Search Results (2,567)

Based on a literature review, the present article aims to present ecotourism as a transformative factor in the economic, social, cultural, and environmental contexts, revealing key elements for the sustainable development of ecotourism. To ensure that this objective is met, the review combines the insights of classical authors and many recent authors who have best addressed the subject. The review carefully selected consensual and contradictory arguments, reflecting on the relevance of each group, particularly in aspects such as the influence of emotional experience on behaviour and satisfaction, strategy and competitive advantage, cooperation and sustainability, and the influence of resilience on ecotourism. The impact of each perspective was presented without ignoring the major constraints that ecotourism faces in its search for a position in the tourism industry. This led the study to accept the fact that the active participation of the community is indispensable in the formula for the success of ecotourism. Some statistical data were consulted and analysed, which enabled the study to determine the quantitative impact of ecotourism on economic, social, and environmental life. In terms of benefits to communities, the review clarifies the fact that ecotourism serves as an instrument that mobilizes not only the additional value of products and services traded in the process, but also the return on investments and job creation. The combination of visiting activities with the involvement of tour guides contributes to maximizing profits in the destinations, thus supporting solid economic, social, and environmental development for the benefit of both ecotourism promoters and local communities. However, the analysis makes it clear that the economic, social, and environmental benefit depends on the degree of involvement of the local population. In terms of usability, for other studies, this review can contribute to the understanding and positioning of ecotourism in the search for a balance between satisfying socioeconomic and environmental interests. Additionally, it can serve as an aid to policy makers in their decisions related to ecotourism. Full article

Background/Objectives: Peripheral artery disease (PAD) impacts more than 200 million individuals globally and leads to mortality and morbidity secondary to progressive limb dysfunction and amputation. However, clinical management of PAD remains suboptimal, in part because of the lack of standardized biomarkers to predict patient outcomes. Growth differentiation factor 15 (GDF15) is a stress-responsive cytokine that has been studied extensively in cardiovascular disease, but its investigation in PAD remains limited. This study aimed to use explainable statistical and machine learning methods to assess the prognostic value of GDF15 for limb outcomes in patients with PAD. Methods: This prognostic investigation was carried out using a prospectively enrolled cohort comprising 454 patients diagnosed with PAD. At baseline, plasma GDF15 levels were measured using a validated multiplex immunoassay. Participants were monitored over a two-year period to assess the occurrence of major adverse limb events (MALE), a composite outcome encompassing major lower extremity amputation, need for open/endovascular revascularization, or acute limb ischemia. An Extreme Gradient Boosting (XGBoost) model was trained to predict 2-year MALE using 10-fold cross-validation, incorporating GDF15 levels along with baseline variables. Model performance was primarily evaluated using the area under the receiver operating characteristic curve (AUROC). Secondary model evaluation metrics were accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). Prediction histogram plots were generated to assess the ability of the model to discriminate between patients who develop vs. do not develop 2-year MALE. For model interpretability, SHapley Additive exPlanations (SHAP) analysis was performed to evaluate the relative contribution of each predictor to model outputs. Results: The mean age of the cohort was 71 (SD 10) years, with 31% (n = 139) being female. Over the two-year follow-up period, 157 patients (34.6%) experienced MALE. The XGBoost model incorporating plasma GDF15 levels and demographic/clinical features achieved excellent performance for predicting 2-year MALE in PAD patients: AUROC 0.84, accuracy 83.5%, sensitivity 83.6%, specificity 83.7%, PPV 87.3%, and NPV 86.2%. The prediction probability histogram for the XGBoost model demonstrated clear separation for patients who developed vs. did not develop 2-year MALE, indicating strong discrimination ability. SHAP analysis showed that GDF15 was the strongest predictive feature for 2-year MALE, followed by age, smoking status, and other cardiovascular comorbidities, highlighting its clinical relevance. Conclusions: Using explainable statistical and machine learning methods, we demonstrated that plasma GDF15 levels have important prognostic value for 2-year MALE in patients with PAD. By integrating clinical variables with GDF15 levels, our machine learning model can support early identification of PAD patients at elevated risk for adverse limb events, facilitating timely referral to vascular specialists and aiding in decisions regarding the aggressiveness of medical/surgical treatment. This precision medicine approach based on a biomarker-guided prognostication algorithm offers a promising strategy for improving limb outcomes in individuals with PAD. Full article

