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Abstract The recent outbreak of mpox epidemic, caused by monkeypox virus (MPXV), poses a new threat to global public health. Here, we initially assessed the preexisting antibody level to the MPXV B6 protein in vaccinia vaccinees born before the end of the immunization program and then identified two monoclonal antibodies (MAbs), hMB621 and hMB668, targeting distinct epitopes on B6, from one vaccinee. Binding assays demonstrate that both MAbs exhibit broad binding abilities to B6 and its orthologs in vaccinia (VACV), variola (VARV) and cowpox viruses (CPXV). Neutralizing assays reveal that the two MAbs showed potent neutralization against VACV. Animal experiments using a BALB/c female mouse model indicate that the two MAbs showed effective protection against VACV via intraperitoneal injection. Additionally, we determined the complex structure of B6 and hMB668, revealing the structural feature of B6 and the epitope of hMB668. Collectively, our study provides two promising antibody candidates for the treatment of orthopoxvirus infections, including mpox.

Abstract Almost all the neutralizing antibodies targeting the receptor-binding domain (RBD) of spike (S) protein show weakened or lost efficacy against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged or emerging variants, such as Omicron and its sub-variants. This suggests that highly conserved epitopes are crucial for the development of neutralizing antibodies. Here, we present one nanobody, N235, displaying broad neutralization against the SARS-CoV-2 prototype and multiple variants, including the newly emerged Omicron and its sub-variants. Cryo-electron microscopy demonstrates N235 binds a novel, conserved, cryptic epitope in the N-terminal domain (NTD) of the S protein, which interferes with the RBD in the neighboring S protein. The neutralization mechanism interpreted via flow cytometry and Western blot shows that N235 appears to induce the S1 subunit shedding from the trimeric S complex. Furthermore, a nano-IgM construct (MN235), engineered by fusing N235 with the human IgM Fc region, displays prevention via inducing S1 shedding and cross-linking virus particles. Compared to N235, MN235 exhibits varied enhancement in neutralization against pseudotyped and authentic viruses in vitro. The intranasal administration of MN235 in low doses can effectively prevent the infection of Omicron sub-variant BA.1 and XBB in vivo, suggesting that it can be developed as a promising prophylactic antibody to cope with the ongoing and future infection.

SF6 is widely developed and applied in gas insulated electrical equipment due to its excellent arc extinguishing performance and insulation performance. However, SF6 is very destructive to the ozone layer and has a strong cumulative effect on the greenhouse effect of global warming. Therefore, the inevitable trend of the development of power grid equipment in the future is to reduce or even prohibit the use of SF6 gas in the power system, power industry and electrical equipment, or to find insulation media that can replace SF6. As a result, the research on environment-friendly insulating gas has become popular. In this paper, power frequency partial discharge platform and GC–MS​ detection platform are used to systematically study the partial discharge and decomposition characteristics of C5F10O under N2 and CO2 background gases. The partial discharge extinction voltage (PDIV) and partial discharge statistical characteristics of C5F10O under two different background gases, as well as the variety and concentration change of mixed gas decomposition products are theoretically analyzed and compared with SF6. It is found that, similar to SF6, the PDIV of C5F10O under two kinds of background gases increases linearly with the increase of air pressure. The PDIV under N2 background is smaller than that under CO2 background under the same conditions, and its insulation performance is slightly better than that under CO2 background. The partial discharge decomposition of C5F10O mixed gas mainly produces fluorocarbons such as CF4, C2F4, C2F6, C3F8, C3F6, and a small amount of CO, COF2, C3HF7, and CHF3. The increase of air pressure inhibits the partial discharge decomposition of mixed gas.

