The endothelium is nowadays described as an entire organ that regulates various processes: vascular tone, coagulation, inflammation, and immune cell trafficking, depending on the vascular site and its specific microenvironment as well as on endothelial cell-intrinsic mechanisms like epigenetic changes. In this review, we will focus on the control of the adaptive immune response by the tumor vasculature. In physiological conditions, the endothelium acts as a barrier regulating cell trafficking by specific expression of adhesion molecules enabling adhesion of immune cells on the vessel, and subsequent extravasation. This process is also dependent on chemokine and integrin expression, and on the type of junctions defining the permeability of the endothelium. Endothelial cells can also regulate immune cell activation. In fact, the endothelial layer can constitute immunological synapses due to its close interactions with immune cells, and the delivery of co-stimulatory or co-inhibitory signals. In tumor conditions, the vasculature is characterized by an abnormal vessel structure and permeability, and by a specific phenotype of endothelial cells. All these abnormalities lead to a modulation of intra-tumoral immune responses and contribute to the development of intra-tumoral immunosuppression, which is a major mechanism for promoting the development, progression, and treatment resistance of tumors. The in-depth analysis of these various abnormalities will help defining novel targets for the development of anti-tumoral treatments. Furthermore, eventual changes of the endothelial cell phenotype identified by plasma biomarkers could secondarily be selected to monitor treatment efficacy. The effects of immunity on tumoral angiogenesis are well-known for several years, but the description of a modulation of immunity by pro-angiogenic molecules, like vascular endothelial growth factor (VEGF), is more recent . The useTo allow its development, the tumor has to promote neoangiogenesis and escape the immune system, which constitutes major hallmarks of cancer . To suppSince several years, endothelium has been considered as a whole organ with a unique situation, as it communicates both with the circulating compartment and the tissue . EndotheImmune modulation by the endothelium is favored by the unique position of ECs, exposing them to T cells during extravasation from the circulation into the tissue or the tumor. Several steps are implied in the regulation of the immune response by the endothelium. Immune cells must firstly adhere and cross the vascular barrier before being effective at the site of inflammation. Moreover, it has been demonstrated that activated ECs could present antigens to lymphocytes. Depending on the presence of various sets of co-stimulatory signals, they could activate memory cells or anergize naïve T cells . Under aTumor vasculature has been described as abnormal, with tortuous, leaky, and immature vessels that are finally less functional . CharactBriefly, rolling of leukocytes occurs by the interaction between the selectins expressed by leukocytes and their ligands on ECs . Then, lin vitro adhesion of leukocytes  on the vasculature has been described, impairing the development of an efficient leukocyte infiltration in tumors. The high level of tumor necrosis factor α (TNF-α) found in tumors, like in inflammatory responses, should upregulate CAM expression on ECs. But tumor microenvironment may deliver other products that exert a negative regulation. Indeed, Griffioen et al. observed a decrease of TEC activation under exposure to pro-inflammatory cytokines, like TNF-α, that could be attributed to angiogenic factors highly expressed in tumors, basic fibroblast growth factor (b-FGF), or VEGF. The slightest activation was characterized by a limited increase of intercellular cell adhesion molecule-1 (ICAM-1) and 2 (ICAM-2), vascular cell adhesion molecule-1 (VCAM-1), and E-selectin expression, and a decreased ukocytes . The NO ukocytes . Conversukocytes , 40. Bouukocytes .Egfl7 also known as Vascular Endothelial–statin gene is mostly expressed in ECs and endothelial progenitors during embryonic and neonatal development. Egfl7 regulates vascular integrity and smooth muscle cell migration  and endothelin-1. igration . An upreigration , 43. Itsigration . In tumoigration . In glioigration . Egfl7 cigration . Endotheigration . ET-1 isigration . Indeed,igration . ET-1 syigration  and ET-1igration –51. An oigration . As for igration .5β1 integrin (VLA-5) expressed on leukocytes  or S-Endo-1 antigen, is a component of the endothelial junction involved in the control of cell cohesion and tumor angiogenesis . CD146 iAnother way to decrease anti-tumoral immunity is the expression of adhesion molecules favoring specific intra-tumoral infiltration of immunosuppressive populations. For example, tumor-associated vessels in hepatocellular carcinoma present an increased level of common lymphatic endothelial and vascular endothelial receptor-1 (CLEVER-1). This recycling and intracellular trafficking receptor has been implicated preferentially in transendothelial migration of CD4+ FoxP3+ regulatory T cells . Its expTwo signals are required for induction of cell proliferation and cytokine production in resting T cells. Occupancy of the T-cell receptor (TCR) by antigen presented by the MHC delivers the first signal to the T cell, while the second signal is provided by the interaction with co-stimulatory ligands on APCs. Formation of an immune synapse may serve to stabilize adhesion and extend the duration of bidirectional signaling between the APC and the T cell. Immune synapses are constituted by a concentration of adhesion molecules at the edge, while TCR/MHC complexes and co-stimulatory molecules are grouped in the center. A modulation in MHC II, co-stimulatory/co-inhibitory molecule, or cytokine expression by TECs could participate to the tumor-induced immunosuppression. The major mechanisms reviewed in this part are presented in Figure Antigen-presenting cell function of TECs is defined by their capacity to present exogenous antigens by class II MHC. The antigen uptake, processing, and presentation are regulated by the microenvironment as pro-inflammatory cytokines like TNF-α and interferon-γ (IFN-γ) can upregulate the expression of class II MHC and accessory molecules . The abiin vitro , adhesion molecules, and activating cytokines. Tumor microenvironment controls T-lymphocyte activation by several mechanisms, among them the inhibition of DC maturation. Indeed, immature DCs express intermediate amounts of MHC class I and II and co-stimulatory molecules, high amounts of co-inhibitory molecules, and immunosuppressive cytokines, thus inducing anergy of effector T cells and expansion of Treg. The tumor microenvironment also induces the expression of co-inhibitory molecules on T lymphocytes that interact with their ligands expressed on tumor cells. Interestingly, TECs can also express co-inhibitory molecules and produce immunosuppressive molecules, and thus may participate to anergize T cells in the tumor microenvironment.Human ECs cultured from different vessel sources express numerous co-stimulatory molecules, including LFA-3, OX40-L, 4-1BB-L (CD137), ICOS-L, and glucocorticoid-induced TNF-receptor-related protein (GITR-L) . Among tAmong co-inhibitory molecules, ECs can express PD-L1, which bind to PD-1 on activated lymphocytes and down-regulate T-cell activation . Like maThe decrease of activating co-stimulatory molecules expressed by ECs could be hypothesized but have not been reported. Molecules involved in the direct stimulation of lymphocytes like ICOS-L, CD40, CD80, CD86 are upregulated by angiostatic molecules and Th1 cytokines. An inhibition of these co-stimulatory signals has also been proposed after the identification of markers of tumor endothelium by Seaman et al. . Among tin vivo amplified cancer T-cell-mediated rejection, in association with an increase of the threshold of TCR signaling required, leading to a resistance to tolerance induction , can form endothelial junctions and promote leukocyte transendothelial cell migration, respectively . But it nduction .Under pro-inflammatory signals, ECs can secrete cytokines and directly activate resting effector cells . Some acIn vitro, interactions between microvessel ECs and tumor cells from head and neck squamous cell carcinoma induced the secretion of PGE2 by ECs through an IL-1 pathway  expression. This inflammatory enzyme is implicated in the catabolism of the essential amino acid tryptophan and participates in immune tolerance and tumor immunoresistance by simultaneously depleting essential tryptophan and generating immunosuppressive tryptophan metabolites. IDO expression is a mechanism to regulate T-cell activation by APCs and has been considered to be a major mechanism involved in the escape of tumors from the host immune response . IDO expTumor infiltration by lymphocytes depends on cancer cell type and individuals but is a prognostic factor of response to treatment. A recent Nature Cancer Review meta-analysis summarizes the impact of different immune cells on clinical outcome from more than 120 published articles . A stronVascular endothelial growth factor-targeted therapies were initially developed in order to inhibit new blood vessel growth and thus starve tumors of necessary oxygen and nutrients. It has become increasingly apparent, however, that the therapeutic benefit associated with VEGF-targeted therapy was complex, probably involving multiple mechanisms, some of them relying on the improvement of the immune status during tumor development. An increase of B and T cells has been observed in patients with metastatic colorectal cancer treated with bevacizumab, an antibody directed against VEGF . This trThe clinical benefit observed on tumor growth made several molecules indicated in first line treatment of renal cell carcinoma and colorectal cancer, in association with chemotherapy. Despite their impact on immunosuppression, no benefit on overall survival has been observed with anti-angiogenic therapies used in monotherapy. These limited results might be explained by the existence of a therapeutic window for their benefit. Indeed, vascular normalization has been observed under anti-angiogenic treatment, with a more mature and functional tumor vasculature able to deliver chemotherapy or immune cells –109. HowBlocking more specifically immune suppressive ECs may help improving the efficacy of existing immunotherapies, particularly those consisting of T cells or NK cells as these cells must pass through the tumor vasculature to infiltrate tumors. Interestingly, EC anergy induced by tumor pro-angiogenic factors Figure  can be rRegulation of immunity is one of the numerous functions of endothelium. It is now well-demonstrated that tumor endothelium is implicated in the suppression of adaptive anti-tumoral immune response exerted by the tumor. Tumor endothelium is a prohibitive barrier that inhibits T-cell homing to the tumor and inactivates immune cells through antigen presentation co-inhibitory signals and the expression of immunosuppressive molecules. The immunosuppressive phenotype of TECs is dependent on the tumor microenvironment. The regulation of immunity by tumor vasculature was initially demonstrated by the reversal of immunosuppression observed under anti-angiogenic treatments. Combination between immunotherapies and these treatments reversing TEC phenotype have shown encouraging results promoting the use of these molecules as adjuvant therapies. For that purpose, validation of immune parameters as predictive biomarkers is required. Alternative tools inducing stable normalization are also in development. Further research is needed to identify new endothelial targets and determine how the modulation of EC phenotype could be combined with immunotherapeutic strategies.