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      string(327) "NCT03964415 - A Study of Heterologous Vaccine Regimen of Adenovirus Serotype 26 Mosaic4 Human Immunodeficiency Virus(Ad26.Mos4.HIV), Adjuvanted Clade C gp140 and Mosaic gp140 to Prevent HIV-1 Infection Among Cis-gender Men and Transgender Individuals Who Have Sex With Cis-gender Men and/​or Transgender Individuals (MOSAICO)"
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  ["project_title"]=>
  string(122) "Regional heterogeneity in behavioral risk factors for HIV acquisition: a longitudinal analysis of the MOSAICO trial cohort"
  ["project_narrative_summary"]=>
  string(729) "HIV incidence varies across regions of the world and is influenced by multiple factors, including behavioral, social, and epidemiological determinants. The MOSAICO study provides an opportunity to examine these factors by analyzing participants from different regions with higher HIV incidence trends over the past decade. Using longitudinal data, we will assess how sociodemographic characteristics, sexual behaviors, and sexually transmitted infections are associated with HIV acquisition. By understanding how these factors interact across different settings, this study aims to identify key drivers of HIV infection among populations at higher risk, helping to inform more effective and region-specific prevention strategies."
  ["project_learn_source"]=>
  string(5) "other"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(10) "Juan José"
    ["last_name"]=>
    string(17) "Montenegro-Idrogo"
    ["degree"]=>
    string(2) "MD"
    ["primary_affiliation"]=>
    string(78) "Centro de Investigaciones Tecnológicas Biomédicas y Medioambientales (CITBM)"
    ["email"]=>
    string(20) "jmontenegro@citbm.pe"
    ["state_or_province"]=>
    string(4) "Lima"
    ["country"]=>
    string(4) "Peru"
  }
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      ["p_pers_f_name"]=>
      string(13) "José Alfredo"
      ["p_pers_l_name"]=>
      string(13) "Hidalgo Vidal"
      ["p_pers_degree"]=>
      string(2) "MD"
      ["p_pers_pr_affil"]=>
      string(10) "VÍA LIBRE"
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      string(7) "Mariana"
      ["p_pers_l_name"]=>
      string(6) "Kundro"
      ["p_pers_degree"]=>
      string(2) "MD"
      ["p_pers_pr_affil"]=>
      string(45) "Hospital General de Agudos J. M. Ramos Mejía"
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      ["requires_data_access"]=>
      string(3) "yes"
    }
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  ["project_ext_grants"]=>
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    ["value"]=>
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    ["label"]=>
    string(68) "No external grants or funds are being used to support this research."
  }
  ["project_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1635) "Background. HIV incidence is a dynamic and heterogeneous phenomenon worldwide, with variations across different settings. The MOSAICO study includes participants from regions with higher HIV incidence trends over the past decade. Understanding how HIV risk factors present and interact across regions may help identify key drivers of HIV acquisition.

Objective. To identify regional differences in HIV risk factors among individuals at high risk of HIV acquisition enrolled in the MOSAICO study.

Study Design. Longitudinal study from baseline (enrollment) through month 24.

Participants. All participants enrolled in the MOSAICO study with follow-up data from baseline through month 24.

Primary and Secondary Outcome Measure(s). The primary outcome is HIV acquisition (seroconversion, including event and time-to-event) during follow-up through month 24. Secondary variables include sociodemographic characteristics (age, sex, sexual orientation, occupation, income, country of origin), sexual behaviors (e.g., number of partners, sexual practices), sexually transmitted infection (STI) events, risk behaviors (alcohol, drugs, tobacco), and use of HIV prevention strategies (PrEP and PEP).

Statistical Analysis. Associations between behavioral exposures and HIV acquisition will be estimated using Cox proportional hazards models. Analyses will be stratified by geographic region to assess regional differences. Sensitivity analyses will be conducted to evaluate the robustness of findings, and consistency in the direction and magnitude of region-specific associations across models will be examined"
  ["project_brief_bg"]=>
  string(3161) "HIV incidence has declined globally since 2010; however, increases have been observed over the past decade in several regions, including Latin America, parts of North America, Africa, and Europe, with an estimated 5,000 new HIV infections occurring daily worldwide [1,2]. In Latin America and the Caribbean, the epidemic remains concentrated in specific geographic areas and key populations, particularly men who have sex with men (MSM) and transgender women (TGW), who experience disproportionately high HIV incidence and prevalence [2–6]. Regional estimates suggest that approximately 1.7 million people are living with HIV in the Americas, with some countries, including Peru and Bolivia, showing increasing trends in new diagnoses over the last decade [5–7].

