Background As the major interface between the body and the external environment, the skin is liable to various injuries. Skin injuries often lead to severe disability, and the exploration of promising therapeutic strategies is of great importance. Exogenous mesenchymal stem cell (MSC)-based therapy is a potential strategy due to the apparent therapeutic effects, while the underlying mechanism is still elusive. Interestingly, we observed the extensive apoptosis of exogenous bone marrow mesenchymal stem cells (BMMSCs) in a short time after transplantation in mouse skin wound healing models. Considering the roles of extracellular vesicles (EVs) in intercellular communication, we hypothesized that the numerous apoptotic bodies (ABs) released during apoptosis may partially contribute to the therapeutic effects. Methods ABs derived from MSCs were extracted, characterized, and applied in mouse skin wound healing models, and the therapeutic effects were evaluated. Then, the target cells of ABs were explored, and the effects of ABs on macrophages were investigated in vitro. Results We found ABs derived from MSCs promoted cutaneous wound healing via triggering the polarization of macrophages towards M2 phenotype. In addition, the functional converted macrophages further enhanced the migration and proliferation abilities of fibroblasts, which together facilitated the wound healing process. Conclusions Collectively, our study demonstrated that transplanted MSCs promoted cutaneous wound healing partially through releasing apoptotic bodies which could convert the macrophages towards an anti-inflammatory phenotype that plays a crucial role in the tissue repair process.

Electroencephalogram (EEG) signals contain vital information on the electrical activities of the brain and are widely used to aid epilepsy analysis. A challenging element of epilepsy diagnosis, accurate classification of different epileptic states, is of particular interest and has been extensively investigated. A new deep learning-based classification methodology, namely epileptic EEG signal classification (EESC), is proposed in this paper. This methodology first transforms epileptic EEG signals to power spectrum density energy diagrams (PSDEDs), then applies deep convolutional neural networks (DCNNs) and transfer learning to automatically extract features from the PSDED, and finally classifies four categories of epileptic states (interictal, preictal duration to 30 min, preictal duration to 10 min, and seizure). It outperforms the existing epilepsy classification methods in terms of accuracy and efficiency. For instance, it achieves an average classification accuracy of over 90% in a case study with CHB-MIT epileptic EEG data.

Preventive care service is considered pivotal on the background of demographic ageing and a rise in chronic diseases in China. The disparity in utilization of preventive care services between urban and rural in China is a serious issue. In this paper, we explored factors associated with urban–rural disparity in utilization of preventive care services in China, and determined how much of the urban–rural disparity was attributable to each determinant of utilization in preventive care services. Using representative sample data from China Health and Nutrition Survey in 2011 (N = 12,976), the present study performed multilevel logistic model to examine the factors that affected utilization of preventive care services in last 4 weeks. Blinder–Oaxaca decomposition method was applied to divide the utilization of preventive care disparity between urban and rural residents into a part that can be explained by differences in observed covariates and unobserved part. The percentage of rural residents utilizing preventive care service in last 4 weeks was lower than that of urban residents (5.1% vs 9.3%). Female, the aged, residents with higher education level and household income, residents reporting self-perceived illness in last 4 weeks and physician-diagnosed chronic disease had higher likelihood of utilizing preventive care services. Household income was the most important factor accounting for 26.6% of urban–rural disparities in utilization of preventive care services, followed by education (21.5%), self-perceived illness in last 4 weeks (7.8%), hypertension (4.4%), diabetes (3.3%), other chronic diseases (0.8%), and health insurance (−1.0%). Efforts to reduce financial barriers for low-income individuals who cannot afford preventive services, increasing awareness of the importance of obtaining preventive health services and providing more preventive health services covered by health insurance, may help to reduce the gap of preventive care services utilization between urban and rural.

Long noncoding RNAs (lncRNAs) play an important role in gene regulation, but their impact on the pathogenesis of colorectal cancer and the biological function of cancer cells is unclear. In this study, we used next‐generation sequencing to study the differences in the expression profiles of lncRNAs and mRNAs in colorectal cancer tissues. We analyzed the differentially expressed genes by Gene Ontology/Kyoto Encyclopedia of Genes and Genomes (GO/KEGG) enrichment and predicted new lncRNA functions. Our results revealed that compared with lncRNAs and mRNAs in nontumor colorectal tissues, 1019 lncRNAs (512 upregulated, 507 downregulated) and 3221 mRNAs (1606 upregulated, 1615 downregulated) were differentially expressed in tumor colorectal tissues (fold change >2 and P < 0.05). We validated some of these genes by qPCR. Furthermore, we identified some new lncRNAs differently expressed in colorectal cancer samples from patients in northern China. We confirmed the function of lncRNA‐FIRRE‐201 and SLCO4A1‐AS1‐202 in colorectal cancer cells to provide an experimental basis for studies on their roles in the occurrence and development of colorectal cancer and in the regulation of networks.

Acid-sensing ion channels, a proton-gated cation channel, can be activated by low extracellular pH and involved in pathogenesis of some tumors such as glioma and breast cancer. However, the role of acid-sensing ion channels in the growth of lung cancer cell is unclear. In this study, we investigated the expression of acid-sensing ion channels in human lung cancer cell line A549 and their possible role in proliferation and migration of A549 cells. The results show that acid-sensing ion channel 1, acid-sensing ion channel 2, and acid-sensing ion channel 3 are expressed in A549 cells at the messenger RNA and protein levels, and acid-sensing ion channel-like currents were elicited by extracellular acid stimuli. Moreover, we found that acidic extracellular medium or overexpressing acid-sensing ion channel 1a promotes proliferation and migration of A549 cells. In addition psalmotoxin 1, a specific acid-sensing ion channel 1a inhibitor, or acid-sensing ion channel 1a knockdown can abolish the effect of acid stimuli on A549 cells. In addition, acid-sensing ion channels mediate increase of [Ca] induced by low extracellular pH in A549 cells. All these results indicate that acid-sensing ion channel-calcium signal mediate lung cancer cell proliferation and migration induced by extracellular acidosis, and acid-sensing ion channels may serve as a prognostic marker and a therapeutic target for lung cancer.

