ABSTRACT
Introduction: Heavy chain disease (HCD) is characterized by the production of a truncated heavy chain monoclonal immunoglobulin lacking the corresponding light chains. The aim is to highlight the difficulties associated with the biological diagnosis of this pathology. Observation: Serum protein electrophoresis showed hypogammaglobulinemia with restricted gammaglobulin heterogeneity and a peak migrating into the beta-2 globulin zone. Immunofixation revealed a monoclonal band in the gamma heavy chain, with no correspondence with the Kappa or Lambda light chains. Conclusion: The observation we have presented reveals that the diagnosis of gamma heavy chain disease is delicate and relies on several techniques.

ABSTRACT
As a result of the pancreas not secreting the hormone insulin or secreting it in a low concentration, as the hormone is important in enhancing the ability of glucose to enter cells and produce energy, and the cumulative glucose analysis is HbA1c, also known as glycated hemoglobin, which is a product of glucose adhesion in the body with red blood cells (RBC). The pellets are effective from two to three months, and for this reason, 3 cumulative glucose readings are taken for the same period. This study was conducted at Al-Zawiya Central Hospital, Children’s Department, for 100 children with type 1 diabetes, of whom 46 were males and 54 were females, the purpose of this study is to evaluate the cumulative sugar levels for children and adolescents with diabetes, as the study showed a weakness in the cumulative rate, which was high at a rate of 10.6%, and a lack of care for diabetes with a lack of regular exercise.

ABSTRACT
The aim of this study was to establish the bacteriological profile of UTIs in children. Method: A retrospective cross-sectional study conducted over one year. Urine samples were taken after local disinfection of the urinary meatus. Children who did not meet the criteria for UTI, i.e., leukocyturia greater than or equal to 104 leukocytes/mL, were excluded from our sample. Susceptibility to antibiotics was studied by agar diffusion. Results: Enterobacteriaceae were the most frequently isolated bacterial family in our series (65.65%). Escherichia coli was the most represented bacterial species (49.07%), followed by Klebsiella sp. (14.11%). Gram-positive cocci accounted for 26.38% of germs, including 18.71% Enterococcus sp. and 7.05% coagulase-negative staphylococci. ESBL was observed in 16.74% of enterobacteria. Carbapenem resistance was observed in 6.52% (3/46). Conclusion: E. coli was the germ most frequently isolated in urinary tract infections, with an average rate of resistance to the combination of amoxicillin and clavulanic acid and 3rd generation cephalosporins, but low for ciprofloxacin and cotrimoxazole. ESBL-producing strains.

ABSTRACT
Background: The most prevalent kind of diabetes that results from insulin resistance is type 2. Hyperglycemia, polyphagia, polyuria, and polydipsia are the traditional trifecta of symptoms associated with this illness. The purpose of this study was to ascertain the impact of petersianthus macrocarpus ethanolic leaf extract on the lipid profile and haematological markers of streptozotocin-induced type 2 diabetic wistar rats. Materials and methods: Five groups of rats were used: non-diabetic, diabetic, untreated, glibenclamide-treated, ethanolic extract-treated, and 100 mg/kg body weight-treated. Results: The body weight of the rats treated with 100 mg/kg body weight increased, while the body weight of the rats treated with 50 mg/kg body weight decreased, according to the results. Conclusion: 100 mg/kg body weight was determined to be the least effective therapeutic dose, indicating the potential of ethanolic leaf extracts.

ABSTRACT
Traffic congestion remains a significant challenge in urban mobility, necessitating innovative and cost-effective monitoring solutions. This research explores the development of intelligent magnetic sensing systems as an alternative to traditional camera-based and GPS-dependent traffic monitoring technologies. Magnetic sensors provide a low-cost, non-intrusive approach to vehicle detection, classification, and congestion analysis, leveraging perturbations in the Earth's magnetic field caused by moving vehicles. The study employs a structured methodology, integrating IoT-based real-time data processing, machine learning models for vehicle classification, and edge computing for congestion prediction. Data analysis results indicate an average vehicle classification accuracy of 89%, with buses achieving the highest detection rate (92%) and motorbikes presenting the lowest (85%) due to smaller magnetic signatures. The study also highlights key research gaps, including limited deployment in urban environments, challenges in differentiating similar vehicle types, and scalability issues. Based on these findings, several recommendations are proposed, such as adaptive sensor placement, multi-sensor fusion, AI-driven classification enhancements, and integration into smart city infrastructures. Future research should focus on developing standardized communication protocols, refining predictive analytics, and ensuring seamless interaction with Vehicle-to-Infrastructure (V2I) networks. The results suggest that intelligent magnetic sensing systems can play a pivotal role in next-generation urban mobility solutions, offering real-time, privacy-friendly, and scalable traffic monitoring capabilities for intelligent transportation systems (ITS).