Unlocking the genetic tapestry of autoimmune diseases: Unveiling common genes across multiple conditions.

OBJECTIVES: This study aimed to unravel the complexities of autoimmune diseases by conducting a comprehensive analysis of gene expression data across 10 conditions, including systemic lupus erythematosus (SLE), psoriasis, Sjögren's syndrome, sclerosis, immune-associated diseases, osteoarthritis, cystic fibrosis, inflammatory bowel disease (IBD), type 1 diabetes, and Guillain-Barré syndrome. METHODS: Gene expression profiles were rigorously examined to identify both upregulated and downregulated genes specific to each autoimmune disease. The study employed visual representation techniques such as heatmaps, volcano plots, and contour-MA plots to provide an intuitive understanding of the complex gene expression patterns in these conditions. RESULTS: Distinct gene expression profiles for each autoimmune condition were uncovered, with psoriasis and osteoarthritis standing out due to a multitude of both upregulated and downregulated genes, indicating intricate molecular interplays in these disorders. Notably, common upregulated and downregulated genes were identified across various autoimmune conditions, with genes like SELENBP1, MMP9, BNC1, and COL1A1 emerging as pivotal players. CONCLUSION: This research contributes valuable insights into the molecular signatures of autoimmune diseases, highlighting the unique gene expression patterns characterizing each condition. The identification of common genes shared among different autoimmune conditions, and their potential role in mitigating the risk of rare diseases in patients with more prevalent conditions, underscores the growing significance of genetics in healthcare and the promising future of personalized medicine.

Perilous paradigm of graphene oxide and its derivatives in biomedical applications: Insight to immunocompatibility.

With advancements in nanotechnology and innovative materials, Graphene Oxide nanoparticles (GONP) have attracted lots of attention among the diverse types of nanomaterials owing to their distinctive physicochemical characteristics. However, the usage at scientific and industrial level has also raised concern to their toxicological interaction with biological system. Understanding these interactions is crucial for developing guidelines and recommendations for applications of GONP in various sectors, like biomedicine and environmental technologies. This review offers crucial insights and an in-depth analysis to the biological processes associated with GONP immunotoxicity with multiple cell lines including human whole blood cultures, dendritic cells, macrophages, and multiple cancer cell lines. The complicated interactions between graphene oxide nanoparticles and the immune system, are highlighted in this work, which reveals a range of immunotoxic consequences like inflammation, immunosuppression, immunostimulation, hypersensitivity, autoimmunity, and cellular malfunction. Moreover, the immunotoxic effects are also highlighted with respect to in vivo models like mice and zebrafish, insighting GO Nanoparticles' cytotoxicity. The study provides invaluable review for researchers, policymakers, and industrialist to understand and exploit the beneficial applications of GONP with a controlled measure to human health and the environment.

An efficient MPPT techniques for inter-harmonic reduction in grid connected hybrid wind and solar energy systems.

In this work, the operation of photovoltaic system, wind turbine driven doubly fed induction generator along with battery has been observed. Also, a searching space minimization-based artificial bee colony scheme is developed for tracking the maximum power in a doubly fed induction generator-based system. To track maximum power in solar systems, an improved adaptive reference voltage approach has been presented. Several conventional and optimization-based techniques are used by DFIG and photovoltaic systems to get around the non-linearity features in the output parameters. Regarding DFIG, the artificial bee colony method based on searching space minimization can be used to solve the shortcomings of the perturb and observe algorithm. Because of its weather-sensitive nature, it can withstand sudden changes in wind speed. The suggested searching space minimization based artificial bee colony strategy uses a mechanism for determining the range of optimal rotor speed in order to track the maximum power point more quickly. The maximum power point tracking performance of the adaptive reference voltage technique is superior to that of current perturb and observed-based systems. However, a huge processing memory is required in order to track the maximum possible power point. This paper proposes an enhanced maximum power point tracking technique based on adaptive reference voltage that does not require a memory unit. Additionally, despite sudden changes in irradiation conditions, improved adaptive reference voltage can drift-free and reliably monitor the maximum power point. The new adaptive reference voltage technique uses temperature and radiation sensors to identify the region nearest to the maximum power point. This helps the system respond more quickly. The proposed system with searching space minimization based artificial bee colony and improved adaptive reference voltage schemes displays lower inter-harmonic content in grid current compared to perturb and observe scheme. The proposed scheme has been implemented in MATLAB & simulink atmosphere and OPAL-RT displayed satisfactory results.