Search Results (1,957)

Urbanization markedly influences the microclimatic conditions in semi-arid regions by elevating land surface temperatures (LST) and contributing to ecological degradation. This study examined the spatial and temporal evolution of LST and urban heat island (UHI) effects in Benguerir, Morocco, over a 30-year period (1994–2024), employing high-resolution satellite imagery and in situ sensor data. Urban expansion was quantified using thermal bands from Landsat imagery, the Normalized Difference Built-up Index (NDBI), and the Built-up Index (BU), whereas thermal comfort was evaluated through the Universal Thermal Climate Index (UTCI) and Predicted Mean Vote (PMV) using air temperature and humidity data collected via spatial sensor and the Sniffer Bike mobile sensor network. These urban transformations have intensified the UHI effect, resulting in a 29.34 °C increase in mean LST to 41.82 °C in 2024 across built-up areas. Statistical modeling revealed strong linear relationships between LST and urban indices, with R² values ranging from 0.93 to 0.96, and correlation coefficients around 0.98 (all p-values < 0.001), indicating a reliable model fit. Furthermore, the analysis of thermal comfort trends underscores urbanization’s impact on human well-being. In 1994, 34.2% of the population experienced slight warmth and 65.8% experienced hot conditions. By 2024, conditions had shifted dramatically, with 76.7% experiencing hot conditions and 16.2% exposed to very hot conditions, leaving only 7.1% in the slight warmth category. These findings highlight the urgent need for adaptive urban planning strategies. The implementation of urban greening initiatives, the use of reflective materials, and the integration of data-driven planning approaches are essential to mitigate thermal stress and enhance urban resilience. Leveraging climate modeling and spatial analytics can support the identification of high-risk zones and inform targeted interventions to effectively address the escalating UHI phenomenon. Full article

A high-performance liquid chromatography (HPLC) method using a diode array detector (DAD) was developed and validated for the simultaneous quantification of chlorogenic acid, rutin, and isoquercitrin, which are key bioactive compounds in Cudrania tricuspidata leaves. The method demonstrated excellent specificity, precision, and accuracy in accordance with the guidelines of the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH). Calibration curves showed outstanding linearity (r² > 0.99), with recovery rates of 101.63%, 101.81%, and 102.18% for chlorogenic acid, rutin, and isoquercitrin, respectively. The limits of detection (LOD) were 0.286, 0.411, and 0.201 μg/mL, and the limits of quantification (LOQ) were 1.246, 0.866, and 0.608 μg/mL for chlorogenic acid, rutin, and isoquercitrin, respectively. Additionally, response surface methodology (RSM) based on a Box–Behnken design was employed to optimize the extraction conditions of the three marker compounds. The second-order regression models showed high coefficients of determination (r²) and significant ANOVA results (p < 0.05). RSM analysis revealed that extraction temperature and ethanol concentration exerted the most significant effects on the extraction yields, while extraction time played a supportive role. The optimal conditions (70 °C, 40% ethanol, 120 min) significantly enhanced compound recovery while reducing solvent and energy consumption, thereby contributing to the development of efficient and sustainable extraction processes. Collectively, the validated HPLC-DAD method and the optimized extraction strategy developed in this study provide a reliable framework for the quality standardization and industrial application of C. tricuspidata leaf extracts in functional food, cosmetic, and pharmaceutical products. Full article

Background: Physiologically based pharmacokinetic (PBPK) modeling is a mathematical approach that integrates human physiological parameters with drug-specific characteristics (including both active pharmaceutical ingredients and excipients), and it has emerged as one of the core technologies for optimizing the efficiency and reliability of drug development. Methods: This study synthesizes applications of PBPK models in FDA-approved drugs (2020–2024), systematically analyzing model utilization frequency, indication distribution, application domains and choice of modeling platforms, to reveal their substantive contributions to regulatory submissions. Additionally, we conducted an in-depth analysis of the PBPK models for 2024, classifying models into three tiers based on critical assessment of FDA reviewer comments. Results: Among 245 FDA-approved new drugs during this period, 65 NDAs/BLAs (26.5%) submitted PBPK models as pivotal evidence. Oncology drugs accounted for the highest proportion (42%). In application scenarios, drug–drug interaction (DDI) was predominant (81.9%), followed by dose recommendations for patients with organ impairment (7.0%), pediatric population dosing prediction (2.6%), and food-effect evaluation. Regarding modeling platforms, Simcyp^® emerged as the industry-preferred modeling platform, with an 80% usage rate. In terms of regulatory evaluation, a core concern for reviewers is whether the model establishes a complete and credible chain of evidence from in vitro parameters to clinical predictions. Conclusions: Detailed regulatory reviews demonstrate that although some PBPK models exhibit certain limitations and shortcomings, this does not preclude them from demonstrating notable strengths and practical value in critical applications. Benefiting from the strong support these successful implementations provide for regulatory decision-making, the technology is gaining increasing recognition across the industry. Looking forward, the integration of PBPK modeling with artificial intelligence (AI) and multi-omics data will unprecedentedly enhance predictive accuracy, thereby providing critical and actionable insights for decision-making in precision medicine and global regulatory strategies. Full article

