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Purpose. To identify significant radiomics features based on MRI and establish effective models for predicting the response to bortezomib-based regimens. Materials and Methods. In total, 95 MM patients treated with bortezomib-based therapy were enrolled, including 77 with bortezomib, cyclophosphamide, and dexamethasone (BCD) and 18 with bortezomib, lenalidomide, and dexamethasone (VRD). Based on T1-weighted imaging (T1WI) and T2-weighted imaging with fat suppression (T2WI-fs), radiomics features were extracted and then selected. The random forest (RF), k -nearest neighbor, support vector machine, logistic regression, decision tree, and Bayes models were built using the selected features. The predictive power of six models for response to BCD and VRD regimens were evaluated. The correlation between the selected features and progression-free survival (PFS) was also analyzed. Results. Four wavelet features were correlated with BCD treatment response. The six models all showed predictive power for BCD regimen (AUC: 0.84-0.896 in the training set, 0.801-0.885 in the validation set), and RF performed relatively better than others. Nevertheless, all the BCD-based models were incapable of predicting the VRD treatment response. The wavelet-HLH_firstorder_kurtosis was also associated with PFS (log-rank P = 0.019 ). Conclusion. The four wavelet features were valuable biomarkers for predicting the response to BCD regimen. The six models based on these features showed predictive power, and RF was the best. One wavelet feature was also a survival-related biomarker. MRI-based radiomics had the potential to guide clinicians in MM management.

Methylation is one of the most common and considerable modifications in biological systems mediated by multiple enzymes. Recent studies have shown that methylation has been widely identified in different RNA molecules. RNA methylation modifications have various kinds, such as 5-methylcytosine (m5C). However, for individual methylation sites, their functions still remain to be elucidated. Testing of all methylation sites relies heavily on high-throughput sequencing technology, which is expensive and labor consuming. Thus, computational prediction approaches could serve as a substitute. In this study, multiple machine learning models were used to predict possible RNA m5C sites on the basis of mRNA sequences in human and mouse. Each site was represented by several features derived from k -mers of an RNA subsequence containing such site as center. The powerful max-relevance and min-redundancy (mRMR) feature selection method was employed to analyse these features. The outcome feature list was fed into incremental feature selection method, incorporating four classification algorithms, to build efficient models. Furthermore, the sites related to features used in the models were also investigated.

Coronaviruses are specific crown-shaped viruses that were first identified in the 1960s, and three typical examples of the most recent coronavirus disease outbreaks include severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and COVID-19. Particularly, COVID-19 is currently causing a worldwide pandemic, threatening the health of human beings globally. The identification of viral pathogenic mechanisms is important for further developing effective drugs and targeted clinical treatment methods. The delayed revelation of viral infectious mechanisms is currently one of the technical obstacles in the prevention and treatment of infectious diseases. In this study, we proposed a random walk model to identify the potential pathological mechanisms of COVID-19 on a virus–human protein interaction network, and we effectively identified a group of proteins that have already been determined to be potentially important for COVID-19 infection and for similar SARS infections, which help further developing drugs and targeted therapeutic methods against COVID-19. Moreover, we constructed a standard computational workflow for predicting the pathological biomarkers and related pharmacological targets of infectious diseases.

Brain metastasis is a major cause of death in breast cancer patients. The greatest event for brain metastasis is the breaching of the blood-brain barrier (BBB) by cancer cells. The role of exosomes in cancer metastasis is clear, whereas the role of exosomes in the integrity of the BBB is unknown. Here, we established a highly brain metastatic breast cancer cell line by three cycles of in vivo selection. The effect of exosomes on the BBB was evaluated in vitro by tracking, transepithelial/transendothelial electrical resistance (TEER), and permeability assays. BBB-associated exosomal long noncoding RNA (lncRNA) was selected from the GEO dataset and verified by real-time PCR, TEER, permeability, and Transwell assays. The cells obtained by the in vivo selection showed higher brain metastatic capacity in vivo and higher migration and invasion in vitro compared to the parental cells. Exosomes from the highly brain metastatic cells were internalized by brain microvascular endothelial cells (BMECs), which reduced TEER and increased permeability of BBB. The exosomes derived from the highly metastatic cells promoted invasion of the breast cancer cells in the BBB model. lncRNA GS1-600G8.5 was highly expressed in the highly brain metastatic cells and their exosomes, as compared to the samples with reduced metastatic behavior. Silencing of GS1-600G8.5 significantly abrogated the BBB destructive effect of exosomes. GS1-600G8.5-deficient exosomes failed to promote the infiltration of cancer cells through the BBB. Furthermore, BMECs treated with GS1-600G8.5-deprived exosomes expressed higher tight junction proteins than those treated with the control exosomes. These data suggest the exosomes derived from highly brain metastatic breast cancer cells might destroy the BBB system and promote the passage of cancer cells across the BBB, by transferring lncRNA GS1-600G8.5.

