Search Results (955)

Syphilis, which is caused by Treponema pallidum, remains one of the most common congenital infection worldwide and has tremendous consequences for the mother and her developing foetus if left untreated. The complexity of the exposure to this pathogen extends beyond the well-established clinical manifestations, as it can profoundly affect placental histomorphology. This study aimed to compare T. pallidum-exposed placental villi structures with healthy placentae at term to evaluate the histomorphological differences using stereology. In this case-control study conducted at term (38 weeks ± 2 weeks), 78 placentae were collected from the hospital delivery suites, comprising 39 cases (T. pallidum-exposed) and 39 controls (non-exposed), who were gestational age-matched with other potential confounders excluded. Blood samples from the umbilical vein and placental basal plate were tested for syphilis, using rapid diagnostic test (RDT) kits for T. pallidum (TP) antibodies (IgG and IgM) to classify placentae as exposed to T. pallidum (cases) and non-exposed (controls). Tissue sections were prepared and stained with haematoxylin and eosin, and the mean volume densities of syncytial knots, foetal capillaries, syncytial denuded areas, and intervillous spaces were estimated using stereological methods. Statistical analysis was performed to compare the mean values between the case and control groups. Stereological assessment revealed significant differences between the T. pallidum-exposed and non-exposed groups with regard to syncytial knots (p < 0.0001), syncytial denudation (p < 0.0001), and foetal capillaries (p < 0.0001), but no significant difference in the intervillous space was found (p = 0.1592). Therefore, our study shows, for the first time, that the histomorphology of human placental villi appears to be altered by exposure to T. pallidum. It will, therefore, be interesting to determine whether these changes in the placental villi translate into long-term effects on the baby. Full article

Coastal blue carbon ecosystems (traditional types such as mangroves, salt marshes, and seagrass meadows; emerging types such as tidal flats and mariculture) play pivotal roles in capturing and storing atmospheric carbon dioxide. Reliable assessment of the spatial and temporal variation and the carbon storage potential holds immense promise for mitigating climate change. Although previous field surveys and regional assessments have improved the understanding of individual habitats, most studies remain site-specific and short-term; comprehensive, multi-decadal assessments that integrate all major coastal blue carbon systems at the national scale are still scarce for China. In this study, we integrated 30 m Landsat imagery (1992–2022), processed on Google Earth Engine with a random forest classifier; province-specific, literature-derived carbon density data with quantified uncertainty (mean ± standard deviation); and the InVEST model to track coastal China’s mangroves, salt marshes, tidal flats, and mariculture to quantify their associated carbon stocks. Then the GeoDetector was applied to distinguish the natural and anthropogenic drivers of carbon stock change. Results showed rapid and divergent land use change over the past three decades, with mariculture expanded by 44%, becoming the dominant blue carbon land use; whereas tidal flats declined by 39%, mangroves and salt marshes exhibited fluctuating upward trends. National blue carbon stock rose markedly from 74 Mt C in 1992 to 194 Mt C in 2022, with Liaoning, Shandong, and Fujian holding the largest provincial stock; Jiangsu and Guangdong showed higher increasing trends. The Normalized Difference Vegetation Index (NDVI) was the primary driver of spatial variability in carbon stock change (q = 0.63), followed by precipitation and temperature. Synergistic interactions were also detected, e.g., NDVI and precipitation, enhancing the effects beyond those of single factors, which indicates that a wetter climate may boost NDVI’s carbon sequestration. These findings highlight the urgency of strengthening ecological red lines, scaling climate-smart restoration of mangroves and salt marshes, and promoting low-impact mariculture. Our workflow and driver diagnostics provide a transferable template for blue carbon monitoring and evidence-based coastal management frameworks. Full article

Thermal camouflage technologies manipulate heat fluxes to conceal objects from thermographic detection, offering potential solutions for thermal management in high-power-density electronics. Most reported approaches are aimed at scenarios where the target is not a heat source; however, any target with a non-zero temperature emits thermal radiation described by the Stefan–Boltzmann law since the thermal radiation of an object is proportional to the fourth power of its temperature (T⁴). To address this issue, this study proposes a thermal camouflage device that considers the influence of radiative thermal transfer from the target. The underlying principle involves maintaining synchronous heat transfer separately along both the device and background surfaces. Numerical simulation confirms the feasibility of this proposed thermal camouflage strategy. Moreover, by altering some parameters related to the target such as geometry, location, temperature, and surface emissivity, excellent performance can be achieved using this device. This work advances thermal management strategies for high-power electronics and infrared-sensitive systems, with applications in infrared stealth, thermal diagnostics, and energy-efficient heat dissipation. Full article

