Blockade of C5aR1 resets M1 via gut microbiota-mediated PFKM stabilization in a TLR5-dependent manner.

Targeting C5aR1 modulates the function of infiltrated immune cells including tumor-associated macrophages (TAMs). The gut microbiome plays a pivotal role in colorectal cancer (CRC) tumorigenesis and development through TAM education. However, whether and how the gut flora is involved in C5aR1 inhibition-mediated TAMs remains unclear. Therefore, in this study, genetic deletion of C5ar1 or pharmacological inhibition of C5aR1 with anti-C5aR1 Ab or PMX-53 in the presence or absence of deletion Abs were utilized to verify if and how C5aR1 inhibition regulated TAMs polarization via affecting gut microbiota composition. We found that the therapeutic effects of C5aR1 inhibition on CRC benefited from programming of TAMs toward M1 polarization via driving AKT2-mediated 6-phosphofructokinase muscle type (PFKM) stabilization in a TLR5-dependent manner. Of note, in the further study, we found that C5aR1 inhibition elevated the concentration of serum IL-22 and the mRNA levels of its downstream target genes encoded antimicrobial peptides (AMPs), leading to gut microbiota modulation and flagellin releasement, which contributed to M1 polarization. Our data revealed that high levels of C5aR1 in TAMs predicted poor prognosis. In summary, our study suggested that C5aR1 inhibition reduced CRC growth via resetting M1 by AKT2 activation-mediated PFKM stabilization in a TLR5-dependent manner, which relied on IL-22-regulated gut flora.

ICSDA: a multi-modal deep learning model to predict breast cancer recurrence and metastasis risk by integrating pathological, clinical and gene expression data.

Breast cancer patients often have recurrence and metastasis after surgery. Predicting the risk of recurrence and metastasis for a breast cancer patient is essential for the development of precision treatment. In this study, we proposed a novel multi-modal deep learning prediction model by integrating hematoxylin & eosin (H&E)-stained histopathological images, clinical information and gene expression data. Specifically, we segmented tumor regions in H&E into image blocks (256 × 256 pixels) and encoded each image block into a 1D feature vector using a deep neural network. Then, the attention module scored each area of the H&E-stained images and combined image features with clinical and gene expression data to predict the risk of recurrence and metastasis for each patient. To test the model, we downloaded all 196 breast cancer samples from the Cancer Genome Atlas with clinical, gene expression and H&E information simultaneously available. The samples were then divided into the training and testing sets with a ratio of 7: 3, in which the distributions of the samples were kept between the two datasets by hierarchical sampling. The multi-modal model achieved an area-under-the-curve value of 0.75 on the testing set better than those based solely on H&E image, sequencing data and clinical data, respectively. This study might have clinical significance in identifying high-risk breast cancer patients, who may benefit from postoperative adjuvant treatment.

Identifying potential microRNA biomarkers for colon cancer and colorectal cancer through bound nuclear norm regularization.

Colon cancer and colorectal cancer are two common cancer-related deaths worldwide. Identification of potential biomarkers for the two cancers can help us to evaluate their initiation, progression and therapeutic response. In this study, we propose a new microRNA-disease association identification method, BNNRMDA, to discover potential microRNA biomarkers for the two cancers. BNNRMDA better combines disease semantic similarity and Gaussian Association Profile Kernel (GAPK) similarity, microRNA function similarity and GAPK similarity, and the bound nuclear norm regularization model. Compared to other five classical microRNA-disease association identification methods (MIDPE, MIDP, RLSMDA, GRNMF, AND LPLNS), BNNRMDA obtains the highest AUC of 0.9071, demonstrating its strong microRNA-disease association identification performance. BNNRMDA is applied to discover possible microRNA biomarkers for colon cancer and colorectal cancer. The results show that all 73 known microRNAs associated with colon cancer in the HMDD database have the highest association scores with colon cancer and are ranked as top 73. Among 137 known microRNAs associated with colorectal cancer in the HMDD database, 129 microRNAs have the highest association scores with colorectal cancer and are ranked as top 129. In addition, we predict that hsa-miR-103a could be a potential biomarker of colon cancer and hsa-mir-193b and hsa-mir-7days could be potential biomarkers of colorectal cancer.

Autistic children's age difference in affiliate stigma and resilience of their parents in China: A cross-sectional study.