Over 1,000 compounds, including ecteinascidin-743 and didemnin B, have been isolated from ascidians, with most having bioactive properties such as antimicrobial, antitumor, and enzyme-inhibiting activities. In recent years, direct and indirect evidence has shown that some bioactive compounds isolated from ascidians are not produced by ascidians themselves but by their symbiotic microorganisms. Isolated culturable bacteria associated with ascidians and investigating their potential bioactivity are an important approach for discovering novel compounds. In this study, a total of 269 bacteria were isolated from the ascidian Styela clava collected from the coast of Weihai in the north of the Yellow Sea, China. Phylogenetic relationships among 183 isolates were determined using their 16S rRNA gene sequences. Isolates were tested for antimicrobial activity against seven indicator strains, and an antiproliferative activity assay was performed to test for inhibition of human hepatocellular carcinoma Bel 7402 and human cervical carcinoma HeLa cell proliferation. Our results showed that the isolates belonged to 26 genera from 18 families in four phyla (Firmicutes, Actinobacteria, Proteobacteria, and Bacteroidetes). Bacillus and Streptomyces were the most dominant genera; 146 strains had potent antimicrobial activities and inhibited at least one of the indicator strains. Crude extracts from 29 strains showed antiproliferative activity against Bel 7402 cells with IC50 values below 500 μg·mL−1, and 53 strains showed antiproliferative activity against HeLa cells, with IC50 values less than 500 μg·mL−1. Our results suggest that culturable bacteria associated with the ascidian Styela clava may be a promising source of novel bioactive compounds.

To establish a nomogram for predicting the overall survival (OS) of patients with newly diagnosed multiple myeloma (MM), 304 patients with newly diagnosed MM were recruited between June 1, 2010, and June 30, 2015, from the Beijing Chaoyang Hospital, Capital Medical University, and randomly divided into training (n=214) and validation (n=90) cohorts. The Kaplan-Meier method and the Cox proportional hazards regression model were used to evaluate the prognostic effects of multiple clinical and laboratory parameters on survival. Significant prognostic factors were combined to build a nomogram. The discriminative ability and predictive accuracy of the nomogram were evaluated using the index of concordance (C-index) and calibration curves and compared with the five staging systems currently used for MM. Multivariate analysis of the training cohort revealed that the age at diagnosis, clonal bone marrow plasma cells, serum lactate dehydrogenase, serumβ2-microglobulin, and del (17p) were independent risk factors for OS and were used to establish the nomogram. The C-index value of the nomogram for predicting OS was 0.749, which was significantly higher than the C-indices of the five most common staging systems currently used for MM. In the validation cohort, the C-index for nomogram-based predictions was 0.711 for OS, and the nomogram discrimination was better than the above mentioned five staging systems (P

