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Due to the advanced studies on stem cells in developmental biology, the roles of stem cells in the body and their phenotypes in related diseases have not been covered clearly. Meanwhile, with the intensive research on the mechanisms of stem cells in regulating various diseases, stem cell therapy is increasingly being attention because of its effectiveness and safety. As one of the most widely used stem cell in stem cell therapies, hematopoietic stem cell transplantation shows huge advantage in treatment of leukemia and other blood-malignant diseases. Besides, due to the effect of anti-inflammatory and immunomodulatory, mesenchymal stem cells could be a potential therapeutic strategy for variety infectious diseases. In this review, we summarized the effects of Staphylococcus aureus (S. aureus) and its components on different types of adult stem cells and their downstream signaling pathways. Also, we reviewed the roles of different kinds of stem cells in various disease models caused by S. aureus, providing new insights for applying stem cell therapy to treat infectious diseases., (© 2024. The Author(s).)

Inflammation is the host's protective response against harmful external stimulation that helps tissue repair and remodeling. However, excessive inflammation seriously threatens the patient's life. Due to anti-inflammatory effects, corticosteroids, immunosuppressants, and monoclonal antibodies are used to treat various inflammatory diseases, but drug resistance, non-responsiveness, and severe side effect limit their development and application. Therefore, developing other alternative therapies has become essential in anti-inflammatory therapy. In recent years, the in-depth study of stem cells has made them a promising alternative drug for the treatment of inflammatory diseases, and the function of stem cells is regulated by a variety of signals, of which dopamine signaling is one of the main influencing factors. In this review, we review the effects of dopamine on various adult stem cells (neural stem cells, mesenchymal stromal cells, hematopoietic stem cells, and cancer stem cells) and their signaling pathways, as well as the application of some critical dopamine receptor agonists/antagonists. Besides, we also review the role of various adult stem cells in inflammatory diseases and discuss the potential anti-inflammation function of dopamine receptors, which provides a new therapeutic target for regenerative medicine in inflammatory diseases., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Background: The identification of chemical-target interaction is key to pharmaceutical research and development, but the unclear materials basis and complex mechanisms of traditional medicine (TM) make it difficult, especially for low-content chemicals which are hard to test in experiments. In this research, we aim to apply the node2vec algorithm in the context of drug-herb interactions for expanding potential targets and taking advantage of molecular docking and experiments for verification., Methods: Regarding the widely reported risks between cardiovascular drugs and herbs, Salvia miltiorrhiza (Danshen, DS) and Ligusticum chuanxiong (Chuanxiong, CX), which are widely used in the treatment of cardiovascular disease (CVD), and approved drugs for CVD form the new dataset as an example. Three data groups DS-drug, CX-drug, and DS-CX-drug were applied to serve as the context of drug-herb interactions for link prediction. Three types of datasets were set under three groups, containing information from chemical-target connection (CTC), chemical-chemical connection (CCC) and protein-protein interaction (PPI) in increasing steps. Five algorithms, including node2vec, were applied as comparisons. Molecular docking and pharmacological experiments were used for verification., Results: Node2vec represented the best performance with average AUROC and AP values of 0.91 on the datasets "CTC, CCC, PPI". Targets of 32 herbal chemicals were identified within 43 predicted edges of herbal chemicals and drug targets. Among them, 11 potential chemical-drug target interactions showed better binding affinity by molecular docking. Further pharmacological experiments indicated caffeic acid increased the thermal stability of the protein GGT1 and ligustilide and low-content chemical neocryptotanshinone induced mRNA change of FGF2 and MTNR1A, respectively., Conclusions: The analytical framework and methods established in the study provide an important reference for researchers in discovering herb-drug interactions, alerting clinical risks, and understanding complex mechanisms of TM., (© 2023. The Author(s).)