BACKGROUND: Parents of children with Autism Spectrum Disorders (ASD) experience high levels of stigma, especially in China where the culture is shame socialized. Resilience can help overcome stigma; while parent characteristics predict resilience, other factors may also be significant such as the child's age. OBJECTIVE: The study sought to identify the differences in affiliate stigma and resilience among Chinese parents of children with ASD according to the child's age, and to determine whether the levels of resilience and experience of stigma are related. METHODS: A cross-sectional survey of 184 parents of children with ASD was conducted. Affiliate stigma and resilience were measured using the Chinese version of the 22-item Affiliate Stigma Scale and the Chinese version of the Connor-Davidson Resilience Scale. Differences were examined by using regression and correlation analysis. RESULTS: Parents of school-age children experienced more affiliate stigma than parents of preschoolers, but there was no difference in resilience when other factors were controlled. CONCLUSIONS: Considering the child's age is important to understand affiliate stigma and resilience, particularly where resilience is protective and could inform the design of support strategies for preschooler parents.

Genomic and resistome analysis of Alcaligenes faecalis strain PGB1 by Nanopore MinION and Illumina Technologies.

BACKGROUND: Drug-resistant bacteria are important carriers of antibiotic-resistant genes (ARGs). This fact is crucial for the development of precise clinical drug treatment strategies. Long-read sequencing platforms such as the Oxford Nanopore sequencer can improve genome assembly efficiency particularly when they are combined with short-read sequencing data. RESULTS: Alcaligenes faecalis PGB1 was isolated and identified with resistance to penicillin and three other antibiotics. After being sequenced by Nanopore MinION and Illumina sequencer, its entire genome was hybrid-assembled. One chromosome and one plasmid was assembled and annotated with 4,433 genes (including 91 RNA genes). Function annotation and comparison between strains were performed. A phylogenetic analysis revealed that it was closest to A. faecalis ZD02. Resistome related sequences was explored, including ARGs, Insert sequence, phage. Two plasmid aminoglycoside genes were determined to be acquired ARGs. The main ARG category was antibiotic efflux resistance and ß-lactamase (EC 3.5.2.6) of PGB1 was assigned to Class A, Subclass A1b, and Cluster LSBL3. CONCLUSIONS: The present study identified the newly isolated bacterium A. faecalis PGB1 and systematically annotated its genome sequence and ARGs.

Prediction of HER2-positive breast cancer recurrence and metastasis risk from histopathological images and clinical information via multimodal deep learning.

HER2-positive breast cancer is a highly heterogeneous tumor, and about 30% of patients still suffer from recurrence and metastasis after trastuzumab targeted therapy. Predicting individual prognosis is of great significance for the further development of precise therapy. With the continuous development of computer technology, more and more attention has been paid to computer-aided diagnosis and prognosis prediction based on Hematoxylin and Eosin (H&E) pathological images, which are available for all breast cancer patients undergone surgical treatment. In this study, we first enrolled 127 HER2-positive breast cancer patients with known recurrence and metastasis status from Cancer Hospital of the Chinese Academy of Medical Sciences. We then proposed a novel multimodal deep learning method integrating whole slide H&E images (WSIs) and clinical information to accurately assess the risk of relapse and metastasis in patients with HER2-positive breast cancer. Specifically, we obtained the whole H&E staining images from the surgical specimens of breast cancer patients, and these images were adjusted to size 512 × 512 pixels. The deep convolutional neural network (CNN) was applied to these images to retrieve image features, which were combined with the clinical data. Based on the combined features. After that, a novel multimodal model was constructed for predicting the prognosis of each patient. The model achieved an area under curve (AUC) of 0.76 in the two-fold cross-validation (CV). To further evaluate the performance of our model, we downloaded the data of all 123 HER2-positive breast cancer patients with available H&E image and known recurrence and metastasis status in The Cancer Genome Atlas (TCGA), which was severed as an independent testing data. Despite the huge differences in race and experimental strategies, our model achieved an AUC of 0.72 in the TCGA samples. As a conclusion, H&E images, in conjunction with clinical information and advanced deep learning models, could be used to evaluate the risk of relapse and metastasis in patients with HER2-positive breast cancer.

Agent Repurposing for the Treatment of Advanced Stage Diffuse Large B-Cell Lymphoma Based on Gene Expression and Network Perturbation Analysis.

