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This research is motivated by the arbitrary nature of decision-making environments and the dynamic changes in decision patterns, particularly in international dispute resolution. These challenges introduce uncertainties that could be effectively managed by fuzzy logic, which provides a robust framework for evaluating alternatives under multiple criteria. In this study, an Interval-Valued Type-2 Fuzzy TOPSIS approach is proposed to assess various dispute resolution methods, including negotiation, good offices, mediation, international inquiry, conciliation, international organization, arbitration, and international jurisdiction. Common criteria are determined by examining academic literature and by interviewing relevant experts.—cost-efficiency, duration, impartiality, binding nature, and generalizability are considered essential in determining the best resolution method. The proposed method allows for a nuanced evaluation by incorporating both primary and secondary levels of uncertainty, enabling decision-makers to determine the best alternative solution more reliably. This method’s application extends not only to the international law field but also to industrial engineering, where complex, uncertain decision environments require similarly sophisticated multicriteria decision-making tools. By systematically analyzing these resolution methods, this study aims to provide a structured, quantifiable approach that enhances the decision-making process for both international legal practitioners and engineers working with uncertain and dynamic systems. The results of this study ultimately contribute to improved decision-making outcomes and greater efficiency in multidisciplinary problem solving. The assessments of experts in international law, international relations, and political science in their respective fields of expertise have been gathered to form a consensus. This study contributes to the literature as it is the pioneering application of fuzzy multicriteria decision-making techniques in the field of international law. The results of this study imply that the best option from the different decision-maker evaluations is international jurisdiction. Consequently, the utilization of multicriteria decision-making tools can result in more informed and effective decisions in complex and uncertain situations, which is advantageous to both legal practitioners and engineers. Additionally, incorporating different disciplines can help streamline the decision-making process and improve overall efficiency in solving multidisciplinary problems.

Background Pre-eclampsia continues to be a significant global health burden with complex pathophysiology, necessitating investigation of novel biomarkers to improve understanding, diagnosis and management of this pregnancy-specific disorder.To investigate the differential expression of Calcium Release-Activated Calcium Channel Protein 1 (ORAI1), Fibroblast Growth Factor 23 (FGF23), Placental Protein 13 (PP13), Palladin, and Supervillin in both maternal and umbilical cord blood as potential biomarkers for late-onset pre-eclampsia. Methods This cross-sectional, case-control study included 61 women with late-onset pre-eclampsia and 61 normotensive pregnant women undergoing cesarean delivery. Maternal blood samples were collected immediately prior to cesarean delivery, and umbilical cord blood was obtained immediately after delivery of the placenta. Protein concentrations in both circulatory compartments were measured using enzyme-linked immunosorbent assay. The unique study design with paired maternal-cord blood sampling provided insights into maternal-fetal protein transfer dynamics in pre-eclamptic conditions. Results Maternal and cord blood ORAI1 concentrations were significantly elevated in pre-eclampsia ( p  = 0.001 and p  = 0.035, respectively), while FGF23 and PP13 were significantly decreased in maternal blood ( p  = 0.022 and p  = 0.018, respectively). Maternal-to-cord blood concentration ratios for ORAI1 and FGF23 were significantly altered in pre-eclampsia ( p  = 0.038 and p  = 0.021, respectively). ORAI1 showed the highest diagnostic accuracy (AUC = 0.733) and correlated positively with disease severity and negatively with birth weight. Combined ORAI1 and FGF23 assessment significantly enhanced diagnostic performance (AUC = 0.782). Conclusion The altered expression of ORAI1, FGF23, and PP13 in late-onset pre-eclampsia suggests disruptions in calcium signaling, phosphate metabolism, and placental function. The parallel measurement of these proteins in both maternal and cord blood provided unique insights into maternal-fetal interface dysfunction in pre-eclampsia. The superior performance of combined ORAI1 and FGF23 measurement underscores the value of a multi-marker approach in capturing pre-eclampsia’s complex pathophysiology, potentially contributing to improved diagnostic strategies and therapeutic interventions.