The aim of this study is to analyze the factors that influence consumer referents or reference points and their interaction during the decision-making process, along with the principles of prospect theory in the metaverse with market and retail examples. We conducted an integrative literature review. Consumers’ preference for reference points is determined and structured during the buying process, which can be affected by potential signals and biased decisions. To guide consumers’ shopping experiences and purchasing behavior in the most effective way, marketers and organizations must investigate the factors that influence consumer reference points beyond physical or tangible attributes. Businesses must be adaptable and adapt their strategies to changing consumer preferences based on reference points. Our findings can advance discussions about how reference points are being used in the market by using consumer decision-making claims in the discursive construction of the metaverse. By comprehending this, developers can create better experiences and assist users in navigating virtual risks. Our research aids us in better comprehending the influence of referents on consumer purchasing decisions in the marketing communications field. Numerous opportunities for academic research into consumer reference points have arisen, in which individuals as digital consumers are influenced by the same biases and heuristics that guide their behavior in reality. Full article

The immune landscape of tumor-draining lymph nodes (TDLNs) plays a critical role in shaping antitumor responses and influencing prognosis in oral squamous cell carcinoma (OSCC). Among patients with lymph node (LN) metastasis, clinical outcomes vary widely, yet reliable biomarkers for prognostic stratification remain limited. This study aimed to identify immune features in tumors and LNs that differentiate between favorable and poor outcomes in OSCC patients with nodal metastasis. We analyzed T cell receptor (TCR) CDR3 repertoires and the expression of immune-related genes in primary tumors and paired sentinel LNs from OSCC patients who underwent tumor resection and lymphadenectomy. Patients were divided into three groups: Group A (no nodal metastasis), Group B1 (metastasis without recurrence), and Group B2 (metastasis with recurrence). TCR diversity was assessed using the Shannon index. The expression of immune-related genes (e.g., CD3E, CD4, CD8B, FOXP3, CTLA4, IL2, IL4) was measured by quantitative PCR and normalized to GAPDH. TCR diversity was lower in tumors than in non-metastatic LNs, reflecting clonal expansion. Metastatic LNs exhibited tumor-like diversity, suggesting infiltration by tumor-reactive clones. Tumor gene expression did not differ across groups, but LNs from metastatic cases showed the reduced expression of several immune genes. Notably, CD3E, CD8B, CTLA4, IL2, and IL4 distinguished B1 from B2. The immune profiling of LNs offers superior prognostic value over tumor analysis in OSCC patients with LN metastasis. LN-based evaluation may aid in postoperative risk stratification and personalized postoperative management and could inform decisions regarding adjuvant therapy and follow-up strategies. Full article

Objective: To evaluate the prognostic utility of the Sequential Organ Failure Assessment (SOFA) and Simplified Acute Physiology Score 3 (SAPS 3) in COVID-19 patients and assess whether incorporating C-reactive protein (CRP), procalcitonin, lactate, and lactate dehydrogenase (LDH) enhances their predictive accuracy. Methods: Single-center, observational, cohort study. We analyzed a database of adult ICU patients with severe or critical COVID-19 treated at a large academic center. We used binary logistic regression for all analyses. We assessed the predictive performance of SAPS 3 and SOFA scores within 24 h of admission, individually and in combination with serum lactate, LDH, CRP, and procalcitonin. We examined the independent association of these biomarkers with hospital mortality. We evaluated discrimination using the C-statistic and determined clinical utility with decision curve analysis. Results: We included 1395 patients, 66% of whom required mechanical ventilation, and 59.7% needed vasopressor support. Patients who died (39.7%) were significantly older (61.1 ± 15.9 years vs. 50.1 ± 14.5 years, p < 0.001) and had more comorbidities than survivors. Among the biomarkers, only procalcitonin was independently associated with higher mortality in the multivariable analysis, in a non-linear pattern. The AUROC for predicting hospital mortality was 0.771 (95% CI: 0.746–0.797) for SAPS 3 and 0.781 (95% CI: 0.756–0.805) for the SOFA score. A model incorporating the SOFA score, age, and procalcitonin demonstrated high AUROC of 0.837 (95% CI: 0.816–0.859). These associations with the SOFA score showed greater clinical utility. Conclusions: The SOFA score may aid clinical decision-making, and incorporating procalcitonin and age could further enhance its prognostic utility. Full article

