G protein–coupled receptors (GPCRs), also known as seven-(pass)-transmembrane domain receptors, 7TM receptors, heptahelical receptors, serpentine receptor, and G protein–linked receptors (GPLR), constitute a large protein family of receptors that detect molecules outside the cell and activate internal signal transduction pathways and, ultimately, cellular responses. Coupling with G proteins, they are called seven-transmembrane receptors because they pass through the cell membrane seven times. G protein–coupled receptors are found only in eukaryotes, including yeast, choanoflagellates, and animals. The ligands that bind and activate these receptors include light-sensitive compounds, odors, pheromones, hormones, and neurotransmitters, and vary in size from small molecules to peptides to large proteins. G protein–coupled receptors are involved in many diseases and are also the target of approximately 34% of all modern medicinal drugs.

A nanoparticle (or nanopowder, or nanocluster, or nanocrystal) is a microscopic particle containing at least one dimension less than 100 Nm.4 Nanoparticles are particles between 1 and 100 nanometers (nm) in size, with the surrounding interfacial layer. Integral parts of nanoscale subject matter, all its properties are profoundly influenced by the interfacial layer. Usually, the interfacial layer is made up of ions, inorganic and organic molecules. Organic molecules that cover inorganic nanoparticles are known as stabilizers, ligands of capping and surface or passivating agents.  In nanotechnology, a particle is characterized as a small object that acts in terms of its transport and properties as a whole. Particles are additionally graded according to their diameter. Nanotechnology refers to the production and use of materials whose nanoscale components exist and, by definition, are up to 100 nm in size. Nanotechnology investigates both electrical, optical, and magnetic activity, and molecular and submolecular structural behavior. It has the ability to revolutionize a range of methods and procedures in medical and biotechnology to make them compact, simpler, safer, and easier to administer.

The oral route of drug administration is speculated as one of the most acceptable route for drug delivery. Recently the orally dispersible tablets have become the most desirable dosage forms especially for a special category of patients i.e. pediatric, geriatric, bedridden, mentally ill, and uncooperative patients. Quick disintegration, better patient compliance, enhanced bioavailability are some of the vital characteristics of orally dispersible tablets which makes it superior from other traditional dosage forms. It is the most prominent dosage form for the patients which face difficulty in swallowing other conventional dosage forms. Basically the oro-dispersible tablets are defined as novel solid dosage form that provide the rapid disintegration or dissolution of solid medicament to exhibit it as a solution or in suspension form before administration. Reportedly, there are numerous drug candidates, which have been successfully formulated as orally dispersible tablets and have resulted in satisfactory invitro as well as in-vivo results. By going through this review, a researcher can easily become familiar to this novel formulation, as this review gives an quick insight of the advantages, disadvantages, ideal characteristics, method of preparation, and evaluation parameters of the oral dispersible tablets.

Recently With whole world India is also facing Pandemic of CORONA Virus disease. Keeping the actions and measures taken by Government of India still COVID infection is not transmitted in its third stage- Stage of Community spread. When it happens it starts spreading among people and it show its lethal form with huge morbidity and mortality as India is second largest democratic population in world. The thought of third stage of transmission is more than lethal and worst, It may not be easily controlled.  In-order to prevent its third stage of transmission rigorous screening is require along with tracking of existing cases. In respect of Uttar Pradesh, largest state of India on population basis, having population more than Brazil and Indonesia if may happen large number of causalities will report. Uttar Pradesh have around 107 thousands of villages. Social Distancing and Lockdown is the biggest implemented way to prevent community from getting infection. At village set-ups where community is unable to reach public or private registered health facilities they prefer to go to local practitioner or quacks which resides within their reach. Here, the local Practitioner and quacks are use to identify the patients who are approach to them for general illment including Cold, Cough and Respiratory distress and related. A format is shared to all Local Practitioners and Quacks on which they update the information of suspected symptomatic cases, a block level supervisor will asked daily from local Practitioner and quacks about the symptomatic cases on daily basis. List of local Practitioner and quacks is obtained from marketing representatives by District Chief Medical Officer (CMO) than block wise list has been shared to Block Medical Officer further Block medical officer appoint around 10 supervisors and distribute their area to get update of suspected symptomatic cases on phone. At district and block level RRT (Rapid Response Team) will go to particular village/region/area and thermally screen the community, If any of suspected cases found this person will go for District Hospital for sample collection of RT-PCR further go to laboratory of Aligarh Medical college for test result.