Hepatocellular carcinoma (HCC) is the most common type among primary liver cancers (PLC). With its poor prognosis and survival rate, it is necessary for HCC patients to have a long-term follow-up. We believe that there are currently no relevant reports or literature about nomograms for predicting the cancer-specific mortality of HCC patients. Therefore, the primary goal of this study was to develop and evaluate nomograms to predict cancer-specific mortality and overall mortality. Data of 45,158 cases of HCC patients were collected from the Surveillance, Epidemiology, and End Results (SEER) program database between 2004 and 2013, which were then utilized to develop the nomograms. Finally, the performance of the nomograms was evaluated by the concordance index (C-index) and the area under the time-dependent receiver operating characteristic (ROC) curve (td-AUC). The categories selected to develop a nomogram for predicting cancer-specific mortality included marriage, insurance, radiotherapy, surgery, distant metastasis, lymphatic metastasis, tumor size, grade, sex, and the American Joint Committee on Cancer (AJCC) stage; while the marriage, radiotherapy, surgery, AJCC stage, grade, race, sex, and age were selected to develop a nomogram for predicting overall mortality. The C-indices for predicted 1-, 3-, and 5-year cancer-specific mortality were 0.792, 0.776, and 0.774; the AUC values for 1-, 3-, and 5-year cancer-specific mortality were 0.830, 0.830, and 0.830. The C-indices for predicted 1-, 3-, and 5-year overall mortality were 0.770, 0.755, and 0.752; AUC values for predicted 1-, 3-, and 5-year overall mortality were 0.820, 0.820, and 0.830. The results showed that the nomograms possessed good agreement compared with the observed outcomes. It could provide clinicians with a personalized predicted risk of death information to evaluate the potential changes of the disease-specific condition so that clinicians can adjust therapy options when combined with the actual condition of the patient, which is beneficial to patients.

COVID-19, a severe respiratory disease caused by a new type of coronavirus SARS-CoV-2, has been spreading all over the world. Patients infected with SARS-CoV-2 may have no pathogenic symptoms, i.e., presymptomatic patients and asymptomatic patients. Both patients could further spread the virus to other susceptible people, thereby making the control of COVID-19 difficult. The two major challenges for COVID-19 diagnosis at present are as follows: (1) patients could share similar symptoms with other respiratory infections, and (2) patients may not have any symptoms but could still spread the virus. Therefore, new biomarkers at different omics levels are required for the large-scale screening and diagnosis of COVID-19. Although some initial analyses could identify a group of candidate gene biomarkers for COVID-19, the previous work still could not identify biomarkers capable for clinical use in COVID-19, which requires disease-specific diagnosis compared with other multiple infectious diseases. As an extension of the previous study, optimized machine learning models were applied in the present study to identify some specific qualitative host biomarkers associated with COVID-19 infection on the basis of a publicly released transcriptomic dataset, which included healthy controls and patients with bacterial infection, influenza, COVID-19, and other kinds of coronavirus. This dataset was first analysed by Boruta, Max-Relevance and Min-Redundancy feature selection methods one by one, resulting in a feature list. This list was fed into the incremental feature selection method, incorporating one of the classification algorithms to extract essential biomarkers and build efficient classifiers and classification rules. The capacity of these findings to distinguish COVID-19 with other similar respiratory infectious diseases at the transcriptomic level was also validated, which may improve the efficacy and accuracy of COVID-19 diagnosis.

Skeletal muscle is the most abundant and a highly plastic tissue of the mammals, especially when it comes to regenerate after trauma, but there is limited information about the mechanism of muscle repair and its regeneration. In the present study, we found that miR-204 is downregulated after skeletal muscle injury. In vitro experiments showed that over-expression of miR-204 by transfecting with miR-204 mimics suppressed C2C12 cell proliferation, migration, and blocked subsequent differentiation, whereas inhibition of miR-204 by transfecting with miR-204 inhibitor showed the converse effects. Furthermore, through the dual luciferase reporter system, we demonstrated that miR-204 can target the 3’UTR regions of Pax7, IGF1, and Mef2c and inhibit their expression. Taken together, our results suggest that Pax7, IGF1, and Mef2c are the target genes of miR-204 in the process of myoblasts proliferation, cell migration, and differentiation, respectively, and may contribute to mouse skeletal muscle regeneration. Our results may provide new ideas and references for the skeletal muscle study and may also provide therapeutic strategies of skeletal muscle injury.

