In 2003, the United States saw an epidemic of monkeypox that was later traced back to rodents of West Africa infected with the monkeypox virus (MPXV). Disease in the United States seemed less severe than the smallpox-like disease in the Democratic Republic of the Congo (DRC). In this study, researchers analyzed data from Central Africa: two distinct MPXV clades were confirmed by sequencing the genomes of MPXV isolates from Western Africa, the United States, and Central Africa. By comparing open reading frames across MPXV clades, scientists can infer which virus proteins might account for the observed variation in pathogenicity in humans. Monkeypox can be prevented and controlled with a better understanding of MPXV’s molecular etiology and epidemiological and clinical features. In light of the current outbreaks worldwide, we provide updated information on monkeypox for medical professionals in this review.

One area of study within machine learning and artificial intelligence (AI) seeks to create computer programs with intelligence that can mimic human focal processes in order to produce results. This technique includes data collection, effective data usage system development, conclusion illustration, and arrangements. Analysis algorithms that are learning to mimic human cognitive activities are the most widespread application of AI. Artificial intelligence (AI) studies have proliferated, and the field is quickly beginning to understand its potential impact on medical services and investigation. This review delves deeper into the pros and cons of AI across the healthcare and pharmaceutical research industries. Research and review articles published throughout the last few years were selected from PubMed, Google Scholar, and Science Direct, using search terms like ‘artificial intelligence’, ‘drug discovery’, ‘pharmacy research’, ‘clinical trial’, etc. This article provides a comprehensive overview of how artificial intelligence (AI) is being used to diagnose diseases, treat patients digitally, find new drugs, and predict when outbreaks or pandemics may occur. In artificial intelligence, neural networks and deep learning are some of the most popular tools; in clinical research, Bayesian non-parametric approaches hold promise for better results, while smartphones and the processing of natural languages are employed in recognizing patients and trial monitoring. Seasonal flu, Ebola, Zika, COVID-19, tuberculosis, and outbreak predictions were made using deep computation and artificial intelligence. The academic world is hopeful that AI development will lead to more efficient and less expensive medical and pharmaceutical investigations and better public services.

Objective: Transdermal patch of timolol maleate was prepared in order to increase the permeability of the drug topically. Methods: The timolol maleate (TM) loaded solid lipid nanoparticles (SLN) were prepared by the solvent evaporation method. For the optimization process full factorial (three-factor and three-level), hydroxypropyl methylcellulose (HPMC) range from 100 to 300 mg, ethylcellulose 100 to 200 gm and almond oil 3 to 4 ml. The response noted in form of tensile strength and percent drug release. These transdermal patches were evaluated for physical characterization like weight variation, thickness, percentage moisture absorption, percentage moisture loss, water vapor transmission rate, folding endurance, tensile strength, and content uniformity. Results: Solid lipid nanoparticles of TM were optimized and prepared, the data presented that drug release percent ranged from 66.12 to 91.75. 2FI model was observed to fit for response % drug permeation with a p and F value of 0.0271 and 4.50. The tensile strength varies from 0.358 to 0.508. The linear model was observed to fit for the tensile strength response with a p-value and F-value of<0.0001 and 52.41. Conclusion: The controlled release formulation of Timolol Maleate was successfully optimized and prepared, a study conducted to investigate the effect of different polymers and type of permeation time profiles from Timolol Maleate patches.