Initiative Consortium Research Articles

Cross-trait GWAS in COVID-19 and systemic sclerosis reveals novel genes implicated in fibrotic and inflammation pathways.

COVID-19 and systemic sclerosis (SSc) share multiple similarities in their clinical manifestations, alterations in immune response, and therapeutic options. These resemblances have also been identified in other immune-mediated inflammatory diseases where a common genetic component has been found. Thus, we decided to evaluate for the first time this shared genetic architecture with SSc. For this study, we retrieved genomic data from two European-ancestry cohorts: 2,597 856 individuals from The COVID-19 Host Genetics Initiative consortium, and 26 679 individuals from the largest genomic scan in SSc. We performed a cross-trait meta-analyses including >9.3 million SNPs. Finally, we conducted functional annotation to prioritize potential causal genes and performed drug repurposing analysis. Our results revealed a total of 19 non-HLA pleiotropic loci, including 2 novel associations for both conditions (BMP1 and PPARG), and 12 emerging as new shared loci. Functional annotation of these regions underscored their potential regulatory role and identified potential causal genes, many of which are implicated in fibrotic and inflammatory pathways. Remarkably, we observed an antagonistic pleiotropy model of the IFN signalling between COVID-19 and SSc, including the well-known TYK2 P1104A missense variant, showing a protective effect for SSc while being a risk factor for COVID-19, along with two additional novel pleiotropic associations (IRF8 and SENP7). Finally, our findings provide new therapeutic options that could potentially benefit both conditions. Our study confirms the genetic resemblance between susceptibility to and severity of COVID-19 and SSc, revealing a novel common genetic contribution affecting fibrotic and immune pathways.

Case-control association study between polygenic risk score and COVID-19 severity in a Russian population using low-pass genome sequencing.

The course of COVID-19 is highly variable, with genetics playing a significant role. Through large-scale genetic association studies, a link between single nucleotide polymorphisms and disease susceptibility and severity was established. However, individual single nucleotide polymorphisms identified thus far have shown modest effects, indicating a polygenic nature of this trait, and individually have limited predictive performance. To address this limitation, we investigated the performance of a polygenic risk score model in the context of COVID-19 severity in a Russian population. A genome-wide polygenic risk score model including information from over a million common single nucleotide polymorphisms was developed using summary statistics from the COVID-19 Host Genetics Initiative consortium. Low-coverage sequencing (5x) was performed for ~1000 participants, and polygenic risk score values were calculated for each individual. A multivariate logistic regression model was used to analyse the association between polygenic risk score and COVID-19 outcomes. We found that individuals in the top 10% of the polygenic risk score distribution had a markedly elevated risk of severe COVID-19, with adjusted odds ratio of 2.9 (95% confidence interval: 1.8-4.6, p-value = 4e-06), and more than four times higher risk of mortality from COVID-19 (adjusted odds ratio = 4.3, p-value = 2e-05). This study highlights the potential of polygenic risk score as a valuable tool for identifying individuals at increased risk of severe COVID-19 based on their genetic profile.

Facilitating multidisciplinary working groups in translational research: Strategies to promote cross-center collaboration and sustain the Cancer Center Cessation Initiative Consortium.

As funding for large translational research consortia increases across the National Institutes of Health (NIH), focused working groups provide an opportunity to leverage the power of unique networks to conduct high-impact science and offer a strategy for building collaborative infrastructure to sustain networks long-term. This sustainment leverages the existing NIH investments, amplifying the impact and creating conditions for future innovative translational research. However, few resources exist that detail practical strategies for establishing and sustaining working groups in consortia. Here, we describe how the Coordinating Center for the National Cancer Institute-funded Cancer Center Cessation Initiative (C3I) utilized principles derived from the Science of Team Science to develop replicable strategies for building and sustaining an effective working group-led consortium. These strategies include continually engaging community members in strategic planning, prioritizing diversity in leadership and membership, creating multi-level opportunities for leadership and participation, providing intensive community management and facilitation, and incentivizing projects that support the consortium sustainment. When assessing the impact of these interventions through qualitative exit interviews, four key themes emerged: through the C3I working group consortium, members co-created new dissemination products, gained new insights and innovations, enhanced local program implementation, and invested in cross-network collaboration to support sustained engagement in the initiative.

A novel classification framework for genome-wide association study of whole brain MRI images using deep learning.