Introduction: Patient comfort is a central concept in nursing practice, and is particularly important in emergency contexts, where clinical complexity and care overload challenge the provision of humanized care. Katharine Kolcaba’s Theory of Comfort offers a robust theoretical framework for assessing and promoting comfort in multiple domains. The main objective is to psychometrically validate the adapted version of Kolcaba’s Comfort Scale—COLCABA-32—in critically ill patients treated in a Portuguese hospital emergency department. Method: A quantitative, descriptive, cross-sectional study was conducted using a sample of 165 adult patients triaged with urgent clinical priority. Data collection was performed through individual interviews. The COLCABA-32 Scale and the Mini-Mental State Examination (MMSE) were used. Statistical analysis included descriptive statistics, principal component analysis (PCA), internal consistency (Cronbach’s alpha), and correlation with clinical priority according to the Manchester Triage. Results: PCA revealed six factors with eigenvalues greater than 1, explaining 59.01% of the total variance of the scale. The dimensions identified were psycho-emotional comfort and autonomy, physical and symptomatic comfort, relational comfort and information, spiritual comfort, environmental comfort and motivational comfort and hope. The overall Cronbach’s alpha was 0.897, indicating excellent internal consistency. Correlations with clinical priority confirmed partial convergent validity. Conclusions: The COLCABA-32 Scale demonstrated adequate psychometric properties for assessing the comfort of critically ill patients in an emergency setting and is a valid, reliable, and sensitive instrument for the multiple dimensions of comfort, as proposed by Kolcaba. Its application can contribute to more person-centered and evidence-based nursing practices. Full article

Gas Detection Systems (GDSs) are critical safety technologies deployed in semiconductor wafer fabrication facilities to monitor the presence of hazardous gases. A GDS receives input from gas detectors equipped with consumable gas sensors, such as electrochemical (EC) and metal oxide semiconductor (MOS) types, which are used to detect toxic, flammable, or reactive gases. However, over time, sensors degradations, accuracy drift, and cross-sensitivity to interference gases compromise their intended performance. To maintain sensor accuracy and reliability, routine manual calibration is required—an approach that is resource-intensive, time-consuming, and prone to human error, especially in facilities with extensive networks of gas detectors. This systematic review (PROSPERO on 11th October 2025 Registration number: 1166004) explored minimizing or eliminating the dependency on manual calibration. Findings indicate that using properly calibrated gas sensor data can support advanced data analytics and machine learning algorithms to correct accuracy drift and improve gas selectivity. Techniques such as Principal Component Analysis (PCA), Support Vector Machines (SVMs), multivariate regression, and calibration transfer have been effectively applied to differentiate target gases from interferences and compensate for sensor aging and environmental variability. The paper also explores the emerging potential for integrating calibration-free or self-correcting gas sensor systems into existing GDS infrastructures. Despite significant progress, key research challenges persist. These include understanding the dynamics of sensor response drift due to prolonged gas exposure, synchronizing multi-sensor data collection to minimize time-related drift, and aligning ambient sensor signals with gas analytical references. Future research should prioritize the development of application-specific datasets, adaptive environmental compensation models, and hybrid validation frameworks. These advancements will contribute to the realization of intelligent, autonomous, and data-driven gas detection solutions that are robust, scalable, and well-suited to the operational complexities of modern industrial environments. Full article

