Search Results (9,438)

Manufacturing enterprises face increasing complexity in managing the complete life cycle of production systems, requiring integration of information from diverse sources to support timely maintenance, diagnostics, and operational decisions. This paper presents a comprehensive service-oriented architecture (SOA) for decision support in industrial life-cycle management, integrating real-time monitoring, predictive maintenance, and collaborative problem-solving across extended manufacturing enterprises. The architecture implements a three-layer service model comprising eight core collaborative services, three application services, and six life-cycle management services, orchestrated through a risk assessment module that monitors life-cycle parameters and triggers appropriate maintenance, diagnostics, or hazard prevention actions. The system was developed in the context of a European research project and validated in two industrial settings: automotive assembly lines at a German SME and air conditioning manufacturing at a Portuguese company. Results demonstrated substantial operational improvements, including reduced problem resolution time, lower diagnostic travel requirements, reduced spare-parts consumption, and increased structured problem registration. The original SOAP-based web-services implementation is further contextualized within the contemporary Industry 4.0 landscape through comparison with microservices architectures and discussion of integration paths involving OPC UA, Asset Administration Shells, and digital twins. The paper contributes a validated reference architecture for service-based industrial life-cycle management and clarifies its relevance as an early precursor of contemporary smart manufacturing approaches. Full article

This review aims to assess the clinical performance and application results of oral wearable devices in in vivo trials. Following a systematic search of PubMed, Cochrane, Embase, and Scopus databases up to 15 October 2025, and strict screening in accordance with PRISMA 2020 guidelines, 13 in vivo human trials were finally included for analysis. These were analyzed across four clinical functions: diagnosis, treatment, monitoring, and prevention. These devices have evolved from bulky prototypes into miniaturized, wireless systems with diverse diagnostic and therapeutic functions. Their applications now extend beyond common conditions like caries and bruxism to postoperative recovery and pediatric dental anxiety intervention. The findings show that some devices already offer practical value for clinical screening and auxiliary diagnosis. They demonstrate significant potential in early disease detection and medical cost control. However, development still faces many challenges. Technical issues include limited battery life, insufficient mechanical durability, and wireless transmission constraints within the oral environment. Furthermore, clinical evidence levels remain low, indications are narrow, and dedicated ethical and regulatory frameworks are lacking. Inconsistent regulatory standards, production costs, and clinician adoption hurdles slow its commercial development. In the future, the integration of AI, breakthroughs in energy harvesting, and the creation of digital health platforms will be key to overcoming technical bottlenecks. Full article

Objective: The integration of artificial intelligence (AI) into computer-aided detection (CAD) is a major innovation in lung cancer diagnosis. However, its reliability in detecting the earliest radiographic sign—faint ground-glass opacities (GGOs) indicating pre-invasive adenocarcinoma—remains a critical, unquantified gap. This study aimed to perform a rigorous failure analysis to define the specific conditions under which commercial AI/CAD systems fail in a low-dose CT (LDCT) screening setting. Methods: In this retrospective diagnostic accuracy study, a primary cohort of 100 patients and an external validation cohort of 50 patients with moderate/low-risk nodules on LDCT were included. An expert reference standard was established by a consensus panel of three thoracic radiologists. Two independent, commercially deployed AI/CAD systems from different vendors (Vendor A & Vendor B) processed all cases. Nodules confirmed by experts but missed by AI were analyzed. Their morphology was categorized, and their mean CT attenuation (HU) was measured via manual region-of-interest placement. Results: The AI systems demonstrated significant and comparable false negative rates in the combined cohort: 12.7% for Vendor A and 14.7% for Vendor B. The vast majority of missed nodules were GGOs (92.3% and 78.6%, respectively, in the primary cohort). Crucially, quantitative analysis revealed a consistent density threshold for AI failure: the mean CT value of missed GGOs was −737 ± 51.50 HU for Vendor A and −727 ± 70.07 HU for Vendor B. This algorithmic blind spot was fully corroborated by the external validation cohort (−741 ± 48.2 HU and −733 ± 62.5 HU, respectively). Anatomical complexity (juxta-pleural/endobronchial location) was a secondary failure factor. Conclusions: This study identifies a quantifiable “−730 HU blind spot” as a common limitation of current commercial AI/CAD systems in diagnosing early lung adenocarcinoma. This finding represents a pivotal advancement in understanding AI’s role in diagnostics: it is not infallible. To innovate and safeguard screening efficacy, radiologists must adopt a human–AI collaborative model with mandated manual verification targeting low-attenuation opacities, ensuring this diagnostic innovation fulfills its promise while mitigating the risks of overdiagnosis. Full article