Abstract Background Surgery is a common treatment strategy for patients with neurofibromatosis type 1 (NF1)-related plexiform neurofibroma (PN) and has limited efficacy. FCN-159 is a novel anti-tumorigenic drug via selective inhibition of MEK1/2. This study assesses the safety and efficacy of FCN-159 in patients with NF1-related PN. Methods This is a multicenter, open-label, single-arm, phase I dose-escalation study. Patients with NF1-related PN that was non-resectable or unsuitable for surgery were enrolled; they received FCN-159 monotherapy daily in 28-day cycles. Results Nineteen adults were enrolled in the study, 3 in 4 mg, 4 in 6 mg, 8 in 8 mg, and 4 in 12 mg. Among patients included in dose-limiting toxicity (DLT) analysis, DLTs (grade 3 folliculitis) were reported in 1 of 8 patients (16.7%) receiving 8 mg and 3 of 3 (100%) patients receiving 12 mg. The maximum tolerated dose was determined to be 8 mg. FCN-159-related treatment-emergent adverse events (TEAEs) were observed in 19 patients (100%); most of which were grade 1 or 2. Nine (47.4%) patients reported grade 3 study-drug–related TEAEs across all dose levels, including four experiencing paronychia and five experiencing folliculitis. Of the 16 patients analyzed, all (100%) had reduced tumor size and six (37.5%) achieved partial responses; the largest reduction in tumor size was 84.2%. The pharmacokinetic profile was approximately linear between 4 and 12 mg, and the half-life supported once daily dosing. Conclusions FCN-159 was well tolerated up to 8 mg daily with manageable adverse events and showed promising anti-tumorigenic activity in patients with NF1-related PN, warranting further investigation in this indication. Trial registration ClinicalTrials.gov, NCT04954001. Registered 08 July 2021.

Highly contagious respiratory illnesses like influenza and COVID-19 pose serious risks to public health. A two-in-one vaccine would be ideal to avoid multiple vaccinations for these diseases. Here, we generated a chimeric receptor binding domain of the spike protein (S-RBD) and hemagglutinin (HA)-stalk-based vaccine for both SARS-CoV-2 and influenza viruses. The S-RBD from SARS-CoV-2 Delta was fused to the headless HA from H1N1 (H1Delta), creating a chimera that forms trimers in solution. The cryo-electron microscopy structure of the chimeric protein complexed with the RBD-targeting CB6 and the HA-stalk-targeting CR9114 antibodies shows that the trimeric protein is stable and accessible for neutralizing antibody binding. Immunization with the vaccine elicited high and long-lasting neutralizing antibodies and effectively protected mice against the challenges of lethal H1N1 or heterosubtypic H5N8, as well as the SARS-CoV-2 Delta or Omicron BA.2 variants. Overall, this study offers a two-in-one universal vaccine design to combat infections caused by both SARS-CoV-2 variants of concern and influenza viruses.

SARS-CoV-2 variants with severe immune evasion are a major challenge for COVID-19 prevention, especially the circulating Omicron XBB/BQ.1.1/BF.7 strains. Thus, the next-generation of broad-spectrum vaccines are urgently needed. Previously, we developed a COVID-19 protein subunit vaccine, ZF2001, based on the RBD-homodimer as the immunogen. To adapt SARS-CoV-2 variants, we developed chimeric RBD-heterodimers to induce broad immune responses. In this study, we further explored the concept of tandem RBD homotrimer and heterotrimer. Prototype SARS-CoV-2 RBD-homotrimer, prototype-Delta-BA.1 (PDO) RBD-heterotrimer and Delta-BA.2-BA.5 (DBA2BA5) RBD-heterotrimer were designed. Biochemical and cryo-EM structural characterization demonstrated total epitope exposure of the RBD-trimers. In mouse experiments, PDO and DBA2BA5 elicited broad SARS-CoV-2 neutralization. Potent protection against SARS-CoV-2 variants was observed in challenge assays and was correlated with neutralizing antibody titer. This study validated the design strategy of tandem RBD-heterotrimers as multivalent immunogens and presented a promising vaccine candidate, DBA2BA5, eliciting broad-spectrum immune responses, including against the circulating XBB/BF.7/BQ.1.1.

The SARS-CoV-2 Omicron variant spreads rapidly. Here the authors show that Omicron S preferentially adopts the one-RBD-up conformation, which leads to a non-RBM-binding monoclonal antibody escape. Mutagenesis reveals that S371L, S373P and S375F substitutions enhance the conformational stability.

