Development, Optimization and Evaluation of Mucoadhesive Microspheres of Amoxicillin for the Treatment of H.Pylori by Full Factorial Design.

OBJECTIVE: This work is aimed to formulate and evaluate Mucoadhesive Microspheres contain Amoxicillin for the effective use in the treatment of H.Pylori. METHODS: Microspheres were prepared using Emulsification-cross linking technique. To this guar gum (GG) and sodium alginate (SA) was dissolved in 200 ml of water and allowed to swell for 24 h at room temperature. And separately chitosan (CH) was dissolved in 2% (v/v) glacial acetic acid and this also kept for 24 h to swell or dissolve properly. After 24 h this swelled mixture was mixed under magnetic stirrer (Remi, India) at specific stirring rate for 1 h in order to find homogeneous mass of both the gum. Then slurry of chitosan also was homogenized for half an hour. The drug, Amoxicillin (1g) was then added to the chitosan solution and mixed homogeneously. RESULTS: The aim of the study was to formulate and evaluate microspheres, for SR of the chosen drug. The particle size of microspheres was in the range of 200-500 µ, maximum mucoadhesive property observed was 57.41% for Optimized formulation F-9, Drug release 68.52% till 8 h, and the maximum entrapment was 94.87% for F-9 formulation. The work also aims to study various parameters affecting the behavior of microspheres in oral dosage form. CONCLUSION: Drugs with short half life that are absorbed from the gastrointestinal tract (GIT) are eliminated rapidly from the blood flow. To avoid this, the oral SR was developed as this formulation released the drug slowly into the GIT and maintained a stable drug concentration in the serum for a longer duration of time.

Effect of Formulation Parameters on Enalapril Maleate Mucoadhesive Buccal Tablet Using Quality by Design (QbD) Approach.

The buccal route has great prospects and possible benefits for the administration of drugs systemically. The present study involves designing, developing and optimising the buccal tablet formulation of Enalapril Maleate (EM) by using the QbD approach. We prepared the EM buccal tablets using the dry granulation method. In the QTPP profile, the CQAs for EM buccal tablets are Mucoadhesive strength, swelling index and drug release (dependent variables); the CMAs identified for EM buccal tablets were Carbopol 934P, HPMC-K100M and chitosan (independent variables). Diluent quantity, blending time and compression force were selected as CPPs; the Box-Behnkentdesign was used to evaluate the relationship between the CMAs and CPPs. Based on the DoE, the composition of the optimised formulation of EM BT-18 consists of 20mg of EM, 15 mg of carbopol 934p, 17 mg of HPMC-K100M, 10mg of chitosan, 30 mg of PVP K-30, 1 mg of magnesium stearate, 16 mg of Mannitol, 1 mg of aspartame, and 50 mg of Ethyl cellulose. The optimised formulation of EM BT 18 was found to have a Mucoadhesive strength of 24.32±0.30g. The swelling index was 90.74±0.25% and drug release was sustained up to 10 hours 98.4±3.62% compared to the marketed product, whose release was up to 8 hours. We attempted to design a buccal tablet of Enalapril Maleate for sustained drug release in the treatment of hypertension. Patients who cannot take oral medication due to trauma or unconscious conditions could receive the formulation. Development of a newly P.ceutical product is very time-consuming, extremely costly and high-risk, with very little chance of a successful outcome. Hence, this study showed EM tablets are already available on the market but we have chosen a buccal drug delivery system using a novel approach using QbD tools to target the quality of the product accurately.

Mucoadhesive Microspheres: A Promising Delivery System for Helicobacter Pylori (H. pylori) Treatment.