Background: To identify operational taxonomy units (OTUs) signaling disease onset in an observational study, a powerful strategy was selecting participants by matched sets and profiling temporal metagenomes, followed by trajectory analysis. Existing trajectory analyses modeled individual OTU or microbial community without adjusting for the within-community correlation and matched-set-specific latent factors., Results: We proposed a joint model with matching and regularization (JMR) to detect OTU-specific trajectory predictive of host disease status. The between- and within-matched-sets heterogeneity in OTU relative abundance and disease risk were modeled by nested random effects. The inherent negative correlation in microbiota composition was adjusted by incorporating and regularizing the top-correlated taxa as longitudinal covariate, pre-selected by Bray-Curtis distance and elastic net regression. We designed a simulation pipeline to generate true biomarkers for disease onset and the pseudo biomarkers caused by compositionality. We demonstrated that JMR effectively controlled the false discovery and pseudo biomarkers in a simulation study generating temporal high-dimensional metagenomic counts with random intercept or slope. Application of the competing methods in the simulated data and the TEDDY cohort showed that JMR outperformed the other methods and identified important taxa in infants' fecal samples with dynamics preceding host disease status., Conclusion: Our method JMR is a robust framework that models taxon-specific trajectory and host disease status for matched participants without transformation of relative abundance, improving the power of detecting disease-associated microbial features in certain scenarios. JMR is available in R package mtradeR at https://github.com/qianli10000/mtradeR., (© 2022. The Author(s).)

Background: Truck drivers have difficulties participating in health education programs delivered at a fixed time and place due to the mobility of their workplace. Interventions conducted via social media can overcome these limitations of time and place. This study aimed to investigate the effect of a nutrition education intervention program delivered via a social media platform on the healthy eating behaviors of truck drivers., Methods: This study adopted a quasi-experimental design. A 12-week intervention program was conducted for a social-media group (n = 125) and a conventional-teaching group (n = 117) from February to May 2020. The social-media group participated in a social-media-based health intervention on the LINE application. The intervention involved the provision of online messages, online instant responses, a picture-based food log, an audio e-book, and a loyalty e-card. The conventional-teaching group participated in a healthy diet course and a hygiene education manual. The generalized estimation equation (GEE) was applied to evaluate the intervention effects on the outcome measures derived from the Health Belief Model., Results: The results of the GEE showed the social-media-based intervention strategies significantly decreased perceived barriers of consuming a healthy diet (p =  < 0.001), increased willingness to follow cues of action (p = 0.036), improved the self-efficacy of healthy eating behaviors (p = 0.001), and increased the score of healthy eating behaviors (p < 0.001) compared with the conventional teaching strategies. For the social-media and conventional-teaching groups, no significant changes occurred in self-perceived health status, self-perceived susceptibility, or self-perceived severity after the intervention. More than 90% of the participants in the social-media group believed the social-media-based intervention strategies could help implement and maintain healthy eating behaviors., Conclusions: The results indicate social-media-based intervention strategies can facilitate approaching a population without a fixed workplace, such as truck drivers. Health promoters and planners focusing on occupational health can consider developing social-media-based intervention strategies for improving truck drivers' health status., (© 2022. The Author(s).)

Background: Although rarely indicated, antibiotics are commonly used for acute diarrhoea in China. We conducted a randomised, double blind exploratory clinical trial of loperamide, berberine and turmeric for treatment of acute diarrhoea., Methods: Adults with acute uncomplicated diarrhoea aged 18 to 70 were randomised to 4 groups: (A) loperamide; (B) loperamide and berberine; (C) loperamide and turmeric; (D) loperamide, berberine and turmeric. All participants were given rescue ciprofloxacin for use after 48 h if symptoms worsened or were unimproved. Primary endpoints were feasibility and ciprofloxacin use during the 2-week follow-up period. Semi-structured interviews were conducted following recruitment and were analysed thematically. Recruiting doctors, delivery pharmacists and research assistants were blinded to treatment allocation., Results: Only 21.5% (278/1295) of patients screened were deemed eligible, and 49% (136/278) of these consented and were entered into the final analysis. Most participants had mild symptoms, because most patients with moderate or severe symptoms wanted to be given antibiotics. Follow-up was good (94% at 2 weeks). Only three participants used rescue antibiotics compared to 67% of acute diarrhoea patients in the hospital during the recruitment period. The median symptom duration was 14 h in group B (interquartile range (IQR) 10-22), 16 h in group D (IQR 10-22), 18 h in group A (IQR 10-33) and 20 h in group C (IQR 16-54). Re-consultation rates were low. There were no serious treatment-related adverse events. Most interviewed participants said that although they had believed antibiotics to be effective for diarrhoea, they were surprised by their quick recovery without antibiotics in this trial., Conclusion: Although recruitment was challenging because of widespread expectations for antibiotics, patients with mild diarrhoea accepted trying an alternative. The three nutraceuticals therapy require further evaluation in a fully powered, randomised controlled trial among a broader sample., Trial Registration: ChiCTR-IPR-17014107.

