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J. Kulbacka

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Open access Aug 2026

Pelargonium graveolens L’Hér. in Traditional and Contemporary Medicine: Phytochemistry, Pharmacology, and Molecular Mechanisms

Pelargonium graveolens L’Hér. is a widely distributed aromatic plant that has been utilized in traditional medicine for an extended period. It is currently experiencing a surge in popularity in modern phytotherapy. The ethnopharmacological uses of this species include treatment of respiratory tract infections, digestive disorders, and analgesia. The plant’s multifaceted biological activity is attributed to the presence of monoterpenes, such as citronellol and geraniol, as well as non-volatile polyphenolic fractions and organic acids. Research has demonstrated the efficacy of PGEO (P. graveolens essential oil) in mitigating inflammation, a phenomenon attributable to the suppression of NF-κB and MAPK signaling pathways, as well as the inhibition of inflammatory mediators such as histamine, prostaglandins (PGs), and nitric oxide (NO). Furthermore, a broad spectrum of antimicrobial activity has been demonstrated against pathogens such as MRSA and Mycobacterium tuberculosis, whilst myricetin derivatives have been shown to enable the effective eradication of bacterial biofilms. It is also noteworthy that the oil’s components can reduce ACE2 receptor expression, indicating their potential to inhibit SARS-CoV-2 infection. Active compounds, such as geraniol, have been shown to modulate metabolic processes through interaction with LXR and FXR nuclear receptors. In addition, these compounds have been observed to exhibit spasmolytic effects within the gastrointestinal tract by blocking calcium channels. The influence on the HPA axis and the GABAergic system provides a scientific rationale for the plant’s traditional use in reducing stress and anxiety. Nevertheless, variations in chemical composition, determined by geographical origin, and an insufficient number of rigorous clinical data hinder the full medical implementation of this plant. It is imperative that further standardization of extracts and their verification in clinical trials is undertaken.

Kamil Bukowiec, Mateusz Sroka, Agata Pałkiewicz et al. · 0 citations
Review Aug 2026

Advancing cancer drug discovery through the integration of machine learning and high-throughput screening.

This review highlights the synergy between AI and HTS, emphasizing DL techniques such as convolutional neural networks for bioactivity prediction, recurrent neural networks for de novo design, and reinforcement learning for property optimization.

K. Herbetko, Katarzyna Herbetko, Magdalena Mikołajek et al. · 0 citations