No abstract

Dietary supplementation with omega-3 (o-3) fatty acids is a safe, economical mean of preventive medicine that has shown protection against several neurologic disorders. The present study tested the hypothesis that this method is protective against controlled cortical impact (CCI). Indeed, mice fed with o-3 polyunsaturated fatty acid (PUFA)-enriched diet for 2 months exhibited attenuated short and long-term behavioral deficits due to CCI. Although o-3 PUFAs did not decrease cortical lesion volume, these fatty acids did protect against hippocampal neuronal loss after CCI and reduced pro-inflammatory response. Interestingly, o-3 PUFAs prevented the loss of myelin basic protein (MPB), preserved the integrity of the myelin sheath, and maintained the nerve fiber conductivity in the CCI model. o-3 PUFAs also directly protected oligodendrocyte cultures from excitotoxicity and blunted the microglial activation-induced death of oligodendrocytes in microglia/oligodendrocyte cocultures. In sum, o-3 PUFAs elicit multifaceted protection against behavioral dysfunction, hippocampal neuronal loss, inflammation, and loss of myelination and impulse conductivity. The present report is the first demonstration that o-3 PUFAs protect against white matter injury in vivo and in vitro. The protective impact of o-3 PUFAs supports the clinical use of this dietary supplement as a prophylaxis against traumatic brain injury and other nervous system disorders.

Quantitative assessment of parkinsonian tremor based on inertial sensors can provide reliable feedback on the effect of medication. In this regard, the features of parkinsonian tremor and its unique properties such as motor fluctuations and dyskinesia are taken into account. Least-square-estimation models are used to assess the severities of rest, postural, and action tremors. In addition, a time-frequency signal analysis algorithm for tremor state detection was also included in the tremor assessment method. This inertial sensor-based method was verified through comparison with an electromagnetic motion tracking system. Seven Parkinson’s disease (PD) patients were tested using this tremor assessment system. The measured tremor amplitudes correlated well with the judgments of a neurologist (r = 0.98). The systematic analysis of sensor-based tremor quantification and the corresponding experiments could be of great help in monitoring the severity of parkinsonian tremor.

Motivation Systematic identification of molecular targets among known drugs plays an essential role in drug repurposing and understanding of their unexpected side effects. Computational approaches for prediction of drug–target interactions (DTIs) are highly desired in comparison to traditional experimental assays. Furthermore, recent advances of multiomics technologies and systems biology approaches have generated large-scale heterogeneous, biological networks, which offer unexpected opportunities for network-based identification of new molecular targets among known drugs. Results In this study, we present a network-based computational framework, termed AOPEDF, an arbitrary-order proximity embedded deep forest approach, for prediction of DTIs. AOPEDF learns a low-dimensional vector representation of features that preserve arbitrary-order proximity from a highly integrated, heterogeneous biological network connecting drugs, targets (proteins) and diseases. In total, we construct a heterogeneous network by uniquely integrating 15 networks covering chemical, genomic, phenotypic and network profiles among drugs, proteins/targets and diseases. Then, we build a cascade deep forest classifier to infer new DTIs. Via systematic performance evaluation, AOPEDF achieves high accuracy in identifying molecular targets among known drugs on two external validation sets collected from DrugCentral [area under the receiver operating characteristic curve (AUROC) = 0.868] and ChEMBL (AUROC = 0.768) databases, outperforming several state-of-the-art methods. In a case study, we showcase that multiple molecular targets predicted by AOPEDF are associated with mechanism-of-action of substance abuse disorder for several marketed drugs (such as aripiprazole, risperidone and haloperidol). Availability and implementation Source code and data can be downloaded from https://github.com/ChengF-Lab/AOPEDF. Supplementary information Supplementary data are available at Bioinformatics online.

Traumatic brain injury (TBI) is the largest non-genetic, non-aging related risk factor for Alzheimers disease (AD). We report here that TBI induces tau acetylation (ac-tau) at sites acetylated also in human AD brain. This is mediated by S-nitrosylated-GAPDH, which simultaneously inactivates Sirtuinl deacetylase and activates p300/CBP acetyltransferase, increasing neuronal ac-tau. Subsequent tau mislocalization causes neurodegeneration and neurobehavioral impairment, and ac-tau accumulates in the blood. Blocking GAPDH S-nitrosylation, inhibiting p300/CBP, or stimulating Sirtuinl all protect mice from neurodegeneration, neurobehavioral impairment, and blood and brain accumulation of ac-tau after TBI. Ac-tau is thus a therapeutic target and potential blood biomarker of TBI that may represent pathologic convergence between TBI and AD. Increased ac-tau in human AD brain is further augmented in AD patients with history of TBI, and patients receiving the p300/CBP inhibitors salsalate or diflunisal exhibit decreased incidence of AD and clinically diagnosed TBI.