Search Results (3,290)

Background and Objectives: The worldwide incidence of renal cell carcinoma (RCC) rose by 22% between 2012 and 2022. In Australia, RCC accounted for 2.8% of all cancer diagnoses and contributing to 1.8% of cancer-related deaths. Identification of RCC biomarkers may aid in diagnosis and management. Methods: A systematic review of immunohistochemical markers of RCC studies published between 1990 and 2019 was undertaken to select candidate biomarkers of RCC. Immunohistochemical staining of 73 clear cell RCC tumors and paired normal tissue was undertaken using selected markers. Semi-quantitative and quantitative analysis of staining intensity between paired samples was undertaken to evaluate utility as potential biomarkers, using Chi-square tests and paired t-tests for analysis. As an exploratory analysis, staining intensity was also compared on clinical/demographic variables using linear and logistic regression. Results: There were 123 candidate biomarkers identified in 91 studies. Four candidate markers were selected for further investigation: aminopeptidase A (APA)/cluster of differentiation (CD)249, aminopeptidase N (APN)/CD13, gamma-glutamyl transferase (GGT), and neuron-specific enolase (NSE). APA, GGT, and APN all demonstrated reduced staining intensity in the tumor compared with normal tissue (p < 0.001 for all). NSE demonstrated a statistically significant increase in expression in tumor compared with normal tissue (p < 0.001), and this was more pronounced in patients aged >60 years (p = 0.038). Conclusions: The utility of APA, APN, and GGT as diagnostic biomarkers in clear cell RCC is limited. NSE may have some role as a biomarker for clear cell RCC, particularly among older patients; however, further investigation is required. Full article

This study examined the distribution and disease associations of non-HLA-B*27 HLA-B alleles in Romanian spondyloarthritis (SpA) patients, aiming to address the underrepresentation of Eastern European populations in immunogenetic research. Methods: We analyzed 263 HLA-B*27-negative patients from Northeastern Romania fulfilling ASAS criteria. HLA-B genotyping was performed at two-digit resolution, and allele distributions were compared with two Romanian HLA-B*27-negative control groups (n = 335 and n = 1705 cases), using chi-square testing and logistic regression. Compared to controls, HLA-B*47 (p = 0.0007) and HLA-B*54 (p = 0.0013) were significantly enriched, while HLA-B*40 was underrepresented (p = 0.0287). Notably, HLA-B*54 was observed exclusively in axial SpA. Within the cohort, both HLA-B*13 and HLA-B*57 alleles were associated with psoriasis, while HLA-B*37 and HLA-B*41 alleles were clustered within the reactive arthritis group. The HLA-B*35 and HLA-B*18 alleles were the most frequently observed alleles across most clinical phenotypes. When comparing the frequency of HLA-B associations, the most common genotypes among SpA patients were B*08-B*18, B*13-B*35, and B*35-B*51. Notably, B*08-B*18 was more frequent in patients with radiographic sacroiliitis grade ≥ 2, while B*35-B*51 was more frequent in those with confirmed systemic inflammation, as indicated by elevated CRP or ESR levels. Analysis of peptide-binding patterns revealed a cluster of risk alleles, HLA-B*08, B*18, B*35, B*40, and B*54, sharing similar features, distinct from the canonical profile of B*27. These findings highlight the contribution of non-B*27 HLA-B alleles to SpA susceptibility in an Eastern European population and support the notion that HLA-B*27-negative SpA may represent a distinct clinical and immunological entity, driven by alternative pathogenic mechanisms. They also emphasize the importance of population-specific immunogenetic profiling and support expanding genetic characterization in HLA-B*27-negative patients. Full article

Background/Objectives: While elevated Lp(a) levels are associated with incident heart failure development, the role of Lp(a) in established heart failure with reduced ejection fraction (HFrEF) versus heart failure with preserved ejection fraction (HFpEF) remains unexplored. Methods: We conducted a retrospective analysis of 387 heart failure patients from our institutional database (January 2018–June 2024). Patients were categorized as HFrEF (n = 201) or HFpEF (n = 186) using ICD-10 codes. Categorical variables were compared between heart failure types using the Chi-square test or Fisher’s Exact test, and continuous variables were compared using the two-sample t-test or Wilcoxon rank-sum test, as appropriate. Logistic regression was utilized to assess heart failure type as a function of Lp(a) levels, adjusting for covariates. Spearman correlation assessed relationships between Lp(a) and pro-BNP levels. Results: Despite significant demographic and clinical differences between HFrEF and HFpEF patients, Lp(a) concentrations showed no significant variation between groups. Median Lp(a) levels were 60.9 nmol/dL (IQR: 21.9–136.7) in HFrEF versus 45.0 nmol/dL (IQR: 20.1–109.9) in HFpEF (p = 0.19). After adjusting for demographic and clinical covariates, Lp(a) showed no association with heart failure subtype (OR: 1.001, 95% CI: 0.99–1.004; p = 0.59). Conclusions: Lp(a) levels do not differ significantly between HFrEF and HFpEF phenotypes, suggesting possible shared pathophysiological mechanisms rather than phenotype-specific biomarker properties. These preliminary findings may support unified screening and treatment strategies for elevated Lp(a) across heart failure, pending confirmation in larger studies. Full article