Manufacturing processes are inherently complex, multi-objective in nature, and highly sensitive to process parameter settings. This paper presents two simple and efficient optimization algorithms—Best–Worst–Random (BWR) and Best–Mean–Random (BMR)—developed to solve both constrained and unconstrained optimization problems of manufacturing processes involving single, multi-, and many-objectives. These algorithms are free from metaphorical inspirations and require no algorithm-specific control parameters, which often complicate other metaheuristics. Extensive testing reveals that BWR and BMR consistently deliver competitive, and often superior, performance compared to established methods. Their multi- and many-objective extensions, named MO-BWR and MO-BMR, respectively, have been successfully applied to tackle 2-, 3-, and 9-objective optimization problems in advanced manufacturing processes such as friction stir processing (FSP), ultra-precision turning (UPT), laser powder bed fusion (LPBF), and wire arc additive manufacturing (WAAM). To aid in decision-making, the proposed BHARAT can be integrated with MO-BWR and MO-BMR to identify the most suitable compromise solution from among a set of Pareto-optimal alternatives. The results demonstrate the strong potential of the proposed algorithms as practical tools for intelligent decision-making in real-world manufacturing applications. Full article

Background/Objectives: Knee osteoarthritis is one of the most significant diseases globally and in Portugal. Despite the availability of international guidelines, there is a lack of tailored, evidence-based recommendations specifically for Portuguese physiotherapists to manage their knee osteoarthritis patients with non-pharmacological and non-surgical interventions. This study aimed to develop a clinical practice guideline that integrates the latest international evidence with local clinical practice data to enhance patient outcomes. Methods: To achieve the objective, a comprehensive search was conducted in November 2024 across major health-related databases, to identify robust and recent evidence regarding the efficacy of non-pharmacological and non-surgical interventions, as well as their usage in the national context. Two key sources were identified: An umbrella and a mixed-methods study. Data from both sources were independently reviewed and integrated through a comparative analysis to identify interventions with robust scientific support and high local acceptability. Recommendations were then formulated and categorized into gold (strong), silver (moderate), and bronze (weak) levels based on evidence quality and clinical relevance. A decision-making flowchart was developed to support guideline implementation and clinical usage. Results: The integrated analysis identified three gold-level interventions, namely Nutrition/Weight Loss, Resistance Exercise, and Self-care/Education. Five silver-level recommendations were Aerobic Exercise, Balneology/Spa, Extracorporeal Shockwave Therapy, Electrical Stimulation, and Manual Therapy. Similarly, five bronze-level recommendations comprised Kinesio Taping, Stretching, Ultrasound Therapy, Thermal Agents, and Walking Aids. Conclusions: This clinical practice guideline provides a context-specific, evidence-based framework for Portuguese physiotherapists managing knee osteoarthritis. By bridging international evidence with local clinical practice, the guideline aims to facilitate optimal patient care and inform future research and guideline updates. Full article

In the last two decades, there has been a considerable surge in the development of artificial intelligence. Imaging is most frequently employed for the diagnostic evaluation of patients, as it is regarded as one of the most precise methods for identifying the presence of a disease. However, a study indicates that approximately 800,000 individuals in the USA die or incur permanent disability because of misdiagnosis. The present study is based on the use of computer-aided diagnosis of colorectal cancer. The objective of this study is to develop a practical, low-cost, AI-based decision-support tool that integrates clinical test data (blood/stool) and, if needed, colonoscopy images to help reduce misdiagnosis and improve early detection of colorectal cancer for clinicians. Convolutional neural networks (CNNs) and artificial neural networks (ANNs) are utilized in conjunction with a graphical user interface (GUI), which caters to individuals lacking programming expertise. The performance of the artificial neural network (ANN) is measured using the mean squared error (MSE) metric, and the obtained performance is 7.38. For CNN, two distinct cases are under consideration: one with two outputs and one with three outputs. The precision of the models is 97.2% for RGB and 96.7% for grayscale, respectively, in the first instance, and 83% for RGB and 82% for grayscale in the second instance. However, using a pretrained network yielded superior performance with 99.5% for 2-output models and 93% for 3-output models. The GUI is composed of two panels, with the best ANN model and the best CNN model being utilized in each. The primary function of the tool is to assist medical personnel in reducing the time required to make decisions and the probability of misdiagnosis. Full article

Oral cancer is typically diagnosed through histological examination; however, the primary issue with this type of procedure is tumor heterogeneity, where a subjective aspect of the examination may have a direct effect on the treatment plan for a patient. To reduce inter- and intra-observer variability, artificial intelligence algorithms are often used as computational aids in tumor classification and diagnosis. This research proposes a two-step approach for automatic multiclass grading using oral histopathology images (the first step) and Grad-CAM visualization (the second step) to assist clinicians in diagnosing oral squamous cell carcinoma. The Xception architecture achieved the highest classification values of 0.929 (±σ = 0.087) AUC_macro and 0.942 (±σ = 0.074) AUC_micro. Additionally, Grad-CAM provided visual explanations of the model’s predictions by highlighting the precise areas of histopathology images that influenced the model’s decision. These results emphasize the potential of integrated AI algorithms in medical diagnostics, offering a more precise, dependable, and effective method for disease analysis. Full article

