[Automatic epilepsy detection with an attention-based multiscale residual network].

The deep learning-based automatic detection of epilepsy electroencephalogram (EEG), which can avoid the artificial influence, has attracted much attention, and its effectiveness mainly depends on the deep neural network model. In this paper, an attention-based multi-scale residual network (AMSRN) was proposed in consideration of the multiscale, spatio-temporal characteristics of epilepsy EEG and the information flow among channels, and it was combined with multiscale principal component analysis (MSPCA) to realize the automatic epilepsy detection. Firstly, MSPCA was used for noise reduction and feature enhancement of original epilepsy EEG. Then, we designed the structure and parameters of AMSRN. Among them, the attention module (AM), multiscale convolutional module (MCM), spatio-temporal feature extraction module (STFEM) and classification module (CM) were applied successively to signal reexpression with attention weighted mechanism as well as extraction, fusion and classification for multiscale and spatio-temporal features. Based on the Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) public dataset, the AMSRN model achieved good results in sensitivity (98.56%), F1 score (98.35%), accuracy (98.41%) and precision (98.43%). The results show that AMSRN can make good use of brain network information flow caused by seizures to enhance the difference among channels, and effectively capture the multiscale and spatio-temporal features of EEG to improve the performance of epilepsy detection.

CLDN1 silencing suppresses the proliferation and migration of airway smooth muscle cells by modulating MMP14.

Airway remodeling is an important pathologic factor in the progression of asthma. Abnormal proliferation and migration of airway smooth muscle cells (ASMCs) are important pathologic mechanisms in severe asthma. In the current study, claudin-1 (CLDN1) was identified as an asthma-related gene and was upregulated in ASMCs stimulated with platelet-derived growth factor BB (PDGF-BB). Cell counting kit-8 and EdU assays were used to evaluate cell proliferation, and transwell assay was carried out to analyze cell migration and invasion. The levels of inflammatory factors were detected using enzyme-linked immunosorbent assay. The results showed that CLDN1 knockdown inhibited the proliferation, migration, invasion, and inflammation of ASMCs treated with PDGF-BB, whereas overexpression of CLDN1 exhibited the opposite effects. Protein-protein interaction assay and co-immunoprecipitation revealed that CLDN1 directly interacted with matrix metalloproteinase 14 (MMP14). CLDN1 positively regulated MMP14 expression in asthma, and MMP14 overexpression reversed cell proliferation, migration, invasion, and inflammation induced by silenced CLDN1. Taken together, CLDN1 promotes PDGF-BB-induced cell proliferation, migration, invasion, and inflammatory responses of ASMCs by upregulating MMP14 expression, suggesting a potential role for CLDN1 in airway remodeling in asthma.

Decoding motor imagery with a simplified distributed dipoles model at source level.

Motor imagery (MI) based brain computer interface significantly oriented the development of neuro-rehabilitation, and the crucial issue is how to accurately detect the changes of cerebral cortex for MI decoding. The brain activity can be calculated based on the head model and observed scalp EEG, providing insights regarding cortical dynamics by using equivalent current dipoles with high spatial and temporal resolution. Now, all the dipoles within entire cortex or partial regions of interest are directly applied to data representation, this may make the key information weakened or lost, and it is worth studying how to choose the most important from numerous dipoles. In this paper, we devote to building a simplified distributed dipoles model (SDDM), which is combined with convolutional neural network (CNN), generating a MI decoding method at source level (called SDDM-CNN). First, all channels of raw MI-EEG signals are subdivided by a series of bandpass filters with width of 1 Hz, the average energies associated with any sub-band signals are calculated and ranked in a descending order to screen the top n sub-bands; then, the MI-EEG signals over each selected sub-band are mapped into source space by using EEG source imaging technology, and for each scout of neuroanatomical Desikan-Killiany partition, a centered dipole is selected as the most relevant dipole and put together to build a SDDM to reflect the neuroelectric activity of entire cerebral cortex; finally, the 4 dimensional (4D) magnitude matrix is constructed for each SDDM and fused into a novel data representation, which is further input to a well-designed 3DCNN with n parallel branches (nB3DCNN) to extract and classify the comprehensive features from time-frequency-space dimensions. Experiments are carried out on three public datasets, and the average ten-fold CV decoding accuracies achieve 95.09%, 97.98% and 94.53% respectively, and the statistical analysis is fulfilled by standard deviation, kappa value and confusion matrix. Experiment results suggest that it is beneficial to pick out the most sensitive sub-bands in sensor domain, and SDDM can sufficiently describe the dynamic changing of entire cortex, improving decoding performance while greatly reducing number of source signals. Also, nB3DCNN is capable of exploring spatial-temporal features from multi sub-bands.