Genome-wide association studies (GWASs) have been widely applied in the neuroimaging field to discover genetic variants associated with brain-related traits. So far, almost all GWASs conducted in neuroimaging genetics are performed on univariate quantitative features summarized from brain images. On the other hand, powerful deep learning technologies have dramatically improved our ability to classify images. In this study, we proposed and implemented a novel machine learning strategy for systematically identifying genetic variants that lead to detectable nuances on Magnetic Resonance Images (MRI). For a specific single nucleotide polymorphism (SNP), if MRI images labeled by genotypes of this SNP can be reliably distinguished using machine learning, we then hypothesized that this SNP is likely to be associated with brain anatomy or function which is manifested in MRI brain images. We applied this strategy to a catalog of MRI image and genotype data collected by the Alzheimer's Disease Neuroimaging Initiative (ADNI) consortium. From the results, we identified novel variants that show strong association to brain phenotypes.

The Environmental Enteric Dysfunction Biopsy Initiative (EEDBI) Consortium: mucosal investigations of environmental enteric dysfunction

Environmental enteric dysfunction (EED) is an asymptomatic acquired disorder characterized by upper small bowel inflammation, villus blunting, and gut permeability. It is a major contributor to poor growth in childhood as well as other highly consequential outcomes such as delayed neuorcognitive development. After decades of intermittent interest in this entity, we are now seeing a resurgence in the field of EED. However, recent studies have been hampered by a lack of investigation of the target tissue—the upper small bowel. In 2016, the EEDBI (Environmental Enteric Dysfunction Biopsy Initiative) Consortium was established as a common scientific platform across 3 independent EED biopsy cohort studies in Bangladesh, Pakistan, and Zambia. Two centers in the United States recruited comparison groups of children undergoing endoscopy for clinical indications. The EEDBI Consortium goal was to augment the contributions of the individual centers and answer high-level questions amenable to analysis and interpretation across the studies. Here, we describe the Consortium and its cohorts and recruitment procedures across studies. We also offer details applicable to all papers in this supplement, which describe EED mucosal histology, morphometry, immunohistochemistry, and transcriptomics as well as histology relationship to pathogens and biomarkers.

Portrait of a UK-Africa Capacity Building Initiative Consortium 2015-2022: the Cameroon, Ghana, South Africa and United Kingdom Materials Initiative (CaGSUMI) for developing materials for solar cells.

The CaGSUMI consortium was funded by the Royal Society-Department for International Development (later the Foreign, Commonwealth & Development Office) on the Africa Capacity Building Initiative programme between the years 2015 and 2022 and involved three Sub-Saharan African universities: Kwame Nkrumah University of Science and Technology, Kumasi, Ghana, University of Yaoundé I, Cameroon, and the University of Zululand, South Africa; and the University of Manchester in the United Kingdom. The project was used to cement an emergent UK-Africa network in the areas of materials chemistry related to renewable energy generation with both thin films and nanomaterials. The consortium's outputs led to numerous publications of African science in international journals, a number of graduated PhDs who went on to permanent academic positions and prestigious fellowships, the establishment of a capacity-building plan relevant to the chemistry departments in each of the African countries, and the installation of a number of first-in-kind pieces of kit for African laboratories that will keep them on a competitive footing at an international level for the next decade and more.

Genetic variations in anti-diabetic drug targets and COPD risk: evidence from mendelian randomization

BackgroundPrevious research has emphasized the potential benefits of anti-diabetic medications in inhibiting the exacerbation of Chronic Obstructive Pulmonary Disease (COPD), yet the role of anti-diabetic drugs on COPD risk remains uncertain.MethodsThis study employed a Mendelian randomization (MR) approach to evaluate the causal association of genetic variations related to six classes of anti-diabetic drug targets with COPD. The primary outcome for COPD was obtained from the Global Biobank Meta-analysis Initiative (GBMI) consortium, encompassing a meta-analysis of 12 cohorts with 81,568 cases and 1,310,798 controls. Summary-level data for HbA1c was derived from the UK Biobank, involving 344,182 individuals. Positive control analysis was conducted for Type 2 Diabetes Mellitus (T2DM) to validate the choice of instrumental variables. The study applied Summary-data-based MR (SMR) and two-sample MR for effect estimation and further adopted colocalization analysis to verify evidence of genetic variations.ResultsSMR analysis revealed that elevated KCNJ11 gene expression levels in blood correlated with reduced COPD risk (OR = 0.87, 95% CI = 0.79–0.95; p = 0.002), whereas an increase in DPP4 expression corresponded with an increased COPD incidence (OR = 1.18, 95% CI = 1.03–1.35; p = 0.022). Additionally, the primary method within MR analysis demonstrated a positive correlation between PPARG-mediated HbA1c and both FEV1 (OR = 1.07, 95% CI = 1.02–1.13; P = 0.013) and FEV1/FVC (OR = 1.08, 95% CI = 1.01–1.14; P = 0.007), and a negative association between SLC5A2-mediated HbA1c and FEV1/FVC (OR = 0.86, 95% CI = 0.74–1.00; P = 0.045). No colocalization evidence with outcome phenotypes was detected (all PP.H4 < 0.7).ConclusionThis study provides suggestive evidence for anti-diabetic medications' role in improving COPD and lung function. Further updated MR analyses are warranted in the future, following the acquisition of more extensive and comprehensive data, to validate our results.

