Search Results (69)

Food allergy diagnosis remains challenging due to the difficulty of distinguishing true clinical allergy from asymptomatic sensitization. Inaccurate diagnosis may result in unnecessary dietary restrictions, reduced quality of life, or, conversely, failure to identify individuals at risk of severe allergic reactions. This review critically analyzes the efficacy, limitations, and clinical utility of currently available diagnostic tests for food allergy, with particular emphasis on their ability to predict true clinical reactivity. A comprehensive literature review was conducted to evaluate the sensitivity, specificity, and predictive values of both traditional and emerging diagnostic modalities. English-language guidelines, systematic reviews, and key clinical studies published primarily within the past 15 years (up to 2025) were identified through PubMed and Google Scholar. Classic diagnostic tools, including skin prick testing (SPT) and serum-specific IgE (sIgE), were assessed alongside novel approaches such as component-resolved diagnostics (CRD), basophil activation test (BAT), mast cell activation test (MAT), atopy patch testing (APT), cytokine profiling, and omics-based diagnostics. Particular attention was given to how these tests compare with the oral food challenge (OFC), which remains the diagnostic gold standard. The findings demonstrate that while conventional tests offer high sensitivity and are valuable for initial risk assessment, their limited specificity often leads to overdiagnosis. Emerging molecular and cellular assays show improved specificity and functional relevance, especially in complex cases involving polysensitization or unclear clinical histories and may reduce reliance on OFCs in the future. However, accessibility, cost, and lack of standardization currently limit their widespread clinical application. Advances in artificial intelligence and data integration hold promise for improving diagnostic accuracy through enhanced interpretation of complex immunological data. Based on the synthesized evidence, this review proposes an evidence-based, stepwise, and individualized diagnostic algorithm for food allergy. Integrating clinical history, targeted testing, and selective use of OFCs can improve diagnostic certainty, enhance food safety, minimize unnecessary dietary avoidance, and optimize patient outcomes. The review underscores the need for continued research, standardization, and validation of novel diagnostic tools to support personalized and precise food allergy management. Full article

Background: Benign Paroxysmal Positional Vertigo (BPPV) is the most common vestibular disorder. Although canalith repositioning procedures (CRPs) typically resolve positional vertigo, several patients still report imbalance or residual dizziness, which remain difficult to quantify with standard tests. Wearable inertial sensors now allow high-resolution, objective gait analysis and may detect subtle vestibular-related impairments. Objectives: This study evaluates the clinical usefulness of sensor-based gait metrics, enhanced by the newly developed φ-bonacci index framework to quantify gait changes and residual dizziness in BPPV before and after CRPs. Methods: Fifteen BPPV patients (BPPV-P) and fifteen age-matched controls completed walking tests under eyes-open (EO) and eyes-closed (EC) conditions using wearable inertial measurement units (IMU). φ-bonacci index components—self-similarity (A1), swing symmetry (A2), and double-support consistency (A4)—were calculated to assess gait harmonicity, symmetry and stability. Results: Before treatment, BPPV-P exhibited significantly higher A1 values than healthy controls (p = 0.038 EO; p = 0.011 EC), indicating impaired gait harmonicity. After CRPs, A1 values normalized to control levels, suggesting restored gait self-similarity. Under visual deprivation, both A1 and A4 showed pronounced increases across all groups, reflecting the contribution of vision to balance control. Among post-treatment patients, those reporting residual dizziness demonstrated persistently elevated A4 values—particularly under EC conditions—indicating incomplete sensory reweighting despite clinical recovery. Conclusions: Wearable sensor–derived φ-bonacci metrics offer sensitive, objective markers of gait abnormalities and residual dizziness in BPPV, supporting their use as digital biomarkers for diagnosis, rehabilitation, and follow-up. Full article