Background: The COVID-19 pandemic has profoundly altered the clinical and microbiological landscape of respiratory tract infections (RTIs), potentially reshaping pathogen distribution and antimicrobial resistance (AMR) profiles across care settings. Objectives: The objective of this study was to assess temporal trends in respiratory bacterial pathogens, antimicrobial resistance, and polymicrobial infections across three pandemic phases—pre-COVID (2018–2019), COVID (2020–2022), and post-COVID (2022–2024)—in hospitalized and ambulatory patients. Methods: We retrospectively analyzed 1827 respiratory bacterial isolates (hospitalized patients, n = 1032; ambulatory patients, n = 795) collected at a tertiary care center in Northern Italy. Data were stratified by care setting, anatomical site, and pandemic phase. Species identification and susceptibility testing followed EUCAST guidelines. Statistical analysis included chi-square and Fisher’s exact tests. Results: In hospitalized patients, a significant increase in Pseudomonas aeruginosa (from 45.5% pre-COVID to 58.6% post-COVID, p < 0.0001) and Acinetobacter baumannii (from 1.2% to 11.1% during COVID, p < 0.0001) was observed, with 100% extensively drug-resistant (XDR) rates for A. baumannii during the pandemic. Conversely, Staphylococcus aureus significantly declined from 23.6% pre-COVID to 13.7% post-COVID (p = 0.0012). In ambulatory patients, polymicrobial infections peaked at 41.2% during COVID, frequently involving co-isolation of Candida spp. Notably, resistance to benzylpenicillin in Streptococcus pneumoniae reached 80% (4/5 isolates) in hospitalized patients during COVID, and carbapenem-resistant P. aeruginosa (CRPA) significantly increased post-pandemic in ambulatory patients (0% pre-COVID vs. 23.5% post-COVID, p = 0.0014). Conclusions: The pandemic markedly shifted respiratory pathogen dynamics and resistance profiles, with distinct trends observed in hospital and community settings. Persistent resistance phenotypes and frequent polymicrobial infections, particularly involving Candida spp. in outpatients, underscore the need for targeted surveillance and antimicrobial stewardship strategies. Full article

Cucumber, a high-yielding crop commonly grown in facility environments, is particularly susceptible to nitrogen (N) deficiency due to its rapid growth and high nutrient demand. This study used cucumber as its experimental subject and established a spectral dataset of leaves under four nutritional conditions, normal supply, nitrogen deficiency, phosphorus deficiency, and potassium deficiency, aiming to develop an efficient and robust method for quantifying N in cucumber leaves using Raman spectroscopy (RS). Spectral data were preprocessed using three baseline correction methods—BaselineWavelet (BW), Iteratively Improve the Moving Average (IIMA), and Iterative Polynomial Fitting (IPF)—and key spectral variables were selected using 4-Dimensional Feature Extraction (4DFE) and Competitive Adaptive Reweighted Sampling (CARS). These selected features were then used to develop a N content prediction model based on Partial Least Squares Regression (PLSR). The results indicated that baseline correction significantly enhanced model performance, with three methods outperforming unprocessed spectra. A further analysis showed that the combination of IPF, 4DFE, and CARS achieved optimal PLSR model performance, achieving determination coefficients (R²) of 0.947 and 0.847 for the calibration and prediction sets, respectively. The corresponding root mean square errors (RMSEC and RMSEP) were 0.250 and 0.368, while the residual predictive deviation (RPDC and RPDP) values reached 4.335 and 2.555. These findings confirm the feasibility of integrating RS with advanced data processing for rapid, non-destructive nitrogen assessment in cucumber leaves, offering a valuable tool for nutrient monitoring in precision agriculture. Full article