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Three meta-analyses and one systematic review have been conducted on the question of whether self-collected specimens are as accurate as clinician-collected specimens for STI screening. However, these reviews predate 2007 and did not analyze rectal or pharyngeal collection sites. Currently, there is no consensus on which sampling method is the most effective for the diagnosis of genital chlamydia (CT), gonorrhea (GC) or human papillomavirus (HPV) infection. Our meta-analysis aims to be comprehensive in that it will examine the evidence of whether self-collected vaginal, urine, pharyngeal and rectal specimens provide as accurate a clinical diagnosis as clinician-collected samples (reference standard).2 test. Sensitivity and specificity estimates with 95% confidence intervals as well as negative and positive likelihood ratios will be pooled and weighted using random effects meta-analysis, if appropriate. A hierarchical summary receiver operating characteristics curve for self-collected specimens will be generated.Inclusion and exclusion criteria: Eligible studies include both randomized and non-randomized controlled trials, pre- and post-test designs, and controlled observational studies. Search strategy: The databases that will be searched include the Cochrane Database of Systematic Reviews, Web of Science, Database of Abstracts of Reviews of Effects (DARE), EMBASE and PubMed/Medline. Data collection and analysis: Data will be abstracted independently by two reviewers using a standardized pre-tested data abstraction form. Heterogeneity will be assessed using the QThis synthesis involves a meta-analysis of self-collected samples  versus clinician-collected samples for the diagnosis of CT, GC and HPV, the most prevalent STIs. Our systematic review will allow patients, clinicians and researchers to determine the diagnostic accuracy of specimens collected by patients compared to those collected by clinicians in the detection of chlamydia, gonorrhea and HPV. Increasing rates of sexually transmitted infections (STIs) remain a growing concern worldwide -3. DespiSelf-collection of urine, vaginal, rectal and pharyngeal samples provides a different option for screening outside of the traditional clinical setting. Settings for self-collection of samples for STI testing can include community-based organizations, street outreach settings and the internet. However, more evidence is required to determine whether self-collected specimens are as accurate as clinician-collected specimens in terms of diagnostic accuracy for CT, GC and HPV, prior to recommending it as standard practice. The purpose of our systematic review and meta-analysis will be to examine whether a self-collected sample is as accurate as a clinical-collected sample for the diagnosis of CT, GC and HPV. Our specific research question is:Are self-collected vaginal, urine, pharyngeal and rectal samples as accurate as clinician-collected samples for the diagnosis of CT, GC and HPV?Eligible studies will include both randomized and non-randomized controlled trials, pre- and post-test designs, and controlled observational studies examining self-collected versus clinician-collected samples using the nucleic acid amplification test (NAAT) to detect GC, CT and HPV. All manuscripts must include a reference standard of a clinician-collected specimen. Manuscripts that only report a kappa statistic (measuring agreement between the self-collected sample and the clinician-collected sample) will also be reviewed. Table Neisseria gonorrhoeae assay), and the ligase chain reaction (discontinued as of 2002) [Our reference standard will be clinician-collected samples. Both self-collected and clinician-collected samples must have used a NAAT to be included. NAATs are highly sensitive and specific for the detection of CT and GC when using swabs from the genital tract or first-catch urine specimens . The difof 2002) -8.Dual assays that test for CT and GC, include AC2, Becton Dickinson ProbeTec™ CT/GC, AC and Abbott Realtime™ CT/NG assays -10. The Our search will include English and non-English databases. Our search will be limited to articles published since 1990 because of two factors: (a) the HC2 test is the most widely used HPV test worldwide and was first used in 1990  and (b) Sexually Transmitted Infections, Sexually Transmitted Diseases and Journal of Clinical Microbiology.MeSH headings, subject headings and keywords will be created using language that describes laboratory test performances for NAAT tests for GC, CT and HPV. The scope notes of the MeSH headings and subject headings and terms will be reviewed to identify additional terms, common usage and previous usage for terms being searched. Keywords in published journals will also be used. Search terms will include but will not be limited to: internet STI testing; sexually transmitted diseases; sexually transmitted infections; chlamydia; gonorrhea; human papillomavirus/HPV; screening; testing; reproductive health; adolescent health; non-clinic testing; urine testing; vaginal testing; pharyngeal testing; self-collected specimens; home sampling; test performance; test accuracy; PCR, polymerase chain reaction and nucleic acid amplification test sensitivity; specificity and diagnostic ratio. Boolean combinations will be created for more specific searches. Authors will also be asked to identify key articles that should be included in the review. The bibliographies of retrieved articles will also be manually searched as well as key journals such as Data will be abstracted independently by two reviewers using a standardized pre-tested form. Any disagreements between the reviewers will be resolved by a third reviewer. The following data elements will be extracted, and if appropriate, used for stratification if heterogeneity is found: true/false positive; true/false negative; sensitivity; specificity; reference (gold) standard used to compare test characteristics; NAAT platform used; specimen ; diagnostic odds ratio; positive likelihood ratio; negative likelihood ratio; kappa statistic; sex and age of participants; presence of symptoms ; HIV status; date of study initiation and publication status (published or unpublished); country of study and author affiliation; number of specimens analyzed; number of tests per patient; any elements of blinding; location of self-collection ; community type ; sex ; population type ; sexual orientation ; religion; clinician setting ; HPV type  and prevalence. If studies involve several self-sampling methods, the first method described will be used in the analysis. We consider any high-risk HPV positive test as standard; however, if the authors have not separated high- and low-risk HPV types, we used their combined data.The numbers of true positives, false negatives, false positives and true negatives will be extracted from each study, and test sensitivity and specificity as well as likelihood ratios will be calculated. Coupled forest plots for paired sensitivity and specificity for each study along with 95% confidence intervals will be created in RevMan 5.0 to detect publication bias, if present. A meta-analysis will be conducted according to the Cochrane Collaboration’s new recommended methodology for systematic reviews of Diagnostic Test Accuracy . HierarcValidity will be assessed using the Quadas-2  checklist . SensitiA search of the Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, EMBASE and PubMed/Medline using a review filter was conducted using the terms self-sampling and noninvasive sampling. One previous 2005 systematic review  evaluateIncreasing rates of STIs remain a growing concern in British Columbia, Canada, and worldwide. Rates of STIs continue to grow despite active and passive surveillance and multiple interventions aimed at increasing the number of cases found and treated. New interventions, such as self-collected sampling, are needed to identify STIs and stop the upward trend.Reported rates of genital chlamydia (CT) have steadily increased in British Columbia since 1998, from 122/100,000 (1998) to 255/100,000 (2011), with the majority of cases being in females between the ages of 15 and 24 .The prevalence of HPV among women aged 13 to 86 is estimated to be 16.8%  and gay, bisexual and other men who have sex with men (MSM) -37. StigGetCheckedOnline, aimed at decreasing barriers to STI and HIV testing with an emphasis on youth and MSM. Focus group discussions with youths, MSM and STI clinic clients revealed that they would use the internet-based testing service or recommend it to others [A number of models for internet-based testing have been developed, all typically including an online risk self-assessment, test recommendations and specimen collection without requiring presentation to a clinic . Self-coo others . The inco others ; howeverSelf-collected samples are also potentially more cost effective compared to clinician-collected samples. Organizing samples through the internet can reduce health service and delivery costs as interventions delivered over the internet are likely to cost less than treatments requiring frequent contact with health-care professionals . Huang aThe outcomes of this systematic review and meta-analysis are important in the detection of STI disease in hard-to-reach populations, in particular, those who may be unwilling to provide samples in traditional settings. The aim of exploring whether self-collected samples have comparable specificity and sensitively to samples collected in traditional settings has the potential to increase testing rates. Presumably, with higher testing levels, transmission could be reduced. Our systematic review will allow patients, clinicians and researchers to determine the diagnostic accuracy of specimens collected by patients compared to those collected by clinicians in the detection of chlamydia, gonorrhea, and HPV.CT: Genital chlamydia; GC: Gonorrhea; HPV: Human papillomavirus; HSROC: Hierarchical summary receiver operating characteristics; MSM: Men who have sex with men; NAAT: Nucleic acid amplification test; PCR: Polymerase chain reaction; SROC: Summary receiver operating characteristics; STI: Sexually transmitted infection.The authors declare that they have no competing interests.DT and NL drafted the manuscript. CL edited and finalized the manuscript. GO, RL and MG contributed to the research design. DT, CL, GO, MG, LH, MK and TW participated in writing the grant application and edited the manuscript. All authors read and approved the final manuscript.


