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Hereditary renal amyloidosis is an autosomal dominant condition with considerable overlap with other amyloidosis types. Differential diagnosis is complicated, but is relevant for prognosis and treatment. We describe a patient with nephrotic syndrome and progressive renal failure, who had a mother with renal amiloidosis. Renal biopsy revealed amyloid deposits in glomerular space, with absence of light chains and protein AA. We suspected amyloidosis with fibrinogen A alpha chain deposits, which is the most frequent cause of hereditary amyloidosis in Europe, with a glomerular preferential affectation. However, the genetic study showed a novel mutation in apolipoprotein AI. On reviewing the biopsy of the patient's mother similar glomerular deposits were found, but there were significant deposits in the renal medulla as well, which is typical in APO AI amyloidosis. The diagnosis was confirmed by immunohistochemistry. Apo AI amyloidosis is characterized by slowly progressive renal disease and end-stage renal disease occurs aproximately 3 to 15 years from initial diagnosis. Renal transplantation offers an acceptable graft survival and in these patients with hepatorenal involvement simultaneous liver and kidney transplantation could be considered.

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BACKGROUND Quite often, patients arrive to consultation when the symptoms of an infectious disease are already serious, forcing doctors to divert them to the emergency services. Particularly, the possible anticipation of the diagnosis -prognostic- for institutionalized people would lead to soften the treatment, increasing resident’s wellness and alleviating the degradation of the emergency services. Big data, mobile communications, cloud services or machine learning technologies applied in medicine -e-Health- assist practitioners with efficient tools. OBJECTIVE This article describes a new data collection system for predicting infectious diseases in elderly people, supporting future telecare and medical recommender applications. METHODS The system provides a medical database updated with vital signs that nurses take with medical sensors from residents. The Cloud database is accessible with a flexible microservices software architecture. RESULTS The e-Health system components are cost-effective, leading to massive implementations for servicing disadvantaged areas. The scalable architecture is prepared for big data applications that may extract valuable knowledge patterns for medical research. CONCLUSIONS The innovation relies in the combination of advanced e-Health technologies and procedures that delivers ubiquitously available quality data to provide multifaceted scalable low-cost applications to improve resident’s wealth and release public health care services.