OBJECTIVES: To investigate the otoprotective effects of mouse nerve growth factor (mNGF) in A/J mice. METHODS: The mice at postnatal day 7 (P7) were randomly separated into a mNGF treated group (mNGF group) and a distilled water (for injection) treated group (control group). The mNGF dissolved in distilled water or distilled water alone was given to the mice once every other day from P7 by intramuscular injection in the hips. The otoprotective effects of mNGF in A/J mice were observed in a time course manner. The thresholds of auditory-evoked brainstem response (ABR) were tested from the age of the 3rd to the 8th week. Sections of the inner ears were stained by hematoxylin and eosin, and spiral ganglion neurons (SGNs) were observed at the age of the 3rd, the 6th,and the 8th week. Counts of whole mount outer hair cells (OHCs) in the cochleae were made at the age of 8 weeks. Expression of apoptosis related genes was determined by quantitative real-time polymerase chain reaction and Western blotting. RESULTS: ABR thresholds of the mNGF group were significantly lower than those of the control group at the age of the 6th and the 8th week. Moreover, the mNGF preserved OHC and SGN in the mouse cochleae in this period. Further experiments showed that the expression of caspase genes (including caspase-3) was inhibited in the mouse inner ears in the mNGF group. CONCLUSION: The mNGF improves hearing in A/J mice by preserving SGN and OHC in the cochleae.
Animals ; Apoptosis ; Blotting, Western ; Brain Stem ; Cochlea ; Ear, Inner ; Eosine Yellowish-(YS) ; Hair Cells, Auditory, Outer ; Hearing* ; Hematoxylin ; Hip ; Injections, Intramuscular ; Mice* ; Nerve Growth Factor* ; Neurons ; Real-Time Polymerase Chain Reaction ; Spiral Ganglion ; Water

Diagnosis of Parkinson's disease (PD) based on speech data has been proved to be an effective way in recent years. However, current researches just care about the feature extraction and classifier design, and do not consider the instance selection. Former research by authors showed that the instance selection can lead to improvement on classification accuracy. However, no attention is paid on the relationship between speech sample and feature until now. Therefore, a new diagnosis algorithm of PD is proposed in this paper by simultaneously selecting speech sample and feature based on relevant feature weighting algorithm and multiple kernel method, so as to find their synergy effects, thereby improving classification accuracy. Experimental results showed that this proposed algorithm obtained apparent improvement on classification accuracy. It can obtain mean classification accuracy of 82.5%, which was 30.5% higher than the relevant algorithm. Besides, the proposed algorithm detected the synergy effects of speech sample and feature, which is valuable for speech marker extraction.

Methods for achieving diagnosis of Parkinson's disease (PD) based on speech data mining have been proven effective in recent years. However, due to factors such as the degree of disease of the data collection subjects and the collection equipment and environment, there are different categories of sample aliasing in the sample space of the acquired data set. Samples in the aliased area are difficult to be identified effectively, which seriously affects the classification accuracy of the algorithm. In order to solve this problem, a partition bagging ensemble learning is proposed in this article, which measures the aliasing degree of the sample by designing the the ratio of sample centroid distance metrics and divides the training set into multiple subsets. And then the method of transfer training of misclassified samples is used to adjust the results of subset partitioning. Finally, the optimized weights of each sub-classifier are used to integrate the test results. The experimental results show that the classification accuracy of the proposed method is significantly improved on two public datasets and the increasement of mean accuracy is up to 25.44%. This method not only effectively improves the classification accuracy of PD speech dataset, but also increases the sample utilization rate, providing a new idea for the diagnosis of PD.
Algorithms ; Data Mining ; Humans ; Machine Learning ; Parkinson Disease ; diagnosis ; Speech