Among MSM and TGW, HIV prevalence in Latin America has been estimated at 10.6% and 18%, respectively, with incidence rates reaching up to 6.7% in some settings such as Peru [4–6]. These disparities are shaped by a combination of structural and behavioral factors, including stigma, violence, limited access to prevention services, low PrEP coverage, and socioeconomic vulnerability [5,8–12]. Persistent inequities in access to HIV testing, prevention, and care further contribute to ongoing transmission in these populations [1–3,6].

The MOSAICO trial [13], a multi-country HIV vaccine efficacy study, provides a unique opportunity to explore regional differences in HIV acquisition within a standardized research setting. Although no difference in HIV acquisition was observed between vaccine and placebo groups, substantial heterogeneity in HIV incidence was observed across regions. Preliminary data suggest marked differences in incidence rates, with higher rates in Peru compared to other regions. For example, estimated incidence rates per 100 person-years during follow-up were approximately 1.06 in North America/Europe, 2.97 in Latin America excluding Peru, and 6.02 in Peru.

These findings raise important questions regarding the underlying drivers of regional differences in HIV acquisition. This study aims to confirm these patterns using the full longitudinal dataset from baseline through month 24 and to examine whether differences in behavioral, sociodemographic, and contextual factors explain the observed heterogeneity. By leveraging the full cohort rather than restricting analyses to the vaccine efficacy period, this study will enhance statistical power and allow a more comprehensive evaluation of HIV risk dynamics.

Importantly, because the MOSAICO trial demonstrated no protective effect of the vaccine, HIV acquisition can be interpreted independently of the intervention, enabling an unbiased assessment of behavioral and contextual risk factors across regions.

This study will generate generalizable knowledge on how HIV risk factors operate across heterogeneous epidemiologic settings. Understanding these differences is critical for improving the interpretation of multi-country HIV prevention trials and for informing region-specific HIV prevention strategies, particularly in high-incidence settings such as Peru.

"
  ["project_specific_aims"]=>
  string(1206) "This study aims to evaluate regional heterogeneity in HIV acquisition and its behavioral determinants using data from the MOSAICO trial.

Aim 1: To estimate HIV incidence across geographic regions, with a primary comparison between Peru and other regions.

Hypothesis 1: HIV incidence is higher in Peru compared to other regions participating in the MOSAICO study.

Aim 2: To identify behavioral, sociodemographic, and clinical risk factors associated with HIV acquisition and to assess whether these associations differ by geographic region.

Hypothesis 2: The magnitude and direction of associations between behavioral risk factors and HIV acquisition vary across regions, reflecting differences in epidemiologic context and prevention access.

Exploratory Aim: To describe baseline HIV prevalence among individuals screened for participation in the MOSAICO study and to compare their characteristics with those of participants who acquired HIV during follow-up.



Hypothesis 3 (exploratory): Baseline HIV prevalence and participant characteristics differ across regions and may help contextualize observed differences in HIV incidence during follow-up.

"
  ["project_study_design"]=>
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    ["value"]=>
    string(14) "indiv_trial_an"
    ["label"]=>
    string(25) "Individual trial analysis"
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  ["project_purposes"]=>
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    [0]=>
    array(2) {
      ["value"]=>
      string(50) "research_on_clinical_prediction_or_risk_prediction"
      ["label"]=>
      string(50) "Research on clinical prediction or risk prediction"
    }
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  ["project_research_methods"]=>
  string(1430) "Data Source and Inclusion/Exclusion Criteria

This study will use participant-level data from the MOSAICO HIV vaccine efficacy trial (HVTN 706/HPX3002), accessed through the YODA Project secure data platform. No additional data sources will be used, and no external data will be pooled with the MOSAICO dataset. All analyses will be conducted within the secure YODA environment using R and/or Stata. No new participant contact will occur.

