Curcumin And Etoricoxib Encapsulated Liposomes: Formulation, Characterization And Anti-Inflammatory Effects In Rat Models

To enhance anti-inflammatory efficacy of Curcumin (CURC) and Etoricoxib (ETRX); and to reduce their notorious side effects, they were loaded into liposomal formulations (LFs). The present study aimed at formulation, characterization and evaluation of anti-inflammatory effects of LFs of CURC in combination with ETRX in experimental acute inflammation (AI) in rat model induced by carrageenan administration. The existing pharmaceuticals for treating arthritis are analgesics, steroids and non-steroidal anti-inflammatory drugs (NSAIDs), which reduce the symptoms such as severe pain and inflammation. Classical NSAIDs are cyclooxygenase (COX) inhibitors that inhibit prostaglandins (PGs) and thromboxane synthesis, thereby reducing inflammation. New NSAIDs selectively inhibit COX-2 and are usually specific to inflamed tissue, which decreases the risk of peptic ulcer. However, their long-term use cannot be sustained due to inadequate pain relief, immune disturbances and serious gastrointestinal and cardiovascular adverse events. Therefore, plant-based product like CURC with anti-inflammatory properties and minimum side effects are needed for the treatment of arthritis, including rheumatoid arthritis (RA) and osteoarthritis, especially after the withdrawal of many Food and Drug Administration (FDA)-approved anti- inflammatory drugs. However, its poor solubility, low chemical stability and short half-life following systemic absorption contribute to CURC being considered a pharmaceutical challenge. Numerous delivery systems have been proposed as means to tailor its biological properties. In this research, we are particularly interested in potential of CURC as an anti-inflammatory agent in combination with ETRX and delivery in the form of LFs. ETRX; a NSAID is proposed to treat inflammation in rat model as it is known for its anti-inflammatory, analgesic and antipyretic effects. ETRX, a widely prescribed anti- inflammatory drug belongs to class IΙ under BCS (biopharmaceutical classification system) and exhibit variable oral bioavailability due to its poor aqueous solubility. This research is aimed to study synergistic effect of a natural compound CURC and an allopathic NSAID moiety ETRX to treat inflammation in rat model, by oral ingestion in the form of LF as an efficient drug delivery system. Hence, we propose LF as a mean to overcome the CURC limitations. Liposomes (LPs) encapsulation of CURC makes this formulation amenable to circumvent the problem of poor oral availability that limits the utility of free CURC. The LFs can potentiate the effects of encapsulated drugs by sustaining the release over an extended period of time. The results of our study demonstrated that the association of CURC with ETRX in the form of LFs could potentiate the anti-inflammatory effects in reduced doses in vitro and in rat model. The LFs were spherical in shape in TEM images at various resolutions. The particle size of optimized CURC-ETRX LPs was found to be 276.1 nm with PDI value of 24.5. The maximum EE for CURC and ETRX in optimized formulation (F2) was found 98.915% and 93.877% respectively. The % EE of CURC-ETRX loaded LFs was found to be dependent on the lipid concentration, resulting almost quantitatively for a maximum 15 mg of total lipids (PC+CL) and progressively decreasing at higher 20 mg of total lipid (PC+CL) content, may be due to precipitation of drugs at higher lipid concentrations. The cumulative percentage release of CURC and ETRX from optimized formulations was found to be 59.64% and 83.11% respectively, for a period of 24 hours. We investigated the in vivo effect of CURC and ETRX loaded LF on local edema in carrageenan-induced paw edema in rat model. The percentage inhibition of edema in rat model was found to be better for CURC-ETRX LF in comparison to conventional CURC and ETRX in solution forms (p<0.05). Hence, the association of CURC and ETRX to a low dose in the form of LFs could be an appropriate combination to decrease NSAID doses used to reduce pain, inflammatory cytokines, and histological changes in AI