Over 50% of diffuse large B-cell lymphoma (DLBCL) patients are diagnosed at an advanced stage. Although there are a few therapeutic strategies for DLBCL, most of them are more effective in limited-stage cancer patients. The prognosis of patients with advanced-stage DLBCL is usually poor with frequent recurrence and metastasis. In this study, we aimed to identify gene expression and network differences between limited- and advanced-stage DLBCL patients, with the goal of identifying potential agents that could be used to relieve the severity of DLBCL. Specifically, RNA sequencing data of DLBCL patients at different clinical stages were collected from the cancer genome atlas (TCGA). Differentially expressed genes were identified using DESeq2, and then, weighted gene correlation network analysis (WGCNA) and differential module analysis were performed to find variations between different stages. In addition, important genes were extracted by key driver analysis, and potential agents for DLBCL were identified according to gene-expression perturbations and the Crowd Extracted Expression of Differential Signatures (CREEDS) drug signature database. As a result, 20 up-regulated and 73 down-regulated genes were identified and 79 gene co-expression modules were found using WGCNA, among which, the thistle1 module was highly related to the clinical stage of DLBCL. KEGG pathway and GO enrichment analyses of genes in the thistle1 module indicated that DLBCL progression was mainly related to the NOD-like receptor signaling pathway, neutrophil activation, secretory granule membrane, and carboxylic acid binding. A total of 47 key drivers were identified through key driver analysis with 11 up-regulated key driver genes and 36 down-regulated key diver genes in advanced-stage DLBCL patients. Five genes (MMP1, RAB6C, ACCSL, RGS21 and MOCOS) appeared as hub genes, being closely related to the occurrence and development of DLBCL. Finally, both differentially expressed genes and key driver genes were subjected to CREEDS analysis, and 10 potential agents were predicted to have the potential for application in advanced-stage DLBCL patients. In conclusion, we propose a novel pipeline to utilize perturbed gene-expression signatures during DLBCL progression for identifying agents, and we successfully utilized this approach to generate a list of promising compounds.

Predicting LncRNA-Disease Association by a Random Walk With Restart on Multiplex and Heterogeneous Networks.

Studies have found that long non-coding RNAs (lncRNAs) play important roles in many human biological processes, and it is critical to explore potential lncRNA-disease associations, especially cancer-associated lncRNAs. However, traditional biological experiments are costly and time-consuming, so it is of great significance to develop effective computational models. We developed a random walk algorithm with restart on multiplex and heterogeneous networks of lncRNAs and diseases to predict lncRNA-disease associations (MHRWRLDA). First, multiple disease similarity networks are constructed by using different approaches to calculate similarity scores between diseases, and multiple lncRNA similarity networks are also constructed by using different approaches to calculate similarity scores between lncRNAs. Then, a multiplex and heterogeneous network was constructed by integrating multiple disease similarity networks and multiple lncRNA similarity networks with the lncRNA-disease associations, and a random walk with restart on the multiplex and heterogeneous network was performed to predict lncRNA-disease associations. The results of Leave-One-Out cross-validation (LOOCV) showed that the value of Area under the curve (AUC) was 0.68736, which was improved compared with the classical algorithm in recent years. Finally, we confirmed a few novel predicted lncRNAs associated with specific diseases like colon cancer by literature mining. In summary, MHRWRLDA contributes to predict lncRNA-disease associations.

Application of Circulating Tumor DNA as a Biomarker for Non-Small Cell Lung Cancer.

BACKGROUND: Non-small cell lung cancer (NSCLC) is one of the most prevalent causes of cancer-related death worldwide. Recently, there are many important medical advancements on NSCLC, such as therapies based on tyrosine kinase inhibitors and immune checkpoint inhibitors. Most of these therapies require tumor molecular testing for selecting patients who would benefit most from them. As invasive biopsy is highly risky, NSCLC molecular testing based on liquid biopsy has received more and more attention recently. OBJECTIVE: We aimed to introduce liquid biopsy and its potential clinical applications in NSCLC patients, including cancer diagnosis, treatment plan prioritization, minimal residual disease detection, and dynamic monitoring on the response to cancer treatment. METHOD: We reviewed recent studies on circulating tumor DNA (ctDNA) testing, which is a minimally invasive approach to identify the presence of tumor-related mutations. In addition, we evaluated potential clinical applications of ctDNA as blood biomarkers for advanced NSCLC patients. RESULTS: Most studies have indicated that ctDNA testing is critical in diagnosing NSCLC, predicting clinical outcomes, monitoring response to targeted therapies and immunotherapies, and detecting cancer recurrence. Moreover, the changes of ctDNA levels are associated with tumor mutation burden and cancer progression. CONCLUSION: The ctDNA testing is promising in guiding the therapies on NSCLC patients.