Sporothrix schenckii is a pathogenic fungus with both clinical and environmental origins that was traditionally described as a single species but is increasingly recognized as being genetically diverse. In this study, we analyzed multiple isolates recovered from human sporotrichosis cases and environmental sources across Latin America (Mexico, Guatemala, Colombia). We conducted a polyphasic analysis of 16 isolates, integrating morphological data with multilocus sequence analysis (MLSA) targeting the internal transcribed spacer (ITS), calmodulin (CAL), β-tubulin (BT2), and translation elongation factor 1-α (TEF) gene regions. Phylogenetic relationships were resolved via maximum likelihood, and genetic structure was corroborated via four independent clustering methods: minimum spanning tree, principal component analysis, multidimensional scaling, and self-organizing maps. MLSA reidentified six isolates as S. globosa and confirmed the absence of S. brasiliensis in the cohort. The remaining S. schenckii s. str. isolates were resolved into three clades (A, B, and C). Notably, clade B (EH748, EH194, and EH257) formed a genetically divergent cluster with the highest nucleotide diversity (π = 0.03556) and was consistently segregated by all clustering algorithms. Clinical and environmental isolates were phylogenetically intermingled, supporting an active environmental reservoir for human infections. Phenotypic data, including colony size and conidial and yeast dimensions, varied but did not clearly distinguish between clinical and environmental origins. Our study provides compelling molecular evidence for a previously unrecognized, highly divergent clade within S. schenckii s. str., indicative of ongoing cryptic speciation. These findings refine the taxonomy of medically important Sporothrix species and reveal a distinct epidemiological profile for sporotrichosis in the studied regions, separate from the S. brasiliensis-driven epizootic. This highlights the critical role of molecular surveillance for accurate diagnosis, treatment, and public health strategies. Full article

Background/Objectives: Endometrial carcinoma is an emerging challenge for public health systems globally, especially in countries with a high development index. Traditionally, histopathological staging and grading have been the main criteria informing treatment modalities. More recently, clinically actionable molecular targets have been developed, following observations from the TCGA project and the ProMisE cohort. Although promising, the cost of these methods is an obstacle for some countries that lack well developed theranostics infrastructure in their public systems. This study aimed to contextualize our center’s diagnostic experience from the perspective of histopathological diagnosis. Methods: This is a retrospective study that selected 109 cases of already diagnosed endometrial carcinoma from the interval of 2017–2023. We analyzed traditional parameters related to staging and grading, using the FIGO 2009 system as well as basic histological parameters (lymphovascular invasion, perineural invasion, necrosis). Excel and SPSS 26 were used for database management and correlations. Findings were contextualized using the more recent studies that reported on similar parameters. Results: Higher-grade tumors were associated with lymphovascular invasion (p = 0.04) and lymph node involvement (p = 0.0006), as well as deeper myoinvasion (p = 0.0018). Myoinvasion (p = 0.013) and lymphovascular invasion (p = 0.0001) were associated with advanced disease (FIGO III and IV). Our cohort showed a relative paucity (6.5%) of non-endometrioid endometrial carcinoma and presence of lymphovascular invasion (9.2%). Perineural invasion was found in 3 cases with extrauterine involvement. Conclusions: Histopathological diagnosis represents an integral component in informing clinical management for endometrial carcinoma and should serve as a means of triage for more expensive molecular techniques. It nevertheless presents reproducibility issues. Further efforts should focus on resolving such issues or possibly introducing less-researched parameters like perineural invasion. Full article

In pharmaceutical manufacturing, the reliability of machines and utility assets is critical to ensuring product quality, regulatory compliance, and uninterrupted operations. Traditional Risk-Based Maintenance (RBM) models quantify asset criticality using the Risk Priority Number (RPN), calculated from the probability and impact of failure alongside detectability. However, these models often neglect the practical challenges involved in diagnosing and resolving equipment issues, particularly in GMP-regulated environments. This study proposes an enhanced RPN framework that replaces the conventional detectability component with Maintainability Complexity (MC), quantified through two practical indicators: Ease of Diagnosis (ED) and Ease of Resolution (ER). Thirteen Key Performance Indicators (KPIs) were developed to assess Probability, Impact, and MC across 185 pharmaceutical utility assets. To enable objective risk stratification, Jenks Natural Breaks Optimization was applied to group assets into Low, Medium, and High risk tiers. Both multiplicative and normalized averaging methods were tested for score aggregation, allowing comparative analysis of their impact on prioritization outcomes. The enhanced model produced stronger alignment with operational realities, enabling more accurate asset classification and maintenance scheduling. A 3D risk matrix was introduced to translate scores into proactive strategies, offering traceability and digital compatibility with Computerized Maintenance Management Systems (CMMS). This framework provides a practical, auditable, and scalable approach to maintenance planning, supporting Industry 4.0 readiness in pharmaceutical operations. Full article