A dual alignment-based multi-source domain adaptation framework for motor imagery EEG classification.

Domain adaptation, as an important branch of transfer learning, can be applied to cope with data insufficiency and high subject variabilities in motor imagery electroencephalogram (MI-EEG) based brain-computer interfaces. The existing methods generally focus on aligning data and feature distribution; however, aligning each source domain with the informative samples of the target domain and seeking the most appropriate source domains to enhance the classification effect has not been considered. In this paper, we propose a dual alignment-based multi-source domain adaptation framework, denoted DAMSDAF. Based on continuous wavelet transform, all channels of MI-EEG signals are converted respectively and the generated time-frequency spectrum images are stitched to construct multi-source domains and target domain. Then, the informative samples close to the decision boundary are found in the target domain by using entropy, and they are employed to align and reassign each source domain with normalized mutual information. Furthermore, a multi-branch deep network (MBDN) is designed, and the maximum mean discrepancy is embedded in each branch to realign the specific feature distribution. Each branch is separately trained by an aligned source domain, and all the single branch transfer accuracies are arranged in descending order and utilized for weighted prediction of MBDN. Therefore, the most suitable number of source domains with top weights can be automatically determined. Extensive experiments are conducted based on 3 public MI-EEG datasets. DAMSDAF achieves the classification accuracies of 92.56%, 69.45% and 89.57%, and the statistical analysis is performed by the kappa value and t-test. Experimental results show that DAMSDAF significantly improves the transfer effects compared to the present methods, indicating that dual alignment can sufficiently use the different weighted samples and even source domains at different levels as well as realizing optimal selection of multi-source domains.

Influence of surfactant on the permeability at different positions of a leaching column.

To solve the problems of poor permeability and low leaching rate in ore heap leaching, solid surface physical chemistry, seepage mechanics theory for porous media, CT scanning and SEM were used to carry out column leaching tests with a homemade segmented removable plexiglass column; the variation law for the permeability coefficients of each segment of the leaching column before and after leaching was analyzed. The experimental results showed that there was little difference in the permeability coefficient of ore at different heights before leaching. After leaching, the permeability coefficients were unevenly distributed along the column height, and the lowest value was located at the bottom of the leaching column. The addition of surfactant provided an obvious improvement in the permeability of the leaching column. The permeability coefficient at the bottom of the leaching column was 6% higher than that of the control group. At the same time, the addition of surfactant increased the leaching rate of ore by nearly 10%. A theoretical analysis showed that the surfactant improved the permeability of ore heaps mainly by preventing physical blockage by fine particles and inhibiting deposition of chemical products.

[Parameter transfer learning based on shallow visual geometry group network and its application in motor imagery classification].

