Analysis of early diagnosis methods for asymmetric dementia in brain MR images based on genetic medical technology.

Alzheimer's disease (AD) is a relatively common senile neurodegenerative disease and the main manifestation of senile dementia. In the pathological changes of AD, the asymmetry of the brain also changes. Therefore, finding an early diagnosis method of AD based on asymmetry is the key to the treatment of Alzheimer's. Magnetic resonance (MR) imaging can quantitatively reflect the structural and functional changes of various tissues in the brain. It has the advantages of non-invasive, high spatial resolution, and non-radiation, and has been widely used in the early diagnosis of AD. In this work, asymmetric images were extracted from multiple brain MR images, and different morphological and texture features were extracted. By establishing a feature selection classification integration model, image features in the image were deeply fused to obtain higher and more stable recognition results than before. By filtering image samples, the corresponding sample feature matrix was obtained. Support vector machine was used for classification, and its classification accuracy had improved significantly compared with that before selection. In the experimental data of normal control group and AD group, the accuracy, sensitivity, and specificity of the feature selection algorithm were 93.34, 90.69, and 95.87%, respectively. In the normal control group and the mild cognitive impairment group, the accuracy, sensitivity, and specificity of the feature selection algorithm in this work were 85.31, 79.68, and 88.54%, respectively. On the whole, the classification accuracy of the feature selection algorithm in this work was much higher than that of other items. In addition, from the classification ability and distribution of asymmetric features, it can be seen that this asymmetric feature had a more significant consistent diagnostic role in clinical practice.

Computed tomography and clinical parameters predict intracerebral hemorrhage expansion.

ABSTRACT: This study aimed to evaluate the association of imaging signs, and to establish a predictive model through selecting highly relevant imaging signs in combination with clinical parameters for hematoma expansion.Intracerebral Hemorrhage (ICH) patients who received 2 consecutive noncontrast computed tomography scans were examined and recruited through January 2014 to December 2020. Demographic information and clinical characteristics were collected. Two experienced radiologists reviewed baseline noncontrast computed tomography images to assess the imaging characteristics. Correlation analysis was analyzed with Pearson and Spearman correlation tests. The association between clinical and imaging predictors with hematoma expansion was evaluated in multivariate models. Receiver operating characteristic (ROC) curve analysis was adopted to evaluate predictive performance.A total of 232 ICH patients, with mean age of 59.73 years, and 31% of female were included, among which, 32 patients occurred with hematoma expansion. For sex, ICH density, low density in hematoma, the midline shift, and Glasgow Coma Scale score, liquid level, H-tra, edema Cor, H Volume, time from onset to examination, there were significant differences between the 2 groups. As for imaging signs, only blend sign showed a significant difference, that patients with blend sign had a higher incidence of ICH expansion. The logistic analysis found that radiation attenuation, liquid level, the midline shift, Glasgow Coma Scale score, history of ischemic stroke, and smoking could predict the occurrence of ICH expansion.In summary, the model combined radiological characteristics with clinical indicators showed considerable predictive performance. Further validation is needed to verify the findings and help transfer to clinical practice.

Application of Magnetic Resonance DTI Technique in Evaluating the Effect of Postoperative Exercise Rehabilitation.

Magnetic resonance diffusion tensor imaging (DTI) is a new kind of magnetic resonance imaging technology. Its imaging principle is to distinguish different pathological tissues according to the movement of water molecules, which is higher than regular magnetic resonance diffusion-weighted imaging. Magnetic resonance diffusion tensor imaging has exact utility price in medical analysis and sickness evaluation. However, there are few researches on the utility of diffusion tensor imaging in the rehabilitation comparison of patients. This paper explores the utility of magnetic resonance DTI science in evaluating the impact of postoperative patients' exercising rehabilitation. Taking stroke patients as an example, through giving patients rehabilitation training method, using magnetic resonance DTI technology, the motor function rehabilitation of patients was evaluated, and FA changes of the affected side and healthy side and Fugl-Meyer score of two groups of patients before and after rehabilitation were observed. The software outcomes exhibit that, in the contrast of rehabilitation therapy impact of motor feature in sufferers with cerebral infarction, the use of magnetic resonance DTI technological know-how gives a foundation for clinicians to deeply apprehend the CST involvement of patients, which helps to scientifically evaluate the effect and quality of limb motor rehabilitation training of patients and provides a basis for disease treatment.

