CLINICAL STUDY OF BABOOL (ACACIA ARABICA WILLD.) AND KUKUNDAR (BLUMEA LACERA D.C.) ON ASRIGDARA

Rakta is known to be a vital substance of body. As the disease is characterized by excess flow of blood out of the body, Raktastambhana Chikitisa is beneficial. Treatment prescribed for Raktatisara, Raktapitta, Raktarsha, Guhyaroga and Garbhasrava is also useful. Considering this aspect, Raktastambhaka, Raktsodhana, Raktapittaghna effects are desired in treatment. Haemostatic drugs should be used by giving due consideration to the association of Doshas diagnosed on the basis of colour and smell of the blood. Aims and Objectives: To evaluate the efficacy of Babool and Kukundar and comparative clinical study of Babool and Kukundar on Asrigdara. Materials and Methods: For the present study, patients fulfilling the clinical criteria for diagnosis of Asrigdara will be selected irrespective of their age, sex, religion, etc, in random from O.P.D. section of M. M. M. Govt. Ayurveda College hospital, Udaipur (Raj.). Case history of all the patients was taken in the specially prepared proforma. Conclusion: Babool due to its astringent taste and Kunkundar due to its bitter taste, both drugs pacify excessive Pitta humour which is main cause of Asrigdara. Their chemical constituents possess haemostatic and anti-inflammatory properties thus considering these facts, these drugs have been selected for present study

Coexistence of carcinoma cheek with tuberculosis: a rarity

Coexistence of tuberculosis and cheek malignancy is a rare phenomenon and it has never been reported to the best of our knowledge. Here, we present a case of carcinoma cheek with tuberculosis in a 50 year old male patient who was successfully managed by multimodality approach by combining anti-tubercular therapy with chemotherapy and radiotherapy

Mechanical properties and quality of plasma sprayed, functionally graded tungsten/steel coatings after process upscaling

The First Wall of a fusion reactor needs to withstand high heat flux as well as particle bombardment. For this, a First Wall made of steel requires a protective coating with a material that may still transfer heat for conversion to energy, such as tungsten. Its thermal expansion mismatch towards steel is overcome by vacuum plasma spraying of a functionally graded material onto the steel wall, followed by a tungsten top coat. This process was recently transferred to industry for upscaling, to develop a coating technology that can cover the large dimensions of First Wall components without deteriorating the substrate steel\u27s properties by overheating. This work represents an instrumented indentation study of the achieved coating quality and properties, combined with microstructural analysis. Hardness profiles within coating and substrate indicate successful establishment of a linearly functionally graded material and only minor substrate overheating. The latter observation is supported by electron backscatter diffraction showing no change in the substrate\u27s microstructure. The substrate hardness was investigated on several positions of coated plates sizing up to 500 × 250 mm2. The results indicate faster cooldown in the plate corners. Cooling channel bores that were pre-fabricated in the plates had no effect on plate hardness after coating. The elastic modulus of the coating\u27s interlayers, determined by instrumented indentation, was found lower than predicted from bulk properties. This is attributed to the heterogeneous microstructure of the thermally sprayed coating

Fracture behaviour of Ti/TiN multilayer thin film modeling and experimental validation

Although titanium nitride is used as a hard protective coating, it is brittle. The multilayering of Ti/TiN film improves the nitride coating’s damage tolerance and expands its applications. The influence of layer spacing on multilayer fracture toughness (KIC) is not known and quantification is important for designing a multilayer with enhanced damage tolerance. The effect of multilayering on the crack tip driving force is investigated using finite element method (FEM) in this work. Elastic-plastic mismatch in Ti/TiN system is incorporated in simulation. Numerical simulations are performed for varying layer spacing for Ti/TiN systems. Please click Download on the upper right corner to see the full abstract

Treatment with clobetasol propionate 0.025% topical therapy in various dermatoses

Owing to their anti-inflammatory and vasoconstrictive properties. Topical corticosteroids (TCs) provide benefits in various dermatological conditions, including atopic eczema, psoriasis, chronic hand eczema, and localized vitiligo. Clobetasol propionate (CP) is the most common topical agent possessing anti-inflammatory, antimitotic, antipruritic, and immunosuppressive properties that are employed in the management of plaque psoriasis. CP 0.025% cream was approved by the United States food and drug administration for the treatment of moderate-to-severe psoriasis in adult patients. The formulation is free from known contact allergens, such as propylene glycol, short-chain alcohols, and sorbitol-based emulsifiers, and has demonstrated hypoallergenic effects. High penetration of active ingredients and a lower degree of systemic absorption make CP 0.025% an effective and safe agent. This case series discusses the clinical experience of using CP 0.025% cream in various dermatologic conditions, focusing on its efficacy and safety

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

A COVID-19 X-ray image classification model based on an enhanced convolutional neural network and hill climbing algorithms

The classification of medical images is significant among researchers and physicians for the early identification and clinical treatment of many disorders. Though, traditional classifiers require more time and effort for feature extraction and reduction from images. To overcome this problem, there is a need for a new deep learning method known as Convolution Neural Network (CNN), which shows the high performance and self-learning capabilities. In this paper,to classify whether a chest X-ray (CXR) image shows pneumonia (Normal) or COVID-19 illness, a test-bed analysis has been carried out between pre-trained CNN models like Visual Geometry Group (VGG-16), VGG-19, Inception version 3 (INV3), Caps Net, DenseNet121, Residual Neural Network with 50 deep layers (ResNet50), Mobile-Net and proposed CNN classifier. It has been observed that, in terms of accuracy, the proposed CNN model appears to be potentially superior to others. Additionally, in order to increase the performance of the CNN classifier, a nature-inspired optimization method known as Hill-Climbing Algorithm based CNN (CNN-HCA) model has been proposed to enhance the CNN model’s parameters. The proposed CNN-HCA model performance is tested using a simulation study and contrasted to existing hybridized classifiers like as Particle Swarm Optimization (CNN-PSO) and CNN-Jaya. The proposed CNN-HCA model is compared with peer reviewed works in the same domain. The CXR dataset, which is freely available on the Kaggle repository, was used for all experimental validations. In terms of Receiver Operating Characteristic Curve (ROC), Area Under the ROC Curve (AUC), sensitivity, specificity, F-score, and accuracy, the simulation findings show that the CNN-HCA is possibly superior than existing hybrid approaches. Each method employs a k-fold stratified cross-validation strategy to reduce over-fitting

Massive pleural effusion and associated pulmonary embolism in a case of Gefitinib responsive lung cancer

Pulmonary embolism (PE) and venous thrombosis is a common complication in lung cancer patients with a high misdiagnosis rate and high mortality. However, when an undiagnosed lung cancer patient presents as PE, cancer as a cause may not always be explored. We present a case of a young male patient presenting with venous thromboembolism causing massive pleural effusion, leading to the diagnosis of epidermal growth factor receptor mutation positive adenocarcinoma, showing good response to gefitinib therapy