Search Results (82)

Feature selection represents a critical step in developing accurate and interpretable models for early breast cancer detection. Despite extensive research in the field of mammographic image analysis, no consensus has yet been reached on the optimal feature subsets that distinguish normal from malignant tissues. To address this gap, the present study aims to identify the most discriminative and significant features through a comprehensive multi-criterion selection framework. The aim is to integrate, as new frameworks, different combinations of t-test, ANOVA, Mutual Information (MI), and Equal Grouping Methods (EGM) to rank 19 linear and nonlinear features extracted from mammographic images. The objective is to maximize feature relevance while minimizing redundancy and enhancing diagnostic and healthcare systems. Linear features were assessed alongside nonlinear descriptors. A framework combining t-test, ANOVA, and EGM, guided by MI relevance, was employed to balance feature contributions across categories. The experimental results demonstrated that hybrid feature selection significantly enhanced diagnostic accuracy using optimal linear and nonlinear attributes. The optimization results suggested using a hybrid of six linear and eight nonlinear features. Linear features were highly accurate for detecting cancer. Haralick entropy obtained the highest average accuracy and performance, 94.14% and 93.45%; followed by kurtosis, 93.49% and 92.59%; perimeter irregularity, 93.43% and 92.65%; skewness, 93.01% and 92.25%; and volume/area, 92.82% and 91.92%. Despite the reliable discriminative power of linear descriptors, their overall effectiveness in representing intricate tissue characteristics was limited. The comparison of statistical characteristics shows a distinct performance benefit of nonlinear descriptors over linear ones for detecting breast cancer. Nonlinear descriptors, however, showcased higher accuracy and performance, with an average accuracy of 97.81% in contrast to 94.43% for linear approaches. Local phase congruency achieved the top average accuracy and performance, 97.81% and 96.61%, respectively; succeeded by wavelet entropy, 97.62% and 96.42%; Laplacian spectrum features, 97.52% and 96.32%; nonlinear diffusion, 97.10% and 95.90%; and clustering coefficient, 96.70% and 95.50%; then Shannon, Tsallis, and Rényi entropies. The results indicate that statistically validated nonlinear characteristics significantly outperform linear ones across accuracy and performance measures. Their combination significantly improves the strength and discriminative power of computer-assisted breast cancer diagnostic systems, affirming their suitability for integration into sophisticated machine learning and deep learning models. The results also show that the new multi-criterion framework’s early detection performance surpassed that of the statistical and deep learning models explored, with an average of 98.6% accuracy, 98% sensitivity, 98.9% precision, and 98.4% F1 score of early detection of breast cancer. The incorporation of statistically validated nonlinear descriptors, particularly local phase congruency and wavelet entropy, improves the discriminative ability, robustness, and clinical understanding of breast cancer computer-assisted diagnostic systems. Overall, the proposed framework confirms that integrating hybrid features substantially enhances robustness and plays a pivotal role in computer-assisted breast cancer detection. These selected features may be fed to more advanced algorithms in the future, potentially yielding improved performance. Full article

Macrophages are highly plastic innate immune cells that adopt context-dependent phenotypes along a continuum, integrating developmental origin with local microenvironmental cues rather than conforming to discrete M1/M2 states. This review delineates the molecular circuits shaping macrophage identity—TLR/cytokine signaling, microRNA networks, metabolic rewiring, and epigenetic mechanisms including histone lactylation—and traces how circulating monocyte subsets contribute to tissue macrophage diversity. We examine macrophage plasticity across a broad disease spectrum—oncology, autoimmune and rheumatic diseases, inflammatory bowel disease, infectious diseases, metabolic disorders, and neurological conditions—showing that the pathogenic phenotype is strikingly context-dependent: for instance, M2-like tumor-associated macrophages promote immune evasion in solid tumors, whereas M1-skewed programs drive tissue damage in autoimmunity. Soluble markers (sCD163, sCD14, soluble mannose receptor) are emerging biomarkers of disease activity and prognosis. High-dimensional flow cytometry and mass cytometry (CyTOF) bridge molecular biology and clinical phenotyping, enabling integrated readouts of surface phenotype, intracellular signaling, and metabolic state. Therapeutic strategies discussed include selective tumor-associated macrophage (TAM) reprogramming, chimeric antigen receptor (CAR)-M cell therapies, and biomaterial-based platforms. Future priorities encompass spatially resolved multi-omics, epigenetic and metabolic targeting, and macrophage-centered vaccine approaches. Standardized cytometry panels will be essential for biomarker-guided stratification and context-specific interventions. Full article