School-based sex education is a cornerstone of HIV prevention for adolescents who continue to bear a disproportionally high HIV burden globally. We systematically reviewed and meta-analyzed the existing evidence for school-based sex education interventions in low- and middle-income countries to determine the efficacy of these interventions in changing HIV-related knowledge and risk behaviors.We searched five electronic databases, PubMed, Embase, PsycInfo, CINAHL, and Sociological Abstracts, for eligible articles. We also conducted hand-searching of key journals and secondary reference searching of included articles to identify potential studies. Intervention effects were synthesized through random effects meta-analysis for five outcomes: HIV knowledge, self-efficacy, sexual debut, condom use, and number of sexual partners.g = 0.63, 95% Confidence Interval (CI): 0.49–0.78, p<0.001), self-efficacy related to refusing sex or condom use , condom use , fewer sexual partners  and less initiation of first sex during follow-up .Of 6191 unique citations initially identified, 64 studies in 63 articles were included in the review. Nine interventions either focused exclusively on abstinence (abstinence-only) or emphasized abstinence (abstinence-plus), whereas the remaining 55 interventions provided comprehensive sex education. Thirty-three studies were able to be meta-analyzed across five HIV-related outcomes. Results from meta-analysis demonstrate that school-based sex education is an effective strategy for reducing HIV-related risk. Students who received school-based sex education interventions had significantly greater HIV knowledge (Hedges The paucity of abstinence-only or abstinence-plus interventions identified during the review made comparisons between the predominant comprehensive and less common abstinence-focused programs difficult. Comprehensive school-based sex education interventions adapted from effective programs and those involving a range of school-based and community-based components had the largest impact on changing HIV-related behaviors. Worldwide, young people aged 15–24 accounted for almost half of all new HIV infections among individuals aged 15 and older in 2010 Contentious debates have raged in the past decade regarding whether abstinence-only or comprehensive sexual education interventions are effective and appropriate. Abstinence-only interventions promote delaying sex until marriage with little to no information provided about contraceptives or condom use, whereas comprehensive sexual education provides information on abstinence as well as information on how to engage in safer sex and prevent pregnancies and sexually transmitted infections (STIs). In the 1990s various groups in the United States invested in abstinence-only education, and with the creation of the President's Emergency Plan for AIDS Relief (PEPFAR) in 2004, money was earmarked for “ABC” programs , with a heavy emphasis on the “A” component, to be implemented in low- and middle-income countries most impacted by HIV Previous research has been conducted on the effectiveness of youth-oriented HIV prevention and sex education interventions in school settings. A review of 35 school-based sex education programs by Kirby and Coyle However, few reviews have attempted to quantitatively synthesize the effects of school-based interventions on HIV-related risk behaviors across studies, and no review to date has attempted to compare the effectiveness of abstinence-only or abstinence plus interventions with comprehensive sex education in low- and middle-income countries. Therefore, the current review seeks to address this gap by conducting a systematic review and meta-analysis on the efficacy of school-based sex education interventions, including abstinence-only/abstinence-plus and comprehensive sex education programs, in changing HIV-related knowledge and risk behaviors in low- and middle-income countries. This review sought to answer the following research question: Does participating in school-based sex education vs. not participating in school-based sex education reduce HIV-related risk behaviors among youth in low- and middle-income countries?This review is part of a large systematic review and meta-analysis project, called The Evidence Project, which is a joint collaboration between investigators at the Medical University of South Carolina and the Johns Hopkins Bloomberg School of Public Health. The Evidence Project reviews the efficacy of behavioral HIV prevention interventions in low- and middle-income countries. Other reviews published with this project include topics such as voluntary counseling and testing School-based sex education was defined as programs designed to encourage sexual risk reduction strategies for HIV prevention delivered in school settings. This definition allowed for the inclusion of abstinence-only, abstinence-plus, and comprehensive sex education programs. Studies included in the review had to meet the following criteria: conducted in a low- or middle-income country as defined by the World Bank There were no restrictions on language; eligible non-English articles were translated by consultants fluent in English and the language in which the article was written. Participant age was also not restricted. Therefore, studies across a variety of educational settings, from primary schools through college and vocational schools, were included. Additionally, in order to include as many studies as possible, a wide range of study designs were eligible for inclusion: randomized controlled trials , non-randomized controlled trials, prospective or retrospective cohorts, time-series, before-after, case-control, cross-sectional, and serial cross-sectional studies.AIDS, AIDS Care, AIDS and Behavior, and AIDS Education and Prevention for relevant citations. Finally, we searched the reference lists of all included studies for additional eligible studies. This process was iterative and continued until no additional studies were identified.Our search strategy involved three methods. First, five electronic databases, including PubMed, PsycInfo, EMBASE, CINAHL, and Sociological Abstracts, were searched using a combination of terms for sex education, schools/youth, and HIV or AIDS (full list available from the authors upon request). The search was limited to a date range of January 1, 1990 to June 16, 2010. We also searched the table of contents of Trained research assistants conducted an initial screening of all citations and excluded studies clearly not relevant to school-based sex education. Two senior study staff members then independently screened all remaining citations and categorized studies as eligible for inclusion, not eligible for inclusion, or questionable. Discrepancies in categorization were resolved through consensus. Full article texts were obtained and discussed by senior researchers to ascertain eligibility if questionable. Articles were retained and included as background studies if they failed to meet the inclusion criteria but still contained information relevant to school-based sex education in low- and middle-income countries, including prior reviews, cost-effectiveness analyses, and qualitative studies.The following data were abstracted from each eligible study using standardized forms: location, year(s) of study implementation, study setting, study population, sample size, study design, sampling frame and sampling methods, description of the intervention, composition of intervention and control groups (if applicable), length of follow-up, description of outcomes, effect sizes, confidence intervals, statistical tests employed, and study limitations. Two trained research assistants independently abstracted data from each study; any discrepancies were resolved through consensus. Data were double entered into EpiData version 3.1  and later exported to an SPSS database .We also evaluated the methodological rigor of studies to assess risk of bias based on the following criteria: whether the study 1) included a cohort of participants, 2) had a control and experimental/intervention group, 3) compared baseline demographic equivalence of control and intervention groups, 4) compared outcome measures between control and intervention groups at baseline (if applicable), 5) contained both pre- and post-intervention data, 6) randomly selected participants for assessment (i.e. sampling strategy), 7) randomly assigned participants to the intervention, and 8) maintained a follow-up rate of greater than 80%.Outcomes were chosen for meta-analysis based on relevance to HIV prevention and frequency in available studies. The five most commonly reported outcomes across studies were: HIV knowledge, condom use, self-efficacy related to HIV prevention , initiation of first sex, and number of sexual partners. All outcomes were based on self-report. Studies containing at least one of these outcomes were included in meta-analysis if they met the following criteria:Provided an estimate of effect size and its variance, or provided statistics needed to calculate an effect size and variance. If enough information was not provided to calculate an effect size, study authors were contacted for clarification or additional statistics. If study authors did not provide this information after one month, the study was removed from the analysis.Presented pre-post or multi-arm results comparing either participants who received the intervention to those who did not, or comparing outcomes before and after the intervention. If results of a repeated measures analysis were reported, authors needed to provide the correlation between pre-post measurements or provide enough information to calculate the correlation between measurements. If these statistics were not available, either in publication