Inclusion criteria (Aims 1 and 2):

All randomized participants in the MOSAICO trial who received at least one study intervention and contributed follow-up time from baseline will be included, regardless of HIV infection status during follow-up.

Exclusion criteria (Aims 1 and 2):

Participants with missing or incomplete data required to define the primary outcome (HIV acquisition) or to construct valid time-to-event intervals (e.g., missing event date or censoring information) will be excluded. The extent and patterns of missing data will be evaluated, and sensitivity analyses will be conducted to assess the potential impact of these exclusions.

Inclusion criteria (Exploratory Aim 3):

All individuals screened for participation in the MOSAICO trial for whom screening data are available in the dataset.

Exclusion criteria (Exploratory Aim 3):

Individuals with missing or incomplete screening data will be excluded.

"
  ["project_main_outcome_measure"]=>
  string(923) "Primary and Secondary Outcome Measure(s) and Definitions

The primary outcome of this study is HIV acquisition (seroconversion) during follow-up from baseline through month 24. HIV acquisition will be defined according to the MOSAICO study protocol, based on centrally confirmed laboratory diagnosis. The outcome will be analyzed as both a binary event (incident HIV infection: yes/no) and as a time-to-event outcome (time from enrollment to first confirmed HIV infection).

Secondary outcomes include region-specific HIV incidence rates. Associations between risk factors and HIV acquisition will be evaluated as secondary analyses. These factors include sexual behaviors, substance use, sexually transmitted infection (STI) events, and use of HIV prevention strategies (PrEP and PEP). Behavioral and prevention-related variables will be modeled as time-varying covariates when measured longitudinally.

"
  ["project_main_predictor_indep"]=>
  string(2032) "The primary independent variable of interest is geographic region, defined based on study site location. The main comparison will be Peru versus other regions, with secondary analyses exploring additional regional groupings (e.g., Latin America excluding Peru, and North America/Europe), depending on sample size and stability of estimates.

In addition, a set of prespecified behavioral, sociodemographic, and clinical variables will be evaluated as independent predictors of HIV acquisition. These variables will be derived from standardized questionnaires and clinical assessments collected during the MOSAICO study and will include:

-Sexual behaviors: number of sexual partners, receptive condomless anal sex, and sex with partners living with HIV who are not virally suppressed or of unknown status 

-Substance use: heavy episodic alcohol use and recreational or injection drug use 

-Sexually transmitted infections (STIs): recent diagnoses of bacterial STIs (e.g., chlamydia or gonorrhea) as markers of exposure intensity 

-Sociodemographic characteristics: age, sex, sexual orientation, occupation, income, and country of origin 

-HIV prevention strategies: use of pre-exposure prophylaxis (PrEP) and post-exposure prophylaxis (PEP) 

Behavioral and prevention-related variables collected at multiple study visits will be modeled as time-varying covariates, reflecting changes in exposure status over time. When appropriate, exposures will be carried forward to the subsequent risk interval.

Geographic region will also be evaluated as an effect modifier, through interaction terms with selected behavioral exposures, to assess whether the association between risk factors and HIV acquisition differs across settings.

All variables will be defined using standardized definitions from the MOSAICO dataset. No data-driven variable selection procedures will be used; instead, variable inclusion will be guided by prior knowledge and a predefined conceptual framework."
  ["project_other_variables_interest"]=>
  string(1737) "In addition to the primary independent variables, several other variables will be included in the analysis to account for potential confounding, improve model specification, and support subgroup and sensitivity analyses.

Demographic variables will include age (modeled as a continuous variable and, in secondary analyses, categorized into age groups), sex assigned at birth, and gender identity when available.

Socioeconomic variables will include education level, occupation, income, and country of origin. These variables will be categorized based on available distributions in the dataset and, when appropriate, harmonized across regions to ensure comparability.

Clinical and baseline risk variables will include baseline HIV risk profile, history of sexually transmitted infections (STIs), and prior use of HIV prevention strategies (PrEP or PEP). These variables will be primarily treated as baseline covariates.