A major worldwide health problem, Helicobacter Pylori (H. pylori) infection is associated with a number of gastrointestinal disorders, such as gastric cancer and peptic ulcers. The shortcomings of traditional treatment plans often include adverse effects, low patient compliance, and the emergence of antibiotic resistance. Investigating different delivery methods is thus necessary to improve the effectiveness of treatment. Mucoadhesive microspheres show promise as a method for delivering anti H. pylori drugs in a targeted and sustained manner. With their ability to stick to the stomach mucosa, these microspheres increase the local concentration of the medication and guarantee a more thorough removal of the pathogen. The potential of Mucoadhesive microspheres in the management of H. pylori infection is examined in this review. We explore the properties and benefits of Mucoadhesive polymers, the production techniques for microspheres, and the variables affecting their functionality. To provide a thorough grasp of this delivery system, a variety of drug-loading strategies, release mechanisms, and in vitro and in vivo assessment methodologies are covered. The potential of Mucoadhesive microspheres to overcome the drawbacks of traditional therapy is shown by highlighting recent developments in their formulation and their therapeutic consequences. Mucoadhesive microspheres constitute an important advancement in the treatment of Helicobacter pylori because they guarantee a regulated release of antibiotics and improve medication absorption at the site of infection. In order to fully appreciate the advantages of this novel delivery method, further study is necessary. Future research paths and the difficulties in the clinical translation of this technology are also discussed.

A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images.

Diabetic retinopathy (DR) is the leading cause of visual impairment globally. It occurs due to long-term diabetes with fluctuating blood glucose levels. It has become a significant concern for people in the working age group as it can lead to vision loss in the future. Manual examination of fundus images is time-consuming and requires much effort and expertise to determine the severity of the retinopathy. To diagnose and evaluate the disease, deep learning-based technologies have been used, which analyze blood vessels, microaneurysms, exudates, macula, optic discs, and hemorrhages also used for initial detection and grading of DR. This study examines the fundamentals of diabetes, its prevalence, complications, and treatment strategies that use artificial intelligence methods such as machine learning (ML), deep learning (DL), and federated learning (FL). The research covers future studies, performance assessments, biomarkers, screening methods, and current datasets. Various neural network designs, including recurrent neural networks (RNNs), generative adversarial networks (GANs), and applications of ML, DL, and FL in the processing of fundus images, such as convolutional neural networks (CNNs) and their variations, are thoroughly examined. The potential research methods, such as developing DL models and incorporating heterogeneous data sources, are also outlined. Finally, the challenges and future directions of this research are discussed.

Exploring the Potential of Natural Products as FoxO1 Inhibitors: an In Silico Approach.

FoxO1, a member of the Forkhead transcription factor family subgroup O (FoxO), is expressed in a range of cell types and is crucial for various pathophysiological processes, such as apoptosis and inflammation. While FoxO1's roles in multiple diseases have been recognized, the target has remained largely unexplored due to the absence of cost-effective and efficient inhibitors. Therefore, there is a need for natural FoxO1 inhibitors with minimal adverse effects. In this study, docking, MMGBSA, and ADMET analyses were performed to identify natural compounds that exhibit strong binding affinity to FoxO1. The top candidates were then subjected to molecular dynamics (MD) simulations. A natural product library was screened for interaction with FoxO1 (PDB ID- 3CO6) using the Glide module of the Schrödinger suite. In silico ADMET profiling was conducted using SwissADME and pkCSM web servers. Binding free energies of the selected compounds were assessed with the Prime-MMGBSA module, while the dynamics of the top hits were analyzed using the Desmond module of the Schrödinger suite. Several natural products demonstrated high docking scores with FoxO1, indicating their potential as FoxO1 inhibitors. Specifically, the docking scores of neochlorogenic acid and fraxin were both below -6.0. These compounds also exhibit favorable drug-like properties, and a 25 ns MD study revealed a stable interaction between fraxin and FoxO1. Our findings highlight the potential of various natural products, particularly fraxin, as effective FoxO1 inhibitors with strong binding affinity, dynamic stability, and suitable ADMET profiles.

Mucoadhesive Drug Delivery System and There Future Prospective: Are a Promising Approach for Effective Treatment?

