Mohammed V University

Ground Penetrating Radar (GPR), has emerged as a powerfulnon-invasive geophysical technique for detecting subsurfaceutilities, voids, and other subsurface anomalies. However, de-spite its widespread use in geophysical investigations, andconstruction management, there is lack of available datasetscontaining B-scan images of the subsurface features publiclythat could be used to train deep learning models for au-tomated anomaly detection. This data article aims at con-tributing to fill up this gap by creating a dataset specifi-cally designed for automatic detection of subsurface utilities,and voids using deep learning. The dataset consists of 2,239Radargram images in JPEG format obtained from GPR surveysconducted in urban environments to identify utilities suchas pipes, cables, and underground voids. The importance ofthis dataset lies in: (1) contribute to fill the gap of lack ofGPR data, (2) the universality of the data, (3) its potential to enhance the accuracy and efficiency to detect subsurfaceanomaly through the application of deep learning models,(4) GPR surveys are highly effective but still expensive, andits processing is time-consuming. By providing this labelleddataset for deep learning model training, this can facilitatethe development of automated systems, capable of detectingsubsurface anomalies effectively, which could reduce manual errors.

Introduction Malaria remains a significant global health concern, particularly in travelers returning from endemic regions. Hematological abnormalities are often associated with malaria and can serve as diagnostic indicators, especially when clinical symptoms are nonspecific. Objective This study aims to identify the most relevant hematological parameters for diagnosing malaria in travelers returning from endemic areas, who sought care at the Mohamed V Military Instruction Hospital in Rabat. Methods We conducted a retrospective comparative study involving 829 patients who returned from malaria-endemic regions between January 2017 and December 2023. Data collected included demographic information, parasitological test results, and comprehensive hematological profiles. Statistical analysis was performed to determine the sensitivity and specificity of various hematological parameters in diagnosing malaria. Results Thrombocytopenia, lymphocytopenia, and anemia were the most significant hematological abnormalities associated with malaria. Thrombocytopenia, defined as a platelet count below 150 x 103/µL, demonstrated a sensitivity of 75.91% and a specificity of 84.11%. Lymphocytopenia, with a threshold of less than 1.5 x 103/µL, showed a sensitivity of 69.47% and a specificity of 78.39%. Anemia, defined by hemoglobin levels below 13 g/dL in men and 12 g/dL in women, also significantly correlated with malaria diagnosis. Discussion This study highlights the significance of hematological abnormalities as key diagnostic markers for imported malaria cases. By analyzing retrospective data, we observed that these abnormalities, especially thrombocytopenia and anemia, are common among returning travelers with confirmed malaria. These findings suggest that clinicians can use such markers as a valuable tool for early malaria diagnosis, potentially improving patient outcomes. Additionally, the study reinforces the need for heightened awareness among healthcare providers in non-endemic regions regarding the presentation of malaria in travelers. Conclusion Hematological parameters such as thrombocytopenia, lymphocytopenia, and anemia are valuable diagnostic tools for malaria in returning travelers. These findings suggest that these parameters should be integrated into diagnostic protocols to improve the accuracy and timeliness of malaria diagnosis, particularly in clinical settings with limited access to advanced diagnostic tools.

Predictive maintenance (PdM) is a proactive approach aimed at anticipating the future point of failure for a machine or a component, with the goal of reducing both the frequency and the expenses associated with unplanned downtime. Recent advances in machine learning (ML) techniques have enabled PdM to be more efficient with diverse and successful applications in various manufacturing industries. The support vector machine (SVM), a fundamental ML algorithm, is renowned for its effectiveness in addressing classification and regression tasks. Nevertheless, the successful application of SVM hinges on the careful tuning of its parameters, a process that significantly influences its predictive performance. This research seeks to optimize the selection of the regularization parameter C and the kernel parameter \sigma using metaheuristic methods. It suggests combining the altruistic dragonfly algorithm (ADA) with SVM to enhance the prediction of maintenance failures. The primary motive for integrating altruism into this research is the unprecedented utilization of altruistic principles within this specific area. In addition, ADA-SVM provides a balance between exploration and exploitation. This balance is achieved through the altruistic behavior of dragonflies, where they help each other find better solutions. Therefore, this model is not trapped in the local optimum. The effectiveness of the model ADA-SVM is assessed on aircraft engine sensor data in comparison with other metaheuristic optimization algorithms, namely, genetic algorithms (GA), particle swarm optimization (PSO), grey wolf optimization (GWO) and dragonfly algorithm (DA). The performance of the SVM has been improved significantly by using parameter optimization. Besides, while GA-SVM, PSO-SVM, and DA-SVM models were able to predict engine failures with 95% accuracy, and the GWO-SVM, which demonstrated a good performance in terms of accuracy compared to other metaheuristics algorithms, achieves an accuracy of 97%, the ADA-SVM reached the best accuracy value which is 98%. The findings, thus, reveal that the proposed model outperforms the other models in optimizing SVM parameters, and, therefore, improves the performance of the engines failures prediction.

