Objective:To explore whether the preoperative use of statins in patients with coronary atherosclerotic heart disease (CAD) undergoing off-pump coronary artery bypass grafting (OPCABG) can effectively reduce the risk of postoperative new-onset cerebral infarction.Methods:A retrospective study was conducted on 7 407 CAD patients who underwent OPCABG in the Department of Cardiac Surgery, Beijing Anzhen Hospital, from February 2019 to March 2023. Among them, 5 559 were males and 1 848 were females, with an age range of 26 to 92 years and a median age of 63 years. Hypertension (5 536 cases, 74.7%) was the most common comorbidity, followed by hyperlipidemia (4 261 cases, 57.5%) and type 2 diabetes mellitus (2 941 cases, 39.7%). The patients were divided into the statin group(5 586 cases, 75.4%)and the control group(1 821 cases, 24.6%)based on whether statins were used preoperatively. The inverse probability treatment weighting (IPTW) method was applied to adjust other risk factors, and the odds ratio ( OR) and 95% confidence interval ( CI) were reported to assess the difference in the risk of postoperative new-onset cerebral infarction between the two groups. Results:A total of 346 patients (4.7%) developed new-onset cerebral infarction after OPCABG. After IPTW adjustment, the incidence of cerebral infarction was significantly lower in the statin group compared to the control group (4.3% vs. 6.8%, OR=0.62, 95% CI: 0.53-0.72, P<0.001), and the incidence of postoperative acute kidney injury (AKI) was also lower in the statin group (10.4% vs. 13.4%, OR=0.75, 95% CI: 0.67-0.94, P<0.001). No statistically significant differences were observed between the two groups in terms of postoperative liver injury, postoperative atrial fibrillation, re-thoracotomy rate, use of extracorporeal membrane oxygenation and intra-aortic balloon pump, or in-hospital mortality ( P>0.05). Regardless of whether patients had preoperative hyperlipidemia or whether their low-density lipoprotein cholesterol (LDL-C) levels were within the target range, preoperative statin use effectively reduced the risk of postoperative new-onset cerebral infarction( P<0.05). Conclusion:Statin therapy can significantly reduce the risk of postoperative new-onset cerebral infarction after OPCABG. Preoperative statin use is effective in lowering this risk, regardless of the presence of preoperative hyperlipidemia or whether baseline LDL-C levels are within the target range.

Post-traumatic stress disorder (PTSD) presents with complex and diverse clinical manifestations, making accurate and objective diagnosis challenging when relying solely on clinical assessments. Therefore, there is an urgent need to develop reliable and objective auxiliary diagnostic models to provide effective diagnosis for PTSD patients. Currently, the application of graph neural networks for representing PTSD is limited by the expressiveness of existing models, which does not yield optimal classification results. To address this, we proposed a multi-graph multi-kernel graph convolutional network (MK-GCN) model for classifying PTSD data. First, we constructed functional connectivity matrices at different scales for the same subjects using different atlases, followed by employing the k-nearest neighbors algorithm to build the graphs. Second, we introduced the MK-GCN methodology to enhance the feature extraction capability of brain structures at different scales for the same subjects. Finally, we classified the extracted features from multiple scales and utilized graph class activation mapping to identify the top 10 brain regions contributing to classification. Experimental results on seismic-induced PTSD data demonstrated that our model achieved an accuracy of 84.75%, a specificity of 84.02%, and an AUC of 85% in the classification task distinguishing between PTSD patients and non-affected subjects. The findings provide robust evidence for the auxiliary diagnosis of PTSD following earthquakes and hold promise for reliably identifying specific brain regions in other PTSD diagnostic contexts, offering valuable references for clinicians.
Humans ; Stress Disorders, Post-Traumatic/diagnostic imaging* ; Neural Networks, Computer ; Algorithms ; Brain/diagnostic imaging* ; Magnetic Resonance Imaging

The extraction of neuroimaging features of migraine patients and the design of identification models are of great significance for the auxiliary diagnosis of related diseases. Compared with the commonly used image features, this study directly uses time-series signals to characterize the functional state of the brain in migraine patients and healthy controls, which can effectively utilize the temporal information and reduce the computational effort of classification model training. Firstly, Group Independent Component Analysis and Dictionary Learning were used to segment different brain areas for small-sample groups and then the regional average time-series signals were extracted. Next, the extracted time series were divided equally into multiple subseries to expand the model input sample. Finally, the time series were modeled using a bi-directional long-short term memory network to learn the pre-and-post temporal information within each time series to characterize the periodic brain state changes to improve the diagnostic accuracy of migraine. The results showed that the classification accuracy of migraine patients and healthy controls was 96.94%, the area under the curve was 0.98, and the computation time was relatively shorter. The experiments indicate that the method in this paper has strong applicability, and the combination of time-series feature extraction and bi-directional long-short term memory network model can be better used for the classification and diagnosis of migraine. This work provides a new idea for the lightweight diagnostic model based on small-sample neuroimaging data, and contributes to the exploration of the neural discrimination mechanism of related diseases.
Humans ; Time Factors ; Migraine Disorders/diagnostic imaging* ; Magnetic Resonance Imaging ; Brain/diagnostic imaging* ; Neuroimaging