The Australian Traumatic Brain Injury Initiative: Single Data Dictionary to Predict Outcome for People With Moderate-Severe Traumatic Brain Injury.

In this series of eight articles, the Australian Traumatic Brain Injury Initiative (AUS-TBI) consortium describes the Australian approach used to select the common data elements collected acutely that have been shown to predict outcome following moderate-severe traumatic brain injury (TBI) across the lifespan. This article presents the unified single data dictionary, together with additional measures chosen to facilitate comparative effectiveness research and data linkage. Consultations with the AUS-TBI Lived Experience Expert Group provided insights on the merits and considerations regarding data elements for some of the study areas, as well as more general principles to guide the collection of data and the selection of meaningful measures. These are presented as a series of guiding principles and themes. The AUS-TBI Aboriginal and Torres Strait Islander Advisory Group identified a number of key points and considerations for the project approach specific to Aboriginal and Torres Strait Islander peoples, including key issues of data sovereignty and community involvement. These are outlined in the form of principles to guide selection of appropriate methodologies, data management, and governance. Implementation of the AUS-TBI approach aims to maximize ongoing data collection and linkage, to facilitate personalization of care and improved outcomes for people who experience moderate-severe TBI.

Harnessing the power of proteomics in precision diabetes medicine.

Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.

Etiologic Spectrum of Pediatric-Onset Leukodystrophies and Genetic Leukoencephalopathies: The Five-Year Experience of a Tertiary Care Center in Southern India

BackgroundWhite matter (WM) disorders with a genetic etiology are classified as leukodystrophies (LDs) and genetic leukoencephalopathies (GLEs). There are very few studies pertaining to the etiologic spectrum of these disorders in the Asian Indian population. MethodsThis study was conducted over a period of five years from January 2016 to December 2020, in the medical genetics department of a tertiary care hospital in southern India. A total of 107 patients up to age 18 years, with a diagnosis of a genetic WM disorder confirmed by molecular genetic testing and/or metabolic testing, were included in the study and categorized into LD or GLE group as per the classification suggested by the Global Leukodystrophy Initiative consortium in 2015. ResultsForty-one patients were diagnosed to have LDs, and 66 patients had GLEs. The two most common LDs were metachromatic LD (16 patients) and X-linked adrenoleukodystrophy (seven patients). In the GLE group, lysosomal storage disorders were the most common (40 patients) followed by mitochondrial disorders (nine patients), with other metabolic disorders and miscellaneous conditions making up the rest. The clinical presentations, neuroimaging findings, and mutation spectrum of the patients in our cohort are discussed. ConclusionsThis is one of the largest cohorts of genetic WM disorders reported till date from the Asian Indian population. The etiologies and clinical presentations identified in our study cohort are similar to those found in other Indian studies as well as in studies based on other populations from different parts of the world.

A three-component Breakfast Quality Score (BQS) to evaluate the nutrient density of breakfast meals.