Mucoadhesive polymers are a new and exciting development in drug delivery systems that have the potential to significantly increase therapeutic efficacy. These polymers stick to mucosal surfaces, increasing the amount of time that medications stay at the site of absorption and improving their bioavailability. These mechanisms include longer contact times with the mucosal surface, better drug solubility, and defence against enzymatic degradation of pharmaceuticals. Mucoadhesive polymers also provide a number of benefits over traditional drug delivery methods, including less frequent dosage, better patient compliance, and fewer adverse effects. Due to their adaptability, Mucoadhesive polymers may be used in the rectal, vaginal, ophthalmic, nasal, and oral routes of drug delivery. Mucoadhesive polymers have advantages now, but they also have potential for the future of medication delivery. Mucoadhesion offers excellent possibilities for the delivery of a range of substances through the nasal, vaginal, buccal, and ocular routes of administration. Furthermore, mucoadhesion facilitates the achievement of an extended local or systemic pharmacological effect. In this study, we covered the mechanisms behind mucoadhesion, possible uses for Mucoadhesive polymers in drug administration, and techniques for assessing Mucoadhesive drug delivery systems. The goal of current research is to create innovative Mucoadhesive polymers that have better biodegradability, biocompatibility, and adhesive qualities. Moreover, it is anticipated that the effectiveness of Mucoadhesive polymers would be increased when combined with other cutting-edge drug delivery technologies, such as micro particles and nanoparticles.

A Comprehensive Review: Personalized Medicine for Rare Disease Cancer Treatment.

In the United States, cancer is one of the major causes of death. In 2010 alone, over 1.5 million fresh instances were recorded and over 0.5 billion died. After the completion of human genome sequence, significant progress in characterizing human epigenomes, proteomes and metabolomes has been made; a stronger knowledge of pharmacogenomics has been established and the capacity for individual personalization of health care has grown considerably. Personalized medicine has recently been primarily used to systematically select or optimize the prevention and therapeutic care of the patient through genetic or other data about the particular patient. Molecular profiling in healthy samples and cancer patients can allow for more personalized medications than is currently available. Patient protein, genetic and metabolic information may be used for adapting medical attention to the needs of that individual. The development of complementary diagnostics is a key attribute of this medicinal model. Molecular tests measuring the level of proteins, genes or specific mutations are used to provide a specific treatment for a particular individual by stratify the status of a disease, selecting the right drugs and tailoring dosages to the particular needs of the patient. These methods are also available for assessing risk factors for a patient for a number of conditions and for tailoring individual preventive therapies. Recent advances of personalized cancer medicine, challenges and futures perspectives are discussed.

Addressing Binary Classification over Class Imbalanced Clinical Datasets Using Computationally Intelligent Techniques.

Nowadays, healthcare is the prime need of every human being in the world, and clinical datasets play an important role in developing an intelligent healthcare system for monitoring the health of people. Mostly, the real-world datasets are inherently class imbalanced, clinical datasets also suffer from this imbalance problem, and the imbalanced class distributions pose several issues in the training of classifiers. Consequently, classifiers suffer from low accuracy, precision, recall, and a high degree of misclassification, etc. We performed a brief literature review on the class imbalanced learning scenario. This study carries the empirical performance evaluation of six classifiers, namely Decision Tree, k-Nearest Neighbor, Logistic regression, Artificial Neural Network, Support Vector Machine, and Gaussian Naïve Bayes, over five imbalanced clinical datasets, Breast Cancer Disease, Coronary Heart Disease, Indian Liver Patient, Pima Indians Diabetes Database, and Coronary Kidney Disease, with respect to seven different class balancing techniques, namely Undersampling, Random oversampling, SMOTE, ADASYN, SVM-SMOTE, SMOTEEN, and SMOTETOMEK. In addition to this, the appropriate explanations for the superiority of the classifiers as well as data-balancing techniques are also explored. Furthermore, we discuss the possible recommendations on how to tackle the class imbalanced datasets while training the different supervised machine learning methods. Result analysis demonstrates that SMOTEEN balancing method often performed better over all the other six data-balancing techniques with all six classifiers and for all five clinical datasets. Except for SMOTEEN, all other six balancing techniques almost had equal performance but moderately lesser performance than SMOTEEN.

