No abstract

Premature infants are vulnerable to infections, partly because of the low transplacental transfer of maternal antibodies. The present study investigated the placental transfer of maternal rubella-specific antibodies to full-term and preterm infants. The study group consisted of 133 healthy, native Israeli mothers and their 159 newborns. Of these, 69 were full-term infants (gestational age > 37 weeks) of 69 mothers, and 90 were preterm infants (gestational age < 35 weeks) of 64 mothers. Antibody titers against rubella were measured in maternal and umbilical cord blood samples by hemagglutination inhibition and microneutralization techniques. There was no significant difference in the level of protection and in geometrical mean titers by hemagglutination between the full-term and preterm groups. Conversely, significant differences in geometric mean titers of neutralizing antibodies were found between full-term and preterm infants, e.g., 65.9 and 39.8, respectively (P < 0.001). Very low birth weight preterm infants are at greater risk of rubella infection during the first year of life, due to the diminished transfer of neutralizing maternal antibodies. Therefore, earlier vaccination of this group may be beneficial.

Anomalies in multi-lepton final states at the Large Hadron Collider (LHC) have been reported in Refs. (von Buddenbrock et al., J Phys G 45(11):115003, arXiv:1711.07874 [hep-ph], 2018; Buddenbrock et al., JHEP 1910:157, arXiv:1901.05300 [hep-ph], 2019). These can be interpreted in terms of the production of a heavy boson, H, decaying into a standard model (SM) Higgs boson, h, and a singlet scalar, S, which is treated as a SM Higgs-like boson. This process would naturally affect the measurement of the Wh signal strength at the LHC, where h is produced in association with leptons and di-jets. Here, h would be produced with lower transverse momentum, $$p_{Th}$$ p Th , compared to SM processes. Corners of the phase-space are fixed according to the model parameters derived in Refs. (von Buddenbrock et al., J Phys G 45(11):115003, arXiv:1711.07874 [hep-ph], 2018; von Buddenbrock et al., Eur Phys J C 76(10):580, arXiv:1606.01674 [hep-ph], 2016) without additional tuning, thus nullifying potential look-else-where effects or selection biases. Provided that no stringent requirements are made on $$p_{Th}$$ p Th or related observables, the signal strength of Wh is $$\mu (Wh)=2.41 \pm 0.37$$ μ ( W h ) = 2.41 ± 0.37 . This corresponds to a deviation from the SM of $$3.8\sigma $$ 3.8 σ . This result further strengthens the need to measure with precision the SM Higgs boson couplings in $$e^+e^-$$ e + e - , and $$e^-p$$ e - p collisions, in addition to pp collisions.

COVID-19 is imposing massive health, social and economic costs. While many developed countries have started vaccinating, most African nations are waiting for vaccine stocks to be allocated and are using clinical public health (CPH) strategies to control the pandemic. The emergence of variants of concern (VOC), unequal access to the vaccine supply and locally specific logistical and vaccine delivery parameters, add complexity to national CPH strategies and amplify the urgent need for effective CPH policies. Big data and artificial intelligence machine learning techniques and collaborations can be instrumental in an accurate, timely, locally nuanced analysis of multiple data sources to inform CPH decision-making, vaccination strategies and their staged roll-out. The Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) has been established to develop and employ machine learning techniques to design CPH strategies in Africa, which requires ongoing collaboration, testing and development to maximize the equity and effectiveness of COVID-19-related CPH interventions.

Background COVID-19 is a virus which has lead to a global pandemic. Worldwide, more than 130 countries have imposed severe restrictions, which form part of a set of non-pharmaceutical interventions (NPI)s. We aimed to quantify the country-specific effects of these NPIs and compare them using the Oxford COVID-19 Government Response Tracker (OxCGRT) stringency index, p, as a measure of NPI stringency. Methods We developed a dual latent/observable Susceptible Infected Recovered Deaths (SIRD) model and applied it on each of 22 countries and 25 states in the US using publicly available data. The observable model parameters were extracted using kernel functions. The regression of the transmission rate, β, as a function of p in each locale was modeled through the interven-: medRxiv preprint tion leverage, α s , an initial transmission rate, β 0 and a typical adjustment time, b −1 r . Results The world average for the intervention leverage, α s = 0.01 (95% CI 0.0102 -0.0112) had an ensemble standard deviation of 0.0017 (95% C.I. 0.0014 -0.0021), strongly indicating a universal behavior. Discussion Our study indicates that removing NPIs too swiftly will result in the resurgence of the spread within one to two months, in alignment with the current WHO recommendations. Moreover, we have quantified and are able to predict the effect of various combinations of NPIs. There is a minimum NPI level, below which leads to resurgence of the outbreak (in the absence of pharmaceutical and clinical advances). For the epidemic to remain subcritical, the rate with which the intervention leverage α s increases should outpace that of the relaxation of NPIs.