Interelectrode distance is a key structural parameter of passive air purifiers based on the particle sink effect, but its influence on particulate matter (PM) dynamics remains unclear. This study experimentally investigates the concentration evolution of PM in four size ranges (≤1, 1–2.5, 2.5–10, >10 μm) under different interelectrode distances in a confined space, combined with multifractal detrended fluctuation analysis (MF-DFA) and coupling detrended fluctuation analysis (CDFA). Results show a non-monotonic effect of interelectrode distance on PM removal, with an optimal range of 3–4 cm. Submicron PM (≤1 μm) exhibits a short-term rise followed by a decline; the rising phase duration increases with larger interelectrode distance, revealing a competition between fragmentation-induced release of large particles and their capture. MF-DFA indicates that concentration series of each PM size range display typical multifractal behavior, and the skewness direction of the multifractal spectrum varies with interelectrode distance and particle size. CDFA further reveals strong coupled multifractal features among concentration series of the same particle size at different distances, with coupling strength increasing with particle size. The skewness evolution of the coupled multifractal spectrum quantitatively uncovers how the interelectrode distance, through non-monotonic matching between electric field intensity and dust collection area, regulates PM concentrations toward higher or lower values. This study provides a mechanistic basis for optimizing passive air purifier structures. Full article

Cutaneous T-cell lymphoma (CTCL), primarily mycosis fungoides (MF) and Sézary syndrome (SS), has long been characterized as a neoplasm of mature memory T cells, based on monoclonal T-cell receptor (TCR) rearrangements and tissue-resident memory (TRM)/central memory (TCM) T-cell phenotypes. This review synthesizes converging population-genetic, multi-omic, and single-cell evidence to argue that this characterization is incomplete and that a progenitor-based model better accounts for the full spectrum of disease biology. We present evidence that initiating mutations arise in hematopoietic stem or early lymphoid progenitor survive thymic selection, and diversify after TCR assembly, resulting in branched evolution across both blood and skin. In SS, paired analyses reveal > 200 shared variants between CD34⁺ progenitors and Sézary cells, as well as signal-joint T-cell receptor excision circle (sjTREC) positivity, providing direct progenitor-level evidence. In MF, convergent signals, multiple malignant clonotypes per lesion, greater blood–skin than skin–skin clonotype overlap, and compartment-specific CNV subclones, implicate hematogenous seeding and reseeding. Population-scale lymphoid clonal hematopoiesis and lymphoid-pattern mosaic chromosomal alterations define a compatible antecedent state. Spatial single-cell atlases and trajectory analyses map site-conditioned programs in skin, including Th2-skewed cytokines, microbial responses, and UV signatures, that select and expand subclones and explain inter- and intra-patient heterogeneity. This framework reconciles mature immunophenotypes with upstream initiation and clarifies why compartment-focused therapies often reshape rather than eradicate disease. It yields testable predictions and actionable implications: trials should pair multicompartment cytoreduction with strategies that attenuate progenitor-derived reservoirs, restore immune balance, and repair skin barrier dysfunction. A progenitor-initiated, niche-adapted model provides a coherent scaffold for more durable control in CTCL. Full article

The genus Trollius L. remains insufficiently studied in Kazakhstan, necessitating comprehensive monitoring of its distribution and population status assessments. In Kazakhstan, this genus is represented by the following five species: T. asiaticus, T. altaicus, T. dschungaricus, T. lilacinus and T. komarovii. Of those, T. altaicus and T. dschungaricus are the most widely distributed. This study focuses on analyzing the population structure of Trollius altaicus and Trollius dschungaricus in relation to varying ecological and geographical conditions within Kazakhstan, along with conducting a comprehensive morphometric assessment. To study plant communities with Trollius L. species, classical geobotanical methods were applied, including the route-reconnaissance method to determine the species’ range and carry out a detailed population survey which involved the assessment of the age structure of populations and species composition of associated vegetation. Population structure analysis showed that the majority of T. altaicus plants were in the generative stage, with the right skewed age spectrum suggesting a decline in population size. Meanwhile, populations of T. dschungaricus were dominated by juvenile and virginal individuals, with the left skewed age spectrum suggesting a high regenerative potential. The morphometric analysis revealed high variability of plant height, number of leaves, flower diameter, diameter of generative bushes, number of basal leaves, and leaf length and width. The obtained results can serve as a basis for developing effective conservation and management strategies for T. altaicus and T. dschungaricus under ongoing climate change and anthropogenic impact. This research demonstrates that a detailed assessment of phenotypic characteristics is vital for formulating preservation frameworks and managing biological resources sustainably. Full article