or after request, and the study was a controlled design, an effect size was generated using post-intervention statistics provided groups were similar at baseline with respect to the outcome of interest and other relevant covariates.Presented an outcome of interest that was measured in such a way as to be comparable to outcomes assessed by other studies. In other words, outcomes needed to be similar enough to synthesize across studies.Presented data based on an individual unit of analysis .g (for continuous outcomes) or odds ratios (for dichotomous outcomes). For several outcomes, including HIV knowledge, self-efficacy, and number of sexual partners, both continuous and dichotomous effect sizes were combined in meta-analysis. In these instances, Comprehensive Meta-Analysis (CMA) was used to either convert the standard mean difference into an odds ratio when transforming the effect size from continuous to dichotomous or vice versa using methods developed by Hasselblad and Hedges Using standard meta-analytic methods CMA V.2.2 was used for all analyses 2 statistic and its confidence interval were calculated for each meta-analysis to describe inconsistencies in effect sizes across studies When possible, data were analyzed in several ways per outcome. Stratifications by age, gender, instructor , intervention type (abstinence vs. comprehensive sex education), and length of follow-up were made when three or more studies could be retained per category. Additionally, when possible, we investigated the role of certain characteristics of the data itself, including comparing differences between continuous and dichotomous effect sizes and whether the effect size was based on data collected pre-post intervention or post-only. Mixed effects meta-regression techniques were used to compare effect sizes across strata when possible. The IOf 6191 studies initially identified, 64 studies in 63 articles met the inclusion criteria for this review . In fiveth graders in Mexico Most studies included both male and female participants; three studies evaluated school based sex education for girls only Nine studies either taught abstinence-only or emphasized abstinence or delay of sexual debut over other risk reduction strategies among in-school youth Many studies reported using a variety of instructional formats to convey information and generate discussion. For example, 35 studies used lectures, 34 employed interactive discussions, 30 incorporated role-plays, and 21 utilized skill-based sessions, such as learning the steps involved in correct condom use. Use of media such as videos and audio tapes was also common. Two studies relied on drama, including creating plays and skits, for the bulk of the intervention Intervention content varied widely. Several interventions were adapted from curricula that had been developed and implemented elsewhere, such as the US-based “Focus on Kids” Interventions ranged from 2 or 3 hours in total HIV-related knowledge was the most commonly reported study outcome. Of all included studies, 49 (76.6%) evaluated the effect of the intervention on HIV-related knowledge. The measures used to assess HIV-related knowledge varied by study but centered on aspects such as HIV biology, HIV transmission, and HIV prevention. Some studies measuring HIV-related knowledge were excluded from meta-analysis for not controlling for baseline differences in socio-demographics or outcome variables (n = 3) g) was 0.63 with a 95% confidence interval of 0.49 to 0.78, p<0.001. Meta-regression indicated no significant standardized mean difference when comparing males and females (p = 0.194); however, only 3 studies reported data disaggregated by gender with two additional studies reporting on entirely female samples. Similarly there was no significant difference in standardized mean knowledge score differences comparing abstinence-focused to comprehensive sex education interventions (p = 0.501). When stratified by instructor type, interventions led by health professionals , appeared to produce more knowledgeable students than those led by either teachers, peers, or a mix of different types of instructors . However, when the two interventions implemented among college students were removed Twenty-six studies were included in the meta-analysis for knowledge; results are presented in g = 0.87, 95% CI 0.60–1.13, p<0.001). The smallest differences were seen when knowledge was measured 3 months or less post-intervention . Studies with the shortest follow-up time generally corresponded to studies of the shortest duration (≤3 weeks). However, duration was difficult to measure due to wide variations in length and intensity of intervention implementation. Two studies evaluated the long-term effects (≥3 years post-intervention) of year-long interventions based in schools in Tanzania When considering length of follow-up time, standardized mean differences in knowledge scores were largest when data were collected 3 to 11 months post-intervention compared to all other categories of follow-up time  suggests that some bias exists across studies as the plot was asymmetrical, indicating a possible underdispersion of less precise effect sizes. Following the assumption of normally distributed effect sizes across studies, finding more studies demonstrating at least a null effect would be expected, but finding studies showing a negative effect is less likely given the nature of the outcome, knowledge. Given the wide range of scales used to assess knowledge and the different facets of sexual education being taught across interventions, the asymmetrical plot could also be interpreted as presenting more evidence of a truly heterogeneous effect of school-based sexual education on HIV-related knowledge. The ITwenty-two studies measured self-efficacy. Fourteen of these studies were excluded from meta-analysis for measuring general self-efficacy or self-efficacy not related to condom use or sex refusal (n = 8) Twenty one studies measured condom use as a primary outcome. Of these, 13 were eligible for meta-analysis Of 10 studies measuring participants' number of sexual partners, four were eligible for meta-analysis th and 7th grade school years for youth who received the intervention .Nine studies measured initiation of first sex. Three were excluded for not containing enough data to convert results into standardized effect sizes for meta-analysis and safe sex strategies give mixed messages to students and may promote sexual activity This review found that school-based sex education is an effective intervention for generating HIV-related knowledge and decreasing sexual risk behaviors among participants, including delaying sexual debut, increasing condom use, and decreasing numbers of sexual partners. Importantly, no individual study included in meta-analysis, including abstinence-only, abstinence-plus, and comprehensive school-based sex education interventions, found detrimental effects of school-based sex education on increased risky sexual behavior. This finding is notable given that some argue programs including information on abstinence Comprehensive school-based sex education comprised the majority of interventions included in this review despite extensive attempts to identify abstinence-only and abstinence-plus interventions. Given PEPFAR's past emphasis on abstinence-only and abstinence-plus interventions Interventions producing the most significant changes in behavior seemed to have several characteristics in common. First, several effective interventions included community-based components that extended beyond school-based sex education by involving resources and activities outside of the school environment, such as training healthcare staff to offer youth-friendly services, distributing condoms, and involving parents, teachers, and community members in intervention development. Additionally, studies that adapted curricula from interventions already deemed efficacious also tended to produce significant changes on HIV-related behaviors. These findings are similar to another review which concluded that replicating effective sex education programs continued to produce significant behavior changes even when programs were implemented in different settings It is encouraging that many of the reviewed studies used controlled designs and randomized intervention assignments, especially as earlier reviews have emphasized the need to improve study designs when evaluating school-based sex education interventions This review must be seen in light of several limitations. Firstly, all outcomes reported in this review were based on self-report, which creates potential for social desirability bias and memory error. Secondly, we combined outcomes in meta-analysis that are not identical, such as combining effect sizes generated from different scales measuring HIV-related knowledge, which could lead to inaccuracies in synthesized effects. We also combined both continuous and dichotomous outcomes in several meta-analyses, which could introduce error School-based sex education is a critical tool for HIV prevention among youth, and research suggests school-based HIV prevention programs are cost-effective when implemented in the context of combination prevention Checklist S1PRISMA Checklist for systematic reviews.(DOC)Click here for additional data file.


