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Background: Granuloma annulare (GA) is a benign, self-limiting inflammatory skin condition of unknown origin that may occur following multiple etiological triggers. GA incited secondary to vaccinations has been rarely reported in the medical literature. The COVID-19 pandemic has introduced extensive global immunization against the SARS-COV-2 virus, bringing a gamut of vaccine-related complications. We elucidate a case report of the spontaneous eventuality of GA following ChAdOx1 nCoV-19 Corona Virus Recombinant Vaccine. Case Report: A healthy 26-year male presented with a one-week history of asymptomatic single, flesh-pink patch with a raised margin over his left ventral forearm. On close examination, the margin of the lesion had multiple annularly arranged papules. Biopsy of lesion was done, and histopathology revealed numerous palisading granulomas in the dermis consistent with findings of localized GA. The patient was managed with once-daily external application high potent topical corticosteroids, which was used intermittently by the patient. However, the lesion showed spontaneous resolution in one month. Conclusion: Identifying ChAdOx1 nCoV- 19 Vaccine-related adverse events following its first dose is paramount, as evidence of the proportion of local or systemic severe cutaneous adverse skin reaction (SCAR) on subsequent dosing is a paucity. A more extensive systematic review corroborating SCARs and safety profile following immunization with ChAdOx1 nCoV-19 Vaccine prevails to be the need of the hour.

Amaranth dye (AD) is trisodium (4E)-3-oxo-4-[(4-sulfonato-1- naphthyl) hydrazono] naphthalene-2, 7-disulfonate and anionic in nature. In the present investigation, waste biomasses such as Terminalia chebula shell (TCS), Peltophorum pterocarpum leaf (PPL) and Psidium guajava bark (PGB) are explored as biosorbents for the first time toward the removal of AD from aqueous solution in a batch method. Influence of biosorption parameters such as pH, initial concentration of AD, biosorbents (TCS, PPL, PGB) dosage, temperature and contact time was studied. Biosorption equilibrium data was analyzed using two parameter isotherms. The kinetics of the biosorption process was analyzed using different models to understand the rate-determining step. The results of the biosorption experiment and modeling investigation illustrated that the pseudo-second-order rate equation fits the experimental data and further the experimental results showed Langmuir isotherm fitted well the biosorption equilibrium data. TCS showed more efficiency toward the removal of AD than PPL and PGB. The value of enthalpy for TCS is 1.527 kJ/mol suggests that the AD removal process is endothermic. The positive value of entropy is 6.429 J/mol K indicates that the particle is randomly disordered and negative values of standard Gibbs free energy (ΔG°) suggested that the biosorption process is spontaneous.Novelty statementBiomasses of Terminalia chebula shell (TCS), Peltophorum pterocarpum leaf (PPL) and Psidium guajava bark (PGB) reported as first time explored biosorbent for amaranth dye (AD) removal from aqueous solution.Optimal biosorption parameter for AD removal determined.Experimental data examined using isotherm, kinetic and thermodynamic analysis.

Measurement of the longitudinal relaxation time in the rotating frame of reference (T₁ₚ) is sensitive to the fidelity of the main imaging magnetic field (B₀) and that of the RF pulse (B₁). The purpose of this study was to introduce methods for producing continuous wave (CW) T₁ₚ contrast with improved robustness against field inhomogeneities and to compare the sensitivities of several existing and the novel T₁ₚ contrast generation methods with the B₀ and B₁ field inhomogeneities. Four hard-pulse and four adiabatic CW-T₁ₚ magnetization preparations were investigated. Bloch simulations and experimental measurements at different spin-lock amplitudes under ideal and non-ideal conditions, as well as theoretical analysis of the hard-pulse preparations, were conducted to assess the sensitivity of the methods to field inhomogeneities, at low (ω₁ << ΔB₀) and high (ω₁ >> ΔB₀) spin-locking field strengths. In simulations, previously reported single-refocus and new triple-refocus hard-pulse and double-refocus adiabatic preparation schemes were found to be the most robust. The mean normalized absolute deviation between the experimentally measured relaxation times under ideal and non-ideal conditions was found to be smallest for the refocused preparation schemes and broadly in agreement with the sensitivities observed in simulations. Experimentally, all refocused preparations performed better than those that were non-refocused. The findings promote the use of the previously reported hard-pulse single-refocus ΔB0 and B₁ insensitive T₁ₚ as a robust method with minimal RF energy deposition. The double-refocus adiabatic B₁ insensitive rotation-4 CW-T₁ₚ preparation offers further improved insensitivity to field variations, but because of the extra RF deposition, may be preferred for ex vivo applications.

Cloud computing have high interest from companies .since ,its inception With its services delivery model, cloud computing add technical and strategic business value to companies. This paper presents a systematic literature review to explore the current key issues related to cloud computing adoption.