Hail is one of the typical manifestations of severe convective weather, characterized by its sudden onset and strong localization. In this study, a compact all-fiber coherent Doppler lidar (CDL) working at the 1.5 μm wavelength is employed to detect a hail event. Combined with ERA5 reanalysis data, Parsivel², and cloud-type products from the Fengyun satellite, the synoptic background of the hail event was analyzed. Owing to its high-precision spectrum measurement capability, the CDL can effectively separate the multi-component power spectra of precipitation particles. By comparing particle velocity, spectrum width and skewness as characteristic parameters from signal separation across light rain, hail and heavy rain, the distinctive power spectrum characteristics of hail were identified. This study verifies that CDL can provide high-spatiotemporal-resolution data support for the short-term forecasting of hail events. Full article

Solute carrier family 25 member 39 (SLC25A39) is a pivotal mitochondrial glutathione transporter and an emerging oncoprotein in hepatocellular carcinoma (HCC). While its cell-intrinsic roles are increasingly recognized, its comprehensive functions in modulating the tumor immune microenvironment (TIME) and epigenetic landscape within HCC remain undefined. To address this, we employed an integrated multi-omics and experimental approach, including TCGA, ssGSEA, CCK-8, Transwell, etc. Our study confirmed SLC25A39 upregulation and its pro-tumorigenic role. Notably, we provide several key novel insights: First, we establish the first link between SLC25A39 promoter hypermethylation at specific CpG sites and poor patient prognosis, revealing an epigenetic regulatory layer in HCC. Second and most importantly, we pioneer the exploration of SLC25A39 in the HCC immune context, demonstrating its association with a distinct immunosuppressive TIME characterized by a Th2-skewed profile, reduced cytotoxic cell infiltration, and elevated immune checkpoint (CTLA-4, PD-1) expression. Furthermore, drug sensitivity analysis linked SLC25A39 to a broader spectrum of pharmacological agents beyond sorafenib. Collectively, our findings not only reinforce SLC25A39 as a therapeutic target but, for the first time, reposition it as a potential modulator at the intersection of tumor metabolism, epigenetics, and immunology in HCC, offering a rationale for its inhibition, particularly combined with immunotherapy. Full article

Heavy and overloaded freight traffic strongly affects pavement performance, yet short-term weigh-in-motion (WIM) measurements are not easily converted into design-oriented traffic inputs. Using the Nairobi Southern Bypass in Kenya as a case study, this study develops axle load spectrum (ALS) and equivalent single axle load (ESAL) indicators from more than 1.5 million axle group records collected between June and December 2025 and proposes an XGBoost-based conditional axle load spectrum (CA-ALS) framework. The data revealed strongly right-skewed load distributions, with a limited number of heavily loaded axle groups dominating pavement damage. Compared with the static ALS by axle group type baseline, the CA-ALS reduced log loss from 2.7563 to 2.6709 in conditional spectrum prediction. In the December 2025 tandem axle benchmark, the CA-ALS increased the ESAL-based verification input by 6.0% at b = 4 and 11.1% at b = 5 relative to the stronger static reference. A legal-load-capped counterfactual analysis further showed that, for all heavy vehicles, observed overloading increased ESAL by 161.0% at b = 4 and 239.4% at b = 5. These results indicate that the CA-ALS provides condition-sensitive traffic inputs for design traffic verification, scenario-based pavement checks, and overload-sensitive evaluation based on short-term WIM observations. Full article

Outlier detection is a fundamental component of data preprocessing and quality monitoring across diverse scientific domains, including engineering, biomedical sciences, and finance. While many variables in controlled environments approximate a normal distribution, real-world data, particularly biological, environmental, and epidemiological measures, are frequently characterized by pronounced right-skewness. To address the shortcomings of conventional methods, this study introduces the Dynamic Threshold for Outlier Detection (DTOD), which reframes outlier detection as a concrete operational workflow. The DTOD framework dynamically adjusts detection thresholds based on a functional relationship between skewness and tail morphology. Validation through large-scale simulation experiments across light-, middle-, and high-skewness levels confirms the method’s versatility. The DTOD proves particularly effective at two ends of the spectrum: enhancing sensitivity for detecting subtle anomalies in light-skewed data while serving as a conservative, high-confidence screening tool that controls false positives in high-skewness environments. In real-world application to North American Association of Central Cancer Registries (NAACCR) data, the method successfully identified outliers with abnormally high unknown tumor size rates in colorectal cancer and maintained a low misclassification rate in highly skewed lung cancer data. Ultimately, the DTOD provides a promising, interpretable solution for improving data quality in skewed scenarios. Full article

