EEGGAN-Net: enhancing EEG signal classification through data augmentation.

Background: Emerging brain-computer interface (BCI) technology holds promising potential to enhance the quality of life for individuals with disabilities. Nevertheless, the constrained accuracy of electroencephalography (EEG) signal classification poses numerous hurdles in real-world applications. Methods: In response to this predicament, we introduce a novel EEG signal classification model termed EEGGAN-Net, leveraging a data augmentation framework. By incorporating Conditional Generative Adversarial Network (CGAN) data augmentation, a cropped training strategy and a Squeeze-and-Excitation (SE) attention mechanism, EEGGAN-Net adeptly assimilates crucial features from the data, consequently enhancing classification efficacy across diverse BCI tasks. Results: The EEGGAN-Net model exhibits notable performance metrics on the BCI Competition IV-2a and IV-2b datasets. Specifically, it achieves a classification accuracy of 81.3% with a kappa value of 0.751 on the IV-2a dataset, and a classification accuracy of 90.3% with a kappa value of 0.79 on the IV-2b dataset. Remarkably, these results surpass those of four other CNN-based decoding models. Conclusions: In conclusion, the amalgamation of data augmentation and attention mechanisms proves instrumental in acquiring generalized features from EEG signals, ultimately elevating the overall proficiency of EEG signal classification.

Achieving Strong Circularly Polarized Luminescence through Cascade Cationic Insertion in Lead-free Hybrid Metal Halides.

Generating circularly polarized luminescence (CPL) with simultaneous high photoluminescence quantum yield (PLQY) and dissymmetry factor (glum) is difficult due to usually unmatched electric transition dipole moment (µ) and magnetic transition dipole moment (m) of materials. Herein we tackle this issue by playing a "cascade cationic insertion" trick to achieve strong CPL (with PLQY of ~100 %) in lead-free metal halides with high glum values reaching -2.3×10-2 without using any chiral inducers. Achiral solvents of hydrochloric acid (HCl) and N, N-dimethylformamide (DMF) infiltrate the crystal lattice via asymmetric hydrogen bonding, distorting the perovskite structure to induce the "intrinsic" chirality. Surprisingly, additional insertion of Cs+ cation to substitute partial (CH3)2NH2 + transforms the chiral space group to achiral but the crystal maintains chiroptical activity. Further doping of Sb3+ stimulates strong photoluminescence as a result of self-trapped excitons (STEs) formation without disturbing the crystal framework. The chiral perovskites of indium-antimony chlorides embedded on LEDs chips demonstrate promising potential as CPL emitters. Our work presents rare cases of chiroptical activity of highly luminescent perovskites from only achiral building blocks via spontaneous resolution as a result of symmetry breaking.

Affect and stress detection based on feature fusion of LSTM and 1DCNN.

The impact of emotions on health, especially stress, is receiving increasing attention. It is important to provide a non-invasive affect detection system that can be continuously monitored for a long period of time. Multi-sensor fusion strategies can better improve the performance of affect detection models, but there are also problems such as insufficient feature extraction and poor spatiotemporal feature fusion. Therefore, this study proposes a feature-level fusion method based on long short-term memory and one-dimensional convolutional neural network to extract temporal and spatial features of electrocardiogram, electromyogram, electrical activity, temperature, accelerator and response data, respectively, and then fuse them in a summation fashion for affect and stress detection. In particular, we added the tanh activation function before feature fusion, which can improve the model's performance. We used the wearable affect and stress detection dataset to train the model, which includes three different emotion states (neutral, stress, and amusement) for three-class emotion classification with accuracy and F1-scores of 87.82% and 86.68%, respectively. Due to the importance of stress, we also studied binary classification for stress detection, where neutral and amusement were combined as non-stress, with accuracy and F1-scores of 94.9% and 94.98%, respectively. The performance of the proposed model outperforms other control models and can effectively improve the performance of affect and stress detection, and promote medical care, health care and elderly care.

Automatic segmentation of brain glioma based on XY-Net.