Nutrient profiling methods can be applied to individual foods or to composite meals. This article introduces a new method to assess the nutrient density of breakfast meals. This study aimed to develop a new breakfast quality score (BQS), based on the nutrient standards previously published by the International Breakfast Research Initiative (IBRI) consortium. BQS was composed of three sub-scores derived from the weighted arithmetic mean of corresponding nutrient adequacy: an eLIMf sub-score (energy, saturated fat, free sugars, and sodium), a PF (protein and fiber) sub-score, and a VMn1 - 14 micronutrient sub-score, where n varied from 0 to 14. The effects of assigning different weights to the eLIMf, PF, and VMn were explored in four alternative models. The micronutrients were calcium, iron, potassium, magnesium, zinc, vitamin A, thiamin, riboflavin, niacin, vitamin B5, vitamin B6, vitamin B12, vitamin C, and vitamin D. Micronutrient permutations were used to develop alternate VMn1 - 14 sub-scores. The breakfast database used in this study came from all breakfasts declared as consumed by adults (>18 years old) in the French dietary survey INCA3. All models were tested with respect to the Nutrient Rich Food Index (NRF9.3). BQS sensitivity was tested using three prototype French breakfasts, for which improvements were made. The correlations of the models with NRF9.3 improved when the VMn>3 sub-score (n > 3) was included alongside the PF and eLIMf sub-scores. The model with (PF+VMn) and eLIMf each accounting for 50% of the total score showed the highest correlations with NRF9.3 and was the preferred final score (i.e., BQS). BQS was sensitive to the changing quality of three prototype breakfasts defined as tartine, sandwich, and cereal. The proposed BQS was shown to valuably rank the nutritional density of breakfast meals against a set of nutrient recommendations. It includes nutrients to limit along with protein, fiber, and a variable number of micronutrients to encourage. The flexible VMn sub-score allows for the evaluation of breakfast quality even when nutrient composition data are limited.

DirKorp

In this paper, we present recent developments on a new version (v3.0) of DirKorp (Korpus direktivnih govornih činova hrvatskoga jezika), the first Croatian corpus of directive speech acts developed for the purposes of pragmatic research. The corpus contains 800 elicited speech acts collected via an online questionnaire with role-playing tasks, a method of simulated communication that is implemented under pre-set conditions. This method is suitable for researching speech acts due to the ability to collect a great number of examples of such acts of equal propositional content and illocutionary purpose used in the same controlled situations. The presented situations are classified into two categories with regard to the relationship between the participants of the communication act: (1) situations involving interlocutors who are not in a familiar relationship; (2) situations involving interlocutors in a familiar relationship. Assignments of the two categories are organized into four pairs, asking respondents to share a speech act of similar propositional content. The respondents were 100 Croatian speakers, all undergraduate (63%) or graduate students (37%) of the Faculty of Humanities and Social Sciences (University of Zagreb). The corpus has been manually annotated on the speech act level, each speech act containing up to 14 features: (1) respondent ID, (2) familiarity/unfamiliarity, (3) utterance type, (4) directive performative verb in 1st person, (5) illocutionary force, (6) propositional content, (7) T/V form, (8) exhortative, (9) lexical marker of request, (10) lexical marker of apology, (11) lexical marker of gratitude, (12) honorific title, (13) grammatical mood, and (14) modal verb in 2nd person. It contains 12,676 tokens and 1,692 types. The corpus is encoded according to the TEI P5: Guidelines for Electronic Text Encoding and Interchange, developed and maintained by the Text Encoding Initiative Consortium (TEI). DirKorp is available for download under the CC BY-SA 4.0 license from GitHub in TEI format. We describe applied pragmatic annotation as well as the structure of the corpus.

Setting International Standards in Analyzing Patient-Reported Outcomes and Quality of Life Endpoints in Cancer Clinical Trials-Innovative Medicines Initiative (SISAQOL-IMI): stakeholder views, objectives, and procedures.

Patient-reported outcomes (PROs), such as symptoms, functioning, and other health-related quality-of-life concepts are gaining a more prominent role in the benefit-risk assessment of cancer therapies. However, varying ways of analysing, presenting, and interpreting PRO data could lead to erroneous and inconsistent decisions on the part of stakeholders, adversely affecting patient care and outcomes. The Setting International Standards in Analyzing Patient-Reported Outcomes and Quality of Life Endpoints in Cancer Clinical Trials-Innovative Medicines Initiative (SISAQOL-IMI) Consortium builds on the existing SISAQOL work to establish recommendations on design, analysis, presentation, and interpretation for PRO data in cancer clinical trials, with an expanded set of topics, including more in-depth recommendations for randomised controlled trials and single-arm studies, and for defining clinically meaningful change. This Policy Review presents international stakeholder views on the need for SISAQOL-IMI, the agreed on and prioritised set of PRO objectives, and a roadmap to ensure that international consensus recommendations are achieved.

Accelerating development of engineered T cell therapies in the EU: current regulatory framework for studying multiple product versions and T2EVOLVE recommendations.