Abstract The Omicron variant of SARS-CoV-2 carries multiple unusual mutations, particularly in the receptor-binding domain (RBD) of the spike (S) protein. Moreover, host-adapting mutations, such as residues 493, 498, and 501, were also observed in the Omicron RBD, which indicates that it is necessary to evaluate the interspecies transmission risk of the Omicron variant. Herein, we evaluated the interspecies recognition of the Omicron BA.1 and Delta RBDs by 27 ACE2 orthologs, including humans. We found that Omicron BA.1 expanded its receptor binding spectra to palm-civet, rodents, more bats (least horseshoe bat and greater horseshoe bat) and lesser hedgehog tenrec. Additionally, we determined the cryo-electron microscopy (cryo-EM) structure of the Omicron BA.1 S protein complexed with mouse ACE2 (mACE2) and the crystal structure of Omicron RBD complexed with palm-civet ACE2 (cvACE2). Several key residues for the host range have been identified. These results suggest that surveillance should be enhanced on the Omicron variant for its broader-species receptor binding to prevent spillover and expansion of reservoir hosts for a prolonged pandemic.

Introduction: Adverse drug reactions (ADR) are directly related to public health and become the focus of public and media attention. At present, a large number of ADR events have been reported on the Internet, but the mining and utilization of such information resources is insufficient. Named entity recognition (NER) is the basic work of many natural language processing (NLP) tasks, which aims to identify entities with specific meanings from natural language texts.Methods: In order to identify entities from ADR event data resources more effectively, so as to provide valuable health knowledge for people, this paper introduces ALBERT in the input presentation layer on the basis of the classic BiLSTM-CRF model, and proposes a method of ADR named entity recognition based on the ALBERT-BiLSTM-CRF model. The textual information about ADR on the website “Chinese medical information query platform” (https://www.dayi.org.cn) was collected by the crawler and used as research data, and the BIO method was used to label three types of entities: drug name (DRN), drug component (COM), and adverse drug reactions (ADR) to build a corpus. Then, the words were mapped to the word vector by using the ALBERT module to obtain the character level semantic information, the context coding was performed by the BiLSTM module, and the label decoding was using the CRF module to predict the real label.Results: Based on the constructed corpus, experimental comparisons were made with two classical models, namely, BiLSTM-CRF and BERT-BiLSTM-CRF. The experimental results show that the F1 of our method is 91.19% on the whole, which is 1.5% and 1.37% higher than the other two models respectively, and the performance of recognition of three types of entities is significantly improved, which proves the superiority of this method.Discussion: The method proposed can be used effectively in NER from ADR information on the Internet, which provides a basis for the extraction of drug-related entity relationships and the construction of knowledge graph, thus playing a role in practical health systems such as intelligent diagnosis, risk reasoning and automatic question answering.

This study documents equine ACE2 (eqACE2) binding to the RBDs of SARS-CoV-2 and related CoVs, revealing a mechanism of eqACE2 binding with RaTG13-RBD, SARS-CoV-2 prototype-RBD and Omicron BA.1-RBD.

Objective: FCN-159 is a highly active mitogen-activated extracellular signal-regulated kinase 1/2 (MEK1/2) inhibitor in patients with advanced melanoma and neurofibromatosis type 1 (NF1). We report a population pharmacokinetic (PopPK) model-based analysis of FCN-159 and its application to inform dose selection for NF1 pediatric trials.Methods: PK data collected from patients with advanced melanoma and NF1 in two clinical studies (NCT03932253 and NCT04954001) were analyzed using a non-linear mixed effects model. The adult model was adapted by incorporating allometric scaling for PK projection in 2–17 years old children. Pediatric exposure in different body surface area (BSA) bins was simulated to identify nominal doses (i.e., dose amounts given as integers) and BSA bin cutoffs to achieve exposure comparable to adults’ optimal exposure across the entire pediatric BSA range.Results: The final dataset consisted of 45 subjects with a total of 1030 PK samples. The PK of FCN-159 was well-described by a 2-compartment model with first-order linear elimination and delayed first-order absorption. Covariates, including BSA, age, sex, albumin, total protein, and cancer type, were identified as statistically significant predictors of FCN-159 disposition. Simulations based on the final model projected daily doses of 4 mg/m2 QD with optimized BSA bin cutoffs would allow fixed nominal doses within each bin and result in steady state exposure approximating the adult exposure observed at the recommended phase 2 dose (RP2D) in NF1, which is 8 mg QD.Conclusion: The developed population PK model adequately described the PK profile of FCN-159, which was adapted using allometric scaling to inform dose selection for NF1 pediatric trials.