Integration of metabolic pathways resources and regulatory metabolic network models, and deploying new tools on the integrated platform can help perform more effective and more efficient systems biology research on understanding the regulation in metabolic networks. Therefore, the tasks of (a) integrating under a single database environment regulatory metabolic networks and existing models, and (b) building tools to help with modeling and analysis are desirable and intellectually challenging computational tasks.PathCase Systems Biology (PathCase-SB) is built and released. The PathCase-SB database provides data and API for multiple user interfaces and software tools. The current PathCase-SB system provides a database-enabled framework and web-based computational tools towards facilitating the development of kinetic models for biological systems. PathCase-SB aims to integrate data of selected biological data sources on the web , and to provide more powerful and/or new capabilities via the new web-based integrative framework. This paper describes architecture and database design issues encountered in PathCase-SB's design and implementation, and presents the current design of PathCase-SB's architecture and database.PathCase-SB architecture and database provide a highly extensible and scalable environment with easy and fast  access to the data in the database. PathCase-SB itself is already being used by researchers across the world. There are many computer science applications that integrate the data of different data sources, and build new tools that are otherwise difficult or impossible to build. PathCase Systems Biology (PathCase-SB)  brings tPathCase-SB has two goals:and biochemical network data sources, and1. Integrate into a single database selected data of systems biology data sources providing added-value as compared to the original data sources.2. Build a database-enabled and SBML-  or CellMThe current version of PathCase-SB, released on August 2010, integrates models from BioModels Database -8 and KEThis paper is about the architecture and database design of PathCase-SB. More specifically, we describe• Data source integration issues encountered during PathCase-SB design, and the approach taken to resolve them,• BioModels Database SBML parser and KEGG parser developed for the original data sources, and problems encountered,• Current PathCase-SB architecture and database design, and the resulting database schema, and• Current database content.BioModels Database and CellML repositories are two leading open-access primary data sources that biologists and modelers use to store, search, exchange, and retrieve published quantitative systems biology models. Models from BioModels Database are available as SBML documents which are enriched with additional annotations in RDF (Resource Description Framework ) format.performance goals in mind.PathCase-SB architecture and database are designed with four Extensibility. The database is currently populated with three parsers, namely, BioModels Database SBML parser, CellML parser, and KEGG FTP data parser. Since biological data sources continually extend or change  their data formats, these three PathCase-SB parsers also continually go through major or minor revisions. Accordingly, (a) the corresponding PathCase database data model is designed to accommodate such  changes with relative ease, and (b) all visualization and graph manipulations are encapsulated within a client-side applet, and delegated to client-side-only computation. In summary, the PathCase-SB architecture and database are designed with extensibility in mind, i.e., the design takes into consideration possible future growth and changes.• Easy data access. User interfaces of computational applications for life sciences are usually very rich and detailed with many types of data. In order to allow such interfaces provide fast and easy data access for many different user interface functionalities, the database designs of applications should be in synch with their user interface designs, which, we hope, is the case for PathCase-SB. And, in addition, PathCase-SB interfaces are multi-faceted, involving sophisticated browsing, querying, visualization, and simulation components. PathCase-SB database is designed with easy access in mind for all the functionalities of PathCase-SB user interfaces.• Fast  response time for all interface functionalities. PathCase-SB tools are available online to users on the web. Consequently, the architecture and database is designed for providing real-time response capabilities for each functionality in PathCase-SB user interfaces. Visualization interface is designed to minimize client-server trips by shipping the original visualization specifications from the server to the client  only once, and then performing all visualization commands only at the client side, with no communications with the server. The design takes into account the requirement that all PathCase-SB queries must be processable in real-time, i.e., in a matter of seconds. That said, in some cases, this requirement also necessitates main-memory-based maintenance and manipulation of subsets of PathCase-SB database tables. We are currently looking into rewriting some of the user interface code  which cashes parts of database tables and retains them in main memory, in order to speed up observed response-time delays of some interface functions.• Scalability. PathCase-SB can be accessed by a large number of users at the same time, and the PathCase architecture and database must scale effortlessly to concurrent accesses by hundreds of users. Fully completed stress tests on PathCase-SB will be reported elsewhere; however, by load balancing on two fast servers and by offloading visualization to the client side, PathCase-SB scales to concurrent access of many (low hundreds) users.• In this section, we briefly summarize the PathCase-SB system architecture and database content.PathCase is a series of applications developed in Microsoft.NET that serve a wide variety of purposes. This paper discusses the database of one of the applications, namely, PathCase-SB. Current PathCase-SB architecture is shown in Figure At the server-side, PathCase-SB data is managed by a relational database management system, namely, Microsoft SQL Server 2008. In addition, an object-oriented data-access interface between the relational database and the application layer is provided in the form of a large set of wrapper class functions, in order to provide easy, extensible, and fast data access as well as to prevent major changes in the application when a schema change occurs during the evolution of PathCase-SB. When designing the object model behind the system, a persistent object model is used which hides the details of the actual database implementation and database access. As an example, users can easily create an object that can be stored into the database or search for a specific object by using the PathCase-SB API (application programmer's interface) without writing any SQL queries.For any browser functionality that requests a new visualization, the server first prepares an XML document providing all visualization details, and sends it to the Graph Viewer (applet) at the client-side. The Graph Viewer visualizes the data, and, handles all subsequent interactive visualization-related user commands of the current visualization directly and without any client-server communication. This speeds up the command execution, and scales the visualization. On the flip-side, if the client machine is old and slow, or is short in main memory, PathCase-SB visualizations also slow down, but, from our own testing, a typical average office PC/laptop can easily run PathCase-SB visualization tools with no noticeable performance problems.Client-side PathCase-SB functionality includes basic HTML that renders the main site interface to the user, and JavaScript with AJAX that makes the site highly responsive to the user.The amount of core data in PathCase-SB database, which is parsed from BioModels Database and KEGG, has been updated periodically since its initial version 1 (2008) to the current release . From Version 1 to Version 2, PathCase-SB database has grown 200 MB in size, and nine new tables are added to the database schema. As a result, Version 2 of the PathCase-SB database contains 20% more models, 37% more reactions, 10% more processes, 4% more basic molecules, and ~4% more proteins when compared to PathCase-SB database in 2008. The detailed statistics are shown in Table Please note that the user interfaces of PathCase-SB system are extensive, covered in a separate paper , and will not be discussed here. Next, in the section on "data model design", we discuss data model design issues. The section on "scalability and efficiency" discusses two optimization schemes for browsing and built-in query processing, namely, the use of labeling schemes and caching small dimension tables for main-memory joins. The section on "BioModels Database SBML parser" presents the details of parsing SBML documents from the BioModels Database site. In the section on "capturing BioModels information in PathCase-SB database", we discuss how BioModels Database data is converted into PathCase-SB database schema . The section on "mappings" presents how BioModels SBML documents "link" models to external data sources; and, based on this knowledge, in the section on "PathCase-SB Database Schema", we discuss the actual mapping- and annotation-related tables of PathCase-SB.Appendix 1, Figure One design criterion used is for PathCase-SB never to involve any data curation. With this in mind, PathCase-SB database design has encountered two data integration choices, discussed next.separate tables for common entities of different systems biology and/or biochemical network data sources, e.g., BioModels Database and KEGG data sources. As an example, PathCase-SB database maintains a species table occurring in BioModels Database models and molecular_entities table for KEGG molecular entities. Such an approach allows the system to cleanly separate data from different data sources, and, to add new data sources seamlessly, without any future data cleansing and data integration problems. The current approach however requires an additional "mapping" effort: PathCase-SB maintains mappings between all pairs of data sources on their corresponding entities. For BioModels and KEGG, there are three major mappings: <species, molecular entities>, <reactions, process-entities>, <models, pathways>.One design choice is whether or not to integrate common information between systems biology  data sources and biochemical reaction network data sources . In an earlier database design, such information was fully integrated, and soon many data integration, data repopulation, and curation problems surfaced. The current approach for capturing data that originate from different systems biology and biochemical network databases is to have different systems biology (or metabolic network) data sources into a single set of tables. As it turns out, the data models of SBML-oriented data sources (such as BioModels) and CellML data source are significantly different, turning any tightly-coupled data integration into a significant data curation issue. Thus, at this point in time, PathCase-SB keeps separate tables for different systems biology data sources as well. As an example, currently, PathCase-SB generates and maintains distinct sets of tables for BioModels Database and CellML data sources.A second design choice is whether or not to integrate data in Briefly, the advantages of keeping separate tables for different systems biology data sources and pathways data sources are as follows.No data curation. Integrating data from different systems biology data sources almost always translates into further data curation, which may be prone to errors, and needs extensive domain expertise. The current PathCase-SB database design approach avoids data curation as a design principle.