Accumulating studies demonstrate that dihydromyricetin (DMY), a compound extracted from Chinese traditional herb, Ampelopsis grossedentata, attenuates atherosclerotic process by improvement of endothelial dysfunction. However, the underlying mechanism remains poorly understood. Thus, the aim of this study is to investigate the potential mechanism behind the attenuating effects of DMY on tumor necrosis factor alpha- (TNF-α-) induced endothelial dysfunction. In response to TNF-α, microRNA-21 (miR-21) expression was significantly increased in human umbilical vein endothelial cells (HUVECs), in line with impaired endothelial dysfunction as evidenced by decreased tube formation and migration, endothelial nitric oxide synthase (eNOS) (ser1177) phosphorylation, dimethylarginine dimethylaminohydrolases 1 (DDAH1) expression and metabolic activity, and nitric oxide (NO) concentration as well as increased asymmetric dimethylarginine (ADMA) levels. In contrast, DMY or blockade of miR-21 expression ameliorated endothelial dysfunction in HUVECs treated with TNF-α through downregulation of miR-21 expression, whereas these effects were abolished by overexpression of miR-21. In addition, using a nonspecific NOS inhibitor, L-NAME, also abrogated the attenuating effects of DMY on endothelial dysfunction. Taken together, these data demonstrated that miR-21-mediated DDAH1/ADMA/NO signal pathway plays an important role in TNF-α-induced endothelial dysfunction, and DMY attenuated endothelial dysfunction induced by TNF-α in a miR-21-dependent manner.

Background. Intervertebral disc degeneration (IDD) at the cervicothoracic junction of spine is clinically relevant, however, little attention had been paid. T2 mapping and magnetic transfer ratio (MTR) are useful magnetic resonance imaging (MRI) techniques to quantitatively evaluate IDD, revealing the biochemical changes within the intervertebral disc. To compare T2 mapping with MTR imaging regarding their accuracy to quantitatively diagnose intervertebral disc degeneration at the cervicothoracic junction, influences of anatomical level, gender, age, and Pfirrmann grade of T2 relaxation time values and MTR values were evaluated. Methods. Sixty-seven patients with neck and upper back pain were included and examined with both T2 mapping and MTR imaging. The Pfirrmann grade, T2 relaxation time values, and MTR value of each disc between C7 and T3 were measured. Differences were investigated among different segmental levels, genders, age ranges, and Pfirrmann grades. The diagnostic accuracy of both MRI techniques was compared using the receiver operating characteristic (ROC) curves. Results. No significant difference was detected comparing T2 relaxation time values or MTR values among different anatomical levels, genders, and segmental levels. And we generally found that T2 relaxation time values decreased, while MTR value increased with increasing age. Importantly, we demonstrated the significant correlation between either T2 relaxation time values or MTR value and Pfirrmann grade. Conclusion. We proved the better accuracy of T2 mapping over MTR imaging to quantitatively evaluate the intervertebral disc degeneration of the cervicothoracic junction.

Background. Hyposmia is one of the most common and best-characterized conditions that is also one of the first nonmotor features of Parkinson’s disease (PD). The association of hyposmia with PD is widely accepted; however the likelihood of developing PD is unclear. Our meta-analysis aimed to investigate the risk of PD in individuals with hyposmia.Methods. Prospective studies on humans published before December4th, 2018, were searched for in PubMed, Embase, Web of Science, and Cochrane Library databases. Two independent reviewers screened studies for inclusion and extracted data. We assessed the quality of studies using the Newcastle–Ottawa Scale and pooled data for analysis using random-effects models.Results. Of the 1774 studies retrieved, seven met the inclusion criteria for this review. A total of 3272 hyposmia and 176 PD events were reported over follow-up periods ranging from 3 to 17 years. Hyposmia was associated with a 3.84-fold risk of developing PD (pooled relative risk: 3.84, 95% CI 2.12−6.95). Subgroup analyses identified few differences between different hyposmia assessment methodologies and follow-up periods.Conclusions. Our findings suggest that deficiencies in olfaction are associated with an increased risk of developing PD. Future studies are needed to investigate whether hyposmia is a promising and feasible biomarker for the early diagnosis of PD.

