Silver nanoparticles (AgNPs) made by green synthesis offer a variety of biochemical properties and are an excellent alternative to traditional medications due to their low cost. In the current study, we synthesised AgNPs from the leaf extract of the medicinal plant Uvaria narum, commonly called narumpanal. The nanoparticles were characterised by ultraviolet-visible (UV-Vis) spectroscopy, Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and transmission electron microscopy (TEM). SEM analysis showed AgNPs are highly crystalline and spherical with an average diameter of 7.13 nm. The outstanding catalytic activity of AgNPs was demonstrated by employing the reduction of 4-nitrophenol to 4-aminophenol. The AgNPs showed antiangiogenic activity in the chick chorioallantoic membrane (CAM) assay. AgNPs demonstrated anticancer activity against Dalton’s lymphoma ascites cells (DLA cells) in trypan blue assay and cytotoxicity against three fish cell lines: Oreochromis niloticus liver (onlL; National Repository of Fish Cell Lines, India (NRFC) Accession number—NRFC052) cells, Cyprinus carpio koi fin (CCKF; NRFC Accession number—NRFC007) cells and Cyprinus carpio gill (CyCKG; NRFC Accession number—NRFC064). Furthermore, the AgNPs demonstrated their ability to inhibit pathogenic microorganisms, Staphylococcus aureus, and Escherichia coli. The results from the study displayed green synthesised AgNPs exhibit antiangiogenic activity, cytotoxicity, antimicrobial and catalytic properties, which are crucial characteristics of a molecule with excellent clinical applications.

Bacterial and fungal secondary infections following COVID-19 disease are widely being reported and are an area that should receive careful attention. Mucormycosis is a fatal fungal condition affect...

To define the subgingival microbial profile associated with Stage II generalized periodontitis using next-generation sequencing and to determine the relative abundance of novel periodontal pathogens and bacterial complexes. Subgingival biofilm samples were collected from 80 subjects diagnosed with Stage II generalized periodontitis. Bacterial DNA was extracted, and 16S rRNA-based bacterial profiling via next-generation sequencing was carried out. The bacterial composition and diversity of microbial communities based on the age and sex of the patients were analyzed. The bacterial species were organized into groups: bacterial complexes (red, orange, purple, yellow, and green), novel periodontal pathogens, periodontal health-related species, and unclassified periodontal species. The results were analyzed and statistically evaluated. The highest number of bacteria belonged to the phylum Bacteroidetes and Firmicutes. In terms of relative abundance, the orange complex represented 18.99%, novel bacterial species (Fretibacterium spp. and Saccharibacteria spp.) comprised 17.34%, periodontal health-related species accounted for 16.75% and unclassified periodontal species represented (Leptotrichia spp. and Selenomonas spp.) 15.61%. Novel periodontal pathogens had outweighed the periodontal disease-related red complex (5.3%). The one-sample z-test performed was statistically significant at p<0.05. The Beta diversity based on the unweighted UniFrac distance at the species level demonstrated a total variance of 15.77% based on age and 39.19% on sex, which was not statistically significant. The bacterial species corresponding to the disease-related orange complex and novel periodontal pathogens are predominant in Stage II generalized periodontitis.

Gingivitis and periodontitis, are widespread conditions with diverse influence on oral and systemic health. Traditional diagnostic methods in periodontology often rely on subjective clinical assessments, which can lead to variability and inconsistencies in care. Imbibing artificial intelligence (AI) facilitates a significant solution by enhancing precision metrics, treatment planning, and personalized care. Studies published between 2018 and 2024 was conducted to evaluate AI applications in periodontal maintenance. Databases such as PubMed, Cochrane, Web of Science and Scopus were searched using keywords like “artificial intelligence,” “machine learning,” and “periodontitis.” Studies employing AI for diagnosis, prognosis, or periodontal maintenance using clinical or radiographic data were included. Deep learning algorithms such as convolutional neural networks (CNNs) and segmentation techniques were analyzed for their diagnostic accuracy. AI demonstrated superior performance in detecting periodontal conditions, with accuracy rates surpassing 90% in some studies. Advanced models, such as Multi-Label U-Net, exhibited high precision in radiographic analyses, outperforming traditional methods. Additionally, AI facilitated predictive analytics for disease progression and personalized treatment strategies. AI has transformed periodontal care, offering accuracy, personalized care, and efficient workflow integration. Addressing challenges like standardization and ethical concerns is critical for its broader adoption.

Aims: This study is the first of its kind in the United Arab Emirates, where these herbal plant extracts are commonly incorporated into toothpaste. The present study compares two herbal toothpastes (Meswak extract and tea tree oil [TTO]) and analyses their efficiency in reducing plaque accumulation. Materials and Methods: A total of 25 patients were examined for plaque and assigned a plaque score. They were then randomly assigned to use either Meswak-based toothpaste or TTO-based toothpaste. After 24 h, a new plaque score was recorded, and the patients used their regular brand for 2 weeks. The patients were then instructed to use the second of the two study toothpastes for 24 h, and a third set of plaque scores was obtained. Statistical data were analyzed using the SPSS software with a significance of P < 0.05 and Kruskal–Wallis test. Results: Analysis of plaque scores showed a reduction in plaque index after using either of the two herbal-based toothpastes; however, when compared with TTO, Meswak-based toothpaste resulted in significantly less plaque. Conclusions: Brushing with Meswak-based toothpaste gave a significant drop in plaque score when compared with TTO-based paste.

As the battle against the deadly Covid-19 pandemic is still continuing worldwide, several complications are being reported in patients who have recovered post-covid. One such lethal complication being reported in patients in India in recent times, who have tested positive for Covid-19 and are gradually recovering, is a fungal disease called Mucormycosis or the black fungus. With several hundreds of cases being reported all over the country, it has triggered an additional wave of panic among the general public. Post-Covid-19 patients who are more vulnerable to Mucormycosis are those with a history of poorly controlled diabetes mellitus and also those who are immuno-compromised and have been treated with steroids and other drugs for Covid-19. The aim of this short review is to briefly cover the epidemiology of mucormycosis, its possible pathophysiology in Post Covid scenario, the clinical presentation and its diagnosis and management.