Glioma is a malignant primary brain tumor, which can easily lead to death if it is not detected in time. Magnetic resonance imaging is the most commonly used technique to diagnose gliomas, and precise outlining of tumor areas from magnetic resonance images (MRIs) is an important aid to physicians in understanding the patient's condition and formulating treatment plans. However, relying on radiologists to manually depict tumors is a tedious and laborious task, so it is clinically important to investigate an automated method for outlining glioma regions in MRIs. To liberate radiologists from the heavy task of outlining tumors, we propose a fully convolutional network, XY-Net, based on the most popular U-Net symmetric encoder-decoder structure to perform automatic segmentation of gliomas. We construct two symmetric sub-encoders for XY-Net and build interconnected X-shaped feature map transmission paths between the sub-encoders, while maintaining the feature map concatenation between each sub-encoder and the decoder. Moreover, a loss function composed of the balanced cross-entropy loss function and the dice loss function is used in the training task of XY-Net to solve the class unevenness problem of the medical image segmentation task. The experimental results show that the proposed XY-Net has a 2.16% improvement in dice coefficient (DC) compared to the network model with a single encoder structure, and compare with some state-of-the-art image segmentation methods, XY-Net achieves the best performance. The DC, HD, recall, and precision of our method on the test set are 74.49%, 10.89 mm, 78.06%, and 76.30%, respectively. The combination of sub-encoders and cross-transmission paths enables the model to perform better; based on this combination, the XY-Net achieves an end-to-end automatic segmentation of gliomas on 2D slices of MRIs, which can play a certain auxiliary role for doctors in grasping the state of illness.

Research on the Relation between Slump Flow and Yield Stress of Ultra-High Performance Concrete Mixtures.

The relation between slump flow and yield stress of ultra-high performance concrete (UHPC) mixtures was studied with theoretical analysis and experimentation. The relational expression between slump flow and yield stress of UHPC mixtures was built and then verified with a rheological test. The results showed that the prediction model, as a function of cone geometry of dimensionless slump flow and dimensionless yield stress of the UHPC mixtures, was constructed based on Tresca criteria, considering the geometric relation of morphological characterization parameters before and after slump of the UHPC mixtures. The rationality and applicability of the dimensionless prediction model was verified with a rheological test and a slump test of UHPC mixtures with different dosages of polycarboxylate superplasticizer. With increase in polycarboxylate superplasticizer dosage, yield stress of the two series of UHPC mixtures (large/small binding material consumption) gradually decreased, leading to a gradual increase in slump flow. Based on the prediction model of dimensionless slump flow and dimensionless yield stress, the relational expression between slump flow and yield stress of the UHPC mixtures was built. The comparison result showed that the calculated data was consistent with the experimental data, which provided a new method for predicting yield stress of UHPC mixtures with a slump test.

Rice nitrogen nutrition monitoring classification method based on the convolution neural network model: Direct detection of rice nitrogen nutritional status.

The nitrogen nutrition status affects the main factors of rice yield. In traditional rice nitrogen nutrition monitoring methods, most experts enter the farmland to observe leaf color and growth and apply an appropriate amount of nitrogen fertilizer according to the results. However, this method is labor- and time-consuming. To realize automatic rice nitrogen nutrition monitoring, we constructed the Jiangxi rice nitrogen nutrition monitoring model based on a convolution neural network (CNN) using the same region rice canopy image in different generation periods. Our CNN model was evaluated using multiple evaluation criteria (Accuracy, Recall, Precision, and F1 score). The results show that the same CNN model could distinguish the rice nitrogen nutrition status in different periods, which can completely realize the automatic discrimination of nitrogen nutrition status so as to guide the scientific nitrogen application of rice in this area. This will greatly improve the discrimination efficiency of the nitrogen nutrition status and reduce the time and labor cost. The application of the proposed method also proved that the CNN model can be applied in the discrimination of the nitrogen nutrition status. Among CNN models, GoogleNet model proposed a CNN architecture named Inception which can improve the depth of the network and extract higher-level features without changing the amount of calculation of the model. The GoogleNet model achieved the highest accuracy, 95.7%.

