Search Results (310)

The intestinal mucosal barrier is a layered structure comprising fundamental components that play important roles in regulating paracellular permeability. Disruption of intestinal barrier homeostasis predisposes to infections, mucosal damage, and metabolic and allergic diseases. To provide protection against potential damage to the intestinal mucosa, agents such as prebiotics and probiotics are recommended due to their ability to secrete components and metabolites (e.g., bacteriocins, organic acids, enzymes) that can exert beneficial biological effects. The aim of this study is to comprehensively investigate the effects of a postbiotic derived from Pediococcus acidilactici on healthy rat intestinal tissue. A total of 78 Wistar Albino rats were used in this study. Following compositional analysis of the postbiotic, the animals were administered the postbiotic orally via gavage for different durations (7, 14, 21, 28 days) and at different doses (250 mg/Kg, 500 mg/Kg, 1000 mg/Kg). Characterization of the produced postbiotic revealed a diverse spectrum of biologically active compounds, including organic acids, phenolics, and volatile compounds. Histopathological examination of intestinal sections (duodenum, jejunum, ileum, cecum, colon, and rectum) showed no pathological lesions in any of the experimental groups. Conversely, immunohistochemical analysis revealed that the postbiotic increased the expression of CLDN3, OCLN, ZO1, AQP4, and AQP8, proteins involved in intestinal permeability and fluid transport, in a dose-dependent manner. These results highlight the potential of Pediococcus acidilactici as a supportive agent in a range of intestinal pathologies, including major intestinal diseases such as Crohn’s disease, ulcerative colitis, and inflammatory bowel disease (IBD). Full article

Helichrysum italicum Mill. (Asteraceae), a perennial evergreen species native to the Mediterranean basin, has been traditionally employed to treat various inflammatory and infectious diseases, as well as respiratory, digestive, gallbladder, and bladder disorders. The plant is valued for its essential oil. It contains phenols and flavonoids, which play a fundamental role in the protective effects associated with the traditional use of extracts of its aerial parts. The goal of the study was to investigate the phytochemical and biological properties of polar extracts, specifically water and hydroalcoholic (50% ethanol) extracts, obtained from the aerial parts of H. italicum. The extracts were evaluated for phenolic composition and concurrently assessed for antioxidant and enzyme-inhibitory activities. Additionally, the biocompatibility of the extracts was investigated using eco-toxicological models, including Artemia salina lethality and Daphnia magna cardiotoxicity assays, as well as allelopathic studies. CCD841CoN colon epithelial cell viability was also assessed in the presence of the extracts. The extracts’ protective effects were examined in an ex vivo inflammatory model using isolated mouse colon and liver tissues exposed to Escherichia coli lipopolysaccharide (LPS). Their influence on cyclooxygenase-2 (COX-2) and interleukin-6 (IL-6) gene expression was investigated, as well. Docking studies were also performed to uncover on the potential mechanisms underpinning the biological effects observed in the study. The phytochemical analysis showed that hydroxycinnamic acids and quercetin derivatives were the primary components in both extracts. In particular, the hydroalcoholic extract showed higher phenol levels and more potent scavenging/reducing and enzyme inhibitory activities against tyrosinase, cholinesterases, glucosidase, and amylase. Using the aforementioned eco-toxicological and in vitro cell models, the extracts’ biocompatibility was determined to be in the range of 200–1000 µg/mL. Within this concentration interval, the extracts effectively mitigated LPS-induced stimulation of COX-2 and IL-6 gene expression. Docking studies suggest that hydroxycinnamic acids (notably chlorogenic acid) and flavonoids (including quercetin, rutin, hyperoside, and isoquercitrin) play a pivotal role in the extracts’ anti-inflammatory activity. In conclusion, this study provides scientific evidence supporting the ethnopharmacological use of H. italicum in managing oxidative stress and inflammatory disorders, especially in the digestive system. Phenolics in the extracts likely enhance their therapeutic potential. These findings warrant further research, including in vivo studies, to assess the extracts’ efficacy and safety profile comprehensively. Full article

