Abstract The olive trituration activity is one of the most important industrial activities in Fez, Morocco. These effluents are highly loaded with organic, inorganic, and phenolic compounds without any preliminary treatment that affects water quality. In this sense, the waters of Oued Fez are deteriorating due to the discharge of wastewater from oil mills and the excessive proliferation of Pistia stratiotes L., an invasive macrophyte that represents a significant stress to the aquatic ecosystem, eutrophication, and a reduction in biodiversity. This ecological situation has prompted us to carry out phytotoxicity bioassays based on the detection of the lethal concentration of P. stratiotes in the wastewater from the olive oil mill to evaluate the sensitivity and tolerance of macrophytes to the different concentrations of total polyphenols (TPP) present in the olive mill wastewater. To estimate their impact on the environment. Fresh whole plants of P. stratiotes were exposed to varying concentrations of olive oil mill wastewater with a series of TPP concentrations (0–30 mg/l) for 1 week in the natural environment. The results also show that P. stratiotes is able to grow rapidly in culture with 20 mg/l of TPP; the highest growth of wet weight of P. stratiotes occurred at 20 mg/l treatment with an average of 13 g wet weight increase. At 0 mg/l, treatment is known to cause very slow growth with an average of 2 g. Olive oil mill wastewater was toxic to the plant at concentrations higher than 30 mg/l, and the phytotoxic effect was manifested by retardation of growth, detachment of roots, wilting, and chlorosis of leaves. This indicates that polyphenols have great potential to inhibit the proliferation of P. stratiotes in aquatic environments.

Introduction: There is considerable concern with rising rates of antimicrobial resistance (AMR) with its subsequent impact on morbidity, mortality and costs. In low- and middle-income countries, a key driver of AMR is the appreciable misuse of antibiotics in ambulatory care, which can account for up to 95% of human utilisation. A principal area is the selling of antibiotics without a prescription. There is conflicting evidence in South Africa regarding this practice alongside rising AMR rates. Consequently, there is a need to explore this further, especially in more rural areas of South Africa. A pilot study was undertaken to address this. Materials and Methods: A two-step descriptive approach involving a self-administered questionnaire amongst pharmacists and their assistants followed by cognitive interviews with some of the participants. Results: Twenty-one responses were obtained from nine of the 11 community pharmacies invited to participate. Participating pharmacies were all independently owned. Ten of the 21 participants admitted dispensing antibiotics without a prescription, including both adults and children, representing five of the nine participating pharmacies. A minority dispensed antibiotics before recommending suitable over-the-counter medicines. These high rates were exacerbated by patient pressure. There were issues with the length of the questionnaire and some of the phraseology, which will be addressed in the main study. Conclusion: There were concerns with the extent of purchasing antibiotics without a prescription in this pilot in South Africa study. Key issues will be explored further in the main study.

Financial performance analysis is of vital importance those involved in a business (e.g., shareholders, creditors, partners, and company managers). An accurate and appropriate performance measurement is critical for decision-makers to achieve efficient results. Integrated performance measurement, by its nature, consists of multiple criteria with different levels of importance. Multiple Criteria Decision Analysis (MCDA) methods have become increasingly popular for solving complex problems, especially over the last two decades. There are different evaluation methodologies in the literature for selecting the most appropriate one among over 200 MCDA methods. This study comprehensively analyzed 41 companies traded on the Borsa Istanbul Corporate Governance Index for 10 quarters using SWARA, CRITIC, and SD integrated with eight different MCDA method algorithms to determine the position of Turkey's most transparent companies in terms of financial performance. In this study, we propose "stock returns" as a benchmark in comparing and evaluating MCDA methods. Moreover, we calculate the "rank reversal performance of MCDA methods". Finally, we performed a "standard deviation" analysis to identify the objective and characteristic trends for each method. Interestingly, all these innovative comparison procedures suggest that PROMETHEE II (preference ranking organization method for enrichment of evaluations II) and FUCA (Faire Un Choix Adéquat) are the most suitable MCDA methods. In other words, these methods produce a higher correlation with share price; they have fewer rank reversal problems, the distribution of scores they produce is wider, and the amount of information is higher. Thus, it can be said that these advantages make them preferable. The results show that this innovative methodological procedure based on 'knowledge discovery' is verifiable, robust and efficient when choosing the MCDA method.