Advancements in farm machinery and power engineering have revolutionized the agriculture industry, leading to increased efficiency, productivity, and sustainability in farming practices. Precision farming techniques, such as the use of GPS, sensors, and satellite imagery, enable precise application of fertilizers, pesticides, and water, resulting in improved crop yields and reduced environmental impact. Autonomous vehicles, including self-driving tractors and harvesters, have reduced labor costs and increased operational efficiency. Drones equipped with cameras and sensors provide valuable data for monitoring crop health, detecting diseases, and optimizing irrigation. Robotics have emerged for labor-intensive tasks like harvesting, pruning, and weeding, enhancing productivity and reducing crop damage. Sensor technology allows real-time monitoring of soil moisture, temperature, and nutrient levels, enabling data-driven decision-making. Integration of renewable energy sources, such as solar panels and wind turbines, reduces reliance on fossil fuels and promotes sustainable farming practices. Data analytics and artificial intelligence algorithms provide valuable insights for optimizing crop management, predicting yield, and detecting diseases. These advancements collectively contribute to a more efficient, sustainable, and technologically advanced agriculture sector.

Good Laboratory Practice (GLP) is a quality assurance system that sets forth guidelines and principles for the conduct of non-clinical laboratory studies. It encompasses a set of standardized practices and procedures that ensure the reliability, integrity, and validity of data generated during research and testing in various scientific fields, including pharmaceuticals, chemicals, agrochemicals, and cosmetics. Adhering to GLP promotes the generation of high-quality and reliable data, enhances the credibility of scientific research, and contributes to the safety and efficacy of products developed and tested in the laboratory. GLP emphasizes standardized practices, comprehensive documentation, proper equipment calibration and maintenance, and the qualification and training of laboratory personnel.

Background: This study aimed to comprehensively evaluate the precision and performance of the AI-based dental disease detection technology “Dental Friend” within a pediatric population. Materials and Methods: The study assessed DentalFriend’s precision through diagnostic accuracy measurements, focusing on its diagnostic potential in pediatric dentistry. Diagnostic accuracy metrics, including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy, were calculated at a precision rate of 96.4%. Subgroup analysis explored Dental Friend’s performance across various participant characteristics. A statistical comparison and agreement analysis were conducted to evaluate Dental Friend’s outcomes against the clinical examination, serving as the gold standard. Results: The present study revealed a remarkable diagnostic accuracy of 96.4% for Dental Friend, indicating its potential as an effective diagnostic tool in pediatric dentistry. Sensitivity, specificity, PPV, NPV, and overall accuracy all demonstrated consistent high values of 96.4%. The subgroup analysis highlighted Dental Friend’s consistent precision across age groups, genders, and specific dental conditions, affirming its versatility in diverse pediatric demographics and conditions. Statistical comparison demonstrated a significant result (p-value = 0.021), reinforcing the technology’s reliability in comparison to the clinical gold standard. The agreement analysis using Cohen’s Kappa coefficient yielded a value of 0.85, indicating substantial alignment between DentalFriend’s diagnoses and clinical examination findings. Conclusions: The study’s findings underscore DentalFriend’s potential as a reliable diagnostic aid in pediatric dentistry. The technology’s high accuracy, significant results in comparison, and substantial agreement with clinical judgments contribute to its credibility in diagnosing dental conditions. However, limitations include the cross-sectional nature of the study and potential variations in different populations. Future longitudinal studies and broader participant samples may provide more insights.