Myocardial fibrosis (MF) can cause heart remodeling and it is an independent risk factor for malignant arrhythmias, sudden cardiac death, and other malignant cardiovascular events. It is often characterized by myocardial interstitial collagen deposition and hyperproliferation of cardiac fibroblasts (CFs). The transforming growth factor-β1 (TGF-β1) is the most influential profibrogenic factor. Resveratrol (RSV) is an active polyphenol substance that inhibits myocardial fibrosis. The mechanism of RSV-mediated inhibition of the proliferation of CFs at the microRNA level is not fully understood. We used TGF-β1 to induce CFs proliferation to simulate the pathogenesis of myocardial fibrosis. Neonatal rat CFs were treated with TGF-β1 in the presence or absence of resveratrol. Cell proliferation was measured using the CCK-8 and EdU assay. Collagen secretion was measured using hydroxyproline kit. Further, qPCR analysis was performed to determine microRNA levels after TGF-β1 or resveratrol treatment. To identify the target gene for miR-17, miR-17 was overexpressed or silenced, and the mRNA and protein levels of Smad7 were assessed. The effects of miR-17 silencing or Smad7 overexpression on cell proliferation and collagen secretion were also examined. Resveratrol treatment significantly decreased the TGF-β1-induced CF proliferation and collagen secretion. Resveratrol also decreased the levels of miR-17, miR-34a, and miR-181a in TGF-β1-treated CFs. Overexpression of miR-17 decreased the Smad7 mRNA and protein levels while silencing miR-17 increased them. Additionally, silencing miR-17 or overexpressing Smad7 decreased the TGF-β1-induced CFs proliferation and collagen secretion. In conclusion, resveratrol inhibits TGF-β1-induced CFs proliferation and collagen secretion. This inhibitory effect of resveratrol is orchestrated by the downregulation of miR-17 and the regulation of Smad7.

A drug side effect is an undesirable effect which occurs in addition to the intended therapeutic effect of the drug. The unexpected side effects that many patients suffer from are the major causes of large-scale drug withdrawal. To address the problem, it is highly demanded by pharmaceutical industries to develop computational methods for predicting the side effects of drugs. In this study, a novel computational method was developed to predict the side effects of drug compounds by hybridizing the chemical-chemical and protein-chemical interactions. Compared to most of the previous works, our method can rank the potential side effects for any query drug according to their predicted level of risk. A training dataset and test datasets were constructed from the benchmark dataset that contains 835 drug compounds to evaluate the method. By a jackknife test on the training dataset, the 1st order prediction accuracy was 86.30%, while it was 89.16% on the test dataset. It is expected that the new method may become a useful tool for drug design, and that the findings obtained by hybridizing various interactions in a network system may provide useful insights for conducting in-depth pharmacological research as well, particularly at the level of systems biomedicine.

Drug combinatorial therapy could be more effective in treating some complex diseases than single agents due to better efficacy and reduced side effects. Although some drug combinations are being used, their underlying molecular mechanisms are still poorly understood. Therefore, it is of great interest to deduce a novel drug combination by their molecular mechanisms in a robust and rigorous way. This paper attempts to predict effective drug combinations by a combined consideration of: (1) chemical interaction between drugs, (2) protein interactions between drugs’ targets, and (3) target enrichment of KEGG pathways. A benchmark dataset was constructed, consisting of 121 confirmed effective combinations and 605 random combinations. Each drug combination was represented by 465 features derived from the aforementioned three properties. Some feature selection techniques, including Minimum Redundancy Maximum Relevance and Incremental Feature Selection, were adopted to extract the key features. Random forest model was built with its performance evaluated by 5-fold cross-validation. As a result, 55 key features providing the best prediction result were selected. These important features may help to gain insights into the mechanisms of drug combinations, and the proposed prediction model could become a useful tool for screening possible drug combinations.

