Search Results (140)

Background: Food allergy is a leading cause of severe allergic reactions in children and often results in restrictive elimination diets. The oral food challenge (OFC) remains the diagnostic gold standard but is resource-intensive and carries a risk of adverse reactions. This study aimed to identify epidemiological, clinical, and laboratory predictors of OFC outcomes and reaction severity in children with suspected immediate-type food allergies. Methods: We conducted a retrospective review of 148 children who underwent hospital-based, open OFCs due to suspected immediate-type food reactions. Data on demographics, comorbidities, characteristics of the initial reaction, sensitisation profiles (specific IgE [sIgE], skin prick test [SPT]), and OFC outcomes were analysed. Reactions were graded using the Ring and Messmer scale. Results: OFC was positive in 44 of 148 children (29.7%). However, no clinical or laboratory parameters—including prior reaction severity and the magnitude of allergy test results—were associated with the severity of reactions during OFC. Comorbidities—specifically asthma, atopic dermatitis, and allergic rhinitis—were significantly associated with a positive OFC (p < 0.01), as were elevated sIgE levels and larger SPT wheal diameters (p < 0.01 for both). The optimal thresholds for predicting a positive OFC were 0.73 IU/mL for sIgE and 3.5 mm for SPT. Conclusions: Oral food challenge (OFC) remains essential for confirming food allergies in children. Given that the severity of reactions during OFCs cannot be reliably predicted and that low cut-off values of allergy tests were identified for predicting a positive OFC outcome, OFCs should be performed in a controlled and fully equipped medical setting, particularly in children with atopic comorbidities. Full article

Migraine is a prevalent and disabling neurological disorder with multifactorial origins and complex clinical manifestations. While pharmacologic therapies remain the cornerstone of management, a growing body of evidence highlights the role of extracranial peripheral nerve compression as a significant contributor to migraine pathophysiology in selected patients. This recognition has expanded the therapeutic role of plastic surgery, offering anatomically targeted interventions that complement or surpass traditional medical approaches for refractory cases. From a plastic surgeon’s perspective, optimal migraine care begins with accurate identification of clinical patterns, trigger-site mapping, and the judicious use of diagnostic tools such as nerve blocks and botulinum toxin. Surgical decompression techniques, including endoscopic and open approaches, address compression of the supraorbital, supratrochlear, zygomaticotemporal, greater and lesser occipital, auriculotemporal, and intranasal contact-point trigger sites. Adjunctive strategies such as autologous fat grafting further enhance outcomes by providing neuroprotective cushioning and modulating local inflammation through adipose-derived stem cell activity. Recent advances, including neuromodulation technologies, next-generation biologics, and innovations in surgical visualization, underscore the ongoing shift toward precision-based, mechanism-driven therapy. As understanding of migraine heterogeneity deepens, the integration of surgical expertise with modern neuroscience offers a comprehensive and personalized therapeutic framework. Plastic surgeons, equipped with detailed knowledge of peripheral nerve anatomy and minimally invasive techniques, play an increasingly pivotal role in the multidisciplinary management of refractory migraine. Full article

Background: Maternal and newborn morbidity and mortality remain a pressing challenge with uneven progress globally and in Tanzania. The capacity of health facilities to provide quality care is critical to improving outcomes. This study aimed to assess changes in health facilities’ readiness to provide quality maternal and newborn care, and hence aimed to inform improvements in quality-of-care interventions in Tanzania. Methods: A before and after assessment of 28 comprehensive emergency obstetric and newborn care health facilities implementing the Safer Births Bundle of Care package in five regions of Tanzania was carried out in December 2020 and January 2023. We adapted the World Health Organization’s Service Availability and Readiness Assessment tool, which covered amenities, equipment, staff, guidelines, medicines, and diagnostic facilities. Composite readiness scores were calculated for each category and results were compared at the health facility level. For categorical variables, we tested for differences by Fisher’s exact test; for readiness scores, differences were tested by linear fixed and mixed model analyses, considering dependencies within the regions. We used p < 0.05 as our level of significance and measured change from baseline using a paired t-test. Results: The overall readiness improved significantly from 67.6% to 83.7% (p < 0.05). Statistically significant improvements were seen in medical equipment (77.1% to 94.0%), diagnostic/treatment commodities (69.3% to 83.1%), and availability of guidelines (50.8% to 96.7%). Changes in amenities (78.1% to 84.2%) and staff (63.0% to 61.7%) were not significant. The overall readiness improved in all facility types and the change was statistically significant in district hospitals and health centres (p < 0.05). There were significant differences in improvement between regions (p < 0.05) Conclusions: The overall readiness has improved significantly, reflecting a positive change. However, there remains a need for further enhancement, particularly in terms of staffing, to ensure high-quality maternal and newborn care. Authorities should take swift action to address the identified gaps, selecting the most effective and practical interventions while closely monitoring progress in readiness and sustaining the gains. Full article