Aluminum alloy 2014 has wide application. In its applications, aerospace and automobile industries are utilized well such Aluminum alloy. This article presents a novel approach of reviewing material related research to pave the path for new researchers. The aluminum is one of the popular materials and can be modified either to form a composite or augmenting the material properties with utilization of heat treatment methods. Friction stir welding is one of the advanced metals joining/ welding technology. This paper reviews how the two popular cases jointly contributing for creating mechanical structures. This papers covers parameter optimizations for friction stir welding of similar and dissimilar metals, mathematical modeling, post welding treatments, new tool contributions, grain structure effects, need for FSW and identified further research scope further research are consolidated and presented as research potential.

In the current work the reinforcement effect of nano and micro scale fillers separately and in combination (dual scale filler reinforcement) in polypropylene thermoplastic was investigated. The composites was prepared using counter rotating twin screw extruder at a screw speed of 100 rpm and the set temperature profile varying from 150 to 210 °C. The morphological investigations performed by Scanning electron microscopy (SEM) and Transmission electron microscopy (TEM) revealed the uniform distribution and dispersion of fillers within the matrix. The tensile strength and modulus obtained was notable at 2 wt% of nanosilica amongst the nanocomposites, whereas in the dual scale filler reinforced composite 4 wt% of nanosilica content displayed the enhanced tensile and barrier properties. The hybrid composite incorporated with 4 wt% of nanosilica and 15 wt% of glass fiber offered higher restrictions to the solvent flow and is evinced with least permeability and diffusivity parameters.

3D printing is a process of producing 3D objects from CAD model. Using additive processes, 3D printing is done where a part is formed layer by layer. A melted affidavit model conveying ordinary surface examples with various element sizes and angle proportions will be produced on a FDM framework with various form directions. The surface finish of each 3D printed part will be inspected. The objective is to evaluate surface finish of different parts by changing the layer thickness and other parameters such as infill, infill pattern, roof thickness, floor thickness, and number of shells were considered for the study of build time optimization and also find out the relation between surface roughness and layer thickness.

Recently deep learning has shown a improved performance than machine learning in many of the areas like pattern recognition, image classification computer vision, video segmentation and many more. But out of all these areas, disease classification is one of the major area in which deep learning has shown a remarkable performance than the traditional machine learning algorithms especially in the area of image recognition. Machine learning algorithms are not enough capable to handle the image so in this work we will apply the deep learning approach on the Alzheimer's disease dataset for performing the early detection and classification of the disease and this has done through using neuroimaging data. Previous work done in this area was based on traditional machine learning algorithm and they have used stacked auto encoder (SAC) for dimensionality reduction and they have achieved a classification accuracy of 83.7% during the prediction from initial symptom to final development of Alzheimer's disease. The deep learning algorithm ResNet which is implemented in this paper has shown a classification accuracy of 93% and this is also achieved without applying any dimensionality reduction approach and this has been considered as the best predictive rate on the neuroimaging data till now. The applied ResNet is the improved ResNet and the comparison of both the Resnet models are shown in this work. This deep learning application will also be useful for other types of disease classification like cancer, diabetics, etc. Keyword : ResNet, mild cognitive impairments (MCI), ADNI, ReLU, Residual Block, Convolutions.

Cystic echinococcosis (CE) is one of the most neglected tropical diseases as per WHO which has an immense public health significance. Diagnosis of CE is difficult as specific clinical signs are manifested only after the hydatid cyst attains a considerable size. Immunodiagnosis is a reliable method of diagnosing CE.SDS-PAGE was performed for the hydatid cyst fluid antigens. The antigen purity was tested by Western blotting and four different immunoassays were evaluated using these two antigens in sheep and buffalo in diagnosis of CE.SDS-PAGE revealed four bands of 72, 64, 48 and 24 kDa for crude antigen and a single 72 kDa band for purified antigen. Among sheep sera, ELISA was most sensitive (70%) using crude antigen and also while using the purified antigen (80%). In case of buffaloes, ELISA, DID and CIEP were more sensitive (83.3%) using crude antigen, whereas DID and CIEP were more sensitive (83.3%) using purified antigen.In sheep, while using the crude antigen ELISA was the most sensitive assay and IHA was the least sensitive assay. While using the purified antigen also, ELISA was the most sensitive and others were absolutely specific except for IHA being less sensitive. In buffaloes, using crude antigen, all the immunoassays CIEP, DID and ELISA were highly sensitive in diagnosing CE infection except IHA, whereas using the purified antigen, both CIEP and DID were more sensitive than ELISA and IHA which were comparatively less sensitive in detecting CE in buffalo sera.