PCA-Based Incremental Extreme Learning Machine (PCA-IELM) for COVID-19 Patient Diagnosis Using Chest X-Ray Images.

Novel coronavirus 2019 has created a pandemic and was first reported in December 2019. It has had very adverse consequences on people's daily life, healthcare, and the world's economy as well. According to the World Health Organization's most recent statistics, COVID-19 has become a worldwide pandemic, and the number of infected persons and fatalities growing at an alarming rate. It is highly required to have an effective system to early detect the COVID-19 patients to curb the further spreading of the virus from the affected person. Therefore, to early identify positive cases in patients and to support radiologists in the automatic diagnosis of COVID-19 from X-ray images, a novel method PCA-IELM is proposed based on principal component analysis (PCA) and incremental extreme learning machine. The suggested method's key addition is that it considers the benefits of PCA and the incremental extreme learning machine. Further, our strategy PCA-IELM reduces the input dimension by extracting the most important information from an image. Consequently, the technique can effectively increase the COVID-19 patient prediction performance. In addition to these, PCA-IELM has a faster training speed than a multi-layer neural network. The proposed approach was tested on a COVID-19 patient's chest X-ray image dataset. The experimental results indicate that the proposed approach PCA-IELM outperforms PCA-SVM and PCA-ELM in terms of accuracy (98.11%), precision (96.11%), recall (97.50%), F1-score (98.50%), etc., and training speed.

Enhancement of Detection of Diabetic Retinopathy Using Harris Hawks Optimization with Deep Learning Model.

In today's world, diabetic retinopathy is a very severe health issue, which is affecting many humans of different age groups. Due to the high levels of blood sugar, the minuscule blood vessels in the retina may get damaged in no time and further may lead to retinal detachment and even sometimes lead to glaucoma blindness. If diabetic retinopathy can be diagnosed at the early stages, then many of the affected people will not be losing their vision and also human lives can be saved. Several machine learning and deep learning methods have been applied on the available data sets of diabetic retinopathy, but they were unable to provide the better results in terms of accuracy in preprocessing and optimizing the classification and feature extraction process. To overcome the issues like feature extraction and optimization in the existing systems, we have considered the Diabetic Retinopathy Debrecen Data Set from the UCI machine learning repository and designed a deep learning model with principal component analysis (PCA) for dimensionality reduction, and to extract the most important features, Harris hawks optimization algorithm is used further to optimize the classification and feature extraction process. The results shown by the deep learning model with respect to specificity, precision, accuracy, and recall are very much satisfactory compared to the existing systems.

Big data integration enhancement based on attributes conditional dependency and similarity index method.

Big data has attracted a lot of attention in many domain sectors. The volume of data-generating today in every domain in form of digital is enormous and same time acquiring such information for various analyses and decisions is growing in every field. So, it is significant to integrate the related information based on their similarity. But the existing integration techniques are usually having processing and time complexity and even having constraints in interconnecting multiple data sources. Many of these sources of information come from a variety of sources. Due to the complex distribution of many different data sources, it is difficult to determine the relationship between the data, and it is difficult to study the same data structures for integration to effectively access or retrieve data to meet the needs of different data analysis. In this paper, proposed an integration of big data with computation of attribute conditional dependency (ACD) and similarity index (SI) methods termed as ACD-SI. The ACD-SI mechanism allows using of an improved Bayesian mechanism to analyze the distribution of attributes in a document in the form of dependence on possible attributes. It also uses attribute conversion and selection mechanisms for mapping and grouping data for integration and uses methods such as LSA (latent semantic analysis) to analyze the content of data attributes to extract relevant and accurate data. It performs a series of experiments to measure the overall purity and normalization of the data integrity, using a large dataset of bibliographic data from various publications. The obtained purity and NMI ratio confined the clustered data relevancy and the measure of precision, recall, and accurate rate justified the improvement of the proposal is compared to the existing approaches.