La decisión de donar leche materna en el contexto de pérdida perinatal. Revisión de la literatura

Introducción: La pérdida perinatal es un evento profundamente impactante que afecta a diversas partes de la sociedad, desde las mujeres y sus parejas hasta los profesionales de la salud y la sociedad en su conjunto. Lamentablemente, en el cuidado de las mujeres que experimentan duelo perinatal, a menudo se pasa por alto la información y el apoyo relacionados con la lactancia materna, lo que puede aumentar sus sentimientos de dolor y ansiedad. Sin embargo, la donación de leche materna se ha reconocido como una opción valiosa que puede desempeñar un papel significativo en dicho proceso de duelo. Objetivo: Analizar la literatura existente sobre el impacto de la donación de leche después de la pérdida perinatal, su función en el proceso de duelo y sus implicaciones. Método: Búsqueda bibliográfica en bases de datos PubMed, Cochrane y PsycINFO para identificar estudios que aborden la donación de leche, la pérdida perinatal y las experiencias de las mujeres. Resultados: Basados en siete artículos cualitativos, resaltaron la necesidad de brindar apoyo a la lactancia a las madres en duelo perinatal, así como la falta de comunicación por parte de los profesionales sanitarios, sensible y libre de juicio. Conclusión: La lactancia materna puede desempeñar un papel esencial en el proceso de duelo perinatal, validando la identidad materna y generando recuerdos significativos. Se enfatiza la importancia de abordar la inhibición de la lactancia de manera individualizada y mejorar la formación de los profesionales de la salud para ofrecer apoyo integr

Applicability of probabilistic graphical models for early detection of SARS-CoV-2 reactive antibodies after SARS-CoV-2 vaccination in hematological patients

Prior studies of antibody response after full SARS-CoV-2 vaccination in hematological patients have confirmed lower antibody levels compared to the general population. Serological response in hematological patients varies widely according to the disease type and its status, and the treatment given and its timing with respect to vaccination. Through probabilistic machine learning graphical models, we estimated the conditional probabilities of having detectable anti-SARS-CoV-2 antibodies at 3–6 weeks after SARS-CoV-2 vaccination in a large cohort of patients with several hematological diseases (n= 1166). Most patients received mRNA-based vaccines (97%), mainly Moderna® mRNA-1273 (74%) followed by Pfizer-BioNTech® BNT162b2 (23%). The overall antibody detection rate at 3 to 6 weeks after full vaccination for the entire cohort was 79%. Variables such as type of disease, timing of anti-CD20 monoclonal antibody therapy, age, corticosteroids therapy, vaccine type, disease status, or prior infection with SARS-CoV-2 are among the most relevant conditions influencing SARS-CoV-2-IgG-reactive antibody detection. A lower probability of having detectable antibodies was observed in patients with B-cell non-Hodgkin’s lymphoma treated with anti-CD20 monoclonal antibodies within 6 months before vaccination (29.32%), whereas the highest probability was observed in younger patients with chronic myeloproliferative neoplasms (99.53%). The Moderna® mRNA-1273 compound provided higher probabilities of antibody detection in all scenarios. This study depicts conditional probabilities of having detectable antibodies in the whole cohort and in specific scenarios such as B cell NHL, CLL, MM, and cMPN that may impact humoral responses. These results could be useful to focus on additional preventive and/or monitoring interventions in these highly immunosuppressed hematological patients.REDCap is developed and supported by Vanderbilt Institute for Clinical and Translational Research. We thank the Spanish Society of Hematology (SEHH) for its support on the study. We sincerely want to thanks the invaluable aid of microbiology services for their commitment in SARS-CoV-2-reactive IgG antibody monitoring in these highly immunosuppressed patients from all participating centers. Finally, we also want to thank the patients, nurses, and study coordinators for their foremost contributions in this study.Peer reviewe

Quantification of specific antibodies against SARS-CoV-2 in breast milk of lactating women vaccinated with an mRNA vaccine

This cohort study assesses the concentration of SARS-CoV-2 antibodies in the breast milk of women who received vaccines for COVID-19 and their correlation with serum antibody levels

Quantification and progress over time of specific antibodies against severe acute respiratory syndrome coronavirus 2 in breast milk of lactating women vaccinated with BNT162b2 Pfizer-BioNTech coronavirus disease 2019 vaccine (LacCOVID)

Background: several observational studies demonstrated the passage of postvaccine antibodies through breast milk in women vaccinated against coronavirus disease 2019 (COVID-19), mostly with messenger RNA (mRNA)-based vaccines, but lacked long-term data. Methods: a 6-month prospective cohort study was performed to determine severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine-induced antibody levels in the breast milk of 33 lactating healthcare workers at different timepoints after mRNA BNT162b2 Pfizer-BioNTech COVID-19 vaccination. Moreover, we examined the correlation of SARS-CoV-2 antibody levels between serum and breast milk, adverse events related to vaccination, and rate of SARS-CoV-2 infections. Results: mothers' median age was 38 (interquartile range [IQR], 36-39) years and 15 (IQR, 10-22) months for infants. Median (IQR) SARS-CoV-2 immunoglobulin G (IgG) spike protein subunit S1 (S1) vaccine-induced levels at different timepoints for serum-milk pairs were 519 (234-937) to 1 (0-2.9) arbitrary units (AU)/mL at 2 weeks after first dose and 18 644 (9923-29 264) to 78 (33.7-128), 12 478 (6870-20 801) to 50.4 (24.3-104), 4094 (2413-8480) to 19.9 (10.8-51.9), 1350 (831-2298) to 8.9 (7.8-31.5) AU/mL at 2, 4, 12 and 24 weeks after second dose, respectively. We observed a positive correlation of antibody levels between serum and breast milk, no serious adverse events related to vaccination, and 2 (6%) COVID-19 vaccine breakthrough infections. Conclusions: women vaccinated with Pfizer-BioNTech transmit antibodies into breast milk with a positive correlation with serum levels. Both decreased over time in a 6-month follow-up

Optimization of adsorptive removal of α-toluic acid by CaO2 nanoparticles using response surface methodology

The present work addresses the optimization of process parameters for adsorptive removal of α-toluic acid by calcium peroxide (CaO2) nanoparticles using response surface methodology (RSM). CaO2 nanoparticles were synthesized by chemical precipitation method and confirmed by Transmission electron microscopy (TEM) and high-resolution TEM (HRTEM) analysis which shows the CaO2 nanoparticles size range of 5–15 nm. A series of batch adsorption experiments were performed using CaO2 nanoparticles to remove α-toluic acid from the aqueous solution. Further, an experimental based central composite design (CCD) was developed to study the interactive effect of CaO2 adsorbent dosage, initial concentration of α-toluic acid, and contact time on α-toluic acid removal efficiency (response) and optimization of the process. Analysis of variance (ANOVA) was performed to determine the significance of the individual and the interactive effects of variables on the response. The model predicted response showed a good agreement with the experimental response, and the coefficient of determination, (R2) was 0.92. Among the variables, the interactive effect of adsorbent dosage and the initial α-toluic acid concentration was found to have more influence on the response than the contact time. Numerical optimization of process by RSM showed the optimal adsorbent dosage, initial concentration of α-toluic acid, and contact time as 0.03 g, 7.06 g/L, and 34 min respectively. The predicted removal efficiency was 99.50%. The experiments performed under these conditions showed α-toluic acid removal efficiency up to 98.05%, which confirmed the adequacy of the model prediction