Background: Matched-set data arise frequently in microbiome studies. For example, we may collect pre- and post-treatment samples from a set of individuals, or use important confounding variables to match data from case participants to one or more control participants. Thus, there is a need for statistical methods for data comprised of matched sets, to test hypotheses against traits of interest (e.g., clinical outcomes or environmental factors) at the community level and/or the operational taxonomic unit (OTU) level. Optimally, these methods should accommodate complex data such as those with unequal sample sizes across sets, confounders varying within sets, and continuous traits of interest., Methods: PERMANOVA is a commonly used distance-based method for testing hypotheses at the community level. We have also developed the linear decomposition model (LDM) that unifies the community-level and OTU-level tests into one framework. Here we present a new strategy that can be used with both PERMANOVA and the LDM for analyzing matched-set data. We propose to include an indicator variable for each set as covariates, so as to constrain comparisons between samples within a set, and also permute traits within each set, which can account for exchangeable sample correlations. The flexible nature of PERMANOVA and the LDM allows discrete or continuous traits or interactions to be tested, within-set confounders to be adjusted, and unbalanced data to be fully exploited., Results: Our simulations indicate that our proposed strategy outperformed alternative strategies, including the commonly used one that utilizes restricted permutation only, in a wide range of scenarios. Using simulation, we also explored optimal designs for matched-set studies. The flexibility of PERMANOVA and the LDM for a variety of matched-set microbiome data is illustrated by the analysis of data from two real studies., Conclusions: Including set indicator variables and permuting within sets when analyzing matched-set data with PERMANOVA or the LDM is a strategy that performs well and is capable of handling the complex data structures that frequently occur in microbiome studies. Video Abstract.

Background: Very large cohorts that span an entire population raise new prospects for the conduct of multiple trials that speed up advances in prevention or treatment while reducing participant, financial and regulatory burden. However, a review of literature reveals no blueprint to guide this systematically in practice. This Statement of Intent proposes how diverse trials may be integrated within or alongside Generation Victoria (GenV), a whole-of-state Australian birth cohort in planning, and delineates potential processes and opportunities., Methods: Parents of all newborns (estimated 160,000) in the state of Victoria, Australia, will be approached for two full years from 2021. The cohort design comprises four elements: (1) consent soon after birth to follow the child and parent/s until study end or withdrawal; retrospective and prospective (2) linkage to clinical and administrative datasets and (3) banking of universal and clinical biosamples; and (4) GenV-collected biosamples and data. GenV-collected data will focus on overarching outcome and phenotypic measures using low-burden, universal-capable electronic interfaces, with funding-dependent face-to-face assessments tailored to universal settings during the early childhood, school and/or adult years., Results: For population or registry-type trials within GenV, GenV will provide all outcomes data and consent via traditional, waiver, or Trials Within Cohorts models. Trials alongside GenV consent their own participants born within the GenV window; GenV may help identify potential participants via opt-in or opt-out expression of interest. Data sharing enriches trials with outcomes, prior data, and/or access to linked data contingent on custodian's agreements, and supports modeling of causal effects to the population and between-trials comparisons of costs, benefits and utility. Data access will operate under the Findability, Accessibility, Interoperability, and Reusability (FAIR) and Care and Five Safes Principles. We consider governance, ethical and shared trial oversight, and expectations that trials will adhere to the best practice of the day., Conclusions: Children and younger adults can access fewer trials than older adults. Integrating trials into mega-cohorts should improve health and well-being by generating faster, larger-scale evidence on a longer and/or broader horizon than previously possible. GenV will explore the limits and details of this approach over the coming years.