Objective To investigate the clinical manifestations,imaging features,molecular genetic characteristics and possible pathogenic mechanisms of hereditary cerebral small vessel disease (CSVD) caused by heterozygous mutation of HtrA serine protease-1 (HTRA1) gene.Methods The clinical data of a Chinese Han family with CSVD carrying a heterozygous mutation of HTRA 1 gene,which came from the Department of Neurology,Henan Provincial People's Hospital in March 2018,were analyzed retrospectively.The clinical and radiographic features were summarized.Several high-throughput whole exon high-throughput sequencing was used to capture the mutation sites and the Sanger sequencing was used to validate the results.The family diagram was drawn and the 3D model construction and mutation function prediction were performed using silico tools.The relevant literature was reviewed and the pathogenesis was explored.Results The pedigree map showed that the family had an autosomal dominant inheritance pattern.Three generations of the family were investigated,and three family members in the same generation suffered from the disease.The first symptom of the proband was diplopia at the age of 39,accompanied by recurrent stroke,cognitive impairment and mood disorders,without alopecia.Head magnetic resonance imaging revealed bilateral diffuse,symmetric lesions,multiple lacunar infarcts,perivascular space,and microbleeds.The elder sister of the proband developed symptoms of left limb weakness at the age of 46,whose other clinical and imaging features were similar to those of the proband.The proband's mother died at the age of 59 due to repeated strokes.Whole exon sequencing indicated heterozygous missense mutation at c.821G＞A locus of HTRA1 gene in the proband and her 4th elder sibling,which was a new pathogenic mutation after consulting several mutation sites of databases.Function prediction suggested pathogenicity.Conclusions The heterozygous mutation of c.821G＞A in HTRA1 gene may lead to autosomal dominant CVSD.This genetic type should be given clinical attention.

In recent years, with the rapid development of machine learning techniques,the deep learning algorithm has been widely used in one-dimensional physiological signal processing. In this paper we used electroencephalography (EEG) signals based on deep belief network (DBN) model in open source frameworks of deep learning to identify emotional state (positive, negative and neutrals), then the results of DBN were compared with support vector machine (SVM). The EEG signals were collected from the subjects who were under different emotional stimuli, and DBN and SVM were adopted to identify the EEG signals with changes of different characteristics and different frequency bands. We found that the average accuracy of differential entropy (DE) feature by DBN is 89.12%±6.54%, which has a better performance than previous research based on the same data set. At the same time, the classification effects of DBN are better than the results from traditional SVM (the average classification accuracy of 84.2%±9.24%) and its accuracy and stability have a better trend. In three experiments with different time points, single subject can achieve the consistent results of classification by using DBN (the mean standard deviation is1.44%), and the experimental results show that the system has steady performance and good repeatability. According to our research, the characteristic of DE has a better classification result than other characteristics. Furthermore, the Beta band and the Gamma band in the emotional recognition model have higher classification accuracy. To sum up, the performances of classifiers have a promotion by using the deep learning algorithm, which has a reference for establishing a more accurate system of emotional recognition. Meanwhile, we can trace through the results of recognition to find out the brain regions and frequency band that are related to the emotions, which can help us to understand the emotional mechanism better. This study has a high academic value and practical significance, so further investigation still needs to be done.

Transoral incisionless fundoplication (TIF)has been used for the treatment of gastroesophageal reflux disease (GERD)via repairing the gastroesophageal junction valve. Both the clinical report and control study confirmed that,with the prerequisite of rational selection of patients,TIF as an emerging technology can effectively cure or improve the clinical symptoms of GERD,esophagitis,and level of esophageal acid exposure. Compared with the traditional treatment,TIF is more effective,safe and having lasting effect for the rationally selected GERD patients. This article reviewed advances in study on TIF for the treatment of GERD.

