Search Results (11,838)

Background/Objectives: Cardiac sarcoidosis (CS) is a granulomatous inflammatory cardiomyopathy with heterogeneous presentations, from palpitations to heart failure and sudden cardiac arrest. Despite advances in imaging and immunosuppressive (IS) therapy, relapse patterns and long-term outcomes remain poorly defined. This study aimed to characterize relapse and identify predictors of relapse and major adverse cardiac events (MACE) in a real-world CS cohort. Methods: This retrospective single-center study included 25 adults diagnosed with CS at Geneva University Hospitals between 2016 and 2024, classified per the 2024 American Heart Association diagnostic criteria. Relapse was defined as clinical, arrhythmic, or imaging deterioration requiring treatment escalation. MACE included cardiovascular hospitalization, device therapy, left ventricular assist device, heart transplant, or death. Statistical methods included Kaplan–Meier analysis with log-rank tests and multivariable Cox regression adjusted for age and sex. Results: Relapse occurred in 13 patients (56%), frequently subclinical (61.5%) and detected incidentally on routine PET-CT during IS tapering. In the multivariate model, predictors of relapse included right ventricular FDG uptake (aHR 13.1; 95% CI 1.3–133.7; p = 0.03) and second-line immunosuppression duration ≤24 months (aHR 20.1; 95% CI 1.1–363.8; p = 0.04). Relapse-free patients were more often maintained on dual or triple IS therapy (71.4% vs. 15.4%; p = 0.02) and low-dose prednisone (<10 mg/day) (57.1% vs. 7.7%; p = 0.03). Conclusions: Relapse is common in CS, often subclinical, and associated with PET-CT findings and premature IS tapering. Maintenance therapy may reduce risk. Multimodal imaging remains critical for disease monitoring, though tracers with higher specificity are needed. Further research should refine relapse definitions and support personalized treatment strategies. Full article

This study characterized the spatial distribution and assessed the ecological risks of carbon, nitrogen, and phosphorus in sediments of the Dongzhai Harbor mangrove wetland, Hainan, China. Analysis of key environmental indicators (grain size, pH, TOC, TN, TP) across twenty-seven sediment cores (0–100 cm depth) revealed distinct decreasing land–sea gradients and vertical stratification of nutrient concentrations. Mangrove plant debris was identified as the primary source of sedimentary organic matter. Elemental ratio analysis indicated terrestrial inputs as the dominant phosphorus source. Significant positive correlations between TOC, TN, and TP in surface sediments suggested coupled nutrient dynamics. Vertical distribution of C/N to C/P ratios increased with depth, which may be related to increased nitrogen and phosphorus inputs due to regional human activities. Pollution assessment showed significantly higher ecological risks in surface sediments (0–50 cm), particularly near inland areas and dense mangroves, indicating co-regulation by vegetation processes and human impacts. These findings highlight significant spatial heterogeneity in ecological risks, underscoring the need for enhanced monitoring and targeted management strategies in critical land–sea transition zones. Full article

Leishmaniasis is a parasitic disease transmitted by female sandflies of the genus Lutzomyia. Ecuador is divided into three distinct natural regions: the Andes, the Coast, and Amazonia, each characterized by significant variations in altitude and climate. While most reported cases of leishmaniasis are associated with humid, low-altitude rural areas, our study uncovered an unexpected trend: confirmed cases occurring in traditionally cold Andean regions. To investigate this issue, we conducted a cross-sectional ecological study using official morbidity and mortality records from the Ecuadorian Health Care Registration Platform, focusing on the cantons of Alausí and Chunchi in Chimborazo Province from 2013 to 2022. Chimborazo Province, in the Andes, is characterized by higher altitudes (2740 m above sea level) and a cold climate (averaging 13 °C throughout the year). Among a population of 44,089 residents in Alausí, we reported a total of 40 confirmed cases, with 97.5% classified as cutaneous and 2.5% as mucocutaneous, predominantly affecting children and males. No mortality cases were recorded during the study period. To further explore environmental influences, we examined the Alausí region, where climate change has led to rising average temperatures, deforestation, and changes in humidity levels. Leishmaniasis cases in Alausí showed seasonal peaks, particularly in 2018 and 2019, correlating with warmer and more humid conditions. Environmental factors such as temperature and humidity were strongly associated with the prevalence of the disease, suggesting that climate change may be increasing transmission risks. These findings point to the value of incorporating environmental monitoring into public health strategies for vector-borne diseases that affect vulnerable populations in the Andes. Full article