The study of qualitative morphological variation is essential for taxonomists and professionals involved in the identification and diagnosis of species of agricultural importance. This becomes particularly critical when quarantine decisions depend on the accurate identification of species belonging to highly diverse genera, poorly reviewed taxonomic groups, or sets of morphologically similar species that lack comprehensive identification keys. Geometric morphometrics has proven to be a powerful tool for resolving taxonomic uncertainties and distinguishing economically significant pest insects, even in the absence of formal taxonomic keys. In this study, we applied geometric morphometrics to analyze pronotum shape variation across 11 species of the genus Acanthocephala, representing nearly half of the currently recognized diversity in the genus, including several species of quarantine relevance to the United States. Our results indicate that principal component analysis accounted for 67% of the total shape variation and identified shape patterns that are useful for distinguishing between several species. Discriminate analysis further supported the differentiation among species, with significant differences confirmed through Mahalanobis distances. Although some species exhibited morphological overlaps, particularly among closely related taxa, most comparisons yielded statistically significant results. These findings demonstrate that the shape of the pronotum is a reliable and informative characteristic for species delimitation within the Acanthocephala group. We propose the use of geometric morphometrics as a reproducible, cost-effective, and robust method for species-level identification in taxonomically complex groups, which has valuable applications in quarantine inspection, pest monitoring, and agricultural biosecurity. Full article

Cultural heritage (CH) relics are irreplaceable records of human civilization, encompassing diverse historical, technological, and artistic achievements. Extracting their structural and compositional information without affecting their physical integrity is a critical challenge. This review summarizes recent advances in non-destructive techniques (NDTs) for CH analysis and emphasizes the balance between the depth of analysis and conservation ethics. Techniques are broadly categorized into spectrum-based, X-ray-based, and digital-based methods. Spectroscopic techniques such as Fourier transform infrared (FTIR), Raman, and nuclear magnetic resonance (NMR) spectroscopy provide molecular-level insights into organic and inorganic components, often requiring minimal or no sampling. X-ray-based techniques, including conventional and spatially resolved XRD/XRF and total reflection XRF (TRXRF), provide powerful means for crystal and elemental analysis, including in situ pigment identification and trace material analysis. Digital-based methods include high-resolution imaging, three-dimensional modeling, data fusion, and AI-driven diagnosis to achieve the non-invasive visualization, monitoring, and virtual restoration of CH assets. This review highlights a methodology shift from traditional molecular-level detection to data-centric and AI-assisted diagnosis, reflecting the paradigm shift in heritage science. Full article

Partial discharge (PD) is a critical indicator of insulation degradation in high-voltage DC systems, necessitating accurate diagnosis to ensure long-term reliability. Conventional AC-based diagnostic methods, such as phase-resolved partial discharge analysis (PRPDA), are ineffective under DC conditions, emphasizing the need for waveform-based analysis. This study presents a novel clustering framework for DC PD pulses, leveraging multi-level wavelet decomposition and statistical feature extraction. Each signal is decomposed into multiple frequency bands, and 70 distinctive waveform features are extracted from each pulse. To mitigate feature redundancy and enhance clustering performance, principal component analysis (PCA) is employed for dimensionality reduction. Experimental data were obtained from multiple defect types and measurement distances using a 22.9 kV cross-linked polyethylene (XLPE) cable system. The proposed method significantly outperformed conventional time-frequency (T-F) mapping techniques, particularly in scenarios involving signal attenuation and mixed noise. Propagation-induced distortion was effectively addressed through multi-resolution analysis. In addition, field noise sources such as HVDC converter switching transients and fluorescent lamp emissions were included to assess robustness. The results confirmed the framework’s capability to distinguish between multiple PD types and noise sources, even in challenging environments. Furthermore, optimal mother wavelet selection and correlation-based feature analysis contributed to improved clustering resolution. This framework supports robust PD classification in practical HVDC diagnostics. The framework can contribute to the development of real-time autonomous monitoring systems for HVDC infrastructure. Future research will explore incorporating temporal deep learning architectures for automated PD-type recognition based on clustered data. Full article

Alternaria alternata is one of the most clinically relevant fungal allergens in pediatric patients with respiratory allergies. Sensitization to this mold has increased in recent decades and is influenced by environmental exposure, geographic location, climate change, and genetic predisposition. In children, Alternaria spp. are strongly associated with the development and worsening of asthma and allergic rhinitis, often contributing to severe and difficult-to-control forms of the disease. The major allergen, Alt a 1, plays a central role in the immunopathogenesis of Alternaria-induced allergies and exhibits molecular features that allow cross-reactivity with other fungal species. Although Alternaria allergy is clinically relevant, its diagnosis remains challenging due to the variability and lack of standardization of fungal extracts. Therefore, it may be necessary to complement traditional diagnostic tools, such as skin prick testing and specific IgE measurement, with component-resolved diagnostics or, in selected cases, nasal provocation tests. Allergen immunotherapy (AIT) has shown promising results in the treatment of Alternaria allergy, particularly with the use of standardized Alt a 1-based extracts or chemically modified allergoids, which offer clinical benefits and immunological modulation. However, AIT is still underused in this context, partly because of the lack of widely available commercial products and long-term efficacy data in the pediatric population. This review provides a comprehensive overview of the current knowledge on the epidemiology, mechanisms, clinical implications, and treatment options related to Alternaria allergy in children, with the aim of supporting early recognition and tailored therapeutic strategies for this important, yet often underestimated, allergen. Full article