Objective To analyze the clinical, imaging characteristics and NOTCH3 mutations of cerebral autosomal dominant arteriopathy with the subcortical infarcts and leukoencephalopathy (CADASIL) in Henan, China.Methods CADASIL patients diagnosed by gene or biopsy in People′s Hospital of Zhengzhou University between 2012-2016 were recruited.Clinical and imaging features of these patients were analyzed retrospectively.The distribution of NOTCH3 gene mutations hotspots was described in Henan region at the same time.Results There were 37 patients from 19 families who were diagnosed as CADASIL by genetic testing or biopsy, 27 of whom had symptoms of CADASIL.Two families were confirmed by pathological examination and 17 by genetic testing.Of these 17 families, 13 mutations were found.Mutations in exon 11 were found in eight families, in exon 4 were detected in four families, and in exon 13 were found in two families.Mutation in exons 3, 8 and 20 was detected in one family respectively.Most patients presented with stroke and several presented with cognitive decline.Twelve patients had been attacked by risk factors.Magnetic resonance imaging (MRI) was performed on 22 patients.White-matter lesions were distributed in brain stem, basal ganglia, subcortical, temporal pole, external capsule.There were 19 patients with white-matter lesions in temporal pole and seven in capsula externa, showed as a high signal in T2WI.Conclusions CADASIL patients can be associated with risk factors.T2WI hyperintensities in the anterior temporal lobe were more common than that in the capsular external.Exon 11 and exon 4 were the hotspots for the NOTCH3 mutation in Henan patients.

Objective To analyze the clinical presentation , the mutation of the pathogenic genes and imaging features in a Chinese Han early-onset Alzheimer's disease pedigree.Methods A pedigree of Alzheimer's disease was collected.The DNA sequence of presenilin 1 (PSEN1), presenilin 2, micro-tubule associated protein tau ,β-amyloid precursor protein gene was analyzed , the clinical presentation , results of accessory examination , neuropsychological evaluation of the proband were investigated and the point mutations of some members of the family , 50 sporadic Alzheimer's disease patients , 50 normal controls were verified.Results The proband of the family appeared as language impairment , memory loss, personality change, repeated language, visuospatial impairment, mental and behavior disorder.The gene detection showed p.L226R mutation in the condon 226 in the exon 7 of PSEN1 gene of the proband and five other family members (Ⅲ1 ,Ⅲ2 ,Ⅲ4 ,Ⅲ6 ,Ⅲ7 ).The mother of the proband had the suspicious symptoms , and the sister and the brother of the proband had the similiar symptoms with the proband , all of whom died.Fifty sporadic Alzheimer'disease patients and 50 unrelated normal subjects did not have the mutation .The computed tomographic angiography showed that the brain blood vessels were normal and 18 F-fludeoxyglucose positron emission tomography (18F-FDG-PET) showed brain atrophy and hypometabolism in frontotemporal regions, parietal regions, hippocampal areas, however, the MRI, MRA and 18F-FDG-PET of the two mutation carriers (Ⅲ6 ,Ⅲ7 ) were all normal.Conclusion We reported a novel mutation in an early-onset Alzheimer's disease family presented as language impairment in the early stage of the disease , the p.L226R mutation of PSEN1, which may be a pathogenic mutation to cause the family's dementia.

Objective To observe any differences in the spontaneous activity of brain areas between Manda rin-speaking and Uyghur-speaking aphasics at rest and explore the reasons.Methods Functional magnetic resonance imaging was used to scan the brains of 15 Mandarin-speaking and 12 Uyghur-speaking subjects.Statistical parametric maps of the active brain areas were prepared and low-frequency amplitude (ALEF) indexes were calculated.Results While at rest the ALEF indexes of the Mandarin-speakers were,on averge,larger than those of the Uyghur-speakers.The difference was primarily in the ALEF of the right thalamus.Conclusion At rest,the average ALEF of the right thalamus of aphasic Mandarin-speakers is larger than that of Uyghur-speakers.It might be that after a left-brain stroke the Mandarin-speakers form new language circuits with the help of the right thalamus which compensates for the loss of language function.

Wearable devices bring us an innovative human-computer interaction which plays an irreplaceable role in enhancing the users' ability in environmental awareness, acquirements of their own state and "ubiquitous" computing power. Since 2013, wearable devices have quickly appeared around us. In this article we classify most of the wearable devices which have been appeared in the markets or reported in the literature according to their functions and the positions where they are worn. Furthermore, we review the technologies related to wearable devices, such as sensing technology, wireless communication, power manager, display technology and big data. At last, we analyze the challenges which the wearable devices will face in near future, and look forward to development trends of wearable devices.
Biomedical Engineering ; Biomedical Technology ; Clothing ; Humans ; Monitoring, Ambulatory ; instrumentation ; Wireless Technology