Current diagnostic assessments of lung disease rely primarily on medical imaging and global pulmonary function tests, which either provide static structural information or collapse complex regional behavior into global indices. As a result, important information about regional mechanical heterogeneity and early pathological changes may remain inaccessible. In this work, we introduce a conceptual diagnostic framework for the lung based on mechanical system identification and evaluate its feasibility using simulation-based analysis. Rather than directly imaging internal lung structure, the lung–thorax system is treated as an identifiable viscoelastic dynamical system whose internal mechanical properties can be inferred from its response to controlled external excitation. A multi-degree-of-freedom mechanical representation of the lung was developed to capture the dominant low-frequency behavior of the chest wall and major lung regions. Sensitivity and Fisher-information analysis confirmed the structural identifiability of regional stiffness parameters (FIM eigenvalues λ₁ = 1.75 × 10⁻⁹ and λ₂ = 8.91 × 10⁻¹⁰). Inverse fitting experiments accurately recovered simulated stiffness perturbations (e.g., k₀₁ = 240 → 239.5; k₀₂ = 154 → 159.5) from noisy frequency response data, while classification experiments achieved the complete separation of simulated pathological configurations in an idealized synthetic scenario, supporting theoretical discriminability rather than clinical performance claims. These findings demonstrate the theoretical feasibility of a diagnostic paradigm in which regional lung mechanical alterations can in principle be identified through mechanical system identification rather than direct imaging, thereby suggesting a complementary approach for a non-invasive assessment of regional lung mechanics from externally measured responses. By quantifying regional stiffness and mechanical heterogeneity, this framework may also support the personalization and monitoring of pulmonary rehabilitation strategies in chronic respiratory disease. Full article

Brain stroke (BS) and heart disease (HD) are leading causes of global mortality and long-term disability, underscoring the critical need for early and accurate diagnostic tools. This research addresses the dual challenge of developing high-performance predictive models while ensuring the privacy of sensitive patient data. We propose a framework that integrates ensemble machine learning (ML) models with a formal differential privacy (DP) mechanism. Using a dataset of 5110 samples with clinical features, we evaluate Extreme Gradient Boosting (XGB), Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Categorical Boosting (CAT) for BS and HD prediction. To protect individual privacy, we apply the Gaussian mechanism of DP with two probabilities of failure (POF) parameters (10–5 and 10–6) and a privacy budget ranging from 0.5 to 5.0. A key novelty of this work is the application of Pareto frontier multi-objective optimization (PFMOO) to systematically identify the optimal trade-off between model accuracy and privacy constraints. Our approach successfully identifies optimal, privacy-preserving models: XGB achieves top performance for BS prediction (92.3% accuracy, 92.29% F1 score), with a POF of 10–6, while RF excels for HD detection (95.61% accuracy, 97.8% precision), with a POF of 10–5. Furthermore, we employ explainable AI (XAI) techniques, SHAP and LIME, to provide interpretability of the model decisions, enhancing clinical trust. This research delivers a robust, interpretable, and privacy-conscious framework for early disease detection, offering a significant advancement over existing methods by holistically balancing accuracy, data security, and transparency. Full article