Background: Parasellar meningioma is a common benign tumour in brain. Both surgery and radiosurgery are important treatment modalities for this tumour. The study was designed to investigate whether prior surgery would affect treatment outcomes of patients with parasellar meningiomas after management with Gamma Knife radiosurgery., Methods: A total of 93 patients who received Gamma Knife surgery were included in this retrospective study. There were 30 males and 63 females, with a median age of 48.6 years (range, 15.2-78.7 years). Prior surgery was performed in 45 patients. The median tumor volume was 5.02 cm 3 (range 1.07-35.46 cm 3 ) and median marginal dose was 12 Gy (range 10-15 Gy). The mean imaging follow-up and clinical follow-up periods were 40.7 and 52.7 months, respectively., Results: In the group without prior surgery, 31 patients had improvement of preexisting symptoms; and in the group with prior surgery, 20 patients were noted to improve. The difference in symptom improvement between the two groups reached statistical significance (P = 0.009). Patients with prior surgery were more likely to have stable symptoms after Gamma Knife surgery (P = 0.012). Tumor recurrence was reported in 8 patients out of 45 patients with prior surgery, and 3 patients out of 48 patents without prior surgery (P = 0.085). After Gamma Knife surgery, 5 and 4 patients in two groups developed new neurological symptoms, respectively (P = 0.651). Cox regression analysis identified follow-up period as prognostic factor of progression-free survival. Ordinal logistic regression analysis identified surgery prior to Gamma Knife surgery as an unfavorable factor of symptom change., Conclusion: Gamma Knife radiosurgery provided long-term effective tumor control and better symptom recovery compared with those with prior surgery. Patients with surgery before Gamma Knife radiosurgery were more likely to have stable symptoms. Further analyses indicated that long follow-up is essential to determine the efficacy of radiosurgery for parasellar meningiomas. Further study needs to include more patients with longer follow-up to draw a more solid conclusion.

Background: Although various machine learning-based predictors have been developed for estimating protein-protein interactions, their performances vary with dataset and species, and are affected by two primary aspects: choice of learning algorithm, and the representation of protein pairs. To improve the performance of predicting protein-protein interactions, we exploit the synergy of multiple learning algorithms, and utilize the expressiveness of different protein-pair features., Results: We developed a stacked generalization scheme that integrates five learning algorithms. We also designed three types of protein-pair features based on the physicochemical properties of amino acids, gene ontology annotations, and interaction network topologies. When tested on 19 published datasets collected from eight species, the proposed approach achieved a significantly higher or comparable overall performance, compared with seven competitive predictors., Conclusion: We introduced an ensemble learning approach for PPI prediction that integrated multiple learning algorithms and different protein-pair representations. The extensive comparisons with other state-of-the-art prediction tools demonstrated the feasibility and superiority of the proposed method.

Background: We sought to understand the epidemiology and characteristics of antimicrobial resistance (AMR) and the impact of the National Action Plan (NAP) on AMR. This information will be critical to develop interventions and strengthen antibiotic stewardship in hospital settings in China., Methods: Cross-sectional data collection from the hospital information management system from 1 January 2015 to 30 August 2018. Variables included patient age, sex, diagnosis, hospital department and antibiotic sensitivity test. T-test for two samples method was applied to compare the results before and after NAP implementation. Multivariate analysis with binary logistic regression was conducted to examine the associations of risk factors for antimicrobial resistance., Results: In total there were 352,238 isolates in the final analysis after excluding contamination strains and isolates with incomplete information. More than 50% of patients were > 66 years old. 62% were male. 40% of the total samples were sputum. Among the total sample, the total resistance rate was 42% among all isolates. The rate of resistance to all antibiotics declined by 5.3% (95% CI 4.96-5.64%, p  < 0.0001) and culture positivity rate declined by 9.8% (95% CI 9.22-10.34%, p  < 0.0001) after NAP. Logistical regression showed that the NAP had effect with an adjusted odds ratio of 0.76 (95% CI 0.71-0.81, p  = 0.002). Being male, age > 65 years, ICU department, diagnosed with certain diseases were more likely to be associated with antimicrobial resistance., Conclusions: Antibiotic resistance rates were high in this teaching hospital. However, the introduction of the China NAP since 2016 followed by hospital policy emphasis was associated with a declining AMR trend. Policies will need to incorporate antimicrobial stewardship with a focus on certain departments, with infection control practices and with increases in vaccination coverage among elderly., Competing Interests: Jiading district hospital ethical committee, reference number: 201803; All information obtained was kept anonymous.Not applicable.The authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Background: The Gnaphalium pensylvanicum willd. is used in China as a folk medicine to treat anti-inflammatory, cough and rheumatism arthritis. The aim of this study was to evaluate the potential of the extract of G. pensylvanicum to treat hyperuricemia and acute gouty arthritis in animal model., Methods: G. pensylvanicum extract was evaluated in an experimental model with potassium oxonate (PO) induced hyperuricemia in mice which was used to evaluate anti-hyperuricemia activity and xanthine oxidase (XO) inhibition. Therapies for acute gouty arthritis was also investigated on monosodium urate (MSU) crystal induced paw edema model., Results: G. pensylvanicum extract showed activity in reducing serum uric acid (Sur) through effect renal glucose transporter 9 (GLUT9), organic anion transporter 1 (OAT1) and urate transporter 1 (URAT1) mainly and inhibited XO activity in vivo of mice with PO induced hyperuricemia. The extract of G. pensylvanicum also showed significant anti-inflammatory activity and reduced the paw swelling on MSU crystal-induced paw edema model. Meanwhile, 13 caffeoylquinic acid derivatives and 1 flavone were identified by UPLC-ESI-MS/MS as the main active component of G. pensylvanicum., Conclusions: The extract of G. pensylvanicum showed significant effect on evaluated models and therefore may be active agents for the treatment of hyperuricemia and acute gouty arthritis.