Abstract The road extraction from high resolution remote sensing image is of great importance in a variety of applications. Recently, the abundant deep convolutional neural networks are proposed for road extraction task. However, the existing approaches lack suitable strategy to utilize multiple views road features for road extraction, which fails to extract road with smooth appearance and accurate boundary under complex scenes. To address this problem, the authors propose a novel deep residual and pyramid pooling network (DRPPNet) for extracting road regions from high resolution remote sensing image. The DRPPNet consists of three parts: deep residual network (DResNet), pyramid pooling module (PPM) and deep decoder (DD). Specially, the DResNet uses several residual blocks to extract deep road features from input images, which can enhance learning ability of DRPPNet and avoid gradient vanish. Then, PPM is proposed to fuse road features from multiple views and it aims to address disadvantage of single view feature. Finally, the DD is used to recover size of feature maps to input size. Extensive experiments on two challenging road datasets demonstrate that proposed method outperforms the state‐of‐the‐art methods greatly on performance of road extraction task.

In order to reduce the mismatching rate and improve the accuracy of disparity calculation, a stereo matching algorithm base on improved Census transform and path cost was proposed. Firstly, a reference pixel is applied to replace the center pixel by setting a threshold to improve the anti-noise ability. The improved Census transform algorithm is normalized weighted fusion with absolute differences (AD) algorithm and gradient information to improve the accuracy of parallax calculation and to enhance the stability of the algorithm. Secondly, a penalty factors determination method based on the pixel color difference on the path is designed to improve the matching effect of different regions. Finally, weighted median filtering and discontinuous disparity adjustment steps are added to further enhance the performance of the algorithm. The effectiveness of the proposed algorithm is verified by using 14 images which are selected from the Middlebury 2.0 test platform. The experimental results show that the average mismatch rate is 4.89%, and the proposed algorithm can effectively reduce the mismatching rate and improve the disparity effect.

ABSTRACT Virus-receptor interactions determine viral host range and tissue tropism. CD55 and human neonatal Fc receptor (FcRn) were found to be the binding and uncoating receptors for some of the echovirus-related enterovirus species B serotypes in our previous study. Echovirus 18 (E18), as a member of enterovirus species B, is a significant causative agent of aseptic meningitis and viral encephalitis in children. However, it does not use CD55 as a critical host factor. We conducted CRISPR/Cas9 knockout screening to determine the receptors and entry mechanisms and identified FcRn working as a dual-function receptor for E18. Knockout of FCGRT and B2M, which encode the two subunits of FcRn, prevented infection by E18 and other echoviruses in the same physiological cluster. We then elucidated the underlying molecular mechanism of receptor recognition by E18 using cryogenic electron microscopy. The binding of the FCGRT subunit to the canyon region rotates the residues around the pocket, triggering the release of the pocket factor as observed for other enterovirus species B members. IMPORTANCE E18 is a member of enterovirus species B. As one of the most common enterovirus serotypes in nonpolio enterovirus detection, it easily infects children and causes various clinical symptoms. Aseptic meningitis and viral encephalitis are the most commonly reported syndromes associated with E18. No effective antiviral drugs or approved vaccines are available. Previous studies showed that CD55 and FcRn were the binding and uncoating receptors for some echoviruses. However, we found that CD55 is not the critical host factor for E18. Thus, we want to determine the receptors and elucidate the entry mechanism of E18. Our findings reveal that FcRn is a two-in-one attachment-uncoating receptor for E18.