Background and Objectives: Adequate bone quality is essential for promoting initial bone ingrowth and preventing early migration during cementless total knee arthroplasty (TKA). However, gold-standard criteria for identifying suitable bone strength have yet to be established. Dual-energy computed tomography (DECT)-based volumetric bone mineral density (vBMD) is an emerging tool for assessing bone quality. This study aimed to determine whether DECT-derived vBMD can accurately predict suitable bone strength for cementless TKA. Materials and Methods: A total of 190 patients undergoing primary TKA with a standardized posterior-stabilized implant were prospectively enrolled. Prior to TKA, DECT-derived vBMD was measured in the femoral box region. Actual bone strength was evaluated using an indentation test on resected femoral box specimens. Correlation and linear regression analyses were performed to assess the relationship between DECT vBMD and actual bone strength. Receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) calculations were used to determine the optimal cut-off value and diagnostic accuracy of DECT vBMD in identifying candidates suitable for cementless TKA. Results: DECT-derived vBMD exhibited a strong correlation with actual bone strength (correlation coefficient = 0.719, p < 0.01), while linear regression analysis revealed a moderate association (R² = 0.51, p < 0.01). In addition, it demonstrated excellent diagnostic performance in predicting adequate bone quality for cementless TKA, yielding an AUC of 0.984, with a sensitivity of 91.9% and a specificity of 92.0%. Conclusions: DECT-derived vBMD is a reliable and accurate tool for assessing bone strength around the knee and predicting the suitable bone quality for cementless TKA. Full article

Background: Mammographic microcalcifications (MCs) are a common early radiological finding in breast cancer, but their significance in relation to molecular subtypes, particularly HER2-positive tumors, remains under investigation. Objectives: To evaluate the association between MCs and HER2 status in invasive breast cancer. Methods: A retrospective study was conducted on 185 patients treated at a breast unit between 2018 and 2023. Clinical, histological, and molecular data were analyzed. Logistic regression was used to identify independent predictors of MCs. Results: MCs were present in 27% of HER2-positive patients and 16.15% of HER2-negative patients (p < 0.001). HER2 positivity was the only significant independent predictor (OR = 5.89; 95% CI: 2.42–14.30; p < 0.001). Age, breast density, and histology were not associated. Conclusions: MCs are significantly associated with HER2 positivity and may serve as an early imaging marker of aggressive disease, supporting the integration of radiologic and molecular diagnostics. Full article

This study analyzes the impact of the type of transported material (overburden, lignite, mixture) on the rate of core damage accumulation in Type St conveyor belts in open-pit mines. The research was conducted using the DiagBelt+ diagnostic system, which enables the assessment of belt core condition without dismantling the belt. Data were collected from over 100 conveyor belt loops, covering segments of varying lengths, ages, and operational histories. Damage density and area were assessed, and differences were analyzed depending on the material type. The results indicate that belt age and damage density vary significantly with material type, while the Resurs indicator (percentage of expected operating time) shows no clear dependence on the material type. A multiple regression analysis was also performed to predict failure density based on operational variables, such as Age, Resurs results, Loop Length, and Segment Length. The regression model explains approximately 46% of the variability in damage density, indicating the need for further research to improve predictive accuracy. The study emphasizes the importance of using non-destructive diagnostic systems to optimize maintenance planning and enhance conveyor belt reliability. Full article

Anoplocephala spp. are common equine tapeworm species in Europe, frequently found in grazing horses. Anoplocephala perfoliata is the most pathogenic, clinically significant species associated with gastrointestinal disorders, particularly colic, and can have a fatal outcome in some horses. The aim of this study was to determine the infection prevalence of Anoplocephala spp. in Serbia and to identify relevant risk factors. A total of 173 horses from various regions were tested using a combination of diagnostic methods: coprological analysis via combined sedimentation–flotation and the Mini-FLOTAC technique, as well as serological testing using a commercial ELISA test. The overall prevalence was 38.7%, with a higher number of positive cases being identified by serology. It was demonstrated that coprology and serology are complementary approaches for prevalence studies. Infection risk factors included high stocking density, free-roaming status and co-infection with nematodes, while lower rainfall and temperate climate were associated with reduced risk of tapeworm infection. These findings highlight the challenges of Anoplocephala spp. detection and provide insight into the epidemiology of equine tapeworm infection in Southeastern Europe. Full article

