Search Results (27,086)

Quasicrystals are ordered phases without periodicity. They are often found in aluminium and other alloys. They can appear in different sizes. Therefore, several metallographic and characterisation techniques are required to fully determine their shape, size, crystallography, and chemical composition. This review paper gives special attention to identifying quasicrystals in aluminium alloys using classical metallographic techniques, such as etching, deep etching, and particle extraction, which allow the investigation of larger areas by light and scanning electron microscope, giving additional information by combining with complementary high-resolution techniques. Full article

Background: Coronary imaging is increasingly used in chronic total occlusion percutaneous coronary intervention (CTO-PCI), but the impact of different imaging strategies on procedural decisions and outcomes remains unclear. Methods: We retrospectively analyzed 171 consecutive patients undergoing CTO-PCI, stratified by imaging strategy into four groups: angiography-only (n = 48), IVUS-guided (n = 42), CT-guided (n = 40) and CT + IVUS-guided (n = 41). Procedural and in-hospital clinical outcomes were compared. A multivariable logistic regression identified predictors of intense debulking techniques (defined as the use of rotational atherectomy or intravascular lithotripsy). Results: Imaging guidance was associated with progressively longer procedural (p < 0.001) and fluoroscopic time (p = 0.007). Similarly, an increased number of guidewires (p = 0.005) and balloons (p = 0.003) was used in the imaging groups, with the CT + IVUS groups showing the highest features. Regarding stenting characteristics, higher stent length and diameter (p = 0.01) were observed in the imaging groups. In patients with J-CTO score > 2, procedural success rates significantly increased with the use of coronary imaging (p = 0.01). Multivariable analysis showed that both J-CTO score (OR 2.0; 95% CI 1.3–3.0; p = 0.001) and imaging strategies (OR 1.6; 95% CI 1.02–2.4; p = 0.04) independently predicted the use of intense debulking techniques. Importantly, no significant differences were observed in in-hospital complications across groups. Conclusions: The use of coronary imaging, particularly the combination of IVUS and CT, is associated with more complex CTO lesions and led to increased procedural time, fluoroscopic time and more extensive stenting, as well as higher debulking usage. In complex CTO cases, coronary imaging was associated with higher procedural success rates. Imaging strategies independently predicted the need for advanced lesion preparation, beyond anatomical complexity, without compromising safety. Despite higher procedural demands, coronary imaging enables a more tailored and successful approach to CTO-PCI, particularly in complex cases. These findings underscore the pivotal role of multimodal imaging in the procedural planning and optimization of CTO-PCI. Full article

Early and accurate diagnosis of dementia is essential to improving patient outcomes and reducing societal burden. The Mini-Mental State Examination (MMSE) is widely used to assess cognitive function, yet traditional statistical and machine learning approaches often face limitations in capturing nonlinear interactions and subtle decline patterns. This study developed a novel deep learning-based dementia prediction model using MMSE data collected from domestic clinical settings and compared its performance with traditional machine learning models. A notable strength of this work lies in its use of item-level MMSE features combined with explainable AI (SHAP analysis), enabling both high predictive accuracy and clinical interpretability—an advancement over prior approaches that primarily relied on total scores or linear modeling. Data from 164 participants, classified into cognitively normal, mild cognitive impairment (MCI), and dementia groups, were analyzed. Individual MMSE items and total scores were used as input features, and the dataset was divided into training and validation sets (8:2 split). A fully connected neural network with regularization techniques was constructed and evaluated alongside Random Forest and support vector machine (SVM) classifiers. Model performance was assessed using accuracy, F1-score, confusion matrices, and receiver operating characteristic (ROC) curves. The deep learning model achieved the highest performance (accuracy 0.90, F1-score 0.90), surpassing Random Forest (0.86) and SVM (0.82). SHAP analysis identified Q11 (immediate memory), Q12 (calculation), and Q17 (drawing shapes) as the most influential variables, aligning with clinical diagnostic practices. These findings suggest that deep learning not only enhances predictive accuracy but also offers interpretable insights aligned with clinical reasoning, underscoring its potential utility as a reliable tool for early dementia diagnosis. However, the study is limited by the use of data from a single clinical site with a relatively small sample size, which may restrict generalizability. Future research should validate the model using larger, multi-institutional, and multimodal datasets to strengthen clinical applicability and robustness. Full article