Precipitation downscaling aims to enhance coarse-resolution data to higher resolutions. Due to the similarity between downscaling and super-resolution (SR), deep learning-based SR approaches have been increasingly adopted in this domain. However, single-image super-resolution (SISR) methods applied to precipitation data face two main challenges: weak high-frequency signals and highly skewed distributions in precipitation datasets, which often lead to overly smooth reconstructions, failure to capture precipitation extremes, and loss of fine-scale variability with predictions biased toward mean values. To address these issues, we propose EdgeGeoDiff, a two-stage diffusion model for precipitation downscaling that leverages both edge information and geographical priors (e.g., terrain-related factors such as elevation). In the first stage, a residual network reconstructs an initial high-resolution precipitation field with preliminary structural details. In the second stage, edge features extracted using the Laplacian operator, together with geographical priors, guide a diffusion model to generate residuals that enhance fine-scale precipitation structures. Experimental results on real-world precipitation datasets show that EdgeGeoDiff effectively reconstructs fine-scale details while preserving large-scale patterns and outperforms conventional SISR methods in terms of its RMSE, PSNR, SSIM, and CSI, particularly demonstrating superior performance in the high-frequency region of the spectrum. Full article

Metformin has stood as the primary clinical tool for type 2 diabetes for decades, yet its potential reach into oncology and gerontology is only now being critically dissected. This review evaluates how metformin might actually pull the levers of cancer progression and biological aging. Evidence from across various models suggests that the drug works by recalibrating cellular energy homeostasis—specifically by triggering AMPK and dampening the mTOR pathway. This signaling shift ripples through downstream processes like autophagy and oxidative stress regulation, theoretically slowing tumor growth and pushing back against cellular senescence. However, our look at the literature from PubMed, Scopus, and Web of Science shows a messy reality where preclinical success often stalls during clinical translation. Even though observational data point toward lower cancer rates in diabetic cohorts, these “wins” are frequently skewed by clinical confounders and inconsistent data. This makes the leap from metabolic control to a broad-spectrum anti-aging or anticancer therapy a point of serious contention. We argue that only large-scale, randomized trials can truly verify if metformin is safe and effective for non-diabetic populations. In the end, untangling these molecular routes is the only way to see if metformin belongs in future oncological or healthy aging strategies. That being said, at least mechanistically, metformin definitely offers potential that warrants such large-scale research. Full article

T-cell malignancies represent a complex spectrum of clinically and biologically heterogeneous diseases. Effective translational research and drug development are critically dependent on preclinical models that faithfully recapitulate this diversity. This review analyzes the current preclinical landscape, identifying a profound disparity between the clinical spectrum of T-cell neoplasms and the available in vitro tools. We demonstrate that the existing armamentarium of cell lines is heavily skewed, with an abundance of models for T-cell lymphoblastic leukemia/lymphoma (T-ALL), cutaneous T-cell lymphoma (CTCL), and anaplastic large cell lymphoma (ALCL). This skew is a direct result of a biological selection bias, as these entities are often driven by potent, TME-independent oncogenes (e.g., NOTCH1 mutations, NPM1-ALK fusions) conducive to immortalization. Conversely, the majority of peripheral T-cell lymphoma (PTCL) subtypes, which are frequently TME-dependent and clinically aggressive, remain “preclinical orphans” with few or no authenticated models. This “preclinical void” constitutes a major bottleneck, impeding mechanistic studies and therapeutic progress. We discuss the limitations of 2D cultures and highlight the necessity of adopting advanced platforms, such as patient-derived xenografts (PDX) and 3D organoid systems. These “avatar” models preserve vital tumor heterogeneity and microenvironmental context, offering superior predictive value. The systematic development and integration of these next-generation models are essential to bridge the translational gap and advance precision medicine for all patients with T-cell malignancies. Full article

