Coeliac disease (CD) is an immune mediated enteropathy caused by the ingestion of gluten in genetically predisposed individuals. It is frequently associated with wide spectrum of extra-intestinal manifestations, including thromboembolism events. We report the case of a young woman known with a CD since the age of 6, who says to be compliant to the gluten free diet (GFD), and presented with bloating and anemic syndrome. The endoscopic and anathomopathological examinations revealed no abnormalities. The CT scan revealed incidentally a chronic obstruction of the inferior vena cava (IVC). the etiological work-up for thromboembolic disease was negative, concluding to a Budd-Chiari syndrome complicating her CD. She was managed with anticoagulants, specifically Direct oral anticoagulants (DOACs).

Aim: To evaluate the role of the Neutrophil-to-Lymphocyte Ratio (NLR) as a biomarker for predicting acute exacerbations in COPD (AECOPD) based on current literature. Background: Chronic obstructive pulmonary disease (COPD) is one of the leading cause of morbidity and mortality worldwide. Acute exacerbations (AECOPD) have led to a major impact on patient health, which in turn lead to hospitalizations and also an increased risk to disease burden. Thus in order to optimize treatment stratergies, one have to identify those reliable biomarkers that help predict exacerbations. The neutrophil-to-lymphocyte ratio (NLR) has been a potential indicator to assess inflammation in COPD patients [1-6]. Review Results: This review critically examines six key studies on NLR and COPD exacerbations, assessing both its clinical utility as well as its limitations. Several studies have found a link between higher NLR and an increased risk of COPD exacerbations. However, changes in cutoff values, study designs and confounding factors like corticosteroid use may have an impact on prediction accuracy [1-6]. Conclusion: The NLR is a promising biomarker for determining AECOPD risk. However, consistent cutoff values and multi marker techniques are necessary for clinical use.

In view of the considerable importance of thiadiazoles and thiazolidinones, which are the core structures in a variety of pharmaceuticals with a broad spectrum of biological activity. Synthesis of series of potential biological active 1, 3, 4 thiadiazole linked 4 thiazolidinone derivatives were obtained via a multistep synthesis sequence with a simple and convenient approach by using substituted benzoic acids, which are expected to possess enhanced antioxidant activity based on the literature survey reports. In the present study the initial compound, 5-phenyl-1, 3, 4-thiadiazol-2-amine was treated with different substituted aromatic aldehydes to produce Schiff base. The resulting Schiff base were subjected to addition reactions with thioglycolic acid to form title compounds of 2-phenyl-3-(5-phenyl-1,3,4-thiadiazol-2-yl)-1,3-thiazolidin-4-one. The structure of the synthesized compounds was characterised by FT-IR, H1NMR and mass spectral analysis. The synthesized compounds were tested for antioxidant activities with standard drug using DPPH method. The results of this study revealed that, among the compound tested for antioxidant activity, TZD 5 and TZD 3 exhibited promising antioxidant activity with the IC50 value 27.50µM and 28.00µM while the value of reference compound, ascorbic acid 29.2µM. The antioxidant screening results indicate that exciting DPPH radical scavenging activity was observed in compounds (TZD 3 and TZD 5) in comparison with standard ascorbic acid. These results may also provide some significance guidance for the development of new class antioxidant.

Background: Interactive teaching strategies have been shown to enhance student engagement and learning outcomes in medical education. However, the extent to which first-year medical students in Bangladesh perceive interactivity in lectures remains unclear. This study aimed to assess students’ perceptions of interactivity in General Embryology lecture classes. Objectives: To analyze the perceptions of Bangladeshi medical undergraduates regarding interactivity in the General Embryology lecture classes Methods: In this study two surveys were conducted on 232 undergraduates of two medical colleges of Bangladesh using a questionnaire designed by the researcher to analyze the perceptions of the undergraduates regarding how the lecturers try to make their General Embryology lectures ‘understandable and interesting’. Results: The survey results regarding the perceptions and views of the medical undergraduates show that out of the 55 questions regarding interactivity, the Embryology lectures scored three (3) or more out of four (4) in case of only two (2) of the questions. Two (2) or more was obtained in case of eighteen (18) questions only. Conclusions: These results broadly suggest lower levels of interactivity regarding General Embryology lecture classes in the eyes of Bangladeshi medical undergraduates.

