Ochi, A. | Search bioRxiv

bioRxiv2022

NF-κB Decoy ODN-Loaded Poly lactic-co-glycolic Acid Nanospheres Inhibit Alveolar Ridge Resorption

Residual ridge resorption combined with dimensional loss resulting from tooth extraction has a prolonged correlation with early excessive inflammation. Nuclear factor-kappa B (NF-{kappa}B) decoy oligodeoxynucleotide (ODN) is a member of a group of double-stranded DNA capable of downregulating the expression of downstream genes of the NF-{kappa}B pathway. The healing action of its embellished effect combined with poly(lactic-co-glycolic acid) (PLGA) nanospheres on tooth extraction socket still remains unknown. Hence, the aim of this study was to investigate the therapeutic effect of NF-{kappa}B decoy ODN-loaded PLGA nanospheres (PLGA-NfD) transfected into extraction sockets in Wistar/ST rats. Micro-computed tomography and trabecular bone analysis following treatment with PLGA-NfD demonstrated inhibition of vertical alveolar bone loss with increased bone volume, smoother trabecular bone surface, thicker trabecular bone, larger trabecular number and separation, and fewer bone porosities. Histomorphometric and reverse transcription-quantitative polymerase chain reaction analysis revealed reduced tartrate-resistant acid phosphatase-expressing osteoclasts, interleukin-1{beta}, tumor necrosis factor-, receptor activator of NF-{kappa}B ligand, turnover rate and increased transforming growth factor-{beta}1 immunopositive reactions and relative gene expressions. These data demonstrate that local delivery of PLGA-NfD could be used as a substantial suppressor of inflammation during the healing process in a tooth extraction socket, with the potential of accelerated new bone formation.

immunology↗

bioRxiv2022

Dissociable default-mode subnetworks subserve childhood attention and cognitive flexibility: evidence from deep learning and stereotaxic electroencephalography

BackgroundCognitive flexibility encompasses the ability to efficiently shift focus and forms a critical component of goal-directed attention. The neural substrates of this process are incompletely understood in part due to difficulties in sampling the involved circuitry. MethodsStereotactic intracranial recordings that permit direct resolution of local-field potentials from otherwise inaccessible structures were employed to study moment-to-moment attentional activity in children with epilepsy during the performance of an attentional set-shifting task. A combined deep learning and model-agnostic feature explanation approach was used to analyze these data and decode attentionally-relevant neural features. Connectomic profiling of highly predictive attentional nodes was further employed to examine task-related engagement of large-scale functional networks. ResultsThrough this approach, we show that beta/gamma power within executive control, salience, and default mode networks accurately predicts single-trial attentional performance. Connectomic profiling reveals that key attentional nodes exclusively recruit dorsal default mode subsystems during attentional shifts. ConclusionsThe identification of distinct substreams within the default mode system supports a key role for this network in cognitive flexibility and attention in children. Furthermore, convergence of our results onto consistent functional networks despite significant inter-subject variability in electrode implantations supports a broader role for deep learning applied to intracranial electrodes in the study of human attention. FundingNo funds supported this specific investigation. Awards and grants supporting authors include: Canadian Institutes of Health Research (CIHR) Vanier Scholarship (NMW, HY); CIHR Frederick Banting and Charles Best Canada Graduate Scholarship Doctoral Award (SMW); CIHR Canada Graduate Scholarship Masters Award (ONA); and a CIHR project grant (GMI).

neuroscience↗