Transcranial direct current stimulation promotes recovery of cognitive function after a stroke / 中华物理医学与康复杂志

Objective:To observe the functional remodeling of the default mode network (DMN) of patients with post-stroke cognitive impairment (PSCI) in response to transcranial direct current stimulation (tDCS) and to explore the mechanisms involved.Methods:Eighteen PSCI patients and twenty healthy controls were enrolled. The PSCI patients were given 20 minutes of tDCS at 2.0mA on the dorsolateral prefrontal cortex every weekday for four weeks. The subjects were scanned using resting state functional magnetic resonance imaging before and after the intervention to explore any functional changes in the DMN. Montreal cognitive assessment (MoCA) scoring was performed before and after the treatment.Results:Compared to the healthy controls, the cognitively impaired patients showed significant abnormalities in the DMN on admission, including decreased functinal connection (FC) in the posterior cingulate cortex and/or precuneus (PCC/PCu), as well as increased FC in the medial prefrontal cortex (MPFC) and left hippocampus. After the treatment, a significant improvement in the PSCI patients′ average MoCA score was observed. The average FC of the PCC/PCu had also increased significantly, while that of the left hippocampus had decreased significantly, on average. The FC in the PCC/PCu after tDCS was found to be positively related to the MoCA score measured at 4 weeks after admission.Conclusions:PSCI patients show functional abnormalities in the DMN. tDCS applied to their dorsolateral prefrontal cortex can significantly improve their average MoCA scores, which may be related to the increasing FC of the PCC/PCu.

Dynamic matching algorithm for viral structure prediction

Most viruses have RNA genomes, their biological functions are expressed more by folded architecture than by sequence

Among the various RNA structures, pseudoknots are the most typical. In general, RNA secondary structures prediction doesn't contain pseudoknots because of its difficulty in modeling

Here we present an algorithm of dynamic matching to predict RNA secondary structures with pseudoknots by combining the merits of comparative and thermodynamic approaches

We have tested and verified our algorithm on some viral RNA

Comparisons show that our algorithm and loop matching method has similar accuracy and time complexity, and are more sensitive than the maximum weighted matching method and Rivas algorithm. Among the four methods, our algorithm has the best prediction specificity. The results show that our algorithm is more reliable and efficient than the other methods