Feline ascites has been reported to have a poor prognosis, with a median survival time of 21 days (considering all etiologies). However, previous studies included relatively small populations (<100 cases) and there is no literature evaluating the prognosis of all causes of feline ascites within the last 20 years. This study aimed to assess the survival times of a large population of cats presenting with ascites and assess the effect of the effusion cause, signalment, clinicopathological and imaging findings on survival. Data was acquired from the medical record system of a referral hospital (including both referrals and first-opinion emergency cases). Four hundred and ninety-eight cats met the inclusion criteria and 55% of all cases survived to discharge. Median survival time post-discharge was 30.5 days. The cause of the effusion was significantly associated with survival to discharge (p = 0.002). Common etiologies of ascites included neoplasia, septic peritonitis, sterile inflammatory disease, uroperitoneum, hemoperitoneum and cardiac disease. Uroperitoneum cases had the highest rate of survival to discharge (77%), while hemoperitoneum cases had the lowest percentage surviving to discharge (40%). Subjectively assessed effusion volume on imaging was significantly associated with survival to discharge (p = 0.012). Subjective assessment of the effusion volume and effusion assessment via abdominocentesis to help obtain a diagnosis can help guide prognosis and are diagnostics that do not require advanced techniques or specialist equipment, which can provide important prognostic information for cats presenting with ascites. Full article

Electronic noses (E-Noses) equipped with metal-oxide semiconductor (MOS) sensors are promising tools for non-invasive medical diagnostics. Their adoption in clinical practice, however, is limited—among others—by sensor variability across devices, which makes individual calibration necessary. This study presents an approach for the development of a calibration transfer (CT) methodology for urine headspace analysis, involving the design and realization of a dedicated experimental setup and protocol. Partial least squares-discriminant analysis (PLS-DA) models were trained on human urine samples enriched with selected biomarkers to simulate pathological states. Models from a reference (“master”) device were transferred to other (“slave”) units in multiple master–slave configurations using Direct Standardization (DS). To overcome the variability of human urine, synthetic urine recipes were formulated to mimic sensor responses and serve as reproducible transfer samples. Several strategies for selecting transfer samples were evaluated, including the Kennard–Stone algorithm, a DBSCAN-based approach, and random selection. Without CT, classification accuracy on slave devices decreased markedly (37–55%) compared to the master’s performance (79%), whereas applying DS with synthetic standards restored accuracy to 75–80%. These results demonstrate that combining reproducible synthetic standards with DS enables effective model transfer across E-Noses, reducing calibration requirements and supporting their broader applicability in medical diagnostics. Full article

This study introduces an explainable neuro-symbolic and large language model (LLM)-driven framework for intelligent interpretation of corneal topography and precision surgical decision support. In a prospective cohort of 20 eyes, comprehensive IOLMaster 700 reports were analyzed through a four-stage pipeline: (1) automated extraction of key parameters—including corneal curvature, pachymetry, and axial biometry; (2) mapping of these quantitative features onto a curated corneal disease and refractive-surgery knowledge graph; (3) Bayesian probabilistic inference to evaluate early keratoconus and surgical eligibility; and (4) explainable multi-model LLM reporting, employing DeepSeek and GPT-4.0, to generate bilingual physician- and patient-facing narratives. By transforming complex imaging data into transparent reasoning chains, the pipeline delivered case-level outputs within ~95 ± 12 s. When benchmarked against independent evaluations by two senior corneal specialists, the framework achieved 92 ± 4% sensitivity, 94 ± 5% specificity, 93 ± 4% accuracy, and an AUC of 0.95 ± 0.03 for early keratoconus detection, alongside an F1 score of 0.90 ± 0.04 for refractive surgery eligibility. The generated bilingual reports were rated ≥4.8/5 for logical clarity, clinical usefulness, and comprehensibility, with representative cases fully concordant with expert judgment. Comparative benchmarking against baseline CNN and ViT models demonstrated superior diagnostic accuracy (AUC = 0.95 ± 0.03 vs. 0.88 and 0.90, p < 0.05), confirming the added value of the neuro-symbolic reasoning layer. All analyses were executed on a workstation equipped with an NVIDIA RTX 4090 GPU and implemented in Python 3.10/PyTorch 2.2.1 for full reproducibility. By explicitly coupling symbolic medical knowledge with advanced language models and embedding explainable artificial intelligence (XAI) principles throughout data processing, reasoning, and reporting, this framework provides a transparent, rapid, and clinically actionable AI solution. The approach holds significant promise for improving early ectatic disease detection and supporting individualized refractive surgery planning in routine ophthalmic practice. Full article