Artificial intelligence (AI) has significantly impacted dentistry by enhancing diagnostic accuracy, treatment planning, and patient care across various specialties, including endodontics, radiology, and periodontology. This review synthesizes findings from five key studies examining AI applications in dentistry, focusing on convolutional neural networks (CNNs) and deep learning models.AI-assisted diagnostics have shown superior accuracy compared to traditional methods, with CNNs achieving up to 94% accuracy in detecting periapical lesions and surpassing human radiologists in specific diagnostic tasks. Additionally, AI-assisted caries detection improves tooth retention and reduces treatment costs, demonstrating its potential economic benefits. However, challenges such as data biases, ethical considerations, and regulatory barriers remain future research should focus on developing transparent AI models, standardizing datasets, and addressing cost-effectiveness concerns to enhance clinical integration. Methods: A comprehensive review of five peer-reviewed articles was conducted, highlighting AI applications in dentistry. The articles were selected based on relevance to diagnostic advancements, clinical decision-making, and patient outcomes. Key methodologies included CNN-based image analysis, deep learning applications for caries detection, and neural networks for treatment optimization. Results: AI applications in dentistry demonstrated superior diagnostic performance. CNNs achieved 94% accuracy in detecting periapical lesions and surpassed human radiologists in specific diagnostic tasks. AI-assisted caries detection improved tooth retention by 62.8 years on average, with cost savings of €378 per patient compared to traditional methods. In endodontics, AI accurately identified root fractures and predicted treatment outcomes with up to 95.6% accuracy. Despite these advancements, limitations such as data biases and interpretability of AI models were noted. Conclusion: AI holds transformative potential for modern dentistry by improving diagnostic precision and clinical efficiency. However, integrating AI into routine practice requires addressing data standardization, ethical frameworks, and regulatory barriers. Future research should focus on developing transparent AI models and exploring their cost-effectiveness and long-term impact on patient care.

Respiratory epithelial adenomatoid hamartoma (REAH) is an uncommon benign lesion of the nasal cavity and paranasal sinuses with an unclear etiology. Although rare, REAH should be considered in the differential diagnosis of nasal lesions. Limited but complete surgical resection is the treatment of choice. We present a 54-year-old male patient with REAH in the left nasal cavity, with differential diagnoses including antrochoanal polyp and inverted papilloma.

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, with atherosclerosis as a central pathological process driven by lipid imbalances. Apolipoproteins A (apoA) and B (apoB) are key regulators of lipid metabolism and atherogenesis, representing protective and pro-atherogenic roles, respectively. ApoA, primarily found in high-density lipoproteins (HDL), facilitates reverse cholesterol transport and exhibits anti-inflammatory and antioxidant properties, thereby reducing cardiovascular risk. In contrast, apoB, a major component of low-density lipoproteins (LDL) and other atherogenic lipoproteins, promotes cholesterol deposition and plaque formation within arterial walls. This article reviews the metabolic pathways of apoA and apoB, elucidates their opposing roles in the initiation and progression of atherosclerotic plaques, and highlights their clinical utility as biomarkers. The apoB/apoA-I ratio emerges as a superior predictor of cardiovascular risk compared to traditional lipid measures, enabling improved risk stratification and personalized management. Advancements in apoB quantification and the therapeutic potential of targeting apolipoproteins underscore their importance in future strategies to prevent and treat CVDs globally.

In the era of artificial intelligence, the proliferation of AI-generated images has blurred the boundaries between reality and digital fabrication. Technologies such as Generative Adversarial Networks (GANs) have enabled the creation of highly realistic synthetic images—commonly known as deepfakes—which pose substantial challenges in domains like digital media, cybersecurity, and legal forensics. While these advancements offer innovative applications in entertainment and simulation, their potential misuse can lead to misinformation, identity theft, and erosion of public trust. This project proposes an AI-powered image authenticity detection system that leverages a Convolutional Neural Network (CNN) to accurately classify images as either real or AI-generated. The system is built with an intuitive graphical user interface (GUI) that allows users to upload and analyse images in both individual and batch modes. Key features include real-time prediction with confidence scoring, visual result displays, confusion matrix generation, and performance metrics such as accuracy, precision, and recall. The model achieves an overall classification accuracy of 82.7%, demonstrating strong potential for real-world applications in detecting synthetic media. By combining deep learning techniques with user-centric design, the system provides a practical and transparent solution for addressing the rising concerns of digital image manipulation. It serves as a critical tool for enhancing media authenticity and combating the spread of AI-generated misinformation.