Optimally positioning renewable-based distributed generation (DG) units is vital for mitigating technical challenges in active distribution networks (ADNs). With the goal of achieving technical goals such as reduced losses and mitigated unstable voltage, two available optimization methods have been combined for positioning wind-energy DGs: grey wolf optimization (GWO) and opposition-based learning (OBL), which tries out opposite possibilities for each assessed population, thus addressing GWO’s susceptibility to becoming stuck in local optima. This new fusion technique enhances the algorithm’s scrutiny of each area under consideration and reduces the likelihood of premature convergence. Results show that, compared with standard GWO, the proposed OBL-GWO reduced active power losses by up to 95.16%, improved total voltage deviation (TVD) by 99.7%, and increased the minimum bus voltage from 0.907 p.u. to 0.994 p.u. In addition, the voltage stability index (VSI) was also enhanced by nearly 30%. The proposed methodology outperformed both standard GWO on the IEEE 33-bus test system and comparable techniques reported in the literature consistently. By accounting for the uncertainty in wind generation, load demand, and future growth, this framework offers a more reliable and practical planning approach that better reflects real operating conditions. Full article

In this paper, a broadband photoconductive antenna (PCA) with enhanced full-band radiation power is proposed based on dual-frequency complementary technology. In the proposed PCA, dual-frequency metallic bar resonators are combined with the coplanar transmission line. Dual-frequency resonant cascades in the meta-atomic electrodes enable effective manipulation of the dissipated terahertz energy along the coplanar lines of PCAs and efficient scattering of terahertz energy into the far field, thereby enhancing far-field radiation power. To validate the proposed antenna, the prototype of the proposed PCA is manufactured and measured. Compared with the conventional PCA, experimental results indicate that our PCA increases the THz radiation power of the entire radiation frequency band (0.02–1.5 THz) by 4.5 times. In addition, our experiments demonstrate that the proposed PCA overcomes the narrowband resonant response characteristics of traditional methods, significantly improving energy utilization efficiency. This design offers a reproducible and universal approach to effectively harness this dissipated terahertz energy, opening a path to rapidly advancing the practicality of terahertz techniques. Full article

Road traffic crashes (RTCs) have emerged as a major global cause of fatalities, with the number of accident-related deaths rising rapidly each day. To mitigate this issue, it is essential to develop early prediction methods that help drivers and riders understand accident statistics relevant to their region. These methods should consider key factors such as speed limits, compliance with traffic signs and signals, pedestrian crossings, right-of-way rules, weather conditions, driver negligence, fatigue, and the impact of excessive speed on RTC occurrences. Raising awareness of these factors enables individuals to exercise greater caution, thereby contributing to accident prevention. A promising approach to improving road traffic accident severity classification is the stacking ensemble method, which leverages multiple machine learning models. This technique addresses challenges such as imbalanced datasets and high-dimensional features by combining predictions from various base models into a meta-model, ultimately enhancing classification accuracy. The ensemble approach exploits the diverse strengths of different models, capturing multiple aspects of the data to improve predictive performance. The effectiveness of stacking depends on the careful selection of base models with complementary strengths, ensuring robust and reliable predictions. Additionally, advanced feature engineering and selection techniques can further optimize the model’s performance. Within the field of artificial intelligence, various machine learning (ML) techniques have been explored to support decision making in tackling RTC-related issues. These methods aim to generate precise reports and insights. However, the stacking method has demonstrated significantly superior performance compared to existing approaches, making it a valuable tool for improving road safety. Full article