Transfer learning is provided with potential research value and application prospect in motor imagery electroencephalography (MI-EEG)-based brain-computer interface (BCI) rehabilitation system, and the source domain classification model and transfer strategy are the two important aspects that directly affect the performance and transfer efficiency of the target domain model. Therefore, we propose a parameter transfer learning method based on shallow visual geometry group network (PTL-sVGG). First, Pearson correlation coefficient is used to screen the subjects of the source domain, and the short-time Fourier transform is performed on the MI-EEG data of each selected subject to acquire the time-frequency spectrogram images (TFSI). Then, the architecture of VGG-16 is simplified and the block design is carried out, and the modified sVGG model is pre-trained with TFSI of source domain. Furthermore, a block-based frozen-fine-tuning transfer strategy is designed to quickly find and freeze the block with the greatest contribution to sVGG model, and the remaining blocks are fine-tuned by using TFSI of target subjects to obtain the target domain classification model. Extensive experiments are conducted based on public MI-EEG datasets, the average recognition rate and Kappa value of PTL-sVGG are 94.9% and 0.898, respectively. The results show that the subjects' optimization is beneficial to improve the model performance in source domain, and the block-based transfer strategy can enhance the transfer efficiency, realizing the rapid and effective transfer of model parameters across subjects on the datasets with different number of channels. It is beneficial to reduce the calibration time of BCI system, which promote the application of BCI technology in rehabilitation engineering.

A cross-sectional study of the psychological status of 33,706 hospital workers at the late stage of the COVID-19 outbreak.

BACKGROUND: Hospital workers have been under intense psychological pressure since the COVID-19 outbreak. We analyzed the psychological status of hospital staff in the late period of the COVID-19 to provide a basis for the construction of global health care after the COVID-19 outbreak. METHODS: We used online surveys to assess participants' self-reported symptoms at the late stage of the outbreak. This study collected data on sociodemographic characteristics, epidemic-related factors, psychological status (PHQ-9, GAD-7, and PHQ-15), psychological assistance needs, perceived stress and support, PTSD symptoms (PCL-C) and suicidal and self-injurious ideation (SSI). Participants were hospital workers in all positions from 46 hospitals. Chi-square tests to compare the scales and logistic regression analysis were used to identify risk factors for PTSD and SSI. RESULTS: Among the 33,706 participants, the prevalences of depression, anxiety, somatic symptoms, PTSD symptoms, and SSI were 35.8%, 24.4%, 49.7%, 5.0%, and 1.3%, respectively. Logistic regression analysis showed that work in a general ward, attention to the epidemic, high education, work in non-first-line departments, insufficient social support, and anxiety and somatization symptoms were influencing factors of PTSD (P<0.05). The independent risk factors for SSI were female gender; psychological assistance needs; contact with severe COVID-19 patients; high stress at work; single or divorced marital status; insufficient social support; and depression, anxiety or PTSD symptoms (P<0.05). LIMITATIONS: This cross-sectional study could not reveal causality, and voluntary participation may have led to selection bias. The longer longitudinal studies are needed to determine the long-term psychological impact. CONCLUSION: This COVID-19 pandemic had a sustained, strong psychological impact on hospital workers, and hospital workers with PTSD symptoms were a high-risk group for SSI in the later period of the epidemic. Continuous attention and positive psychological intervention are of great significance for specific populations.

The status and advances of intravitreal injection in treatment of noninfectious uveitic macular edema / 中华眼底病杂志

Noninfectious uveitic macular edema (NIU-ME) is a major cause of visual impairment in patients with uveitis. Intravitreal route can control inflammation rapidly, reduce macular edema, and improve vision with relatively lower doses of the drug. Currently, several intravitreal injection drugs have been used for the treatment of NIU-ME. Cataract and elevated intraocular pressure are the major complications. Due to its efficacy and safety, intravitreal drugs have gradually become an effective alternative to systemic treatment, especially in patients with unilateral disease. However, more studies are needed on drug selection, timing of injection and combination therapy in clinical practice. There are various treatments for NIU-ME, and the ultimate treatment should be individualized based on the severity of the disease, the risk/benefit ratio of each therapy, and the patient's tolerance.