Background: Difficult intubation is an important clinical problem faced by anesthesiologists and is one of the most important causes of anesthesia-related morbidity. According to various sources, the frequency of encountering a difficult airway is stated as 1–4%. Aim: We thought that difficult tracheal intubation could be predicted by photographic analysis using artificial intelligence. Methods: Sixteen photographs were taken in the preoperative period in the sitting and lying positions anteriorly, laterally, with the mouth open, with the mouth closed, with the neck straight, and with the neck extended. Intubations performed without intervention for the first time were considered easy. Intubations with external tracheal intervention and with more than one attempt were evaluated as medium. Intubations requiring more than three attempts; intubation with stylets, fiberoptic bronchoscopes, or video laryngoscopes; or cases in which patients could not be intubated and provided airway with a laryngeal mask were considered difficult. Results: In our study, the CNN (convolutional neural network) model performed well overall, with the best results generally obtained using batch sizes of 32 and 128 and learning rates ranging from 0.1 to 0.001. Conclusions: The prominent aspects of our study are that it can be conducted with an easily accessible mobile phone, can be performed at the bedside, and is successful in predicting difficult intubation. The sensitivity of methods currently used to assess difficult airways is generally low, and the likelihood of clinicians successfully identifying this condition using available information varies widely; thus far, there is no gold standard for prediction. We believe that our study will bring a different perspective to estimating the difficulty of intubation, which occupies a very important place in anesthesia practice. Full article

Many construction tenders conducted by public institutions and organizations are canceled for various reasons, leading to project delays, resource inefficiencies, and disruptions to public services. This research aims to analyze the reasons for construction tender cancellations and the factors that influence the likelihood of cancellation, with a focus on institutional capacity and transaction costs. The cancelled tenders were obtained from the Electronic Public Procurement Platform (EKAP), which is officially used by public bodies. A total of 2483 construction tenders canceled in 2024 were analyzed. This figure represents 15.44% of the construction tenders conducted in Turkey in 2024. The construction tenders examined were subjected to a categorical frequency analysis using administrative reason codes. Additionally, weighted logistic regression was used to identify factors associated with the likelihood of cancellation among the 16,105 construction tenders held in 2024. According to the analysis results, region, type of administration, time, and tender type have a statistically significant impact on cancellation. The primary causes for cancellation include bids substantially above the estimated cost, the absence of submitted tenders, and the issuance of a published circular. The municipal elections held in March 2024 and the accompanying circular have led to an increase in tender cancellations. Inadequate institutional capacity may lead to uncertainty in the process; this, in turn, may result in the suspension of tenders and a rise in transaction costs. In this context, strengthening institutional resilience can be seen as a facilitator in resolving many issues. The factors that lead to tender cancellations and the suggested approaches can offer useful guidance for both the administration and contractors in normalizing processes. Furthermore, public authorities might consider investments to enhance institutional capacity to reduce the risk of tender cancellations. Full article

