OBJECTIVE: To review the research advance of the preparation and characteristics of small intestinal submucosa(SIS). METHODS: Recent original articles related to such aspects of small intestinal submucosa were reviewed extensively. RESULTS: Small intestinal submucosa was an easily obtained biomaterial. SIS was a bio-absorbable and degradable material. SIS had tissue specific regeneration properties. CONCLUSION: SIS is a suitable bio-derived material for tissue engineering of blood vessel, muscle tendon, urinary bladder and abdomen.
ObjectiveTo explore the application of cardiac enhanced MRI in acute myocardial infarction with normal result of coronary angiography. MethodsOn October 18, 2013, a male patient underwent coronary angiography under the local anesthesia. Mild coronary stenosis both in left and right side were found in the surgery, but the results of dynamic cardiogram and myocardial markers were abnormal, which accorded with the clinical procedure of myocardial infarction. The patient underwent cardiac enhanced MRI at the 6th day and was finally diagnosed as acute myocardial infarction. We reviewed the database to find out the significance of cardiac enhanced MRI in diagnosis of acute myocardial infarction. ResultsAlthough coronary angiography was the gold standard for the diagnosis of coronary diseases, it had limitations in the diagnosis of coronary eccentric stenosis, branch vascular stenosis and coronary spasm. Cardiac enhanced MRI had the advantages of accurate measurement of the attenuation of myocardium and exhibition of functional changes of ischemic myocardium. ConclusionCardiac enhanced MRI is important for the diagnosis of myocardial infarction with normal result of coronary angiography.
ObjectiveTo explore the echocardiography characteristics of aortic valve disease (AVD) among different ethnic groups in Xinjiang.MethodsThe data of a large sample (n=130 358) of different ethnic groups in Xinjiang based on the results of echocardiography were analyzed between January 2011 and December 2016, and the echocardiography characteristics of AVD among the Han nationality and different ethnic minorities in Xinjiang were summarized.ResultsThe study recruited 130 358 patients, involving Han nationality (58.49%) and 33 ethnic minorities. The ethnic minorities included the Uygur (27.42%), Kazak (7.47%), Hui nationality (3.48%) and other minorities (3.13%). Apart from Uygur, Kazak and Hui nationality, no description was given due to the small sample sizes of other minorities (3.13%). In the total study population, the prevalence of aortic valve stenosis (AS) was 0.44%, and the prevalence of severe AS was 0.10%; the prevalence of aortic valve regurgitation (AR) was 0.37%, and the prevalence of severe AR was 0.02%; the prevalence of aortic valve calcification (AVC) was 6.51%, and the highest AVC prevalence existed in ≥75 years old age group (24.45%); the prevalence of bicuspid aortic valve (BAV) was 0.54%, and the highest BAV prevalence existed in 18-44 years old age group (0.86%). Among different ethnic groups, the Uygur had the highest prevalence in terms of AS (0.60%), AR (0.63%) and BAV (0.88%), while the Han had the lowest prevalence in terms of AS (0.37%) and AR (0.24%), but the highest AVC prevalence existed in the Han nationality (7.83%). The etiology of AVD showed that the degenerative valve changes was the main cause of AS with the largest proportion of 61.97%. While the aorta root diseases (35.97%) and BAV (22.87%) were the main etiology of AR.ConclusionsIn Xinjiang the overall prevalence of AVD is low. In the elderly population, the Uygur, Kazak and Hui nationality have the higher AS prevalence than the Han nationality does. Different ethnic groups have different AVD characteristics based on the echocardiography. In the Uygur group, AVD presents the younger age of onset; the prevalence of BAV is the highest in the Uygur population, while the lowest in the Hui nationality.