Electroacupuncture Pretreatment Prevents Cognitive Impairment Induced by Cerebral Ischemia-Reperfusion via Adenosine A1 Receptors in Rats.

A previous study has demonstrated that pretreatment with electroacupuncture (EA) induces rapid tolerance to focal cerebral ischemia. In the present study, we investigated whether adenosine receptor 1 (A1 R) is involved in EA pretreatment-induced cognitive impairment after focal cerebral ischemia in rats. Two hours after EA pretreatment, focal cerebral ischemia was induced by middle cerebral artery occlusion for 120 min in male Sprague-Dawley rats. The neurobehavioral score, cognitive function [as determined by the Morris water maze (MWM) test], neuronal number, and the Bax/Bcl-2 ratio was evaluated at 24 h after reperfusion in the presence or absence of CCPA (a selective A1 receptor agonist), DPCPX (a selective A1 receptor antagonist) into left lateral ventricle, or A1 short interfering RNA into the hippocampus area. The expression of the A1 receptor in the hippocampus was also investigated. The result showed that EA pretreatment upregulated the neuronal expression of the A1 receptor in the rat hippocampus at 90 min. And EA pretreatment reversed cognitive impairment, improved neurological outcome, and inhibited apoptosis at 24 h after reperfusion. Pretreatment with CCPA could imitate the beneficial effects of EA pretreatment. But the EA pretreatment effects were abolished by DPCPX. Furthermore, A1 receptor protein was reduced by A1 short interfering RNA which attenuated EA pretreatment-induced cognitive impairment.

Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe-Disease Association Prediction.

Microbes are closely associated with the formation and development of diseases. The identification of the potential associations between microbes and diseases can boost the understanding of various complex diseases. Wet experiments applied to microbe-disease association (MDA) identification are costly and time-consuming. In this manuscript, we developed a novel computational model, NLLMDA, to find unobserved MDAs, especially for colon cancer and colorectal carcinoma. NLLMDA integrated negative MDA selection, linear neighborhood similarity, label propagation, information integration, and known biological data. The Gaussian association profile (GAP) similarity of microbes and GAPs similarity and symptom similarity of diseases were firstly computed. Secondly, linear neighborhood method was then applied to the above computed similarity matrices to obtain more stable performance. Thirdly, negative MDA samples were selected, and the label propagation algorithm was used to score for microbe-disease pairs. The final association probabilities can be computed based on the information integration method. NLLMDA was compared with the other five classical MDA methods and obtained the highest area under the curve (AUC) value of 0.9031 and 0.9335 on cross-validations of diseases and microbe-disease pairs. The results suggest that NLLMDA was an effective prediction method. More importantly, we found that Acidobacteriaceae may have a close link with colon cancer and Tannerella may densely associate with colorectal carcinoma.

A Computational Framework to Identify Biomarkers for Glioma Recurrence and Potential Drugs Targeting Them.

Background: Recurrence is still a major obstacle to the successful treatment of gliomas. Understanding the underlying mechanisms of recurrence may help for developing new drugs to combat gliomas recurrence. This study provides a strategy to discover new drugs for recurrent gliomas based on drug perturbation induced gene expression changes. Methods: The RNA-seq data of 511 low grade gliomas primary tumor samples (LGG-P), 18 low grade gliomas recurrent tumor samples (LGG-R), 155 glioblastoma multiforme primary tumor samples (GBM-P), and 13 glioblastoma multiforme recurrent tumor samples (GBM-R) were downloaded from TCGA database. DESeq2, key driver analysis and weighted gene correlation network analysis (WGCNA) were conducted to identify differentially expressed genes (DEGs), key driver genes and coexpression networks between LGG-P vs LGG-R, GBM-P vs GBM-R pairs. Then, the CREEDS database was used to find potential drugs that could reverse the DEGs and key drivers. Results: We identified 75 upregulated and 130 downregulated genes between LGG-P and LGG-R samples, which were mainly enriched in human papillomavirus (HPV) infection, PI3K-Akt signaling pathway, Wnt signaling pathway, and ECM-receptor interaction. A total of 262 key driver genes were obtained with frizzled class receptor 8 (FZD8), guanine nucleotide-binding protein subunit gamma-12 (GNG12), and G protein subunit ß2 (GNB2) as the top hub genes. By screening the CREEDS database, we got 4 drugs (Paclitaxel, 6-benzyladenine, Erlotinib, Cidofovir) that could downregulate the expression of up-regulated genes and 5 drugs (Fenofibrate, Oxaliplatin, Bilirubin, Nutlins, Valproic acid) that could upregulate the expression of down-regulated genes. These drugs may have a potential in combating recurrence of gliomas. Conclusion: We proposed a time-saving strategy based on drug perturbation induced gene expression changes to find new drugs that may have a potential to treat recurrent gliomas.