This paper investigates the effect of O2 on the partial discharge (PD) and decomposition characteristics of C5F10O/CO2/O2 gas mixture employed as an eco-friendly gas insulating medium. We found that the insulation performance of the C5F10O/CO2/O2 gas mixture is less degraded as the content of O2 is less than 4%. However, PDIV+ is significantly reduced, and the discharge repetition rate increases drastically when the O2 content exceeds 4%. The PD decomposition products of C5F10O/CO2/O2 mainly include CF4, C2F6, C3F8, C3F6, CF2O, C2F6O3, and C3F7H. The addition of 2% O2 has a strong inhibitory effect on the generation of CF4, C2F6, C3F6, C3F8, CF2O, and C2F6O3. This work provides guidance for the application of the C5F10O/CO2/O2 gas mixture.

As an emerging computing model, edge computing greatly expands the collaboration capabilities of the servers. It makes full use of the available resources around the users to quickly complete the task request coming from the terminal devices. Task offloading is a common solution for improving the efficiency of task execution on edge networks. However, the peculiarities of the edge networks, especially the random access of mobile devices, brings unpredictable challenges to the task offloading in a mobile edge network. In this paper, we propose a trajectory prediction model for moving targets in edge networks without users’ historical paths which represents their habitual movement trajectory. We also put forward a mobility-aware parallelizable task offloading strategy based on a trajectory prediction model and parallel mechanisms of tasks. In our experiments, we compared the hit ratio of the prediction model, network bandwidth and task execution efficiency of the edge networks by using the EUA data set. Experimental results showed that our model is much better than random, non-position prediction parallel, non-parallel strategy-based position prediction. Where the task offloading hit rate is closed to the user’s moving speed, when the speed is less 12.96 m/s, the hit rate can reach more than 80%. Meanwhile, we we also find that the bandwidth occupancy is significantly related to the degree of task parallelism and the number of services running on servers in the network. The parallel strategy can boost network bandwidth utilization by more than eight times when compared to a non-parallel policy as the number of parallel activities grows.

Mycobacterium tuberculosis (Mtb) caused an estimated 10 million cases of tuberculosis and 1.2 million deaths in 2019 globally. The increasing emergence of multidrug-resistant and extensively drug-resistant Mtb is becoming a public health threat worldwide and makes the identification of anti-Mtb drug targets urgent. Elongation factor G (EF-G) is involved in tRNA translocation on ribosomes during protein translation. Therefore, EF-G is a major focus of structural analysis and a valuable drug target of antibiotics. However, the crystal structure of Mtb EF-G1 is not yet available, and this has limited the design of inhibitors. Here, we report the crystal structure of Mtb EF-G1 in complex with GDP. The unique crystal form of the Mtb EF-G1-GDP complex provides an excellent platform for fragment-based screening using a crystallographic approach. Our findings provide a structure-based explanation for GDP recognition, and facilitate the identification of EF-G1 inhibitors with potential interest in the context of drug discovery.

The traditional video passive forensics method using only the principle of similarity between adjacent frames will cause a lot of false detection for the video with severe motion.Aiming at this problem, a video tamper detection method combining spatial constraints and gradient structure information was proposed. Firstly, the low motion region and the high texture region were extracted by using spatial constraint criteria. The two regions were merged to obtain the robust quantitative correlation rich regions for extracting video optimal similarity features. Then improving the extraction and description methods of the original features, and using the similarity of the gradient structure in accordance with the characteristics of the human visual system to calculate the spatial constraint correlation value. Finally, the tampering points were located by the Chebyshev inequality. Experiments show that the proposed algorithm has lower false detection rate and higher accuracy.

