SPRDA: a link prediction approach based on the structural perturbation to infer disease-associated Piwi-interacting RNAs.

piRNA and PIWI proteins have been confirmed for disease diagnosis and treatment as novel biomarkers due to its abnormal expression in various cancers. However, the current research is not strong enough to further clarify the functions of piRNA in cancer and its underlying mechanism. Therefore, how to provide large-scale and serious piRNA candidates for biological research has grown up to be a pressing issue. In this study, a novel computational model based on the structural perturbation method is proposed to predict potential disease-associated piRNAs, called SPRDA. Notably, SPRDA belongs to positive-unlabeled learning, which is unaffected by negative examples in contrast to previous approaches. In the 5-fold cross-validation, SPRDA shows high performance on the benchmark dataset piRDisease, with an AUC of 0.9529. Furthermore, the predictive performance of SPRDA for 10 diseases shows the robustness of the proposed method. Overall, the proposed approach can provide unique insights into the pathogenesis of the disease and will advance the field of oncology diagnosis and treatment.

iGRLCDA: identifying circRNA-disease association based on graph representation learning.

While the technologies of ribonucleic acid-sequence (RNA-seq) and transcript assembly analysis have continued to improve, a novel topology of RNA transcript was uncovered in the last decade and is called circular RNA (circRNA). Recently, researchers have revealed that they compete with messenger RNA (mRNA) and long noncoding for combining with microRNA in gene regulation. Therefore, circRNA was assumed to be associated with complex disease and discovering the relationship between them would contribute to medical research. However, the work of identifying the association between circRNA and disease in vitro takes a long time and usually without direction. During these years, more and more associations were verified by experiments. Hence, we proposed a computational method named identifying circRNA-disease association based on graph representation learning (iGRLCDA) for the prediction of the potential association of circRNA and disease, which utilized a deep learning model of graph convolution network (GCN) and graph factorization (GF). In detail, iGRLCDA first derived the hidden feature of known associations between circRNA and disease using the Gaussian interaction profile (GIP) kernel combined with disease semantic information to form a numeric descriptor. After that, it further used the deep learning model of GCN and GF to extract hidden features from the descriptor. Finally, the random forest classifier is introduced to identify the potential circRNA-disease association. The five-fold cross-validation of iGRLCDA shows strong competitiveness in comparison with other excellent prediction models at the gold standard data and achieved an average area under the receiver operating characteristic curve of 0.9289 and an area under the precision-recall curve of 0.9377. On reviewing the prediction results from the relevant literature, 22 of the top 30 predicted circRNA-disease associations were noted in recent published papers. These exceptional results make us believe that iGRLCDA can provide reliable circRNA-disease associations for medical research and reduce the blindness of wet-lab experiments.

SiSGC: A Drug Repositioning Prediction Model Based on Heterogeneous Simplifying Graph Convolution.

Drug repositioning plays a key role in disease treatment. With the large-scale chemical data increasing, many computational methods are utilized for drug-disease association prediction. However, most of the existing models neglect the positive influence of non-Euclidean data and multisource information, and there is still a critical issue for graph neural networks regarding how to set the feature diffuse distance. To solve the problems, we proposed SiSGC, which makes full use of the biological knowledge information as initial features and learns the structure information from the constructed heterogeneous graph with the adaptive selection of the information diffuse distance. Then, the structural features are fused with the denoised similarity information and fed to the advanced classifier of CatBoost to make predictions. Three different data sets are used to confirm the robustness and generalization of SiSGC under two splitting strategies. Experiment results demonstrate that the proposed model achieves superior performance compared with the six leading methods and four variants. Our case study on breast neoplasms further indicates that SiSGC is trustworthy and robust yet simple. We also present four drugs for breast cancer treatment with high confidence and further give an explanation for demonstrating the rationality. There is no doubt that SiSGC can be used as a beneficial supplement for drug repositioning.

[Expression and significance of hypoxia-inducible factor 1α and Bcl-2/adenovirus E1B19kDa-interacting protein 3 in children with traumatic brain injury]. / ç¼ºæ°§è¯±å¯¼å› å­1α、Bcl-2/è ºç— æ¯’E1B19kDa相互作用蛋白3在儿童创伤性脑损伤中的表达及意义.