Study-related variables will include time since enrollment, visit timing, and follow-up duration, which will be used to structure time-to-event analyses and define risk intervals.

Geographic and contextual variables beyond the primary regional categorization may be explored, including country-level indicators if available within the dataset. These will be used in secondary analyses to better characterize regional heterogeneity.

These variables will be included in multivariable models based on prior knowledge and a predefined conceptual framework, rather than data-driven selection procedures. Where appropriate, variables will be categorized to improve interpretability, while continuous modeling will be retained when it better reflects underlying relationships.

"
  ["project_stat_analysis_plan"]=>
  string(2822) "Descriptive analysis

Baseline characteristics will be summarized overall and by geographic region using appropriate descriptive statistics. Continuous variables will be described using means and standard deviations or medians and interquartile ranges, as appropriate. Categorical variables will be summarized using frequencies and proportions. Comparisons across regions will be exploratory and used to characterize the study population.



Primary analysis (Aim 1)

HIV incidence will be estimated from baseline through month 24 using time-to-event methods. Kaplan–Meier curves will be used to describe time to HIV acquisition by geographic region. Differences between regions, with a primary comparison of Peru versus other regions, will be assessed using Cox proportional hazards models. Hazard ratios (HRs) and 95% confidence intervals (CIs) will be reported.



Risk factor analysis (Aim 2)

Associations between behavioral, sociodemographic, and clinical variables and HIV acquisition will be evaluated using Cox proportional hazards regression. Behavioral exposures and prevention variables measured repeatedly over time will be modeled as time-varying covariates when appropriate.

Geographic region will be evaluated both as a main exposure and as an effect modifier. Interaction terms between region and selected behavioral variables will be included to assess whether associations differ across regions.



Handling of time-varying confounding

Because PrEP and PEP use may both influence and be influenced by prior behaviors, sensitivity analyses will evaluate different approaches to handling these variables, including time-updated adjustment in Cox models and, if supported by data structure and completeness, inverse probability weighting methods.



Exploratory analysis (Aim 3)

Baseline HIV prevalence among screened individuals will be summarized by region, if available. Descriptive comparisons will be conducted to assess differences between individuals with HIV identified at screening and those who acquired HIV during follow-up.



Sensitivity analyses

Several sensitivity analyses will be conducted to assess robustness of findings, including:

Alternative categorizations of geographic regions

Models excluding participants with missing data

Alternative specifications of time-varying exposures

Analyses restricted to key subgroups (e.g., MSM and transgender women), if sample size permits



Missing data:

Patterns of missing data will be examined. Primary analyses will use complete-case approaches, and sensitivity analyses will assess the impact of missing data using appropriate methods (e.g., multiple imputation), when feasible.

"
  ["project_software_used"]=>
  array(2) {
    [0]=>
    array(2) {
      ["value"]=>
      string(1) "r"
      ["label"]=>
      string(1) "R"
    }
    [1]=>
    array(2) {
      ["value"]=>
      string(5) "stata"
      ["label"]=>
      string(5) "STATA"
    }
  }
  ["project_timeline"]=>
  string(841) "The proposed study is expected to be completed within 9 months from the time of data access approval.

Months 1–2:

Data access, dataset familiarization, and variable harmonization. This phase will include review of data structure and construction of analysis datasets, including time-to-event variables and time-varying covariates.

Months 3–6:

Iterative data analysis phase, including descriptive analyses, estimation of HIV incidence (Aim 1), and multivariable modeling of risk factors (Aim 2). Model development, interaction analyses, and sensitivity analyses will be conducted in parallel and refined iteratively.

Months 6–7:

Exploratory analyses (Aim 3) and integration of findings across aims.

Months 7–9:

Manuscript preparation, internal review, and dissemination.

"
  ["project_dissemination_plan"]=>
  string(1180) "Findings from this study will be disseminated through multiple channels to maximize scientific, clinical, and public health impact. 

First, results will be submitted for publication in peer-reviewed journals focused on HIV prevention, implementation science, and global health. Priority targets include high-impact journals in the fields of infectious diseases and digital health. 