Data masking is an inborn defect of measures of disproportionality in adverse drug reactions signal detection. Some improved methods which used gender and age for data stratification only considered the patient-related confounding factors, ignoring the drug-related influencing factors. Due to a large number of reports and the high proportion of antibiotics in the Chinese spontaneous reporting database, this paper proposes a decision tree-stratification method for the minimization of the masking effect by integrating the relevant factors of patients and drugs. The adverse drug reaction monitoring reports of Jiangsu Province in China from 2011 to 2018 were selected for this study. First, the age division interval was determined based on the statistical analysis of antibiotic-related data. Secondly, correlation analysis was conducted based on the patient’s gender and age respectively with the drug category attributes. Thirdly, the decision tree based on age and gender was constructed by the J48 algorithm, which was used to determine if drugs belonged to antibiotics as a classification label. Fourthly, some performance evaluation indicators were constructed based on the data of drug package inserts as a standard signal library: recall, precision, and F (the arithmetic harmonic mean of recall and precision). Finally, four experiments were carried out by means of the proportional reporting ratio method: non-stratification (total data), gender-stratification, age-stratification and decision tree-stratification, and the performance of the signal detection results was compared. The experimental results showed that the decision tree-stratification was superior to the other three methods. Therefore, the data-masking effect can be further minimized by comprehensively considering the patient and drug-related confounding factors.

Adverse drug reactions (ADRs) are increasingly becoming a serious public health problem. Spontaneous reporting systems (SRSs) are an important way for many countries to monitor ADRs produced in the clinical use of drugs, and they are the main data source for ADR signal detection. The traditional signal detection methods are based on disproportionality analysis (DPA) and lack the application of data mining technology. In this paper, we selected the spontaneous reports from 2011 to 2018 in Jiangsu Province of China as the research data and used association rules analysis (ARA) to mine signals. We defined some important metrics of the ARA according to the two-dimensional contingency table of ADRs, such as Confidence and Lift, and constructed performance evaluation indicators such as Precision, Recall, and F1 as objective standards. We used experimental methods based on data to objectively determine the optimal thresholds of the corresponding metrics, which, in the best case, are Confidence = 0.007 and Lift = 1. We obtained the average performance of the method through 10-fold cross-validation. The experimental results showed that F1 increased from 31.43% in the MHRA method to 40.38% in the ARA method; this was a significant improvement. To reduce drug risk and provide decision making for drug safety, more data mining methods need to be introduced and applied to ADR signal detection.

Human beings have been recognizing and treating epilepsy for more than 3000 years. The cause of this disease was understood as demons in ancient times. A theory of brain disease was firstly proposed by Hippocrates. Although there were many years of case observation and development of anatomy, epilepsy had not been better recognized. Before 1000 years, the word "epilepsy" was firstly called by Avicenna. Afterward scholars of many countries had made elementary progress in classification, signs and symptoms, pathology and treatment of epilepsy. The first monograph of epilepsy was published in 1881 by Gower. Then drug treatment and surgical treatment had been applied and developed. In recent 100 years, due to the establishment of International League Against Epilepsy (ILAE), publishing of Epilepsia and clinical employment of EEG, rapid progress has been made in basic and clinical study of epilepsy under the help of many other relative scientific knowledge. As early as over 3000 years before, there was already treatment of traditional Chinese medicine for epilepsy recorded in China. During the past 30 years, obvious advancement of learning and growing number of specialized personnel have been pushing epilepsy research forward in China. doi: 10.3969/j.issn.1672-6731.2014.11.003