There has been much interest in the mathematical investigation of critical sets and unavoidable sets in Latin Squares, Sudoku, and their applications to practical problems in areas such as agriculture and cryptology. This paper considers the associated structures of Strictly Concentric Magic Squares (SCMSs) and Prime Strictly Concentric Magic Squares (PSCMSs). A framework of formal definitions is given that leads to the definitions of critical sets and unavoidable sets. Minimal critical sets are of interest in Latin Squares, and in this article, the cardinality of minimal critical sets of SCMS is given for all n, n odd. Two families of unavoidable sets are established for SCMS, leading to a complete classification of unavoidable sets of minimum PSCMS of order 5. Full article

Background/Objectives: Direct oral anticoagulants (DOACs) are now the guideline-recommended alternative to vitamin K antagonists (VKAs) for long-term anticoagulation in patients with non-valvular atrial fibrillation. However, accurately assessing their impact on ischemic stroke outcomes remains challenging, primarily due to uncertainty regarding anticoagulation status at the time of hospital admission. This preliminary study addresses this gap by using point-of-care testing (POCT) to confirm DOAC activity at bedside, allowing for a more accurate comparison of 90-day functional outcomes between anticoagulated and non-anticoagulated stroke patients. Methods: We conducted a retrospective cohort study of 786 ischemic stroke patients admitted to the University of Pécs between February 2023 and February 2025. Active DOAC therapy was confirmed using the ClotPro^® viscoelastic testing platform, with ecarin Clotting Time (ECT) employed for thrombin inhibitors and Russell’s Viper Venom (RVV) assays for factor Xa inhibitors. Patients were categorized as non-anticoagulated (n = 767) or DOAC-treated with confirmed activity (n = 19). Mahalanobis distance-based matching was applied to account for confounding variables including age, sex, pre-stroke modified Rankin Scale (mRS), and National Institutes of Health Stroke Scale (NIHSS) scores at admission and 72 h post-stroke. The primary outcome was the change in mRS from baseline to 90 days. Statistical analysis included ordinary least squares (OLS) regression and principal component analysis (PCA). Results: After matching, 90-day functional outcomes were comparable between groups (mean mRS-shift: 2.00 in DOAC-treated vs. 1.78 in non-anticoagulated; p = 0.745). OLS regression showed no significant association between DOAC status and recovery (p = 0.599). In contrast, NIHSS score at 72 h (p = 0.004) and age (p = 0.015) were significant predictors of outcome. PCA supported these findings, identifying stroke severity as the primary driver of outcome. Conclusions: This preliminary analysis suggests that ischemic stroke patients with confirmed active DOAC therapy at admission may achieve 90-day functional outcomes comparable to those of non-anticoagulated patients. The integration of bedside POCT enhances the reliability of anticoagulation assessment and underscores its clinical value for real-time management in acute stroke care. Larger prospective studies are needed to validate these findings and to further refine treatment strategies. Full article

Background: Minimally invasive cardiac surgery (MICS) via right minithoracotomy is a safe, reproducible approach with excellent outcomes and reduced costs compared to median sternotomy. Despite careful patient selection, conversion to sternotomy occurs in 1–3% of cases and is associated with significantly higher morbidity and mortality. Small body habitus, particularly a short anteroposterior (AP) diameter, may increase the risk of conversion, but this has not been previously studied. This study aims to identify preoperative factors associated with conversion to improve patient selection for MICS. As cardiovascular surgery becomes increasingly personalized, identifying anatomical factors that predict technical complexity is essential. Methods: This retrospective study included 254 adult patients who underwent elective MICS between 2015 and 2024 at a tertiary hospital. Patient characteristics, computed tomography (CT) scans, intraoperative parameters, and postoperative outcomes were reviewed. AP diameter was defined as the distance from the posterior sternum to the anterior vertebral body at the mitral valve level on CT. Statistical analyses included Mann−Whitney and Fisher’s exact/chi-square tests. Results: Conversion to sternotomy occurred in 1.6% of patients (n = 4). All converted patients were female. The converted group had a significantly shorter median AP diameter (100 mm vs. 124 mm, p = 0.020). Conversion was associated with higher rates of stroke and infection (25.0% vs. 0.8%, p = 0.047 for both), but no significant differences in hospital stay, bleeding, or renal failure. Conclusions: An AP diameter of less than 100 mm was associated with a higher risk of conversion to sternotomy in MICS. Incorporating simple, reproducible preoperative imaging metrics into surgical planning may advance precision-guided cardiac surgery and optimize patient outcomes. Full article

