Thyroid hormone resistance (THR) is a rare syndrome of reduced end organ sensitivity. Patients with THR have elevated serum free thyroxine (FT4), free triiodothyronine (FT3), but normal or slightly elevated serum thyrotropin values. The characteristic clinical feature is goitre without symptoms and metabolic consequences of thyroid hormone excess. THR can be classified on the basis of tissue resistance into pituitary, peripheral or generalised (both pituitary and peripheral) types. Mutations in the TRbeta gene, cell membrane transporter and genes controlling intracellular metabolism of thyroid hormone have been implicated. THR is differentiated from thyroid stimulating hormone (TSH) secreting pituitary adenoma by history of THR in the family. No specific treatment is often required for THR; patients with features of hypo- or hyperthyroidism are appropriately treated with levo-triiodothyronine (L-T3), levo-thyroxine (L-T4), dextro-thyroxine(D-T4) or 3,3,5 triiodo-thyroacetic acid (TRIAC). The diagnosis helps in appropriate genetic counselling of the family.

The ongoing coronavirus disease-2019 (COVID-19) pandemic has been the focus of health care workers as it has affected millions of people and cost hundreds of thousands of lives around the world. As hospitals struggle to identify and care for those afflicted with COVID-19, it is easy to overlook endemic diseases that potentially worsen or mimic the pulmonary manifestations or may coinfect those with COVID-19. In this case report, we present the case of a 48-year-old Hispanic female who was admitted with respiratory distress from an acute COVID-19 infection but was also found to have acute pulmonary coccidioidomycosis infection and was treated successfully.

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Purpose This paper aims to discuss that a conventional Al2O3, 1.5 Wt.% carbon nanotubes (CNTs)-Al2O3, 2 Wt.% CNTs-Al2O3 and 4 Wt.% CNTs-Al2O3 composite coatings were deposited with the help of Plasma spray process. Design/methodology/approach To better understand the effect of CNT reinforcement on oxidation resistance, high-temperature oxidation behaviour of conventional Al2O3, 1.5 Wt.% CNTs-Al2O3, 2 Wt.% CNTs-Al2O3 and 4 Wt.% CNTs-Al2O3 composite coatings at 900°C was compared with the performance of the uncoated ASME-SA213-T11 boiler tube steel substrate. Findings The results showed that the CNT-reinforced alumina coatings exhibited better oxidation resistance and thermal stability than uncoated ASME-SA213-T11 boiler tube steel. The coated steel substrates had a lower mass gain rate than the substrate after different oxidation times. Originality/value Limited literature is available where the CNT have been reinforced into the composite alloy powders and has been thermally spray-deposited for various surface engineering applications. This research showed that with the increase in the percentage of CNTs into the alloy powder mixture, there is a significant reduction in weight gain and hence higher resistance to oxidation.

Carbon Nanotubes (CNT) reinforced Cr 3 C 2 -20NiCr composite coatings were prepared and deposited on alloy steel using high-velocity oxyfuel (HVOF) thermal spraying method. The comparative effects of variation in CNT content (from 1 to 2 weight percentage) on mechanical properties and hot corrosion behaviour in super-heater zone of actual coal-fired boiler of a thermal power plant at 900°C have been investigated. After exposure to boiler environment, the corrosion products have been analysed by thermogravimetric analysis, Xray diffraction, scanning electron microscopy, energy dispersive and cross-sectional analysis techniques. The results confirmed that variation in CNT content improved the mechanical and microstructural properties of surface coatings by modifying their surface characteristics. The variation in CNT content improved the corrosion protection property of composite coating at high temperature of exposure. Reduction in corrosion rate was observed with increase in CNT content from 1 to 2 weight percentage in the Cr 3 C 2 -20NiCr coating matrix.

The scope of research in the domain of activation functions remains limited and centered around improving the ease of optimization or generalization quality of neural networks (NNs). However, to develop a deeper understanding of deep learning, it becomes important to look at the non linear component of NNs more carefully. In this paper, we aim to provide a generic form of activation function along with appropriate mathematical grounding so as to allow for insights into the working of NNs in future. We propose "Self-Learnable Activation Functions" (SLAF), which are learned during training and are capable of approximating most of the existing activation functions. SLAF is given as a weighted sum of pre-defined basis elements which can serve for a good approximation of the optimal activation function. The coefficients for these basis elements allow a search in the entire space of continuous functions (consisting of all the conventional activations). We propose various training routines which can be used to achieve performance with SLAF equipped neural networks (SLNNs). We prove that SLNNs can approximate any neural network with lipschitz continuous activations, to any arbitrary error highlighting their capacity and possible equivalence with standard NNs. Also, SLNNs can be completely represented as a collections of finite degree polynomial upto the very last layer obviating several hyper parameters like width and depth. Since the optimization of SLNNs is still a challenge, we show that using SLAF along with standard activations (like ReLU) can provide performance improvements with only a small increase in number of parameters.