Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 3 appraisals
Analytical methods : advancing methods and applications
Spatial metabolomics in medicinal plant research: current advances, technical challenges, and future perspectives
Secondary metabolites derived from medicinal plants represent a critical resource for drug discovery and play an indispensable role in the prevention and treatment of major human diseases. Spatial metabolomics has emerged as a transformative approach that overcomes the inherent limitations of traditional metabolomics, particularly tissue homogenisation, by enabling precise, in situ profiling of the spatial heterogeneity and dynamic accumulation of key bioactive compounds. When integrated with complementary technologies such as spatial multi-omics (SMO), single-cell analysis, and artificial intelligence (AI)-assisted imaging, this paradigm provides a powerful framework for deciphering the spatiotemporal regulatory networks that govern secondary metabolite biosynthesis. This review systematically compares conventional and spatial metabolomics, synthesises recent advances in medicinal plant research, critically evaluates current technical challenges and optimisation strategies, and outlines future directions enabled by multi-omics integration and intelligent computational analytics. Collectively, these insights aim to establish a theoretical foundation for the in-depth investigation of medicinal plants and to support their translational application and industrial advancement.
19 July 2026
Read appraisal →Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences
Deep fused explainable neural architecture for the automatic recognition of therapeutic medicinal plants
BACKGROUND: Medicinal plants have been utilized for centuries for their beneficial properties and significant impact on traditional medical systems worldwide. Indian medicinal plants and their therapeutic properties have recently gained increased attention from the global healthcare community. Accurate identification and classification of medicinal plants ensure proper therapeutic applications. However, botanists manually identify these plants, which is subjective, labor-intensive, and time-consuming. OBJECTIVE: The main objective of this study is to develop an accurate and automated classification of these medicinal plants for diverse applications such as conservation initiatives, quality assurance, and the formulation of novel herbal treatments. METHODS: A Deep Fused Explainable Neural Architecture termed MedPlantNet is proposed for robust recognition of medicinal plant leaves. The deep discriminative features are extracted from the medicinal plant images using a fine-tuned Deep Convolutional Neural Network (DCNN). A customized Support Vector Machine is developed to efficiently classify these extracted deep features. To enhance model interpretability, a meta-visualization framework incorporating Grad-CAM and Occlusion Sensitivity visualization is implemented to identify the regions of the image that contribute to the classification decisions. We utilized the Mendeley benchmark Medicinal plant leaf database with 30 classes to train and evaluate the model. RESULTS: To assess the effectiveness of the proposed model, accuracy, specificity, sensitivity, and AUC are calculated. The proposed system outperforms existing state-of-the-art (SOTA) algorithms for medicinal plant classification, achieving an optimal maximum validation accuracy of 99.87% and AUC of 1. To further validate the generalizability external validation was performed on Bangladeshi medicinal plant dataset and MedLeaves dataset where the proposed model MedPlantNet attained 99.27% and 99.80% validation accuracy respectively. CONCLUSION: MedPlantNet provides an automatic framework for medicinal plant identification based on deep feature extraction, classification and explainable visualization techniques. The results obtained indicate its potential utility in supporting biodiversity conservation, quality assurance and herbal medicine research.
13 July 2026
Read appraisal →Oral health & preventive dentistry
Natural Agents for the Improvement of Gingival Health: Systematic Review and Meta-Analysis of Randomized Clinical Trials
PURPOSE: This systematic review and meta-analysis evaluated the efficacy of natural ingredients used in toothpastes and gels in improving gingival health. MATERIALS AND METHODS: A comprehensive search strategy using different databases was conducted in accordance with PRISMA guidelines to identify randomized clinical trials (RCTs) on the use of natural ingredients for improving gingival health. Included studies focussed on outcomes measuring gingival health parameters, such as gingival index (GI), or bleeding on probing (BoP). Out of 249 identified studies, 10 RCTs met the inclusion criteria. Formulations included natural ingredients from plant extracts such as Aloe vera or Pudilan, and hydroxyapatite (as tooth-like calcium phosphate). RESULTS: The meta-analysis demonstrated a statistically significant improvement in gingival health when natural ingredients were used compared to control toothpastes and gels (p=0.0051). These findings suggest that natural ingredients may offer comparable or superior efficacy to conventional oral care products in terms of gingival health. Notably, Aloe vera and hydroxyapatite consistently demonstrated clinical benefits. CONCLUSIONS: Natural ingredients used in oral care products represent a promising strategy in gingivitis prevention and management. Their use in toothpastes and gels may therefore provide clinicians and patients with evidence-based, well-tolerated alternatives or adjuncts to conventional formulations for maintaining and improving gingival health.
20 June 2026
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