This paper examines Imbolo Mbue’s How Beautiful We Were through the lens of the Integrated Model of Ideological Representation in Discourse (IMIRD) to explore how environmental risk is communicated as a tension between silence and resistance. Developed by Ogungbemi (2016), IMIRD synthesizes the ideological depth of Critical Discourse Analysis (CDA), the structural precision of Systemic Functional Linguistics (SFL), and lexical analysis to investigate how language encodes power, agency, and ideological positioning. Applying this model, the paper analyzes transitivity structures, agency assignment, and discursive silencing in Mbue’s depiction of a fictional African village devastated by corporate oil pollution. Through a close reading of narrative voice, clause structure, and dialogic framing, we demonstrate how Mbue’s linguistic choices—such as collective narration, passive constructions, and high-transitivity clauses—represent the villagers’ oscillation between voicelessness and defiant resistance. The study reveals how narrative grammar functions as a site of ideological struggle: one where corporate actors are obscured or backgrounded, while subaltern voices struggle for recognition. Ultimately, we argue that How Beautiful We Were is not only a literary account of environmental injustice but also a compelling discourse of resistance, showing how storytelling—when examined through IMIRD—operates as a vehicle for reclaiming agency, memory, and ecological justice.

Bone marrow invasion in pediatric solid tumors is an essential area of research, as it has a significant impact on prognosis and treatment strategies. Bone marrow metastases occur when non-hematopoietic malignant cells infiltrate the bone marrow, often leading to severe hematological disorders and high mortality rates if not identified early. The mechanisms of bone marrow invasion involve tumor cells escaping into the bone marrow, forming micrometastases which can then take on aggressive forms. Bilateral bone marrow aspirates and trephine biopsies (BMAT) are essential for accurate classification, as discrepancies in results can occur. In addition to their progressive nature, pediatric solid tumors are characterized by a high metastatic potential, particularly in the bone marrow. This retrospective study analyzes a series of 52 cases of solid tumors with bone marrow invasion, diagnosed in the hematology laboratory and pediatric hemato-oncology unit (UHOP) of the Hassan II University Hospital in Fez over a six-year period, from January 2016 to December2022. Evaluation of bone marrow involvement is a key factor in therapeutic orientation and assessment of response to treatment. The presence of bone marrow metastases is associated with a poor prognosis. The haematology-biology laboratory plays an essential role in detecting extra-haematopoietic cells suggestive of bone marrow metastases, over and above standard tests. Despite advances in our understanding of spinal cord metastases, challenges remain in early detection and effective treatment, necessitating ongoing research to improve outcomes for affected children. In this article, we review the epidemiology, molecular mechanisms, clinical diagnosis, treatment and prognosis of bone marrow invasion in solid tumors in the pediatric population.

Against the backdrop of national policies advocating for the integration of ideological and political education into college courses and the strengthening of Chinese cultural dissemination globally, this paper aims to explore effective strategies for incorporating the narration of China’s stories into the College English curriculum. Focusing on North China Electric Power University (Baoding), the study employs a combination of teaching practice and a questionnaire to investigate the current status, pathways, and methods of integrating China’s stories in College English instruction. The research findings reveal that the integration of China’s stories in class teaching enhances students’ cross-cultural communication abilities and deepens their understanding of Chinese culture. The study highlights the feasibility of merging language learning with cultural narrative and calls for expanded extracurricular practice and long-term impact research. These findings offer practical insights for college English curriculum innovation and cultural soft power enhancement.

Schwannoma is a benign peripheral nerve sheath tumor originating from Schwann cells. Its occurrence in the tongue is rare. We report the case of a 19-year-old patient presenting with a painless, slowly enlarging mass on the mobile portion of the tongue. MRI revealed a well-defined, encapsulated lesion. The mass was completely excised surgically, and histopathological examination confirmed the diagnosis of a benign schwannoma. The postoperative course was uneventful, with no recurrence observed during follow-up.

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.

Cubosomes, lipid-based nanoparticles characterized by a bicontinuous cubic phase structure, show significant promise as drug delivery systems (DDS) due to their stability, enhanced bioavailability, and targeted delivery capabilities. This review examines the development, structural features, and drug release mechanisms of cubosomes, including diffusion-controlled, stimuli-responsive, and targeted release strategies. Applications in oncology, infectious diseases, and anti-inflammatory therapies highlight their improved therapeutic efficacy and minimized systemic side effects. Challenges related to large-scale manufacturing, particle stability, and regulatory compliance are addressed, along with potential solutions and emerging trends. The review emphasizes the potential of cubosomes in precision medicine and personalizing treatment plans, encouraging further research to meet unmet clinical needs.

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).

Beginning Syntax: An Introduction to Syntactic Analysis aims to present the basic concepts of syntactic theory to readers without requiring prior linguistic knowledge. Starting from the ideas of modern generative linguistics, the author systematically introduces basic concepts and the latest developments in linguistic theory in a step-by-step fashion. Topics covered include Phrase Structure Rules, X’-theory, Wh-movement Rules, Universal Grammar, Movement Parameters, and the Architecture of Grammar. The book explores multiple perspectives in natural languages, emphasizing the relationship between linguistics and cognition, society, and politics. It highlights the importance of formal and cognitive theories, treating language as a program running on the hardware of the brain and positioning syntactic theory at the core of cognitive theory. Chapters One through Six provide a detailed exposition of the foundational knowledge in syntax, while Chapter Seven expands the explanatory scope of Generative Grammar. It describes the distribution of word order features of world languages and proposes the wh-movement parameter hypothesis, offering the necessary analysis for understanding Parameter Hierarchies. Then, it systematically introduces the five components in a particular overall grammar model. The publication of this book is considered a milestone, as it utilizes the Parametric Comparison Method to analyze universal patterns and diversities in world languages. It develops Chomsky’s (2005) theory of parameter differences related to the third factor in language and Greenberg’s (1963) hypotheses on language universals and word order typology. The book focuses on providing scholars with in-depth foundational knowledge in syntax, inspiring thoughtful consideration of deep-seated issues in language structure.

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.

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.

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.