Parameter transfer learning based on shallow visual geometry group network and its application in motor imagery classification / 生物医学工程学杂志

Transfer learning is provided with potential research value and application prospect in motor imagery electroencephalography (MI-EEG)-based brain-computer interface (BCI) rehabilitation system, and the source domain classification model and transfer strategy are the two important aspects that directly affect the performance and transfer efficiency of the target domain model. Therefore, we propose a parameter transfer learning method based on shallow visual geometry group network (PTL-sVGG). First, Pearson correlation coefficient is used to screen the subjects of the source domain, and the short-time Fourier transform is performed on the MI-EEG data of each selected subject to acquire the time-frequency spectrogram images (TFSI). Then, the architecture of VGG-16 is simplified and the block design is carried out, and the modified sVGG model is pre-trained with TFSI of source domain. Furthermore, a block-based frozen-fine-tuning transfer strategy is designed to quickly find and freeze the block with the greatest contribution to sVGG model, and the remaining blocks are fine-tuned by using TFSI of target subjects to obtain the target domain classification model. Extensive experiments are conducted based on public MI-EEG datasets, the average recognition rate and Kappa value of PTL-sVGG are 94.9% and 0.898, respectively. The results show that the subjects' optimization is beneficial to improve the model performance in source domain, and the block-based transfer strategy can enhance the transfer efficiency, realizing the rapid and effective transfer of model parameters across subjects on the datasets with different number of channels. It is beneficial to reduce the calibration time of BCI system, which promote the application of BCI technology in rehabilitation engineering.

Automatic feature extraction and fusion recognition of motor imagery EEG using multilevel multiscale CNN.

A motor imagery EEG (MI-EEG) signal is often selected as the driving signal in an active brain computer interface (BCI) system, and it has been a popular field to recognize MI-EEG images via convolutional neural network (CNN), which poses a potential problem for maintaining the integrity of the time-frequency-space information in MI-EEG images and exploring the feature fusion mechanism in the CNN. However, information is excessively compressed in the present MI-EEG image, and the sequential CNN is unfavorable for the comprehensive utilization of local features. In this paper, a multidimensional MI-EEG imaging method is proposed, which is based on time-frequency analysis and the Clough-Tocher (CT) interpolation algorithm. The time-frequency matrix of each electrode is generated via continuous wavelet transform (WT), and the relevant section of frequency is extracted and divided into nine submatrices, the longitudinal sums and lengths of which are calculated along the directions of frequency and time successively to produce a 3 × 3 feature matrix for each electrode. Then, feature matrix of each electrode is interpolated to coincide with their corresponding coordinates, thereby yielding a WT-based multidimensional image, called WTMI. Meanwhile, a multilevel and multiscale feature fusion convolutional neural network (MLMSFFCNN) is designed for WTMI, which has dense information, low signal-to-noise ratio, and strong spatial distribution. Extensive experiments are conducted on the BCI Competition IV 2a and 2b datasets, and accuracies of 92.95% and 97.03% are yielded based on 10-fold cross-validation, respectively, which exceed those of the state-of-the-art imaging methods. The kappa values and p values demonstrate that our method has lower class skew and error costs. The experimental results demonstrate that WTMI can fully represent the time-frequency-space features of MI-EEG and that MLMSFFCNN is beneficial for improving the collection of multiscale features and the fusion recognition of general and abstract features for WTMI.

A novel decoding method for motor imagery tasks with 4D data representation and 3D convolutional neural networks.

Objective. Motor imagery electroencephalography (MI-EEG) produces one of the most commonly used biosignals in intelligent rehabilitation systems. The newly developed 3D convolutional neural network (3DCNN) is gaining increasing attention for its ability to recognize MI tasks. The key to successful identification of movement intention is dependent on whether the data representation can faithfully reflect the cortical activity induced by MI. However, the present data representation, which is often generated from partial source signals with time-frequency analysis, contains incomplete information. Therefore, it would be beneficial to explore a new type of data representation using raw spatiotemporal dipole information as well as the possible development of a matching 3DCNN.Approach.Based on EEG source imaging and 3DCNN, a novel decoding method for identifying MI tasks is proposed, called ESICNND. MI-EEG is mapped to the cerebral cortex by the standardized low resolution electromagnetic tomography algorithm, and the optimal sampling points of the dipoles are selected as the time of interest to best reveal the difference between any two MI tasks. Then, the initial subject coordinate system is converted to a magnetic resonance imaging coordinate system, followed by dipole interpolation and volume down-sampling; the resulting 3D dipole amplitude matrices are merged at the selected sampling points to obtain 4D dipole feature matrices (4DDFMs). These matrices are augmented by sliding window technology and input into a 3DCNN with a cascading architecture of three modules (3M3DCNN) to perform the extraction and classification of comprehensive features.Main results.Experiments are carried out on two public datasets; the average ten-fold CV classification accuracies reach 88.73% and 96.25%, respectively, and the statistical analysis demonstrates outstanding consistency and stability.Significance.The 4DDFMs reveals the variation of cortical activation in a 3D spatial cube with a temporal dimension and matches the 3M3DCNN well, making full use of the high-resolution spatiotemporal information from all dipoles.