Recent research has shown that Kiswahili is faced with lack of full implementation of language policy at various levels yet scholars have argued that language policy performs a substantial role in the enhancement of performance as well as the comprehension of the language. It is based on this thrust that this study was carried out to explore the challenges associated with enforcing school language policy in secondary schools. The study was conducted in 30 out of 300 public secondary schools found in Bungoma County. Stratified, purposive and simple random sampling techniques were used to sample the target groups. The questionnaire and focused group discussion guide and document analysis guide were used to collect data. The data was analyzed using descriptive statistics which included the use of frequencies, percentages and narrations. The study established that the challenges associated with the enforcement of school language policy were dependent on the type of school. In view of the above findings, it was recommended that day sub-county secondary school administrators, both in rural and urban areas should come up with stringent measures in enforcing the school language policies because of the nature of their schools. It is hoped the finding of this study would help MOEST, principals of schools and teachers in formulating school language policy that aim at enhancing students achievements in languages offered in the curriculum.

Background: Malaria remains a significant public health challenge in Somalia, with varying prevalence influenced by environmental, demographic, and regional factors. Mogadishu, as a major urban center, presents unique malaria dynamics requiring localized data for effective control. This study analyzes malaria prevalence and testing patterns at Kalkaal Hospital, using gold standard diagnostic test microscopy and malaria rapid diagnostic tests, from January to October 2024. Methods: A retrospective analysis was conducted on malaria diagnostic records, including 11,497 tests performed at Kalkaal Hospital's hematology department. Data collected included test results, patient demographics, service settings (inpatient/outpatient), and department-specific testing patterns. Standard microscopy and malaria rapid diagnostic were employed for diagnosis. Statistical analysis assessed positivity rates and identified testing trends. Results: Out of 11,497 tests, 10 cases (0.087%) were confirmed positive, while 11,487 (99.913%) were negative. The outpatient department accounted for 80% of positive cases, with obstetrics and gynecology contributing the highest proportion (30%), followed by internal medicine/cardiology and pediatric services (20% each). The highest testing volumes were recorded in emergency (3,247 tests) and pediatric services (2,683 tests). Monthly data showed a peak in positive cases during January (6 cases) and April (2 cases), suggesting seasonal influences on malaria transmission. Conclusions: This study found an exceptionally low malaria prevalence in Mogadishu, reflecting low malaria infection rates in Mogadishu Somalia. This is in coherence with recent studies that suggest Mogadishu, Somalia, may be transitioning towards a non-malaria zone. However, the low positivity rate suggests potential overuse of malaria testing for febrile illnesses in this urban setting.

Dentigerous cyst (DCs) is the type of odontogenic cyst and is the one of the most common type of cyst occurring in the jaw. Dentigerous cyst is clinically asymptomatic and is found radiographically. It encloses the crown of impacted tooth. Histological evaluation is important in such cases as sometimes radiographically interpretated Dentigerous cyst is diagnosed as Unicystic ameloblastoma, dental follicle or an odontogenic keratocyst. In this case report, 18years old boy is reported with clinical diagnosis of pericoronitis and radiographic finding of Dentigerous cyst which on histopathological investigation revealed features of plexiform ameloblastoma on post-operative excisional biopsy.