ObjectivesTo analyze epidemiological characteristics of leprosy in Sichuan province from 2000 to 2017.MethodsCase data of all new leprosy patients in Sichuan province from 2000 to 2017 were collected. A retrospective analysis of its epidemiological characteristics was performed by using SPSS 19.0 software.ResultsA total of 3 208 cases of leprosy were detected during 2000 to 2017, of whom 2 197 (71.28%) were male, 885 (28.72%) were female. The younger cases whose ages were less than 14 were 82 (2.66%), and the cases with grade 2 disabilities were 614 (19.92%). The mean age of male was older than female (41.64±14.26 vs. 38.89±15.12 years, P<0.05). The grade 2 disability rate of male was significantly higher than that of female (20.94% vs. 17.40%, P<0.05). Self-report was the most common method of discovery. But the ratio of male who were detected through contact examination was significantly lower than that detected through dermatological clinic, self-report, clues check and report (the ratio of male to female was 1.57, 2.38, 2.88, 2.48, 2.37, respectively, P<0.05).ConclusionsThe case detection of leprosy declines annually in Sichuan province from 2000 to 2017, especially in high-endemic area and male patients. Female patients are younger than male patients when they are detected. The grade 2 disability situation of male patients is significantly more serious than that of female patients. Self-report is the most common way of discovery, while women are more passive.
ObjectiveTo construct a prediction model of diabetics distal symmetric polyneuropathy (DSPN) based on neural network algorithm and the characteristic data of traditional Chinese medicine and Western medicine. MethodsFrom the inpatients with diabetes in the First Affiliated Hospital of Anhui University of Chinese Medicine from 2017 to 2022, 4 071 cases with complete data were selected. The early warning model of DSPN was established by using neural network, and 49 indicators including general epidemiological data, laboratory examination, signs and symptoms of traditional Chinese medicine were included to analyze the potential risk factors of DSPN, and the weight values of variable features were sorted. Validation was performed using ten-fold crossover, and the model was measured by accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and AUC value. ResultsThe mean duration of diabetes in the DSPN group was about 4 years longer than that in the non-DSPN group (P<0.001). Compared with non-DSPN patients, DSPN patients had a significantly higher proportion of Chinese medicine symptoms and signs such as numbness of limb, limb pain, dizziness and palpitations, fatigue, thirst with desire to drink, dry mouth and throat, blurred vision, frequent urination, slow reaction, dull complexion, purple tongue, thready pulse and hesitant pulse (P<0.001). In this study, the DSPN neural network prediction model was established by integrating traditional Chinese and Western medicine feature data. The AUC of the model was 0.945 3, the accuracy was 87.68%, the sensitivity was 73.9%, the specificity was 92.7%, the positive predictive value was 78.7%, and the negative predictive value was 90.72%. ConclusionThe fusion of Chinese and Western medicine characteristic data has great clinical value for early diagnosis, and the established model has high accuracy and diagnostic efficacy, which can provide practical tools for DSPN screening and diagnosis in diabetic population.
Objective To investigate biological markers that differentiate states during various seizure periods of childhood absence epilepsy (CAE) by examining the spatiotemporal dynamics of magnetoencephalographic (MEG) signals from Default Mode Network (DMN) nodes, revealing the neurophysiological mechanisms underlying changes in consciousness during CAE seizures. MethodsThirty-six drug-native children diagnosed with CAE were recruited. The interictal data, ictal data of CAE children were collected using a CTF-225 channel MEG system. Conduct temporal homogeneity partitioning for all seizure period data, co-registering 14 distinct seizure states. Identify 12 brain regions associated with the default mode network (DMN) as regions of interest (ROI); employ minimum norm estimation in conjunction with the Welch method to compute the power spectral density (PSD) of the ROI; conduct differential analysis on the relative PSD values; and use a random forest model to identify significant PSD features that differentiate between states of epilepsy. ResultsPower changes in DMN-related brain regions across various frequency bands show significant synchrony. During a seizure, the power in the δ band rapidly increases at the onset and quickly decreases at the end. Meanwhile, the power in the θ-γ2 bands decreases at the beginning and gradually recovers after the seizure. During the O+2 phase following seizure onset, the power in the β band peaks briefly before rapidly declining. The medial frontal cortex has lower power in the δ frequency band during seizures compared to other DMN brain regions, but higher power in the α frequency band. The random forest model's feature importance analysis reveals that the precuneus, lateral temporal lobe and medial temporal lobe play important roles in identifying seizure states. Power changes in the precuneus in the α and δ frequency bands improve the model's classification accuracy. ConclusionsThis study investigated the dynamic spatiotemporal characteristics of the DMN during CAE seizures, revealing the typical manifestations of power changes in specific brain regions and frequency bands at the onset, maintenance, and termination of seizures. It was discovered that power of the precuneus can act as an important feature to distinguish between different stages of CAE seizures. These findings shed new light on the pathophysiological mechanisms underlying changes in consciousness states in CAE.