• Simple and easy to maintain application. The clean separation of data originating from different data sources, and creating selective "mappings" is a simpler and easier-to-maintain approach, and is used by PathCase-SB. The interfaces of PathCase-SB are designed in such a way that data in different data sources are naturally presented separately, while model data is linked to biochemical network data  and relevant annotations  at the presentation level on an as-needed basis.• Easy data replenishment. With a data separation-and-mapping approach, replenishing data from a data source becomes a relatively straightforward work, in comparison to the task of curating and integrating data of different data sources with each data reload.• Easier adaptation to data source format/version changes. The exception to "easy data replenishment" occurs when, for various reasons, the data source itself makes significant format/version changes that translate into significant data source parser changes on the PathCase-SB side, which unfortunately occurs sometimes. We have already encountered such data source format/version changes when both BioModels Database and KEGG data formats have changed recently--BioModels Database changes involving relatively easy-to-handle SBML version improvements, and KEGG changes involving significant format changes, with an overall effect of significant changes in our parser applications. Nevertheless, if the data of PathCase-SB were to be tightly coupled, we believe that PathCase-SB database repopulation would have been even more difficult.• At the present time, PathCase-SB database has data from the BioModels Database (as the systems biology data source), and from the KEGG data source (as a metabolic pathways data source).Currently, the PathCase-SB database contains four classes of tables capturing the following information:a. Biochemical reaction network-related tables: These tables capture information in a biochemical reaction network as defined by a well-defined web-based data source on biochemical networks. As the biochemical network and its database, we use the KEGG metabolic pathways and the PathCase database (see [ase (see ). The Paase  to the systems biology models , andd. Tables annotating systems biology data with other ontologies or taxonomies: These tables capture annotations of systems biology models .Towards the goals of improving time efficiency and scalability, the PathCase-SB system has utilized two optimization schemes for browsing and built-in query processing, namely, the use of labeling schemes for efficiently evaluating descendant/ancestor queries within hierarchical structures such as Gene Ontology (GO) , and thebiological process, cellular component, and molecular function, are defined in GO. They are represented as directed acyclic graphs (DAGs) in which the terms form nodes and the relations between terms form edges.The Gene Ontology is the result of an effort aimed at providing a controlled vocabulary for describing roles of genes and gene products in any organism. Three ontologies, t, obtains all descendant terms of the term t within the GO, and lists all models that refer to terms in all descendants of t as well as the term t. This functionality can be implemented in an iterative manner, which becomes time-inefficient .The use of labeling schemes when traversing the Gene Ontology helps improve the performance of the PathCase-SB browser when browsing for "BioModels Database models by GO terms". More specifically, the PathCase-SB browser functionality "Browse BioModels Database models by GO terms" takes a GO term s and making all roots the children of s). For each node u in the graph, the set of NodeCodes of u, denoted NC(u), are assigned using a breadth-first-search traversal starting from the source node as follows: If u is the virtual source node, then NC(u) contains only one element, the empty string. Let u be a node with a set of NodeCodes, NC(u), and 0v, 1v,...k vbe u's children in sibling order, then for each x in NC(u), a code xi is added to NC(iv), where 0 ≤ I ≤ k. While we have chosen to use the NodeCodes labeling in PathCase-SB, there are also interval-based labeling and prefix-based labeling schemes, which are efficient for evaluating descendant/ancestor queries in GO as well. Next we briefly evaluate the time efficiency of using the NodeCodes labeling approach in PathCase-SB.To overcome the limitations of traditional iterative methods for descendant/ancestor query evaluation, we have chosen to use a labeling scheme for modeling DAG-shaped hierarchies (such as GO) in order to efficiently process descendant/ancestor queries. In PathCase-SB, we use NodeCodes labeling ,16 for qIterative and NodeCodes. The Iterative method refers to the method where the descendants of a given term are retrieved by iteratively traversing the GO graph. The Iterative method is our baseline approach which is commonly utilized by existing tools [NodeCodes labeling method performs 2.50 times faster than the Iterative method.As of Aug. 2010, Gene Ontology has 10,268 terms having one or more descendants. In our first experiment on time efficiency, we have run descendant queries for these 10,268 terms, and compared two methods: ng tools ,18. The selectivity is defined as the proportion of terms that it returns. In Gene Ontology, the term (GO: 0008150) biological_process has the maximum number of descendants. This term has 14,889 descendants, which leads to the maximum selectivity (58.7%) for the descendant query. In this experiment, we have chosen to use selectivity values ranging from 58.7% to 0.01%. We have run these five query types 50 times each, and the average query processing time is shown in Table In our next experiment, we have run descendant queries on five different classes: each class corresponds to a different choice of input node type, and, therefore, of different query selectivity. Here, query NodeCodes requires close to constant time for processing each class. For Class 1 and Class 2, NodeCodes Labeling performs 190.52 times and 90.45 times faster than the Iterative method, respectively. Figure From Table Attribute, EntityName, GraphNode, MolecularEntityType, NameId, NameType, PathwaysType, ProcessEntityRole, ReactionSpeciesRole, RnaType, RuleType, and UnitDefinitonTables. As an example, consider the ReactionSpeciesRole table. The cached table is utilized by four built-in queries:PathCase-SB utilizes a caching scheme for optimizing browsing and built-in query processing. More specifically, for fact-dimension tables, PathCase-SB caches all small dimension tables  in the database subschemas to avoid multiple disk-based joins with the fact table  in that subschema. In total, there are twelve such cached tables, which include Find models containing different expressions of metabolic flux associated to the same reaction;1) Find models that contain metabolites of a given pathway;2) Find models that contain reactions of a given pathway; and3) Find kinetic models corresponding to a given reaction in a given pathway.4) Species Name (Reaction Role) column which is obtained from a join between ReactionSpecies and ReactionSpeciesRole tables on the column roleId from ReactionSpecies table. Since joins are expensive, and such a caching approach improves the efficiency of query processing, our caching approach helps with PathCase-SB real-time query processing and scalability requirements.In the third query "Find models that contain reactions of a given pathway", the tabular results include The chart in Figure After parsing the relevant SBML elements or annotation, the parser populates the database not by direct inline SQL calls, but by employing a large number (about 200) of database wrapper class functions. One problem with having inline SQL code is that, since there is no abstraction from the data source, one is bound to a particular schema because table and column names are specified directly in the code, and, this may have negative implications: (i) when the data source is changed, the whole parser application code needs to be scanned, and the corresponding inline SQL calls need to be changed one by one, instead of revising just the wrapper class library; and (ii) error handling becomes difficult and decentralized.PathwaysSBLib. There are two classes that all database access classes use, DBWrapper and DBRow. DBWrapper implements a singleton pattern via the use of an instance, and creates one single database connection at a time, which is shared by multiple database calls. DBRow is used to abstract the idea of a database tuple  into a class in an object-oriented manner. These two classes centralize all database access through a series of public functions. All pertinent information such as connection string (which is stored in configuration files) and exception handling are processed inside these classes. This approach of "single point of database contact" allows us to centralize all data access information and customization, making future updates much easier.PathCase-SB system uses object-oriented wrapper classes to access the data. Wrapper classes share common interface and data access code, as well as common utilities such as logging  and error handling. The new class library is called Server" wrapper class associated with it. Each one of these classes exposes public properties that correspond to columns in that table. This approach allows us to change the database schema without breaking an existing class that might reference the class at hand. It also allows for type-safe access to data without any knowledge of column names or database schema. Type-safe access means, for example, that the code specifies in property declaration that ID is an integer), compared to inline SQL usage where one would cast a column to a data type without any guarantees that it will work.Each table in the PathCase-SB schema has a "In Section "BioModels Database Entities and Relationships", we present entities and relationships of data in the BioModels Database data source. Section "PathCase-SB Schema: BioModels Database-Related Information" lists database table schemata, and presents additional details for each table. Since BioModels Database uses SBML documents to capture its data, we view a model from a BioModels Database as an SBML document. A BioModel Database model also captures additional information, mostly in the form of annotations.Model, Species, SpeciesType, Reaction, Compartment, CompartmentType, Event, Rule, RuleType, Constraint, FunctionDefinition, UnitDefinition, Parameter, InitialAssignment; for more details on these entities, please see the SBML Level 2, Version 3 specification [BioModel Database SBML documents capture all entities as defined by the Systems Biology Markup Language (SBML) Level 2, Version 3 specification. SBML is a model representation format for systems biology, and allows models of arbitrary