Expression of Robo4 in the fibrovascular membranes from patients with proliferative diabetic retinopathy and its role in RF/6A and RPE cells

Purpose: Robo4, a member of the roundabout (Robo) family, acts as a neuronal guidance receptor and plays some role in vasculogenesis and angiogenesis. This study investigated the effect of Robo4 on the formation of fibrovascular membranes (FVMs) from patients with proliferative diabetic retinopathy and its roles in choroid-retina endothelial (RF/6A) and human retinal pigment epithelial (RPE) cells. Methods: RT-PCR and immunohistochemistry were used to determine the levels of mRNA and the presence and distribution of Robo4 in FVMs. Small interfering RNA (siRNA) technology was used to knock down Robo4 expression and to study its effects on RF/6A and RPE cells in vitro. Cell proliferation, migration, spreading, cycling, and apoptosis were assessed with MTT assay, Boyden chamber assay, immunocytochemistry, and flow cytometry. Tube formation by RF/6A on Matrigel was also analyzed. Results: The level of Robo4 mRNA was high in FVMs. Robo4 was expressed in the vessels and fibrous-like tissue co-immunostained for CD31 and GFAP, respectively. Robo4 siRNA knockdown inhibited cell proliferation and migration. Tube formation by RF/6A cells was also disturbed. Under hypoxic conditions, more apoptotic cells were evident among the knockdown cells than among the control cells (p < 0.01). Conclusions: Robo4 may play a role in the formation of FVMs. Silencing the expression of Robo4 in RF/6A and RPE cells inhibited their proliferation and reduced their tolerance of hypoxic conditions, suggesting physiologic functions of Robo4 in the cells of the retina.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000267136400001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Biochemistry & Molecular BiologyOphthalmologySCI(E)PubMed15ARTICLE112-131057-10691

Automatic Data Transformation Using Large Language Model: An Experimental Study on Building Energy Data

Existing approaches to automatic data transformation are insufficient to meet the requirements in many real-world scenarios, such as the building sector. First, there is no convenient interface for domain experts to provide domain knowledge easily. Second, they require significant training data collection overheads. Third, the accuracy suffers from complicated schema changes. To bridge this gap, we present a novel approach that leverages the unique capabilities of large language models (LLMs) in coding, complex reasoning, and zero-shot learning to generate SQL code that transforms the source datasets into the target datasets. We demonstrate the viability of this approach by designing an LLM-based framework, termed SQLMorpher, which comprises a prompt generator that integrates the initial prompt with optional domain knowledge and historical patterns in external databases. It also implements an iterative prompt optimization mechanism that automatically improves the prompt based on flaw detection. The key contributions of this work include (1) pioneering an end-to-end LLM-based solution for data transformation, (2) developing a benchmark dataset of 105 real-world building energy data transformation problems, and (3) conducting an extensive empirical evaluation where our approach achieved 96% accuracy in all 105 problems. SQLMorpher demonstrates the effectiveness of utilizing LLMs in complex, domain-specific challenges, highlighting the potential of their potential to drive sustainable solutions.Comment: 10 pages, 7 figure