Background: Aikeqing (AKQ) has been shown in clinical studies to improve quality of life of HIV/AIDS patients, but anti-HIV activity has not been determined. The SHIV-infected macaque is an important animal model for testing antiviral drugs. This study aimed to determine the anti-HIV activity of AKQ in chronically SHIV89.6-infected Chinese rhesus macaques., Methods: Nine Chinese rhesus macaques were inoculated intravenously with SHIV89.6 virus. At 11 weeks post-infection, the animals were arbitrarily divided into three groups: high-dose (AKQ 1.65 g/kg; n = 3), low-dose (AKQ 0.55 g/kg; n = 3), and control (water 1 mL/kg; n = 3). Treatment was administered by the intragastric gavage route once-daily for 8 weeks. Blood (5 mL) was collected biweekly. Viral loads were analyzed by real-time quantitative RT-PCR assays, and T cell counts were monitored by FACS analyses throughout the treatment., Results: AKQ induced a persistent decline (P = 0.02) in plasma viral loads during treatment in the high-dose group compared with their baseline levels, and cessation of the therapy caused viral load rebound to the pretreatment levels. No significant difference (P = 0.06) was found in the plasma viral loads during treatment in the low-dose group. The CD4(+) T cell counts and CD4/CD8 ratios remained at stable high levels during the treatment period., Conclusion: AKQ reduced plasma viral loads in the SHIV89.6-infected Chinese rhesus macaque model.

Background: With the increasing popularity of mobile phones, the potential hazards of radiofrequency electromagnetic radiation (RF-EMR) on the auditory system remain unclear. Apart from RF-EMR, humans are also exposed to various physical and chemical factors. We established a lipopolysaccharide (LPS)-induced inflammation in vitro model to investigate whether the possible sensitivity of spiral ganglion neurons to damage caused by mobile phone electromagnetic radiation (at specific absorption rates: 2, 4 W/kg) will increase., Methods: Spiral ganglion neurons (SGN) were obtained from neonatal (1- to 3-day-old) Sprague Dawley® (SD) rats. After the SGN were treated with different concentrations (0, 20, 40, 50, 100, 200, and 400 μg/ml) of LPS, the Cell Counting Kit-8 (CCK-8) and alkaline comet assay were used to quantify cellular activity and DNA damage, respectively. The SGN were treated with the moderate LPS concentrations before RF-EMR exposure. After 24 h intermittent exposure at an absorption rate of 2 and 4 W/kg, DNA damage was examined by alkaline comet assay, ultrastructure changes were detected by transmission electron microscopy, and expression of the autophagy markers LC3-II and Beclin1 were examined by immunofluorescence and confocal laser scanning microscopy. Reactive oxygen species (ROS) production was quantified by the dichlorofluorescin-diacetate assay., Results: LPS (100 μg/ml) induced DNA damage and suppressed cellular activity (P < 0.05). LPS (40 μg/ml) did not exhibit cellular activity changes or DNA damage (P > 0.05); therefore, 40 μg/ml was used to pretreat the concentration before exposure to RF-EMR. RF-EMR could not directly induce DNA damage. However, the 4 W/kg combined with LPS (40 μg/ml) group showed mitochondria vacuoles, karyopyknosis, presence of lysosomes and autophagosome, and increasing expression of LC3-II and Beclin1. The ROS values significantly increased in the 4 W/kg exposure, 4 W/kg combined with LPS (40 μg/ml) exposure, and H2O2 groups (P < 0.05, 0.01)., Conclusions: Short-term exposure to radiofrequency electromagnetic radiation could not directly induce DNA damage in normal spiral ganglion neurons, but it could cause the changes of cellular ultrastructure at special SAR 4.0 W/kg when cells are in fragile or micro-damaged condition. It seems that the sensitivity of SGN to damage caused by mobile phone electromagnetic radiation will increase in a lipopolysaccharide-induced inflammation in vitro model.