Air pollution is a leading environmental risk that causes respiratory morbidity and mortality. The increasing availability of high-resolution environmental data and air pollution-related health cases have accelerated the use of machine learning models (ML) to estimate environmental exposure–response relationships, forecast health risks and call for the needed policy and practical interventions. Unfortunately, ML models are opaque, in a sense that, it is unclear how these models combine various data inputs to make a concise decision. Thus, limiting its trust and use in clinical matters. Explainable artificial intelligence (xAI) models offer the necessary techniques to ensure transparent and interpretable models. This systematic review explores online data repositories through the lens of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline to synthesize articles from 2020 to 2025. Various inclusion and exclusion criteria were established to narrow the search to a final selection of 92 articles, which were thoroughly reviewed by independent researchers to reduce bias in article assessment. Equally, the ROBINS-I (Risk Of Bias In Non-randomized Studies of Interventions) domain strategy was helpful in further reducing any possible risk in the article assessment and its reproducibility. The findings reveal a growing adoption of ML techniques such as random forests, XGBoost, parallel lightweight diagnosis models and deep neural networks for health risk prediction, with SHAP (SHapley Additive exPlanations) emerging as the dominant technique for these models’ interpretability. The extremely randomized tree (ERT) technique demonstrated optimal performance but lacks explainability. Moreover, the limitations of these models include generalizability, data limitations and policy translation. Conclusion: This review’s outcome suggests limited research on the integration of LIME (Local Interpretable Model-Agnostic Explanations) in the current ML model; it recommends that future research could focus on causal-xAI-ML models. Again, the use of such models in respiratory health issues may be complemented with a medical professional’s opinion. Full article

Aptamers have many advantages, including facile synthesis and a high affinity and good selectivity toward their targets. Therefore, aptamer-based biosensors have become increasingly popular for the detection of different bioanalytical substances. Microchip-based analytical detection platforms offer significant advantages for the detection of different analytes, including their ease of operation, high throughput, cost-effectiveness, and high sensitivity. Aptamer-based signal amplification techniques have been combined with microchips to sensitively detect bioanalytical substances due to their stable reactions, easy operation, and specificity in biomedical science and environmental fields. This review summarizes representative articles about aptamer signal amplification strategies on microchips for the detection of bioanalytical substances, as well as their advantages and challenges for specific applications. We highlight the accomplishments and shortcomings of aptamer signal amplification strategies on microchips and discuss the direction of development and prospects of aptamer signal amplification strategies on microchips. Full article

Liquid biopsies promise major advantages for cancer screening and diagnosis. By detecting biomarkers in peripheral blood samples, liquid biopsies reduce the need for invasive techniques and provide important genetic information integral to the emerging molecular classification of cancers. Unfortunately, the concentrations of most biomarkers, particularly circulating tumour nucleic acids, are vanishingly small—beyond the sensitivity and specificity of most assays. Clustered Regularly Interspaced Short Palindromic Repeats diagnostics (herein labelled ‘CRISPR-Dx’) use gene editing tools to detect, rather than modify, nucleic acids with extremely high specificity. These tools are commonly combined with isothermal nucleic acid amplification to also achieve sensitivities comparable to high-performance laboratory-based techniques, such as digital PCR. CRISPR assays, however, are inherently well suited to adaptation for point-of-care (POC) use, and unlike antigen-based POC assays, are significantly easier and faster to develop. In this review, we summarise current CRISPR-Dx platforms and their analytical potential for cancer biomarker discovery, with an emphasis on enhancing early diagnosis, disease monitoring, point-of-care testing, and supporting cancer therapy. Full article

