from Tourism and Tourism and Hospitality Management programmes. These sources are interpreted as exploratory and formative evidence rather than as a representative adoption study or a causal impact evaluation. The results suggest perceived usefulness for structuring sustainability information, supporting indicator monitoring and informing decision making, while also revealing operational constraints related to usability, data-entry flexibility, privacy communication, validation mechanisms, data availability in micro and small enterprises and the need for close onboarding support. The article contributes a refined platform architecture, a governance requirements matrix, design principles, an operationalisation roadmap and an evaluation protocol for sustainable tourism platform governance. Full article

To address the complex spatiotemporal dependencies and dynamically evolving spatial relationships in tunnel traffic flow prediction, a macro–micro collaborative two-stage prediction method is proposed. The Grey Wolf Optimizer (GWO) is first employed to optimize the GRU model for predicting incoming traffic flow at the tunnel entrance, providing reliable macro-level input for subsequent modeling. Based on this, a spatiotemporal graph structure is constructed, and an FSE-ST-GCN model integrating an adaptive adjacency matrix with spatial and channel attention mechanisms is developed to capture dynamic spatial dependencies and enhance key feature representation. Experiments are conducted using real-world traffic flow data collected from the Shizuizi Tunnel on the Jilin–Caoshi Expressway. The results show that the proposed method outperforms baseline models in terms of MAE, RMSE, and MAPE, achieving superior prediction accuracy and stability. This work provides effective technical support for refined tunnel traffic management and lighting control. Full article

Hepatocellular carcinoma (HCC) remains a major global health burden, characterized by late diagnosis, limited therapeutic options, and high mortality rates. Conventional diagnostic tools such as serum α-fetoprotein testing and imaging lack sufficient sensitivity for early detection. In recent years, liquid biopsy has emerged as a minimally invasive approach that enables real-time molecular profiling of tumors through the analysis of circulating biomarkers such as nucleic acids, proteins, and extracellular vesicles. Recent advances have underscored exosomes—nano-sized extracellular vesicles (EVs) secreted by nearly all cell types—as pivotal mediators of intercellular communication and dynamic carriers of tumor-derived molecular information, offering exciting prospects for early cancer detection and personalized therapy. In HCC, EV microRNAs (miRNAs) participate in multiple oncogenic processes, including proliferation, angiogenesis, epithelial–mesenchymal transition, and immune modulation. Specific EV-associated miRNAs, such as miR-21, miR-122, miR-224, and miR-221, show distinctive expression profiles in HCC and correlate with tumor stage, metastasis, and patient prognosis. Moreover, panels of circulating EV-associated miRNAs demonstrate superior diagnostic accuracy compared with traditional biomarkers, underscoring their potential as non-invasive tools for early detection and disease monitoring. Their inherent stability in biofluids and resistance to enzymatic degradation further support their application in liquid biopsy approaches. Despite promising results, continued research is essential to validate EV-associated miRNA signatures and to integrate these “silent messengers” into routine clinical practice for precision management of hepatocellular carcinoma. Full article