Maritime piracy and border crimes pose significant threats to Cameroon’s security and regional stability and this justifies why the Gulf of Guinea has emerged as a focal point of global concern in Africa. This tumultuous maritime environment has weathered ongoing challenges over time, necessitating a meticulous examination of its security landscape. The study argues that the ineffectiveness of Cameroon’s maritime security is largely due to weak legal framework, colonialism, inadequate enforcement mechanisms, corruption and lack of regional cooperation. It further highlights the fact that the country’s piracy laws are often ineffective, and enforcement agencies lack the capacity and resources to combat piracy and related crimes. In addition, Cameroon faces significant maritime security challenges in the Gulf of Guinea, including limited resources for maritime patrols, surveillance, and judicial capacity, inadequate ports and coastal infrastructures, corruption and the involvement of organized criminal networks in maritime crimes and the lack of trust between coastal communities and law enforcement agencies. The study that made use of primary and secondary data, reveals that poverty, unemployment, and lack of economic opportunities in coastal communities are the root causes of maritime insecurity, piracy, armed robbery and oil related crimes in the Gulf of Guinea. To address these threats, Gulf of Guinea countries need to strengthen their legal framework to ensure effective prosecution of maritime criminals and to align its laws with international standards and invest in maritime security capacity building, including training for coast guard personnel, intelligence gathering, and judicial personnel. The study makes necessary policy recommendations and concludes that, for peace to return in the Gulf of Guinea, the evolving nature of challenges necessitates continued efforts to address emerging threats in the dynamic maritime security landscape.

Students’ views on their assessment play a pivotal role in shaping their learning experiences and trajectories. This qualitative study aimed to explore the views of Greek university graduates regarding their assessment during their undergraduate studies. The sample consisted of 30 graduates, and data were collected through written narratives in response to a given prompt. Qualitative content analysis revealed key categories, including assessment methods and frequency, interpersonal relationships with professors and peers, advantages and disadvantages of assessment, feedback, and objectivity. The findings suggest that, although assessment was primarily traditional, graduates’ interpretations and expectations align with contemporary trends in higher education assessment, such as sustainable assessment and assessment for learning. Furthermore, there were indications that graduates’ views on assessment influenced their learning approaches.

Retirement presents a pivotal life transition that can offer both liberation and adversity. This qualitative study explores the personal experiences of Nigerian retirees, shedding light on post-retirement challenges. Using purposive sampling, an in-depth interviews were conducted involving two (2) focus group discussions with six (6) retirees as participants in each group, who are residents of Government Residential Area (G.R.A) Trans-Ekulu, Enugu State, Nigeria. The data of the study participants’ showed that majority served an average of 35 years in active service. Despite this, the study revealed that retirees struggle to balance their newfound freedom with (1) financial constraints, (2) health issues, (3) demands of family obligations, and (4) lack of proper records/profiling in active service. Also, participants expressed dissatisfaction of their pensions and gratuities arrangements, which are not properly managed by Pension Fund Administrators (PFAs). The study underscores the urgent need for comprehensive retirement education, transparent pension fund management, improved healthcare access, and institutional reforms aimed at easing the transition into retirement. Policy implications and suggestions for future research were examined to enhance retirees’ post-retirement success.

Computational chemistry is becoming a key tool for comprehending and creating new treatment materials due to the growing need for sustainable and effective wastewater treatment methods. This study thoroughly examines the molecular modeling, material design techniques, and mechanistic insights that support the creation of novel wastewater treatment solutions. Researchers can decipher the molecule-level adsorption, degradation, and catalytic processes of pollutants by utilizing density functional theory (DFT), molecular dynamics (MD) simulations, and quantum chemistry computations. In addition to making, it easier to identify active sites and reaction pathways, these insights also make it possible to rationally design functional materials with improved specificity and efficiency, such as metal-organic frameworks (MOFs), covalent organic frameworks (COFs), graphene-based composites, and photocatalysts. The optimization of structural and electrical characteristics is made possible by molecular modeling, which also helps forecast the physicochemical interactions between pollutants and treatment materials. Furthermore, machine learning integration and computational screening are becoming effective strategies for speeding up the search for new therapeutic ingredients. Recent developments in computational methods for wastewater treatment are summarized in this study, emphasizing the cooperation between theoretical forecasts and experimental confirmations. The focus is on how computational insights drive process optimization and material innovation in the removal of new pollutants, heavy metals, dyes, and medicines. To create next-generation materials for water purification, the paper ends by describing upcoming potential and difficulties in combining data-driven design, multiscale simulations, and green chemistry principles. This integrated computational method has the potential to transform wastewater treatment technology and advance public health and environmental sustainability.