OBJECTIVES: To dynamically observe the changes in hypoxia-inducible factor 1α (HIF-1α) and Bcl-2/adenovirus E1B19kDa-interacting protein 3 (BNIP3) in children with traumatic brain injury (TBI) and evaluate their clinical value in predicting the severity and prognosis of pediatric TBI. METHODS: A prospective study included 47 children with moderate to severe TBI from January 2021 to July 2023, categorized into moderate (scores 9-12) and severe (scores 3-8) subgroups based on the Glasgow Coma Scale. A control group consisted of 30 children diagnosed and treated for inguinal hernia during the same period, with no underlying diseases. The levels of HIF-1α, BNIP3, autophagy-related protein Beclin-1, and S100B were compared among groups. The predictive value of HIF-1α, BNIP3, Beclin-1, and S100B for the severity and prognosis of TBI was assessed using receiver operating characteristic (ROC) curves. RESULTS: Serum levels of HIF-1α, BNIP3, Beclin-1, and S100B in the TBI group were higher than those in the control group (P<0.05). Among the TBI patients, the severe subgroup had higher levels of HIF-1α, BNIP3, Beclin-1, and S100B than the moderate subgroup (P<0.05). Correlation analysis showed that the serum levels of HIF-1α, BNIP3, Beclin-1, and S100B were negatively correlated with the Glasgow Coma Scale scores (P<0.05). After 7 days of treatment, serum levels of HIF-1α, BNIP3, Beclin-1, and S100B in both non-surgical and surgical TBI patients decreased compared to before treatment (P<0.05). ROC curve analysis indicated that the areas under the curve for predicting severe TBI based on serum levels of HIF-1α, BNIP3, Beclin-1, and S100B were 0.782, 0.835, 0.872, and 0.880, respectively (P<0.05), and for predicting poor prognosis of TBI were 0.749, 0.775, 0.814, and 0.751, respectively (P<0.05). CONCLUSIONS: Serum levels of HIF-1α, BNIP3, and Beclin-1 are significantly elevated in children with TBI, and their measurement can aid in the clinical assessment of the severity and prognosis of pediatric TBI.

Anterior column realignment via a minimally invasive hybrid approach in adult spinal deformity surgery: a short-term retrospective study.

BACKGROUND: Anterior column realignment (ACR) is a novel surgical method for correcting spinal sagittal balance. meanwhile, oblique lumbar interbody fusion (OLIF) and anterior lumbar interbody fusion (ALIF) are considered minimally invasive surgical methods through natural anatomical space. This study aimed to explore the corrective effects and clinical outcomes of OLIF or ALIF combined with ACR technology in patients with adult spinal deformity (ASD). METHODS: We retrospectively analyzed patients with sagittal imbalance who received OLIF and/or ALIF and ACR treatment from 2018 to 2021. Surgical time and intraoperative bleeding volume are recorded, the corrective effect is determined by the intervertebral space angle (IVA), lumbar lordosis (LL), the sagittal vertical axis (SVA), clinical outcome is evaluated by preoperative and final follow-up visual analog pain score (VAS), Japanese orthopedic association scores (JOA) and complications. RESULTS: Sixty-four patients were enrolled in the study, average age of 65.1(range, 47-82) years. All patients completed 173 fusion segments, for 150 segments of ACR surgery. The operation time of ALIF-ACR was 50.4 ± 22.1 min; The intraoperative bleeding volume was 50.2 ± 23.6 ml. The operation time and intraoperative bleeding volume of single-segment OLIF-ACR was 66.2 ± 19.4 min and 70.2 ± 31.6 ml. At the follow-up of 6 months after surgery, the intervertebral space angle correction for OLIF-ACR and ALIF-ACR is 9.2° and 12.2°, the preoperative and postoperative lumbar lordosis were 16.7° ± 6.4°and 47.1° ± 3.6° (p < 0.001), VAS and JOA scores were improved from 6.8 to 1.8 and 7.8 to 22.1 respectively, statistically significant differences were observed in these parameters. The incidence of surgical related complications is 29.69%, but without serious complications. CONCLUSION: ACR via a minimally invasive hybrid approach for ASD has significant advantages in restoring local intervertebral space angulation and correcting the overall sagittal balance. Simultaneously, it can achieve good clinical outcomes and fewer surgical complications.