Objective: The aim of this study was to determine the optimal region-of-interest (ROI) pixel size for fractal dimension analysis on panoramic radiographs that best reflects implant stability assessed by resonance frequency analysis (ISQ) and to investigate whether implant stability can be directly estimated from radiographic images. Materials and Methods: This retrospective cross-sectional study included 65 patients for whom panoramic radiographs and resonance frequency analysis measurements were available. All panoramic images were converted to TIFF format and standardized to a resolution of 2627 × 1646 pixels. All radiographic images were obtained using the same panoramic imaging device and standardized acquisition protocol. Exposure parameters were adjusted within the manufacturer’s recommended range to ensure optimal image quality while maintaining methodological consistency across patients. During ROI selection, care was taken to avoid cortical bone margins, overlapping anatomical structures, and radiographic artifacts in order to ensure that the analyzed regions represented trabecular bone adjacent to the implant surface. Fractal dimension analysis was performed in the cervical peri-implant bone region, starting from the first bone–implant contact and extending apically, using three different ROI configurations. The ROI size was defined as 30 pixels apically and 10 pixels horizontally for FMD1, 30 × 20 pixels for FMD2, and 30 × 30 pixels for FMD3. Implant stability was assessed using ISQ values. Data distribution was evaluated using the Shapiro–Wilk test. Associations between ISQ and fractal dimension measurements were analyzed using Pearson and Spearman correlation analyses. Multiple linear regression models adjusted for age and sex were constructed to assess independent associations. Results: The mean age of the participants was 50.0 ± 9.9 years, and the mean ISQ value was 78.6 ± 5.9. The mean fractal dimension values were 1.466 ± 0.055 for FMD1, 1.595 ± 0.031 for FMD2, and 1.655 ± 0.046 for FMD3. No significant association was found between ISQ and FMD1 or FMD3. A weak positive correlation was observed between ISQ and FMD2; however, this association did not remain statistically significant after correction for multiple comparisons. In multiple linear regression analysis, ISQ was identified as an independent predictor of FMD2, but not of FMD1 or FMD3. Age and sex had no significant effect on fractal dimension measurements. Conclusions: Fractal dimension measurements derived from panoramic radiographs showed a weak association with implant stability that was dependent on the selected ROI pixel size. Among the evaluated configurations, the 30 × 20-pixel ROI at the cervical peri-implant region demonstrated the strongest association with ISQ values, suggesting that this ROI configuration showed the most consistent association with ISQ values among the tested ROI sizes. Full article

Objective: To evaluate the diagnostic and prognostic utility of the hemoglobin–albumin–lymphocyte–platelet (HALP) score and several systemic inflammatory indices derived from routine blood parameters—including the systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR), pan-immune inflammation value (PIV), and systemic inflammatory response index (SIRI)—for pathogen differentiation and clinical assessment in culture-proven late-onset neonatal sepsis (LOS). Methods: A retrospective analysis was conducted on a cohort of 150 neonates with culture-proven LOS. Systemic inflammatory indices were calculated at baseline (first week of life) and at the time of septic insult. The discriminative power of these indices was assessed via ROC curve analysis, with optimal cut-off points determined by the Youden Index. Risk stratification was performed using Odds Ratio (OR) modeling with 95% Confidence Intervals (CIs) to evaluate the predictive strength of each marker according to its respective threshold. Results: Diagnosis-phase assessments identified SII as the premier discriminator for microbiological etiology (AUC = 0.869; OR = 44.57), outperforming PLR and PIV. Although HALP demonstrated moderate efficacy in distinguishing pathogens, it lacked prognostic value regarding mortality. Conversely, SIRI displayed limited clinical utility, yielding the lowest predictive performance in our cohort. Conclusions: In neonatal sepsis, the HALP score provided additional clinical information when compared with several hematological inflammatory indices. Although HALP was not associated with mortality, prospective multicenter studies are needed to clarify the role of these cost-effective markers in pathogen differentiation and clinical assessment of LOS. Full article

Wind energy has expanded rapidly as a key low-carbon technology; however, its environmental performance cannot be assessed solely based on the operational phase. Floating wind turbines introduce additional structural components and offshore activities that significantly affect life cycle impacts. This study provides a comprehensive review of the life cycle environmental performance of floating wind systems by synthesizing existing life cycle assessment studies from a cradle-to-grave perspective. The analysis covers manufacturing, transportation, installation, operation and maintenance, and end-of-life stages, with particular focus on offshore-specific processes. Reported global warming potential values for floating wind turbines range from 7.23 to 31.4 g CO₂-eq/kWh, demonstrating competitive low-carbon performance. Manufacturing, driven largely by steel-intensive floating platforms and mooring systems, is identified as the dominant contributor, while vessel operations during installation and maintenance also play a significant role. The findings highlight the importance of holistic and site-specific life cycle modelling to support sustainable deep-water wind deployment. Full article