Introduction: Huntington’s disease (HD) disrupts cortico-striato-thalamocortical circuits decades before clinical onset. Electroencephalography (EEG) offers millisecond temporal resolution, low cost, and broad accessibility, yet its mechanistic and biomarker potential in HD remains underexplored. We conducted a mechanistic review to synthesize half a century of EEG findings, identify reproducible electrophysiological signatures, and outline translational next steps. Methods: Two independent reviewers searched PubMed, Scopus, Google Scholar, ResearchGate, and the Cochrane Library (January 1970–April 2025) using the terms “EEG” OR “electroencephalography” AND “Huntington’s disease”. Clinical trials published in English that reported raw EEG (not ERP-only) in human HD gene carriers were eligible. Abstract/title screening, full-text appraisal, and cross-reference mining yielded 22 studies (~700 HD recordings, ~600 controls). We extracted sample characteristics, acquisition protocols, spectral/connectivity metrics, and neuroclinical correlations. Results: Across diverse platforms, a consistent spectral trajectory emerged: (i) presymptomatic carriers show a focal 7–9 Hz (low-alpha) power loss that scales with CAG repeat length; (ii) early-manifest patients exhibit widespread alpha attenuation, delta–theta excess, and a flattened anterior-posterior gradient; (iii) advanced disease is characterized by global slow-wave dominance and low-voltage tracings. Source-resolved studies reveal early alpha hypocoherence and progressive delta/high-beta hypersynchrony, microstate shifts (A/B ↑, C/D ↓), and rising omega complexity. These electrophysiological changes correlate with motor burden, cognitive slowing, sleep fragmentation, and neurovascular uncoupling, and achieve 80–90% diagnostic accuracy in shallow machine-learning pipelines. Conclusions: EEG offers a coherent, stage-sensitive window on HD pathophysiology—from early thalamocortical disinhibition to late network fragmentation—and fulfills key biomarker criteria. Translation now depends on large, longitudinal, multi-center cohorts with harmonized high-density protocols, rigorous artifact control, and linkage to clinical milestones. Such infrastructure will enable the qualification of alpha-band restoration, delta-band hypersynchrony, and neurovascular coupling as pharmacodynamic readouts, fostering precision monitoring and network-targeted therapy in Huntington’s disease. Full article

Malaria remains a major public health issue, especially near hydroelectric dams that often promote mosquito breeding. This study aimed to establish baseline epidemiological data during the construction of the Singrobo–Ahouaty dam to support assessment and decision-making for short- and long-term health impacts on surrounding communities. A cross-sectional survey was carried out in randomly selected households. Blood samples were analyzed using thick/thin smears and rapid diagnostic tests, while sociodemographic and behavioral data were collected via questionnaires. Statistical analyses included chi-square, Mann–Whitney, Kruskal–Wallis tests, and logistic regression. The malaria prevalence was 43.1% (394/915). The parasite density averaged 405.7 parasites/µL. School-age children (6–13 years) showed the highest prevalence (74.3%, p < 0.0001), while younger children (0–5 years) had the highest parasite density (1218.0 parasites/µL, p < 0.0001). Highly elevated infection rates (>51%) occurred in Sokrogbo, N’Dènou, and Amani-Menou, with the highest density in Ahérémou 1 (5663.9 parasites/µL). Risk factors included being an informal worker (ORa = 1.5), working in the raw material sector (ORa = 1.4) or market gardening/rice farming (ORa = 0.9; p = 0.043), and frequent mosquito bites (OR = 0.4; p = 0.017). These results underscore the need for stronger vector control strategies, improved bed net distribution and follow-up, and enhanced intersectoral collaboration in dam-influenced areas to reduce malaria transmission. Full article