Non-negative constrained dictionary learning for compressed sensing of ECG signals.

Objective. Overcomplete dictionaries are widely used in compressed sensing (CS) to improve the quality of signal reconstruction. However, dictionary learning under theℓ0-norm orℓ1-norm constraint inevitably produces dictionary atoms that are negatively correlated with the original signal; meanwhile, when we use a sparse linear combination of dictionary atoms to represent a signal, it is suboptimal for the dictionary atoms to "cancel each other out" by addition and subtraction to approximate the sample. In this paper, we propose a non-negative constrained dictionary learning (NCDL) algorithm to improve the reconstruction performance of CS with electrocardiogram (ECG) signals.Approach.Non-NCDL was divided into an encoding stage and a dictionary learning stage. In the encoding stage, non-negative constraints were imposed on the encoding coefficients and obtained the sparse solution using the alternating direction method of multipliers. At the same time, a penalty term was integrated into the objective function in order to remove small coding coefficients and achieve the effect of sparse coding. In the dictionary learning stage, the block coordinate descent algorithm was utilized to update the dictionary with a view to obtaining an overcomplete dictionary.Results.The performance of the proposed NCDL algorithm was evaluated using the standard MIT-BIH database. Quantitative performance metrics, such as percent root mean square difference (PRD1) and root mean square error, were compared with existing CS approaches to quantify the efficacy of the proposed scheme. For a PRD1 value of 9%, the compression ratio (CR) of the NCDL approach was around 2.78. When CR ranged from 1.05 to 2.78, the proposed NCDL approach outperformed the method of optimal direction, k-means singular value decomposition, and online dictionary learning approaches in ECG signal reconstruction based on CS.Significance.This promising preliminary result demonstrates the capability and feasibility of the proposed bioimpedance method and may open up a new direction for this application. The non-NCDL method proposed in this paper can be used to obtain a sparse basis and improve the performance of CS reconstruction.

Deep Compressive Sensing on ECG Signals with Modified Inception Block and LSTM.

In practical electrocardiogram (ECG) monitoring, there are some challenges in reducing the data burden and energy costs. Therefore, compressed sensing (CS) which can conduct under-sampling and reconstruction at the same time is adopted in the ECG monitoring application. Recently, deep learning used in CS methods improves the reconstruction performance significantly and can removes of some of the constraints in traditional CS. In this paper, we propose a deep compressive-sensing scheme for ECG signals, based on modified-Inception block and long short-term memory (LSTM). The framework is comprised of four modules: preprocessing; compression; initial; and final reconstruction. We adaptively compressed the normalized ECG signals, sequentially using three convolutional layers, and reconstructed the signals with a modified Inception block and LSTM. We conducted our experiments on the MIT-BIH Arrhythmia Database and Non-Invasive Fetal ECG Arrhythmia Database to validate the robustness of our model, adopting Signal-to-Noise Ratio (SNR) and percentage Root-mean-square Difference (PRD) as the evaluation metrics. The PRD of our scheme was the lowest and the SNR was the highest at all of the sensing rates in our experiments on both of the databases, and when the sensing rate was higher than 0.5, the PRD was lower than 2%, showing significant improvement in reconstruction performance compared to the comparative methods. Our method also showed good recovering quality in the noisy data.

A pre-impact fall detection data segmentation method based on multi-channel convolutional neural network and class activation mapping.

