Outer Retinal Tubulation in Geographic Atrophy.

BACKGROUND AND OBJECTIVE: Outer retinal tubulation (ORT) is observed on optical coherence tomography images from patients with geographic atrophy (GA), but its clinical implications are unclear. The objective of this study was to investigate the prevalence of ORT and its association with GA lesion growth rates. MATERIALS AND METHODS: This post hoc longitudinal analysis assessed 62 eyes randomized to sham treatment in the phase 2 FILLY trial. ORT prevalence was estimated at baseline, month 12, and month 18 and change in GA lesion growth from baseline to month 18 was calculated. RESULTS: ORT prevalence rates were 24%, 43%, and 43% at baseline, month 12, and month 18, respectively. Slower mean GA lesion growth was observed in eyes with ORT present at baseline in the overall population as well as the subfoveal and nonsubfoveal GA subgroups. CONCLUSION: ORT presence may indicate a slower-growing GA lesion phenotype, independent of foveal involvement. [Ophthalmic Surg Lasers Imaging Retina 2024;55:XX-XX.].

Loss of TXNIP enhances peritoneal metastasis and can be abrogated by dual TORC1/2 inhibition.

Peritoneal metastasis (PM) is a debilitating consequence of multiple cancers. As cancer cells lose tonic signaling related to attachment dependence, critical morphologic shifts result in alteration of the transcriptome. Identifying key genes associated with this transformation may lead to targeted therapies for this devastating complication. TC71, CHLA9, PANC1, YOU and HEYA8 cell lines were grown as tumor spheroids in polyHEMA coated plates. Temporal profiling of transcriptomic alterations over 72 hrs was used to develop a comprehensive PM model. We identified transcriptomic outliers using Gaussian mixtures model clustering to identify drivers of spheroid formation. Outliers were validated in The Cancer Genome Atlas (TCGA) and an ovarian tissue microarray (TMA) and by modulation in ovarian cancer models in vitro and in peritoneal xenograft models. Outlier analysis of PM genes identified the gene TXNIP and the TORC signaling as central to PM. Ovarian cancer spheroids isolated from patient ascites had significantly higher TXNIP than their attached counterparts (p = 0.047). TXNIP levels predicted progression-free (log-rank p = 0.026) survival in stage 1/2 ovarian cancer and overall survival (log rank p = 0.047) in stage 3/4 ovarian cancer. In vitro, TXNIP silencing was associated with increased mTOR signaling and enhanced spheroid development which could be overcome by TAK228, a TORC1/2 inhibitor. Similarly, in vivo peritoneal xenograft models of carcinomatosis were prevented by TAK228. PM is driven by TXNIP-associated TORC1/2 signaling. This work provides the first evidence that TORC1/2 inhibition may prevent PM.

The large graph limit of a stochastic epidemic model on a dynamic multilayer network.

We consider a Markovian SIR-type (Susceptible → Infected → Recovered) stochastic epidemic process with multiple modes of transmission on a contact network. The network is given by a random graph following a multilayer configuration model where edges in different layers correspond to potentially infectious contacts of different types. We assume that the graph structure evolves in response to the epidemic via activation or deactivation of edges of infectious nodes. We derive a large graph limit theorem that gives a system of ordinary differential equations (ODEs) describing the evolution of quantities of interest, such as the proportions of infected and susceptible vertices, as the number of nodes tends to infinity. Analysis of the limiting system elucidates how the coupling of edge activation and deactivation to infection status affects disease dynamics, as illustrated by a two-layer network example with edge types corresponding to community and healthcare contacts. Our theorem extends some earlier results describing the deterministic limit of stochastic SIR processes on static, single-layer configuration model graphs. We also describe precisely the conditions for equivalence between our limiting ODEs and the systems obtained via pair approximation, which are widely used in the epidemiological and ecological literature to approximate disease dynamics on networks. The flexible modeling framework and asymptotic results have potential application to many disease settings including Ebola dynamics in West Africa, which was the original motivation for this study.

Network-based analysis of a small Ebola outbreak.

We present a method for estimating epidemic parameters in network-based stochastic epidemic models when the total number of infections is assumed to be small. We illustrate the method by reanalyzing the data from the 2014 Democratic Republic of the Congo (DRC) Ebola outbreak described in Maganga et al. (2014).