Background: One of the major challenges in the field of vaccine design is identifying B-cell epitopes in continuously evolving viruses. Various tools have been developed to predict linear or conformational epitopes, each relying on different physicochemical properties and adopting distinct search strategies. We propose a meta-learning approach for epitope prediction based on stacked and cascade generalizations. Through meta learning, we expect a meta learner to be able integrate multiple prediction models, and outperform the single best-performing model. The objective of this study is twofold: (1) to analyze the complementary predictive strengths in different prediction tools, and (2) to introduce a generic computational model to exploit the synergy among various prediction tools. Our primary goal is not to develop any particular classifier for B-cell epitope prediction, but to advocate the feasibility of meta learning to epitope prediction. With the flexibility of meta learning, the researcher can construct various meta classification hierarchies that are applicable to epitope prediction in different protein domains., Results: We developed the hierarchical meta-learning architectures based on stacked and cascade generalizations. The bottom level of the hierarchy consisted of four conformational and four linear epitope prediction tools that served as the base learners. To perform consistent and unbiased comparisons, we tested the meta-learning method on an independent set of antigen proteins that were not used previously to train the base epitope prediction tools. In addition, we conducted correlation and ablation studies of the base learners in the meta-learning model. Low correlation among the predictions of the base learners suggested that the eight base learners had complementary predictive capabilities. The ablation analysis indicated that the eight base learners differentially interacted and contributed to the final meta model. The results of the independent test demonstrated that the meta-learning approach markedly outperformed the single best-performing epitope predictor., Conclusions: Computational B-cell epitope prediction tools exhibit several differences that affect their performances when predicting epitopic regions in protein antigens. The proposed meta-learning approach for epitope prediction combines multiple prediction tools by integrating their complementary predictive strengths. Our experimental results demonstrate the superior performance of the combined approach in comparison with single epitope predictors.

Background: For treatment and prevention of metastatic disease, one of the premier challenges is the identification of pathways and proteins to target for clinical intervention. Micro RNAs (miRNAs) are short, non-coding RNAs, which regulate cellular activities by either mRNA degradation or translational inhibition. Our studies focused on the invasive properties of hsa-mir30c based on its high expression in MDA-MB-231 metastatic cells and our bioinformatic analysis of the Cancer Genome Atlas that identified aberrant hsa-mir-30c to be associated with poor survival., Methods: Contributions of hsa-mir-30c to breast cancer cell invasion were examined by Matrigel invasion transwell assays following modulation of hsa-mir-30c or hsa-mir-30c* levels in MDA-MB-231 cells. hsa-mir-30c in silico predicted targets linked to cell invasion were screened for targeting by hsa-mir-30c in metastatic breast cancer cells by RT-qPCR. The contribution to invasion by a target of hsa-mir-30c, Nephroblastoma overexpressed (NOV), was characterized by siRNA and invasion assays. Significant effects were determined using Student's T-tests with Welch's correction for unequal variance., Results: MCF-7 and MDA-MB-231 cells were used as models of poorly invasive and late-stage metastatic disease, respectively. By modulating the levels of hsa-mir-30c in these cells, we observed concomitant changes in breast cancer cell invasiveness. From predicted targets of hsa-mir-30c that were related to cellular migration and invasion, NOV/CCN3 was identified as a novel target of hsa-mir-30c. Depleting NOV by siRNA caused a significant increase in the invasiveness of MDA-MB-231 cells is a regulatory protein associated with the extracellular matrix., Conclusions: NOV/CCN3 expression, which protects cells from invasion, is known in patient tumors to inversely correlate with advanced breast cancer and metastasis. This study has identified a novel target of hsa-mir-30c, NOV, which is an inhibitor of the invasiveness of metastatic breast cancer cells. Thus, hsa-mir-30c-mediated inhibition of NOV levels promotes the invasive phenotype of MDA-MB-231 cells and significantly, the miR-30/NOV pathways is independent of RUNX2, a known target of hsa-mir-30c that promotes osteolytic disease in metastatic breast cancer cells. Our findings allow for mechanistic insight into the clinical observation of poor survival of patients with elevated hsa-mir-30c levels, which can be considered for miRNA-based translational studies.