The study sheds light on the subtle interactions among financial development, foreign direct investment (FDI), and the quality of regulatory frameworks, with particular reference to their deep influence on renewable energy use and carbon emissions across 22 developed countries from 2002–2021. The results show an interesting tendency: Financial development and FDI will reduce reliance on renewable energy, whereas a significant increase in GDP per capita will increase reliance. Secondly, carbon emissions have a negative association with the adoption of renewable energy and financial development, though both reduce environmental quality; there is a positive relation between real gross domestic product (GDP) and energy depletion in terms of these toxic emissions. The significant role of regulatory quality as a moderator in this process is particularly striking. There is a direct correlation between financial stability and more robust regulation, resulting in reduced financial liquidity available for investing in renewable projects and restricting the free flow of clean FDI. Crucially, the paper argues that when combined with strong regulation, FDI is more likely to contribute to reductions in emissions, while FYGD, nevertheless regulated at a high level of quality, should raise emissions. Winding up, the result indicates that neither financial depth nor institutional quality, in isolation, is sufficient to deliver significant environmental improvement. Thus, it is urgent to adopt sound green finance policies and to formulate focused regulatory systems that integrate financial development and foreign direct investment with a broader sustainability agenda. Full article

Fiber-reinforced polymer (FRP) composites used in marine environments suffer progressive degradation due to hydrothermal aging, which undermines their structural, physical and morphological integrity. In this study, a novel polyester-based nano-gelcoat reinforced with hexagonal boron nitride (h-BN) nanoparticles was developed as an advanced FRP composite coating for marine applications. Glass fiber/epoxy laminates coated with h-BN/polyester nano-gelcoat were subjected to accelerated hydrothermal aging (immersion in 80 °C artificial seawater for 90 days). Mechanical (tensile/flexural tests), viscoelastic (creep and stress relaxation), thermal (DSC/TGA), and morphological (optical microscopy/SEM) analyses were performed on aged and unaged samples. The h-BN-enhanced nano-gelcoat increased the composite’s resistance to hydrothermal aging. In particular, the optimally doped nano-gelcoat (~1 wt% h-BN) retained the highest tensile and flexural strength and modulus, reducing the property losses seen in the unreinforced system by about half (flexural strength 531.29 MPa vs. 1070.52 MPa for the uncoated laminate). Thermal analysis indicated elevated decomposition onset temperatures and higher char yields with h-BN, confirming improved thermal stability. Morphological observations revealed well-dispersed h-BN at 1 wt% with minimal microcracking, whereas higher filler loadings led to agglomeration. Additionally, a TOPSIS-based multi-criteria decision-making (MCDM) analysis was performed across mechanical, viscoelastic, and thermal metrics, which identified the 1 wt% h-BN coating as the most balanced formulation after hydrothermal aging. Full article

Precise water quality forecasting is vital for sustainable resource management and public health, especially in semi-arid environments. This study investigates the predictive capabilities of ten Machine Learning (ML) algorithms using a dataset of 308 drinking water samples collected from various districts in Şanlıurfa Province, Türkiye. We evaluated ten predictive models, including Support Vector Regressor (SVR) and Extreme Gradient Boosting (XGBoost), both integrated with dimensionality reduction and hyperparameter optimization. Nineteen physicochemical and microbiological parameters—Temperature, chlorine (Cl⁻), pH, Electrical Conductivity (EC), Total Dissolved Solids (TDS), nitrite (NO₂⁻), nitrate (NO₃⁻), ammonium (NH₄⁺), sulfate (SO₄²⁻), Free Chlorine (Cl₂), calcium (Ca²⁺), magnesium (Mg²⁺), sodium (Na⁺), potassium (K⁺), fluoride (F⁻), trihalomethanes (THMs), Escherichia coli, Enterococci, Total Coliform—were used as input features. The dataset was split into training (75%) and testing (25%) subsets, and model performance was assessed through 10-fold cross-validation and hold-out testing procedures. To improve model generalization and mitigate the effects of class imbalance, we implemented the Adaptive Synthetic Sampling (ADASYN) technique. ML algorithms were evaluated using standard regression metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R²). The LSTM model optimized using Randomized Search outperformed the SVR and XGBoost models, demonstrating the highest accuracy and generalization capability, as evidenced by the superior R² value of 0.999 following ADASYN balancing and the lowest RMSE (1.206). These findings underscore the effectiveness of the LSTM framework in modeling the complex variance of the Weighted Arithmetic Water Quality Index (WAWQI). The findings of this study are expected to support future water quality monitoring strategies, inform policy development, and contribute to sustainable water resource management in arid and semi-arid regions. Full article

