Alkaptonuria is a rare genetic metabolic disorder of autosomal recessive inheritance characterised by the accumulation of homogentisic acid in the body. It is diagnosed upon identification of characteristic symptoms, using various biochemical investigations, radiographic pictures, and a variety of specialised tests.Here we are discussing the case of an 80-year-old female patient with incidental findings of alkaptonuria. It is crucial to understand the fundamental diagnostic investigations that can be used in low-income nations or facilities where investigations like genetic testing, gas chromatography, and mass spectrometry are not readily available for the diagnosis of alkaptonuria.
Ground water contamination with Arsenic (As) is one of the foremost issues in the South Asian countries where ground water is one of the foremost sources of drinking water. In Asian countries, especially people of Pakistan living in rural areas are devouring ground water for drinking purpose, and cleaned water is not accessible to them. This arsenic contaminated water is hazardous for human health. The persistence of this study is to study the increasing level of arsenic in ground water in coming years for Khairpur, Sindh Pakistan, which is also increasing the cancer rate (skin cancer, blood cancer) gradually in human body. To predict the arsenic value and cancer risk for the next five years, we have developed two models via Microsoft Azure machine learning with algorithms include Support Vector Machine (SVM), Linear Regression (LR), Bayesian Linear Regression (BLR), Boosted Decision tree (BDT), exponential smoothing ETS, Autoregressive Integrated Moving Average (ARIMA). The developed predictive model named as Arsenic Contamination and Cancer Risk Assessment Prediction Model (ACCRAP model) will help us to forecast the arsenic contamination levels and the cancer rate. The results demonstrated that BLR pose highest prediction accuracy of cancer rate among the four deployed machine learning algorithms.
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