Periodontitis (PD) is a multifactorial, progressive inflammatory disease affecting the teeth-supporting tissues, characterized by an imbalance of the oral microbiota and the presence of bacterial biofilms leading to host response. Nowadays, reliable biochemical markers for early and objective diagnosis, and for predicting disease progression, are still lacking. Our previous proteomic investigations revealed the significant overexpression of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) in periodontal pocket tissue, gingival crevicular fluid (GCF), and tooth-surface-collected material (TSCM) from PD patients in comparison to periodontally healthy controls, proposing it as a possible biomarker of PD. This study aimed to evaluate the expression of GAPDH in saliva, a more accessible, non-invasive, and clinically relevant oral sample. The whole saliva was analyzed by a preliminary mass spectrometry-based proteomic approach, identifying significantly increased levels of GAPDH also in salivary samples from periodontal-affected subjects. These data were further validated by enzyme-linked-immunosorbent assay (ELISA). Additionally, protein–protein interaction networks were generated through the Human Protein Atlas database, using different datasets (OpenCell, IntAct, and BioGRID). Bioinformatic analysis provided noteworthy GAPDH-associated networks potentially relevant to periodontal pathology. The scientific significance of this study lies in the detection of salivary GAPDH as a novel strategy to advance periodontal clinical diagnostics from the perspective of a non-invasive screening test. In correlation with other protein markers, salivary GAPDH could constitute a promising set of distinctive and predictive targets to enhance early diagnosis of PD, disease monitoring, and treatment planning in periodontology. Full article

Background. West Nile Virus (WNV) represents a significant public health concern in Europe, with Italy—particularly its northern regions—experiencing recurrent outbreaks. Climate variables and vector dynamics are known to significantly influence transmission patterns, highlighting the need for reliable predictive models to enable timely outbreak detection and response. Methods. We integrated epidemiological data on human WNV infections in Italy (2012–2024) with high-resolution climate variables (temperature, humidity, and precipitation). Using advanced feature engineering and a gradient boosting framework (XGBoost), we developed a predictive model optimized through time-series cross-validation. Results. The model achieved high predictive accuracy at the national level (R² = 0.994, MAPE = 5.16%) and maintained robust performance across the five most affected provinces, with R² values ranging from 0.896 to 0.996. SHAP analysis identified minimum temperature as the most influential climate predictor, while maximum temperature and rainfall demonstrated considerably weaker associations with case incidence. Conclusions. This machine learning approach provides a reliable framework for forecasting WNV outbreaks and supports evidence-based public health responses. The integration of climate and epidemiological data enhances surveillance capabilities and enables informed decision-making at regional and local levels. Full article

Omsk hemorrhagic fever is an acute zoonotic disease caused by Omsk hemorrhagic fever virus, a member of the genus Flavivirus (family Flaviviridae), with a reported case-fatality rate of approximately 3%. Historically confined to southwestern Siberia, ecological changes raise concerns about possible spread to non-endemic regions. Although no Omsk hemorrhagic fever cases have been reported in the Republic of Korea, the risk of accidental importation highlights the importance of establishing a reliable diagnostic protocol. We established and validated an institutionally developed diagnostic protocol employing real-time reverse transcription polymerase chain reaction targeting the NS2A and C genes of Omsk hemorrhagic fever virus. Primers and probes were designed from all available genomes to ensure broad strain coverage. Human ribonuclease P was used as an internal control to verify nucleic acid extraction and amplification. Using synthetic deoxyribonucleic acid fragments and in vitro-transcribed ribonucleic acid, assay performance was optimized, and analytical sensitivity was determined using probit analysis. The limits of detection were 74.50 copies/µL (threshold cycle 32.99) for NS2A and 70.41 copies/µL (threshold cycle 35.38) for C. Specificity testing using representative flaviviruses (West Nile virus, Yellow fever virus, Zika virus, St. Louis encephalitis virus, and Tick-borne encephalitis virus) and an alphavirus (Venezuelan equine encephalitis virus) demonstrated no cross-reactivity. The assay demonstrated high sensitivity, specificity, and reproducibility, supporting its potential application in national and international Omsk hemorrhagic fever virus surveillance systems. Full article

