Research on phase modulation to enhance the feature of high-frequency steady-state asymmetric visual evoked potentials / 生物医学工程学杂志

High-frequency steady-state asymmetric visual evoked potential (SSaVEP) provides a new paradigm for designing comfortable and practical brain-computer interface (BCI) systems. However, due to the weak amplitude and strong noise of high-frequency signals, it is of great significance to study how to enhance their signal features. In this study, a 30 Hz high-frequency visual stimulus was used, and the peripheral visual field was equally divided into eight annular sectors. Eight kinds of annular sector pairs were selected based on the mapping relationship of visual space onto the primary visual cortex (V1), and three phases (in-phase[0º, 0º], anti-phase [0º, 180º], and anti-phase [180º, 0º]) were designed for each annular sector pair to explore response intensity and signal-to-noise ratio under phase modulation. A total of 8 healthy subjects were recruited in the experiment. The results showed that three annular sector pairs exhibited significant differences in SSaVEP features under phase modulation at 30 Hz high-frequency stimulation. And the spatial feature analysis showed that the two types of features of the annular sector pair in the lower visual field were significantly higher than those in the upper visual field. This study further used the filter bank and ensemble task-related component analysis to calculate the classification accuracy of annular sector pairs under three-phase modulations, and the average accuracy was up to 91.5%, which proved that the phase-modulated SSaVEP features could be used to encode high- frequency SSaVEP. In summary, the results of this study provide new ideas for enhancing the features of high-frequency SSaVEP signals and expanding the instruction set of the traditional steady state visual evoked potential paradigm.

Accurately Differentiating COVID-19, Other Viral Infection, and Healthy Individuals Using Multimodal Features via Late Fusion Learning

Effectively identifying COVID-19 patients using non-PCR clinical data is critical for the optimal clinical outcomes. Currently, there is a lack of comprehensive understanding of various biomedical features and appropriate technical approaches to accurately detecting COVID-19 patients. In this study, we recruited 214 confirmed COVID-19 patients in non-severe (NS) and 148 in severe (S) clinical type, 198 non-infected healthy (H) participants and 129 non-COVID viral pneumonia (V) patients. The participants clinical information (23 features), lab testing results (10 features), and thoracic CT scans upon admission were acquired as three input feature modalities. To enable late fusion of multimodality data, we developed a deep learning model to extract a 10-feature high-level representation of the CT scans. Exploratory analyses showed substantial differences of all features among the four classes. Three machine learning models (k-nearest neighbor kNN, random forest RF, and support vector machine SVM) were developed based on the 43 features combined from all three modalities to differentiate four classes (NS, S, V, and H) at once. All three models had high accuracy to differentiate the overall four classes (95.4%-97.7%) and each individual class (90.6%-99.9%). Multimodal features provided substantial performance gain from using any single feature modality. Compared to existing binary classification benchmarks often focusing on single feature modality, this study provided a novel and effective breakthrough for clinical applications. Findings and the analytical workflow can be used as clinical decision support for current COVID-19 and other clinical applications with high-dimensional multimodal biomedical features. One sentence summaryWe trained and validated late fusion deep learning-machine learning models to predict non-severe COVID-19, severe COVID-19, non-COVID viral infection, and healthy classes from clinical, lab testing, and CT scan features extracted from convolutional neural network and achieved predictive accuracy of > 96% to differentiate all four classes at once based on a large dataset of 689 participants.