Second, findings will be presented at major international and regional scientific conferences, such as the International AIDS Society (IAS) Conference, CROI, and relevant Latin American infectious diseases meetings, to engage both global and regional audiences. 

Third, results will be shared with key stakeholders, including public health authorities, HIV prevention programs, and community organizations in participating regions. Particular emphasis will be placed on communicating findings relevant to Peru and Latin America, where results may inform region-specific prevention strategies. 

In addition, summary materials tailored to non-academic audiences (e.g, policy briefs or executive summaries) will be developed to facilitate translation into practice.

"
  ["project_bibliography"]=>
  string(3842) "

Govender, R., Hashim, M., Khan, M., Mustafa, H., & Khan, G. Global Epidemiology of HIV/AIDS: A Resurgence in North America and Europe. Journal of Epidemiology and Global Health. 2021; 11. https://doi.org/10.2991/jegh.k.210621.001
Challacombe, S. Global inequalities in HIV infection. Oral diseases. 2020; 26 Suppl 1. https://doi.org/10.1111/odi.13386

 

De Boni, R., Veloso, V., & Grinsztejn, B. Epidemiology of HIV in Latin America and the Caribbean. Current Opinion in HIV and AIDS. 2014; 9. https://doi.org/10.1097/coh.0000000000000031
Torres, T., Teixeira, S., Hoagland, B., Konda, K., et al. Recent HIV infection and annualized HIV incidence rates among sexual and gender minorities in Brazil and Peru (ImPrEP seroincidence study): a cross-sectional, multicenter study. Lancet Regional Health – Americas. 2023; 28. https://doi.org/10.1016/j.lana.2023.100642
Garcia, P., Bayer, A., & Cárcamo, C. The Changing face of HIV in Latin America and the Caribbean. Current HIV/AIDS Reports. 2014; 11. https://doi.org/10.1007/s11904-014-0204-1
Luz, P., Veloso, V., & Grinsztejn, B. The HIV epidemic in Latin America: accomplishments and challenges on treatment and prevention. Current Opinion in HIV and AIDS. 2019; 14. https://doi.org/10.1097/coh.0000000000000564
Salomón, H., de los Ángeles Pando, M. (2017). HIV Epidemiology in Latin America. In: Ludert, J., Pujol, F., Arbiza, J. (eds) Human Virology in Latin America. Springer, Cham. https://doi.org/10.1007/978-3-319-54567-7_19
Huff, H., Carcamo, P., Diaz, M., Conklin, J., Salvatierra, J., Aponte, R., & García, P. HIV and Substance Use in Latin America: A Scoping Review. International Journal of Environmental Research and Public Health. 2022; 19. https://doi.org/10.3390/ijerph19127198
Mimiaga, M., Biello, K., Robertson, A., Oldenburg, C., Rosenberger, J., O’Cleirigh, C., Novak, D., Mayer, K., & Safren, S. High Prevalence of Multiple Syndemic Conditions Associated with Sexual Risk Behavior and HIV Infection Among a Large Sample of Spanish- and Portuguese-Speaking Men Who Have Sex with Men in Latin America. Archives of Sexual Behavior. 2015; 44. https://doi.org/10.1007/s10508-015-0488-2
Anjos, T., Monteiro, A., Sardinha, D., Figueira, L., Silva, M., Marinho, R., Kimura, M., Soares, T., & Lima, L. HIV incidence trends in Brazil and neighboring countries: an ecological and analytical study on public health. Frontiers in Public Health. 2025; 13. https://doi.org/10.3389/fpubh.2025.1625475
Dias, B., Rodrigues, T., Botelho, E., Oliveira, M., Feijão, A., & Polaro, S. Integrative review on the incidence of HIV infection and its socio-spatial determinants. Revista brasileira de enfermagem. 2021; 74 2. https://doi.org/10.1590/0034-7167-2020-0905
12. Montana, J., Ferreira, G., Cunha, C., De Queiroz, A., Fernandes, W., Polaro, S., Gonçalves, L., Couto, D., Gir, E., Reis, R., Sorensen, W., & Botelho, E. The HIV epidemic in Colombia: spatial and temporal trends analysis. BMC Public Health. 2020; 21. https://doi.org/10.1186/s12889-021-10196-y
Buchbinder SP, Spinosa Guzman S, Sanchez J, Willems W, Stieh DJ, van Duijn J, van Rosmalen MGM, Hendriks J, Nijs S, Lavreys L, Paez CA, Grinzstejn B, Hutter J, Mann P, Sierra Madero JG, Cahn P, Castagna A, Truyers C, Roels S, Gilbert PB, Carone M, Luedtke A, Corey L, Pau MG, Tomaka F; HVTN 706/HPX3002/Mosaico Study Team. Efficacy and safety of a mosaic HIV-1 vaccine regimen in men who have sex with men and transgender individuals (HVTN 706/HPX3002/Mosaico): a global, randomised, double-blind, placebo-controlled, phase 3 trial. Lancet HIV. 2025 Dec;12(12):e823-e835. doi: 10.1016/S2352-3018(25)00195-X.