complexity to be represented. More specifically, SBML allows the specification of the following entities: DataSource, Sbase, ModelLayout , KineticLaw, ReactionSpeciesRole, ReactionSpecies, StoichiometryMath, EventTrigger, EventDelay, EventAssignment, EventAssignmentArgument, and KineticLawParameter. To save space, below, we describe only a few of these PathCase-SB-specific entities and relationships. Please see Appendix 1, Figure In addition to SBML-based entities, PathCase-SB has other entities and relationships, namely, DataSource entity retains all information specific to a systems biology data source.Sbase entity serves as a base object for the actual entities. Each entity (except DataSource) in the database extends Sbase entity. Sbase contains a sboTerm attribute for SBO (Systems Biology Ontology) annotations. Most SBML elements have SBO annotations which define the semantics of the corresponding term. For instance, for a modifier in a reaction, its SBO term (if available) may specify its specific modifier role  in that reaction. Storing SBO annotations helps in terms of establishing connections both between different models and between metabolic pathways and models.Model entity contains basic information for each systems biology model.StoichiometryMath entity specifies the full stoichiometry of a given reaction. SBML specification allows the definition of stoichiometry through either double values, or MathML expressions (for more complicated stoichiometry representation). This entity includes two stoichiometry-related fields: (i) stoichiometry (float), and (ii) stoichiometryMath (mathml) where the expression in parentheses shows the type of the stored information.DataSource, Sbase, Model, ModelLayout, FunctionDefinition, UnitDefinition, Unit, UnitComposition, CompartmentType, SpeciesType, Compartment, CompartmentClass, CompartmentClassDictionary, Species, Common_Species, Parameter, InitialAssignment, RuleType, Rule, Constraint, Reaction, KineticLaw, ReactionSpeciesRole, ReactionSpecies, StoichiometryMath, Event, EventTrigger, EventDelay, EventAssignment, Author, ModelMetadata, DesignedBy. Please see Appendix 2, Figure In this section, we specify the database tables of the BioModels-related part of PathCase-SB database, omitting the CellML- and KEGG-related tables in PathCase-SB. The complete list of BioModels-related tables in the database include For ease of understanding, in naming tables, we use the same naming conventions as defined by the SBML Level 2, Version 3, Release 2 specification ). UnderlDataSource  table captures data source information. name is a key  in this table.Sbase . Unless noted otherwise, each entity (except DataSource) in this schema extends Sbase entity. Therefore, in each table (including Model table), id field is a foreign key referring to the id field in Sbase table. Whenever a table contains a field with name "id", it is a primary key in that table. Unless noted otherwise, all foreign keys adopt "ON DELETE CASCADE" property.Model  table captures basic information for each systems biology model. dataSourceId is a foreign key referring to the id field in DataSourcetable.ModelLayout  table. One lesson we learned in PathCase [id, where id is a foreign key referring to the id field in Model table.PathCase  is that Compartment .CompartmentClass  table contains a biologically well-defined compartment classification class hierarchy. parentId is a foreign key referring to the id field in CompartmentClass table. Parent of root compartments is null.CompartmentClassDictionary  table holds information needed to match the correct compartment class with a compartment. This table is mainly used by SBML parser during the creation of each compartment. Compartment names in this table are unique.Species  table captures species information and the relationships it is involved in. Attribute speciesTypeId is a foreign key referring to the id field in SpeciesType table; substanceUnitsId is a foreign key referring to the id field in UnitDefinition table; and compartmentId is a foreign key referring to the id field in Compartment table.Reaction  table captures reaction information in models.ReactionSpecies  table captures species information in reactions.Author table contains general information about authors.DesignedBy table captures information about the creator(s) of each model. It represents many-to-many relationship between Model and Author tables which are linked through modelKey and authorKey foreign keys, respectively.ModelMetadata  table contains version (date) information about the model. publicationId is a foreign key which is used to link the Model to BioModels[BioModels originalBelow we briefly discuss a number of additional issues that have arisen when pushing data in SBML documents into database tables.uniqueidentifier. On the other hand, each SBML model specification contains an id field of type alphanumeric string for the corresponding entities. Ids in an SBML model are used for cross-referencing among different entity definitions, and, most of the time, id is the only meaningful name  for the corresponding entity. Although each database table has an id field, such fields store an arbitrarily generated unique identifier which is independent of the corresponding model. This information is usually crucial, especially when models omit the optional name field, and use id for that purpose. Thus, we have added the sbmlId field into the corresponding relations, for which SBML specification defines an id field.Each table in a PathCase-SB database needs to have an id field of type Most of the mathematical expressions (represented in MathML) such as kinetic laws, rules, etc., in a model are allowed to contain references to pre-defined mathematical functions in the model. To accommodate such a relationship in the database model, one alternative is to consider it as an argument like species, parameter, and compartment names in math expressions. And, adding an attribute function_id into the argument entity accommodates references to function names in different MathML expressions throughout the model.units and models, model ids can be included as a field in units table. For built-in base units, the model id is set as null. And, base units are pre-specified before any database population.In SBML, there are built-in "base" units, and user-specified units that are defined based on built-in units. User-specified units cannot be built upon other user-specified units. Furthermore, since user-specified units are defined in the context of a specific model to provide connection between Pathcase_SB_UserUploads in order to temporarily save user-uploaded data. This database has a schema similar to the Pathcase-SB schema. The user-uploaded data is presently kept in a separate database than the PathCase-SB database due to (i) privacy and intellectual property rights concerns, and (ii) possible manageability issues to maintain, purge, and integrate the uploaded data into the existing permanent data.To implement the functionality of users uploading their own models for visualization and simulation, PathCase-SB maintains a separate database, namely, Part of the BioModels' curation effort involves linking components of a systems biology model to other external and related databases. Such external links are located in the annotation field of each major entity in an SBML model. Annotation fields in SBML are formatted in RDF . Note that some models may have only a subset of the five external links.• Linking Reactions. The annotation field of each reaction element contains links to four external related databases, namely, KEGG, Reactome, EC number, and Gene Ontology. Note that some reactions may have only a subset of the four external links.• Linking Species to Metabolites/Compounds. The annotation field of each species element contains links to two external related databases, namely, KEGG and CHEBI [• nd CHEBI ,22,23 whLinking Compartments to GO. The annotation field of each compartment element contains link to GO Cellular Component Subontology, which may be used to reconcile compartments from different models.• We have designed and populated three tables that map BioModels Database entities and KEGG entities:Species of systems biology models and Molecular Entities of KEGG,(a) Reactions of systems biology models and Process Entities of KEGG, and(b) Models of systems biology models and Pathways of KEGG.(c) Below we briefly specify the schema of the three relations.MapSpeciesMolecularEntities . In this table, molecularEntityId is a foreign key referring to the id field in molecular_entities table; speciesId is a foreign key referring to the id field in species table, and qualifierId is a foreign key referring to the id field in AnnotationQualifier table.MapReactionsProcessEntities . In this table, processId is a foreign key referring to the id field in processes table; reactionId is a foreign key referring to the id field in reaction table; and qualifierId is a foreign key referring to the id field in AnnotationQualifier table.MapModelsPathways . modelId is a foreign key referring to the id field in models table; pathwayId is a foreign key referring to the id field in pathways table; qualifierId is a foreign key referring to the id field in AnnotationQualifier table; and organismGroupId is a foreign key referring to the id field in organismGroup table.We maintain three annotation tables for BioModels Database models:models with organisms from the NCBI organism taxonomy,(a) Annotating models with Gene Ontology (GO) terms,(b) Annotating reactions in models with EC (Enzyme Commission) numbers.