An Interpretable Machine Learning Framework for Accurate Severe vs Non-severe COVID-19 Clinical Type Classification

Effectively and efficiently diagnosing COVID-19 patients with accurate clinical type is essential to achieve optimal outcomes for the patients as well as reducing the risk of overloading the healthcare system. Currently, severe and non-severe COVID-19 types are differentiated by only a few clinical features, which do not comprehensively characterize complicated pathological, physiological, and immunological responses to SARS-CoV-2 invasion in different types. In this study, we recruited 214 confirmed COVID-19 patients in non-severe and 148 in severe type, from Wuhan, China. The patients comorbidity and symptoms (26 features), and blood biochemistry (26 features) upon admission were acquired as two input modalities. Exploratory analyses demonstrated that these features differed substantially between two clinical types. Machine learning random forest (RF) models using features in each modality were developed and validated to classify COVID-19 clinical types. Using comorbidity/symptom and biochemistry as input independently, RF models achieved >90% and >95% predictive accuracy, respectively. Input features importance based on Gini impurity were further evaluated and top five features from each modality were identified (age, hypertension, cardiovascular disease, gender, diabetes; D-Dimer, hsTNI, neutrophil, IL-6, and LDH). Combining top 10 multimodal features, RF model achieved >99% predictive accuracy. These findings shed light on how the human body reacts to SARS-CoV-2 invasion as a unity and provide insights on effectively evaluating COVID-19 patients severity and developing treatment plans accordingly. We suggest that symptoms and comorbidities can be used as an initial screening tool for triaging, while biochemistry and features combined are applied when accuracy is the priority. One Sentence SummaryWe trained and validated machine learning random forest (RF) models to predict COVID-19 severity based on 26 comorbidity/symptom features and 26 biochemistry features from a cohort of 214 non-severe and 148 severe type COVID-19 patients, identified top features from both feature modalities to differentiate clinical types, and achieved predictive accuracy of >90%, >95%, and >99% when comorbidity/symptom, biochemistry, and combined top features were used as input, respectively.

Study on the Informed Consent Right of Patients with Mental Disorders / 中国医学伦理学

Mental Health Law has specify the mental disorder patients’ informed consent right,but in practice, the problems,the mental health status of patients, namely the families’ right of subrogation exercise,“loss of self-control or deny having mental disorder”, have prevented the exercising of informed consent right. Therefore,it is necessary to effectively solve this plight of rebuilding a harmonious relationship between doctors and patients,estab-lishing mentally disordered patients’ right to advance directives, especially choosing the instrument of assessing the individual’ s capacity to consent.

Intraocular Pressure Sensor Based on a Contact Lens / 生物医学工程学杂志

Intraocular pressure detection has a great significance for understanding the status of eye health, prevention and treatment of diseases such as glaucoma. Traditional intraocular pressure detection needs to be held in the hospital. It is not only time-consuming to doctors and patients, but also difficult to achieve 24 hour-continuous detection. Microminiaturization of the intraocular pressure sensor and wearing it as a contact lens, which is convenient, comfortable and noninvasive, can solve this problem because the soft contact lens with an embedded micro fabricated strain gauge allows the measurement of changes in corneal curvature to correlate to variations of intraocular pressure. We fabricated a strain gauge using micro-electron mechanical systems, and integrated with the contact lens made of polydimethylsiloxane (PDMS) using injection molding. The experimental results showed that the sensitivity was 100. 7 µV/µm. When attached to the corneal surface, the average sensitivity of sensor response of intraocular pressure can be 125.8 µV/mm Hg under the ideal condition.

The application of a revised Chinese version of the state-trait anxiety inventory in migrant children / 中华行为医学与脑科学杂志

Objective To conduct a preliminary examination of the factor structure and the reliability and validity of a revised Chinese version of the State-Trait Anxiety Inventory-Form Y (STAI-Y) by using the large sample of migrant children.Methods Perform Exploratory Factor Analysis with half of the data (n =5477) from a large-scale questionnaire survey of students in Grades 4-9 at 58 schools for migrants in Beijing,then conduct Confirmatory Factor Analysis with the other half (n =5476).Results The results of state anxiety and trait anxiety both showed two factors.However,the indicators of trait anxiety were not ideal as seen from the following:the total explained variance was 39.22％,the relationship between the factors and items was not in accordance with the original English version,and the correlation between the two factors was unstable in the different samples (r =-0.17,P ＜ 0.001 ; r =-0.06,P ＜ 0.001).In addition,the factor loading of items 24 ( I wish I could be as happy as others) was low.Conclusion The state anxiety portion of this revised Chinese version of the STAI-Y is structured by the factors state anxiety present and state anxiety absent,and with good reliability and validity as well.Nevertheless,the trait anxiety portion of this revised Chinese version of the STAI-Y is structured by the factors trait anxiety present and trait anxiety absent,it shows unstable reliability and validity.