Low-light image enhancement (LLIE) remains a challenging problem due to spatially varying illumination degradation, compressed tonal distributions, and structural detail loss. This paper presents Adaptive Multi-Branch Feature Fusion (AMBFF), a unified framework that formulates LLIE as a multi-domain representation alignment task. The proposed architecture explicitly models complementary feature domains, including hierarchical spatial context, luminance–chrominance decoupling, edge–texture structures, frequency-domain information, and differentiable tonal histogram representations. A spatially adaptive gating mechanism dynamically weights multi-feature branches through a convex fusion strategy, enabling location-aware illumination correction while preserving structural integrity and color fidelity. Extensive evaluations on widely used benchmark datasets demonstrate that AMBFF consistently outperforms representative conventional and deep learning-based approaches in terms of PSNR, SSIM, and LPIPS. Ablation analyses confirm the complementarity of the proposed feature domains and the robustness benefits of adaptive fusion. Despite its multi-branch design, AMBFF maintains a favorable performance–complexity trade-off, highlighting the effectiveness of structured multi-domain modeling for low-light image enhancement. Full article

This study examined general attitudes toward animals among staff working in licensed animal hospitals in Türkiye. Using the 10-item Animal Attitude Scale (AAS-10), an online survey was administered to animal hospital staff; 193 questionnaires were completed from 17 provinces. Because total scores deviated from normality, group comparisons were conducted using non-parametric tests, and a multiple linear regression model was fitted to examine joint associations with demographic and professional variables. Overall, participants reported generally positive attitudes (mean AAS-10 = 36.7 ± 5.85; range 10–50). Women scored higher than men (p < 0.001), and respondents aged 20–29 years scored higher than those aged ≥40 years (p = 0.029) in unadjusted comparisons; however, the age pattern was small and did not persist after adjustment for gender and other covariates. Professional variables, including occupational role and length of service, were not meaningfully associated with total scores. Exploratory item-level analyses suggested gender-related differences in acceptance of specific forms of animal use, but these should be interpreted cautiously given multiple comparisons. These findings provide a descriptive baseline of AAS-10 scores in a heterogeneous animal hospital workforce and support hypothesis generation for future research that incorporates practice-specific measures. Full article

This study explores the interconnection between the variables of ChatGPT usage, energy consumption, water consumption, and carbon dioxide CO₂ emissions. A new integer and fractional order model using the Caputo derivative is proposed to comprehend the long-term dependencies of these variables. Boundedness, and global and local stability are examined for the fractional order model. The equilibrium points of these variables are shown to determine the stability of the model. The Runge–Kutta 7 numerical method is employed for the integer order model, whereas the semi-implicit linear interpolation (L1) method is used for the fractional order model. The parameter sensitivity is conducted on the system’s parameters to understand the variables’ impact by varying the relevant parameters for the system. To increase the efficacy of our analysis, we used machine learning approaches to model and predict the dynamics of CO₂ emissions, energy and water consumption, and ChatGPT usage. The Prophet ML model stood out among the other methods because it is adept at identifying long-term growth trends, seasonal changes, and the impact of outside variables in intricate time-series data. It is extremely beneficial for research centered on sustainability, where accurate projections are essential for wellinformed decision-making, because it can produce robust, interpretable forecasts against missing values and outliers. Using the Prophet ML model, our research guarantees precise and expandable predictions and provides valuable information that can direct tactics to balance environmental sustainability and technological progress. Full article