Background/Objectives: Oral infections are caused by a wide spectrum of bacterial and fungal species and remain clinically challenging, particularly against the background of increasing antimicrobial resistance and efforts to reduce antibiotic use in dentistry. Platelet concentrates are widely applied in periodontal and oral surgery due to their regenerative and immunomodulatory properties, and accumulating evidence suggests additional antimicrobial effects. This study evaluated the antimicrobial activity of platelet-rich plasma (PRP), platelet-rich fibrin (PRF), and injectable PRF (i-PRF) against clinically relevant oral microorganisms. Methods: PRP, PRF, and i-PRF were prepared from venous blood of five healthy donors and evaluated using diffusion-dependent, qualitative-semiquantitative agar diffusion assays against Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, Prevotella intermedia, Staphylococcus aureus, Streptococcus mutans, Streptococcus mitis, Enterococcus faecalis, and Candida albicans, with inhibition zones assessed after species-specific incubation times. Chlorhexidine (2%) and amoxicillin served as positive controls and NaCl (0.9%) as negative control. Inhibition zones were digitally quantified and analyzed using non-parametric statistics (Kruskal–Wallis, Friedmann) due to skewed distributions and frequent zero values. Results: All platelet concentrates demonstrated microorganism-dependent inhibition zones in vitro. Overall, i-PRF demonstrated the strongest inhibitory effect across all pathogens (p < 0.001). Significant differences were detected for E. faecalis and C. albicans, where i-PRF produced markedly larger inhibition zones compared to PRP and PRF. Descriptively, anaerobic periodontal pathogens and S. aureus tended to be more susceptible, while streptococci and C. albicans demonstrated lower inhibition. Conclusions: These findings support a potential adjunctive antimicrobial role of platelet-derived preparations in dental infection management but should be interpreted with caution, as agar diffusion results do not necessarily reflect clinical performance. Full article

Variance-driven decomposition based on the singular spectrum analysis of the European Project for Ice Coring in Antarctica (EPICA) δD, CO₂, and CH₄ records allowed a novel quantitative structural interpretation of all glacial/interglacial cycles and glacial terminations of the last 800 kyr. This bottom-up approach used the response components of EPICA stacked records to reconstruct the envelope of the thermal response through a physical interference model. The aim was to improve understanding of the intensity, amplitude, and asymmetry features of 73 marine isotope stages/substages (MISs) and seven glacial terminations. The Antarctic stack record can be described by a variance-weighted superposition of ten thermal waves of different origins (mid-term oscillation, orbitals, and suborbitals) that stochastically interfere at a given time according to their relative differences in frequency, amplitude, and polarity. Interglacial/glacial stages resulted from constructive interference and bipolar amplification of warming/cooling responses, respectively. The low-intensity MISs (including 90% of substages) and the unbiased-dated terminations fell in the low-interference regions, where dominant destructive patterns minimize the thermal envelope. The positive skewness of the EPICA stack resulted from constructive interference with a strong bias in the warming direction, especially after the Mid-Brunhes Event. Duration analysis of short eccentricity hemicycles exhibited an intrinsic unexpectedly prolonged mean cooling in the nominal solution (5.8 kyr) and its EPICA response as well (8.6 kyr), along with an interference-induced asymmetry (21.1 kyr). The overall effect has led to the saw-tooth shape of glacial cycles, which was strongly induced by interference. Full article

Accurate short-term (10–20 days) natural gas load forecasting is crucial for the “tactical planning” of gas utilities, yet it faces significant challenges from high volatility, strong noise, and the high-dimensional multicollinearity of influencing factors. To address these issues, this paper proposes a novel hybrid forecasting framework: PCCA-ISSA-GRU. The framework first employs Principal Component Correlation Analysis (PCCA), which improves upon traditional PCA by incorporating correlation analysis to effectively select orthogonal features most relevant to the load, resolving multicollinearity. Concurrently, an Improved Singular Spectrum Analysis utilizes statistical criteria (skewness and kurtosis) to adaptively separate signals from Gaussian noise, denoising the historical load sequence. Finally, the dually optimized data is fed into a Gated Recurrent Unit (GRU) neural network for prediction. Validated on real-world data from a large city in Northern China, the PCCA-ISSA-GRU model demonstrated superior performance. For a 20-day forecast horizon, it achieved a Mean Absolute Percentage Error (MAPE) of 6.09%. Results show its accuracy is not only significantly better than single models (BPNN, LSTM, GRU) and classic hybrids (ARIMA-ANN), but also outperforms the state-of-the-art (SOTA) model, Informer, within the 10–20 days tactical window. This superiority was confirmed to be statistically significant by the Diebold–Mariano test (p < 0.05). More importantly, the model exhibited exceptional robustness, with its error increase during extreme weather scenarios (e.g., cold waves, rapid temperature changes) being substantially lower (+56.7%) than that of Informer (+109.2%). The PCCA-ISSA-GRU framework provides a high-precision, highly robust, and cost-effective solution for urban gas short-term load forecasting, offering significant practical value for critical operational decisions and high-risk scenarios. Full article