Abstract The impact of logistics performance in the era of sustainable mobility on the overall economic development of a country is inevitable. It can even be said to represent an extremely important component in identifying economic conditions and provides the possibility of defining adequate strategies. In this article, the evaluation of the member countries of the European Union was carried out on the basis of the logistics performance index (LPI) according to the latest report of the World Bank (WB). A unique and original Multiple-Criteria Decision Making (MCDM) approach has been created, and it involves the application of four methods: Criteria Importance Through Intercriteria Correlation, Method based on the Removal Effects of Criteria, and Entropy and Fuzzy ROV (Range of Value). The weighting coefficients of six factors were obtained with the first three methods in crisp form, so they were converted into Triangular Fuzzy Number. The Fuzzy ROV method has been created for the first time in the literature and represents a great contribution from the methodological aspect. The results of the developed model and the applied steps show that there are certain differences in the rankings compared to the World Bank report, with a note that the best-ranked countries have maintained their positions. In addition, verification tests of the originally obtained results were created, with an emphasis on the importance of evaluation parameter values and their impact on the LPI ranking.

The increasing prevalence of colon and lung cancer presents a considerable challenge to healthcare systems worldwide, emphasizing the critical necessity for early and accurate diagnosis to enhance patient outcomes. The precision of diagnosis heavily relies on the expertise of histopathologists, constituting a demanding task. The health and well‐being of patients are jeopardized in the absence of adequately trained histopathologists, potentially leading to misdiagnoses, unnecessary treatments, and tests, resulting in the inefficient utilization of healthcare resources. However, with substantial technological advancements, deep learning (DL) has emerged as a potent tool in clinical settings, particularly in the realm of medical imaging. This study leveraged the LC25000 dataset, encompassing 25,000 images of lung and colon tissue, introducing an innovative approach by employing a self‐organized operational neural network (Self‐ONN) to accurately detect lung and colon cancer in histopathology images. Subsequently, our novel model underwent comparison with five pretrained convolutional neural network (CNN) models: MobileNetV2‐SelfMLP, Resnet18‐SelfMLP, DenseNet201‐SelfMLP, InceptionV3‐SelfMLP, and MobileViTv2_200‐SelfMLP, where each multilayer perceptron (MLP) was replaced with Self‐MLP. The models’ performance was meticulously assessed using key metrics such as precision, recall, F1 score, accuracy, and area under the receiver operating characteristic (ROC) curve. The proposed model demonstrated exceptional overall accuracy, precision, sensitivity, F1 score, and specificity, achieving 99.74%, 99.74%, 99.74%, 99.74%, and 99.94%, respectively. This underscores the potential of artificial intelligence (AI) to significantly enhance diagnostic precision within clinical settings, portraying a promising avenue for improving patient care and outcomes. The synopsis of the literature provides a thorough examination of several DL and digital image processing methods used in the identification of cancer, with a primary emphasis on lung and colon cancer. The experiments use the LC25000 dataset, which consists of 25,000 photos, for the purposes of training and testing. Various techniques, such as CNNs, transfer learning, ensemble models, and lightweight DL architectures, have been used to accomplish accurate categorization of cancer tissue. Various investigations regularly show exceptional performance, with accuracy rates ranging from 96.19% to 99.97%. DL models such as EfficientNetV2, DHS‐CapsNet, and CNN‐based architectures such as VGG16 and GoogleNet variations have shown remarkable performance in obtaining high levels of accuracy. In addition, methods such as SSL and lightweight DL models provide encouraging outcomes in effectively managing large datasets. In general, the research emphasizes the efficacy of DL methods in successfully diagnosing cancer from histopathological pictures. It therefore indicates that DL has the potential to greatly improve medical diagnostic techniques.