Experimental Realization of Weyl Exceptional Rings in a Synthetic Three-Dimensional Non-Hermitian Phononic Crystal.

Weyl points-topological monopoles of quantized Berry flux-are predicted to spread to Weyl exceptional rings in the presence of non-Hermiticity. Here, we use a one-dimensional Aubry-Andre-Harper model to construct a Weyl semimetal in a three-dimensional parameter space comprising one reciprocal dimension and two synthetic dimensions. The inclusion of non-Hermiticity in the form of gain and loss produces a synthetic Weyl exceptional ring (SWER). The topology of the SWER is characterized by both its topological charge and non-Hermitian winding numbers. We experimentally observe the SWER and synthetic Fermi arc in a one-dimensional phononic crystal with the non-Hermiticity introduced by active acoustic components. Our findings pave the way for studying the high-dimensional non-Hermitian topological physics in acoustics.

DF-MDA: An effective diffusion-based computational model for predicting miRNA-disease association.

It is reported that microRNAs (miRNAs) play an important role in various human diseases. However, the mechanisms of miRNA in these diseases have not been fully understood. Therefore, detecting potential miRNA-disease associations has far-reaching significance for pathological development and the diagnosis and treatment of complex diseases. In this study, we propose a novel diffusion-based computational method, DF-MDA, for predicting miRNA-disease association based on the assumption that molecules are related to each other in human physiological processes. Specifically, we first construct a heterogeneous network by integrating various known associations among miRNAs, diseases, proteins, long non-coding RNAs (lncRNAs), and drugs. Then, more representative features are extracted through a diffusion-based machine-learning method. Finally, the Random Forest classifier is adopted to classify miRNA-disease associations. In the 5-fold cross-validation experiment, the proposed model obtained the average area under the curve (AUC) of 0.9321 on the HMDD v3.0 dataset. To further verify the prediction performance of the proposed model, DF-MDA was applied in three significant human diseases, including lymphoma, lung neoplasms, and colon neoplasms. As a result, 47, 46, and 47 out of top 50 predictions were validated by independent databases. These experimental results demonstrated that DF-MDA is a reliable and efficient method for predicting potential miRNA-disease associations.

Hidden blood loss of minimally invasive hybrid lumbar interbody fusion: an analysis of influencing factors.

BACKGROUND: Lumbar interbody fusion(LIF) is the leading way to treat Lumbar Degenerative Diseases(LDD). At present, there is a lack of research on the influencing factors of hidden blood loss in minimally invasive hybrid lumbar interbody fusion. This study comprehensively explores the definite factors affecting the hidden blood loss in minimally invasive hybrid lumbar interbody fusion. MATERIALS AND METHODS: One hundred patients with Lumbar degenerative diseases who underwent minimally invasive hybrid lumbar interbody fusion in our center were included. Demographics, laboratory data, surgical data, and radiographic data were collected. The Gross equation and Sehat equation were used to calculate the estimated value of hidden blood loss. Multi-factor linear regression analysis was used to determine the influencing factors of hidden blood loss. RESULT: We reviewed and collected 100 patients who underwent minimally invasive hybrid approach, mean age 65 ± 10 years, male: female 37:63; 17 patients of diabetes and 83 patients of non-diabetes; Total blood loss was 645.59 ± 376.37 ml, hidden blood loss was 421.39 ± 337.45 ml, the hidden blood loss percentage was 57 ± 26%. Results from the multi-factorial linear regression model: Diabetes (p < 0.05), hypertension (p < 0.05), psoas thickness (p < 0.05) and dorsal extensor group thickness (p < 0.05) were potential risk factors for postoperative hidden blood loss. CONCLUSION: Although minimally invasive hybrid approach is minimally invasive surgery, there is still a significant amount of hidden blood loss. There is a greater risk of blood loss in diabetes, hypertension and preoperative MRI assessment of thickness of the psoas, thickness of the dorsal extensor group.

microRNA-4500 inhibits human glioma cell progression by targeting IGF2BP1.

Insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1) overexpression promotes glioma cell progression. The aim of the current study is to silence IGF2BP1 in glioma cells by the microRNA (miRNA) strategy. The bio-informatic analyses identified that microRNA-4500 (miR-4500) putatively targets 3'-UTR (3'-untranslated region) of IGF2BP1. In A172 cells and primary human glioma cells ectopic overexpression of the wild-type miR-4500 (but not the mutant form) downregulated IGF2BP1 and its target genes (Gli1, IGF2 and c-Myc). Functional studies show that ectopic miR-4500 overexpression inhibited glioma cell growth, survival, proliferation, migration and invasion. Conversely, in A172 cells miR-4500 inhibition, by a lentiviral construct, increased expression of IGF2BP1 and its targets, promoting cell survival, proliferation and migration. Furthermore, IGF2BP1 knockout by the CRISPR/Cas9 method inhibited A172 cell progression. Significantly, miR-4500 overexpression or miR-4500 inhibition was ineffective in IGF2BP1 knockout A172 cells. At last, we show that miR-4500 levels are downregulated in human glioma tissues, correlating with IGF2BP1 upregulation. Together, we conclude that miR-4500 inhibits human glioma cell progression by targeting IGF2BP1.

MLMDA: a machine learning approach to predict and validate MicroRNA-disease associations by integrating of heterogenous information sources.

BACKGROUND: Emerging evidences show that microRNA (miRNA) plays an important role in many human complex diseases. However, considering the inherent time-consuming and expensive of traditional in vitro experiments, more and more attention has been paid to the development of efficient and feasible computational methods to predict the potential associations between miRNA and disease. METHODS: In this work, we present a machine learning-based model called MLMDA for predicting the association of miRNAs and diseases. More specifically, we first use the k-mer sparse matrix to extract miRNA sequence information, and combine it with miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity information. Then, more representative features are extracted from them through deep auto-encoder neural network (AE). Finally, the random forest classifier is used to effectively predict potential miRNA-disease associations. RESULTS: The experimental results show that the MLMDA model achieves promising performance under fivefold cross validations with AUC values of 0.9172, which is higher than the methods using different classifiers or different feature combination methods mentioned in this paper. In addition, to further evaluate the prediction performance of MLMDA model, case studies are carried out with three Human complex diseases including Lymphoma, Lung Neoplasm, and Esophageal Neoplasms. As a result, 39, 37 and 36 out of the top 40 predicted miRNAs are confirmed by other miRNA-disease association databases. CONCLUSIONS: These prominent experimental results suggest that the MLMDA model could serve as a useful tool guiding the future experimental validation for those promising miRNA biomarker candidates. The source code and datasets explored in this work are available at http://220.171.34.3:81/ .

DRMDA: deep representations-based miRNA-disease association prediction.

Recently, microRNAs (miRNAs) are confirmed to be important molecules within many crucial biological processes and therefore related to various complex human diseases. However, previous methods of predicting miRNA-disease associations have their own deficiencies. Under this circumstance, we developed a prediction method called deep representations-based miRNA-disease association (DRMDA) prediction. The original miRNA-disease association data were extracted from HDMM database. Meanwhile, stacked auto-encoder, greedy layer-wise unsupervised pre-training algorithm and support vector machine were implemented to predict potential associations. We compared DRMDA with five previous classical prediction models (HGIMDA, RLSMDA, HDMP, WBSMDA and RWRMDA) in global leave-one-out cross-validation (LOOCV), local LOOCV and fivefold cross-validation, respectively. The AUCs achieved by DRMDA were 0.9177, 08339 and 0.9156 ± 0.0006 in the three tests above, respectively. In further case studies, we predicted the top 50 potential miRNAs for colon neoplasms, lymphoma and prostate neoplasms, and 88%, 90% and 86% of the predicted miRNA can be verified by experimental evidence, respectively. In conclusion, DRMDA is a promising prediction method which could identify potential and novel miRNA-disease associations.

PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction.