Fuzzy support vector machine with joint optimization of genetic algorithm and fuzzy c-means.

BACKGROUND: Motor imagery electroencephalogram (MI-EEG) play an important role in the field of neurorehabilitation, and a fuzzy support vector machine (FSVM) is one of the most used classifiers. Specifically, a fuzzy c-means (FCM) algorithm was used to membership calculation to deal with the classification problems with outliers or noises. However, FCM is sensitive to its initial value and easily falls into local optima. OBJECTIVE: The joint optimization of genetic algorithm (GA) and FCM is proposed to enhance robustness of fuzzy memberships to initial cluster centers, yielding an improved FSVM (GF-FSVM). METHOD: The features of each channel of MI-EEG are extracted by the improved refined composite multivariate multiscale fuzzy entropy and fused to form a feature vector for a trial. Then, GA is employed to optimize the initial cluster center of FCM, and the fuzzy membership degrees are calculated through an iterative process and further applied to classify two-class MI-EEGs. RESULTS: Extensive experiments are conducted on two publicly available datasets, the average recognition accuracies achieve 99.89% and 98.81% and the corresponding kappa values are 0.9978 and 0.9762, respectively. CONCLUSION: The optimized cluster centers of FCM via GA are almost overlapping, showing great stability, and GF-FSVM obtains higher classification accuracies and higher consistency as well.

Activation of Akt-dependent Nrf2/ARE pathway by restoration of Brg-1 remits high glucose-induced oxidative stress and ECM accumulation in podocytes.

Brahma-related gene 1 (Brg-1) is perceived as a cytoprotective protein due to its role in alleviating oxidative stress and apoptosis. Our study aimed to explore the role and mechanism of Brg-1 in high glucose (HG)-stimulated podocytes. The HG exposure downregulated Brg-1 and inactivated the protein kinase B (Akt) pathway in podocytes. Restoration of Brg-1 inhibited HG-induced viability reduction of podocytes. The HG-induced increase of reactive oxygen species and malondialdehyde levels and decrease of superoxide dismutase activity in podocytes were reversed by the Brg-1 overexpression. The Brg-1 overexpression terminated the HG-induced production of fibronectin, collagen IV, transforming growth factor-ß1, and connective tissue growth factor. In addition, the Brg-1 overexpression activated Akt-dependent nuclear factor E2-related factor 2 (Nrf2)/antioxidant response element (ARE) signaling in HG-stimulated podocytes. However, inhibition of the Akt pathway or Nrf2 silencing counteracted the protective effects of Brg-1 in HG-stimulated podocytes. In conclusion, the Brg-1 overexpression suppressed HG-induced oxidative stress and extracellular matrix accumulation by activation of Akt-dependent Nrf2/ARE signaling in podocytes.

The psychological status of 8817 hospital workers during COVID-19 Epidemic: A cross-sectional study in Chongqing.