Background: Artificial intelligence (AI) is increasingly integrated into healthcare, offering transformative potential across diagnostics, treatment, and clinical decision-making. As its adoption grows, understanding how medical doctors perceive and respond to AI, particularly in relation to their specialty, experience, and job security, is critical for effective implementation and acceptance. This study investigates the confidence of medical doctors in AI technologies and their role in healthcare, focusing on the impact of specialty, experience, and perceived job security. Method: A cross-sectional survey was conducted among 187 medical doctors across various specialties in Riyadh, Saudi Arabia, with a final sample of 176 participants. The survey assessed awareness, confidence, and concerns regarding AI integration into clinical practice. The survey was conducted across multiple healthcare hospitals in Riyadh, Saudi Arabia. Hospitals from both public and private sectors were included to ensure a diverse sample of healthcare professionals from different organizational structures. Results: A statistically significant association was found between specialty and confidence level (χ² = 14.5, p = 0.001). Among specialists, the majority (80%) reported high confidence in AI use compared to 45% of general practitioners and 38% of surgeons. Conversely, moderate confidence was most common among surgeons (46%), followed by general practitioners (35%) and specialists (13%). Additionally, participants with 11–20 years of experience reported the highest confidence, whereas those aged 55+ years showed the lowest perceived impact of AI on patient outcomes. Multivariate regression analysis identified specialty as the strongest predictor of AI confidence, with specialists being four times more likely to express high confidence in AI use (β = 0.89, p = 0.001) compared to general practitioners. Job displacement concerns negatively influenced confidence in AI, while age and years of experience had less impactful effects. Conclusions: The study concludes that addressing barriers to AI adoption will be crucial for enhancing its integration into healthcare and improving patient care. These findings underscore the importance of specialty-specific training and highlight the need for targeted educational programs, particularly for lower confidence groups such as general practitioners and surgeons. Lower confidence levels in these groups may result in a hesitant or incorrect use of AI tools, potentially compromising patient safety. Therefore, equipping all healthcare professionals with the necessary knowledge and confidence is essential for the safe and effective use of AI in clinical practice. Full article

New technologies and equipment for medicine analysis and diagnostics have always been critical in clinical medication and pharmaceutical production. Especially in the field of traditional Chinese medicine (TCM) where the chemical composition is not fully clear, cross-device analysis and identification using the same technology can sometimes even lead to misjudgments. Akebia species, capable of inducing heat clearing, diuresis, and anti-inflammatory effects, show great potential in clinical applications. However, the three commonly used species differ in pharmacological effects and therefore should not be used interchangeably. We proposed a method combining LIBS with random forest for species identification and established a modeling and verification scheme across device platforms. Spectra of three Akebia species were collected using two LIBS systems equipped with spectrometers of different resolutions. The data acquired from the low-resolution spectrometer were used for model training, while the data from the high-resolution spectrometers were used for testing. A spectral correction and feature selection (SCFS) method was proposed, in which spectral data were first corrected using a standard lamp, followed by feature selection via analysis of variance (ANOVA) to determine the optimal number of discriminative features. The highest classification accuracy of 80.61% was achieved when 28 features were used. Finally, a post-processing (PP) strategy was applied, where abnormal spectra in the test set were removed using density-based spatial clustering of applications with noise (DBSCAN), resulting in a final classification accuracy of 85.50%. These results demonstrate that the proposed “SCFS-PP” framework effectively enhances the reliability of cross-instrument data utilization and expands the applicability of LIBS in the field of TCM. Full article

The analysis of household health financing vulnerability and its impact on health service utilization during the COVID-19 pandemic remains inadequately explored in Kenya. This study was designed to examine the impact of health financing vulnerability on health services utilization during the COVID-19 period. A health financing vulnerability index (HFVI) was constructed to assess the financial risk that individuals faced in accessing essential health services. A pooled panel probit model was estimated to measure the effect of HFVI on service uptake. The study found a significant negative association between HFVI and health service utilization, indicating that a high level of health financing vulnerability is linked to poor health in periods of emergencies. To address this issue, the study recommends implementation of multiple policy measures during crisis periods, including enhancing social health insurance, providing financial support to vulnerable households, and increasing public expenditure on primary healthcare systems across counties, especially on drugs, referral logistics, personnel, medical equipment, and diagnostic technologies. Full article