Background: Appropriate postoperative pain management contributes to earlier mobilization, shorter hospitalization, and reduced cost. The under treatment of pain may impede short-term recovery and have a detrimental long-term effect on health. This study focuses on Patient Controlled Analgesia (PCA), which is a delivery system for pain medication. This study proposes and demonstrates how to use machine learning and data mining techniques to predict analgesic requirements and PCA readjustment., Methods: The sample in this study included 1099 patients. Every patient was described by 280 attributes, including the class attribute. In addition to commonly studied demographic and physiological factors, this study emphasizes attributes related to PCA. We used decision tree-based learning algorithms to predict analgesic consumption and PCA control readjustment based on the first few hours of PCA medications. We also developed a nearest neighbor-based data cleaning method to alleviate the class-imbalance problem in PCA setting readjustment prediction., Results: The prediction accuracies of total analgesic consumption (continuous dose and PCA dose) and PCA analgesic requirement (PCA dose only) by an ensemble of decision trees were 80.9% and 73.1%, respectively. Decision tree-based learning outperformed Artificial Neural Network, Support Vector Machine, Random Forest, Rotation Forest, and Naïve Bayesian classifiers in analgesic consumption prediction. The proposed data cleaning method improved the performance of every learning method in this study of PCA setting readjustment prediction. Comparative analysis identified the informative attributes from the data mining models and compared them with the correlates of analgesic requirement reported in previous works., Conclusion: This study presents a real-world application of data mining to anesthesiology. Unlike previous research, this study considers a wider variety of predictive factors, including PCA demands over time. We analyzed PCA patient data and conducted several experiments to evaluate the potential of applying machine-learning algorithms to assist anesthesiologists in PCA administration. Results demonstrate the feasibility of the proposed ensemble approach to postoperative pain management.

Background: Oral mucosal melanoma (OMM) is a clinically rare disease with poor prognosis. Various treatment methods have been investigated with the aim of improving the prognosis. This study aimed to analyze the data of a single institution in the management of OMM., Methods: A total of 78 consecutive OMM patients were included in this retrospective study. The intraoral lesion was treated either by cryotherapy, surgery or both; the neck was treated by neck dissection or observation; post-operative chemotherapy with dimethyl triazeno imidazole carboxamide and cisplatin was performed in some patients. The Kaplan-Meier method was used for statistical analysis., Results: Among the 78 patients, there were 50 males and 28 females with an average age of 53.8 years (ranging from 27 to 85 years). The most common sites of OMM were the hard palate and gingiva. The main cause of death in OMM was distant metastasis. No significant difference was found between the intraoral/cervical lesion recurrence/post-operative distant metastasis and the intraoral lesion site/biopsy method/treatment method. The metastasis rate of cervical lymph node was high in the OMM patients, even in the patients with clinically negative necks. Cervical lesion recurrence was correlated with N stage and intraoral lesion recurrence. The survival period was longer in the patients with T3 staging, clinical stage III disease, with post-operative chemotherapy and without post-operative distant metastasis when compared to those patients with T4a staging, clinical stage IV disease, without post-operative chemotherapy and with post-operative distant metastasis., Conclusions: Radical surgery including wide intraoral resection and neck dissection is recommended for OMM patients. Post-operative chemotherapy may also be beneficial for both primary and recurrent OMM patients.