Occurrence of CpG island methylation phenotype and expression of p27 in insulinoma / 中华胰腺病杂志

ObjectiveTo study the expression of p27 and its relationship with CpG island methylation phenotype (CIMP) in insulinoma.MethodsExpression of p27 was tested in 27 insulinoma tissues and 11 paired control tissues by immunohistochemistry staining.CpG island methylation of p16,MLH1,RAR-β,MGMT,THBS1 (CIMP) was detected in 27 insulinoma tissues and 11 paired cantrol tissues by methylation specific PCR (MSP).The data of p27 and CIMP expression were correlated with the clinicopathological characteristics.ResultsThe positive expression rate of p27 in insulinoma tissues was significantly lower than that in paired control tissues (48％ vs 91％,P =0.008).High rate of CIMP occurrence in insulinoma tissues was 33％ (9/27),while it was 18％ (2/11) in paired control tissues,and difference between the two groups was not statistically significant ( P =0.350 ).The methylation of MGMT was reversely associated with p16 methylation ( P =0.004).p27 expression in insulinoma tissues was reversely associated high rate of CIMP occurrence but it was not statistically significant ( P =0.420).Neither the expression of p27 nor the occurrence of CIMP was associated with the clinicopathological features.ConclusionsDown-regulation of p27 and high rate of CIMP occurred in insulinomas,suggesting that the inactivation of p27 and epigenetic alterations of several genes might contribute to the carcinogenesis of insulinoma.

Effect of the mixture of radix sophorae flavescentis on cellular immunologic response in rats with immunosuppressant-induced pneumocystis carinii pneumonia induced / 中国组织工程研究

AIM: To study the effect of the radix sophorae flavescentis on cellular immunity in rats with Pneumocystis Carinii Pneumonia (PCP) induced by long-term use of immunosuppressant, and explore the action of traditional Chinese medicine for the immunological regulation and infectious prevention after organ transplantation. METHODS: The experiment was conducted at Department of Pathobiology, Jilin Medical College from May 2005 to March 2006. Forty adult healthy female SD rats were selected from Harbin Medical University (Certification: 02473146) and randomly divided into experiment group and control group, with 20 rats in each. The model of PCP was set up by glucocorticoid injection subcutaneously to SD rats (25 mg once, 2 times/week). The mixture of sophorae flavescentis was given to stomach with tube in experiment group (3 mL/kg, 2 times/day), and was consisted of radix sophorae flavescentis, ash bark, amur cork-tree, malt, milkvetch root and danshen root. Six weeks later, all the rats were anesthetized and broncholveolar lavage fluids were collected.①Alveolar washing fluid was concentrated 10 times and the levels of the soluble interleukin-2 receptor (sIL-2R) were examined by double antigen sandwich ELISA.②Blood was sampled from rat eyes and the count of lymphocytes in peripheral blood were detected.③The percentages of CD3+ and CD4+ subgroups were assessed with erythrocyte chaplet kit sensitized by antigen. RESULTS: All 40 rats were involved in the result analysis without drop.①The count of lymphocytes in peripheral blood in experiment group was significantly higher than that in control group (5.1?1.3)%, (0.8?0.3)%, P

Observation of curative effects of human growth factor on vascular dementia / 中国组织工程研究

Objective To explore the treatment effect of human growth factor (NGF) on vascular dementia. Method 47 cases with vascular dementia were randomly divided into two groups (study and control). The study group (24 cases) were treated with NGF, the control group (23 cases) were treated with ordinary medicines.Results The intelligence and cognitive ability in the study group were obviously higher than those in the control group (P<0.01). Conclusion Compared with other methods, NGF has a significant curative effect.