American Journal of PharmTech Research

Peptic ulcer disease remains a pressing health issue worldwide, most often linked to Helicobacter pylori infection and long-term use of non-steroidal anti-inflammatory drugs (NSAIDs). While conventional therapies such as proton pump inhibitors (PPIs), histamine-2 receptor antagonists (H2RAs), cytoprotective agents, and antibiotics have transformed patient care by reducing acid secretion and eradicating infection, they are not without drawbacks. Rising antibiotic resistance, drug-related side effects, and recurrence of ulcers continue to challenge clinicians and patients alike. In recent years, herbal medicine has gained attention as a complementary or alternative approach. Plant-derived compounds rich in flavonoids, tannins, alkaloids, and terpenoids offer anti-inflammatory, antioxidant, and antisecretory effects, while also strengthening the stomach’s natural defenses. Traditional remedies such as Anogeissus latifolia, Alchornea castaneaefolia, Decalepis salicifolia, Solanum nigrum, Ocimum tenuiflorum, Asparagus racemosus, and Curcuma longa have shown promising results in experimental models, not only reducing ulcer formation but also accelerating healing. This review brings together evidence on both synthetic and herbal strategies, comparing their mechanisms, effectiveness, safety, and cost considerations. While PPIs and antibiotic regimens remain indispensable for H. pylori eradication and NSAID-induced ulcer prevention, herbal therapeutics stand out for their lower side-effect profile and potential to provide long-term mucosal protection. Looking ahead, integrated treatment approaches that combine modern pharmacology with traditional phytomedicine may offer the most balanced and sustainable path for managing peptic ulcer disease.

ABSTRACT Urolithiasis is a recurrent renal disorder characterized by calcium oxalate crystal deposition, oxidative stress, inflammation, and renal epithelial injury. Despite available therapies, recurrence rates remain high. Bryophyllum pinnatum (Lam.) Oken, traditionally used in urinary disorders, contains bioactive flavonoids with reported antioxidant and diuretic properties. However, the specific flavonoid(s) responsible for anti-urolithiatic activity and their mechanistic pathways remain unidentified. This study aims to isolate and characterize a flavonoid lead compound from B. pinnatum and evaluate its anti-urolithiatic activity through in vitro crystal assays, renal epithelial cell protection studies, and an ethylene glycol-induced rat model. Mechanistic pathways involving oxidative stress, inflammatory signaling, apoptosis, and renal transporter modulation will be explored. Translational biomarkers (KIM-1, NGAL, osteopontin) will be assessed to bridge preclinical findings with clinical relevance. The study is expected to validate a mechanistically defined phytopharmaceutical lead candidate for kidney stone prevention. Keywords: Urolithiasis, Bryophyllum pinnatum

Periodontitis is a multifactorial inflammatory disease characterized by progressive destruction of the periodontal supporting tissues, resulting from complex interactions between microbial biofilm and host immune responses. Conventional diagnostic approaches, including periodontal probing and radiographic evaluation, are limited by examiner variability and challenges in interpreting multiple interacting risk factors. In recent years, Artificial Intelligence (AI) has emerged as a promising adjunct in periodontology, enhancing diagnostic precision, risk assessment, and personalized treatment planning. AI applications in clinical periodontology include Natural Language Processing (NLP) for structured data extraction and improved clinical documentation, as well as machine learning and deep learning models for radiographic and clinical analysis. Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs) have demonstrated high accuracy in detecting periodontal bone loss, classifying disease severity, identifying implant systems, and predicting disease progression. Integration of radiographic, clinical, and multi-omics datasets further supports comprehensive risk profiling and precision-based care. Additionally, AI-assisted biomarker analysis using saliva and gingival crevicular fluid shows potential for non-invasive early detection. Emerging technologies such as smartphone-based monitoring systems, AI-enabled oral hygiene devices, and augmented/virtual reality–based educational tools enhance patient engagement and professional training. Despite challenges including data privacy concerns, ethical considerations, high implementation costs, and limited large-scale clinical validation, AI represents a valuable assistive technology that strengthens clinical decision-making and advances personalized periodontal care. This article aims to comprehensively discuss the current applications of artificial intelligence in periodontology, highlighting recent advancements, clinical implications, limitations, and future perspectives for integrating AI into routine periodontal practice. Keywords: Periodontitis; Artificial Intelligence; Deep Learning; Convolutional Neural Networks; Artificial Neural Networks; Natural Language Processing; Periodontal Diagnosis; Radiographic Analysis; Biomarkers;