Abstract Objective. Studies that have evaluated correlation between body mass index (BMI) and novel lipid indices such as triglycerides (TG)/high-density lipoprotein-cholesterol (HDL-C), total cholesterol (TC)/HDL-C, and low-density lipoprotein cholesterol (LDL-C)/HDL-C in type 2 diabetes mellitus (T2DM) are scarce. Hence, the aim of the present study was to explore the correlation between BMI and novel lipid indices in Bosnian patients with T2DM. Methods. Present study included 117 patients with T2DM (mean age: 66.51 years) and 68 controls (mean age: 68.37 years). BMI was calculated as weight/height². Lipids were measured by standard methods. TG/HDL-C, TC/HDL-C, and LDL-C/HDL-C ratios were separately calculated. The differences between the groups were assessed by Student’s t-test or Man Whitney U test. Correlations were determined by Spearman’s test. Results. In a total sample of T2DM patients, 41.0% were overweight and 44.4% were obese. In the control group, 51.5% of subjects were overweight and 25.0% were obese. In T2DM group, a significant correlation was observed between BMI and HDL-C, LDL-C, TG/HDL, TC/HDL-C, and LDL-C/HDL-C ratios. In the control group, there was a significant correlation found between BMI and HDL-C, TG, TG/HDL, TC/HDL-C, and LDL-C/HDL-C-ratios. Correlation between BMI and other lipid parameters in T2DM and the control group was not determined. Conclusion. The present study showed significant correlation between BMI and novel lipid indices in both T2DM patients and the control group of subjects. Possible explanation for the observed results might be prevalence of overweight and obese participants in this study sample. Since novel lipid indices are used in the prediction of cardiometabolic risk, results obtained in the present study have valuable clinical implications.

The mucosal pellicle (MP) is a biological film protecting the oral mucosa. It is composed of bounded salivary proteins and transmembrane mucin MUC1 expressed by oral epithelial cells. Previous research indicates that MUC1 expression enhances the binding of the main salivary protein forming the MP, MUC5B. This study investigated the influence of MUC1 structure on MP formation. A TR146 cell line, which does not express MUC1 natively, was stably transfected with genes coding for three MUC1 isoforms differing in the structure of the two main extracellular domains: the VNTR domain, exhibiting a variable number of tandem repeats, and the SEA domain, maintaining the two bound subunits of MUC1. Semi-quantification of MUC1 using dot blot chemiluminescence showed comparable expression levels in all transfected cell lines. Semi-quantification of MUC5B by immunostaining after incubation with saliva revealed that MUC1 expression significantly increased MUC5B adsorption. Neither the VNTR domain nor the SEA domain was influenced MUC5B anchoring, suggesting the key role of the MUC1 N-terminal domain. AFM-IR nanospectroscopy revealed discernible shifts indicative of changes in the chemical properties at the cell surface due to the expression of the MUC1 isoform. Furthermore, the observed chemical shifts suggest the involvement of hydrophobic effects in the interaction between MUC1 and salivary proteins.

This scoping review examines the use of CRISPR/Cas9 gene editing in glioblastoma (GBM), a predominant and aggressive brain tumor. Categorizing gene targets into distinct groups, this review explores their roles in cell cycle regulation, microenvironmental dynamics, interphase processes, and therapy resistance reduction. The complexity of CRISPR-Cas9 applications in GBM research is highlighted, providing unique insights into apoptosis, cell proliferation, and immune responses within the tumor microenvironment. The studies challenge conventional perspectives on specific genes, emphasizing the potential therapeutic implications of manipulating key molecular players in cell cycle dynamics. Exploring CRISPR/Cas9 gene therapy in GBMs yields significant insights into the regulation of cellular processes, spanning cell interphase, renewal, and migration. Researchers, by precisely targeting specific genes, uncover the molecular orchestration governing cell proliferation, growth, and differentiation during critical phases of the cell cycle. The findings underscore the potential of CRISPR/Cas9 technology in unraveling the complex dynamics of the GBM microenvironment, offering promising avenues for targeted therapies to curb GBM growth. This review also outlines studies addressing therapy resistance in GBM, employing CRISPR/Cas9 to target genes associated with chemotherapy resistance, showcasing its transformative potential in effective GBM treatments.