[Study on efficacy specificity of electroacupuncture at "Zusanli "(ST36) and "Baihui" (GV20) in cerebral ischemia and inflammatory pain rats].

OBJECTIVE: To explore the efficacy difference between electroacupuncture (EA) at "Zusanli" (ST36) and "Baihui" (GV20) for inflammatory pain and cerebral ischemia-reperfusion injury (CIRI) in rats. METHODS: In 1st part of this study, 90 male SD rats were randomly divided into sham-operation, model (induced by occlusion of the middle cerebral artery and reperfusion), GV20 EA, ST36 EA，and sham EA groups (n=16 in each group). In the 2nd part of the study, 40 male SD rats were randomized into saline injection (control), inflammatory pain model (subcutaneous injection of complete Freund's adjuvant ［CFA］ into the right paw), ST36 EA, GV20 EA, and sham EA groups (n=8 in each group). In these two parts, EA (2 Hz/15 Hz, 1 mA) was applied to ST36 or GV20. The mechanical withdrawal threshold (MWT) and thermal withdrawal latency （TWL） were detected 2.5 h after administration of CFA by using Von Frey and plantar tester, respectively. The neurological deficit scores (NDS) were assessed by using Longa's method and the infarct size of the brain assessed after staining with 2% triphenyltetrazolium chloride (TTC). The expression of c-fos protein in the dorsal horns (DHs) of the spinal cord was detected by immunohistochemistry. RESULTS: (1) Twenty-four hours following CIRI, the NDS and infarct volume were significantly increased in the model group compared with the sham-operation group (P<0.01), and obviously decreased in the GV20 EA and ST36 EA groups relevant to the CIRI model group (P<0.05, P<0.01). There were no significant differences between the two EA groups in the NDS and infarct volume levels (P>0.05). (2) After administration of CFA, both the MPT and TPT were notably decreased in the inflammatory pain model group in contrast to the saline-injection group (P<0.01), but were considerably increased in both ST36 EA and GV20 EA groups (P<0.05), rather than in the sham EA group (P>0.05). The number of c-fos positive cells was significantly increased in the medial half of I-II and III-IV lamina of DHs in the L4-L6 segments of spinal cord in the inflammatory pain model group relevant to the saline-injection group (P<0.01,P<0.05), and was remarkably decreased in the lamina I-II (not in the deeper lamina) in both ST36 EA and GV20 EA groups (P<0.01), rather than in the sham EA group (P>0.05). No significant differences were found in the number of c-fos positive cells between the ST36 EA and GV20 EA groups (P>0.05). CONCLUSION: Our data do not support the specificity of functions at least between GV20 EA and ST36 EA in both CIRI and inflammatory pain model rats. This is the first study reporting the effect of EA at GV20 for relieving CFA-induced inflammatory pain.

Correlation Between Perceived Social Support and Loneliness Among Chinese Adolescents: Mediating Effects of Psychological Capital.

BACKGROUND: Adolescence is a period characterized by high frequency of mental health problems. Loneliness, in particular, is a common psychological problem. This study aims to explore the effects of psychological capital on adolescents' loneliness and the mediating effects of perceived social support. SUBJECTS AND METHODS: A total of 694 adolescents from six regions in Hunan Province, China were selected. The survey was conducted using the Perceived Social Support Scale, Loneliness Scale, and Psychological Capital Questionnaire. The study analyzed the correlations between perceived social support, psychological capital, and loneliness among adolescents and the mediating role of psychological capital in the relationship between perceived social support and loneliness. RESULTS: Significant correlations were observed between adolescents' total scores in perceived social support, psychological capital, and loneliness and between each dimension. The following observations were made: a significant negative correlation between perceived social support and loneliness (r=0.440, P<0.01), a significant positive correlation between perceived social support and psychological capital (r=0.493, P<0.01), and a significant negative correlation between psychological capital and loneliness (r=-0.303, P<0.01). Psychological capital plays a statistically significant mediating role on the relationship between perceived social support and loneliness (P<0.01). CONCLUSIONS: Perceived social support and psychological capital can reduce adolescents' loneliness and perceived social support can alleviate loneliness by enhancing psychological capital.