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  ["project_year_access"]=>
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  ["project_rep_publ"]=>
  bool(false)
  ["project_assoc_data"]=>
  array(0) {
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  ["project_due_dil_assessment"]=>
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    ["id"]=>
    int(19526)
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    string(51) "YODA-Project-Due-Diligence-Assessment-2026-0400.pdf"
    ["filesize"]=>
    int(107589)
    ["url"]=>
    string(100) "https://yoda.yale.edu/wp-content/uploads/2026/05/YODA-Project-Due-Diligence-Assessment-2026-0400.pdf"
    ["link"]=>
    string(93) "https://yoda.yale.edu/data-request/2026-0400/yoda-project-due-diligence-assessment-2026-0400/"
    ["alt"]=>
    string(0) ""
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    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(47) "yoda-project-due-diligence-assessment-2026-0400"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(19321)
    ["date"]=>
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    ["modified"]=>
    string(19) "2026-06-25 18:38:44"
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    string(15) "application/pdf"
    ["type"]=>
    string(11) "application"
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    string(3) "pdf"
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    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_title_link"]=>
  array(21) {
    ["ID"]=>
    int(19527)
    ["id"]=>
    int(19527)
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    string(46) "YODA Project Protocol - 2026-0400 - 2026-05-31"
    ["filename"]=>
    string(46) "YODA-Project-Protocol-2026-0400-2026-05-31.pdf"
    ["filesize"]=>
    int(133621)
    ["url"]=>
    string(95) "https://yoda.yale.edu/wp-content/uploads/2026/05/YODA-Project-Protocol-2026-0400-2026-05-31.pdf"
    ["link"]=>
    string(88) "https://yoda.yale.edu/data-request/2026-0400/yoda-project-protocol-2026-0400-2026-05-31/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(42) "yoda-project-protocol-2026-0400-2026-05-31"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(19321)
    ["date"]=>
    string(19) "2026-06-25 18:39:02"
    ["modified"]=>
    string(19) "2026-06-25 18:39:02"
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    string(3) "pdf"
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    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_review_link"]=>
  array(21) {
    ["ID"]=>
    int(19528)
    ["id"]=>
    int(19528)
    ["title"]=>
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    ["filename"]=>
    string(38) "YODA-Project-Review-2026-0400_site.pdf"
    ["filesize"]=>
    int(1074793)
    ["url"]=>
    string(87) "https://yoda.yale.edu/wp-content/uploads/2026/05/YODA-Project-Review-2026-0400_site.pdf"
    ["link"]=>
    string(80) "https://yoda.yale.edu/data-request/2026-0400/yoda-project-review-2026-0400_site/"
    ["alt"]=>
    string(0) ""
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    string(4) "1885"
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    ["caption"]=>
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  ["project_highlight_button"]=>
  string(0) ""
  ["request_data_partner"]=>
  string(0) ""
}
data partner
array(1) {
  [0]=>
  string(0) ""
}


pi country
array(0) {
}


pi affil
array(0) {
}


products
array(0) {
}


num of trials
array(1) {
  [0]=>
  string(1) "0"
}


res
array(1) {
  [0]=>
  string(1) "3"
}