Optical science and photonics are driving the world’s advancement of knowledge and economy, encompassing important areas such as green photonics, bio-photonics, agro-photonics, environmental photonics, and so on. These areas are at the heart of the needed development for the African continent. This feature issue will discuss some of the works being conducted in these areas in African photonics research laboratories and will inform researchers in Africa of the current works in optics and photonics taking place. The special issue will not only cover research advances but also will present a perspective on challenges and opportunities, including impact on society, through studies that address continental issues, highlighting the excellence in optical science and photonics research presently underway in Africa. With its emerging excellence, it is high time to showcase optics by Africans to Africans, and to the world.

Background Female genital tract (FGT) diseases such as bacterial vaginosis (BV) and sexually transmitted infections are prevalent in South Africa, with young women being at an increased risk. Since imbalances in the FGT microbiome are associated with FGT diseases, it is vital to investigate the factors that influence FGT health. The mycobiome plays an important role in regulating mucosal health, especially when the bacterial component is disturbed. However, we have a limited understanding of the FGT mycobiome since many studies have focused on bacterial communities and have neglected low-abundance taxonomic groups, such as fungi. To reduce this knowledge deficit, we present the first large-scale metaproteomic study to define the taxonomic composition and potential functional processes of the FGT mycobiome in South African reproductive-age women. Results We examined FGT fungal communities present in 123 women by collecting lateral vaginal wall swabs for liquid chromatography-tandem mass spectrometry. From this, 39 different fungal genera were identified, with Candida dominating the mycobiome (53.2% relative abundance). We observed changes in relative abundance at the protein, genus, and functional (gene ontology biological processes) level between BV states. In women with BV, Malassezia and Conidiobolus proteins were more abundant, while Candida proteins were less abundant compared to BV-negative women. Correspondingly, Nugent scores were negatively associated with total fungal protein abundance. The clinical variables, Nugent score, pro-inflammatory cytokines, chemokines, vaginal pH, Chlamydia trachomatis, and the presence of clue cells were associated with fungal community composition. Conclusions The results of this study revealed the diversity of FGT fungal communities, setting the groundwork for understanding the FGT mycobiome. -WgVkTUVMn1xysf-XxHxojVideo Abstract

Background Amiodarone is an antiarrhythmic drug known for its potential side effects, one of which is neuromyopathy, though it remains relatively rare. This condition can present with muscle weakness, pain, and tremors, potentially leading to functional impairment. The exact mechanisms underlying amiodarone-induced neuromyopathy are not fully understood but may involve both direct muscle toxicity and effects on nerve conduction. Case presentation We present the case of a 68-year-old man with symptomatic arrhythmogenic right ventricular dysplasia, receiving long-term amiodarone, experiencing bilateral leg pain and weakness associated with amiodarone use. On clinical examination, motor strength in the lower limbs was rated at 2/5, with decreased tactile sensation. The biological assessment showed normal level of creatine kinase and C-reactive protein. The spinal MRI was normal. Electromyography “EMG” revealed a non-length dependent sensorimotor demyelinating polyneuropathy. After discontinuing amiodarone, both mobility and function showed significant improvement. Conclusion These observations highlight the importance of performing neurologic examinations in patients treated with amiodarone to identify even rare complications, such as neuromyopathy. Importantly, neuromyopathy is often reversible following discontinuation of the drug.