A rare clinical condition associated with numerous diagnostic and treatment challenges, pregnancy-associated melanoma (PAM), is defined as melanoma diagnosed either during pregnancy or within the first year postpartum. The physiological changes in pregnancy (hormonal changes and immune modulation), along with the normal changes in the pregnant woman’s skin (skin color changes, etc.), may all hinder early detection of this disease and create concerns regarding the advancement of melanoma and the well-being of both the mother and her fetus. The purpose of this review article was to summarize the current literature on the incidence, biology, diagnostic methods and treatments of PAM, with an emphasis on comparison between the two forms of melanoma. More recent research indicates that pregnancy itself is not typically associated with decreased melanoma-specific survival rates. However, when worse results are reported, it appears that this may be more due to delays in initial diagnoses (diagnosis of cancer after delivery) or detection of cancer postpartum, as well as the increased number of stages of melanoma at which women were diagnosed at the time of their first evaluation compared to non-pregnant controls, rather than being a result of enhanced biologic aggressiveness in melanoma driven by pregnancy itself. The preclinical and translational models have suggested that pregnancy may influence melanoma biology through the mechanisms of hormonal signaling, immune system modulation and vascular remodeling; however, these mechanisms remain hypothesis-generating, and current clinical evidence does not indicate that changes in hormone levels during pregnancy negatively affect melanoma survival. Surgical excision is the mainstay of treatment and can be performed safely during pregnancy. In select patients, a sentinel lymph node biopsy may also be performed. Due to the risk of fetal harm, systemic therapy (targeted agents and/or immune checkpoint inhibitors) cannot be used for the treatment of PAM during pregnancy. Post-pregnancy treatment of PAM will follow standard melanoma treatment guidelines; however, the treatment options will need to take into consideration whether or not the patient is breastfeeding and if she desires to become pregnant again in the future. In summary, PAM will require a multidisciplinary, individualized approach to maximize oncologic outcomes while protecting the health of both the mother and her fetus. Awareness of this disease and timely diagnosis are critical to maximizing the prognosis. Full article

Background: Age-related cognitive deccline is a significant health issue in Spain, especially among adults over 60 years of age. Addressing this involves establishing intervention guidelines and identifying early diagnostic biomarkers. Objective: To evaluate changes in urine of Brain-Derived Neurotrophic Factor, concentration and cognitive performance after the implementation of the multicomponent CESPORT program (incorporating a Mediterranean Diet, nutritional education, and continuous support). Methods: This controlled trial included 76 older adults, divided into an experimental group (n = 58; mean age 66.9 years; 75.9% female) that participated in the CESPORT program, and a control group (n = 18; mean age 68.8 years; 72.2% female). Cognitive performance was assessed using the Mini-Mental State Examination (MMSE) and the Cognifit^® battery. Urinary BDNF concentrations were quantified via ELISA. Results: After adjusting for baseline scores via ANCOVA, the experimental group demonstrated significantly higher post-intervention outcomes compared to the control group (p < 0.001). Substantial improvements with medium-to-large effect sizes were observed in global cognition, reasoning, attention, coordination and perception. Furthermore, urinary BDNF levels were significantly elevated in the experimental group. Positive correlations were found between Brain-Derived Neurotrophic Factor concentrations and cognitive performance in multiple domains (p < 0.05), particularly regarding global status and reasoning. Conclusions: The multicomponent CESPORT intervention demonstrates a potential protective effect against age-related cognitive decline. Furthermore, urinary BDNF emerges as a promising, non-invasive early biomarker for cognitive health. Further research is warranted to validate these findings. Full article