In the recent few years, an increasing number of studies have shown that microRNAs (miRNAs) play critical roles in many fundamental and important biological processes. As one of pathogenetic factors, the molecular mechanisms underlying human complex diseases still have not been completely understood from the perspective of miRNA. Predicting potential miRNA-disease associations makes important contributions to understanding the pathogenesis of diseases, developing new drugs, and formulating individualized diagnosis and treatment for diverse human complex diseases. Instead of only depending on expensive and time-consuming biological experiments, computational prediction models are effective by predicting potential miRNA-disease associations, prioritizing candidate miRNAs for the investigated diseases, and selecting those miRNAs with higher association probabilities for further experimental validation. In this study, Path-Based MiRNA-Disease Association (PBMDA) prediction model was proposed by integrating known human miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases. This model constructed a heterogeneous graph consisting of three interlinked sub-graphs and further adopted depth-first search algorithm to infer potential miRNA-disease associations. As a result, PBMDA achieved reliable performance in the frameworks of both local and global LOOCV (AUCs of 0.8341 and 0.9169, respectively) and 5-fold cross validation (average AUC of 0.9172). In the cases studies of three important human diseases, 88% (Esophageal Neoplasms), 88% (Kidney Neoplasms) and 90% (Colon Neoplasms) of top-50 predicted miRNAs have been manually confirmed by previous experimental reports from literatures. Through the comparison performance between PBMDA and other previous models in case studies, the reliable performance also demonstrates that PBMDA could serve as a powerful computational tool to accelerate the identification of disease-miRNA associations.

Fatigue Performance of RC Beams Strengthened with CFRP under Overloads with a Ladder Spectrum.

Vehicle overload is detrimental to bridges and traffic safety. This paper presents a study on the fatigue performance of typical reinforced concrete (RC) beams of highway bridges under vehicle overload. A definition method of vehicle overload and a construction method of overload ladder spectrum were first proposed based on traffic data acquisition, statistical analysis and structural calculation of the highway bridges in Guangzhou. A fatigue experimental method was also proposed with the three-ladder vehicle overload spectrum, and the fatigue tests of 15 RC beams strengthened with carbon fiber reinforced polymer (CFRP) under three loading levels were then carried out. The fatigue performance and the failure mechanism of the strengthened beams were presented and discussed, and two fatigue life prediction methods were proposed with the established modified Palmgren-Miner rule and the loading level equivalent method respectively. The results showed that the fatigue performance of the strengthened RC beams was severely degraded under overload ladder spectrum compared with that under constant amplitude cyclic load, and the life prediction methods were proved effective.

Cdc42 Promotes Schwann Cell Proliferation and Migration Through Wnt/ß-Catenin and p38 MAPK Signaling Pathway After Sciatic Nerve Injury.

Schwann cells (SCs) are unique glial cells in the peripheral nerve and may secrete multiple neurotrophic factors, adhesion molecules, extracellular matrix molecules to form the microenvironment of peripheral nerve regeneration, guiding and supporting nerve proliferation and migration. Cdc42 plays an important regulatory role in dynamic changes of the cytoskeleton. However, there is a little study referred to regulation and mechanism of Cdc42 on glial cells after peripheral nerve injury. The present study investigated the role of Cdc42 in the proliferation and migration of SCs after sciatic nerve injury. Cdc42 expression was tested, showing that the mRNA and protein expression levels of Cdc42 were significantly up-regulated after sciatic nerve injury. Then, we isolated and purified SCs from injuried sciatic nerve at day 7. The purified SCs were transfected with Cdc42 siRNA and pcDNA3.1-Cdc42, and the cell proliferation, cell cycle and migration were assessed. The results implied that Cdc42 siRNA remarkably inhibited Schwann cell proliferation and migration, and resulted in S phase arrest. While pcDNA3.1-Cdc42 showed a contrary effect. Besides, we also observed that Cdc42 siRNA down-regulated the protein expression of ß-catenin, Cyclin D1, c-myc and p-p38, which were up-regulated by pcDNA3.1-Cdc42. Meanwhile, the inhibitor of Wnt/ß-catenin and p38 MAPK signaling pathway IWP-2 and SB203580 significantly inhibited the effect of pcDNA3.1-Cdc42 on cell proliferation and migration. Overall, our data indicate that Cdc42 regulates Schwann cell proliferation and migration through Wnt/ß-catenin and p38 MAPK signaling pathway after sciatic nerve injury, which provides further insights into the therapy of the sciatic nerve injury.