The Rehamna Massif is located in the southwestern Moroccan Meseta. This study area is for its diverse geologic events and mineral richness. By comparison, the Jbilet Massif, which lies in the immediate vicinity of the Rehamna, is well known for its rich vein deposits of copper, gold and silver. Preliminary observations suggest that the outcropping geologic structures of the Rehamna Massif may follow orientations similar to those of the Jbilet faults. This indicates that analogous tectonic processes may have influenced the formation of mineral deposits in both Massifs, offering significant potential for mineral discoveries in the Rehamna. To highlight potential mineral-rich zones in the Rehamna Massif, we conducted a geophysical study combined to a field research. While the field study is predicated on the map of mineral showings, the geophysical treatment depends on both gravity and residual magnetic data. It was possible to identify deep prospective magmatic features, primarily granitic plutons and vein bodies thought to be connected to the main NNE-SSW trending faults, by looking at the potential maps of the research area. They are the causative sources of the anomalies shown in the potential fields maps. To inspect these anomalies, several treatments were applied to the magnetic and gravity data, respectively. Starting by the reduction-to-pole of the magnetic data to situate the anomalies over their causative sources, generating then the reduced-to-pole map (RTP). As for the gravity data, it was necessary to extract the residual field map of the Bouguer anomaly map using a first-degree polynomial surface. The RTP map and residual gravity-field map have been upwarded to 30 km, and then submitted to the titlt-angle transformation employing the Oasis Montaj Software. The analysis of titlt-angle transformation outcome reveals the presence of subsurface lineaments in the Rehamna. The field structural data indicate that these lineaments represent the geologic borders between the host rocks and the magmatic formations, the primary faults and dykes that trend from N-S to NE-SW. By determining the vertical extent of underlying geologic formations, we were able to update the region’s geologic maps based on these findings in conjunction with the mineral showings maps. Second, through superposing the mineral showings on the geophysical anomalies, it was possible to identify the deep geometry of the silver/gold-rich granitic bodies of the area, currently exploited in the surface, in the mining district of Oulad Hassine in the southeast of Skhour Rehamna.

Cancer is one of the leading causes of death worldwide, responsible for nearly 10 million deaths in 2020, with approximately 50% of patients receiving radiation therapy as part of their treatment (Baskar et al 2012). Preclinical investigations studies have shown that FLASH radiotherapy (FLASH-RT), delivering radiation in ultra-high dose rates (UHDR), preserves healthy tissue integrity and reduces toxicity, all while maintaining an effective tumor response compared to conventional radiotherapy (CONV-RT), the combined biological benefit was termed as FLASH effect. This article comprehensively surveys pertinent research conducted within FLASH-RT, explores the facilities used in this realm, delves into hypothesized mechanism perspectives, and addresses the challenges to trigger the FLASH effect. In addition, we discuss the potential prospects of FLASH-RT and examine the obstacles that require resolution before its clinical implementation can become a reality.

Mortars composed of a blend of earth and cement are widely employed in masonry, particularly in the construction of earth blocks. This study is primarily focused on investigating the influence of mineralogical and chemical constituents on the mechanical characteristics of two types of mortars. Furthermore, we have assessed the water absorption coefficient of these mortars, which vary in cement content, with the aim of enhancing their longevity. The findings reveal a significant enhancement in compressive strength, attributed to the interaction between calcium hydroxide in cement and clay minerals containing silico-aluminous compounds. Importantly, the incorporation of cement not only affects the macroscopic properties concerning particle size fractions but also heavily depends on the mineralogical composition, particularly the clay content. Moreover, the addition of cement results in a reduction in water absorption, whereas an increase in magnesium oxide content within the clay leads to higher water absorption rates.

Diaphragm walls play an important role in the majority of structures built on non-rocky ground. In the particular case of dams, one way of dealing with tightness issues in alluvial valleys lies in the installation of a diaphragm wall, making it possible to connect the watertight components of the main structure to the rock foundation. This article analyses the specific case of a Concrete Face Rockfill Dam, resting on approximately 35 m of alluvium. To overcome tightness issues caused by alluvium at the bottom of the valley, a plastic concrete diaphragm wall was built. The objective of this paper is to analyse the behaviour of this diaphragm wall during all phases of the dam’s construction. It turns out that the plastic concrete wall undergoes a double behaviour: on the one hand, it deforms laterally with the alluvium which settles and widens on both sides of the axis of the Dam; and on the other hand, it withstands these displacements in term of stresses, limiting the large deformations. It emerges from this study that the plastic property of the concrete used in the diaphragm wall plays a major role in the consistency of the entire structure.

Biliary tract carcinomas (BTC) are malignant epithelial neoplasms subdivided anatomically into: gallbladder carcinomas and carcinomas of the bile duct or cholangiocarcinomas (CCA); including intrahepatic, hilar/perihilar, and distal CCA. Adenocarcinoma accounts for the most common BTC (over 90% of all carcinomas), while other histological subtypes represent rarer forms including: poorly cohesive/signet ring cell carcinoma, which has a greater malignant potential than conventional BTC and a poorer prognosis. Only few cases have been reported in the literature to date. The positive diagnosis remains on histology. Herein, we describe a new case of poorly cohesive carcinoma of bile duct extending to the gallbladder in a 60 years old women with a fatal outcome, to raise awareness of this rare entity and to provide data for larger series.