BACKGROUND: There was an outbreak of COVID-19 towards the end of 2019 in China, which spread all over the world rapidly. The Chinese healthcare system is facing a big challenge where hospital workers are experiencing enormous psychological pressure. This study aimed to (1) investigate the psychological status of hospital workers and (2) provide references for psychological crisis intervention in the future. METHOD: An online survey was conducted to collect sociodemographic features, epidemic-related factors, results of PHQ-9, GAD-7, PHQ-15, suicidal and self-harm ideation (SSI), and the score of stress and support scales. Chi-square test, t-test, non-parametric, and logistic regression analysis were used to detect the risk factors to psychological effect and SSI. RESULTS: 8817 hospital workers participated in this online survey. The prevalence of depression, anxiety, somatic symptoms, and SSI were 30.2%, 20.7%, 46.2%, and 6.5%, respectively. Logistic regression analysis showed that female, single, Tujia minority, educational background of junior or below, designated or county hospital, need for psychological assistance before or during the epidemic, unconfident about defeating COVID-19, ignorance about the epidemic, willingness of attending parties, and poor self-rated health condition were independent factors associated with high-level depression, somatic symptom, and SSI among hospital workers (P<0.05). LIMITATIONS: This cross-sectional study cannot reveal the causality, and voluntary participation could be prone to selection bias. A modified epidemic-related stress and support scale without standardization was used. The number of hospital workers in each hospital was unavailable. CONCLUSION: There were a high level of psychological impact and SSI among hospital workers, which needed to be addressed. County hospital workers were more severe and easier to be neglected. More studies on cognitive and behavioral subsequence after a public health disaster among hospital workers are needed.

Systematic evaluation of non-suicidal self-injury in adolescent depressive patients / 中华行为医学与脑科学杂志

Non-suicidal self-injury, which is especially common in depressed adolescents, has become an important public health issue of global concern.It’s an independent risk factor for suicide, which not only seriously affects the physical and mental health of individuals, but also brings a great burden to families and society.The research status of non-suicidal self-injury in adolescent depression were systematically evaluated, aiming to improve the understanding of non-suicidal non-suicidal self-injury in adolescent depression, and provide a basis for clinical treatment and prevention of serious suicidal behavior.

Thoughts on applying MOOC to the abnormal psychology teaching of graduate students majoring in applied psychology in medical colleges and universities / 中华医学教育探索杂志

Combining with the currently applied psychology teaching mode of medical colleges and universities, regarding the core curriculum, abnormal psychology, as the object, we have explored the new teaching mode of graduate students majoring in applied psychology based on massive open online courses (MOOC), and promoted the fusion of classroom theory teaching, hospital practice, and MOOC course, so as to guide teaching reform of applied psychology and improve the quality of personnel training.

An evaluation of the effect of examination reform on the teaching of Psychiatry / 中华医学教育探索杂志

Objective To determine the impact of new methods in teaching and examination on students' learning of psychiatry by increasing the proportion of objective questions in the final exam papers through examination reform, so as to help students adapt to the Practitioner Examination. Methods A total of 483 students from grade 2011&2012 were selected as research subjects. Through the comparative study of the test scores under different teaching methods before and after the examination reform, the SPSS 22.0 statistical software was used to analyze the structure, reliability, difficulty and level of differentiation of the examine papers via t tests. Results Examination papers after the reform showed an increase in difficulty in all question types; some question types also had higher levels of differentiation. Students receiving PBL-based education had higher scores compared to those under traditional teaching methods. The difference was statistically significant. Conclusion PBL teaching mode is more conducive to the mastering of knowledge points and helps students improve grades in objective questions. Medical schools should increase the use of PBL teaching reform in psychiatry courses and conduct examination reform, so as to improve the teaching effect and the passing rate of Practitioners Examination.

A huge completely isolated duplication cyst complicated by torsion and lined by 3 different mucosal epithelial components in an adult: A case report.

