Georgios Daskalakis

Preeclampsia is a leading cause of maternal and perinatal morbidity and mortality, and effective prevention depends on the early identification of women at risk. Because the disorder is fundamentally a state of systemic endothelial dysfunction and microvascular maladaptation, the retina has long attracted interest as a candidate in vivo biomarker. It is the only vascular bed that can be visualised directly, repeatedly and non-invasively. This review appraises retinal assessment across four modality classes, classifying each study by the timing of imaging relative to diagnosis to distinguish early prediction from diagnostic discrimination, severity assessment and long-term follow-up. Qualitative fundoscopy reliably grades hypertensive retinopathy but is subjective and insensitive in mild disease. Quantitative retinal vessel analysis links narrower retinal vessels with the hypertensive disorders of pregnancy; in one longitudinal cohort, pressure-corrected arteriolar and venular calibre was reduced from 13 weeks in women who later developed preeclampsia, although effects were modest. Optical coherence tomography (OCT) and OCT angiography studies, almost all performed after diagnosis, report choroidal and microvascular changes of inconsistent direction and, therefore, inform diagnosis and severity assessment rather than prediction. Ophthalmic artery Doppler may offer adjunctive discrimination. Two single-centre deep learning models applied to early-pregnancy fundus photographs reported areas under the curve of 0.87–0.88 for preeclampsia, but without external validation, calibration or head-to-head comparison with established first-trimester screening. Retinal imaging is, therefore, a mechanistically grounded but unproven avenue for preeclampsia prediction. Prospective pre-diagnostic cohorts, standardised acquisition, adjustment for blood pressure and other confounders, and externally validated, calibrated models tested for incremental value over established screening are required. Only then can retinal biomarkers, particularly artificial intelligence-assisted analysis with portable fundus cameras, be adopted as a scalable screening adjunct, including in low-resource settings.

A first observation of the rare decay η → μ + μ − e + e − is reported by the CMS Collaboration at the CERN LHC. The result is based on a proton-proton collision data sample at s = 13.6 TeV corresponding to an integrated luminosity of 38.0 fb − 1 , acquired in 2022 using a high-rate dimuon trigger. Using the η → μ + μ − γ decay channel for normalization, the branching fraction is measured to be B ( η → μ + μ − e + e − ) = ( 2.4 ± 0.8 ) × 10 − 6 , with the uncertainty including statistical and systematic sources as well as the B ( η → μ + μ − γ ) uncertainty. This result is about a factor of 3 smaller than the existing limit and is consistent with recent theoretical predictions.

The first evidence of electroweak (EW) production of pairs of Z bosons in association with two jets (jj) in the final state Z Z jj → ℓ ℓ ν ν jj , where ℓ = e , μ , is reported by the CMS experiment. The analysis is based on a data sample of proton-proton ( p p ) collisions at s = 13 TeV , corresponding to an integrated luminosity of 138 fb − 1 . Events are selected by requiring exactly two leptons of same flavor and opposite charge, large missing transverse momentum, and two jets with a large rapidity separation and large invariant mass. The EW production cross section in a fiducial volume is σ EW ( p p → Z Z jj → ℓ ℓ ν ν jj ) = 0.3 7 − 0.12 + 0.14 ( stat ) − 0.06 + 0.06 ( syst ) fb , in agreement with the standard model prediction of 0.39 ± 0.06 fb . The observed (expected) significance of the signal is 3.1 (2.8) standard deviations. Limits on anomalous quartic gauge couplings are derived in terms of dimension-8 effective field theory operators. A combination with the previously reported result from the Z Z decay channel with four charged leptons yields an observed (expected) significance of 5.0 (4.5) standard deviations for the EW production of Z boson pairs.

A bstract: This paper presents a search for beyond the standard model physics using single-lepton events with a high multiplicity of jets, including those identified as bottom quark jets, without a requirement on missing transverse momentum. The analysis is based on proton-proton collision data collected with the CMS detector at the CERN LHC at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 138 fb ^{− 1} . This search is sensitive to R -parity violating supersymmetry models, where supersymmetric particles can decay into standard-model particles through interactions that violate baryon number conservation. In particular, the signal model considered is gluino pair production, where each gluino decays into top, bottom, and strange quarks. The sum of large-radius jet masses is used to distinguish the signal from background, as it effectively captures the features of high jet multiplicity and high interaction energy. No significant excess of data over the background predictions is observed. Gluinos in this model have been excluded for masses below 1890 GeV at 95% confidence level.

A bstract: A novel deep neural network classifier, a “particle transformer” (P ar T), is introduced for the identification of highly Lorentz-boosted resonances reconstructed as single, multipronged jets in measurements and searches performed by the CMS Collaboration at the CERN LHC. Based on a self-attention mechanism that allows the model to weigh the importance of different particles, P ar T is trained on a wide variety of topologies, notably demonstrating strong performance for the first time on jets originating from boosted Higgs boson decays to W bosons. The P ar T algorithm achieves a tagging efficiency of more than 50% for such jets at a background efficiency of 1%, while maintaining decorrelation from the jet mass. A calibration is performed in proton-proton collision data collected by CMS at a center-of-mass energy of 13 TeV, with a data set corresponding to a total luminosity of 138 fb ^{− 1} . Data-to-simulation selection efficiency scale factors are measured to be in the 0.9–1.0 range, with relative uncertainties between 7 and 23%. The tagging capability of P ar T enhances the sensitivity of standard model measurements and searches for beyond-the-standard-model resonances decaying to hadronic diboson systems.