Morphine is an opioid extracted from the poppy plant and highly effective for moderate to severe pain management. Development of techniques to measure the concentration of this highly addictive drug in various matrices is very important. This work was aimed at the development of a sensitive electrochemical method for detection of morphine in wastewater. Molecularly imprinted (MIP) electrodes were made by the electro-polymerization process using pyrrole as a monomer. Electro-polymerization was performed on glassy carbon electrodes in the presence of morphine before the extraction of the entrapped morphine molecules. Various techniques were employed to monitor the polymerization and response of the fabricated electrodes toward morphine. These techniques included Fourier transform infrared spectroscopy (FTIR), cyclic voltammetry (CV), square wave voltammetry (SWV), and electrochemical impedance spectroscopy (EIS). The morphine concentration was determined using SWV and CV by measuring the change in the redox peak current of [Fe(CN)₆]^−3/−4. These MIP electrode sensors were used to analyze morphine concentrations between 0 and 80.0 nM solution. The SWV showed a wider linear response region than CV. The detection limit using SWV was found to be 1.9 nM, while using CV, the detection limit was 2.75 nM. This MIP electrode sensor exhibited specificity when other closely related molecules were included and hence has potential as a cheap alternative technique for analysis of morphine. Full article

As augmented reality (AR) advertising becomes increasingly prevalent across digital platforms, understanding how its unique features influence consumer responses is critical for both theory and practice. Based on the elaboration likelihood model (ELM), this study develops and validates a dual-dimension content–dual-route processing model to investigate how different features of AR advertising influence consumer engagement. Specifically, it examines how product-related attributes (attractiveness, informativeness) and technology-related attributes (interactivity, augmentation) shape attitudes toward the ad and purchase intentions through cognitive (information credibility) and affective (enjoyment) pathways. Using data from an online survey (N = 299), the study applies partial least squares structural equation modeling (PLS-SEM) to test the proposed model. The results show that informativeness and augmentation significantly enhance information credibility, while attractiveness primarily influences emotional responses. Interactivity and augmentation positively influence cognitive and affective responses. Mediation analysis confirms the simultaneous activation of central and peripheral processing routes, with flow experience emerging as a significant moderator in selected pathways. By introducing a structured framework for AR advertising content, this study extends the applicability of the ELM in immersive media contexts. It underscores the combined impact of rational evaluation and emotional engagement in shaping consumer behavior and offers practical insights for designing effective AR advertising strategies. Full article

The evidence for photobiomodulation in reducing postoperative pain after endodontic instrumentation is classified as low or very low certainty, indicating a need for further research. Longitudinal pain assessments over 24 h are crucial, and studies should explore these pain periods. Background/Objectives: This double-blind, randomized controlled clinical trial evaluated the effect of PBM on pain following single-visit endodontic treatment of maxillary molars at 4, 8, 12, and 24 h. Primary outcomes included pain at 24 h; secondary outcomes included pain at 4, 8, and 12 h, pain during palpation/percussion, OHIP-14 analysis, and frequencies of pain. Methods: Approved by the Research Ethics Committee (5.598.290) and registered in Clinical Trials (NCT06253767), the study recruited adults (21–70 years) requiring endodontic treatment in maxillary molars. Fifty-eight molars were randomly assigned to two groups: the PBM Group (n = 29), receiving conventional endodontic treatment with PBM (100 mW, 333 mW/cm², 9 J distributed at 3 points near root apices), and the control group (n = 29), receiving conventional treatment with PBM simulation. Pain was assessed using the Visual Analog Scale. Results: Statistical analyses used chi-square and Mann–Whitney tests, with explained variance (η²). Ten participants were excluded, leaving 48 patients for analysis. No significant differences were observed in postoperative pain at 24, 4, 8, or 12 h, or in palpation/percussion or OHIP-14 scores. Pain frequencies ranged from 12.5% to 25%. Conclusions: PBM does not influence post-treatment pain in maxillary molars under these conditions. These results emphasize the importance of relying on well-designed clinical trials to guide treatment decisions, and future research should focus on personalized dosimetry adapted to the anatomical characteristics of the treated dental region to enhance the accuracy and efficacy of therapeutic protocols. Full article