RATIONALE: Intestinal or enteric duplication (ED) does exit as a rare congenital malformation of the gastrointestinal system clinically. It is a separate entity, but can be communicated with the gastrointestinal tract. It is characterized by a well-developed muscular wall and lumen endowed with ectopic mucosa, simulating a portion of normal bowel. A completely isolated duplication cyst (CIDC) refers to an extremely uncommon variant of ED, which is secluded from the alimentary tract and possesses its own exclusive blood supply. Surgical procedure is the treatment of choice, because most often, a definitive diagnosis can only be confirmed intraoperatively. PATIENT CONCERNS: A 20-year-old male patient presented with a 10-day history of intermittent episodes of abdominal pain. The pain evolved from dull into progressive and intolerable, accompanied by vomiting, nausea, and abdominal distention. DIAGNOSES: Closed-loop small-bowel obstruction with volvulus. INTERVENTIONS: The patient underwent an emergency exploratory laparotomy. OUTCOMES: A huge CIDC was observed upon operation, which was affixed to the mesentery with only a narrow base, just like a pedicle; 720° counterclockwise twisting around its base was definitely noted, provoking the compromised blood supply. Complete excision of the cyst was performed along its base safely without violating the intestinal tract. Furthermore, the ectopic mucosa of the cyst exhibited 3 different epithelial lining components histopathologically. LESSONS: Clinicians should be aware of the possibility of the existence of a duplication and raise a high index of suspicion in case of equivocal diagnosis, particularly in adult population. A low threshold for surgical management should be recommended in order to prevent lethal outcomes.

Correlation of polymorphism of glial high-affinity glutamate transporter (SLC1A3) gene and non-suicidal self-injury in adolescents / 中华行为医学与脑科学杂志

Objective To explore the correlation between polymorphisms (SNPs) of locus rs2269272 of glial high-affinity glutamate transporter (SLC1A3) gene and suicide attempt among Chinese adolescents.Methods iMLDRTM genotyping technology was used to detect the polymorphism of rs2269272 SLC1A3 gene loci in 55 suicide attempters and 112 healthy controls,and survival analysis was applied to analyze the relationship between allele (T) and the age of suicide attempt.Results The differences of rs2269272 locus allele distribution between two groups were statistically significant(x2=4.208,P=0.040),but genotype distribution of two groups had no significantly differences(x2 =4.011,P=0.135).Non-suicidal self-injury adolescents with locus rs2269272 (15.6 ± 0.4) were younger than adolescents without locus rs2269272(16.4±0.7),but the difference was not statistically significant.Conclusion Preliminary findings suggest that rs2269272 SLC1 A3 may be relevant to non-suicidal self-injury acts,and rs2269272 locus allele is not related to earlier suicide attempt.

Expression and its clinical significance of RBP4 in vitreous in patients with proliferative diabetic retinopathy / 国际眼科杂志(Guoji Yanke Zazhi)

AIM:To observe the concentration of RBP4 and IL-6 in vitreous of proliferative diabetic retinopathy (PDR). METHODS: A total of 65 patients (66 eyes) were enrolled in Department of Ophthalmology, Renmin Hospital of Wuhan University from February 2017 to July 2017 with the informed consent. The patients were divided into PDR group (23 cases) and NPDR group (16 cases). Twenty- six patients without diabetic mellitus (DM) served as control group. The demography was matched among the groups, but the course of DM, the blood glucose level and the HbA1c level were elevated in the PDR group and the NPDR group (all P<0.05). Vitreous samples were collected during the procedure of vitrectomy. RBP4,IL-6,TNF-α concentrations in vitreous specimens were detected by ELISA. The differences of vitreous RBP4, IL-6 and TNF-α in various groups were statistically analyzed by ANOVA, respectively. The correlations between RBP4 and IL - 6, TNF - α were calculated by Pearson correlation analysis. RESULTS:The concentration of RBP4 in PDR group,the NPDR group and control group were 13.68士2.66, 11 03士1 12,10.45士1.17μ g/Ml, and the concentration of IL-6 were 56.0士10.27, 20.92士5.77, 10.26士1.91pg/Ml. RBP4 and IL- 6 concentrations were elevated in PDR group compared with NPDR group and control group, with significant difference among three groups (F = 12. 135, 161.167; P < 0. 01). IL - 6 concentrations in vitreous increased in the NPDR group in comparison with control group(P<0.05). RBP4 concentrations had no significant difference between the NPDR group and the group(P>0 05). Pearson correlation coefficient was significant positive between RBP4 concentration and IL - 6 concentration(r=0.606,P=0.001). CONCLUSION: RBP4 is probability involved in the inflammation pathogenesis of PDR. These results indicate that RBP4 could be a new target for the diagnosis and treatment of PDR.