Cardiovascular disease remains the world’s leading cause of death, and even when patients reach guideline low-density lipoprotein cholesterol targets, a substantial “residual risk” persists, underscoring the need for more nuanced assessment and intervention. At the same time, rapid advances in high-resolution lipidomics, connected point-of-care diagnostics, and RNA- or gene-based lipid-modifying therapies are transforming what clinicians can measure, monitor, and treat. Integrating multimodal data through machine learning algorithms capable of handling high-dimensional datasets has the potential to improve cardiovascular risk prediction and re-stratification compared to traditional models. This narrative review therefore sets out to (i) trace how these emerging technologies expand our understanding of dyslipidemia beyond the traditional lipid panel, (ii) examine their potential to enable earlier, more personalized and durable cardiovascular risk reduction, and (iii) highlight the scientific, regulatory and ethical hurdles that must be cleared before such innovations can deliver widespread, equitable benefit. Full article

This paper introduces a novel and flexible class of continuous probability distributions, termed the Alpha Power Survival Ratio-X (APSR-X) family. Unlike many existing transformation-based families, the APSR-X class integrates an alpha power transformation with a survival ratio structure, offering a new mechanism for enhancing shape flexibility while maintaining mathematical tractability. This construction enables fine control over both the tail behavior and the symmetry properties, distinguishing it from traditional alpha power or survival-based extensions. We focus on a key member of this family, the two-parameter Alpha Power Survival Ratio Exponential (APSR-Exp) distribution, deriving essential mathematical properties including moments, quantile functions and hazard rate structures. We estimate the model parameters using eight frequentist methods: the maximum likelihood (MLE), maximum product of spacings (MPSE), least squares (LSE), weighted least squares (WLSE), Anderson–Darling (ADE), right-tailed Anderson–Darling (RADE), Cramér–von Mises (CVME) and percentile (PCE) estimation. Through comprehensive Monte Carlo simulations, we evaluate the estimator performance using bias, mean squared error and mean relative error metrics. The proposed APSR-X framework uniquely enables preservation or controlled modification of the symmetry in probability density and hazard rate functions via its shape parameter. This capability is particularly valuable in reliability and survival analyses, where symmetric patterns represent balanced risk profiles while asymmetric shapes capture skewed failure behaviors. We demonstrate the practical utility of the APSR-Exp model through three real-world applications: economic (tax revenue durations), engineering (mechanical repair times) and medical (infection durations) datasets. In all cases, the proposed model achieves a superior fit over that of the conventional alternatives, supported by goodness-of-fit statistics and visual diagnostics. These findings establish the APSR-X family as a unique, symmetry-aware modeling framework for complex lifetime data. Full article

Currently, the visual study of the structure of building materials and products is gradually supplemented by intelligent algorithms based on computer vision technologies. These algorithms are powerful tools for the visual diagnostic analysis of materials and are of great importance in analyzing the quality of production processes and predicting their mechanical properties. This paper considers the process of analyzing the visual structure of non-autoclaved aerated concrete products, namely their porosity, using the YOLOv11 convolutional neural network, with a subsequent prediction of one of the most important properties—thermal conductivity. The object of this study is a database of images of aerated concrete samples obtained under laboratory conditions and under the same photography conditions, supplemented by using the author’s augmentation algorithm (up to 100 photographs). The results of the porosity analysis, obtained in the form of a log-normal distribution of pore sizes, show that the developed computer vision model has a high accuracy of analyzing the porous structure of the material under study: Precision = 0.86 and Recall = 0.88 for detection; precision = 0.86 and recall = 0.91 for segmentation. The Hellinger and Kolmogorov–Smirnov statistical criteria, for determining the belonging of the real distribution and the one obtained using the intelligent algorithm to the same general population show high significance. Subsequent modeling of the material using the ANSYS 2024 R2 Material Designer module, taking into account the stochastic nature of the pore size, allowed us to predict the main characteristics—thermal conductivity and density. Comparison of the predicted results with real data showed an error less than 7%. Full article