Adaption of microbial community during the start-up stage of a thermophilic anaerobic digester treating food waste.

A successful start-up enables acceleration of anaerobic digestion (AD) into steady state. The microbial community influences the AD performance during the start-up. To investigate how microbial communities changed during the start-up, microbial dynamics was analyzed via high-throughput sequencing in this study. The results confirmed that the AD was started up within 25 d. Thermophilic methanogens and bacterial members functioning in hydrolysis, acidogenesis, and syntrophic oxidation became predominant during the start-up stage, reflecting a quick adaption of microorganisms to operating conditions. Such predominance also indicated the great contribution of these members to the fast start-up of AD. Redundancy analysis confirmed that the bacterial abundance significantly correlated with AD conditions. The stable ratio of hydrogenotrophic methanogens to aceticlastic methanogens is also important to maintain the stability of the AD process. This work will be helpful to understand the contribution of microbial community to the start-up of AD.

Highly Accurate Prediction of Protein-Protein Interactions via Incorporating Evolutionary Information and Physicochemical Characteristics.

Protein-protein interactions (PPIs) occur at almost all levels of cell functions and play crucial roles in various cellular processes. Thus, identification of PPIs is critical for deciphering the molecular mechanisms and further providing insight into biological processes. Although a variety of high-throughput experimental techniques have been developed to identify PPIs, existing PPI pairs by experimental approaches only cover a small fraction of the whole PPI networks, and further, those approaches hold inherent disadvantages, such as being time-consuming, expensive, and having high false positive rate. Therefore, it is urgent and imperative to develop automatic in silico approaches to predict PPIs efficiently and accurately. In this article, we propose a novel mixture of physicochemical and evolutionary-based feature extraction method for predicting PPIs using our newly developed discriminative vector machine (DVM) classifier. The improvements of the proposed method mainly consist in introducing an effective feature extraction method that can capture discriminative features from the evolutionary-based information and physicochemical characteristics, and then a powerful and robust DVM classifier is employed. To the best of our knowledge, it is the first time that DVM model is applied to the field of bioinformatics. When applying the proposed method to the Yeast and Helicobacter pylori (H. pylori) datasets, we obtain excellent prediction accuracies of 94.35% and 90.61%, respectively. The computational results indicate that our method is effective and robust for predicting PPIs, and can be taken as a useful supplementary tool to the traditional experimental methods for future proteomics research.

Brain natriuretic peptide suppresses pain induced by BmK I, a sodium channel-specific modulator, in rats.

BACKGROUND: A previous study found that brain natriuretic peptide (BNP) inhibited inflammatory pain via activating its receptor natriuretic peptide receptor A (NPRA) in nociceptive sensory neurons. A recent study found that functional NPRA is expressed in almost all the trigeminal ganglion (TG) neurons at membrane level suggesting a potentially important role for BNP in migraine pathophysiology. METHODS: An inflammatory pain model was produced by subcutaneous injection of BmK I, a sodium channel-specific modulator from venom of Chinese scorpion Buthus martensi Karsch. Quantitative PCR, Western Blot, and immunohistochemistry were used to detect mRNA and protein expression of BNP and NPRA in dorsal root ganglion (DRG) and dorsal horn of spinal cord. Whole-cell patch clamping experiments were conducted to record large-conductance Ca2+-activated K+ (BKCa) currents of membrane excitability of DRG neurons. Spontaneous and evoked pain behaviors were examined. RESULTS: The mRNA and protein expression of BNP and NPRA was up-regulated in DRG and dorsal horn of spinal cord after BmK I injection. The BNP and NPRA was preferentially expressed in small-sized DRG neurons among which BNP was expressed in both CGRP-positive and IB4-positive neurons while NPRA was preferentially expressed in CGRP-positive neurons. BNP increased the open probability of BKCa channels and suppressed the membrane excitability of small-sized DRG neurons. Intrathecal injection of BNP significantly inhibited BmK-induced pain behaviors including both spontaneous and evoked pain behaviors. CONCLUSIONS: These results suggested that BNP might play an important role as an endogenous pain reliever in BmK I-induced inflammatory pain condition. It is also suggested that BNP might play a similar role in other pathophysiological pain conditions including migraine.