Alpha-1 antitrypsin deficiency (AATD) is a genetic disorder characterized by reduced circulating levels and/or impaired function of alpha-1 antitrypsin (AAT), a key serine protease inhibitor, in which loss of effective antiprotease protection results in unchecked neutrophil elastase activity and progressive lung tissue destruction. Although AATD accounts for approximately 1% of chronic obstructive pulmonary disease (COPD) cases and up to 2% of emphysema, AATD-related COPD remains largely underdiagnosed, despite guideline recommendations for systematic evaluation in patients with COPD, particularly in high-risk clinical settings. Pathologically, AATD-related COPD is not limited to the typical early-onset, lower-lobe-predominant emphysema, also including upper-lobe or mixed emphysema patterns, airway-predominant disease, small airways dysfunction, and bronchiectasis. Clinically, AATD-related COPD is distinguished from smoking-related COPD by its earlier onset, physiological impairment that is often disproportionate to smoking exposure, and its potential presence of certain extrapulmonary manifestations. Diagnosis and monitoring are also challenged by the frequent discordance between airflow limitation and gas transfer impairment, as well as the early involvement of small airways, limiting reliance on spirometry alone. A multimodal assessment incorporating more sensitive functional techniques and CT densitometry may provide a more precise evaluation of disease burden, progression, and prognosis. Management generally follows standard COPD principles, with intravenous AAT augmentation therapy remaining currently the only established disease-modifying therapy for selected patients with severe deficiency. The advent of new pharmacological and gene-based therapies emphasizes the importance of developing personalized management strategies that integrate genotype and longitudinal disease behavior. This narrative review summarizes current evidence on AATD-associated COPD, focusing on its genetic basis and pathophysiological features, clinical and functional heterogeneity, current and emerging diagnostic and monitoring approaches, and disease-specific management considerations. Full article

Background and Clinical Significance: Malignant glaucoma, also described as aqueous misdirection syndrome, most commonly occurs following trabeculectomy in patients with primary angle-closure glaucoma and a shallow anterior chamber. Management aims to restore normal anterior segment anatomy, re-establish aqueous flow from the posterior to the anterior chamber, and achieve adequate intraocular pressure control. This report presents a case of malignant glaucoma developing after trabeculectomy, with emphasis on current diagnostic and therapeutic approaches. Case Presentation: A patient with primary angle-closure glaucoma, pseudophakia of the right eye, and a patent laser peripheral iridotomy, receiving maximal tolerated topical antiglaucoma therapy, was referred to the Department of Ophthalmology due to uncontrolled intraocular pressure. The patient was scheduled for trabeculectomy of the right eye. In the immediate postoperative period, intraocular pressure was adequately controlled. However, on postoperative day five, a significant elevation in intraocular pressure was observed, accompanied by persistent shallowing of the anterior chamber. Topical and systemic hypotensive therapy, posterior capsulotomy and hyaloidotomy were performed without improvement of the local condition. The patient was qualified for irido-zonulectomy with pars plana vitrectomy. Following surgical intervention, normalization of intraocular pressure was achieved, and the anatomy of the anterior chamber was restored. Conclusions: Malignant glaucoma remains a challenging postoperative complication and is frequently refractory to conservative pharmacological and laser-based interventions. Early recognition and prompt surgical management, particularly irido-zonulectomy combined with pars plana vitrectomy, significantly increase the likelihood of anatomical and functional success. Full article

Invasive candidiasis (IC) remains a significant cause of morbidity and mortality among critically ill, hematologic, and neonatal patients worldwide. Rapid and accurate diagnosis is essential to guide timely antifungal therapy and improve outcomes. Among available diagnostic tools, 1,3-β-D-glucan (BDG), a polysaccharide component of the fungal cell wall, has emerged as a key biomarker. BDG assays allow for early detection of probable IC, often preceding positive blood cultures, and offer prognostic information based on serial measurements. Species-specific differences in Candida cell wall composition influence BDG release and diagnostic sensitivity. Candida albicans generally correlates with high BDG levels, whereas Nakaseomyces glabrata, Candida parapsilosis, and Candida auris exhibit variable or lower glucan exposure, limiting assay sensitivity. BDG performance is affected by patient-specific factors, such as prior surgery, transfusions, or coexisting bacterial infections, which may lead to false-positive results. Molecular techniques, including PCR-based assays, provide complementary diagnostic accuracy and species identification, and their combination with BDG testing enhances sensitivity up to 90%. Serial BDG monitoring supports risk stratification and treatment response assessment, with persistent elevations predicting worse outcomes. In neonatal and pediatric populations, optimal cut-off values remain under investigation, highlighting the need for integration with clinical and microbiological data. Overall, BDG represents a valuable adjunct in a multimodal diagnostic workflow, providing both diagnostic and prognostic insights in invasive candidiasis management. Full article

