Tissue-type Differences in Focused Ultrasound and Microbubble-mediated Drug Delivery to the Brain Exist at Vessel Level
Notice bibliographique
Résumé
Rationale:The efficacy of drug delivery to the brain is constrained by the impermeability of the blood-brain barrier (BBB) in healthy tissues and the heterogeneous permeability of the blood-tumor barrier (BTB) in gliomas.Focused ultrasound (FUS) has emerged as a promising technique to transiently modulate vascular permeability, however its effects vary across different brain tissues.This study systematically evaluates the effects of FUS-induced vascular permeability modulation in the gray matter (GM), white matter (WM), and brain tumors, considering their distinct tissue architectures, vascular densities, and permeability profile.Additionally, we compare the delivery of bevacizumab (antiangiogenic monoclonal antibody) and methotrexate (small-molecule chemotherapeutic) to determine how molecular size influences vascular-level permeability and extravasation distances.Methods: A total of n = 48 Fischer-344 rats, including both healthy and tumor-bearing cohorts, underwent magnetic resonance imaging (MRI)-guided FUS using a feedback-controlled algorithm to modulate microbubble pressure based on microbubble emissions.Tumors were either untreated or received a single FUS exposure, while healthy tissues, including GM and WM, were treated with either a single exposure, or a repeated exposure administered 30 minutes after the first one.MR images were used to assess contrast enhancement before and after sonication.Drug deposition was quantified via fluorescence microscopy in terms of local signal intensities and distances of extravasation.Tissue-specific vascular characteristics, including vessel diameters, densities, and inter-vessel distances, were also analysed. Results:The lack of MRI contrast enhancement in untreated tissues suggested a healthy permeability status of the BBB in GM and WM, while a compromised BTB was observed in tumors.Following FUS treatments, contrast enhancement significantly increased in all tissues, with tumors exhibiting the most pronounced effects.Repeated FUS further enhanced permeability in GM and WM, achieving drug deposition levels comparable to those observed in tumors after a single treatment.At the vascular level, FUS exposure led to significant increases in drug extravasation distances, particularly in tumors.Vascular densities were approximately threefold higher in GM, compared to WM and tumors (GM:WM:Tumor 3.2:1:1), yet both drug signal intensities and extravasation distance correlated more strongly with the number of treatments than with baseline vascularity.Fluorescence microscopy revealed that bevacizumab extravasation was primarily localized near vessel lumens, whereas methotrexate exhibited significantly greater extravascular diffusion, reaching distances spanning entire inter-vessel spaces, consistent with its lower molecular weight.At the individual vessel level, white matter showed significantly lower drug signal intensity than gray matter following a single treatment. Conclusion:This study provides vascular-level insights into how FUS-mediated drug delivery is influenced by tissue architecture, vascular properties, treatment regimen, and drug molecular weight.Notably, at the individual vessel level, drug extravasation varies between the different tissue types, and thus vascular density is not the sole driver of differences in drug deposition in these tissues.The study findings highlight the potential of repeated FUS exposures for enhancing the deposition of therapeutics across the physiologically intact BBB of both the gray and white matter, reaching levels comparable to those observed in the pathologically compromised BTB of gliomas.Thus, sonications prescribed over previously permeabilized tissues facilitate deeper drug penetration into interstitial compartments, allowing therapeutics to reach cells further from vessel lumens despite inherent tissue-specific differences.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».