The diagnosis of allergic diseases and anaphylaxis is complex and encompasses a broad spectrum of in vitro and in vivo diagnostic tests. The choice of diagnostic tests is related to the presumed pathophysiological mechanism of the allergic reaction. In the past decade the implementation of component-resolved diagnostics (CRD) into clinical practice has significantly improved the depicting of sensitization profiles, which has aided in the assessment of clinically relevant allergen components that are associated with true allergy, as well as the levels of risk of severe anaphylactic reactions. Recently, multiplex-specific immunoglobulin E (IgE) platforms have emerged for better selection of patients at risk for anaphylaxis and have improved the selection criteria for patients undergoing allergen immunotherapy, including novel regimes such as oral immunotherapy. This review describes the advantages of the utilization of component-resolved diagnostics and multiplex assays in clinical settings, especially in cases of anaphylaxis when no clear trigger is recognized or where multiple culprits are suspected. As multiplex component-resolved diagnostics becomes more readily available globally and with the use of novel approaches, CRD will certainly be a crucial tool in personalized and individually tailored management plans and reduce the financial burden of anaphylaxis. Full article

Accurate detection of surface defects such as wear, cracks, and flaws in metallic components is critical for equipment reliability and longevity, representing a core challenge in surface integrity engineering. To solve the information loss, low estimation accuracy and poor noise immunity associated with Multiscale Dispersion Entropy (MDE) are utilized to address the sensitivity to parameter selection and overfitting susceptibility of the Least Squares Twin Support Vector Machines (LSTSVM). A brand new fault diagnosis method which combined Time Shift Multiscale Ensemble Fuzzy Dispersion Entropy (TSMEFuDE) with binary tree LSTSVM (BT LSTSVM) was proposed. Firstly, a time shift method based on Higuchi Fractal Dimension was introduced to TSMEFuDE, resolving the continuity loss between coarse-grained levels. Second, four mapping techniques, linear, NCDF, tansig and logsig, are introduced. This synergetic combination of each advantage results in the improvement of entropy output stability. Furthermore, triangular and trapezoidal membership functions are incorporated into dispersion patterns and abolished in the round function, therefore enhancing the boundaries between the classes after signal mapping to discrete classes. Lastly, the proposed BT LSTSVM algorithm decomposes the multi-classification problem to a binary classification problem, which promotes the robustness of the algorithm. Simulation experiments maintain that TSMEFuDE has stronger adaptability, higher stability, and better noise resistance. In the fault diagnosis experiment, when compared to the Multiscale Fuzzy Dispersion Entropy (MFuDE) combined with the BT TSVM method, the TSMEFuDE combined with BT LSTSVM method improved the accuracy of bearing fault diagnosis by 5.65% and 2.82%. Full article

Automatic segmentation of nuclei on breast cancer histology images is a basic and important step for diagnosis in a computer-aided diagnostic approach and helps pathologists discover cancer early. Nuclei segmentation remains a challenging problem due to cancer biology and the variability of tissue characteristics; thus, their detection in an image is a very tedious and time-consuming task. In this context, overlapping nuclei objects present difficulties in separating them by conventional segmentation methods; thus, active contours can be employed in image segmentation. A major limitation of the active contours method is its inability to resolve image boundaries/edges of intersecting objects and segment multiple overlapping objects as a single object. Therefore, we present a hybrid active contour (connected component + active contours) method to segment cancerous lesions in unsupervised human breast histology images. Initially, this approach prepares and pre-processes data through various augmentation methods to increase the dataset size. Then, a stain normalization technique is applied to these augmented images to isolate nuclei features from tissue structures. Secondly, morphology operation techniques, namely erosion, dilation, opening, and distance transform, are used to highlight foreground and background pixels while removing overlapping regions from the highlighted nuclei objects on the image. Consequently, the connected components method groups these highlighted pixel components with similar intensity values and assigns them to their relevant labeled component to form a binary mask. Once all binary-masked groups have been determined, a deep-learning recurrent neural network (RNN) model from the Keras architecture uses this information to automatically segment nuclei objects having cancerous lesions on the image via the active contours method. This approach, therefore, uses the capabilities of connected components analysis to solve the limitations of the active contour method. This segmentation method is evaluated on an unsupervised, augmented human breast cancer histology dataset of 15,179 images. This proposed method produced a significant evaluation result of $98.71\%$ accuracy score. Full article