Background and Objectives: Effective postoperative analgesia is essential for enhanced recovery after bariatric surgery. The erector spinae plane block (ESPB) has emerged as a promising regional anesthesia technique, but its impact on postoperative pain control, opioid requirement, patient and surgeon satisfaction, and stress response in obese patients undergoing sleeve gastrectomy remains unclear. This study aimed to evaluate the effects of bilateral ESPB on postoperative analgesia requirements, pain scores, patient and surgeon satisfaction, hemodynamic stability, postoperative stress response, and perioperative hematologic and biochemical parameters in ASA II–III patients with a body mass index (BMI) > 30 undergoing sleeve gastrectomy. Study design was a prospective, randomized, single-blind clinical trial. Materials and Methods: After obtaining ethics committee approval (Şanlıurfa Harran University Hospital, date: 23 January 2023; decision no: HRÜ/23.02.09) and written/verbal informed consent, 60 patients aged 18–65 years, BMI > 30, ASA II–III scheduled for elective sleeve gastrectomy were included. Patients were randomized into two groups: those receiving bilateral ESPB (Group E, n = 30) and those without ESPB (Group C, n = 30). Demographic characteristics, ASA scores, comorbidities, and surgical duration were recorded. Preoperative venous samples were collected into hemogram (WBC, lymphocyte, neutrophil) and biochemistry tubes (CRP, cortisol, glucose). Standard monitoring (ECG, SpO₂, NIBP) was applied intraoperatively, and vital parameters (HR, MAP) were recorded throughout. Postoperatively, HR, MAP, Numerical Rating Scale (NRS) scores at 0, 2, 4, 8, and 24 h, opioid requirement, patient and surgeon satisfaction (Likert scale), postoperative hemogram and biochemistry values, and side effects or complications were documented. All patients received dexketoprofen as baseline analgesia, with tramadol HCl administered as rescue analgesic. Results: All 60 patients completed the study. There were no statistically significant differences between the groups regarding age, BMI, or surgery duration. Comorbidities were similar between groups. Intraoperative and postoperative HR and MAP values showed no significant differences. Postoperative NRS scores at the 0, 2, 8, and 24 hours were significantly lower in Group E compared with Group C. Both patient and surgeon satisfaction scores were higher in Group E. Rescue analgesic (tramadol HCl) consumption in the postoperative ward was significantly reduced in Group E. Cortisol levels, particularly at the 24th postoperative hour, showed a significantly smaller increase in Group E, suggesting a reduced surgical stress response. No significant differences were found between the groups regarding postoperative side effects or complications. Conclusions: Preoperative bilateral ESPB is an effective component of multimodal analgesia in sleeve gastrectomy. The block significantly reduces postoperative pain intensity, lowers NRS scores, improves patient and surgeon satisfaction, and decreases opioid requirements. Additionally, ESPB appears to attenuate the postoperative stress response, as reflected by smaller increases in cortisol levels. These findings support the routine incorporation of ESPB in perioperative pain management strategies for gastric sleeve surgery. Full article

Background/Objectives: This study aims to evaluate mandibular condylar asymmetry in individuals with different types of skeletal malocclusions using a three-dimensional imaging technique, and to determine the relationship between these anomalies and condylar asymmetry. Methods: The study included 100 individuals who visited the Department of Orthodontics Faculty of Dentistry between 2015 and 2020 and underwent Cone-Beam Computed Tomography (CBCT) imaging for various reasons. The evaluation of condylar asymmetry was performed using the Habets method, and measurements were carried out with the NemoCeph V.2017 software. Participants were categorized into skeletal Class I (2–4°), Class II (>4°), and Class III based on their ANB angles. For statistical analysis, frequency distribution, the Kruskal–Wallis H test, and Spearman’s correlation coefficient were used. Results: No statistically significant relationship was found between gender and skeletal classifications (p > 0.05). In terms of age, the mean age of individuals in the Class III group was significantly lower than that of those in the Class II group (p < 0.05). A weak positive correlation was observed between condylar and ramal indices in the overall sample (p = 0.029); however, this correlation was found to be moderate and statistically significant only within the Class III group (p = 0.002). Conclusions: The presence of a significant relationship between condylar and ramal asymmetries in individuals with Class III malocclusion indicates an increased risk of developing facial asymmetry if left untreated. These findings underscore the importance of skeletal malocclusions in influencing condylar morphology. Full article