Articles

Immunopathology and Laboratory Diagnosis of Rheumatoid Arthritis: A Review of Literature

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This review explores rheumatoid arthritis, an autoimmune disorder whose immunopathology involves a convergence of genetic susceptibility (notably HLA-DRB1 shared epitope alleles) and environmental exposures (smoking, infections) leading to an aberrant immune response. RA is a prevalent autoimmune disease globally, and though historically considered uncommon in Africa, emerging data show it is an important and likely under-recognized health issue in regions like Nigeria. Epidemiologically, ~0.5% of the world’s population is affected with millions suffering chronic pain and disability. In Africa and Southeastern Nigeria, true prevalence is uncertain due to diagnostic gaps, but RA cases are increasingly reported as awareness grows. Autoimmune processes – generation of RF and ACPA autoantibodies, activation of T cells and macrophages, and a cytokine-driven inflammation – result in synovial damage and systemic effects. Understanding these mechanisms explains why specific biomarkers (RF, ACPA) are useful in diagnosis and why therapies targeting cytokines (like TNF or IL-6 inhibitors) are effective. In laboratory diagnosis, we identified the core tools: RF and ACPA testing for confirming autoantibodies, ESR and CRP for gauging inflammation, and newer panels for disease activity. In resource-constrained settings, basic assays can be performed with relatively low-cost methods (e.g. ESR by Westergren, RF by latex agglutination), but introducing more specific tests like anti-CCP is vital for improving diagnostic specificity. We provided practical outlines for these assays, emphasizing adherence to SOPs and quality control to ensure accuracy of results.

Evaluation of the Use of Artificial Neural Networks to Predict the Photovoltaic Power Generation Factors by Using Feed Forward Back Propagation (FFBP) Technique

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The solar power generation (renewable energy) is the cleanest form of energy generation method and the solar power plant has a very long life and also is maintenance-free, but due to the high unpredictability of the generated solar power due to dynamically changing environmental factors it cannot be used as the reliable source of power. This prevents the maximum utilization of solar energy. In this project we are designing the artificial neural network model to predict the power generated depending on the various environmental factors like visibility, cloud cover (sky cover), etc. the intensity of the incident of the solar radiation decreases and thus the plant is not able to work at its rated capacity. We use Artificial Neural Network (ANN) with Feed Forward Back Propagation (FFBP) technique and predicted the percentage of the maximum plant capacity which will be generated by considering the environmental factors like temperature, pressure, distance to solar noon, day light, sky cover, visibility, humidity, wind speed, wind direction and compared our results with available data and find quite encouraging results.