Objective.A segmentation method for pre-impact fall detection data is investigated. Specifically, it studies how to partition data segments that are important for classification from continuous inertial sensor data for pre-impact fall detection.Approach.In this study, a trigger-based algorithm combining multi-channel convolutional neural network (CNN) and class activation mapping was proposed to solve the problem of data segmentation. First, a pre-impact fall detection training dataset was established and divided into two parts. For falls, the 1 s data was divided from the peak value of the acceleration signal magnitude vector to the starting direction. For activities of daily living, the cycle segmentation was performed for a 1 s window size. Second, a heat map of the class activation regions of the sensor data was formed using a multi-channel CNN and a class activation mapping algorithm. Finally, the data segmentation strategy was established based on the heat map, the basic law of falls and the real-time requirements.Main results.This method was verified by the SisFall dataset. The obtained segmentation strategy (i.e. to start segmenting a small data segment with a window duration of 325 ms when the acceleration signal magnitude vector is less than 9.217 m s-2) met the real-time requirements for pre-impact fall detection. Moreover, it was suitable for various machine learning algorithms, and the accuracy of the machine learning algorithms used exceeded 94.8%, with the machine learning algorithms verifying the data segmentation strategy.Significance.The proposed method can automatically identify the class activation area, save the computing resources of wearable devices, shorten the duration of segmentation window, and ensure the real-time performance of pre-impact fall detection.

Effects of Ordered Grasping Movement on Brain Function in the Performance Virtual Reality Task: A Near-Infrared Spectroscopy Study.

Objective: Virtual reality (VR) grasping exercise training helps patients participate actively in their recovery and is a critical approach to the rehabilitation of hand dysfunction. This study aimed to explore the effects of active participation and VR grasping on brain function combined with the kinematic information obtained during VR exercises. Methods: The cerebral oxygenation signals of the prefrontal cortex (LPFC/RPFC), the motor cortex (LMC/RMC), and the occipital cortex (LOC/ROC) were measured by functional near-infrared spectroscopy (fNIRS) in 18 young people during the resting state, grasping movements, and VR grasping movements. The EPPlus plug-in was used to collect the hand motion data during simulated interactive grasping. The wavelet amplitude (WA) of each cerebral cortex and the wavelet phase coherence (WPCO) of each pair of channels were calculated by wavelet analysis. The total difference in acceleration difference of the hand in the VR grasping movements was calculated to acquire kinematic characteristics (KCs). The cortical activation and brain functional connectivity (FC) of each brain region were compared and analyzed, and a significant correlation was found between VR grasping movements and brain region activation. Results: Compared with the resting state, the WA values of LPFC, RPFC, LMC, RMC, and ROC increased during the grasping movements and the VR grasping movements, these changes were significant in LPFC (p = 0.0093) and LMC (p = 0.0007). The WA values of LMC (p = 0.0057) in the VR grasping movements were significantly higher than those in the grasping movements. The WPCO of the cerebral cortex increased during grasping exercise compared with the resting state. Nevertheless, the number of significant functional connections during VR grasping decreased significantly, and only the WPCO strength between the LPFC and LMC was enhanced. The increased WA of the LPFC, RPFC, LMC, and RMC during VR grasping movements compared with the resting state showed a significant negative correlation with KCs (p < 0.001). Conclusion: The VR grasping movements can improve the activation and FC intensity of the ipsilateral brain region, inhibit the FC of the contralateral brain region, and reduce the quantity of brain resources allocated to the task. Thus, ordered grasping exercises can enhance active participation in rehabilitation and help to improve brain function.

Activity and toxicity of intramuscular 1000 iu/m2 polyethylene glycol-E. coli L-asparaginase in the UKALL 2003 and UKALL 2011 clinical trials.

In successive UK clinical trials (UKALL 2003, UKALL 2011) for paediatric acute lymphoblastic leukaemia (ALL), polyethylene glycol-conjugated E. coli L-asparaginase (PEG-EcASNase) 1000 iu/m2 was administered intramuscularly with risk-stratified treatment. In induction, patients received two PEG-EcASNase doses, 14 days apart. Post-induction, non-high-risk patients (Regimens A, B) received 1-2 doses in delayed intensification (DI) while high-risk Regimen C patients received 6-10 PEG-EcASNase doses, including two in DI. Trial substudies monitored asparaginase (ASNase) activity, ASNase-related toxicity and ASNase-associated antibodies (total, 1112 patients). Median (interquartile range) trough plasma ASNase activity (14 ± 2 days post dose) following first and second induction doses and first DI dose was respectively 217 iu/l (144-307 iu/l), 265 iu/l (165-401 iu/l) and 292 iu/l (194-386 iu/l); 15% (138/910) samples showed subthreshold ASNase activity (<100 iu/l) at any trough time point. Older age was associated with lower (regression coefficient -9.5; p < 0.0001) and DI time point with higher ASNase activity (regression coefficient 29.9; p < 0.0001). Clinical hypersensitivity was observed in 3.8% (UKALL 2003) and 6% (UKALL 2011) of patients, and in 90% or more in Regimen C. A 7% (10/149) silent inactivation rate was observed in UKALL 2003. PEG-EcASNase schedule in UKALL paediatric trials is associated with low toxicity but wide interpatient variability. Therapeutic drug monitoring potentially permits optimisation through individualised asparaginase dosing.