Islet-cell autoantigen 69 mediates the antihyperalgesic effects of electroacupuncture on inflammatory pain by regulating spinal glutamate receptor subunit 2 phosphorylation through protein interacting with C-kinase 1 in mice.

Electroacupuncture (EA) is widely used in clinical settings to reduce inflammatory pain. Islet-cell autoantigen 69 (ICA69) has been reported to regulate long-lasting hyperalgesia in mice. ICA69 knockout led to reduced protein interacting with C-kinase 1 (PICK1) expression and increased glutamate receptor subunit 2 (GluR2) phosphorylation at Ser880 in spinal dorsal horn. In this study, we evaluated the role of ICA69 in the antihyperalgesic effects of EA and the underlying mechanism through regulation of GluR2 and PICK1 in spinal dorsal horn. Hyperalgesia was induced in mice with subcutaneous plantar injection of complete Freund adjuvant (CFA) to cause inflammatory pain. Electroacupuncture was then applied for 30 minutes every other day after CFA injection. When compared with CFA group, paw withdrawal frequency of CFA+EA group was significantly decreased. Remarkable increases in Ica1 mRNA expression and ICA69 protein levels on the ipsilateral side were detected in the CFA+EA group. ICA69 expression reached the peak value around day 3. More importantly, ICA69 deletion impaired the antihyperalgesic effects of EA on GluR2-p, but PICK1 deletion could not. Injecting ICA69 peptide into the intrathecal space of ICA69-knockout mice mimicked the effects of EA analgesic and inhibited GluR2-p. Electroacupuncture had no effects on the total protein of PICK1 and GluR2. And, EA could increase the formation of ICA69-PICK1 complexes and decrease the amount of PICK1-GluR2 complexes. Our findings indicate that ICA69 mediates the antihyperalgesic effects of EA on CFA-induced inflammatory pain by regulating spinal GluR2 through PICK1 in mice.

Molecular Network-Based Drug Prediction in Thyroid Cancer.

As a common malignant tumor disease, thyroid cancer lacks effective preventive and therapeutic drugs. Thus, it is crucial to provide an effective drug selection method for thyroid cancer patients. The connectivity map (CMAP) project provides an experimental validated strategy to repurpose and optimize cancer drugs, the rationale behind which is to select drugs to reverse the gene expression variations induced by cancer. However, it has a few limitations. Firstly, CMAP was performed on cell lines, which are usually different from human tissues. Secondly, only gene expression information was considered, while the information about gene regulations and modules/pathways was more or less ignored. In this study, we first measured comprehensively the perturbations of thyroid cancer on a patient including variations at gene expression level, gene co-expression level and gene module level. After that, we provided a drug selection pipeline to reverse the perturbations based on drug signatures derived from tissue studies. We applied the analyses pipeline to the cancer genome atlas (TCGA) thyroid cancer data consisting of 56 normal and 500 cancer samples. As a result, we obtained 812 up-regulated and 213 down-regulated genes, whose functions are significantly enriched in extracellular matrix and receptor localization to synapses. In addition, a total of 33,778 significant differentiated co-expressed gene pairs were found, which form a larger module associated with impaired immune function and low immunity. Finally, we predicted drugs and gene perturbations that could reverse the gene expression and co-expression changes incurred by the development of thyroid cancer through the Fisher's exact test. Top predicted drugs included validated drugs like baclofen, nevirapine, glucocorticoid, formaldehyde and so on. Combining our analyses with literature mining, we inferred that the regulation of thyroid hormone secretion might be closely related to the inhibition of the proliferation of thyroid cancer cells.

Protein sequence information extraction and subcellular localization prediction with gapped k-Mer method.

