COL4A3/COL4A4 mutations: from familial hematuria to autosomal-dominant or recessive Alport syndrome.

COL4A3/COL4A4 mutations: From familial hematuria to autosomal-dominant or recessive Alport syndrome. BACKGROUND: Mutations of the type IV collagen COL4A5 gene cause X-linked Alport syndrome (ATS). Mutations of COL4A3 and COL4A4 have been reported both in autosomal-recessive and autosomal-dominant ATS, as well as in benign familial hematuria (BFH). In the latter conditions, however, clinical features are less defined, few mutations have been reported, and other genes and non-genetic factors may be involved. METHODS: We analyzed 36 ATS patients for COL4A3 and COL4A4 mutations by polymerase chain reaction-single strand conformational polymorphism (PCR-SSCP) and direct sequencing. Sporadic patients who had tested negative for COL4A5 mutations were included with typical cases of autosomal recessive ATS to secure a better definition of the phenotype spectrum. RESULTS: We identified seven previously undescribed COL4A3 mutations: in two genetic compounds and three heterozygotes, and one in COL4A4. In agreement with the literature, some of the mutations of compound heterozygotes were associated with microhematuria in healthy heterozygous relatives. The mutations of heterozygous patients are likely dominant, since no change was identified in the second allele even by sequencing, and they are predicted to result in shortened or abnormal chains with a possible dominant-negative effect. In addition, both genes showed rare variants of unclear pathogenicity, and common polymorphisms that are shared in part with other populations. CONCLUSIONS: This study extends the mutation spectrum of COL4A3 and COL4A4 genes, and suggests a possible relationship between production of abnormal COL IV chains and dominant expression of a continuous spectrum of phenotypes, from ATS to BFH

A smart aeroponic system for sustainable indoor farming

Population growth requires a significant increase in agricultural production to ensure food security. However, the further increase in such production is limited by the environmental crises and by the negative impacts of open-field agricultural practices. Vertical farming techniques, such as aeroponics, can be exploited to optimize the use of resources. This paper presents a methodology for developing a smart aeroponic systems, based on IoT and artificial intelligence algorithms. The proposed methodology is used to identify the parameters that affect plant growth and their correlations with the plant performance indicators. The obtained smart aeroponic system will be able to automatically balance resource utilization (e.g., water, nutrients, energy) and crop productivity