MétaCan
Menu
Retour à la cohorte
Enregistrement W4412911782 · doi:10.1093/mam/ozaf048.943

Recent Advances in Focused Ion Beam Methodologies for 3D Analysis of Biomineralizing Tissues across Multiple Length Scales

2025· article· en· W4412911782 sur OpenAlexaff
Tian Tang

Notice bibliographique

RevueMicroscopy and Microanalysis · 2025
Typearticle
Langueen
DomaineEngineering
ThématiqueBone Tissue Engineering Materials
Établissements canadiensMcMaster University
Organismes subventionnairesnon disponible
Mots-clésFocused ion beamMaterials scienceIon beam analysisNanotechnologyIon beamBeam (structure)IonOpticsPhysics

Résumé

récupéré en direct d'OpenAlex

Biomineralizing tissues such as bone, cartilage, and tendon exhibit a remarkable hierarchical organization, with structural features spanning the macro- to nanometer length scales [1,2]. Historically, capturing these features in 3D posed a significant challenge because conventional methods such as transmission electron microscopy (TEM) and laboratory micro-CT cannot simultaneously provide ultrastructural details and a sufficiently large volume to encompass entire cells and their surrounding matrix. Focused ion beam-scanning electron microscopy (FIB-SEM), originally developed in the semiconductor industry, has emerged as a powerful solution to bridge this gap, enabling researchers to examine how minerals and organic matrices interrelate at multiple length scales – a key step for understanding tissue formation, growth, adaptation, and disease progression. Early FIB tomography studies of bone often involved partial or complete demineralization, following approaches pioneered by Reznikov et al., to circumvent difficulties in milling the hard mineral phase of the tissue [3–5]. While these studies yielded valuable insights into collagen fibril organization [4], the required sample processing risked introducing artifacts or altering the native bone ultrastructure. More recently, direct FIB tomography of fully mineralized bone has been demonstrated using Ga FIB, providing deeper understanding of lamellar organization, cement sheaths, and osteocyte canaliculi within the mineralized matrix [6]. Among the most notable findings from these direct 3D studies is the discovery of an extensive network of nanochannels, each roughly an order of magnitude smaller in diameter than osteocyte canaliculi yet the network possessing a much higher overall volume fraction [7–9]. The nanochannels appear to serve as alternative pathways for ion and small molecule transport within the bone extracellular matrices, a result supported by subsequent work on human cortical bone showing an inverse correlation between nanochannel volume and local calcium content [8]. In addition, unpublished data from lactation mouse models suggest that their nanochannels may participate in osteocytic osteolysis, enabling rapid mineral turnover in the pericellular bone matrix. Despite these advances, the milling volume achievable with Ga FIB remains a key limitation to this technique, generally restricting the field of view to tens of micrometers. Plasma FIB (PFIB) systems – often employing xenon ions – have overcome this barrier, allowing faster material removal and the reconstruction of larger volumes. This capability has allowed the capture of more complete aspects of osteocyte lacunocanalicular networks (LCN) and mineral ellipsoids across mesoscale volumes [10]. By integrating PFIB with X-ray microscopy (XRM), subsurface features of interest at the scale of hundreds of micrometers or more can be identified and selectively milled, an approach recently applied to human trabecular bone [11]. An emerging next step is femtosecond laser FIB (LaserFIB), which uses ultra-short laser pulses to ablate millimeter-scale volumes rapidly before final polishing or tomography with either Ga or Xe FIB. This correlative pipeline has already been shown to expose deeply buried trabeculae in human bone with minimal damage [11], and the application of direct LaserFIB serial sectioning – though still under development – could further accelerate throughput and expand sampling volumes for 3D structural analysis. Meanwhile, other ion species (e.g., oxygen, argon, helium, and neon) have seen specialized use in semiconductor and materials science for milling with low level damage or enhanced chemical contrast [12], but their utility remains largely unexplored for biomineralizing tissues. Some preliminary work has explored oxygen PFIB to mill resin-embedded brain tissue [13] and argon PFIB to prepare cryogenic cellular samples [14], but comprehensive 3D tomography with oxygen, argon, or neon beams for mineralized tissues is largely uncharted. Nonetheless, the distinct sputtering properties and reduced sample damage profiles of these ions suggest potential advantages in future studies of calcified tissues. Altogether, these FIB-based innovations from conventional Ga FIB to PFIB and now LaserFIB are transforming our ability to study 3D mineral-organic interactions in biomineralizing tissues. As these technologies continue to evolve, combining high-resolution volume imaging with targeted site-specific sample preparation should further elucidate how nanoscale pathways, mineral distributions, and structural hierarchies converge to determine tissue function in health and disease [15].

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,003
Score d'incertitude au seuil0,018

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,002
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,001
Communication savante0,0020,002
Science ouverte0,0020,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

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 Opus0,024
Tête enseignante GPT0,329
Écart entre enseignants0,304 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

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

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
Publié2025
Routes d'admission1
Résumé présentnon

Explorer davantage

Même revueMicroscopy and MicroanalysisMême sujetBone Tissue Engineering MaterialsTravaux en français237 207