Background This study aimed to investigate the effects of Lopinavir/Ritonavir (Lop/r) on chondrocyte structure and extracellular matrix (ECM) integrity, as well as its impact on key proteins involved in anabolic and catabolic pathways, using both in vitro and in silico approaches. Methods Drug-target interaction networks were constructed through bioinformatics analyses, and molecular docking was performed. Human primary chondrocytes were treated with Lop/r, and untreated cells served as controls. Cell viability, proliferation, and protein expression levels were assessed using standard in vitro techniques, including spectrophotometric assays and Western blotting. Results Molecular docking analyses revealed strong binding affinities between Lop/r and osteoarthritis-related targets such as HIF-1α, EP300, TNF, IL-6, KCNA5, and IL-1β, suggesting modulation of hypoxia, inflammatory, and epigenetic pathways. In vitro, Lop/r did not alter chondrocyte morphology or ECM structure and was not cytotoxic ( p  < 0.05). However, it significantly reduced the expression of critical proteins including HIF-1α, SOX9, and IL-1β ( p  < 0.05). Conclusion These findings suggest that Lop/r may exert regulatory effects on cartilage-related molecular pathways and holds promise as a repurposed therapeutic agent for osteoarthritis. Further studies are warranted to confirm its potential in clinical applications.

The production of renewable energy is becoming one of the most important issues for communities due to the increasing energy demand. The purpose of this paper is to develop a systematized, sustainability-focused evaluation framework for determining the efficiency of wind farms in Turkey. The environmental impact and long-term viability of wind farms are evaluated using an evaluation framework centered on sustainability. The evaluation of their sustainability involves analyzing their energy production, environmental impacts and economic viability. In this study, DEA–Fuzzy COPRAS aims to evaluate the efficiency of 11 wind power plants located in Turkey in the Marmara Region. As inputs, the number of wind turbines, investment cost and distance from the grid are selected. As output, electricity is produced, and daily production time is considered. The proposed DEA–Fuzzy COPRAS aims to eliminate the disadvantages of the conventional methods and to be able to make better decisions regarding the weight value under uncertain conditions. The main advantages of the proposed DEA–Fuzzy COPRAS include a more accurate evaluation of efficiency and the ability to consider multiple criteria simultaneously. Additionally, the proposed DEA–Fuzzy COPRAS considers uncertainty in the inputs and outputs of wind energy production. The results of the proposed work are validated by comparing them with those obtained from a sensitivity analysis of the criteria. Therefore, decision makers can evaluate the efficiency of wind power plants accurately under an imprecise environment. Wind power plant managers or investors and other renewable energy projects can benefit from the proposed method’s implementation by allowing governments and stakeholders to save money and make better use of resources during the planning phase.

The present study aims to develop a new molecular diagnostic modality based upon mid–infrared (mid–IR) spectroscopy allied with chemometrics for the diagnosis of gastric cancer (GC) from diverse biofluids. The characterization over the spectral data specified the changes at molecular level caused from the synthesis of main biomolecules like lipids, proteins and carbohydrates was performed. The spectral data from the cancer and their control cases were analyzed by practicing the supervised and unsupervised chemometrics analysis including PCA, SIMCA, LDA and HCA. The results indicated that remarkable changes were occurred in the content of biomolecules for GC cases due to energy necessities. 100% of the cancer cases from blood serum, blood plasma and saliva biofluids were successfully discriminated from their control specimens by LDA. Consequently, this research established the potential of mid–IR spectroscopy allied with chemometrics for evaluating biochemical changes of GC cases with high sensitivity and precision.

Aim: Cervical cancer development is influenced by a complex interaction of socio-demographic, behavioral, and clinical factors, which can be systematically analyzed using large datasets. Therefore, this study aimed to evaluate the effectiveness of machine learning (ML) models applied to the University of California, Irvine (UCI), cervical cancer risk factors dataset in predicting cervical health outcomes and supporting early detection strategies. Methods: This study was designed as a retrospective data analysis covering a random sampling of patients between 2012 and 2013 who attended the gynecology service at Hospital Universitario de Caracas in Caracas, Venezuela. The publicly available UCI cervical cancer risk factors dataset was utilized for the analysis. A correlation heatmap was generated to explore the relationships among various risk factors. To address the class imbalance present in the dataset, the synthetic minority over-sampling technique (SMOTE) was applied. Subsequently, different ML classifiers were trained and evaluated to predict cervical cancer outcomes with improved accuracy. Results: The correlation analysis revealed strong correlations among smoking-related measures and diagnostic variables, indicating internal consistency. After applying SMOTE, the dataset achieved a balanced distribution of healthy and diseased individuals. The ensemble classifiers demonstrated high accuracy, up to 97%, and precision, with random forest and light gradient boosting machine performing particularly well. However, the recall for cancer detection was lower: 0.80, indicating potential missed diagnoses. Conclusion: The findings support the integration of ML in clinical diagnostics for cervical cancer, highlighting its potential for improving early detection and patient outcomes while also emphasizing the need for ongoing refinement in model performance.