To accelerate the development of Advanced Therapy Medicinal Products (ATMPs) for patients suffering from life-threatening cancer with limited therapeutic options, regulatory approaches need to be constantly reviewed, evaluated and adjusted, as necessary. This includes utilizing science and risk-based approaches to mitigate and balance potential risks associated with early clinical research and a more flexible manufacturing paradigm. In this paper, T2EVOLVE an Innovative Medicine Initiative (IMI) consortium explores opportunities to expedite the development of CAR and TCR engineered T cell therapies in the EU by leveraging tools within the existing EU regulatory framework to facilitate an iterative and adaptive learning approach across different product versions with similar design elements or based on the same platform technology. As understanding of the linkage between product quality attributes, manufacturing processes, clinical efficacy and safety evolves through development and post licensure, opportunities are emerging to streamline regulatory submissions, optimize clinical studies and extrapolate data across product versions reducing the need to perform duplicative studies. It is worth noting that this paper is focusing on CAR- and TCR-engineered T cell therapies but the concepts may be applied more broadly to engineered cell therapy products (e.g., CAR NK cell therapy products).

Transfusion therapy of neonatal and paediatric patients: They are not just little adults.

Patient blood management (PBM) strategies are needed in the neonate and paediatric population, given that haemoglobin thresholds used are often higher than recommended by evidence, with exposure of children to potential complications without meaningful benefit. A literature review was performed on the following topics: evidence-based transfusions of blood components and pharmaceutical agents. Other topics reviewed included perioperative coagulation assessment and perioperative PBM. The Transfusion and Anaemia Expertise Initiative (TAXI) consortium published a consensus statement addressing haemoglobin (Hb) transfusion threshold in multiple subsets of patients. A multicentre trial (PlaNeT-2) reported a higher risk of bleeding and death or serious new bleeding among infants who received platelet transfusion at a higher (50 000/μl) compared to a lower (25 000/μl) threshold. Recent data support the use of a restrictive transfusion threshold of 25 000/μl for prophylactic platelet transfusions in preterm neonates. The TAXI-CAB consortium mentioned that in critically ill paediatric patients undergoing invasive procedures outside of the operating room, platelet transfusion might be considered when the platelet count is less than or equal to 20 000/μl and there is no benefit of platelet transfusion when the platelet count is more than 50 000/μl. There are limited controlled studies in paediatric and neonatal population regarding plasma transfusion. Blood conservation strategies to minimise allogenic blood exposure are essential to positive patient outcomes neonatal and paediatric transfusion practices have changed significantly in recent years since randomised controlled trials were published to guide practice. Additional studies are needed in order to provide practice change recommendations.

The Rural Opioid Initiative Consortium description: providing evidence to Understand the Fourth Wave of the Opioid Crisis

ObjectiveTo characterize and address the opioid crisis disproportionately impacting rural U.S. regions.MethodsThe Rural Opioid Initiative (ROI) is a two-phase project to collect and harmonize quantitative and qualitative data and develop tailored interventions to address rural opioid use. The baseline quantitative survey data from people who use drugs (PWUD) characterizes the current opioid epidemic (2018–2020) in eight geographically diverse regions.ResultsAmong 3,084 PWUD, 92% reported ever injecting drugs, 86% reported using opioids (most often heroin) and 74% reported using methamphetamine to get high in the past 30 days; 53% experienced homelessness in the prior 6 months; and 49% had ever overdosed. Syringe service program use varied by region and 53% had ever received an overdose kit or naloxone prescription. Less than half (48%) ever received medication for opioid use disorder (MOUD).ConclusionsThe ROI combines data across eight rural regions to better understand drug use including drivers and potential interventions in rural areas with limited resources. Baseline ROI data demonstrate extensive overlap between opioid and methamphetamine use, high homelessness rates, inadequate access to MOUD, and other unmet needs among PWUD in the rural U.S. By combining data across studies, the ROI provides much greater statistical power to address research questions and better understand the syndemic ofinfectious diseases and drug use in rural settings including unmet treatmentneeds.

Structured, Harmonized, and Interoperable Integration of Clinical Routine Data to Compute Heart Failure Risk Scores.

