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  1. Pazikadin AR, Rifai D, Ali K, Malik MZ, Abdalla AN, Faraj MA
    Sci Total Environ, 2020 May 01;715:136848.
    PMID: 32040994 DOI: 10.1016/j.scitotenv.2020.136848
    The increased demand for solar renewable energy sources has created recent interest in the economic and technical issues related to the integration of Photovoltaic (PV) into the grid. Solar photovoltaic power generation forecasting is a crucial aspect of ensuring optimum grid control and power solar plant design. Accurate forecasting provides significant information to grid operators and power system designers in generating an optimal solar photovoltaic plant and to manage the power of demand and supply. This paper presents an extensive review on the implementation of Artificial Neural Networks (ANN) on solar power generation forecasting. The instrument used to measure the solar irradiance is analysed and discussed, specifically on studies that were published from February 1st, 2014 to February 1st, 2019. The selected papers were obtained from five major databases, namely, Direct Science, IEEE Xplore, Google Scholar, MDPI, and Scopus. The results of the review demonstrate the increased application of ANN on solar power generation forecasting. The hybrid system of ANN produces accurate results compared to individual models. The review also revealed that improvement forecasting accuracy can be achieved through proper handling and calibration of the solar irradiance instrument. This finding indicates that improvements in solar forecasting accuracy can be increased by reducing instrument errors that measure the weather parameter.
  2. Dashti M, AlKandari H, Malik MZ, Nizam R, John SE, Jacob S, et al.
    Front Cell Infect Microbiol, 2024;14:1444216.
    PMID: 39844836 DOI: 10.3389/fcimb.2024.1444216
    BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) is a severe complication arising from SARS-CoV-2 infection, with indications that rare inborn errors of immunity may play a role in its pathogenesis. Recent studies suggest that genetic predispositions, particularly monogenic forms, could significantly influence the immune responses to SARS-CoV-2 in MIS-C.

    METHODS: We analysed 24 children under 12 years old, all of whom met the criteria provided by the World Health Organization, 2020 for MIS-C diagnosis, from the Paediatric COVID-19 Registry in Kuwait (PCR-Q8). Demographic and clinical data were collected from medical records, and exome sequencing was performed on the children and their parents to identify rare exonic variants. These variants were prioritized using two approaches: a candidate genes approach employing trio segregation analysis, and a candidate variants approach using a gene panel informed by previous studies on MIS-C-related genetic variants and datasets of differentially expressed genes in MIS-C patients.

    RESULTS: The candidate genes approach identified 53 unique genes in 20 of the 24 probands, including DDX60 and TMEM154, which were also differentially expressed between MIS-C and control groups. The candidate variants approach identified 33 rare, predicted deleterious heterozygous variants across 19 unique genes in 19 of the 24 probands, including both previously described and novel candidate variants for MIS-C. Pathway analysis of the identified genes from both approaches revealed significant involvement in immune response, viral defence, and inflammatory pathways.

    CONCLUSION: This study underscores the monogenic susceptibility to MIS-C, enhancing the evidence base through comprehensive genetic analysis. The findings highlight the critical role of genetic predispositions in MIS-C and suggest that further functional genomics work is necessary to explore the mechanistic contributions of these genes, facilitating the development of targeted diagnostic strategies.

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