Background/Objectives: Adenomyosis is increasingly being identified in women of childbearing age as a cause of infertility and adverse pregnancy outcomes. As hysterectomies are not suitable for fertile women, conservative surgical management has become a promising solution. We aimed to synthesize current evidence on conservative uterus-sparing surgical techniques for adenomyosis, focusing on implications for infertility treatment and pregnancy outcomes. Methods: A search of PubMed, Google Scholar, and Europe PMC from 2022 to July 2025 was conducted using combinations of the words “adenomyosis,” “fertility,” “infertility,” “pregnancy outcomes,” “adenomyomectomy,” and “uterine-sparing surgery.” Sixteen high-relevance studies were chosen that included reproductive-aged women who had conservative surgery for adenomyosis. Results: Excisional techniques such as adenomyomectomy yield pregnancy rates of >50% and live birth rates of up to 70% in focal disease, with less success in diffuse disease. Non-excisional approaches—high-intensity focused ultrasound (HIFU), radiofrequency ablation (RFA), and uterine artery embolization (UAE)—yield equivalent pregnancy (40–53%) and live birth (35–74%) rates in selected patients, with fewer surgical complications. Adjunctive hormonal therapy, particularly GnRH agonists, appears to improve outcomes. Risks include placenta accreta spectrum disorders and uterine rupture (≤6%), especially in diffuse adenomyosis. The type of lesion, location, and junctional zone thickness are strong predictors of fertility outcomes. Conclusions: Conservative surgery can augment fertility in appropriately chosen women with adenomyosis, with removal being the preferred treatment for focal disease and non-removal techniques offering encouraging alternatives in mild or intracorporeal disease. The addition of adjunct hormonal therapy and standardized patient selection criteria will optimize results. The lack of European professional society guidelines underscores the need for harmonized protocols in order to standardize the diagnosis, surgery, and reporting of results. Full article

The grassland caterpillar Gynaephora qinghaiensis (Lepidoptera: Lymantriidae) is a dominant pest species in the alpine meadows of the Tibetan Plateau. Elucidating changes in key gene expression patterns will provide molecular insights into the adaptive evolutionary mechanisms of insects. Quantitative real-time PCR (qRT-PCR) is currently the predominant analytical methodology for assessing gene expression levels. However, variability among samples can compromise result reliability. Thus, selecting stably expressed reference genes for target gene normalization under diverse scenarios is critical. To date, suitable reference genes for G. qinghaiensis under varying experimental conditions have remained unidentified. In this study, the transcriptome data of G. qinghaiensis were obtained using the RNA-seq technique, and 13 candidate reference genes were selected. Four independent algorithms—ΔCt, geNorm, NormFinder, and BestKeeper—as well as a comprehensive online platform, RefFinder, were employed to evaluate the stability under six experimental conditions (tissues, developmental stages, sexes, temperatures, starvation, and insecticide treatments). Our findings identified the following optimal reference gene combinations for each experimental condition: RPS18, RPS15, and RPL19 for tissue samples; RPL19, RPS15, and RPL17 across developmental stages; RPS18 and RPS15 for different sexes; RPS8 and EF1-α under varying temperature conditions; RPL17 and RPL15 during starvation; and RPL19 and RPL17 following insecticide treatments. To validate the feasibility of the reference genes, we examined the expression of the target gene HSP60 in different tissues and under different temperatures. Our results established essential reference standards for qRT-PCR with G. qinghaiensis samples, laying the foundation for precise gene expression quantification in the future. Full article

Cancer is one of the most deadly diseases, costing millions of lives and billions of USD every year. There are different ways to identify the biomarkers that can be used to detect cancer types and subtypes. RNA sequencing is steadily taking the lead as the method of choice due to its ability to access global gene expression in biological samples and facilitate more flexible methods and robust analyses. Numerous studies have employed artificial intelligence (AI) and specifically machine learning techniques to detect cancer in its early stages. However, most of the models provided are very specific to particular cancer types and do not generalize. This paper proposes a deep learning and explainable AI (XAI) combined approach to classifying cancer subtypes and a deep learning-based approach for the classification of cancer types using BARRA:CuRDa, an RNA-seq database with 17 datasets for seven cancer types. One architecture is designed to classify cancer subtypes with around 100% accuracy, precision, recall, F1 score, and G-Mean. This architecture outperforms the previous methodologies for all individual datasets. The second architecture is designed to classify multiple cancer types; it classifies eight types within the neighborhood of 87% of validation accuracy, precision, recall, F1 score, and G-Mean. Within the same process, we employ XAI, which identifies 99 genes out of 58,735 input genes that could be potential biomarkers for different cancer types. We also perform Pathway Enrichment Analysis and Visual Analysis to establish the significance and robustness of our methodology. The proposed methodology can classify cancer types and subtypes with robust results and can be extended to other cancer types. Full article