Risk prediction in patients with heart failure (HF) is essential to improve the tailoring of preventive, diagnostic, and therapeutic strategies for the individual patient, and effectively use health care resources. Risk scores derived from controlled clinical studies can be used to calculate the risk of mortality and HF hospitalizations. However, these scores are poorly implemented into routine care, predominantly because their calculation requires considerable efforts in practice and necessary data often are not available in an interoperable format. In this work, we demonstrate the feasibility of a multi-site solution to derive and calculate two exemplary HF scores from clinical routine data (MAGGIC score with six continuous and eight categorical variables; Barcelona Bio-HF score with five continuous and six categorical variables). Within HiGHmed, a German Medical Informatics Initiative consortium, we implemented an interoperable solution, collecting a harmonized HF-phenotypic core data set (CDS) within the openEHR framework. Our approach minimizes the need for manual data entry by automatically retrieving data from primary systems. We show, across five participating medical centers, that the implemented structures to execute dedicated data queries, followed by harmonized data processing and score calculation, work well in practice. In summary, we demonstrated the feasibility of clinical routine data usage across multiple partner sites to compute HF risk scores. This solution can be extended to a large spectrum of applications in clinical care.

Building knowledge, optimising physical and mental health and setting up healthier life trajectories in South African women (Bukhali): a preconception randomised control trial part of the Healthy Life Trajectories Initiative (HeLTI)

IntroductionSouth Africa’s evolving burden of disease is challenging due to a persistent infectious disease, burgeoning obesity, most notably among women and rising rates of non-communicable diseases (NCDs). With two thirds...

Exploring ITM2A as a new potential target for brain delivery

BackgroundIntegral membrane protein 2A (ITM2A) is a transmembrane protein expressed in a variety of tissues; little is known about its function, particularly in the brain. ITM2A was found to be highly enriched in human brain versus peripheral endothelial cells by transcriptomic and proteomic studies conducted within the European Collaboration on the Optimization of Macromolecular Pharmaceutical (COMPACT) Innovative Medicines Initiative (IMI) consortium. Here, we report the work that was undertaken to determine whether ITM2A could represent a potential target for delivering drugs to the brain.MethodsA series of ITM2A constructs, cell lines and specific anti-human and mouse ITM2A antibodies were generated. Binding and internalization studies in Human Embryonic Kidney 293 (HEK293) cells overexpressing ITM2A and in brain microvascular endothelial cells from mouse and non-human primate (NHP) were performed with these tools. The best ITM2A antibody was evaluated in an in vitro human blood brain barrier (BBB) model and in an in vivo mouse pharmacokinetic study to investigate its ability to cross the BBB.ResultsAntibodies specifically recognizing extracellular parts of ITM2A or tags inserted in its extracellular domain showed selective binding and uptake in ITM2A-overexpressing cells. However, despite high RNA expression in mouse and human microvessels, the ITM2A protein was rapidly downregulated when endothelial cells were grown in culture, probably explaining why transcytosis could not be observed in vitro. An attempt to directly demonstrate in vivo transcytosis in mice was inconclusive, using either a cross-reactive anti-ITM2A antibody or in vivo phage panning of an anti-ITM2A phage library.ConclusionsThe present work describes our efforts to explore the potential of ITM2A as a target mediating transcytosis through the BBB, and highlights the multiple challenges linked to the identification of new brain delivery targets. Our data provide evidence that antibodies against ITM2A are internalized in ITM2A-overexpressing HEK293 cells, and that ITM2A is expressed in brain microvessels, but further investigations will be needed to demonstrate that ITM2A is a potential target for brain delivery.

Application of deterministic, stochastic, and hybrid methods for cloud provider selection

Cloud Computing popularization inspired the emergence of many new cloud service providers. The significant number of cloud providers available drives users to complex or even impractical choice of the most suitable one to satisfy his needs without automation. The Cloud Provider Selection (CPS) problem addresses that choice. Hence, this work presents a general approach for solving the CPS problem using as selection criteria performance indicators compliant with the Cloud Service Measurement Initiative Consortium - Service Measurement Index framework (CSMIC-SMI). To accomplish that, deterministic (CPS-Matching and CPS-DEA), stochastic (Evolutionary Algorithms: CPS-GA, CPS-BDE, and CPS-DDE), and hybrid (Matching-GA, Matching-BDE, and Matching-DDE) selection optimization methods are developed and employed. The evaluation uses a synthetic database created from several real cloud provider indicator values in experiments comprising scenarios with different user needs and several cloud providers indicating that the proposed approach is appropriate for solving the cloud provider selection problem, showing promising results for a large-scale application. Particularly, comparing which approach chooses the most appropriate cloud provider the better, the hybrid one presents better results, achieving the best average hit percentage, dealing with simple and multi-cloud user requests.