This study proposes a task planning approach for a distributed constellation dedicated to space target monitoring, grounded in an adaptive genetic algorithm. The approach is designed to address challenges such as the growing number of space targets and the complex constraints inherent in space target monitoring activities. After reviewing the research progress of distributed satellite task planning and adaptive genetic algorithms, a distributed task model featuring master-slave satellites was developed. This model integrates multi-constraint modeling and aims to optimize key performance indicators: task yield rate, task completion rate, resource utilization rate, and load balancing. To enhance the approach, the contract net algorithm is fused with the adaptive genetic algorithm: Firstly, in the tendering phase, centralized tendering is adopted to reduce communication overhead; Secondly, in the bidding phase, improved genetic mechanisms (e.g., dynamic reverse adjustment of crossover and mutation probabilities) and a dynamic population strategy are employed to generate task allocation schemes; Thirdly, in the bid evaluation and winning phase, differentiated strategies are applied to non-repetitive and repetitive tasks. Simulation validation shows that this approach can complete 80% of space target monitoring tasks, balance satellite loads effectively, and manage space target catalogs efficiently. Full article

Sensor-based approaches are transforming the diagnosis and treatment of neurodegenerative diseases by offering more sensitive, non-invasive tools and are capable of real-time monitoring. Integrating advanced materials, nanotechnology, and artificial intelligence presents promise for earlier detection, enhanced disease management, and improved patient outcomes. From a clinical perspective, these technologies facilitate the shift toward precision medicine by enabling early intervention strategies, real-time treatment monitoring, and more refined patient stratification in practice and research contexts. This review provides an overview of recent advancements in sensor-based technologies aimed at enhancing the diagnosis and monitoring of neurodegenerative diseases (NDDs) such as Alzheimer’s and Parkinson’s, among others. Sensor-based technologies are adjunct tools and integral components of a next-generation framework for diagnosing, monitoring, and understanding neurodegenerative disorders. Full article

The Mediterranean subpopulation of great white sharks (Carcharodon carcharias) is elusive and likely in decline, though long-term trends remain uncertain due to opportunistic record-keeping, misidentifications, and changing observation effort. We investigated whether spatial changes in shark occurrences during the 21st century correspond with shifts in Atlantic bluefin tuna (Thunnus thynnus) distribution and habitat conditions. We compiled geographically validated sighting and capture records from 1900 onward, mapped 20th- and 21st-century hotspots, and overlaid these with bluefin tuna potential habitat and long-term sea surface temperature (SST) data. Results reveal a clear redistribution of great white shark hotspots: historic coastal focal areas (e.g., Balearic Islands, Maltese Islands, eastern Adriatic, Sea of Marmara) have diminished or disappeared, while offshore zones (southern Strait of Sicily–Gulf of Gabes) and the Aegean coast of Turkey have emerged as contemporary hotspots. These patterns appear to align closely with shifts in tuna feeding grounds and cooler SST (<18 °C). We highlight limitations in using opportunistic and citizen-reported data due to detection biases and misidentifications, underscoring the need for models that correct detectability. Our findings are consistent with the hypothesis of a link between predator distribution, prey dynamics, and changing ocean conditions, and point toward targeted strategies for future conservation and monitoring of this apex predator in a warming Mediterranean. Full article