Grass pollen allergies significantly contribute to atopic diseases such as asthma and allergic rhinitis, resulting in considerable healthcare burdens. Objective: In this study, molecular sensitization patterns to grass pollen in Swiss patients were addressed. The research utilized a retrospective cohort approach using ImmunoCAP™ ISAC testing from October 2015 to July 2020. Clinical histories, demographics, and skin prick test results were collected for analysis. The minimum patient age was 18 years and the average patient age was 41.3 years, with a female predominance (68.5%). In total, 4814 measurements were analyzed. Allergic rhinitis was the most common clinical symptom, followed by asthma and urticaria. A total of 1963 patients (40.8%) revealed sensitization to grass pollen. The most common sensitizations were found to the major allergens Phl p 1 (86%) and Phl p 5 (65%), but also to Phl p 4 (62%). Monosensitization was mostly found to allergens Phl p 1 (266/13.5%) and Phl p 4 (157/7.9%), and less so to Phl p 5 (33/1.7%). Notably, the Phl p 4-monosensitized subgroup showed only an 18% positivity rate in skin prick tests and presented mostly with urticaria. This study gives insights into the spectrum of grass pollen allergies in a Central European setting and underscores the possibly underestimated role of Phl p 4 among grass pollen allergens, especially in a subgroup that suffers mainly from seasonal urticaria. Monovalent sensitization to Phl p 4 can also cause seasonal rhinitis and might therefore be missed if only Phl p 1/p 5 are tested. A better understanding of sensitization patterns will further improve diagnosis and treatment options. Full article

Accurate mechanical fault diagnosis of On-Load Tap Changers (OLTCs) remains crucial for power system reliability yet faces challenges from vibration signals’ non-stationary characteristics and limitations of conventional methods. This paper develops a hybrid framework combining metaheuristic-optimized decomposition with hierarchical temporal learning. The methodology employs a Subtraction-Average-Based Optimizer (SABO) to adaptively configure Time-Varying Filtered Empirical Mode Decomposition (TVFEMD), effectively resolving mode mixing through optimized parameter selection. The decomposed components undergo dual-stage temporal processing: A Temporal Convolutional Network (TCN) extracts multi-scale dependencies via dilated convolution architecture, followed by Gated Recurrent Unit (GRU) layers capturing dynamic temporal patterns. An experimental platform was established using a KM-type OLTC to acquire vibration signals under typical mechanical faults, subsequently constructing the dataset. Experimental validation demonstrates superior classification accuracy compared to conventional decomposition–classification approaches in distinguishing complex mechanical anomalies, achieving a classification accuracy of 96.38%. The framework achieves significant accuracy improvement over baseline methods while maintaining computational efficiency, validated through comprehensive mechanical fault simulations. This parameter-adaptive methodology demonstrates enhanced stability in signal decomposition and improved temporal feature discernment, proving particularly effective in handling non-stationary vibration signals under real operational conditions. The results establish practical viability for industrial condition monitoring applications through robust feature extraction and reliable fault pattern recognition. Full article

Background/Objectives: Sensitization to the carbohydrate antigen α-Gal is associated with allergic reactions against different types of food that contain α-Gal (e.g., mammalian meat). This form of allergy is termed α-Gal syndrome (AGS), and the diagnosis is challenging due to delayed symptom onset and cross-reactivity with multiple mammalian products. It is estimated that AGS is underdiagnosed, pointing to an unmet need for patient care. Methods: Sera from patients with suspected AGS based on clinical history were analyzed by ImmunoCAP and the IgE cross-reactivity immune profiling (ICRIP) system specifically developed by us. IgE from patient sera against different forms of α-Gal was analyzed using α-Gal-containing analytes and negative controls. Results: Sera from 33 patients with suspected AGS were analyzed. Sera from 22 patients yielded a clearly positive signal (>0.35 kU/L) for IgE against α-Gal in ImmunoCAP. For 7 of the remaining 11 patients with negative or ambiguous (IgE level between 0.1 and 0.35 kU/L) results in ImmunoCAP, ICRIP analyses supported the suspected association of the allergy symptoms with IgE against α-Gal components. This component-resolved analysis helps the allergist to provide an individual diagnosis for each patient. Conclusions: The diagnosis of AGS is challenging. An interplay between clinical history and lab analysis via ImmunoCAP and the specifically developed ICRIP system helps patients and allergists in establishing the correct diagnosis, thereby preventing accidental exposure and recurrent AGS episodes. Full article