Background: Atrial fibrillation (AF) is a common arrhythmia with a high incidence, and patients with chronic obstructive pulmonary disease (COPD) are at particularly high risk. However, there are currently no tools available for early risk stratification of AF in this population. Objectives: To develop and validate a neural network diagnostic model based on transthoracic echocardiography to address two clinical challenges in patients with COPD: risk stratification for AF; and detection of occult supraventricular arrhythmias (including “micro-AF”) based on 24 h ECG monitoring data. Methods: The study consisted of three consecutive stages: development of a neural network (NN) based on transthoracic echocardiography (TTE) parameters, validation of the model’s predictive ability in patients (n = 311, including 99 with COPD), and assessment of the ability to detect occult atrial arrhythmias (n=207) in patients with COPD. The model architecture consists of a fully connected multilayer perceptron (MLP) with 13 inputs, 4 hidden layers of 130 neurons each, and 2 output neurons. Training was performed on 684 TTE scans (292 without AF, 392 with AF). The echocardiographic parameters were validated on an independent test set (n = 100). Statistical analysis included pairwise and multiple comparisons, logistic regression analysis, and ROC analysis with assessment of the area under the ROC curve (AUC). The median follow-up period for study participants was 18 months. Results: The neural network demonstrated high classification metrics for AF on the test set (AUC = 0.80). A threshold value of the first output layer neuron > 0.75 allowed for the identification of a high-risk subgroup, in which the incidence of AF in patients with COPD was 14.8% versus 0% in the low-risk subgroup (p = 0.0073). Logistic regression models of the relationship between AF development and the neural network output value were statistically significant in both patients with COPD and patients without COPD (p < 0.0001). In patients with COPD without a history of AF, the neural network identified a high-risk group. In this group, 24 h ECG monitoring more frequently recorded episodes of AF, group supraventricular extrasystoles, and the combined endpoint (AF + GSE) compared to the low-risk group (55.32% vs. 17.5%; p < 0.0001). The area under the ROC curve for detecting latent AF in patients with sinus rhythm based on the neural network prediction was 0.93. Conclusions: The developed neural network model, which integrates a set of TTE parameters into a single quantitative measure of the severity of myocardial remodeling, is an effective tool for risk stratification for AF. The model may help identify COPD patients who could benefit from intensified rhythm monitoring; however, external validation is required before clinical implementation. Full article

Background/Objectives: Barley malting is an agro-industrial process that produces malt, an essential ingredient for the brewing and distilling industries. Previously, tran-scriptome profiling has revealed mRNA changes during malting but less is known about their regulation. Methods: The spring 2-row barley variety ‘Conrad’ was sampled at five stages of malt-ing. Using small RNA (sRNA)-sequencing and degradome-sequencing data from these malting stages, de novo discovery of mature microRNA (miRNA), as well as cognate mRNAs targeted for slicing, was identified. ShortStack v4.1.0 was used to map sRNA reads to the Hordeum vulgare Morex V3 genome. Results: In total, 33 expressed MIRs were identified, six of which may be novel. Using the degradome-sequencing data from the same malting stages, CleaveLand4 v4.5 pre-dicted 64 sliced mRNA targets, predominantly transcription factors associated with root development. Conclusions: This study provides an overview of post-transcriptional modulations of miRNAs-cognate mRNA targets, as well as plausible interactions between miRNAs during barley malting. Full article

Background: Breast cancer is the most common cancer in women both in Peru and worldwide. Although mammography remains the standard for early detection, its effectiveness may be limited by unequal access to this technology. In this context, liquid biopsy emerges as a minimally invasive complementary technique that allows the identification of circulating biomarkers, such as microRNAs (miRNAs), whose differential expression has been associated with breast cancer. Methods: The present study evaluated the diagnostic potential of cell-free circulating miRNAs for the early detection of breast cancer. In the screening phase, seven candidate miRNAs were quantified by qPCR in plasma from patients with early-stage breast cancer and healthy controls. In the validation phase, miR-191, miR-182, miR-335, and miR-125b were analyzed in an independent cohort of 30 untreated patients to evaluate their diagnostic performance. Results: miR-125b and miR-335 showed the best individual diagnostic performance, with AUCs of 0.81 and 0.78, respectively, and presented a significant moderate correlation (ρ = 0.608; p < 0.05), supporting their biological consistency and potential as complementary biomarkers. The multivariable binary logistic regression model that integrated both miRNAs showed a moderate improvement in discriminatory ability; however, the expanded multivariable model that incorporated the four validated miRNAs achieved an AUC of 0.91, with a sensitivity of 92% and a specificity of 90%. Conclusions: The panel composed of miR-191, miR-182, miR-335, and miR-125b represents a promising set of circulating biomarkers for the early detection of breast cancer. Full article

