University City Science Center

The care of patients with cholestatic liver diseases such as primary sclerosing cholangitis (PSC) is challenging, partly due to the lack of knowledge of disease pathogenesis and suitable in vitro models for disease modeling and drug screening. Although the pathogenesis of cholestatic liver diseases like PSC remains unknown, the importance of the vascular-biliary interface is clear. Cholangiocyte injury not only impairs barrier function such that bile leaks and damages periductal tissue, but also activates cholangiocytes to secret pro-inflammatory and pro-fibrogenic mediators to stimulate immune cells and mesenchymal cells, ultimately causing damage to the liver. Here we describe a detailed protocol for fabricating a human vascularized bile duct-on-a-chip (VBDOC) that consists of a vascular channel, biliary channel, and neighboring mesenchymal cells in a collagen gel that models the vascular-biliary interface structurally and functionally in three dimensions. This device is notable in maintaining cholangiocyte polarity and barrier function, recapitulating physiological functions and responses of the large bile ducts, and enabling manipulation of components of the mechanical microenvironment such as matrix stiffness and shear flow in the lumens. This practical workflow could help researchers manufacture the VBDOC in their own labs and apply it to studies of various cholestatic liver diseases.

A major concern of COVID-19 is immune escape. While T cells are implicated in protection against severe disease, direct evaluation of their capacity to specifically target SARS-CoV-2 variants remains limited. We aim to narrow this gap by profiling the ability of CD8+ T cells to recognize SARS-CoV-2 mutations after vaccination and observe a high degree of phenotype and repertoire diversity. To better understand the breadth of antigen repertoire coverage, we interrogate the ability of SARS-CoV-2 specific TCRs to bind all point mutations of the immunodominant HLA-A2:S269-277 epitope. While mutation space coverage is surprisingly vast, specific substitutions are not recognized by the vaccine-elicited repertoire. Structural analyses reveal a TCR sequence-based antigen recognition limitation intrinsic to vaccination. Further investigation reveals that this repertoire ‘hole’ is not present in the naive repertoire, suggesting future immune escape through these potential escape mutations could be relieved by modifications to vaccines.

As extreme heat events intensify due to climate change and urbanization, cities face increasing challenges in mitigating outdoor heat stress. While traditional physical models such as SOLWEIG and ENVI-met provide detailed assessments of human-perceived heat exposure, their computational demands limit scalability for city-wide planning. In this study, we propose GSM-UTCI, a multimodal deep learning framework designed to predict both average and hourly Universal Thermal Climate Index (UTCI) at 1-meter hyperlocal resolution across daytime hours. The model fuses surface morphology (nDSM), high-resolution land cover data, and hourly meteorological conditions using a feature-wise linear modulation (FiLM) architecture that dynamically conditions spatial features on atmospheric context. Trained on SOLWEIG-derived UTCI maps, GSM-UTCI effectively emulates the physical model, achieving an R 2 of 0.9151 and a mean absolute error of 0.41 °C for daytime average UTCI prediction.Across 11 daytime hours (8 a.m.–6 p.m.), it maintains robust hourly performance ( R 2 = 0.8044; MAE = 0.64 °C). Additionally, GSM-UTCI reduces inference time from days to under five minutes for an entire city, enabling rapid city-wide simulations. To demonstrate its planning relevance, we apply GSM-UTCI to simulate systematic landscape transformation scenarios in Philadelphia. Results show spatially heterogeneous but consistently strong cooling effects. Notably, converting impervious surfaces to tree canopy yields the most significant public health benefit, reducing the number of residents exposed to strong heat stress (UTCI > 32 °C) by over 374,000, alongside an average cooling of 4.18 °C. Tract-level analysis further reveals strong alignment between thermal reduction potential and land cover proportions. These findings highlight the value of the GSM-UTCI framework as a scalable decision-support tool for climate adaptation in Philadelphia, while its extension to other cities remains a key direction for future validation.