Leaf Classification for Crop Pests and Diseases in the Compressed Domain.

Crop pests and diseases have been the main cause of reduced food production and have seriously affected food security. Therefore, it is very urgent and important to solve the pest problem efficiently and accurately. While traditional neural networks require complete processing of data when processing data, by compressed sensing, only one part of the data needs to be processed, which greatly reduces the amount of data processed by the network. In this paper, a combination of compressed perception and neural networks is used to classify and identify pest images in the compressed domain. A network model for compressed sampling and classification, CSBNet, is proposed to enable compression in neural networks instead of the sensing matrix in conventional compressed sensing (CS). Unlike traditional compressed perception, no reduction is performed to reconstruct the image, but recognition is performed directly in the compressed region, while an attention mechanism is added to enhance feature strength. The experiments in this paper were conducted on different datasets with various sampling rates separately, and our model was substantially less accurate than the other models in terms of trainable parameters, reaching a maximum accuracy of 96.32%, which is higher than the 93.01%, 83.58%, and 87.75% of the other models at a sampling rate of 0.7.

Simulation of the critical nitrogen dilution curve in Jiangxi double-cropped rice region based on leaf dry matter weight.

In order to investigate the feasibility of using rice critical nitrogen concentration as a nitrogen nutrition diagnosis index, a two-year positioning field gradient experiment using four rice varieties and four nitrogen levels (0, 75, 150, 225 kg·ha-1 for early rice; 0, 90, 180, 270 kg·ha-1 for late rice) was conducted for early and late rice. The critical dilution curves (Nc%) of the double-cropped rice based on leaf dry matter (LDM) were constructed and verified using the field data. Two critical nitrogen dilution curves and nitrogen nutrition indexes (NNI) of rice LDM were constructed for early rice [Nc% = 2.66LDM-0.79, R2 = 0.88, NNI ranged between 0.29-1.74, and the average normalized root mean square error (n-RMSE = 19.35%)] and late rice [Nc% = 7.46LDM-1.42, R2 = 0.91, NNI was between 0.55-1.53, and the average (n-RMSE = 15.14%)]. The relationship between NNI and relative yield was a quadratic polynomial equation and suggested that the optimum nitrogen application rate for early rice was sightly smaller than 150 kg·ha-1, and that for late rice was about 180 kg·ha-1. The developed critical nitrogen concentration dilution curves, based on leaf dry matter, were able to diagnose nitrogen nutrition in the double-cropped rice region.

Improved Transductive Support Vector Machine for a Small Labelled Set in Motor Imagery-Based Brain-Computer Interface.

Long and tedious calibration time hinders the development of motor imagery- (MI-) based brain-computer interface (BCI). To tackle this problem, we use a limited labelled set and a relatively large unlabelled set from the same subject for training based on the transductive support vector machine (TSVM) framework. We first introduce an improved TSVM (ITSVM) method, in which a comprehensive feature of each sample consists of its common spatial patterns (CSP) feature and its geometric feature. Moreover, we use the concave-convex procedure (CCCP) to solve the optimization problem of TSVM under a new balancing constraint that can address the unknown distribution of the unlabelled set by considering various possible distributions. In addition, we propose an improved self-training TSVM (IST-TSVM) method that can iteratively perform CSP feature extraction and ITSVM classification using an expanded labelled set. Extensive experimental results on dataset IV-a from BCI competition III and dataset II-a from BCI competition IV show that our algorithms outperform the other competing algorithms, where the sizes and distributions of the labelled sets are variable. In particular, IST-TSVM provides average accuracies of 63.25% and 69.43% with the abovementioned two datasets, respectively, where only four positive labelled samples and sixteen negative labelled samples are used. Therefore, our algorithms can provide an alternative way to reduce the calibration time.

