Functional Gait Assessment Using Manual, Semi-Automated and Deep Learning Approaches Following Standardized Models of Peripheral Nerve Injury in Mice
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Notice bibliographique
Résumé
Objective: To develop a standardized model of stretch−crush sciatic nerve injury in mice, and to compare outcomes of crush and novel stretch−crush injuries using standard manual gait and sensory assays, and compare them to both semi-automated as well as deep-learning gait analysis methods. Methods: Initial studies in C57/Bl6 mice were used to develop crush and stretch−crush injury models followed by histologic analysis. In total, 12 eight-week-old 129S6/SvEvTac mice were used in a six-week behavioural study. Behavioral assessments using the von Frey monofilament test and gait analysis recorded on a DigiGait platform and analyzed through both Visual Gait Lab (VGL) deep learning and standardized sciatic functional index (SFI) measurements were evaluated weekly. At the termination of the study, neurophysiological nerve conduction velocities were recorded, calf muscle weight ratios measured and histological analyses performed. Results: Histological evidence confirmed more severe histomorphological injury in the stretch−crush injured group compared to the crush-only injured group at one week post-injury. Von Frey monofilament paw withdrawal was significant for both groups at week one compared to baseline (p < 0.05), but not between groups with return to baseline at week five. SFI showed hindered gait at week one and two for both groups, compared to baseline (p < 0.0001), with return to baseline at week five. Hind stance width (HSW) showed similar trends as von Frey monofilament test as well as SFI measurements, yet hind paw angle (HPA) peaked at week two. Nerve conduction velocity (NCV), measured six weeks post-injury, at the termination of the study, did not show any significant difference between the two groups; yet, calf muscle weight measurements were significantly different between the two, with the stretch−crush group demonstrating a lower (poorer) weight ratio relative to uninjured contralateral legs (p < 0.05). Conclusion: Stretch−crush injury achieved a more reproducible and constant injury compared to crush-only injuries, with at least a Sunderland grade 3 injury (perineurial interruption) in histological samples one week post-injury in the former. However, serial behavioral outcomes were comparable between the two crush groups, with similar kinetics of recovery by von Frey testing, SFI and certain VGL parameters, the latter reported for the first time in rodent peripheral nerve injury. Semi-automated and deep learning-based approaches for gait analysis are promising, but require further validation for evaluation in murine hind-limb nerve injuries.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| 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) | 0,000 | 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 découle