Expression of the 60 kDa and 71 kDa heat shock proteins and presence of antibodies against the 71 kDa heat shock protein in pediatric patients with immune thrombocytopenic purpura

BACKGROUND: Immune thrombocytopenic purpura (ITP) is an autoimmune disease characterized by platelet destruction resulting from autoantibodies against platelet proteins, particularly platelet glycoprotein IIb/IIIa. Heat shock proteins (Hsp) have been shown to be major antigenic determinants in some autoimmune diseases. Antibodies to Hsps have also been reported to be associated with a number of pathological states. METHODS: Using western blot, we measured the levels of the 60 kDa heat shock protein (Hsp60) and of the inducible 71 kDa member of the Hsp70 family (Hsp71) in lymphocytes and the presence of antibodies against these hsps in plasma of 29 pediatric patients with ITP before the treatment and in 6 other patients before and after treatment. RESULTS: Interestingly only one out of 29 patients showed detectable Hsp60 in lymphocytes while this heat shock protein was detected in the 30 control children. Hsp71 levels were slightly lower in lymphocytes of patients with ITP than in controls (1567.8 ± 753.2 via 1763.2 ± 641.8 integrated optical density (IOD) units). There was a small increase of Hsp71 after recovery from ITP. The titers of plasma antibodies against Hsp60 and Hsp71 were also examined. Antibodies against Hsp71 were more common in ITP patients (15/29) than in control children (5/30). The titer of anti-Hsp71 was also higher in children patients with ITP. The prevalence of ITP children with antibodies against Hsp71 (51.7%) was as high as those with antibodies against platelet membrane glycoproteins (58.3%). CONCLUSIONS: In summary, pediatric patients with ITP showed no detectable expression of Hsp60 in lymphocytes and a high prevalence of antibody against Hsp71 in plasma. These changes add to our understanding of the pathogenesis of ITP and may be important for the diagnosis, prognosis and treatment of ITP

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

OBJECTIVE: To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. DESIGN: Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. DATA SOURCES: PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. STUDY SELECTION: Studies that developed or validated a multivariable covid-19 related prediction model. DATA EXTRACTION: At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). RESULTS: 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. CONCLUSION: Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. SYSTEMATIC REVIEW REGISTRATION: Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. READERS' NOTE: This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity

Investigating attachment, caregiving, and mental health: a model of maternal-fetal relationships

Background Maternal-fetal relationships have been associated with psychosocial outcomes for women and children, but there has been a lack of conceptual clarity about the nature of the maternal relationship with the unborn child, and inconsistent findings assessing its predictors. We proposed and tested a model whereby maternal-fetal relationship quality was predicted by factors relating to the quality of the couple relationship and psychological health. We hypothesized that the contribution of individual differences in romantic attachment shown in past research would be mediated by romantic caregiving responsiveness, as maternal-fetal relationships reflect the beginnings of the caregiving system. Methods 258 women in pregnancy (13, 23, and 33-weeks gestation) completed online measures of attachment to partner, caregiving responsiveness to partner, mental health, and thoughts about their unborn baby. Structural equation modeling was used to test a model of maternal-fetal relationships. Results Maternal-fetal relationship quality was higher for women at 23-weeks than 13-weeks gestation. Women in first pregnancies had higher self-reported scores of psychological functioning and quality of maternal-fetal relationships than women in subsequent pregnancies. Structural equation models indicated that the quality of the maternal-fetal relationship was best predicted by romantic caregiving responsiveness to partner and women's own psychological health, and that the association between adult romantic attachment avoidance and maternal-fetal relationships was fully mediated by caregiving responsiveness to partner, even after controlling for other factors. These data support the hypothesis that maternal-fetal relationships better reflect the operation of the caregiving system than the care-seeking (i.e., attachment) system. Conclusions Models of maternal-fetal relationships and interventions with couples should consider the role of caregiving styles of mothers to partners and the relationship between expectant parents alongside other known predictors, particularly psychological health

Is spleen tyrosine kinase inhibition an effective therapy for patients with RA?

Despite the success of biologic therapeutic agents that target cytokines and lymphocytes, clinical needs remain unmet in the treatment of rheumatoid arthritis (RA). The development of small-molecule inhibitors that can block critical immune signal-transduction pathways are of particular interest as novel therapies for RA. Spleen tyrosine kinase (SYK) subserves the function of Fc receptors and the B-cell receptor; as such, it is attractive as a potential therapeutic target. Weinblatt and colleagues recently performed a proof-of-concept study, which demonstrated that inhibition of SYK reduced RA disease activity and levels of disease-relevant biomarkers. Dose-limiting adverse effects include diarrhea, neutropenia and hypertension, which result from both target-dependent and off-target effects. This novel study provides the first evidence that SYK could be a useful therapeutic target in RA