Acid-sensing ion channel 3 mediates peripheral anti-hyperalgesia effects of acupuncture in mice inflammatory pain

Background  

Peripheral tissue inflammation initiates hyperalgesia accompanied by tissue acidosis, nociceptor activation, and inflammation mediators. Recent studies have suggested a significantly increased expression of acid-sensing ion channel 3 (ASIC3) in both carrageenan- and complete Freund's adjuvant (CFA)-induced inflammation. This study tested the hypothesis that acupuncture is curative for mechanical hyperalgesia induced by peripheral inflammation.     

Protein structure similarity from principle component correlation analysis

Background  

Owing to rapid expansion of protein structure databases in recent years, methods of structure comparison are becoming increasingly effective and important in revealing novel information on functional properties of proteins and their roles in the grand scheme of evolutionary biology. Currently, the structural similarity between two proteins is measured by the root-mean-square-deviation (RMSD) in their best-superimposed atomic coordinates. RMSD is the golden rule of measuring structural similarity when the structures are nearly identical; it, however, fails to detect the higher order topological similarities in proteins evolved into different shapes. We propose new algorithms for extracting geometrical invariants of proteins that can be effectively used to identify homologous protein structures or topologies in order to quantify both close and remote structural similarities.     

A note on the effectiveness of behavioural rehabilitation for reducing inter-dog aggression in shelter dogs

The effectiveness of a rehabilitation program for reducing inter-dog aggression was evaluated at the municipal animal shelter. Sixteen dogs (of 60 examined) met the study criteria of medium inter-dog aggression as determined by an inter-dog aggression test. These dogs received a 10-day treatment of daily rehabilitation for 30 min (rehabilitation group, n = 9) or daily release into an outdoor enclosure for 30 min (control group, n = 7). Rehabilitation consisted of desensitising and counter-conditioning dogs to the approach of other “stimulus” dogs. Most dogs in the rehabilitation group showed a decline in aggression scores when re-tested after the last treatment (day 11), and differed significantly from the control dogs which showed either an increase or no change in aggression scores (U = 8.5, P < 0.01). Rehabilitation dogs also showed lower frequencies of aggressive body postures (“facing the stimulus dog”, P < 0.05, and “stiff posture”, P < 0.10) and higher frequencies of less assertive postures (“ears back”, P < 0.05, and “lowered neck”, P < 0.10) on day 11. The differences between groups were no longer significant when a reduced sample of dogs was tested 1 week after rehabilitation ended (day 18). The study shows short-term reduction of inter-dog aggression through rehabilitation, but further work is needed on effective ways of maintaining the behavioural change.     

Conditional random pattern model for copy number aberration detection

Background  

DNA copy number aberration (CNA) is very important in the pathogenesis of tumors and other diseases. For example, CNAs may result in suppression of anti-oncogenes and activation of oncogenes, which would cause certain types of cancers. High density single nucleotide polymorphism (SNP) array data is widely used for the CNA detection. However, it is nontrivial to detect the CNA automatically because the signals obtained from high density SNP arrays often have low signal-to-noise ratio (SNR), which might be caused by whole genome amplification, mixtures of normal and tumor cells, experimental noise or other technical limitations. With the reduction in SNR, many false CNA regions are often detected and the true CNA regions are missed. Thus, more sophisticated statistical models are needed to make the CNAs detection, using the low SNR signals, more robust and reliable.     

Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces

Background

Predicting drug-protein interactions from heterogeneous biological data sources is a key step for in silico drug discovery. The difficulty of this prediction task lies in the rarity of known drug-protein interactions and myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often generates better results than using the labeled data alone. Furthermore, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data.

Results

Using the proposed method, we predicted certain drug-protein interactions on the enzyme, ion channel, GPCRs, and nuclear receptor data sets. Some of them are confirmed by the latest publicly available drug targets databases such as KEGG.

Conclusions

We report encouraging results of using our method for drug-protein interaction network reconstruction which may shed light on the molecular interaction inference and new uses of marketed drugs.