This study presents a comparative analysis of electrocardiogram (ECG) and accelerometer (ACC) data in the context of unsupervised human activity recognition and subject identification. Recordings were obtained from 30 participants performing activities of daily living such as walking, sitting, lying, cleaning the floor, and climbing stairs. Distance-based signal comparison methods and clustering techniques were employed to evaluate the feasibility of each modality, both individually and in combination, to discriminate between individuals and activities. Results indicate that ACC signals provide superior performance in activity recognition (NMI = 0.728, accuracy = 0.817), while ECG signals show higher discriminative power for subject identification (NMI = 0.641, accuracy = 0.500). In contrast, combining ACC and ECG signals yielded lower scores in both tasks, suggesting that multimodal fusion introduced additional variability. These findings highlight the importance of selecting the most appropriate modality depending on the recognition objective and emphasize the challenges associated with multimodal approaches in unsupervised scenarios. Full article

Desertification threatens the sustainability of dryland ecosystems, yet many existing monitoring frameworks rely on static maps, coarse spatial resolution, or lack temporal forecasting capacity. To address these limitations, this study introduces PyGEE-ST-MEDALUS, a novel spatiotemporal framework combining the full MEDALUS desertification model with deep learning (CNN, LSTM, DeepMLP) and machine learning (RF, XGBoost, SVM) techniques on the Google Earth Engine (GEE) platform. Applied across Tebessa Province, Algeria (2001–2028), the framework integrates MODIS and Sentinel-1/-2 data to compute four core indices—climatic, soil, vegetation, and land management quality—and create the Desertification Sensitivity Index (DSI). Unlike prior studies that focus on static or spatial-only MEDALUS implementations, PyGEE-ST-MEDALUS introduces scalable, time-series forecasting, yielding superior predictive performance (R² ≈ 0.96; RMSE < 0.03). Over 71% of the region was classified as having high to very high sensitivity, driven by declining vegetation and thermal stress. Comparative analysis confirms that this study advances the state-of-the-art by integrating interpretable AI, near-real-time satellite analytics, and full MEDALUS indicators into one cloud-based pipeline. These contributions make PyGEE-ST-MEDALUS a transferable, efficient decision-support tool for identifying degradation hotspots, supporting early warning systems, and enabling evidence-based land management in dryland regions. Full article

A group of Spanish experts of different specialties participated in the ENFOCA2 project, promoted by the Spanish Oncology Genitourinary Group (SOGUG), which was designed to provide updated information on current and novel aspects contributing to the optimal care of prostate cancer (PCa) patients. In localized disease, it is important to implement strategic alliances with other institutions for improving adherence to active surveillance in low-risk groups and to explore genetic testing for a better indication of focal therapy. Local control of the disease should be maximized to prevent local failure and biochemical recurrence. In patients with locally advanced disease, with PSMA PET/CT-positive lesions in M0 staging on conventional imaging techniques, therapeutic decisions should be carefully evaluated due to insufficient evidence regarding the gold standard in this setting. In patients with metastatic castration-resistant PCa (mCRPC), assessment of BRCA somatic and germline mutations provides prognostic information and familial cancer risk and informs treatment decisions. Combinations of androgen receptor signaling inhibitor (ARSi) agents and poly-ADP ribose polymerase inhibitors (PARPi) are emerging alternatives for advanced PCa. The oldest segment of PCa patients (>70 years of age) may require geriatric assessment to evaluate physical and functional reserves, tailoring treatment to their individual characteristics and circumstances. The concept of a comprehensive multidisciplinary approach together with inter-center and/or inter-specialty therapeutic alliances should be implemented in the routine care of patients with PCa. Full article