Background This study evaluates the effects of exenatide (EXE), a glucagon-like peptide-1 (GLP-1) receptor agonist, on bone healing in rats using a single radius cortical defect model and histopathological, biochemical, and in silico methods. Methods Forty-two male Sprague–Dawley rats, excluding controls, were divided into 7 groups after receiving a standard radius defect. The serum levels of total protein (TP), calcium (Ca 2+ ), phosphorus (P), alkaline phosphatase (ALP), osteocalcin (OC), tumor necrosis factor-alpha (TNF-α), and interleukin-6 (IL-6) in each specimen were measured. Radius samples were examined histopathologically using hematoxylin and eosin (H&E) and Masson’s trichrome staining. Molecular docking analyses were used to assess EXE interactions with the GLP-1 receptor and osteogenic transcription factors. Statistical significance was set at p < 0.05. Results Changes in the selected serum markers were observed in the blood samples obtained from the specimens; however, these changes may not have been due to EXE administration. No significant negative effect on bone healing was observed in the groups that received subcutaneous EXE after the bone defect was created. By contrast, it was observed that for the treatment group that received EXE for 7 consecutive days before the bone defect was created on Day 7, bone healing progressed more slowly than in the groups treated with saline. Regarding the binding of EXE to the other target receptors, root mean square deviation (RMSD) values were low, bruised surface area (BSA) was high, and electrostatic interactions were strong, indicating that the ligand (i.e., EXE) binds to the selected receptor surfaces. Conclusion Although the data obtained from the in vitro analyses in this study were verified using molecular docking, it should be noted that its design is preclinical. Given the widespread clinical use of GLP-1 receptor agonists in the management of type 2 diabetes mellitus (T2DM), our research findings may have translational relevance. Although derived from an experimental animal model, these results suggest that GLP-1 agonists such as EXE can exert additional effects on bone healing and inflammatory processes, thus warranting further studies, including controlled clinical investigations, to elucidate the potential implications for patient care.

Mixed integer linear programming (MILP) is known as a type of programming that can combine continuous variables, integer variables, and (0-1) variables in the same algorithm and generate fitting results for the data. Using this technique, it is possible to model and solve complex problems in many different fields such as economics, biology, engineering, etc. In the present study, a regional planning model was developed using MILP technique for the conversion of manure from dairy and beef cattle into biogas and electrical energy. For this regional planning study, considering the locations of future facilities, data on dairy and beef cattle in the Isparta province of Türkiye were used. According to the model written and solution outputs, to utilize all manure obtained from dairy and beef cattles in Isparta, 5 biogas plants with a total manure processing capacity of approximately 522,000 tons should be built in different districts. It is possible to produce a total of approximately 21,000,000 m3 of biogas and 38,500 MW of electricity per year in these biogas plants. This electrical energy obtained can meet 3.83% of the annual electricity consumption of Isparta province.

The present study aimed to investigate the effects of paracetamol, an analgesic and antipyretic that is used in emergency departments and neurosurgery departments for postoperative pain management on intervertebral disc tissue. Paracetamol-treated human primary cell cultures and untreated cell cultures were compared using molecular analyses. Cell proliferation and gene expression were statistically analyzed. Cell proliferation was suppressed on days 10 (P=0.05) and 20 (P<0.05) in the paracetamol-treated groups. Gene expression of chondroadherin, matrix metalloproteinase (MMP)-7, MMP-13 and MMP-19 was higher in the paracetamol-treated samples while gene expression of Cartilage Oligomeric Matrix Protein and interleukin-1β was lower (P<0.05). Paracetamol, which appears innocuous compared with many analgesics, may increase the expression of MMPs, which serve a significant role in catabolic reactions and suppress the proliferation of intact intervertebral disc tissue cells.

Pseudothrombocytopenia (PTCP) can be an analytical error in the automatic blood cell count. Blood samples containing autoantibodies against platelets collected in ethylenediaminetetraacetic acid (EDTA) tubes can lead to platelet accumulation at room temperature. Agglutinated platelets are detected as larger cells using automated counters, which incorrectly leads to falsely low results. Hence, when a low platelet count is noted, PTCP must also be considered. In this study, we report a case of EDTA-dependent PTCP in a patient who has Hashimoto’s thyroiditis with the current literature review.