INTRODUCTION: Follicular helper T cells are essential for helping in the maturation of B cells and the production of neutralizing antibodies (NAbs) during primary viral infections. However, their role during recall responses is unclear. Here, we used hepatitis C virus (HCV) reinfection in humans as a model to study the recall collaborative interaction between circulating CD4 T follicular helper cells (cTfh) and memory B cells (MBCs) leading to the generation of NAbs.

METHODS: We evaluated this interaction longitudinally in subjects who have spontaneously resolved primary HCV infection during a subsequent reinfection episode that resulted in either another spontaneous resolution (SR/SR, = 14) or chronic infection (SR/CI, = 8).

RESULTS: Both groups exhibited virus-specific memory T cells that expanded upon reinfection. However, early expansion of activated cTfh (CD4CXCR5PD-1ICOSFoxP3) occurred in SR/SR only. The frequency of activated cTfh negatively correlated with time post-infection. Concomitantly, NAbs and HCV-specific MBCs (CD19CD27IgME2-Tet) peaked during the early acute phase in SR/SR but not in SR/CI. Finally, the frequency of the activated cTfh1 (CXCR3CCR6) subset correlated with the neutralization breadth and potency of NAbs.

CONCLUSION: These results underscore a key role for early activation of cTfh1 cells in helping antigen-specific B cells to produce NAbs that mediate the clearance of HCV reinfection.

[This corrects the article DOI: 10.1007/s12639-023-01637-z.].

BACKGROUND: Children with sickle cell disease (SCD) who are chronically transfused often, require iron chelation therapy. There are limited data that allow for comparison of the efficacy and safety of the iron chelator deferiprone versus deferoxamine in children with SCD.

METHODS: This post hoc analysis of the phase 3b/4, randomized, open-label FIRST (Ferriprox in Patients with IRon Overload in Sickle Cell Disease Trial) study (NCT02041299) included patients 17 years and younger with SCD or other anemias receiving deferiprone or deferoxamine.

RESULTS: Overall, 142 patients were evaluated; mean ages were 10.5 and 11.7 years in the deferiprone and deferoxamine groups, respectively. At 12 months: mean change from baseline in liver iron concentration was -3.3 mg/g dry weight (dw) with deferiprone and -3.4 mg/g dw with deferoxamine (p = .8216); relative mean change (coefficient of variation %) in log cardiac T2* magnetic resonance imaging was 1.02 (21.8%) with deferiprone and 0.95 (19.5%) with deferoxamine (p = .0717); and the mean (standard error) change in serum ferritin levels was -133.0 (200.3) μg/L with deferiprone and -467.1 (244.1) μg/L with deferoxamine (p = .2924). The most common deferiprone-related adverse events (AEs) were upper abdominal pain (20.2%), vomiting (13.8%), pyrexia (9.6%), decreased neutrophil count (9.6%), increased alanine aminotransferase (ALT; 9.6%), and increased aspartate aminotransferase (AST; 9.6%). All cases of increased ALT, increased AST, and neutropenia resolved, most without intervention.

CONCLUSIONS: This post hoc analysis of pediatric patients from FIRST corroborated previous findings in adults that deferiprone is comparable to deferoxamine in reducing iron overload. No new safety concerns were observed. Deferiprone is an oral chelation option that could improve adherence and outcomes in children.

Estimating smallholder crop yields robustly and timely is crucial for improving agronomic practices, determining yield gaps, guiding investment, and policymaking to ensure food security. However, there is poor estimation of yield for most smallholders due to lack of technology, and field scale data, particularly in Egypt. Automated machine learning (AutoML) can be used to automate the machine learning workflow, including automatic training and optimization of multiple models within a user-specified time frame, but it has less attention so far. Here, we combined extensive field survey yield across wheat cultivated area in Egypt with diverse dataset of remote sensing, soil, and weather to predict field-level wheat yield using 22 Ml models in AutoML. The models showed robust accuracies for yield predictions, recording Willmott degree of agreement, (d > 0.80) with higher accuracy when super learner (stacked ensemble) was used (R2 = 0.51, d = 0.82). The trained AutoML was deployed to predict yield using remote sensing (RS) vegetative indices (VIs), demonstrating a good correlation with actual yield (R2 = 0.7). This is very important since it is considered a low-cost tool and could be used to explore early yield predictions. Since climate change has negative impacts on agricultural production and food security with some uncertainties, AutoML was deployed to predict wheat yield under recent climate scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6). These scenarios included single downscaled General Circulation Model (GCM) as CanESM5 and two shared socioeconomic pathways (SSPs) as SSP2-4.5and SSP5-8.5during the mid-term period (2050). The stacked ensemble model displayed declines in yield of 21% and 5% under SSP5-8.5 and SSP2-4.5 respectively during mid-century, with higher uncertainty under the highest emission scenario (SSP5-8.5). The developed approach could be used as a rapid, accurate and low-cost method to predict yield for stakeholder farms all over the world where ground data is scarce.

Tamoxifen (TAM) is a key player in estrogen receptor-positive (ER+) breast cancer (BC); however, ∼30% of patients experience relapse and a lower survival rate due to TAM resistance. TAM resistance was related to the over expression of SOX-2 gene, which is regulated by the E2F3 transcription factor in the Wnt signaling pathway. It was suggested that SOX-2 overexpression was suppressed by dexamethasone (DEX), a glucocorticoid commonly prescribed to BC patients. The aim of the present study is to explore the effect of combining DEX and TAM on the inhibition of TAM-resistant LCC-2 cells (TAMR-1) through modulating the E2F3/SOX-2-mediated Wnt signaling pathway. The effect of the combination therapy on MCF-7 and TAMR-1 cell viability was assessed. Drug interactions were analyzed using CompuSyn and SynergyFinder softwares. Cell cycle distribution, apoptotic protein expression, gene expression levels of SOX-2 and E2F3, and cell migration were also assessed. Combining DEX with TAM led to synergistic inhibition of TAMR-1 cell proliferation and migration, induced apoptosis, reduced SOX-2 and E2F3 expression and was also associated with S and G2-M phase arrest. Therefore, combining DEX with TAM may present an effective therapeutic option to overcome TAM resistance, by targeting the E2F3/SOX-2/Wnt signaling pathway, in addition to its anti-inflammatory effect.