Breast cancer detection through non-invasive and accurate techniques remains a critical challenge in medical diagnostics. This study introduces a deep learning-based framework that leverages a microwave radar system equipped with an arc-shaped array of six antennas to estimate key tumor parameters, including position, size, and depth. This research begins with the evolutionary design of an ultra-wideband octagram ring patch antenna optimized for enhanced tumor detection sensitivity in directional near-field coupling scenarios. The antenna is fabricated and experimentally evaluated, with its performance validated through S-parameter measurements, far-field radiation characterization, and efficiency analysis to ensure effective signal propagation and interaction with breast tissue. Specific Absorption Rate (SAR) distributions within breast tissues are comprehensively assessed, and power adjustment strategies are implemented to comply with electromagnetic exposure safety limits. The dataset for the deep learning model comprises simulated self and mutual S-parameters capturing tumor-induced variations over a broad frequency spectrum. A core innovation of this work is the development of the Attention-Based Feature Separation (ABFS) model, which dynamically identifies optimal frequency sub-bands and disentangles discriminative features tailored to each tumor parameter. A multi-branch neural network processes these features to achieve precise tumor localization and size estimation. Compared to conventional attention mechanisms, the proposed ABFS architecture demonstrates superior prediction accuracy and interpretability. The proposed approach achieves high estimation accuracy and computational efficiency in simulation studies, underscoring the promise of integrating deep learning with conformal microwave imaging for safe, effective, and non-invasive breast cancer detection. Full article

Background/Objectives: Gastrointestinal diseases (GID), such as oesophagitis, polyps, and ulcerative colitis, contribute significantly to global morbidity and mortality. Conventional diagnostic methods employing white light imaging (WLI) in wireless capsule endoscopy (WCE) provide limited spectrum information, therefore influencing classification performance. Methods: A new technique called Spectrum Aided Vision Enhancer (SAVE), which converts traditional WLI images into hyperspectral imaging (HSI)-like representations, hence improving diagnostic accuracy. HSI involves the acquisition of image data across numerous wavelengths of light, extending beyond the visible spectrum, to deliver comprehensive information regarding the material composition and attributes of the imaged objects. This technique facilitates improved tissue characterisation, rendering it especially effective for identifying abnormalities in medical imaging. Using a carefully selected dataset consisting of 6000 annotated photos taken from the KVASIR and ETIS-Larib Polyp Database, this work classifies normal, ulcers, polyps, and oesophagitis. The performance of both the original WLI and SAVE transformed images was assessed using advanced deep learning architectures. The principal outcome was the overall classification accuracy for normal, ulcer, polyp, and oesophagitis categories, contrasting SAVE-enhanced images with standard WLI across five deep learning models. Results: The principal outcome of this study was the enhancement of diagnostic accuracy for gastrointestinal disease classification, assessed through classification accuracy, precision, recall, and F1 score. The findings illustrate the efficacy of the SAVE method in improving diagnostic performance without requiring specialised equipment. With the best accuracy of 98% attained using EfficientNetB7, compared to 97% with WLI, experimental data show that SAVE greatly increases classification metrics across all models. With relative improvement from 85% (WLI) to 92% (SAVE), VGG16 showed the highest accuracy. Conclusions: These results confirm that the SAVE algorithm significantly improves the early identification and classification of GID, therefore providing a potential development towards more accurate, non-invasive GID diagnostics with WCE. Full article

The integration of artificial intelligence (AI) with wearable bioelectronics is revolutionizing digital healthcare by enabling proactive, personalized, and data-driven medical solutions. These advanced devices, equipped with multimodal sensors and AI-powered analytics, facilitate real-time monitoring of physiological and biochemical parameters—such as cardiac activity, glucose levels, and biomarkers—allowing for early disease detection, chronic condition management, and precision therapeutics. By shifting healthcare from reactive to preventive paradigms, AI-driven wearables address critical challenges, including rising chronic disease burdens, aging populations, and healthcare accessibility gaps. However, their widespread adoption faces technical, ethical, and regulatory hurdles, such as data interoperability, privacy concerns, algorithmic bias, and the need for robust clinical validation. This review comprehensively examines the current state of AI-enhanced wearable bioelectronics, covering (1) foundational technologies in sensor design, AI algorithms, and energy-efficient hardware; (2) applications in continuous health monitoring, diagnostics, and personalized interventions; (3) key challenges in scalability, security, and regulatory compliance; and (4) future directions involving 5G, the IoT, and global standardization efforts. We highlight how these technologies could democratize healthcare through remote patient monitoring and resource optimization while emphasizing the imperative of interdisciplinary collaboration to ensure equitable, secure, and clinically impactful deployment. By synthesizing advancements and critical gaps, this review aims to guide researchers, clinicians, and policymakers toward responsible innovation in the next generation of digital healthcare. Full article