Abstract Objective Fluid overload (FO) after pediatric septic shock resuscitation increases the risk of secondary organ failure and long-term morbidity. We hypothesized that early hemodynamic point-of-care ultrasound (hPOCUS) use in pediatric septic shock would be associated with decreased FO. Methods Retrospective, observational study between 2015 and 2018 in a large academic Pediatric Intensive Care Unit (PICU) of children < 18 years receiving ≥ 40 mL/kg of bolus fluids or vasoactive infusion for treatment of septic shock. %FO and severe FO (%FO ≥ 15%) at 72 h after shock onset were compared between children with hPOCUS exposure < 6 h after shock onset and those without. %FO was calculated: [(fluid in – out)/weight on PICU admission]*100. The association between hPOCUS < 6 h after septic shock onset and %FO and severe FO were evaluated. Results Of 591 children included, 115 (19.4%) had hPOCUS within 6 h. Children with early hPOCUS vs. without had higher PIM-3; median 4.3, IQR 2.9–9.4 vs. 3.2, IQR 0.9–5.7, p = 0.0012. %FO was not significantly different in the early hPOCUS group, median difference -1.6%, 95% CI -3.2 to 0.03, p = 0.06. After controlling for confounders, %FO was significantly lower in the early hPOCUS group, ß-coefficient= -2.76, 95% CI -4.7 to -0.6, p = 0.012. Those with early hPOCUS had lower occurrence of severe FO 11.3% vs. 22.9%, p = 0.006; adjusted OR 0.41, 95% CI 0.22 to 0.76, p = 0.005. Conclusions Early hPOCUS assessment during septic shock resuscitation was independently associated with decreased FO. Prospective research is needed to optimize hPOCUS use in pediatric septic shock management.

The brain integrates multiple scales of description, from the level of cells and molecules to large-scale networks and behavior. Understanding relationships across these scales may be fundamental to advancing understanding of brain function in health and disease. Recent neuroimaging research has shown that functional brain alterations that are associated with schizophrenia spectrum disorders (SSDs) are already present in young adults at clinical high risk for psychosis (CHR-P), but the cellular and molecular determinants of these alterations remain unclear. Here, we used regional cerebral blood flow (rCBF) data from 425 individuals (122 with an SSD compared with 116 healthy control participants [HCs] and 129 individuals at CHR-P compared with 58 HCs) and applied a novel pipeline to integrate brainwide rCBF case-control maps with publicly available transcriptomic data (17,205 gene maps) and neurotransmitter atlases (19 maps) from 1074 healthy volunteers. We identified significant correlations between astrocyte, oligodendrocyte, oligodendrocyte precursor cell, and vascular leptomeningeal cell gene modules for both SSD and CHR-P rCBF phenotypes. Additionally, endothelial cell genes were correlated in SSD, and microglia in CHR-P. Receptor distribution significantly predicted case-control rCBF differences, with dominance analysis highlighting dopamine (D1, D2, dopamine transporter), acetylcholine (VAChT, M1), gamma-aminobutyric acid A (GABAA), and glutamate (NMDA) receptors as key predictors for SSD (R2adjusted = 0.58, false discovery rate [FDR]-corrected p < .05) and CHR-P (R2adjusted = 0.6, pFDR < .05) rCBF phenotypes. These associations were primarily localized in subcortical regions and implicate cell types involved in stress response and inflammation, alongside specific neuroreceptor systems, in shared and distinct rCBF phenotypes in psychosis. Our findings underscore the value of integrating multiscale data as a promising hypothesis-generating approach toward decoding biological pathways involved in neuroimaging-based psychosis phenotypes, potentially guiding novel interventions.

Transcription factors (TFs) are core components of the regulatory toolkits that control gene expression. The sophistication of these regulatory toolkits dramatically increased during Eukaryotic evolution, accomplished in part by the duplication of existing TFs and the subsequent repurposing of these new paralogs. This process, termed functional divergence, drove the evolution of increasingly elaborate programs of gene expression and, in turn, cellular and organismal complexity. Mechanisms generating functional divergence of TF paralogs are thus of significant interest. Here, we review the numerous mechanisms that can lead to divergence of TF paralogs, drawing on studies from across Eukaryota but with a special emphasis on the plant kingdom. We end by placing these mechanisms back into a broader evolutionary context.