Endometriosis affects a large number of women of reproductive age, and its pathogenesis is still unclear. It causes severe chronic pelvic pain, which is often misdiagnosed as irritable bowel syndrome, or other disorders. Metabolomics and transcriptomic approaches enable the study of changes in various physiological or pathological pathways to identify new potential biomarkers. We employed gas chromatography–mass spectrometry (GC–MS) to investigate metabolic alterations, and quantitative real-time polymers-chain reaction (RT-qPCR) to assess changes in miR-451a and miR-125b in saliva in endometriosis. Serum and saliva samples of patients with symptomatic endometriosis and volunteers without it were collected and subjected to GC–MS and qPCR-RT analysis, respectively. Multivariate and univariate statistical analyses were performed. Orthogonal partial least squares discriminant analysis has shown the differences between the two groups. Eicosadienoic acid, arachidonic acid, and miR-451a increased significantly in endometriosis patients. Machine learning methods were used to build the predictive model, which can be used in early low-invasive diagnostics of endometriosis. Receiver operating characteristics analysis has tested the diagnostic power of metabolites. The combination of metabolic and microRNA profiling may improve our knowledge of the pathophysiological and signaling mechanisms in endometriosis and the discovery of new efficient biomarkers of endometriosis. Full article

Background: Duchenne muscular dystrophy (DMD) is a rare, disabling, and life-threatening X-linked recessive disorder caused by mutations in the dystrophin gene. The current standard of care is treatment with corticosteroids, which aim to decrease inflammation-induced muscle damage and delay disease progression. Here, we aim to describe clinical, genetic, and diagnostic characteristics and evaluate current management practices of DMD patients in the Kingdom of Saudi Arabia (KSA). Methods: This was an ambispective (prospective and retrospective) observational multicenter study evaluating characteristics of patients aged 1–14 years with genetically confirmed DMD in the KSA. The variables of interest were demographics, genetic mutations, clinical characteristics, and initial management. The relationship between the age at diagnosis, initial management plan (standard of care), and age at initiation of treatment on disease outcomes was also evaluated. Results: A total of 226 patients (181 in the retrospective part and 45 in the prospective part) were enrolled. The most common type of genetic mutation was large deletions (134 patients, 59.3%). The median age of first symptom was 2.7 years (IQR: 2.0–4.6 years) and the median age at diagnosis was 7.0 years (IQR: 4.8–8.5 years). Among these patients, the most common initial symptoms were difficulty in walking (87.7%) and waddling gait (41%). The initial management plan for DMD patients involved medication (75.6%) and physical therapy (71.0%). The most frequently prescribed initial medications were vitamin D (82%) and corticosteroids (62.3%). In total, 6/226 patients (2.6%) received ataluren; they all had identified nonsense mutations. The median age of corticosteroid initiation was 7.1 years (IQR: 5.7–8.7). The median age at loss of ambulation (LoA) was 9.8 years (IQR: 8.0–11.4 years) in the non-treated patients; it was 10.1 years (IQR: 9.3–11.2 years) in the steroid-only group and 10.8 years (10.8, 10.8) in the combined ataluren and steroid treatment group. Discussion: Age of diagnosis and age of treatment initiation is relatively late in the KSA. However, early diagnosis and early treatment onset is associated with better clinical outcomes, mainly a delay in LoA. Therefore, there is an urgent need for raising awareness and enhancing early screening in the KSA. Full article