Indoor sports facilities face distinctive indoor air quality (IAQ) challenges due to high occupant density, elevated metabolic emissions, and diverse pollutant sources associated with physical activity. This review presents a narrative synthesis of multidisciplinary evidence concerning IAQ in sports environments. It explores major pollutant categories, including carbon dioxide (CO₂), particulate matter (PM), volatile organic compounds (VOCs), and airborne microbial agents, highlighting their sources, behavior during exercise, and associated health risks. Research shows that physical activity can increase PM concentrations by up to 300%, and CO₂ levels frequently exceed 1000 ppm in inadequately ventilated spaces. The presence of semi-volatile organics and bioaerosols further complicates pollutant dynamics, especially in humid and densely occupied areas. Measurement technologies such as optical sensors, chromatographic methods, and molecular techniques are reviewed and compared for their applicability to dynamic indoor settings. Existing IAQ standards across China, the USA, the EU, the UK, and WHO are examined, revealing a lack of activity-specific thresholds and insufficient responsiveness to real-time conditions. Mitigation strategies (e.g., including demand-controlled ventilation, use of low-emission materials, liquid chalk substitutes, and integrated HEPA-UVGI purification systems) are evaluated, many demonstrating pollutant removal efficiencies over 80%. The integration of intelligent building management systems is emphasized for enabling real-time monitoring and adaptive control. This review concludes by identifying research priorities, including the development of activity-sensitive IAQ control frameworks and long-term health impact assessments for athletes and vulnerable users. Full article

Alzheimer’s disease (AD) presents a significant challenge in modern healthcare, prompting exploration into novel therapeutic strategies. This review clearly classifies different types of gold (Au) nanoparticles (NPs) (AuNPs), links them to the gut–brain axis, highlights recent advances, and points out future research needs, offering a more updated perspective than earlier reviews. Diverse approaches have emerged from single to hybrid and functionalized AuNPs, including innovative nanotherapeutic agents like Au nanorods-polyethylene glycol-angiopep-2 peptide/D1 peptide and noninvasive dynamic magnetic field-stimulated NPs. AuNPs have been reported for the neuroprotective properties. Clinical applications of AuNPs highlight their promise in diagnosis and therapeutic monitoring. However, challenges persist, notably in overcoming blood–brain barrier limitations and refining drug delivery systems. Furthermore, the incomplete understanding of AD’s physiological and pathological mechanisms hinders therapeutic development. Future research directions should prioritize elucidating these mechanisms and optimizing AuNPs physicochemical properties for therapeutic efficacy. Despite limitations, nanomaterial-based therapies hold promise for revolutionizing AD treatment and addressing other central nervous system disorders. It also emphasizes the importance of further investigation into the potential of AuNPs, envisioning a future where they serve as a cornerstone in advancing neurological healthcare. Full article

As tropical forests become increasingly vulnerable to land use changes, fragmentation, and climate shifts, efforts to minimise species loss are essential. Prevalent in most environments and having complex vocalisations, birds are key indicators of ecosystem health and a good model for acoustic monitoring. In Brazil, the Caparaó National Park (CNP) is a preserved remnant of the Atlantic Forest with great avian endemism. Despite having >600 species, limited research has utilised bioacoustics for species assessment. This study employed bioacoustics to examine soundscapes and community composition at two CNP locations—one with ombrophilous montane forest (OMF) and another with semi-deciduous seasonal forest (SSF). Four SongMeters were deployed, recording bird choruses from 08:00 to 11:00 a.m. for two months. Soundscape profiles and species composition were characterised using Raven Pro. Acoustic indices assessed correlations with avian species richness, and sites were compared using the Mann–Whitney U test. Ninety-eight species were detected, and species richness was greater within SSF. While acoustic indices had little impact on richness, they identified differing soundscapes: more ambient noise in OMF, and gunshots detected in SSF. The results indicate that bioacoustics can aid monitoring strategies. Given the presence of rare species and illegal activities, more studies are needed to support the conservation of birds in this critical environment. Full article