Unconditionally stable WLP-FDTD method for the modeling of electromagnetic wave propagation in gyrotropic materials.

The unconditional stable finite-difference time-domain (FDTD) method based on field expansion with weighted Laguerre polynomials (WLPs) is applied to model electromagnetic wave propagation in gyrotropic materials. The conventional Yee cell is modified to have the tightly coupled current density components located at the same spatial position. The perfectly matched layer (PML) is formulated in a stretched-coordinate (SC) system with the complex-frequency-shifted (CFS) factor to achieve good absorption performance. Numerical examples are shown to validate the accuracy and efficiency of the proposed method.

Spinal 5-HT3AR contributes to BmK I-induced inflammatory pain in rats.

Subcutaneous injection of BmK I could be adopted to well establish a novel pain model. Moreover, 5-hydroxytryptamine (serotonin, 5-HT) receptor is involved in regulating animal pain-related behaviors. However, the underlying mechanism of 5-HT3R on BmK I-induced pain remains unclear. Animal behavioral testing, RT-PCR and Western blotting were used to yield the following results: first, intraplantar (i.pl.) injection of BmK I (10 µg) induced elevated mRNA and protein levels of 5-HT3AR in bilateral L4-L5 spinal cord; Second, intrathecal (i.t.) injection of ondansetron (a specific antagonist of 5-HT3AR) reduced spontaneous pain responses, attenuated unilateral thermal and bilateral mechanical hypersensitivity elicited by BmK I; Microglia could be activated by BmK I (i.pl.) in both sides of L4-L5 spinal cord, and this effect was reversed by intrathecal pre-treatment with 5-HT3AR antagonist. Meanwhile, the 5-HT3AR in L4-L5 spinal cord was almost co-localized with NeuN (a marker of nerve cell), but not co-expressed with Iba-1 (a marker of microglia). Finally, the expression level of CX3CL1 and CX3CR1 was reduced by intrathecal pre-treatment with ondansetron. Our results indicate that both 5-HT3AR signaling pathway and microglia are activated in the process of induction and maintenance of BmK I-induced pain nociception. Meanwhile, our results suggest that the neuronal 5-HT3AR may communicate with microglia indirectly via CX3CL1 which is involved in regulating the BmK I-induced hyperalgesia and sensitization.

[Risk Factors of the Mortality among Senile HIV/AIDS Patients Intervened by Comprehensive Intervention of Chinese Medicine and Pharmacy].

OBJECTIVE: To observe the effect of Chinese medicine and pharmacy (CMP) on the mortality of senile HIV/AIDS patients as adjunctive therapy. METHODS: HIV/AIDS patients of a certain rural area of Hanna Province, who were recruited in national CMP HIV treatment trial program (NTCMTP) in 2004, were enrolled as the CMP treatment group. HIV/AIDS patients in the same village without recruiting in NTCMTP were enrolled as the non-CMP treatment group. Data related to subjects were collected from the database of NTCMTP and National HAART Reporting System. Multiple regression analysis under Cox proportional hazard model was applied to examine the risk factors for death of senile HIV/AIDS patients. RESULTS: A total of 436 HIV/AIDS were enrolled in this study, 204 in the CMP treatment group and 232 in the non-CMP treatment group. There were 70 AIDS-relative deaths in the CMP treatment group, with 8-year mortality rate of 37.74%. There were 111 AIDS-relative deaths in the non-CMP treatment group, with 8-year mortality rate of 48.34%. The 8-year mortality rate was higher in the non-CMP treatment group than in the CMP treatment group (chi2 = 5.136, P < 0.05). Results of univariate Cox proportional hazards regression analysis showed the hazard ratio in the non-CMP treatment group was 1.41 times that of the CMP treatment group (P < 0.05). Result of multivariate Cox proportional hazards regression analysis showed the hazard ratio in the non-CMP treatment group was 1.44 times that of the CMP treatment group (P < 0.05). Besides, gender and marital conditions were significantly associated with death of HIV/AIDS patients. CONCLUSION: CMP treatment was favorable to lower the mortality rate of senile HIV/AIDS patients, and its objective evaluation awaits for further prospective study.