This study attempted to find novel age-related macular degeneration (AMD) related genes based on 36 known AMD genes. The well-known shortest path algorithm, Dijkstra’s algorithm, was applied to find the shortest path connecting each pair of known AMD related genes in protein-protein interaction (PPI) network. The genes occurring in any shortest path were considered as candidate AMD related genes. As a result, 125 novel AMD genes were predicted. The further analysis based on betweenness and permutation test indicates that there are 10 genes involved in the formation or development of AMD and may be the actual AMD related genes with high probability. We hope that this contribution would promote the study of age-related macular degeneration and discovery of novel effective treatments.

Background. Our study aimed to assess the burden of depression and evaluate factors associated with depression and status of HIV risk behaviors among female sex workers (FSWs) in Guangdong, China. Method. We recruited FSWs from massage parlors, saunas, restaurants, hotels, hair salons, and streets in Guangdong, China, in 2014. Information on demographic characteristics, HIV testing history, and sexual behaviors was collected using a questionnaire. A blood sample was collected to test for HIV, syphilis, and HCV. A participant was defined as being depressed if she obtained 6 points or above using the 12-item General Health Questionnaire. Results. Among the 653 participants, 41.7% were 21–30 years old and 43.6% married. Overall, 52.4% were found to be depressed. FSWs who had correct syphilis related knowledge [aOR = 1.45; 95% CI: 1.04–2.03] and had primary sex partner (1.63, 1.14–2.33) were more likely to be depressed. FSWs who did not use a condom during their last sex with the primary sex partner were less likely to be depressed (0.47, 0.31–0.71). Conclusion. Our study observed high level of depression and HIV risk behaviors among Chinese FSWs. Future interventions should integrate mental health services in comprehensive interventions to prevent depression among Chinese FSWs.