Nickel’s durability and catalytic properties make it essential in the aerospace, automotive, electronics, and fuel cell technology industries. Wastewater analysis typically relies on sensitive but costly techniques such as ICP-MS, AAS, and ICP-AES, which require complex equipment and are unsuitable for on-site testing. This study introduces a novel screen-printed electrode array with 16 chemically and, optionally, electrochemically coated Au electrodes. Its electrochemical response to Ni²⁺ was tested using Na₂SO₃ and ChCl-EG deep eutectic solvents as electrolytes. Ni²⁺ solutions were prepared from NiCl₂·6H₂O, NiSO₄·6H₂O, and dry NiCl₂. In Na₂SO₃, the linear detection ranges were 20–196 mM for NiCl₂·6H₂O and 89–329 mM for NiSO₄·6H₂O. High Ni²⁺ concentrations (10–500 mM) were used to simulate industrial conditions. Two linear ranges were observed, likely due to differences in electrochemical behaviour between NiCl₂·6H₂O and NiSO₄·6H₂O, despite the identical Na₂SO₃ electrolyte. Anion effects (Cl⁻ vs. SO₄²⁻) may influence response via complexation or ion pairing. In ChCl-EG, a linear range of 0.5–10 mM (R² = 0.9995) and a detection limit of 1.6 µM were achieved. With a small electrolyte volume (100–200 µL), nickel detection in the nanomole range is possible. A key advantage is the array’s ability to analyze multiple analytes simultaneously via customizable electrode configurations. Future research will focus on nickel detection in industrial wastewater and its potential in the multiplexed analysis of toxic metals. The array also holds promise for medical diagnostics and food safety applications using thiol/Au-based capture molecules. Full article

The increasing demand for accurate and accessible medical imaging has driven efforts to develop technologies that overcome limitations associated with conventional imaging techniques, such as MRI and CT scans. This study presents the design and implementation of an electronic interface for acquiring signals from a piezoelectric ultrasound sensor with the aim of improving image reconstruction quality by addressing electromagnetic interference and speckle noise, two major factors that degrade image fidelity. The proposed interface is installed between the ultrasound transducer and acquisition system, allowing real-time signal capture without altering the medical equipment’s operation. Using a printed circuit board with 110-pin connectors, signals from individual piezoelectric elements were analyzed using an oscilloscope. Results show that noise amplitudes occasionally exceed those of the acoustic echoes, potentially compromising image quality. By enabling direct observation of these signals, the interface facilitates the future development of analog filtering solutions to mitigate high-frequency noise before digital processing. This approach reduces reliance on computationally expensive digital filtering, offering a low-cost, real-time alternative. The findings underscore the potential of the interface to enhance diagnostic accuracy and support further innovation in medical imaging technologies. Full article

Background: Antimicrobial resistance (AMR) is a public health concern worldwide, particularly in low-to-middle-income countries with few antimicrobial stewardship programs and few laboratories equipped for diagnosis. Methods: As point-prevalence surveys (PPSs) are a well-known tool for assessing antimicrobial use, we adjusted standardized Global-PPS for use in two hospitals in Benin and included an analysis based on the 2021 WHO AWaRe classification. Results: Of the 450 patients enrolled, 148 received antimicrobials (AMs) (overall prevalence 32.9%), most of them orally (54.2%). Both hospitals had a high rate of Access and Watch antibiotics use, and both prescribed mainly metronidazole. In four prescriptions, hospital A used a non-recommended association of antibiotics, such as ceftriaxone + sulbactam and ofloxacin + ornidazole. While hospital A prescribed predominantly amoxicillin + clavulanic acid (19/92; 21%) and ceftriaxone (14/92; 15%), hospital B prescribed ampicillin (24/120; 20%) and cefuroxime (14/120; n = 12%). In hospital B, surgical antimicrobial prophylaxis (SAP) was suboptimal. While there were no single-dose prophylaxis prescriptions, all one-day prophylaxis (SP2) involved ampicillin for cesarean sections. In patients in intensive care units, prolonged prophylaxis (>1 day, SP3) accounted for all postoperative prescriptions. Conclusions: These findings highlight the critical need for implementing antimicrobial stewardship programs, expanding diagnostic laboratory capacity to minimize empirical prescribing, and strengthening medical student training to ensure quality and rational antibiotic use, thereby addressing the growing challenge of resistance in resource-limited settings. Full article