Background/Objectives: Upper tract urothelial carcinoma (UTUC) is often complicated by intravesical recurrence and cancer progression following radical nephroureterectomy (RNU). Identifying reliable prognostic biomarkers remains crucial for optimizing postoperative surveillance. The goal of this study was to assess the prognostic value of the De Ritis ratio (AST/ALT) in predicting bladder recurrence and oncologic outcomes in patients with clinically localized UTUC undergoing RNU. Methods: This retrospective study analyzed 87 patients treated with RNU between 2018 and 2025. Preoperative De Ritis ratios were calculated, and an optimal cut-off value of 1.682 was determined using ROC analysis. Recurrence-free survival (RFS), cancer-specific survival (CSS), and overall survival (OS) were analyzed using the Kaplan–Meier and Cox regression methods. Logistic regression was used to identify independent predictors of bladder recurrence. Results: A high De Ritis ratio was significantly associated with increased bladder recurrence and worse RFS and CSS, but not OS. Multivariate analysis confirmed that an elevated De Ritis ratio, current smoking, positive surgical margins, and synchronous bladder cancer were the independent predictors of bladder recurrence. The De Ritis ratio demonstrated strong discriminatory performance (AUC: 0.807), with good sensitivity and specificity for predicting recurrence. Conclusions: The De Ritis ratio is a simple, cost-effective preoperative biomarker that may aid in identifying UTUC patients at higher risk for intravesical recurrence and cancer-specific mortality. Incorporating this ratio into clinical decision-making could enhance risk stratification and guide tailored follow-up strategies. Full article

This paper presents a visual active SLAM method considering measurement and state uncertainty for space exploration in urban search and rescue environments. An uncertainty evaluation method based on the Fisher Information Matrix (FIM) is studied from the perspective of evaluating the localization uncertainty of SLAM systems. With the aid of the Fisher Information Matrix, the Cramér–Rao Lower Bound (CRLB) of the pose uncertainty in the stereo visual SLAM system is derived to describe the boundary of the pose uncertainty. Optimality criteria are introduced to quantitatively evaluate the localization uncertainty. The odometry information selection method and the local bundle adjustment information selection method based on Fisher Information are proposed to find out the measurements with low uncertainty for localization and mapping in the search and rescue process. By adopting the method above, the computing efficiency of the system is improved while the localization accuracy is equivalent to the classical ORB-SLAM2. Moreover, by the quantified uncertainty of local poses and map points, the generalized unary node and generalized unary edge are defined to improve the computational efficiency in computing local state uncertainty. In addition, an active loop closing planner considering local state uncertainty is proposed to make use of uncertainty in assisting the space exploration and decision-making of MAV, which is beneficial to the improvement of MAV localization performance in search and rescue environments. Simulations and field tests in different challenging scenarios are conducted to verify the effectiveness of the proposed method. Full article

Introduction: Near-infrared fluorescence (NIRF) imaging with indocyanine green (ICG) is now widely regarded as a valuable aid in decision-making for complex hepatobiliary procedures, with increasing support from recent studies. Methods: We performed a systematic review following PRISMA guidelines, utilizing PubMed, CINAHL, and EMBASE databases to locate studies on the perioperative use ICG in pediatric hepatobiliary surgeries. Two independent reviewers assessed all articles for eligibility based on predefined inclusion criteria. We collected data on study design, patient demographics, surgical indications, ICG dosing, timing of ICG injection, and perioperative outcomes. Results: Forty-three articles, including 930 pediatric patients, from 1989 to 2025 met the inclusion criteria for narrative synthesis in our systematic review, of which 22/43 (51.2%) were retrospective studies, 15/43 were case reports (34.9%), 3/43 (7.0%) were experimental studies, and the other three were prospective comparative studies (7.0%). The current clinical applications of ICG in hepatobiliary pediatric surgery include bile duct surgery (cholecystectomy, choledochal cyst, biliary atresia), reported in 17 articles (39.5%), liver tumor resection, reported in 15 articles (34.9%), liver transplantation, reported in 6 articles (14.6%), and liver function determination, reported in 5 articles (12.2%). Conclusions: ICG fluorescence navigation in pediatric hepatobiliary surgery is a highly promising and safe technology that allows for the intraoperative localization of anatomic biliary structures, aids in the identification and resection of liver tumors, and can accurately determine hepatic function. The lack of comparative and prospective studies, and the variability of the dose and timing of administration are the main limitations. Full article

