Mast Cell Activation Profile and TFH13 Detection Discriminate Food Anaphylaxis Versus Sensitization.

BACKGROUND AND OBJECTIVE: The prevalence of food allergy (FA) has increased significantly, and the risk of developing anaphylaxis is unpredictable. Thus, discriminating between sensitized patients and those at risk of having a severe reaction is of utmost interest. To explore mast cell activation pattern and T follicular helper (TFH) 13 presence in sensitized and food anaphylaxis patients. METHODS: Patients sensitized to Lipid transfer protein (LTP) were classified as anaphylaxis or sensitized depending on the symptoms elicited by LTP-containing food. CD34+-derived MCs from patients and controls were obtained, sensitized with pooled sera, and challenged with Pru p 3 (peach LTP). Degranulation, PGD2, and cytokine/chemokine release were measured. The TFH13 population was examined by flow cytometry in the peripheral blood of all groups. In parallel, LAD2 cells were activated similarly to patients' MCs. RESULTS: A distinguishable pattern of mast cell activation was found in anaphylaxis compared to sensitized patients. Robust degranulation, PGD2, and IL-8 and GM-CSF secretion were higher in anaphylaxis, whereas TFG- and CCL2 secretion increased in sensitized patients. Concomitantly, anaphylaxis patients had a larger TFH13 population. MC activation profile was dependent on the sera rather than the MC source. In agreement with that, LAD2 cells reproduce the same pattern as MCs from anaphylactic and sensitized patients. CONCLUSION: The distinct profile of mast cell activation allows to discriminate between anaphylaxis and sensitized patients. Pooled sera may determine mast cell activation independently of mast cell origin. Besides, the presence of TFH13 cells in anaphylaxis patients points to an essential role of IgE affinity.

COVID-19 in children and young adults with moderate/severe inborn errors of immunity in a high burden area in pre-vaccine era.

BACKGROUND: Information regarding inborn error of immunity (IEI) as a risk factor for severe COVID-19 is scarce. We aimed to determine if paediatric patients with moderate/severe IEI got COVID-19 at the same level as the general population, and to describe COVID-19 expression. MATERIAL AND METHODS: We included patients with moderate/severe IEI aged 0-21 years old: cross-sectional study (June2020) to determine the prevalence of COVID-19; prospective study (January2020-January2021) including IEI patients with COVID-19. Assays used: nasopharyngeal swab SARS-CoV-2 PCR and SARS-CoV-2-specific immunoglobulins. RESULTS: Seven from sixty-five patients tested positive (prevalence: 10.7% (7%-13%)) after the first SARS-COV-2 wave and 13/15 patients diagnosed with COVID-19 had an asymptomatic/mild course. CONCLUSIONS: In our area, prevalence of COVID-19 in moderate/severe IEI paediatric patients after the first wave was slightly higher than in the general population. The majority of patients presented a benign course, suggesting a possible protective factor related with age despite IEI.

FAIR data sharing: The roles of common data elements and harmonization.

The value of robust and responsible data sharing in clinical research and healthcare is recognized by patients, patient advocacy groups, researchers, journal editors, and the healthcare industry globally. Privacy and security concerns acknowledged, the act of exchanging data (interoperability) along with its meaning (semantic interoperability) across studies and between partners has been difficult, if not elusive. For shared data to retain its value, a recommendation has been made to follow the Findable, Accessible, Interoperable, Reusable (FAIR) principles. Without applying appropriate data exchange standards with domain-relevant content standards and accessible rich metadata that uses applicable terminologies, interoperability is burdened by the need for transformation and/or mapping. These obstacles to interoperability limit the findability, accessibility and reusability of data, thus diminishing its value and making it impossible to adhere to FAIR principles. One effort to standardize data collection has been through common data elements (CDEs). CDEs are data collection units comprising one or more questions together with a set of valid values. Some CDEs contain standardized terminology concepts that define the meaning of the data, and others include links to unique terminology concept identifiers and unique identifiers for each CDE; however, usually CDEs are defined for specific projects or collaborations and lack traceable or machine readable semantics. While the name implies that these are 'common', this has not necessarily been a requirement, and many CDEs have not been commonly used. The National Institutes of Health (NIH) CDEs are, in fact, a conglomerate of CDEs developed in silos by various NIH institutes. Therefore, CDEs have not brought the anticipated benefit to the industry through widescale interoperability, nor is there widespread reuse of CDEs. Certain institutes in the NIH recommend, albeit do not enforce, institute-specific preferred CDEs; however, at the NIH level a preponderance of choice and a lack of any overarching harmonization of CDEs or consistency in linking them to controlled terminology or common identifiers create confusion for researchers in their efforts to identify the best CDEs for their protocol. The problem of comparing data among studies is exacerbated when researchers select different CDEs for the same variable or data collection field. This manuscript explores reasons for the disappointingly low adoption of CDEs and the inability of CDEs or other clinical research standards to broadly solve the interoperability and data sharing problems. Recommendations are offered for rectifying this situation to enable responsible data sharing that will help in adherence to FAIR principles and the realization of Learning Health Systems for the sake of all of us as patients.