This study investigates the operation and management of an advanced Italian liquid waste treatment platform, focusing on its dual-line configuration and the challenges posed by increasingly heterogeneous waste streams. The main objectives are to (i) characterize the technological and operational features of the system, (ii) evaluate strategies for dealing with variable waste compositions and non-compliant inputs, and (iii) propose governance measures to strengthen cooperation between producers and operators. The methodology integrates the analysis of operational data from 2022 to 2024 (waste volumes, European Waste Catalogue Codes, reagent consumption, sludge production, and energy use) with a critical assessment of acceptance procedures and monitoring protocols. Results show a 10% increase in liquid waste treated over the study period, a growing predominance of complex EWC codes, higher oxygen demand in the thermophilic reactor, and seasonal fluctuations in sludge production. At the same time, the plant achieved stable or improved performance indicators, with specific energy consumption decreasing to 2.08 kWh/kg COD removed in 2024. The study concludes that modular, flexible treatment systems, supported by rigorous waste characterization and real-time decision-making, are essential to ensuring efficiency, regulatory compliance, and long-term environmental sustainability in liquid waste management. Full article

Anomaly detection plays a vital role in ensuring product quality and operational safety across various industrial applications, from manufacturing to infrastructure monitoring. However, current methods often struggle with challenges such as limited generalization to complex multimodal anomalies, poor adaptation to domain-specific patterns, and reduced feature discriminability due to domain gaps between pre-trained models and industrial data. To address these issues, we propose AnomNet, a novel deep anomaly detection framework that integrates a lightweight feature adapter module to bridge domain discrepancies and enhance multi-scale feature discriminability from pre-trained backbones. AnomNet is trained using a dual-stage centroid learning strategy: the first stage employs separation and entropy regularization losses to stabilize and optimize the centroid representation of normal samples; the second stage introduces a centroid-based contrastive learning mechanism to refine decision boundaries by adaptively managing inter- and intra-class feature relationships. The experimental results on the MVTec AD dataset demonstrate the superior performance of AnomNet, achieving a 99.5% image-level AUROC and 98.3% pixel-level AUROC, underscoring its effectiveness and robustness for anomaly detection and localization in industrial environments. Full article

Power transformers are critical components in electrical power systems, where failures can cause significant outages and economic losses. Traditional maintenance strategies, typically based on offline inspections, are increasingly insufficient to meet the reliability requirements of modern digital substations. This work presents an integrated multi-sensor monitoring framework that combines online frequency response analysis (OnFRA^® 4.0), capacitive tap-based monitoring (FRACTIVE^® 4.0), dissolved gas analysis, and temperature measurements. All data streams are synchronized and managed within a SCADA system that supports real-time visualization and historical traceability. To enable automated fault diagnosis, a Random Forest classifier was trained using simulated datasets derived from laboratory experiments that emulate typical transformer and bushing degradation scenarios. Principal Component Analysis was employed for dimensionality reduction, improving model interpretability and computational efficiency. The proposed model achieved perfect classification metrics on the simulated data, demonstrating the feasibility of combining high-fidelity monitoring hardware with machine learning techniques for anomaly detection. Although no in-service failures have been recorded to date, the monitoring infrastructure is already tested and validated through laboratory conditions, enabling continuous data acquisition. Full article

Background: Postoperative wound complications following total joint arthroplasty (TJA) significantly impact patient outcomes and healthcare costs. Reliable preoperative biomarkers for identifying patients at increased risk are critical for optimizing patient management and reducing complication rates. This study evaluated the predictive utility of the C-reactive protein to albumin ratio (CAR) and the prognostic nutritional index (PNI) for periprosthetic joint infection (PJI) and postoperative wound complications in patients undergoing total hip arthroplasty (THA) and total knee arthroplasty (TKA). Methods: We retrospectively studied patients who underwent primary THA and TKA in our department from March 2019 to April 2024. The study included a total of 842 patients (568 knees and 274 hips). Preoperative blood samples were assessed for serum CRP, albumin, and total lymphocyte count, facilitating the calculation of CAR and PNI values. Patient outcomes were monitored, identifying PJI and aseptic wound complications such as persistent wound drainage, hematoma, seroma, skin erosion, and wound dehiscence within 2 weeks post-surgery. Results: The average follow-up time for patients was 39.2 months (range 13–73 months). PJI was significantly linked with elevated admission CAR and diminished PNI ratio (p < 0.001 and p < 0.001). ROC analysis demonstrated optimal predictive cut-off values for CAR at 3.1 (Area under curve [AUC]: 0.92, specificity 97.4%, sensitivity 92.3%) and PNI at 49.4 (AUC: 0.93, specificity 94.7%, sensitivity 91.7%). Furthermore, both CAR (Odds ratio [OR]: 3.84, 95% confidence interval [CI]: 1.6–9.1, p = 0.002) and PNI (OR: 21.8, 95% CI: 9–48.6, p < 0.001) were identified as two independent risk factors associated with the development of PJI following THA or TKA. Further subgroup analysis revealed distinct predictive thresholds for CAR and PNI according to surgical procedure type (TKA and THA), enhancing diagnostic accuracy. Conclusions: Preoperative admission elevated CAR and decreased PNI effectively predict PJI and postoperative wound complications in THA and TKA, supporting their utility as simple, cost-effective biomarkers in clinical practice. Incorporating CAR and PNI evaluations into preoperative assessments can enhance patient stratification and preventive strategies, thus mitigating risks and improving surgical outcomes. Full article