The rapid integration of artificial intelligence (AI) into surgical practice necessitates a comprehensive evaluation of its applications, challenges, and physiological impact. This systematic review synthesizes current AI applications in surgery, with a particular focus on machine learning (ML) and its role in optimizing preoperative planning, intraoperative decision-making, and postoperative patient management. Using PRISMA guidelines and PICO criteria, we analyzed key studies addressing AI’s contributions to surgical precision, outcome prediction, and real-time physiological monitoring. While AI has demonstrated significant promise—from enhancing diagnostics to improving intraoperative safety—many surgeons remain skeptical due to concerns over algorithmic unpredictability, surgeon autonomy, and ethical transparency. This review explores AI’s physiological integration into surgery, discussing its role in real-time hemodynamic assessments, AI-guided tissue characterization, and intraoperative physiological modeling. Ethical concerns, including algorithmic opacity and liability in high-stakes scenarios, are critically examined alongside AI’s potential to augment surgical expertise. We conclude that longitudinal validation, improved AI explainability, and adaptive regulatory frameworks are essential to ensure safe, effective, and ethically sound integration of AI into surgical decision-making. Future research should focus on bridging AI-driven analytics with real-time physiological feedback to refine precision surgery and patient safety strategies.

The concrete industry is confronted with persistent challenges, such as the need for extensive experimentation, time limitations, and high costs. Machine learning (ML) has become an extremely useful tool, providing diverse applications to tackle these challenges. This paper reviews the growing influence of ML on the concrete industry, highlighting its potential to revolutionize different aspects of concrete research and practical applications. The review explores the evolution of ML in this field, identifying key techniques, algorithms, and data sources commonly used in concrete related studies. It discusses the diverse applications of ML, including material characterization, mix design optimization, prediction of concrete properties, enhancement of nonlinear finite element analysis, crack detection, improvements in sustainability, and structural health monitoring. Additionally, the paper addresses challenges faced in the implementation of ML and offers recommendations to enhance its accuracy and effectiveness for concrete researchers, engineers, and practitioners. Graphical abstract

Background Stevens-Johnson syndrome (SJS), a rare and severe toxic epidermal necrolysis, is reported here for the first time at the University Hospital Center of Libreville (CHUL), suspected to be related to fluconazole administration. Objective To inform clinicians about the risks associated with fluconazole in immunocompromised patients and the related healthcare expenses. Case presentation The patient is a 39-year-old immunocompromised woman who received a single dose 400 mg of fluconazole. Two weeks later, she developed a rash affecting approximately 10 % of her body surface, confirmed as SJS. During clinical examinations, no signs of infection, such as fever, dizziness, or chills, were present, suggesting a drug-induced SJS reaction. Results Causality assessment assigned an intrinsic score of I6 and extrinsic score of B2 according to the French method, and a probable temporal relationship was confirmed using the world Health Organization (WHO) method. The ALDEN scale identified fluconazole as the probable cause (score = 4). No additional risk factors were identified (SCORTEN = 0, predicted mortality: 3.2 %). Economically, the total direct medical cost of hospitalization amounted to 605,700 CFA francs, or 923.38 €. Conclusion Immunocompromised HIV-positive patients treated with 400 mg of fluconazole are likely to develop SJS and incur costs.

The present study investigates how the acidity of the medium influences the nucleation of Cu–Sn–S elements to develop co-electrodeposited Cu2SnS3 films suitable for solar cells. The effect of solution acidity on CTS film properties was investigated by varying the pH from 1.4 to 4.4, using tartrate as a stabilizing agent for Cu and Sn through complexation. After co-deposition at a selected potential of − 0.95 (vs. SCE), and after a sulfurization step at 400 °C, the obtained results have clearly demonstrated the effect of acidity on the development of CTS films and, consequently on their physico-chemical properties. More specifically, X-ray diffraction and Raman spectroscopy confirmed this effect of pH, since a Cu2SnS3 phase was progressively formed as the pH increased, to crystallize at the end in a triclinic structure at pH = 4.4. EDS analysis and calculated structural parameters showed a higher pH significantly increased the crystallinity and minimized the formation of secondary phases. Similarly, SEM mapping analysis revealed a very uniform grain distribution with an optimal Cu/Sn ratio stoichiometry of ~1.99, particularly at pH = 4.4, along with a slight copper deficiency favoring p-type conductivity. Furthermore, the experimentally found optical bandgap decreased from 1.34 eV at pH = 1.4 to 1.25 eV at pH = 4.4. Finally, to highlight these results, a simulative calculation was conducted within the framework of density functional theory (DFT). The comparison of experimental and theoretical results showed good agreement, with confirmation of the semi-conductive character of the CTS material, as well as its direct bandgap of the same order of magnitude 1.15 and 1.29 eV. Graphical Abstract