Impact of dose and duration of therapy on dexamethasone pharmacokinetics in childhood acute lymphoblastic leukaemia-a report from the UKALL 2011 trial.

INTRODUCTION: The use of dexamethasone in acute lymphoblastic leukaemia therapy contributes to short- and long-term toxicities. The UKALL 2011 randomised trial investigated whether a more intense dexamethasone dose (10 mg/m2/d x 14d, short vs 6 mg/m2/d x 28d, standard) would lead to a more rapid cytoreduction and reduced adverse effects associated with longer durations of steroids in induction. The impact of dose and duration on dexamethasone pharmacokinetics was investigated. METHODS: Blood samples were obtained on one of the first three and last three days of induction dexamethasone dosing at time points up to 8 h after oral administration. Plasma dexamethasone levels were quantified in 1084 plasma samples obtained from 174 children and a population pharmacokinetic model developed. RESULTS: Drug exposure varied significantly between patients, with a >12-fold variation in AUC0-12h values and a marked overlap in dexamethasone exposures between dose levels. Intuitively, AUC0-12h was significantly higher with short dosing (10 mg/m2/d), but cumulative exposure was significantly higher with standard dosing over 28 days, after a higher cumulative dose. Concomitant rasburicase administration was associated with a 60% higher dexamethasone clearance. Day 8 bone marrow response was comparable between dosing arms, but those with <5% blast count exhibited a greater mean dexamethasone exposure than those with >5%. No statistical differences were observed between arms in terms of steroid-related toxicity or minimal residual disease at the end of induction. CONCLUSION: The potential significance of dexamethasone AUC0-12h on early response and higher cumulative exposure on the standard arm suggest that duration of therapy and exposure may be more important factors than absolute dose from a clinical pharmacology perspective.

Differential regulation of cell death pathways by the microenvironment correlates with chemoresistance and survival in leukaemia.

Glucocorticoids (GCs) and topoisomerase II inhibitors are used to treat acute lymphoblastic leukaemia (ALL) as they induce death in lymphoid cells through the glucocorticoid receptor (GR) and p53 respectively. Mechanisms underlying ALL cell death and the contribution of the bone marrow microenvironment to drug response/resistance remain unclear. The role of the microenvironment and the identification of chemoresistance determinants were studied by transcriptomic analysis in ALL cells treated with Dexamethasone (Dex), and Etoposide (Etop) grown in the presence or absence of bone marrow conditioned media (CM). The necroptotic (RIPK1) and the apoptotic (caspase-8/3) markers were downregulated by CM, whereas the inhibitory effects of chemotherapy on the autophagy marker Beclin-1 (BECN1) were reduced suggesting CM exerts cytoprotective effects. GCs upregulated the RIPK1 ubiquitinating factor BIRC3 (cIAP2), in GC-sensitive (CEM-C7-14) but not in resistant (CEM-C1-15) cells. In addition, CM selectively affected GR phosphorylation in a site and cell-specific manner. GR is recruited to RIPK1, BECN1 and BIRC3 promoters in the sensitive but not in the resistant cells with phosphorylated GR forms being generally less recruited in the presence of hormone. FACS analysis and caspase-8 assays demonstrated that CM promoted a pro-survival trend. High molecular weight proteins reacting with the RIPK1 antibody were modified upon incubation with the BIRC3 inhibitor AT406 in CEM-C7-14 cells suggesting that they represent ubiquitinated forms of RIPK1. Our data suggest that there is a correlation between microenvironment-induced ALL proliferation and altered response to chemotherapy.

