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Enregistrement W4387743881 · doi:10.1097/gh9.0000000000000284

Role of Shankhpushpi (Convolvulus pluricaulis) in neurological disorders

2023· article· en· W4387743881 sur OpenAlexaff
Mahesh Rachamalla, Ravinder K. Kaundal, Hitesh Chopra, Saikat Dewanjee, Saurabh Kumar Jha, Niraj Kumar Jha, Talha Bin Emran

Notice bibliographique

RevueInternational Journal of Surgery Global Health · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueMedicinal Plants and Neuroprotection
Établissements canadiensUniversity of Saskatchewan
Organismes subventionnairesnon disponible
Mots-clésNootropicPharmacologyMedicineCoumarinTraditional medicinePhytochemistryChemistry

Résumé

récupéré en direct d'OpenAlex

Shankhpushpi (Convolvulus pluricaulis) has emerged as a promising natural plant in the treatment of a variety of inflammatory and neurological disorders over the last two decades. Sharma et al.1 investigated and comprehensively reviewed the various health benefits of crude C. pluricaulis herb and its extracts, as well as metabolites, in alleviating inflammation, oxidative stress, stress, anxiety, and neurological disorders such as Alzheimer’s, memory impairment, and sleep disorders. Several preclinical and clinical studies suggested the potential benefits of Shankhpushpi for neurological complications. We agree with the author’s assessment and provide some new viewpoints on numerous areas relating to Shankhpushpi’s potential in this discussion. Lack of phytochemistry on active ingredients: Sharma et al. reviewed the currently available literature on various extracts from the plant and their role in various neurological models. C. pluricaulis contains several phytochemicals like alkaloids, anthocyanin, coumarin, flavonoid, phytosterol, and triterpenoid components. Interestingly, in silico ADME (absorption, distribution, metabolism, and excretion) screening identified only five active phytoconstituents viz. scopoletin, 4-hydroxycinnamic acid, kaempferol, quercetin, and ayapanin of C. pluricaulis that possess drug-likeness and blood–brain barrier permeability2. Considering the diverse pharmacological profile of these bioactive compounds, the beneficial effects of Shankhpushpi in a range of neurological ailments are not surprising. However, further investigations are warranted to explore the therapeutic potential and safety profile of individual phytoconstituents of Shankhpushpi, especially because of concerns about hepatotoxicity. Coumarin, a bioactive consistent of Shankhpushpi, has been reported to exhibit hepatoxicity in rodent models. As the authors mentioned, coumarin has been restricted by the United States Food and Drug Administration and is not permitted for use in human food. There is still a huge knowledge gap on phytochemistry and specific molecules that play a protective role in the various ailments mentioned above, but there is much more research to be done in this area to better understand the molecular mechanisms. Clear knowledge gap on molecular mechanisms: Sharma et al. thoroughly reviewed studies on the neurological benefits of several constituents of Shankhpushpi, but there is no clear understanding of the molecular mechanisms, which poses a lot of uncertainty on its potential benefits. Many of the studies, including the 166 in the review, can hardly explain the precise molecular mechanisms and which active phytoconstituent is responsible for the beneficial effects. A recent study utilizing integrated network pharmacology and in silico approach provided insight into the potential molecular mechanisms of phytoconstituents from Shankhpushpi. The five key bioactive metabolites of Shankhapushpi (scopoletin, 4-hydroxycinnamic acid, kaempferol, quercetin, and ayapanin) are predicted to modulate several molecular targets including PTGS1, PTGS2, NOS3, INSR, HMOX1, ACHE, PPARG, MAOA, MAOB, and TRKB. These molecular targets participate in many cellular pathways important for neuronal growth, survival, and functionality. The in-silico analysis predicted that PI3K/Akt signaling, neurotrophin signaling, and insulin signaling are the key pathways that are most likely to be modulated by Shankhapushpi. Another in-silico study looked at the relationships between Shankhapushpi metabolites and dopaminergic receptors, mitogen-activated protein kinases, 5-hydroxytryptamine receptors, and histone deacetylases. The hypothesized network of gene–gene interactions and its investigations have established the framework for understanding Shankhapushpi’s nootropic function. Furthermore, the network suggests unique insights into the future scope of investigation on Shankhapushpi’s nootropic action. The combined network pharmacology research from animal models and in-silico observations provides a scientific foundation for memory augmentation and the prevention of aging/pathological cognitive impairments. However, more studies are needed to explore the memory-enhancing and neuroprotective mechanisms of phytoconstituents and their metabolites before extrapolating the findings from preclinical and in silico models to clinical subjects2,3. Unknown toxicity might impact the potential of Shankhpushpi applications: Despite such extensive use of Shankhpushpi, data on the toxicity profile of its phytoconsitutents is lacking. Shankhpushpi has been reported to display mild hypotensive effects. It is also indicated to interact with phenytoin and decrease its antiepileptic activity. Furthermore, the hepatotoxicity of coumarin also raises serious safety concerns regarding Shankhpushpi use. Unfortunately, there is not enough literature to suggest that the critical evaluation of several bioactive compounds present in Shankhpushpi does not have toxicity data in various models. Therefore, it is essential to understand and critically evaluate the toxicity potential of all its phytoconstituents. Preliminary research using QSAR (quantitative structure–activity relationship) models and in silico approaches can provide some insight into the toxicity of these bioactive compounds for additional testing in animal models. Need for clinical validation: As the authors noted, multicentric trials involving diverse ethnic and regional populations are required to understand the clinical uses and commercial viability of bioactive compounds. Overall, the authors thoroughly investigated the Shankhpushpi literature (C. pluricaulis) and presented potential molecular targets such as the PI3K–Akt signaling pathway, cholinergic synapse, serotonergic synapse, MAPK (mitogen-activated protein kinase) signaling pathway, long-term depression, Alzheimer’s disease, and neurotrophin signaling pathway. Because these approaches have been taken by other researchers, as mentioned in the preceding sections, this type of study may not result in a concrete understanding of molecular mechanisms. However, pursuing the research of toxicity potential for the presented bioactive compounds might have revealed some unique perspectives. Nonetheless, more human clinical trials will be required to establish the safety, tolerability, and efficacy of each bioactive phytochemical in decreasing neuroinflammation in the brain, as well as highlight the implications for neurodegenerative disease treatment. Ethical approval This case report has been reported in line with the CAse REport (CARE) guidelines. Consent Not applicable. Source of funding None. Author contribution M.R.: conceptualization, data curation, and writing – original draft preparation, reviewing, and editing; R.K.K.: conceptualization, data curation, and writing – original draft preparation, reviewing, and editing; H.C.: data curation and writing – original draft preparation, reviewing, and editing; S.D.: data curation and writing – original draft preparation, reviewing, and editing; S.K.J.: data curation and writing – original draft preparation, reviewing, and editing; N.K.J.: writing – reviewing and editing, visualization, and supervision; T.B.E.: writing – reviewing and editing, visualization, and supervision. All authors critically revised the manuscript concerning intellectual content and approved the final manuscript. Conflicts of interest disclosure There are no conflicts of interest. Research registration unique identifying number (UIN) Not applicable. Provenance and peer review Not commissioned, externally peer-reviewed.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,206
Score d'incertitude au seuil0,275

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,036
Tête enseignante GPT0,352
Écart entre enseignants0,316 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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