The most recent research has demonstrated that oscillatory nano-structures found on the lumenal walls of deep cerebral veins likely contribute significantly to the regulation of the function of deep cerebral veins. The oscillatory nano-structures consist of very small, intricately organized “nanoflaps,” each consisting of a hinge element with an attached lipid bilayer architecture. These nanoflaps have distinct mechanical properties, are in close proximity to mechanically sensitive protein assemblies, and therefore it is hypothesized that the nanoflaps generate rhythmic oscillations that control the distribution of both pressure and fluid flow through the veins and also regulate the metabolic condition of the surrounding tissue. In addition, the behavior of the nanoflaps indicate that there exists a hitherto unappreciated level of venous biomechanics at the nanometer scale that regulates the hydraulic stability of the veins and may also contribute to the structural integrity of the surrounding tissues. The purpose of this review is to provide a theoretical framework for understanding the recent discoveries of the structure, oscillation and hydrodynamic effects of nanoflaps, including resonance drift, waveform irregularity, and multi-scale biomechanical interactions. Additionally, this review will present the idea that disruption of the normal oscillatory processes that occur in the nanoflaps may lead to the development of abnormal micro-environments in the early stages of neurodegenerative diseases, abnormalities of compliance, dysautonomic states, traumatic injury and micro-circulatory stress. Finally, this review will describe several pharmacological strategies that may be used to stabilize the oscillations generated by the nanometer-scale oscillatory nano-structure by modifying the torque applied to the hinge, the viscoelasticity of the membrane and the feedback pathways for mechanotransduction. Full article

High-speed permanent magnet synchronous motors (PMSMs) used in electric pump-fed liquid rocket engines require stator shielding sleeves to prevent corrosive propellants from causing harm under cyclic pressure. However, metallic sleeves suffer significant losses due to eddy currents. Conversely, pure carbon fiber reinforced polymer (CFRP) sleeves have failed when exposed to 98% H₂O₂. Micro-CT analysis of a failed pump sleeve reveals a four-stage failure mechanism. Manufacturing defects caused matrix cracking, which propagated under pressure and thermal cycling. This progression resulted in the formation of through-thickness leakage paths, which ultimately triggered catalytic decomposition and explosion. To address these issues, an improved dual-layer sleeve is proposed, featuring a 2.5 mm PEEK 450G liner and a 2.0 mm T700S/epoxy CFRP overwrap. Finite Element Analysis (FEA) indicates peak von-Mises stresses of 86.25 MPa and 112.16 MPa, yielding Tsai–Wu safety factors of 2.9 and 1.7. Furthermore, various tests, including immersion, fatigue, burst, hydraulic, and thermal evaluations, demonstrate a burst margin of 2.37× at 7.12 MPa, with only 0.19% increase in mass. This design effectively eliminates leakage pathways while preserving zero eddy-current loss and ensuring a low weight. Full article

Oral squamous cell carcinoma (OSCC) is characterized by consistently high mortality rates (≤60%) despite therapeutic advances. This is attributable to diagnostic delays arising from the asymptomatic early stages and time-consuming protocols. Hence, the establishment of reliable biomarkers for the routine assessment of the oral mucosa is imperative. MicroRNAs (miRNAs), key epigenetic regulators of gene expression, represent ideal candidates given their characteristic dysregulation across different pathologies. Here, we aimed to identify novel OSCC-specific miRNAs for the saliva-based detection of OSCC from the presymptomatic stage of early invasion. Through a multistep bioinformatic workflow, four miRNAs (miR-20b-5p, miR-484, miR-185-5p and miR-181d-5p) were identified as disease-specific since they simultaneously regulated >65% of a panel encompassing the 15 primarily overexpressed oncogenes in OSCC and a stage-specific panel including the six upregulated genes that genetically define the malignant stages of sequential oral carcinogenesis. The salivary expression of the identified miRNAs was studied in 31 OSCC patients and 31 healthy controls, using quantitative real-time PCR, followed by statistical analysis and an evaluation of the diagnostic accuracy. All studied miRNAs were significantly downregulated in the saliva of OSCC patients compared to controls (miR-484, p < 0.001; miR-181d-5p, p < 0.001; miR-185, p = 0.008; miR-20b, p = 0.026) and exhibited combinatory diagnostic performance of 95.4% (p < 0.001) for OSCC detection. Their expression remained uninfluenced by lifestyle and clinicopathological parameters, including smoking/alcohol, tumor site, grade and disease stage. The proposed 4-miRNA panel exhibits high diagnostic performance for the early, saliva-based detection of OSCC, irrespective of histopathological and lifestyle confounders, highlighting its potential as a robust non-invasive screening tool. Full article