The Nutraceutical value of Horticultural Crops

Nutraceuticals are one of the secondary metabolites that are being produced by diverse group of plants including the horticultural crops. The secondary metabolites in diverse horticultural crops are produced in significant amounts when encountered with different stresses such as wounding stress, abiotic stress, biotic stress, exposure to ultraviolet radiation etc. The secondary metabolite production in the plants enhances their response towards different stresses and help the plants to fight against the stresses in addition to their growth and development. The secondary metabolites expressed in horticultural crops such as phenolic compounds, flavonoids, alkaloids, polyphenols, terpenoids have proven to exhibit number of health benefits such as anti-inflammatory, anti-diabetic, anticancer response. The indepth knowledge of the biosynthetic pathways could lead to the enhancement of the secondary metabolites. Also, the stress responses can be modulated in a ways that could lead to the optimal expression of the genes involved in the production of these secondary metabolites. In the present review article, various horticultural crops including two vegetable and four fruit crops were assessed for the production of secondary metabolites under stress conditions, the biosynthetic pathways leading to the specific secondary metabolite production along with their health benefits have been discussed in detail

A Secure IoT-Based Cloud Platform Selection Using Entropy Distance Approach and Fuzzy Set Theory

With the growing emergence of the Internet connectivity in this era of Gen Z, several IoT solutions have come into existence for exchanging large scale of data securely, backed up by their own unique cloud service providers (CSPs). It has, therefore, generated the need for customers to decide the IoT cloud platform to suit their vivid and volatile demands in terms of attributes like security and privacy of data, performance efficiency, cost optimization, and other individualistic properties as per unique user. In spite of the existence of many software solutions for this decision-making problem, they have been proved to be inadequate considering the distinct attributes unique to individual user. This paper proposes a framework to represent the selection of IoT cloud platform as a MCDM problem, thereby providing a solution of optimal efficacy with a particular focus in user-specific priorities to create a unique solution for volatile user demands and agile market trends and needs using optimized distance-based approach (DBA) aided by Fuzzy Set Theory

A brief study on entrepreneurship and its classification

Entrepreneurship refers to a person usually someone who wants to implement that idea with the idea of disrupting the market with a new product or service. Perfect for research and development with practices, entrepreneurs are new, they bring innovations that open new ventures, markets, products and technology Opens the doors. Entrepreneurs need to play a role in solving problems that are still unresolved by existing products and technology. Traditionally, Entrepreneurship is classified into four main categories: small businesses, scalable start-ups, large companies and social entrepreneurs. These models cover the basics of starting a business and focus more the company is more than the qualities of an entrepreneur. An entrepreneur will usually start a new business and run it. At the same time, they are responsible for the risks involved. Entrepreneurship is the process of starting a new business, which involves risks and opportunities preparing one for both. An entrepreneur coordinates essential needs a company. Make sure you do the work, and no one will look over your shoulder. As an entrepreneur, you must learn to take responsibility for yourself, otherwise you will not succeed.&nbsp

Analytical Method Validation of Tablet Dosage Form of Lurasidone HCl

<p>This research is mainly focused on development of an Excellent Gradient method by reverse phase - high performance liquid chromatography (RP-HPLC) using UV Visible Detector. The main objective of method validation of Lurasidone HCl as a tablet dosage form is to validate the method as in-house method. As this tablet dosage form is not registered in any of Pharmacopeia, so the method is developed and validated for further studies. The sample is analyzed by RP-HPLC using octadecylsilane (C18) column (Inertsil LC-GC) as stationary phase with UV-Visible detector. The 'Gradient Method' is used as instrumental method. The Mobile phase (A) was prepared using potassium phosphate buffer by adjusting the pH of value 4.0 and mobile phase (B) was 100% acetonitrile; the ratio used for gradient was buffer: acetonitrile (40:60) respectively. The wavelength for λ max was selected by UV-Visible detector on spectrophotometer at wavelength 254nm. This method complies with Linearity , Accuracy , Recovery , Specificity , precision , stability , LOD , LOQ and Robustness.</p&gt

Analytical Method Validation of Tablet Dosage Form of Lurasidone HCl

<p>This research is mainly focused on development of an Excellent Gradient method by reverse phase - high performance liquid chromatography (RP-HPLC) using UV Visible Detector. The main objective of method validation of Lurasidone HCl as a tablet dosage form is to validate the method as in-house method. As this tablet dosage form is not registered in any of Pharmacopeia, so the method is developed and validated for further studies. The sample is analyzed by RP-HPLC using octadecylsilane (C18) column (Inertsil LC-GC) as stationary phase with UV-Visible detector. The 'Gradient Method' is used as instrumental method. The Mobile phase (A) was prepared using potassium phosphate buffer by adjusting the pH of value 4.0 and mobile phase (B) was 100% acetonitrile; the ratio used for gradient was buffer: acetonitrile (40:60) respectively. The wavelength for λ max was selected by UV-Visible detector on spectrophotometer at wavelength 254nm. This method complies with Linearity , Accuracy , Recovery , Specificity , precision , stability , LOD , LOQ and Robustness </p&gt

