Objective To observe the clinical characteristics and influencing factors of post-stroke epilepsy. Methods Our research wasaretrospective study, the data came from the information of patients diagnosed with post-stroke epilepsy from our hospital on October 2000 to December 2014 withatotal of 160 cases. With the general collection of clinical data, including gender, past history, clinical manifestations, laboratory examinations and treatment informations. Results The shortest time of post-stroke seizures were occur immediately, the longest was 15 years after the stroke. Peak onset is as early as onset of stroke immediately, late-onset seizures after stroke peaks between 6 months to 1.5 years. 59 patients occurred early epileptic seizures, partial seizures were the most common, accounting for 47.46%; 101 patients occurred late epilepsy, generalized tonic-clonic seizures were the most common, accounting for 56.44%. 25% of patients wereasingle-site lesions, the most common site was temporal lobe; 75% of patients were multifocal lesions. Most were located in the temporal lobe, frontal lobe and the basal ganglia. 42 cases of patients performed EEG, 30 patients (71.43%) of the EEG abnormalities, including 22 cases (73.33%) recurrent epileptic seizures; 12 cases (28.57%) patients with an edge or normal EEG, including 3 cases (25%) relapsed. 54.38% patients with drug therapy to single-agent therapy, two patients with refractory epilepsy to be combination therapy. Conclusions This group of post stroke epilepsy patients were more common as late-onset epilepsy, early onset of stroke peaks is the first day, and delayed the onset of the peak after stroke is within 6 months to 18 months. Lesions in the cortex:alarge area and multiple lesions were risk factors for post-stroke epilepsy, cortical damage to the temporal lobe is most prevalent. 71.43% of patients may have abnormal EEG, EEG abnormalities have higher relapse rate in patients with epilepsy.
ObjectiveTo systematically evaluate the association between Toll like receptor 2 (TLR2) gene I/D polymorphism and the risk of cancer. MethodsWe searched PubMed, EMbase, The Cochrane Library (Issue 7, 2015), CBM, CNKI, VIP and WanFang Data to collect case-control studies about the association between TLR2 gene I/D polymorphism and the risk of cancer from inception to July 2015. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Then, meta-analysis was conducted using RevMan 5.3 software. ResultsA total of 11 case-control studies involving 3 250 cancer patients and 4 332 controls were included. The results of meta-analysis showed that significant association was found between TLR2 gene I/D polymorphism and the risk of cancer (DD+DI vs. Ⅱ:OR=1.60, 95%CI 1.13 to 2.27, P=0.009; DD vs. Ⅱ+DI:OR=1.73, 95%CI 1.13 to 2.66, P=0.01; DD vs. Ⅱ:OR=1.99, 95%CI 1.22 to 3.24, P=0.006; DI vs. Ⅱ:OR=1.52, 95%CI 1.09 to 2.11, P=0.01; D vs. I:OR=1.54, 95%CI 1.14 to 2.09, P=0.005). ConclusionTLR2 gene L/D polymorphism may be associated with cancer risk. Due to the limited quantity and quality of included studies, the conclusion should be verified in further studies.
Skin aging principles play important roles in skin disease diagnosis, the evaluation of skin cosmetic effect, forensic identification and age identification in sports competition, etc. This paper proposes a new method to evaluate the skin aging objectively and quantitatively by skin texture area. Firstly, the enlarged skin image was acquired. Then, the skin texture image was segmented by using the iterative threshold method, and the skin ridge image was extracted according to the watershed algorithm. Finally, the skin ridge areas of the skin texture were extracted. The experiment data showed that the average areas of skin ridges, of both men and women, had a good correlation with age (the correlation coefficient r of male was 0.938, and the correlation coefficient r of female was 0.922), and skin texture area and age regression curve showed that the skin texture area increased with age. Therefore, it is effective to evaluate skin aging objectively by the new method presented in this paper.
To realize the accurate positioning and quantitative volume measurement of tumor in head and neck tumor CT images, we proposed a level set method based on augmented gradient. With the introduction of gradient information in the edge indicator function, our proposed level set model is adaptive to different intensity variation, and achieves accurate tumor segmentation. The segmentation result has been used to calculate tumor volume. In large volume tumor segmentation, the proposed level set method can reduce manual intervention and enhance the segmentation accuracy. Tumor volume calculation results are close to the gold standard. From the experiment results, the augmented gradient based level set method has achieved accurate head and neck tumor segmentation. It can provide useful information to computer aided diagnosis.