Characteristic gas-based detection technology can facilitate the warning of lithium-ion battery thermal runaway with a high accuracy at an early stage. Microelectromechanical system (MEMS) metal–oxide–semiconductor (MOS) gas sensors have advantages of a low cost, a high accuracy, and low power consumption; therefore, they are ideal candidates for the lithium-ion battery thermal-runaway warning. MEMS MOS gas sensors are composed of a micro-hotplate and gas-sensitive materials. The micro-hotplate component strongly influences the device’s mechanical and thermal properties. Initially, we used COMSOL to optimize the micro-hotplate component. Then, we fabricated the device based on the optimal micro-hotplate. Next, gas-sensitive materials made of ZnO and ZnO-Au were deposited on the micro-hotplate by radio-frequency magnetic sputtering. The self-made and commercial MEMS MOS sensors were integrated to form an electronic nose. The as-made electronic nose can classify hydrogen, ethylene, acetylene, methane, carbon monoxide, and ethanol with a maximum accuracy of 99.4% using gas response data acquired over only 20 s. The reported work can provide a solution for an early and accurate lithium-ion battery thermal runaway warning. Full article

Accurately predicting the capacity degradation of lithium-ion batteries is crucial for ensuring the reliability and safety of electric vehicles and energy storage systems. However, existing methods—including those based on physical principles, deep learning, and traditional machine learning—all face challenges in balancing accuracy, computational efficiency, and adaptability to non-linear aging dynamics. This study proposes a new framework that combines multi-scale data preprocessing and a divide-and-conquer strategy to address these limitations. Firstly, a hybrid Wavelet–SG filter is applied to suppress noise, and a set of specialized XGBoost micro models is trained, with each model predicting capacity for a specific cycle, enabling precise trajectory prediction at different aging stages. The evaluation on the Toyota-MIT-Stanford dataset (118 batteries under different operating protocols) shows that this method achieves an average MAPE of 1.16% and a maximum of no more than 2.5% on the unfamiliar protocol test set. In terms of accuracy, it achieves performance comparable to CNN, LSTM, and CNN-LSTM benchmarks. Importantly, its parallel architecture enables fast inference (400 milliseconds on CPU), making it suitable for edge deployment in battery management systems. The model also has interpretability consistent with physical laws and can autonomously capture stage-dependent degradation mechanisms. This work provides a reliable, efficient, and interpretable solution for real-world battery health monitoring. Full article

Aneurysmal subarachnoid hemorrhage (aSAH) is a life-threatening cerebrovascular condition characterized by a dynamic clinical course spanning distinct pathophysiological stages, including aneurysm rupture, early brain injury (EBI), delayed cerebral vasospasm, and long-term neurological outcome. Despite extensive research, no clinically applicable molecular biomarkers exist to predict disease trajectory across these stages. MicroRNAs (miRNAs), small non-coding RNA molecules detectable in blood and cerebrospinal fluid (CSF), have emerged as promising candidates due to their stability and close association with vascular, inflammatory, and neuronal processes. However, existing studies have largely evaluated miRNAs in isolation, without integrating findings into a unified temporal framework. This review provides a structured, translational synthesis of miRNA dynamics in aSAH and proposes a stage-specific conceptual model integrating prospective clinical evidence with the broader literature. Dual-biofluid profiling has identified miR-29a, miR-200a-3p, and miR-451a as robust rupture-associated biomarkers, with distinct compartment-specific expression patterns. CSF-based profiling has demonstrated that miR-221-3p, miR-9-3p, and miR-183-5p predict vasospasm within 24 h of hemorrhage, while miR-24 and miR-21-5p correlate with disease severity and poor outcome. Integrating these findings with the broader literature, we categorize miRNA signatures across four stages: rupture discrimination, early brain injury, vasospasm prediction, and outcome stratification. This stage-specific framework highlights the biological continuum linking endothelial injury, vascular dysfunction, and secondary brain damage. The proposed model provides a foundation for multi-marker biomarker development, prospective validation studies, and future precision medicine strategies in aSAH. Full article