With the explosively increasing high-throughput omics data, it is highly desired to develop effective computational methods and tools that can mine useful information to support the development of biochemistry, biomedicine, and drug design. Furthermore, in order to understand the protein-protein, protein-D/RNA, and other complex interactions, systems biology approaches are applied. In this collection, diverse topics were covered and there are many novel methods and intriguing findings. Y. Jiang et al. compared the gene expressions among the colorectal cancer patients in different stages and obtained the early and late stage biomarkers. Then, these two kinds of biomarkers were both mapped onto the protein interaction network, and the signal propagation path from the early stage biomarker to the late one was identified. Their findings may provide useful insights for revealing the mechanism of colorectal cancer progression at the cellular systems biology level. L. N. Lili et al. investigated the process of stroma activation in human ovarian cancer by molecular analysis of matched sets of cancer and surrounding stroma tissues. They found that functionally significant variability exists among ovarian cancer patients in the ability of the microenvironment to modulate cancer development. B. Yang et al. constructed a network-based inference framework for identifying cancer genes from gene expression data. Six identified genes (TSPYL5, CD55, CCNE2, DCK, BBC3, and MUC1) susceptible to breast cancer were verified through the literature mining, GO analysis, and pathway functional enrichment analysis. Lung cancer is one of the most malignant cancers. B. Q. Li et al. identified 25 NSCLC and 38 SCLC genes with the shortest path approach in PPI networks. These candidate genes contained more cancer genes and more functional similarity with cancer genes than those identified from the gene expression profiles. A. R. Iskandar et al. evaluated the perturbation of xenobiotic metabolism in response to cigarette smoke exposure in nasal and bronchial tissues. Their observation suggested that the effects of cigarette smoke exposure on the xenobiotic responses in the bronchial and nasal epithelium of smokers were similar to those observed in their respective organotypic models exposed to cigarette smoke, and nasal tissue could be a used as a reliable surrogate to measure the xenobiotic responses in the bronchial tissue. E. G. Maiorov et al. identified interconnected markers for T-cell acute lymphoblastic leukemia (T-ALL). Their identified genes may serve as biomarkers, alternative to the traditional ones used for the diagnosis of T-ALL, and help understand the pathogenesis of the disease. M. Kalita et al. used a multiplex gene expression profiling platform to investigate the perturbations of the innate pathways induced by TGF in a primary airway epithelial cell model of epithelial mesenchymal transition (EMT). Their results indicated that epigenetic changes produced by EMT induce dynamic state changes of the innate signaling pathway. C. Lu et al. studied the functions of microRNAs related to the liver regeneration of the whitespotted bamboo shark, Chiloscyllium plagiosum. Their work deepened the understanding of mechanisms of liver regeneration and resulted in the addition of a significant number of novel miRNAs sequences to GenBank. T. Alioto et al. presented a lightweight pipeline for first-pass gene prediction on newly sequenced genomes. The two main components are ASPic, a program that derives highly accurate, albeit not necessarily complete, EST-based transcript annotations from EST alignments. The other component is GeneID, a standard gene prediction program, which we have modified to take as evidence intron annotations. The pipeline was successfully tested on the entire C. elegans genome and the 44 ENCODE human pilot regions. J. Zou et al. reviewed advanced systems biology methods in drug discovery and translational biomedicine. Their review provided a framework for addressing disease mechanism and approaching drug discovery. L. Chen et al. proposed a computational method to predict the side effects of drugs, which integrated the information of chemical-chemical and protein-chemical interactions. Compared to most of the previous studies, the proposed method can provide the order information of the side effects for any query drug. K. Wang et al. proposed an accurate method for protein-ligand binding site on protein surface using SVM and statistical depth function. The accuracy, sensitivity, and specificity on training set are 77.55%, 56.15%, and 87.96%, respectively, and on the independent test set the accuracy, sensitivity, and specificity are 80.36%, 53.53%, and 92.38%, respectively. K. K. Tseng et al. presented a new system and novel approaches to classify different kinds of sperm images in order to assess their health. In their evaluation, the method reached accuracy of 87.5% and has better performance than the existing approaches to sperm classification. A rapid method is required to mitigate complexity and computation challenges on high throughput protein identification. In Method for Rapid Protein Identification in a Large Database, an accelerated open method is presented by W. Zhang et al. to satisfy this requirement to some extent. Q. Zou et al. proposed a novel method for distinguishing cytokine from other proteins. It is of vital importance of identifying cytokine in silicon. Ensemble classification strategy was employed for improving the prediction performance, and a friendly prediction web server was also developed. Du and Yu introduced a novel method, SubMito-PSPCP, which embeds the PSSM into the pseudoamino acid compositions, to predict protein submitochondrial locations. T. Gu et al. applied the Support Vector Regression and a two stage feature selection to developing the computational model which maps DPP-IV inhibitors to the activity. They also developed the online server. Based on nonlinear mapping and Coulomb function, X. Liu et al. applied 3D kernel approach to predict the four protein tertiary structural classes and five membrane protein types with satisfactory results. It has not escaped our notice that kernel approaches may hold a high potential for predicting the other protein features. T. H. Zhao et al. proposed a new method to predict protein disordered regions based on sequence features. The accuracy and MCC (Matthew's correlation coefficient) of their method are higher than three popular disordered region predictors: DISOPRED, DISOclust, and OnD-CRF. M. S. M. Ali et al. studied the structure and function of LipA8 which is able to adapt to extreme temperatures. Simulations show that it is most stable at 0°C and 5°C. In extreme temperature, the catalytic domain (N-terminus) maintained its stability than the noncatalytic domain (C-terminus), but the noncatalytic domain showed higher flexibility than the catalytic domain. A Boolean network (BN) is widely used as a model of gene regulatory networks. K. Kobayashi et al. proposed a BN model with two types of the control inputs and an optimal control method with duration of drug effectiveness. The optimal control problem is reduced to an integer programming problem. J. Zhang et al. studied the microRNA-mediated regulation in biological systems with oscillatory behavior. They started with two specific microRNA-mediated regulatory circuits which show their fine-tuning roles in the modulation of periodic behavior and then applied these results to study the effects of miR369-3 regulation of cell cycle. B. Yan et al. developed a mathematical model to study the mechanisms underlying the size checkpoint in fission yeast. They found that when the spatiotemporal regulation is coupled to the positive feedback loops, the mitosis-promoting factor (MPF) exhibits a bistable steady-state relationship with the cell size. The switch-like response from the positive feedback loops naturally generates the cell size checkpoint. Detection of potential siRNA off-targets is crucial for High Content Screening (HCS) using small interfering RNAs (siRNAs). S. Das et al. performed a detailed off-target analysis of three most commonly used kinome siRNA libraries based on latest RefSeq version and created SeedSeq database, a new unique format to store off-target information. L. Zhu et al. systematically investigated the characteristics and evolutionary pattern of actin gene family in primates. Phylogenetic analysis of 233 actin genes in human, chimpanzee, gorilla, orangutan, gibbon, rhesus monkey, and marmoset genomes showed that actin genes in the seven species could be divided into two major types of clades: orthologous group versus complex group. Codon usages and gene expression patterns of actin gene copies were highly consistent among the groups because of basic functions needed by the organisms but much diverged within species due to functional diversification. J. Ping et al. performed long time-scale molecular dynamics simulations on both open and closed states of Escherichia coli adenylate kinase (ADK); based on which a conformational selection mechanism was proposed to explain the large scale domain motion of this enzyme. Yudong Cai Tao Huang Lei Chen Bin Niu