Breast cancer remains one of the leading causes of mortality among women worldwide, highlighting the critical need for early and accurate detection methods. Traditional mammography, although widely used, has limitations, including radiation exposure and challenges in detecting early-stage lesions. Electrical Impedance Mammography (EIM) has emerged as a non-invasive and radiation-free alternative that assesses the density and electrical conductivity of breast tissue. EIM images consist of seven layers, each representing different tissue depths, offering a detailed representation of the breast structure. However, analyzing these layers individually can be redundant and complex, making it difficult to identify relevant features for lesion classification. To address this issue, advanced computational techniques are employed for image integration, such as the Root Mean Square ($C_{\mathrm{RMS}}$) Contrast and Contrast-Limited Adaptive Histogram Equalization (CLAHE), combined with the Coefficient of Variation (CV), CLAHE-based fusion, weighted average fusion, Gaussian pyramid fusion, and Wavelet–PCA fusion. Each method enhances the representation of tissue features, optimizing the image quality and diagnostic utility. This study evaluated the impact of these integration techniques on EIM image analysis, aiming to improve the accuracy and reliability of computational diagnostic models for breast cancer detection. According to the obtained results, the best performance was achieved using Wavelet–PCA fusion in combination with XGBoost as a classifier, yielding an accuracy rate of 89.5% and an F1-score of 81.5%. These results are highly encouraging for the further investigation of this topic. Full article

Lithium–ion (Li–ion) batteries are fundamental for advancing intelligent and sustainable transportation, particularly in electric vehicles, due to their long lifespan, high energy density, and strong power efficiency. Ensuring the safety and reliability of EV batteries remains a critical challenge, as undetected faults can lead to hazardous failures or gradual performance degradation. While numerous studies have addressed battery fault detection, most existing reviews adopt isolated perspectives, often overlooking interdisciplinary and intelligent approaches. This paper presents a comprehensive review of advanced battery fault detection using modern machine learning, deep learning, and hybrid methods. It also discusses the pressing challenges in the field, including limited fault data, real-time processing constraints, model adaptability across battery types, and the need for explainable AI. Furthermore, emerging AI approaches such as transformers, graph neural networks, physics-informed models, edge computing, and large language models present new opportunities for intelligent and scalable battery fault detection. Looking ahead, these frameworks, combined with AI-driven strategies, can enhance diagnostic precision, extend battery life, and strengthen safety while enabling proactive fault prevention and building trust in EV systems. Full article

Background/Objectives: Anorexia nervosa (AN) is a chronic eating disorder with the highest mortality rate among psychiatric conditions. Malnutrition and starvation lead to long-term impairments in metabolic processes, hormonal regulation, and immune function, offering potential diagnostic and prognostic value. This study aimed to identify immune–metabolic–hormonal markers associated with treatment response and nutritional rehabilitation. Methods: Fifty hospitalized female patients with AN were included. Anthropometric measurements and venous blood samples were collected at admission and discharge, following partial nutritional recovery. Blood analyses included complete blood count, serum levels of total cholesterol, LDL and HDL, triglycerides, glucose, NT-pro-BNP, TSH, free thyroxine (fT4), sodium, chloride, potassium, calcium, iron, and vitamin D. Composite immune-inflammatory indices calculated were neutrophil-to-lymphocyte (NLR), monocyte-to-lymphocyte (MLR), platelet-to-lymphocyte (PLR); neutrophil-to-high-density lipoprotein (NHR), monocyte-to-high-density lipoprotein (MHR), platelet-to-high-density lipoprotein (PHR) and lymphocyte-to-high-density lipoprotein (LHR) ratios; systemic immune-inflammation (SII), and systemic inflammation response (SIRI) indexes. Results: Responders (R) and non-responders (NR) differed significantly at baseline in levels of sodium, chloride, fT4, monocyte count, MCV, NLR, MLR, SII, and SIRI (all: R < NR; p < 0.05). Predictive ability for treatment response was confirmed by AUC values (95%CI): sodium = 0.791 (0.622–0.960), chloride = 0.820 (0.690–0.950), fT4 = 0.781 (0.591–0.972), monocytes = 0.785 (0.643–0.927), MCV = 0.721 (0.549–0.892), NLR = 0.745 (0.578–0.913), MLR = 0.785 (0.643–0.927), SII = 0.736 (0.562–0.911), SIRI = 0.803 (0.671–0.935). The lower levels of inflammation and chloride are particularly predictive of better nutritional recovery, accounting for 26% of the variability in treatment response. Conclusions: The study demonstrated important insights into the hematological, metabolic, hormonal, and immune-inflammatory mechanisms associated with nutritional recovery in AN. Full article