Background: Diffuse alveolar haemorrhage (DAH) is a rare but potentially life-threatening pulmonary complication of systemic autoimmune diseases. Although interstitial lung disease (ILD) is a hallmark of antisynthetase syndrome (ASyS), DAH has been only exceptionally reported in this setting. Methods: We present two patients with ASyS who developed DAH confirmed by bronchoalveolar lavage. Results: The first case involved a 52-year-old woman initially diagnosed with rheumatoid arthritis, later reclassified as rheumatoid arthritis–ASyS overlap, who developed DAH in the context of progressive ILD. The second case concerned a 37-year-old man with newly diagnosed ASyS who presented with recurrent DAH early in the disease course. Both patients required high-dose corticosteroids, followed by escalation of immunosuppressive therapy with rituximab or mycophenolate mofetil, resulting in clinical and radiological improvement. These cases illustrate the diagnostic challenges posed by DAH in ASyS, particularly due to overlapping features with infection and ILD exacerbation. They also highlight the importance of early bronchoscopy and timely intensification of immunosuppression. Conclusions: Increased awareness of DAH as a rare manifestation of ASyS may facilitate earlier recognition and improve outcomes in patients presenting with acute respiratory deterioration, anaemia, or haemoptysis. Full article

Hepatocellular carcinoma (HCC) remains a major global health burden, with an increasing incidence driven by metabolic syndrome-related cases. Early detection is critical; however, current diagnostic tools, including ultrasonography and alpha-fetoprotein (AFP), lack adequate sensitivity and specificity, particularly for non-viral HCC. In this “discovery-stage” study, we identified CD98, a transmembrane oncoprotein, as a robust biomarker highly expressed across all HCC stages, independent of etiological factors. CD98 is enriched on extracellular vesicles (EVs) released by HCC cells and can be accurately quantified using Blu-ray-based ExoCounter technology. In plasma samples from patients with early-stage (stage I/II) non-viral HCC (n = 136) and healthy controls (n = 50), CD98+ EV levels were significantly elevated (p < 0.001) and demonstrated strong diagnostic performance (AUC = 0.743; sensitivity 64%, specificity 86%). Importantly, CD98+ EV levels detected small tumors (≤3 cm) with 59% sensitivity, outperforming AFP (33%). These findings highlight circulating CD98+ EVs as a promising, noninvasive biomarker for early non-viral HCC detection, providing a clinically applicable platform that integrates EV quantification with a novel anti-CD98 monoclonal antibody for precision oncology. Full article

Telemedicine, driven by the Internet of Things (IoT) and wireless connectivity, is essential for managing cardiovascular diseases, where hypertension remains the primary risk factor. In preclinical research, rabbits are superior biological models compared to rodents due to their human-like lipid metabolism. However, continuous blood pressure monitoring in this species remains challenging. The gold-standard technique (direct carotid catheterization) requires terminal procedures, and indirect methods (Doppler, oscillometric) show limited agreement with direct measurements. Furthermore, commercially available implantable telemetry platforms, while enabling real-time monitoring in freely moving animals, require costly surgical implantation, specialized proprietary hardware, and post-operative recovery periods that may confound early hemodynamic data. To address these limitations, this study presents a low-cost, customizable, and minimally invasive monitoring system utilizing a pressure transducer in the central auricular artery. The device integrates an ESP32 microcontroller with IoT technology for digital signal processing and seamless wireless data transmission to the ThingSpeak cloud platform. Unlike implantable telemetry, the proposed approach avoids surgical implantation and its associated costs and recovery time, while still enabling continuous, real-time hemodynamic tracking throughout the experimental period. A pilot evaluation against the BIOPAC MP100 reference (carotid artery) demonstrated relative errors of 1.60% for mean arterial pressure, 8.58% for systolic blood pressure, and 2.43% for diastolic blood pressure. By reducing invasiveness and enhancing remote data accessibility, this system provides a promising framework for the preclinical evaluation of antihypertensive agents and cardiovascular mechanisms, bridging the gap between edge computing and remote clinical diagnostics. Full article