Doxorubicin (DOXO) is an antineoplastic drug that is used extensively in managing multiple cancer types. However, DOXO-induced cardiotoxicity is a limiting factor for its widespread use and considerably affects patients’ quality of life. Farnesol (FSN) is a sesquiterpene with antioxidant, anti-inflammatory, and anti-tumor properties. Thus, the current study explored the cardioprotective effect of FSN against DOXO-induced cardiotoxicity. In this study, male Wistar rats were randomly divided into five groups (n = 7) and treated for 14 days. Group I (Control): normal saline, p.o. daily for 14 days; Group II (TOXIC): DOXO 2.4 mg/kg, i.p, thrice weekly for 14 days; Group III: FSN 100 mg/kg, p.o. daily for 14 days + DOXO similar to Group II; Group IV: FSN 200 mg/kg, p.o. daily for 14 days + DOXO similar to Group II; Group V (Standard): nifedipine 10 mg/kg, p.o. daily for 14 days + DOXO similar to Group II. At the end of the study, animals were weighed, blood was collected, and heart-weight was measured. The cardiac tissue was used to estimate biochemical markers and for histopathological studies. The observed results revealed that the FSN-treated group rats showed decrease in heart weight and heart weight/body weight ratio, reversed the oxidative stress, cardiac-specific injury markers, proinflammatory and proapoptotic markers and histopathological aberrations towards normal, and showed cardioprotection. In summary, the FSN reduces cardiac injuries caused by DOXO via its antioxidant, anti-inflammatory, and anti-apoptotic potential. However, more detailed mechanism-based studies are needed to bring this drug into clinical use.

Coronavirus disease-2019 is a serious health threat around the globe. Across the world, approximately 142 million people were infected, and three million deaths happened. The fast propagation is also associated with constant anxiety, mental stress, and discomfort in public and health-care professionals. Lack of approved drugs regimen to combat the pandemic challenge concretely is a challenging project for all who are committed to developing remedial assistance. However, the successful development of three vaccines gives a solid roadmap to combat this disease. In this review, we highlighted the current development and challenges of this pandemic.

The main objective of the proposed work was the development of a thermosensitive gel (containing clove and tea tree oil) for the management of vaginal candidiasis. Both oils have been recommended to be used separately in a topical formulation for vaginal candidiasis. Incorporating two natural ingredients (clove and tea tree oil) into a product give it a broad antimicrobial spectrum and analgesic properties. The two oils were mixed together at a 3:1 ratio and converted into o/w nanoemulsion using the aqueous titration method and plotting pseudo ternary phase diagrams. Further transformations resulted in a gel with thermosensitive properties. To determine the final formulation’s potential for further clinical investigation, in vitro analyses (viscosity measurement, MTT assay, mucoadhesion, ex vivo permeation) and in vivo studies (fungal clearance kinetics in an animal model) were conducted. The current effort leveraged the potential of tea tree and clove oils as formulation ingredients and natural therapeutic agents for vaginal infections. Its synergy generated a stable and effective thermosensitive gel that can be utilized for recurrent candidiasis and other infections.

Machine learning has become ubiquitous across all industries, including the relatively new application of predicting antimicrobial resistance. As the first bibliometric review in this field, we expect it to inspire further research in this area. The review employs standard bibliometric indicators such as article count, citation count, and the Hirsch index (H-index) to evaluate the relevance and impact of the leading countries, organizations, journals, and authors in this field. VOSviewer and Biblioshiny programs are utilized to analyze citation and co-citation networks, collaboration networks, keyword co-occurrence, and trend analysis. The United States has the highest contribution with 254 articles, accounting for over 37.57% of the total corpus, followed by China (103) and the United Kingdom (78). Among 58 publishers, the top four publishers account for 45% of the publications, with Elsevier leading with 15% of the publications, followed by Springer Nature (12%), MDPI, and Frontiers Media SA with 9% each. Frontiers in Microbiology is the most frequent publication source (33 articles), followed by Scientific Reports (29 articles), PLoS One (17 articles), and Antibiotics (16 articles). The study reveals a substantial increase in research and publications on the use of machine learning to predict antibiotic resistance. Recent research has focused on developing advanced machine learning algorithms that can accurately forecast antibiotic resistance, and a range of algorithms are now being used to address this issue.

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