To efficiently save cost and reduce risk in drug research and development, there is a pressing demand to develop in silico methods to predict drug sensitivity to cancer cells. With the exponentially increasing number of multi-omics data derived from high-throughput techniques, machine learning-based methods have been applied to the prediction of drug sensitivities. However, these methods have drawbacks either in the interpretability of the mechanism of drug action or limited performance in modeling drug sensitivity. In this paper, we presented a pathway-guided deep neural network (DNN) model to predict the drug sensitivity in cancer cells. Biological pathways describe a group of molecules in a cell that collaborates to control various biological functions like cell proliferation and death, thereby abnormal function of pathways can result in disease. To take advantage of the excellent predictive ability of DNN and the biological knowledge of pathways, we reshaped the canonical DNN structure by incorporating a layer of pathway nodes and their connections to input gene nodes, which makes the DNN model more interpretable and predictive compared to canonical DNN. We have conducted extensive performance evaluations on multiple independent drug sensitivity data sets and demonstrated that our model significantly outperformed the canonical DNN model and eight other classical regression models. Most importantly, we observed a remarkable activity decrease in disease-related pathway nodes during forward propagation upon inputs of drug targets, which implicitly corresponds to the inhibition effect of disease-related pathways induced by drug treatment on cancer cells. Our empirical experiments showed that our method achieves pharmacological interpretability and predictive ability in modeling drug sensitivity in cancer cells. The web server, the processed data sets, and source codes for reproducing our work are available at .

Background Major depressive disorder (MDD) is a highly prevalent psychiatric disorder, and inflammation has been considered crucial components of the pathogenesis of depression. NLRP1 inflammasome-driven inflammatory response is believed to participate in many neurological disorders. However, it is unclear whether NLRP1 inflammasome is implicated in the development of depression. Methods Animal models of depression were established by four different chronic stress stimuli including chronic unpredictable mild stress (CUMS), chronic restrain stress (CRS), chronic social defeat stress (CSDS), and repeat social defeat stress (RSDS). Depressive-like behaviors were determined by sucrose preference test (SPT), forced swim test (FST), tail-suspension test (TST), open-field test (OFT), social interaction test (SIT), and light-dark test (LDT). The expression of NLRP1 inflammasome complexes, BDNF, and CXCL1/CXCR2 were tested by western blot and quantitative real-time PCR. The levels of inflammatory cytokines were tested by enzyme-linked immunosorbent assay (ELISA) kits. Nlrp1a knockdown was performed by an adeno-associated virus (AAV) vector containing Nlrp1a-shRNA-eGFP infusion. Results Chronic stress stimuli activated hippocampal NLRP1 inflammasome and promoted the release of pro-inflammatory cytokines IL-1β, IL-18, IL-6, and TNF-α in mice. Hippocampal Nlrp1a knockdown prevented NLRP1 inflammasome-driven inflammatory response and ameliorated stress-induced depressive-like behaviors. Also, chronic stress stimuli caused the increase in hippocampal CXCL1/CXCR2 expression and low BDNF levels in mice. Interestingly, Nlrp1a knockdown inhibited the up-regulation of CXCL1/CXCR2 expression and restored BDNF levels in the hippocampus. Conclusions NLRP1 inflammasome-driven inflammatory response contributes to chronic stress induced depressive-like behaviors and the mechanism may be related to CXCL1/CXCR2/BDNF signaling pathway. Thus, NLRP1 inflammasome could become a potential antidepressant target.

BackgroundEpilepsy is a common neurological disorder and is not well controlled by available antiepileptic drugs (AEDs). Inflammation is considered to be a critical factor in the pathophysiology of epilepsy. Sinomenine (SN), a bioactive alkaloid with anti-inflammatory effect, exerts neuroprotective activity in many nervous system diseases. However, little is known about the effect of SN on epilepsy.MethodsThe chronic epilepsy model was established by pentylenetetrazole (PTZ) kindling. Morris water maze (MWM) was used to test spatial learning and memory ability. H.E. staining and Hoechst 33258 staining were used to evaluate hippocampal neuronal damage. The expression of nucleotide oligomerization domain (NOD)-like receptor protein 1 (NLRP1) inflammasome complexes and the level of inflammatory cytokines were determined by western blot, quantitative real-time PCR and enzyme-linked immunosorbent assay (ELISA) kits.ResultsSN (20, 40, and 80 mg/kg) dose-dependently disrupts the kindling acquisition process, which decreases the seizure scores and the incidence of fully kindling. SN also increases the latency of seizure and decreases the duration of seizure in fully kindled rats. In addition, different doses of SN block the hippocampal neuronal damage and minimize the impairment of spatial learning and memory in PTZ kindled rats. Finally, PTZ kindling increases the expression of NLRP1 inflammasome complexes and the levels of inflammatory cytokines IL-1β, IL-18, IL-6, and TNF-α, which are all attenuated by SN in a dose- dependent manner.ConclusionsSN exerts anticonvulsant and neuroprotective activity in PTZ kindling model of epilepsy. Disrupting the kindling acquisition, which inhibits NLRP1 inflammasome-mediated inflammatory process, might be involved in its effects.Electronic supplementary materialThe online version of this article (10.1186/s12974-018-1199-0) contains supplementary material, which is available to authorized users.