This study analyzed the probability of damage in heat transport pipelines buried in urban areas using pipeline attribute information and damage history data and developed an AI-based predictive model. A dataset was constructed by collecting spatial and attribute data of pipelines and defining basic units according to specific standards. Damage trends were analyzed based on pipeline attributes, and correlation analysis was performed to identify influential factors. These factors were applied to three machine learning algorithms: Random Forest, eXtreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). The model with optimal performance was selected by comparing evaluation indicators including the F2-score, accuracy, and area under the curve (AUC). The LightGBM model trained on data from pipelines in use for over 20 years showed the best performance (F2-score = 0.804, AUC = 0.837). This model was used to generate a risk map visualizing the probability of pipeline damage. The map can aid in the efficient management of urban heat transport systems by enabling preemptive maintenance in high-risk areas. Incorporating external environmental data and auxiliary facility information in future models could further enhance reliability and support the development of a more effective maintenance decision-making system. Full article

Accurate preoperative distinction between in situ and invasive Breast Cancer (BC) is critical for clinical decision-making and treatment planning. Radiomics and Machine Learning (ML) have shown promise in enhancing diagnostic performance from breast MRI, yet their application to this specific task remains underexplored. The aim of this study was to evaluate the performance of several ML classifiers, trained on radiomic features extracted from DCE–MRI and supported by basic clinical information, for the classification of in situ versus invasive BC lesions. In this study, we retrospectively analysed 71 post-contrast DCE–MRI scans (24 in situ, 47 invasive cases). Radiomic features were extracted from manually segmented tumour regions using the PyRadiomics library, and a limited set of basic clinical variables was also included. Several ML classifiers were evaluated in a Leave-One-Out Cross-Validation (LOOCV) scheme. Feature selection was performed using two different strategies: Minimum Redundancy Maximum Relevance (MRMR), mutual information. Axial 3D rotation was used for data augmentation. Support Vector Machine (SVM), K Nearest Neighbors (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) were the best-performing models, with an Area Under the Curve (AUC) ranging from 0.77 to 0.81. Notably, KNN achieved the best balance between sensitivity and specificity without the need for data augmentation. Our findings confirm that radiomic features extracted from DCE–MRI, combined with well-validated ML models, can effectively support the differentiation of in situ vs. invasive breast cancer. This approach is quite robust even in small datasets and may aid in improving preoperative planning. Further validation on larger cohorts and integration with additional imaging or clinical data are recommended. Full article

Background/Objectives: The oral cavity represents a convenient route of administration for drugs that exhibit significant hepatic first-pass extraction. In this study, the mucosal permeation properties of selected active pharmaceutical ingredients (APIs) incorporated into oral cavity drug products that are approved by the U.S. Food and Drug Administration were quantified using the human-derived sublingual HO-1-u-1 and buccal EpiOral™ in vitro tissue models. Methods: Epithelial barrier properties were monitored using propranolol and Lucifer Yellow as prototypic transcellular and paracellular markers. APIs were dissolved in artificial saliva, pH 6.7, and transepithelial flux from the apical to the basolateral compartment was quantified using HPLC. Results: Apparent permeability coefficients (Papp) calculated for these APIs in the sublingual HO-1-u-1 tissue model varied from Papp = 2.72 ± 0.06 × 10⁻⁵ cm/s for asenapine to Papp = 6.21 ± 2.60 × 10⁻⁵ cm/s for naloxone. In contrast, the buccal EpiOral™ tissue model demonstrated greater discrimination power in terms of permeation properties for the same APIs, with values ranging from Papp = 3.31 ± 0.83 × 10⁻⁷ cm/s for acyclovir to Papp = 2.56 ± 0.68 × 10⁻⁵ cm/s for sufentanil. The tissue-associated dose fraction recovered at the end of the transport experiment was significantly increased in the buccal EpiOral™ tissue model, reaching up to 8.5% for sufentanil. Conclusions: Experimental permeation data collected for selected APIs in FDA-approved oral cavity products will serve as a training set to aid the development of predictive computational models for improving algorithms that describe drug absorption from the oral cavity. Following a robust in vitro–in vivo correlation analysis, it is expected that such innovative in silico modeling strategies will the accelerate development of generic oral cavity products by facilitating the utility of model-integrated evidence to support decision making in generic drug development and regulatory approval. Full article