[Seasonal variation of phytoplankton community in Xiaojiang backwater area during the preliminary operation stage of the Three Gorges Reservoir].

According to one year's continuous observation on algae in the Xiaojiang backwater area in Three Gorges Reservoir, our group analyzed algae community and its succession in the Xiaojiang backwater area at the beginning of the function of the reservoir. The algae cell density and biomass are the highest in spring and the lowest in winter. The maximal value of cell density is 421.64 x 10(3) cells x L(-1), and the minimal value is 2.06 x 10(5) cells x L(-1); and maximal value of biomass is 39,231.84 microg x L(-1), and the minimal value is 226.17 microg x L(-1). From May 2007 to May 2008, there are 7 phylum, 101 category, 262 genus appeared in the Xiaojiang backwater area, in which 51 categories are Chlorophyta accounting for 50.5%, 22 categories are Bacillariophyta accounting for 21.8%, 18 categories are Cyanophyta accounting for 17.8%, and 4 categories are Dinophyta, 2 categories are Cryptophyta, 3 categories are Euglenophyta, 1 category is Xanthophyta and others. Cryptomonas, Chlorella, Cyclotella, Scenedesmus, Oocystis, Chlamydomonas, Schroederia, Aulacoseira, Stephanodiscus and Fragilaria are familiar categories in the Xiaojiang backwater area. Asterionnella, Aulacoseira, Coelastrunm, Chlorella, Scenedesmus, Aclinastnrum, Dictyosphaerium, Anabaena, Aphanizomenon, Merismopedia, Ceratium, Peridinium and Cryptomonas are the preponderant categories in the Xiaojiang backwater area.

[Seasonal variation of cyanobacteria and its potential relationship with key environmental factors in Xiaojiang backwater area, Three Gorges Reservoir].

Eutrophication and algal blooms occurred in the backwater areas of tributaries in Three Gorges Reservoir were the hot ecological issues in recent years. A one year field survey on cyanobacteria was put forward from May, 2007 to May, 2008 in Xiaojiang backwater area. 15 genera and 40 species of cyanophyta were detected. Mean value of cyanobacterial cell density was (23.50 +/- 10.30) x 10(5) cells x L(-1), i.e., 24.1% in the algal community, while the biomass was (768.70 +/- 287.40) microg x L(-1) which was 8.9% in the total biomass of algal community. Seasonal variation of cyanobacteria was apparent. Generally, cyanobacteria bloom occurred during late spring and early summer. Its abundance decreased after summer and reached the minimum level in winter. Anabaena, Aphanizomenon, Microcystis, Merismopedia and Phormidium were the common cynaobacterial genera. The cumulated cell density of these five genera of cyanobacteria above accounted up to 79.1% in the total cell density of cyanobacteria, while that of biomass accounted up to 77.6%. Spearman correlation analysis among the cell density, biomass as well as the key environmental factors indicated that utilization of inorganic nitrogen and phosphorus for anabolism was evident; however, nitrate would be the major nitrogen source for cyanobacteria. Moreover, increase of temperature and decrease of depth of euphotic zone had a significant effect on the abundance of cyanobacteria. According to co-analysis of hydrology and rainfall data in the whole year circle, it was found that nitrogen and phosphorus were input by the heavy rain and surface runoff with inorganic sediments, offering the enriched nutrients in water column. Meanwhile, turbidity increased by the inorganic suspended sediments decreased the depth of euphotic zone. The physiological advantage of cyanobacteria in low light and high turbidity environment might be the cause of cyanobacteria bloom in Xiaojiang backwater area.