Gender- and sex-based disparities in response to immune-checkpoint inhibitors (ICI) has been reported in a variety of tumor types. Women have different anatomy with recurrent urinary tract infections, a different sex hormonal profile, and intrinsic differences in local and systemic immune systems and urobiome composition. Existing literature data in a pan-cancer context reveal contradictory results, and real-world evidence in urothelial carcinoma (UC) is lacking. This was a real-world, multicenter, international, observational study to determine the sex effects on the clinical outcomes in metastatic urothelial carcinoma (mUC) patients progressing or recurring after platinum-based therapy and treated with pembrolizumab as a part of routine clinical care. A total of 1039 patients, treated from January 1st, 2016 to December 31st, 2023 in 68 cancer centers were included. Our data showed that women with metastatic urothelial carcinoma treated with pembrolizumab had shorter OS than men, with a 13% advantage in the 5-year OS rate for male patients. A deeper understanding of these results may inform sex-stratification in future prospective clinical trials and help develop strategies to reduce the magnitude of the sex disparities observed in urothelial cancer outcomes.

KEY POINTS • OPN levels in both tissue and plasma are strongly associated with poor survival outcomes in HNC patients, emphasizing the potential use of OPN as a prognostic biomarker for risk stratification in clinical practice. • Association with Tumor Aggressiveness: High OPN expression correlates with aggressive tumor characteristics, such as advanced stage, lymph node metastasis, and poor differentiation, suggesting that OPN could guide treatment intensity and follow-up strategies. • Clinical Translation and Future Directions: Despite strong prognostic value, the variability in measurement protocols limits clinical application. Standardized OPN assays and prospective validation studies are essential for integrating OPN testing into routine HNC management. BACKGROUND: Osteopontin (OPN) is linked to cancer progression, metastasis, and treatment resistance in head and neck cancer (HNC). This meta-analysis evaluated OPN as a prognostic biomarker in HNC. METHOD: A comprehensive search was conducted for studies published up to December 2023, including English papers on HNC analyzing OPN expression. Data extraction, quality assessment, and quantitative analysis were performed using fixed and random effect models with 95% CI. Heterogeneity and publication bias were assessed with I2 and Egger's regression test. RESULTS: Fifty-one studies were included. OPN expression was significantly elevated in tissue and plasma in HNC compared to control (SMD 0.98; 95% CI 0.47-1.49; I2 = 13%; p < 0.00). High plasma OPN predicted poor survival (HR: 2.00; I2 = 64%; P = 0.03), as did high tissue OPN (hazard ratio: 2.71, 95% confidence interval = 1.51-4.87; I2 = 49.4%, p > 0.05). Elevated plasma OPN correlated with smoking, poorly differentiated neoplasms, larger tumors, advanced stage, and lymph node metastasis. Positive tissue OPN was associated with nodal involvement, advanced stage, male gender, and smoking. CONCLUSION: OPN is a robust prognostic biomarker in HNC, indicating tumor aggressiveness and poor prognostic outcomes. Standardized measurement protocols and further validation in prospective studies are necessary. Evidence-Based Dentistry; https://doi.

Cardiovascular disorders, including atrial fibrillation and bundle branch blockages, have a significant impact on global health and are associated with higher mortality rates. The accurate categorization of these disorders through ECG data is essential in order to improve the results for patients. This investigation addresses the challenge of classifying major cardiac conditions by utilizing seven ECG datasets and employing machine learning techniques. We seek to classify patients exhibiting atypical ECGs through advanced methodologies. We combined datasets, incorporating 12 leads, and utilized preprocessing techniques to enhance data quality. By employing various models, such as CNN, SVM, KNN, random forest, and logistic regression, our CNN combined with logistic regression attained an accuracy of 94% and a sensitivity of 94% on a test set comprising 1500 ECGs. This comprehensive approach minimizes false negatives and enhances diagnostic precision. The integration of multiple datasets and sophisticated preprocessing improves the dependability of our findings, highlighting the importance of data quality and comprehensive analysis in cardiac diagnostics. The results demonstrate how machine learning can improve cardiac diagnoses through thorough data integration and analysis.