Addison's disease caused by adrenal tuberculosis may lead to misdiagnosis of major depressive disorder: A case report

BACKGROUND Addison’s disease(AD)is a rare but potentially fatal disease in Western countries,which can easily be misdiagnosed at an early stage.Severe adrenal tuberculosis(TB)may lead to depression in patients.CASE SUMMARY We report a case of primary adrenal insufficiency secondary to adrenal TB with TB in the lungs and skin in a 48-year-old woman.The patient was misdiagnosed with depression because of her depressed mood.She had hyperpigmentation of the skin,nails,mouth,and lips.The final diagnosis was adrenal TB that resulted in the insufficient secretion of adrenocortical hormone.Adrenocortical hormone test,skin biopsy,T cell spot test of TB,and adrenal computed tomography scan were used to confirm the diagnosis.The patient’s condition improved after hormone replacement therapy and TB treatment.CONCLUSION Given the current status of TB in high-burden countries,outpatient doctors should be aware of and pay attention to TB and understand the early symptoms of AD.

Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture

Various indices are used for assessing vegetation and soil properties in satellite remote sensing applications. Some indices,such as normalized difference vegetation index(NDVI) and normalized difference water index(NDWI), are capable of simply differentiating crop vitality and water stress. Nowadays, remote sensing capabilities with high spectral, spatial and temporal resolution are available to analyse classification problems in precision agriculture. Many challenges in precision agriculture can be addressed by supervised classification, such as crop type classification, disease and stress(e.g., grass, water and nitrogen) monitoring. Instead of performing classification based on designated indices, this paper explores direct classification using different bands information as features. Land cover classification by using the recently launched Sentinel-2A image is adopted as a case study to validate our method. Four approaches of featured band selection are compared to classify five classes(crop, tree, soil, water and road) with the support vector machines(SVMs)algorithm, where the first approach utilizes traditional empirical indices as features and the latter three approaches adopt specific bands(red, near infrared and short wave infrared) related to indices, specific bands after ranking by mutual information(MI), and full bands of on-board sensors as features, respectively. It is shown that a better classification performance can be achieved by directly using the selected bands after MI ranking compared with the one using empirical indices and specific bands related to indices, while the use of all 13 bands can marginally improve the classification accuracy than MI based one. Therefore, it is recommended that this approach can be applied for specific Sentinel-2A image classification problems in precision agriculture.