This paper presents a novel harmonic-selective clocking scheme that facilitates concurrent downconversion of spectrally distant radio frequency (RF) signals using a single low-frequency local oscillator (LO) in an N-path receiver architecture. The proposed scheme selectively generates LO harmonics aligned with multiple RF bands, enabling simultaneous downconversion without modification of the passive mixer topology. The receiver employs a 4-path passive mixer configuration to enhance harmonic selectivity and provide flexible frequency planning.The architecture is implemented on a printed circuit board (PCB) and validated through comprehensive simulation and experimental measurements under continuous wave and modulated signal conditions. Measured results demonstrate a sensitivity of $-55\mathrm{dBm}$ and a conversion gain varying from $-2.5\mathrm{dB}$ to $-9\mathrm{dB}$ depending on the selected harmonic pair. The receiver’s performance is further corroborated by concurrent (dual band) reception of real-world signals, including a GPS signal centered at 1575 MHz and an LTE signal at 1179 MHz, both downconverted using a single 393 MHz LO. Signal fidelity is assessed via Normalized Mean Square Error (NMSE) and Error Vector Magnitude (EVM), confirming the proposed architecture’s effectiveness in maintaining high-quality signal reception under concurrent multiband operation. The results highlight the potential of harmonic-selective clocking to simplify multiband receiver design for wireless communication and global navigation satellite system (GNSS) applications. Full article

Timely and accurate monitoring of crop nitrogen (N) status is essential for precision agriculture. UAV-based hyperspectral remote sensing offers high-resolution data for estimating plant nitrogen concentration (PNC), but its cost and complexity limit large-scale application. This study compares the performance of UAV hyperspectral data (S185 sensor) with simulated multispectral data from DJI Phantom 4 Multispectral (P4M), PlanetScope (PS), and Sentinel-2A (S2) in estimating winter wheat PNC. Spectral data were collected across six growth stages over two seasons and resampled to match the spectral characteristics of the three multispectral sensors. Three variable selection strategies (one-dimensional (1D) spectral reflectance, optimized two-dimensional (2D), and three-dimensional (3D) spectral indices) were combined with Random Forest Regression (RFR), Support Vector Machine Regression (SVMR), and Partial Least Squares Regression (PLSR) to build PNC prediction models. Results showed that, while hyperspectral data yielded slightly higher accuracy, optimized multispectral indices, particularly from PS and S2, achieved comparable performance. Among models, SVM and RFR showed consistent effectiveness across strategies. These findings highlight the potential of low-cost multispectral platforms for practical crop N monitoring. Future work should validate these models using real satellite imagery and explore multi-source data fusion with advanced learning algorithms. Full article

Recently, 3D-printed resins have been introduced as materials for definitive indirect restorations. Herein, a comparative assessment of the bond strengths of 3D-printed resins to a resin cement was performed. Methods: four definitive restorative materials were selected, i.e., a Feldspar ceramic (VITA Mark II, VM), a polymer-infiltrated ceramic network (VITA Enamic, VE), a nanohybrid resin composite (Grandio Bloc, GB), and one 3D-printed resin (Crown Permanent, CP). VM and VE were etched and silanized, GB was sandblasted, and CP was glass bead blasted; for one further experimental group, this was followed by sandblasting (CPs). A resin cement (RelyX Unicem) was then used for bonding, and then a notched shear bond strength test (nSBS) was performed. Failure modes were observed and classified as adhesive, cohesive, or mixed, and SEM representative images were taken. Data were statistically analyzed with one-way ANOVA, Tukey, and Chi-square tests. Significant differences were detected in nSBS among materials (p < 0.001). The highest nSBS was found in VM (30.3 ± 1.8 MPa) a, followed by CP^b, GB^bc, CP^bc, and VE^c. Failure modes were significantly different (p < 0.001), and with different prevalent failure modes. The bond strength for 3D-printed permanent resin materials was shown to be lower than that of the felspathic ceramic but comparable to that of the resin block and PICN substrates. Full article

Indonesia’s worsening air pollution and traffic emissions have thrust electric vehicles (EVs) into the spotlight, but what really drives Indonesians to make the switch? This study integrates Protection Motivation Theory with green branding and policy frameworks to explain electric vehicle (EV) adoption in Indonesia. Using a nationwide survey (n = 986) and partial-least-squares structural-equation modeling, we test how environmental awareness, consumer expectancy, threat appraisal, and coping appraisal shape adoption both directly and through green brand image (GBI), while perceived policy incentives moderate the GBI–adoption link. The model accounts for 54% of the variance in adoption intention. These findings highlight that combining public awareness campaigns, compelling green brand messaging, and carefully calibrated policy incentives is essential for accelerating Indonesia’s transition to cleaner transport. Full article