Objective To explore the risk factors for epilepsy associated with ischemic stroke in order to strengthen the early prevention, guide clinical treatment and improve prognosis. Methods One hundred and one patients with epilepsy associated with ischemic stroke within 24 hours and 101 ischemic stroke patients without epilepsy were randomly treated in Tianjin Huanhu Hospital from May 2007 to January 2009. According to the development of seizures they were divided into 2 groups: epilepsy group and stroke group. The score of clinical neurologic function deficiency was assessed according to the Scandinavian Stroke Scale (SSS) at the time of admission. All the patients accepted computerized tomography (CT) or magnetic resonance imaging (MRI) within 48 h after onset. Recorded age, gender, past history which included hypertension, coronary heart disease, atrial fibrillation, Type Ⅱ diabetes and hyperlipidemia, serum electrolyte (potassium ion, sodium ion and chloridion), disease status (quiet or active), ischemic stroke subtype which included atherosclerotic thrombotic cerebral infarction, cerebral embolism and lacunar infarct, angiorrhea after cerebral infarction, affected lobar or basal ganglia, affected hemisphere (left, right or bilateral), brain atrophy, white matter demyelination and neurological deficit scores. Unconditional multivariate Logistic regression analysis was performed for those factors which were significant after univariate analysis. Results The univariate analysis on epilepsy group and stroke group showed that significant differences in ischemic stroke subtype (cerebral embolism), angiorrhea after cerebral infarction, lesion location, especially affected frontal which achieved 48.72%, affected right hemisphere and neurological deficit scores (the SSS) were seen between the two groups (P ≤ 0.05, for all). Age, gender, past history which included hypertension, coronary heart disease, atrial fibrillation, diabetes and hyperlipidemia, serum electrolyte (potassium ion, sodium ion and chloridion), disease status (quiet or active), brain atrophy and white matter demyelination presented no significant differences (P > 0.05, for all). In unconditional multivariate Logistic regression analysis, cerebral embolism (OR = 0.152, 95% CI: 0.065-0.496; P = 0.011), angiorrhea after cerebral infarction (OR = 0.105, 95% CI: 0.020-0.549; P = 0.008), lobar cortical location (OR = 0.099, 95%CI:0.044-0.225; P = 0.000) and the SSS < 30 (OR = 0.145, 95%CI: 0.062-0.337; P = 0.000) were the main risk factors for epilepsy associated with ischemic stroke, whereas, affected right hemisphere (OR = 0.638, 95%CI: 0.311-1.308; P = 0.220) may not increase the probability of epilepsy associated with ischemic stroke. Conclusion Ischemic stroke patients who have cerebral embolism, angiorrhea after cerebral infarction, cortical location (especially affected frontal), the SSS < 30 are prone to develop epilepsy associated with ischemic stroke. DOI：10.3969/j.issn.1672-6731.2010.02.016

The objectives of this study were to define the pharmacokinetics of recombinant human interleukin-1 receptor antagonist (rhIL-1ra) and its effects on allergic asthma, cell adhesion molecules, and upper respiratory tract following non-parenteral administration in animals. Pharmacokinetics and immunomodulating effects of rhIL-1ra were investigated in Sprague-Dawley rats and asthmatic guinea pigs, respectively. Effects on the upper respiratory tract following the applications of rhIL-1ra were investigated on the ex vivo nasal mucosa of Sprague-Dawley rats and in situ in the upper palate of Chinese toads. Absolute bioavailabilities after intratracheal and intranasal administrations of rhIL-1ra were 94.3% and 24.8%, respectively. After administration of rhIL-1ra solution as ultrasonic spraying, the asthmatic symptom in guinea pigs was obviously attenuated. The plasma soluble intercellular cell adhesion molecule (sICAM-1) and P-selectin levels in asthmatic guinea pigs were each dose-dependently reduced with the increase of rhIL-1ra dose. The rhIL-1ra solution after administration via the airway seemed to have no impact on the integrity of nasal mucosa and mucocilia clearance in the upper respiratory tract. The present study provides evidence that rhIL-1ra effectively suppresses allergen-induced asthmatic symptoms through spraying, which corresponds to nasal and pulmonary absorption or both, and the efficacy is associated with downregulation of sICAM-1 and P-selectin expressions. Keywords:: recombinant human interleukin-1 receptor antagonist (rhIL-1ra), pharmacokinetics, asthma, soluble intercellular cell adhesion molecule (sICAM-1), P-selectin

Under the conditions of high demand for energy saving and environmental protection, the thermal power unit is required to phase out the traditional extensive operation mode—a method of oxygen-enriched combustion in a furnace, considering safety first. Achieving efficient and economic operation with an optimal proportion of air distribution in these thermal power units is crucial. The high-precision simulation equipment could provide an experimental basis for optimal operation of field units. This paper starts by improving the accuracy of simulation equipment. In this work, the method of dividing nodes and branches in the boiler was based on signal flow graph theory. According to the flow characteristics of the working substance, the method for calculating the node and branch pressure drop was analyzed and set up. Subsequently, a fluid network model of the multi-dimensional flue gas system was constructed. With the help of our self-developed simulation model and data-driven platform, a modular simulation algorithm was designed. The simulation analysis of the boiler showed the accuracy of the model.