BACKGROUND: Subcellular localization prediction of protein is an important component of bioinformatics, which has great importance for drug design and other applications. A multitude of computational tools for proteins subcellular location have been developed in the recent decades, however, existing methods differ in the protein sequence representation techniques and classification algorithms adopted. RESULTS: In this paper, we firstly introduce two kinds of protein sequences encoding schemes: dipeptide information with space and Gapped k-mer information. Then, the Gapped k-mer calculation method which is based on quad-tree is also introduced. CONCLUSIONS: >From the prediction results, this method not only reduces the dimension, but also improves the prediction precision of protein subcellular localization.

BPLLDA: Predicting lncRNA-Disease Associations Based on Simple Paths With Limited Lengths in a Heterogeneous Network.

In recent years, it has been increasingly clear that long noncoding RNAs (lncRNAs) play critical roles in many biological processes associated with human diseases. Inferring potential lncRNA-disease associations is essential to reveal the secrets behind diseases, develop novel drugs, and optimize personalized treatments. However, biological experiments to validate lncRNA-disease associations are very time-consuming and costly. Thus, it is critical to develop effective computational models. In this study, we have proposed a method called BPLLDA to predict lncRNA-disease associations based on paths of fixed lengths in a heterogeneous lncRNA-disease association network. Specifically, BPLLDA first constructs a heterogeneous lncRNA-disease network by integrating the lncRNA-disease association network, the lncRNA functional similarity network, and the disease semantic similarity network. It then infers the probability of an lncRNA-disease association based on paths connecting them and their lengths in the network. Compared to existing methods, BPLLDA has a few advantages, including not demanding negative samples and the ability to predict associations related to novel lncRNAs or novel diseases. BPLLDA was applied to a canonical lncRNA-disease association database called LncRNADisease, together with two popular methods LRLSLDA and GrwLDA. The leave-one-out cross-validation areas under the receiver operating characteristic curve of BPLLDA are 0.87117, 0.82403, and 0.78528, respectively, for predicting overall associations, associations related to novel lncRNAs, and associations related to novel diseases, higher than those of the two compared methods. In addition, cervical cancer, glioma, and non-small-cell lung cancer were selected as case studies, for which the predicted top five lncRNA-disease associations were verified by recently published literature. In summary, BPLLDA exhibits good performances in predicting novel lncRNA-disease associations and associations related to novel lncRNAs and diseases. It may contribute to the understanding of lncRNA-associated diseases like certain cancers.

Carnosic acid attenuates neuropathic pain in rat through the activation of spinal sirtuin1 and down-regulation of p66shc expression.

BACKGROUND: It has been reported that carnosic acid (CA) exhibits a range of biological activities including hepatoprotective, antioxidant and anti-inflammatory. However, the effect of carnosic acid in neuropathic pain remained elusive. METHODS: A neuropathic pain model of chronic constriction injury (CCI) was established in adult male Sprague-Dawley rats. Mechanical withdrawal threshold (MWT) and thermal withdrawal latency (TWL) were recorded, and western blot was performed to detect sirtuin1 and p66shc content. RESULTS: Intrathecal administration of carnosic acid attenuated mechanical allodynia and thermal hyperalgesia in rats following chronic constriction injury. Interestingly, carnosic acid analgesic effect was positively associated with spinal sirtuin1 activation; however, p66shc was inhibited by carnosic acid in the spinal cord. In additional, sirtuin1 inhibitor EX-527 reversed the anti-nociceptive effect of carnosic acid. CONCLUSIONS: Carnosic acid is effective in the treatment of the established CCI-induced pain. It may be possible that spinal sirtuin1 activition by carnosic acid attenuates neuropathic pain through a mechanism involving the down-regulation of p66shc expression.

Predictors of health-related quality of life in Chinese caregivers of children with autism spectrum disorders: a cross-sectional study.

The purpose of this study was to identify the predictors of health-related quality of life (HRQOL) among caregivers of children with autism spectrum disorders (ASD) in China. Two hundred and seventy-three caregivers were surveyed using questionnaires on HRQOL, family functioning, coping style, social support, and caregiver burden. Besides socio-demographic characteristics of children with ASD and their caregivers, results demonstrate that family functioning, coping style, social support, caregiver burden are predictors of HRQOL in caregivers of children with ASD, and these predictors correlated with each other. These results indicate that comprehensive intervention, which focuses on improving caregivers' coping strategies, social support (especially from family members and friends) and family functioning, and on releasing caregiver burden, should be provided to caregivers of children with ASD.