Background: At present, the organization of system modules is typically limited to either a multilevel hierarchy that describes the "vertical" relationships between modules at different levels (e.g., module A at level two is included in module B at level one), or a single-level graph that represents the "horizontal" relationships among modules (e.g., genetic interactions between module A and module B). Both types of organizations fail to provide a broader and deeper view of the complex systems that arise from an integration of vertical and horizontal relationships., Results: We propose a complex network analysis tool, Pyramabs, which was developed to integrate vertical and horizontal relationships and extract information at various granularities to create a pyramid from a complex system of interacting objects. The pyramid depicts the nested structure implied in a complex system, and shows the vertical relationships between abstract networks at different levels. In addition, at each level the abstract network of modules, which are connected by weighted links, represents the modules' horizontal relationships. We first tested Pyramabs on hierarchical random networks to verify its ability to find the module organization pre-embedded in the networks. We later tested it on a protein-protein interaction (PPI) network and a metabolic network. According to Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), the vertical relationships identified from the PPI and metabolic pathways correctly characterized the inclusion (i.e., part-of) relationship, and the horizontal relationships provided a good indication of the functional closeness between modules. Our experiments with Pyramabs demonstrated its ability to perform knowledge mining in complex systems., Conclusions: Networks are a flexible and convenient method of representing interactions in a complex system, and an increasing amount of information in real-world situations is described by complex networks. We considered the analysis of a complex network as an iterative process for extracting meaningful information at multiple granularities from a system of interacting objects. The quality of the interpretation of the networks depends on the completeness and expressiveness of the extracted knowledge representations. Pyramabs was designed to interpret a complex network through a disclosure of a pyramid of abstractions. The abstraction pyramid is a new knowledge representation that combines vertical and horizontal viewpoints at different degrees of abstraction. Interpretations in this form are more accurate and more meaningful than multilevel dendrograms or single-level graphs. Pyramabs can be accessed at http://140.113.166.165/pyramabs.php/.

Background: Structural similarities among proteins can provide valuable insight into their functional mechanisms and relationships. As the number of available three-dimensional (3D) protein structures increases, a greater variety of studies can be conducted with increasing efficiency, among which is the design of protein structural alphabets. Structural alphabets allow us to characterize local structures of proteins and describe the global folding structure of a protein using a one-dimensional (1D) sequence. Thus, 1D sequences can be used to identify structural similarities among proteins using standard sequence alignment tools such as BLAST or FASTA., Results: We used self-organizing maps in combination with a minimum spanning tree algorithm to determine the optimum size of a structural alphabet and applied the k-means algorithm to group protein fragnts into clusters. The centroids of these clusters defined the structural alphabet. We also developed a flexible matrix training system to build a substitution matrix (TRISUM-169) for our alphabet. Based on FASTA and using TRISUM-169 as the substitution matrix, we developed the SA-FAST alignment tool. We compared the performance of SA-FAST with that of various search tools in database-scale search tasks and found that SA-FAST was highly competitive in all tests conducted. Further, we evaluated the performance of our structural alphabet in recognizing specific structural domains of EGF and EGF-like proteins. Our method successfully recovered more EGF sub-domains using our structural alphabet than when using other structural alphabets. SA-FAST can be found at http://140.113.166.178/safast/., Conclusion: The goal of this project was two-fold. First, we wanted to introduce a modular design pipeline to those who have been working with structural alphabets. Secondly, we wanted to open the door to researchers who have done substantial work in biological sequences but have yet to enter the field of protein structure research. Our experiments showed that by transforming the structural representations from 3D to 1D, several 1D-based tools can be applied to structural analysis, including similarity searches and structural motif finding.

Background: DNA-binding proteins are of utmost importance to gene regulation. The identification of DNA-binding domains is useful for understanding the regulation mechanisms of DNA-binding proteins. In this study, we proposed a method to determine whether a domain or a protein can has DNA binding capability by considering evolutionary conservation of DNA-binding residues., Results: Our method achieves high precision and recall for 66 families of DNA-binding domains, with a false positive rate less than 5% for 250 non-DNA-binding proteins. In addition, experimental results show that our method is able to identify the different DNA-binding behaviors of proteins in the same SCOP family based on the use of evolutionary conservation of DNA-contact residues., Conclusion: This study shows the conservation of DNA-contact residues in DNA-binding domains. We conclude that the members in the same subfamily bind DNA specifically and the members in different subfamilies often recognize different DNA targets. Additionally, we observe the co-evolution of DNA-contact residues and interacting DNA base-pairs.