Stromal cell-mediated mitochondrial redox adaptation regulates drug resistance in childhood acute lymphoblastic leukemia.

Despite the high cure rates in childhood acute lymphoblastic leukemia (ALL), relapsed ALL remains a significant clinical problem. Genetic heterogeneity does not adequately explain variations in response to therapy. The chemoprotective tumor microenvironment may additionally contribute to disease recurrence. This study identifies metabolic reprogramming of leukemic cells by bone marrow stromal cells (BMSC) as a putative mechanism of drug resistance. In a BMSC-extracellular matrix culture model, BMSC produced chemoprotective soluble factors and facilitated the emergence of a reversible multidrug resistant phenotype in ALL cells. BMSC environment induced a mitochondrial calcium influx leading to increased reactive oxygen species (ROS) levels in ALL cells. In response to this oxidative stress, drug resistant cells underwent a redox adaptation process, characterized by a decrease in ROS levels and mitochondrial membrane potential with an upregulation of antioxidant production and MCL-1 expression. Similar expanded subpopulations of low ROS expressing and drug resistant cells were identified in pre-treatment bone marrow samples from ALL patients with slower response to therapy. This suggests that the bone marrow microenvironment induces a redox adaptation in ALL subclones that protects against cytotoxic stress and potentially gives rise to minimal residual disease. Targeting metabolic remodeling by inhibiting antioxidant production and antiapoptosis was able to overcome drug resistance. Thus metabolic plasticity in leukemic cell response to environmental factors contributes to chemoresistance and disease recurrence. Adjunctive strategies targeting such processes have the potential to overcome therapeutic failure in ALL.

Local blockage of EMMPRIN impedes pressure ulcers healing in a rat model.

Excessive extracellular matrix degradation caused by the hyperfunction of matrix metalloproteinases (MMPs) has been implicated in the failure of pressure ulcers healing. EMMPRIN, as a widely expressed protein, has emerged as an important regulator of MMP activity. We hypothesize that EMMPRIN affects the process of pressure ulcer healing by modulating MMP activity. In the rat pressure ulcer model, the expression of EMMPRIN in ulcers detected by Western blot was elevated compared with that observed in normal tissue. To investigate the role of EMMPRIN in regulating ulcer healing, specific antibodies against EMMPRIN were used via direct administration on the pressure ulcer. Local blockage of EMMPRIN resulted in a poor ulcer healing process compared with control ulcers, which was the opposite of our expectation. Furthermore, inhibiting EMMPRIN minimally impacted MMP activity. However, the collagen content in the pressure ulcer was reduced in the EMMPRIN treated group. Angiogenesis and the expression of angiogenic factors in pressure ulcers were also reduced by EMMPRIN local blockage. The results in the present study indicate a novel effect of EMMPRIN in the regulation of pressure ulcer healing by controlling the collagen contents and angiogenesis rather than MMPs activity.

[Preparation of highly hydrophilic strong cation exchangers and their applications in protein analysis].

Based on the needs of new packing materials for rapid and efficient separation, purification and analysis of biomacromolecules, a novel sulfonic acid-type strong cation exchange resin (SP-G-PGMA SCX resin) was prepared. The porous poly(glycidyl methacrylate) microspheres (PGMA) were selected as the matrix and glucose was used as the hydrophilic modifier to block the hydrophobic domains of PGMA beads. Glucose modification on PGMA beads improved the biocompatibility and reduced the non-specific adsorption so as to increase the recoveries of protein. The PGMA beads possess the porous structure and the relatively high specific surface area, which make the PGMA-based resins good permeability and high loading capacity. The application of such SP-G-PGMA SCX resin for the chromatographic separation of biomacromolecules was explored. Four basic proteins were baseline separated within 6 min with the column size of 100 mm x 4.6 mm. The adsorption capacity of lysozyme on SP-G-PGMA SCX resin was determined as 39.5 g/L. The results make the material promising for the separation and purification of biomacromolecules.