Objective. To examine the biocompatibility of a novel nanohydroxyapatite/poly[lactic-co-glycolic acid] (nHA/PLGA) composite and evaluate its feasibility as a scaffold for cartilage tissue engineering.Methods. Chondrocytes of fetal rabbit were cultured with nHA/PLGA scaffoldin vitroand the cell viability was assessed by MTT assay first. Cells adhering to nHA/PLGA scaffold were then observed by inverted microscope and scanning electron microscope (SEM). The cell cycle profile was analyzed by flow cytometry.Results. The viability of the chondrocytes on the scaffold was not affected by nHA/PLGA comparing with the control group as it was shown by MTT assay. Cells on the surface and in the pores of the scaffold increased in a time-dependent manner. Results obtained from flow cytometry showed that there was no significant difference in cell cycle profiles between the coculture group and control (P>0.05).Conclusion. The porous nHA/PLGA composite scaffold is a biocompatible and good kind of scaffold for cartilage tissue engineering.

Most drugs have beneficial as well as adverse effects and exert their biological functions by adjusting and altering the functions of their target proteins. Thus, knowledge of drugs target proteins is essential for the improvement of therapeutic effects and mitigation of undesirable side effects. In the study, we proposed a novel prediction method based on drug/compound ontology information extracted from ChEBI to identify drugs target groups from which the kind of functions of a drug may be deduced. By collecting data in KEGG, a benchmark dataset consisting of 876 drugs, categorized into four target groups, was constructed. To evaluate the method more thoroughly, the benchmark dataset was divided into a training dataset and an independent test dataset. It is observed by jackknife test that the overall prediction accuracy on the training dataset was 83.12%, while it was 87.50% on the test dataset—the predictor exhibited an excellent generalization. The good performance of the method indicates that the ontology information of the drugs contains rich information about their target groups, and the study may become an inspiration to solve the problems of this sort and bridge the gap between ChEBI ontology and drugs target groups.

Lung cancer is one of the leading causes of cancer mortality worldwide. The main types of lung cancer are small cell lung cancer (SCLC) and nonsmall cell lung cancer (NSCLC). In this work, a computational method was proposed for identifying lung-cancer-related genes with a shortest path approach in a protein-protein interaction (PPI) network. Based on the PPI data from STRING, a weighted PPI network was constructed. 54 NSCLC- and 84 SCLC-related genes were retrieved from associated KEGG pathways. Then the shortest paths between each pair of these 54 NSCLC genes and 84 SCLC genes were obtained with Dijkstra’s algorithm. Finally, all the genes on the shortest paths were extracted, and 25 and 38 shortest genes with a permutationPvalue less than 0.05 for NSCLC and SCLC were selected for further analysis. Some of the shortest path genes have been reported to be related to lung cancer. Intriguingly, the candidate genes we identified from the PPI network contained more cancer genes than those identified from the gene expression profiles. Furthermore, these genes possessed more functional similarity with the known cancer genes than those identified from the gene expression profiles. This study proved the efficiency of the proposed method and showed promising results.