Background: Cardiovascular disease (CVD) remains a leading cause of morbidity and mortality, particularly in socioeconomically disadvantaged populations. Telemedicine offers a potential strategy to support risk factor management in such groups with limited access to care. Our aim was to assess the effectiveness of a telemedicine-supported intervention compared to usual care in improving cardiovascular risk parameters among adults from a socially deprived urban population. Materials and Methods: In this controlled intervention study, adult patients with one or more cardiovascular risk factors were recruited from a primary care center in a low-income urban neighborhood in Timişoara, Romania. Participants were allocated to either usual care or a six-month telemedicine-supported intervention group. The intervention consisted of regular phone calls by trained staff focusing on medication adherence, self-monitoring of blood pressure and glucose, smoking cessation, and lifestyle advice. No physical visits were delivered. Primary outcomes included changes in systolic and diastolic blood pressure, fasting glucose, and lipid profile. Data were collected at baseline and at six months. Results: A total of 144 patients were allocated to the telemedicine group and 142 to the usual care group. After 6 months, diastolic blood pressure decreased by 3.9 mmHg in the telemedicine group compared to 0.3 mmHg in the standard care group (p < 0.001). LDL-cholesterol was reduced by 18.0 mg/dL with telemedicine versus 5.7 mg/dL with usual care (p < 0.001). In contrast, fasting glucose improved more in the standard care group (–10.9 mg/dL vs. –2.0 mg/dL, p < 0.001). Patient satisfaction in the telemedicine group was high, with 84% rating the program as very useful. Conclusions: Basic telemedicine-supported interventions may represent a feasible and effective strategy for improving cardiovascular risk factors such as diastolic blood pressure and LDL-cholesterol in socially deprived populations. High satisfaction suggests strong acceptability; however, given the small sample size, short follow-up, and single-center design, these findings should be interpreted cautiously and confirmed in larger studies. Full article

Major human impacts on New Zealand’s ecology began about 800 years ago with immigration firstly from Polynesia, then Europe starting a few centuries later. The humans cleared habitat, hunted species to extinction, and introduced biota, including plants, birds, fish, invertebrates, and mammals. Over the last 70 years, government-funded campaigns have been waged against some of the introduced mammals that became considered harmful to native biota. These campaigns spread poisonous food baits from aircraft to kill and suppress target animals (mainly brushtail possums (Trichosurus vulpecula) and rats (Rattus spp.) over large areas. Increased intensity, frequency, and scale of poisoning are being trialled under a new conservation strategy (Predator Free 2050) to eradicate several mammalian species. The present study investigates the opportunity for a paradigm shift in conservation, emphasizing the rationales for transitioning from spreading of pesticides to a more targeted approach. NZ’s poison- and predator-focused ecological management has been criticized internationally as cruel and unnecessary, while independent NZ ecologists have called for, and outlined, a new system of conservation management based on ecological knowledge, which embraces all threats to native biota. A central tenet of proposed new methods is the engagement of all relevant stakeholders. Efficient management tools include remote monitoring, and smart, self-resetting kill traps for targeted small mammal control. Ecology-driven, commercially sound, targeted, monitored, relatively humane management can be implemented to protect the remnants of NZ’s natural heritage. Full article