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Context: Gastric cancer is one of the most prevalent types of cancer in developing countries and ranks fourth in terms of death causes. Helicobacter pylori infection is a significant contributor to the emergence of gastric cancer. Lack of early diagnosis of gastric cancer is a leading cause of death. Aims: To identify the key genes and pathways involved in gastric cancer. Methods: This study performed a comprehensive analysis of RNA-Seq data from human gastric cancer and adjacent normal tissues. Raw data passed quality checks with FastQC and were aligned to GRCh38 using HISAT2. Subread's FeatureCounts handled transcript assembly and quantification. DESeq2 pinpointed significant genes, while ClueGO explored gene ontology and KEGG pathways. Protein-protein interaction networks, constructed with StringApp, aided in identifying hub genes through CytoHubba. This holistic approach yields insights into the molecular mechanisms underpinning gastric cancer. Results: This study detected 711 differentially expressed genes (DEGs) between normal and gastric samples. A total of 594 genes were identified as upregulated and 117 as downregulated. Major DEGs are enriched in signal transduction, stimulus-response regulation, transmembrane signaling receptor activity, and signal transduction pathways involving cytokines. In addition, 20 hub genes from the PPI network were identified based on MCC rank analysis from the CytoHubba plugin, contributing to the progression of gastric cancer. Conclusions: The top six hub genes, CD4, CTLA4, CD28, CD80, CD27, and SELL, are expected to regulate several pathways and may serve as potential biomarkers for the early detection and treatment of gastric cancer patients.

Background: Anatomy education relies heavily on Greek and Latin terminology, which often poses a challenge for undergraduate students because of its complexity and unfamiliarity. Traditional rote memorization methods limit conceptual understanding and long-term retention. Objective: To systematically review the evidence on Greekand Latin-based teaching strategies in anatomy education for undergraduate students and evaluate their effectiveness in improving comprehension, retention, and clinical application. Methods: A systematic search of educational and anatomical literature was conducted using databases and academic sources focusing on anatomy teaching strategies, terminology learning, and etymology-based instruction. Studies included undergraduate medical and allied health education contexts. Data were analyzed to identify key teaching strategies and outcomes. Results: Evidence suggests that root-based learning, contextualized instruction, multimodal teaching approaches, and active learning strategies significantly enhance students' understanding of anatomical terminology. Linguistic based approaches improved knowledge retention, confidence, and the ability to interpret unfamiliar terms. Conclusion: Greekand Latin-based teaching strategies transform anatomy learning from memorization to meaningful understanding. Integrating etymology with clinical and experiential learning improves academic performance and supports the development of professional competencies.

Respiratory medicine is transitioning from symptom-driven, standardized care to a more precise, patient-specific approach guided by molecular profiling. This evolution is being enabled by advances in liquid biopsy, multiomics, and artificial intelligence (AI) analytics. Fractional exhaled nitric oxide (FeNO) and blood eosinophils, the two commonly used markers in asthma, are now being joined by more precise airway markers such as galectin-10, which could aid clinicians in making more informed decisions for biological treatments. In chronic obstructive pulmonary disease (COPD) similar progress is underway, with treatment now emphasizing inflammation endotypes, especially eosinophilic patterns, to direct therapeutic choices. Alongside these developments, routine blood-based ratios (e.g., platelet-to-lymphocyte and neutrophil-to-lymphocyte) are being explored as predictors of exacerbation risk, and forced oscillation testing (FOT) is proving useful for picking up early disease shifts. In more severe conditions, biomarkers are linked to an early and better prognosis, enabling timely intervention. Markers like Matrix metalloproteinase-7 (MMP-7) and CC chemokine ligand 18 (CCL18) have proven to be reliable indicators of mortality and disease progression in idiopathic pulmonary fibrosis. Meanwhile, in lung cancer, liquid biopsies, especially those measuring circulating tumor DNA and micro-RNA (miRNA) panels, are enhancing screening accuracy while helping to cut down on the high false-positive rates seen with low-dose computerised tomography (CT). Other respiratory conditions such as bronchiectasis, pulmonary embolism, pneumonia, and acute respiratory distress syndrome (ARDS) are also benefiting from biomarker advances. At the same time there is a growing push to standardize how these biomarkers are measured. AI-based clinical decision support systems are also playing an increasingly important role in the translation of all these complicated data into actionable clinical insights. Together these developments pave the way for improved respiratory care that is precise and responsive to individual patient needs.