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.

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.

In the public safety sector, 5G offers immense opportunities for enhancing mission-critical services by provisioning virtualized service functions at the network edge, which enables achieving high reliability and low-latency. One of these mission-critical services is Back Situation Awareness (BSA) that supports Emergency Vehicles (EmVs) by increasing awareness about them on the roads. In this article, we introduce an on-demand BSA application service, which has been developed for multi-domain Multi-Access Edge Computing (MEC) systems, enabling early notification for vehicles on the Estimated Time of Arrival (ETA) of an approaching EmV. The state-of-the-art approaches inform civilian vehicles about EmVs only when they are in a close proximity (up to 300 m). However, in some situations (e.g., in congested areas), this may not be enough for the civilian vehicles to safely and timely maneuver out of the lane of an EmV. Our approach is, to the best of our knowledge, a unique way to significantly extend this awareness by creating an orchestrated 5G-based MEC deployment of BSA application service on optimally selected edges, thereby stretching over multiple edge domains and even countries. While consuming the real-time location, speed, and heading of an EmV, such application service affords the drivers with sufficient time to create a clear corridor, allowing the EmV to pass through unhindered in a safe manner thereby increasing the mission success. The detailed design and the performance analysis of the BSA application service that has been created following modern cloud-native principles based on Docker and Kubernetes, is presented in terms of the impact of emergency scale on the MEC system resources and service response time. Moreover, we also introduce a metric called panic indicator, which depicts how the proposed BSA service can potentially help in enabling drivers to calmly maneuver out of the path of an EmV, thereby increasing road safety.

: This study explores the accessibility of critical infrastructures (CRITIS) in urban planning, focusing on the City of Sarajevo. CRITIS, essential for societal functioning, encompasses diverse services vital to social, economic, political, health, educational, and administrative systems. The authors leverage geographic information system (GIS) tools to construct an accessibility model for Sarajevo, analysing the spatial availability of critical functions. Six groups of CRITIS indicators, composed of 29 CRITIS elements, were used in the derivation of critical infrastructure accessibility index. The methodological framework was based on implementation of network GIS analysis, interpolation method (IDW) and GIS multi-criteria analysis, which could be applicable to similar research studies. Local communities concentrated in the strict urban core (Ferhadija, Baščaršija) have the best accessibility of CRITIS, while peripheral local communities with a large area, such as Mošćanica and Reljevo, have the lowest. Results suggest a zonal categorization of the urban area, providing valuable insights for spatial planning and future urban development management. The study reveals that the highest value of CRITIS accessibility doesn't necessarily align with the most densely populated areas at local community level.

Background: Radical surgical resection for pancreatic head carcinoma offers a chance for cure but unfortunately is only available to a limited number of patients. For a significant number of patients, palliative surgery remains the only option. The question of the most effective approach for patients with borderline resectable pancreatic head carcinoma (BRPHC) remains unresolved. Objective: The aim of the study was to compare the morbidity and mortality following R1 duodenocephalic pancreatectomy and double palliative bypass to explore the most optimal surgical treatment for patients with BRPHC. Methods: Our retrospective cohort study included 64 patients with BRPHC who underwent surgery from 2012 to 2019, with postoperative follow-up for three years. Morbidity and mortality parameters were examined based on the type of surgical treatment: R1 duodenocephalic pancreatectomy or palliative double bypass. Chi-square test, univariate regression, and Kaplan-Meier analysis were used as basic statistical methods in the analysis of the results. Results: Patients undergoing R1 duodenocephalic pancreatectomy had a 3.69 times higher risk of developing biliary leak (p=0.039; 95%CI:1.066, 1.181) and shorter survival compared to those undergoing palliative double bypass (p=0.022). No statistically significant association was found between the type of surgical procedure and other postoperative complications. Conclusion: Our study suggests that the double palliative bypass procedure may be a better option than R1 resection for patients with BRPHC.

Background: Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of cancer-related deaths worldwide. Studies often consider colon and rectal cancers together. The combination of CA 19-9 and CEA markers is used to improve diagnostic accuracy, but there are no reports on the use of this combination as a prognostic predictor for CRC. The study by Kamada et al. was the first to use the "tumor marker index" (TMI), the geometric mean of normalized CEA and CA 19-9 in CRC, demonstrating the prognostic capabilities of this novel marker. However, it is not known how the preoperative value of TMI compares and fits into the current system of prognostic factors for preoperative prediction of overall survival (OS) and disease-free survival (DFS). Objective: The aim was to investigate the significance of preoperatively determined TMI in predicting three-year overall survival (3Y-OS) and three-year disease-free survival (3Y-DFS) in patients with stage III adenocarcinoma of the upper and middle rectum. Methods: Our retrospective cohort study included 93 patients who underwent open anterior resection of the rectum between January 2015 and December 2020. Optimal cut-off values of the markers were determined by ROC analysis. Statistical analysis was performed using IBM SPSS Statistics 22 and R 4.4.0. Results: Patients with TMI ≥1.0158 had a statistically significantly higher likelihood of mortality within three years (p=0.012). Patients with TMI ≥0.979 had a statistically significantly higher likelihood of disease-free survival of less than three years (p=0.003). Compared to CEA and CA 19-9, TMI had the highest AUC for predicting 3Y-OS (0.740, p=0.020) and 3Y-DFS (0.780, p=0.012). Adding TMI to other predictors increased the AUC for predicting both 3Y-OS (from 0.748 to 0.853) (p=0.001) and 3Y-DFS (from 0.711 to 0.850) (p=0.001). Conclusion: Our study confirmed previous findings on the usefulness of preoperative TMI as a prognostic marker, further expanding knowledge about its accuracy by comparing and combining it with established prognostic factors, including CEA and CA 19-9.