Noise Suppression and Edge Preservation for Low-Dose COVID-19 CT Images Using NLM and Method Noise Thresholding in Shearlet Domain

In the COVID-19 era, it may be possible to detect COVID-19 by detecting lesions in scans, i.e., ground-glass opacity, consolidation, nodules, reticulation, or thickened interlobular septa, and lesion distribution, but it becomes difficult at the early stages due to embryonic lesion growth and the restricted use of high dose X-ray detection. Therefore, it may be possible for a patient who may or may not be infected with coronavirus to consider using high-dose X-rays, but it may cause more risks. Conclusively, using low-dose X-rays to produce CT scans and then adding a rigorous denoising algorithm to the scans is the best way to protect patients from side effects or a high dose X-ray when diagnosing coronavirus involvement early. Hence, this paper proposed a denoising scheme using an NLM filter and method noise thresholding concept in the shearlet domain for noisy COVID CT images. Low-dose COVID CT images can be further utilized. The results and comparative analysis showed that, in most cases, the proposed method gives better outcomes than existing ones

An improved deep learning-based optimal object detection system from images

Computer vision technology for detecting objects in a complex environment often includes other key technologies, including pattern recognition, artificial intelligence, and digital image processing. It has been shown that Fast Convolutional Neural Networks (CNNs) with You Only Look Once (YOLO) is optimal for differentiating similar objects, constant motion, and low image quality. The proposed study aims to resolve these issues by implementing three different object detection algorithms—You Only Look Once (YOLO), Single Stage Detector (SSD), and Faster Region-Based Convolutional Neural Networks (R-CNN). This paper compares three different deep-learning object detection methods to find the best possible combination of feature and accuracy. The R-CNN object detection techniques are performed better than single-stage detectors like Yolo (You Only Look Once) and Single Shot Detector (SSD) in term of accuracy, recall, precision and loss

A Method Noise-Based Convolutional Neural Network Technique for CT Image Denoising

Medical imaging is a complex process that capitulates images created by X-rays, ultrasound imaging, angiography, etc. During the imaging process, it also captures image noise during image acquisition, some of which are extremely corrosive, creating a disturbance that results in image degradation. The proposed work addresses the challenge to eliminate the corrosive Gaussian additive white noise from computed tomography (CT) images while preserving the fine details. The proposed approach is synthesized by amalgamating the concept of method noise with a deep learning-based framework of a convolutional neural network (CNN). The corrupted images are obtained by explicit addition of Gaussian additive white noise at multiple noise variance levels (σ = 10, 15, 20, 25). The denoised images obtained are then evaluated according to their visual quality and quantitative metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). These metrics for denoised CT images are then compared with their respective values for the reference CT image. The average PSNR value of the proposed method is 25.82, the average SSIM value is 0.85, and the average computational time is 2.8760. To better understand the proposed approach’s effectiveness, an intensity profile of denoised and original medical images is plotted and compared. To further test the performance of the proposed methodology, the results obtained are also compared with that of other non-traditional methods. The critical analysis of the results shows the commendable efficiency of the proposed methodology in denoising the medical CT images corrupted by Gaussian noise. This approach can be utilized in multiple pragmatic areas of application in the field of medical image processing

Abstracts of AICTE Sponsored International Conference on Post-COVID Symptoms and Complications in Health

This book presents the selected abstracts of the International Conference on Post-COVID Symptoms and Complications in Health, hosted from the 28th to 29th of April 2022 in virtual mode by the LR Institute of Pharmacy, Solan (H.P.)-173223 in Collaboration with AICTE, New Delhi. This conference focuses on the implications of long-term symptoms on public health, ways to mitigate these complications, improve understanding of the disease process in COVID-19 patients, use of computational methods and artificial intelligence in predicting complications, and the role of various drug delivery systems in combating the complications. Conference Title:  International Conference on Post-COVID Symptoms and Complications in HealthConference Sponsor: AICTE, New Delhi.Conference Date: 28-29 April 2022Conference Location: OnlineConference Organizer: LR Institute of Pharmacy, Solan (H.P.)-173223