Lakes, as key freshwater reserves and ecosystem cores, supply human water, regulate climate, sustain biodiversity, and are vital for global ecological balance and human sustainability. Lake Chaohu, as a crucial ecological barrier in the middle and lower reaches of the Yangtze River, faces significant environmental challenges to regional sustainable development due to water quality deterioration and consequent eutrophication issues. To address the limitations of conventional monitoring techniques, including insufficient spatiotemporal coverage and high operational costs in lake water quality assessment, this study proposes an enhanced Informer model optimized by the Whale Optimization Algorithm (WOA) for predictive analysis of concentration trends of key water quality parameters—dissolved oxygen (DO), permanganate index (CODMn), total phosphorus (TP), and total nitrogen (TN)—across multiple time horizons (4 h, 12 h, 24 h, 48 h, and 72 h). The results demonstrate that the WOA-optimized Informer model (WOA-Informer) significantly improves long-term water quality prediction performance. Comparative evaluation shows that the WOA-Informer model achieves average reductions of 9.45%, 8.76%, 7.79%, 8.54%, and 11.80% in RMSE metrics for 4 h, 12 h, 24 h, 48 h, and 72 h prediction windows, respectively, along with average improvements of 3.80%, 5.99%, 11.23%, 17.37%, and 23.26% in R² values. The performance advantages become increasingly pronounced with extended prediction durations, conclusively validating the model’s superior capability in mitigating error accumulation effects and enhancing long-term prediction stability. Spatial visualization through Kriging interpolation confirms strong consistency between predicted and measured values for all parameters (DO, CODMn, TP, and TN) across all time horizons, both in concentration levels and spatial distribution patterns, thereby verifying the accuracy and reliability of the WOA-Informer model. This study successfully enhances water quality prediction precision through model optimization, providing robust technical support for water environment management and decision-making processes. Full article

Myxomatous mitral valve disease (MMVD) is the most common acquired cardiac disoder in dogs and a relevant model for human mitral valve disease. However, the molecular drivers of disease progression remain unclear, and reliable biomarkers for early diagnosis still hamper clinical management. This study investigated microRNA (miRNA) expression directly in histologically characterized mitral valve tissues. Formalin-fixed paraffin-embedded samples were obtained from control dogs (n = 7), low-grade MMVD (n = 8), and high-grade MMVD (n = 5). A bioinformatics workflow identified candidate miRNAs converging on extracellular matrix remodeling and canonical signaling pathways, including TGF-β, PI3K–Akt, and MAPK. Selected candidates, let-7 family, miR-98, miR-21, miR-30b, miR-133b, and miR-103, were validated by qPCR. Results revealed a general upregulation of the panel in MMVD compared with controls, with stage-dependent differences between low- and high-grade lesions. In particular, miR-21, let-7b, and miR-133b were markedly increased in advanced disease, while miR-30b emerged as an early-stage marker with potential prognostic value. These findings provide molecular evidence linking miRNA dysregulation to progressive valvular degeneration. By combining histologically defined tissue analysis with stage-based comparisons, this study identifies miRNAs with potential diagnostic and prognostic utility for canine MMVD. Full article

Toxoplasma gondii is a globally distributed protozoan parasite, and reliable serodiagnosis is essential for effective management of toxoplasmosis. Conventional assays rely on tachyzoite lysate antigen (TLA), which suffers from limited standardization and reproducibility. In this study, immunodominant fragments of six dense granule proteins—GRA29, GRA35, GRA36, GRA45, GRA54, and GRA64—were expressed in Escherichia coli, purified, and evaluated as candidate antigens in IgG ELISAs using human sera. This study represents the first assessment of their diagnostic utility. Initial screening identified GRA29, GRA45, and GRA54 as promising candidates, with AUC values of 0.9983, 0.8507, and 0.9323, respectively, while GRA35-, GRA36-, and GRA64-based ELISA showed poor discrimination between seropositive and seronegative samples. Extended evaluation of GRA29-based assay with a larger serum panel (n = 286) confirmed excellent diagnostic performance, yielding an AUC of 0.9942 and higher sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) compared with TLA-ELISA. A comparative analysis revealed that GRA29 produced stronger reactivity in positive sera and lower background in negatives. These findings highlight GRA29 as a promising recombinant antigen for the serodiagnosis of human toxoplasmosis and a potential standardized alternative to TLA. Full article

