Background and objective
Action observation training (AOT) has been used as a new intervention for improving upper limb motor functions in people with stroke. This systematic review and meta-analysis aims to investigate the effects of AOT on improving upper limb motor functions in people with stroke.
Methods
We searched ten electronic databases to identify randomized controlled trials (RCTs) about the effects of AOT on upper limb motor functions in stroke survivors. Methodological quality of included studies was assessed by the Risk of Bias Tool in the Cochrane Handbook for Systematic Reviews of Interventions. A random-effects meta-analysis was performed by pooling the standardized mean difference (SMD) of upper limb motor outcomes.
Results
Seven studies of 276 participants with stroke were included. Meta-analysis showed a significant effect favoring AOT on improving upper limb motor functions in patients with stroke [SMD = 0.35, 95% confidence interval [CI], 0.10 to 0.61, I
2
= 10.14%,
p
= 0.007].
Conclusions
AOT appears to be an effective intervention for improving the upper limb motor functions in people after stroke. Further studies need to investigate the neural mechanism underlying the effects of AOT.
The evaluation of absorption, distribution, metabolism, exclusion, and toxicity (ADMET) properties plays a key role in a variety of domains including industrial chemicals, agrochemicals, cosmetics, environmental science, food chemistry, and particularly drug development. Since molecules are often intrinsically described as molecular graphs, graph neural networks have recently been studied to improve the prediction of ADMET properties. Among many graph neural networks published in recent years, Graph Isomorphism Network (GIN) is a relatively recent and very promising one. In this paper, we propose an enhanced GIN, called MolGIN, via exploiting the bond features and differences influence of the atom neighbors to end-to-end predict ADMET properties. Based on GIN, MolGIN concatenates the bond feature together with node feature in the feature aggregator and applies a gate unit to adjust the atomic neighborhood weights to map the differences in the interaction strength between the central atom and its neighbors, such that more meaningful structural patterns of molecules can be explored toward better molecular modeling. Extensive experiments were conducted on seven public datasets to evaluate MolGIN against four baseline models with benchmark metrics. Experimental results of MolGIN were also compared with state-of-the-art results published in the last three years on each dataset. Experimental results in terms of RMSE and AUC show that MolGIN significantly boosts the prediction performance of GIN and markedly outperforms the baseline models, and achieves comparable or superior performance to state-of-the-art results.
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