(c) Annotating Below we briefly specify the schemata involving the above-listed three mappings.ModelOrganism  table annotates BioModels Database systems biology models with organisms/organism groups (as specified in the NCBI organism taxonomy). This information is not part of SBML document; however, BioModels Database curators have captured it as metadata (annotation). modelId is a foreign key referring to the id field in model table; organismGroupId is a foreign key referring to the id field in organism_groups table; qualifierId is a foreign key referring to the id field in AnnotationQualifier table.MapSbaseGO  table annotates systems biology models with GO terms.sbaseId is a foreign key referring to the id field in sbase table. goId is a foreign key referring to the id field in go_terms table ; and quMapReactionECNumber . reactionId is a foreign key referring to the id field in reaction table; ecNumber is a foreign key referring to the ec_number field in ec_numbers table ; and qualifierId is a foreign key referring to the id field in AnnotationQualifier table.For efficiency purposes involving browser interface functionalities of PathCase-SB, we also maintain three distinct tables, as listed below.AnnotationQualifier. BioModels' MIRIAM specification [AnnotationQualifier table. id is an auto-increment field. This table is pre-filled with the following name attribute values: is, isDescribedBy, encodes, hasPart, hasVersion, isEncodedBy, isHomologTo, isPartOf, isVersionOf, occursIn, unknown.fication  defines GONodeCodes  is a secondary table which is added to make "Browse Models by GO terms" functionality of the PathCase-SB web interface more efficient. The table stores precomputed NodeCodes (See section on "Labeling Schemes") for GO terms. goId is a foreign key referring to the id field in go_terms table .ec_numbers  table is designed to make "Browse Reactions by EC Numbers" functionality of the web interface more efficient through the use of NodeCodes [nodeCode attribute.odeCodes . The tabodeCodes , and modBelow, we summarize the current results from mapping KEGG pathways to Biomodels Database models based on the annotation information provided in each model. The extracted annotations are stored in the PathCase-SB database. Currently, the PathCase-SB main browsing interface now has four browsing functions: "Models by Name", "Models by KEGG Pathways", "Models by Organisms" and "Models by GO Terms" in order to aid users with faster and intuitive way to browse models.http://www.genome.jp/kegg/pathway.html for the complete list of pathway categories in KEGG).Out of 252 models in the database, (a) 30 models have a reference to a KEGG pathway, and (b) 229 models have a reference to a GO term. Due to the fact that multiple models are mapped to the same pathway (possibly in different organisms), the number of distinct KEGG pathways referred in 30 models is 30. Among 30 referenced KEGG pathways, only 14 of them are metabolic pathways, and the remaining 16 pathways belong to other categories listed in KEGG, such as signaling pathways, gene-regulation pathways, and so on . There is no model that refers to a metabolic pathway which is not included in the PathCase KEGG database.The PathCase KEGG database contains 8,182 processes among which 173 processes have at least one mapped reaction in a BioModels Database model. In the database, there are 6,957 reactions stored (extracted from 252 models), and 421 reactions have a mapping to one or more of the above 173 processes.The PathCase KEGG database contains 27,220 molecular entities among which 223 molecular entities have at least one mapped species in a BioModels Database model. In the database, there are 4,963 species stored, and 1110 species have a mapping to one or more of the above 223 molecular entities.In this paper, we have described the architecture and database design and modeling issues of PathCase-SB, which integrates data from different systems biology data sources (BioModels Database and CellML) and a metabolic network data source (KEGG).We believe that integrating data from multiple biology data sources and then providing additional computational tools is a commonly occurring theme in life sciences, and Pathcase-SB is one such example. And, in designing an application architecture and database for such an integrated environment, there are many lessons to be learned, some easily generalizable to different environments , and some data source instance-specific . There are also tradeoffs involving extensibility, data access, realtime response time, and scalability. This paper, in the context of PathCase-SB, has highlighted a number of lessons and tradeoffs.http://nashua.case.edu/PathwaysSB/Web.PathCase-SB is freely available for use at Operating Systems: PathCase-SB is accessed from a browser; therefore, it is platform-independent.Browsers: PathCase-SB is extensively tested with browsers Internet Explorer and Mozilla Firefox. It is also tested to a lesser degree with Google Chrome and with Safari browsers.Other Requirements: (a) Ajax and JavaScript. PathCase-SB makes use of Ajax, which is a way of sending data between a web server and client asynchronously with normal page requests. This helps PathCase-SB remain quick and responsive even if there are a substantial amount of data to display at once. For this and other features of the site to work (such as collapsing windows), JavaScript must be enabled in the browser. All modern web browsers such as those mentioned above support JavaScript, but it is possible to have personal security settings in place that prevent JavaScript from running. If certain portions of the site do not appear to be working properly, the user must ensure that JavaScript is enabled and that the browser security settings allow scripting elements to run. (b) Cookies. In order to view certain portions of the site correctly PathCase-SB occasionally uses, which are small bits of text stored on a local system's hard drive. Users must ensure that cookies are enabled in the browser in order to get the most out of PathCase-SB. (c) Java Runtime Environment. The PathCase-SB Graph Viewer uses the Java software platform. In order to view the applet, version 1.6 or later of the Java Runtime Environment must be installed on the system from which the viewer is accessed. Users should download and install the latest version of the free plug-in, JRE 1.6 . Some browsers may not allow Java applets to run because of security concerns; if the JRE is installed properly and the Graph Viewer still does not appear, the user should make sure that the browser's security settings allow Java applets . (d) Monitor Resolution. While PathCase-SB has been designed to gracefully fit into just about any monitor resolution, it is best viewed at resolutions of 1024 × 768 pixels and up. 800 × 600 and 640 × 480 resolutions will work, too, but the results may not be quite as pleasing to the eye. (e) Server-side implementation. Server-side implementation of PathCase-SB is done with ASP.NET Framework using the C#.NET language.Database access support and extensibility. For safety, integrity, and other reasons, the Pathcase-SB application's database is accessed only through its API (Application Programmers Interface), which has about 50 API functions. Third-party application developers can use the existing API functions currently available in the PathCase-SB API to develop new applications. New API functionalities can also be developed if they are needed for third-party developers.Any restrictions to use: PathCase-SB is free to use by academics and non-academics; there are no restrictions.PathCase-SB architecture and databases design was a collaborative task, led by AC, ZMO, GO, NL, and (late) MC. PathCase-SB implementation was done by AC (both server-side and client-side components), XQ , SAC , MD (CellML parser), EC (KEGG data parser), and AEC (software testing). Manuscript has been read and approved by all authors and that all authors agree to the submission of the manuscript to BMC Systems Biology journal.In Figure In Figure In Figure Appendix 4 is a summary from the following web page (retrieved on September 2011)http://www.ebi.ac.uk/compneur-srv/miriam-main/mdb?section=qualifiersThe qualification of an annotation specifies the relation between a model component and its annotation.is: The specified modeling object is the subject of the referenced resource.isDescribedBy: The specified modeling object is described by the referenced resource.isDerivedFrom: The specified modeling object is described by the modeling object in the referenced resource.encodes: The specified biological entity encodes, directly or transitively, the subject of the referenced resource.hasPart: The specified biological entity includes the subject of the referenced resource, either physically or logically.hasProperty: The subject of the referenced resource is a property of the biological entity represented by the model component.hasTaxon: The biological entity represented by the model element is taxonomically restricted, where the restriction is the subject of the referenced resource.hasVersion: The subject of the referenced resource is a version or an instance of the specified biological entity.is: The biological entity represented by the model component is the subject of the referenced resource. This relation might be used to link a reaction to its exact counterpart in KEGG or Reactome for instance.isPropertyOf: The biological entity represented by the model component is a property of the referenced resource.isDescribedBy: The specified biological entity is described by the referenced resource.isEncodedBy: The specified biological entity is encoded, directly or transitively, by the subject of the referenced resource.isHomologTo: The biological entity represented by the model component is homolog, to the subject of the referenced resource, i.e. they share a common ancestor.isPartOf: The specified biological entity is a physical or logical part of the subject of the referenced resource.isVersionOf: The specified biological entity is a version or an instance of the subject of the referenced resource.occursIn: The specified biological entity represented by the model component takes place in the subject of the reference resource.model element contains links to external pathway-related databases which are listed below with example entries. Note that some models may have only a subset of the external links listed below.The annotation field of each a. KEGG<bqbiol:is><rdf:Bag><rdf:li rdf:resource="urn:miriam:kegg.pathway:tbr00010"/></rdf:Bag></bqbiol:is>b. Reactome<bqbiol:isHomologTo><rdf:Bag><rdf:li rdf:resource="urn:miriam:reactome:REACT_1383"/></rdf:Bag></bqbiol:isHomologTo>c. NCBI's Organism Taxonomy<bqbiol:is><rdf:Bag><rdf:li rdf:resource="urn:miriam:taxonomy:5691"/></rdf:Bag></bqbiol:is>d. PubMed<bqmodel:isDescribedBy><rdf:Bag><rdf:li rdf:resource="urn:miriam:pubmed:11415442"/></rdf:Bag></bqmodel:isDescribedBy>e. GO<bqbiol:isVersionOf><rdf:Bag><rdf:li rdf:resource="urn:miriam:obo.go:GO%3A0006096"/></rdf:Bag></bqbiol:isVersionOf>a. KEGG<bqbiol:is><rdf:Bag><rdf:li rdf:resource="urn:miriam:kegg.reaction:R00771"/></rdf:Bag></bqbiol:is>b. Reactome<bqbiol:isHomologTo><rdf:Bag><rdf:li rdf:resource="urn:miriam:reactome:REACT_1164"/></rdf:Bag></bqbiol:isHomologTo>c. EC#<bqbiol:isVersionOf><rdf:Bag><rdf:li rdf:resource="urn:miriam:ec-code:5.3.1.9"/></rdf:Bag></bqbiol:isVersionOf>d. GO<bqbiol:isVersionOf><rdf:Bag><rdf:li rdf:resource="urn:miriam:obo.go:GO%3A0046323"/></rdf:Bag></bqbiol:isVersionOf>a. KEGG<bqbiol:is><rdf:Bag><rdf:li rdf:resource="urn:miriam:kegg.compound:C00074"/></rdf:Bag></bqbiol:is>b. CHEBI<bqbiol:is><rdf:Bag><rdf:li rdf:resource="urn:miriam:obo.chebi:CHEBI%3A18021"/></rdf:Bag></bqbiol:is>a. GO<bqbiol:is><rdf:Bag><rdf:li rdf:resource="urn:miriam:obo.go:GO%3A0005829"/></rdf:Bag></bqbiol:is>