Background: Cervical cancer (CC) is the most common cancer among Ugandan women and the leading cause of cancer mortality. Screening has proven to be a cost-effective method in reducing its burden, yet uptake among women of reproductive age remains alarmingly low, with national adherence rates under 10%. Objective: This study explored healthcare workers’ (HWs) perspectives on barriers and facilitators to screening and attitudes toward implementing human papillomavirus (HPV) DNA testing with self-collection. Methods: A qualitative research design was employed. Twenty semi-structured interviews were conducted with purposively sampled healthcare providers and administrators across different cadres at a referral hospital and three peripheral health centres in Northern Uganda. Interviews were analysed thematically using the Social Ecological Model. Data collection and analysis proceeded iteratively until thematic saturation. Reporting follows the Consolidated Criteria for Reporting Qualitative Research (COREQ). Results: Participants described individual and interpersonal barriers such as limited awareness, poor preventive health-seeking, fear of results, stigma, and limited male involvement. Organisational barriers included staff shortages, weak referral practices, and stock-outs of supplies, while policy constraints included limited governmental support and competing priorities. Facilitators included targeted health education, routine referrals from all service entry points, outreach screening, and donor support. Most respondents favoured scaling up of self-collected HPV testing, citing higher acceptability and feasibility for outreach, contingent on sustained supplies, laboratory capacity, and training. Conclusions: Multi-level interventions are needed to strengthen facility workflows, staff capability, community engagement, and reliable supply chains. Expanding access to self-collected HPV testing may overcome major barriers and represents a promising strategy to increase screening uptake in Uganda and similar low resource settings. Full article

Background/Objectives: Dogs are widely regarded as reliable sources of companionship and emotional support. In many instances, they are not merely considered pets, but valued as integral members of the family who significantly influence their caregivers’ emotional and psychological health. Within this framework, this research examines how dog ownership through adoption may serve as both a protective and empowering factor against feelings of loneliness, while also fostering emotional resilience and a renewed sense of purpose in everyday life. Methods: Employing a qualitative research design, this study involved ten Greek participants, five women and five men, aged between 26 and 72, all of whom were the primary caregivers of their dogs. Data were collected through semi-structured interviews aimed at eliciting rich, in-depth personal narratives. Thematic analysis was used to identify recurring emotional patterns and explore the meanings embedded in participants’ accounts. Results: The findings revealed that the human–dog bond functions as a stable emotional anchor, promoting non-judgmental connection and emotional security. Participants reported experiencing greater emotional expression, enhanced social engagement, and improved psychological balance. Conclusions: Overall, the results demonstrate how dog ownership through adoption may act as a protective factor against loneliness while fostering resilience and emotional balance, pointing to the broader mental health benefits of nurturing human–animal bonds. Full article

This study introduces SHAP-Rule, a novel explainable artificial intelligence method that integrates Shapley additive explanations with fuzzy logic to automatically generate interpretable linguistic IF-THEN rules for diagnostic tasks. Unlike purely numeric SHAP vectors, which are difficult for decision-makers to interpret, SHAP-Rule translates feature attributions into concise explanations that humans can understand. The method was rigorously evaluated and compared with baseline SHAP and AnchorTabular explanations across three distinct and representative datasets: the CWRU Bearing dataset for industrial predictive maintenance, a dataset for failure analysis in power transformers, and the medical Pima Indians Diabetes dataset. Experimental results demonstrated that SHAP-Rule consistently provided clearer and more easily comprehensible explanations, achieving high expert ratings for simplicity and understanding. Additionally, SHAP-Rule exhibited superior computational efficiency and robust consistency compared to alternative methods, making it particularly suitable for real-time diagnostic applications. Although SHAP-Rule showed minor trade-offs in coverage, it maintained high global fidelity, often approaching 100%. These findings highlight the significant practical advantages of linguistic fuzzy explanations generated by SHAP-Rule, emphasizing its strong potential for enhancing interpretability, efficiency, and reliability in diagnostic decision-support systems. Full article

River ecosystems provide essential services that sustain human well-being and ecological integrity, yet their contributions are often underestimated in management and policy decisions. This study aims to develop and validate indicators for quantifying river ecosystem services to support evidence-based decision-making. A review of previous studies was conducted to compile a preliminary list of indicators. Expert evaluations were then applied using the Analytical Hierarchy Process (AHP) and content validity ratio (CVR) analysis to assess their representativeness and reliability. The results identified priority indicators across provisioning, regulating, and cultural services. The findings revealed that, in the AHP analysis, regulating services received the highest weight (0.4567), followed by provisioning services (0.3811) and cultural services (0.1622). In the CVR analysis, four valid indicators were identified for provisioning services, sixteen for regulating services, and eight for cultural services. These findings highlight the importance of careful indicator selection and methodological transparency. The study contributes a refined set of indicators that can inform river restoration initiatives, sustainable water management, and integration into national ecosystem accounting systems. Full article