Development Of A Liposomal Formulation For Brain Targeting

The blood-brain barrier (BBB), formed by the endothelial cells of the brain capillaries, inhibits the penetration of many therapeutic compounds into the brain. Liposomes, which have unique physicochemical characteristics, have been widely investigated for the drug delivery system across BBB. In the current study, FITC-dextran was used as a model drug that encapsulated in a liposomal formulation to investigated its ability to overcome the blood-brain barrier for targeted brain delivery of therapeutic agents. Here, non-targeted liposome (NT LPs) and RVG-modified liposome (RVG LPs) were prepared. The NT LPs and RVG LPs were about 97 and 101 nm in diameter with the zeta potential of -27.0 mV and -21.2 mV, respectively. In vitro study in mouse SH-SY5Y neuroblastoma cells and mixed glial cells demonstrated that the RVG LPs were taken up with enhanced efficiency comparing to the NT LPs. In vitro release study results indicated the sustained release of FITC-dextran from NT LPs. A preliminary pharmacokinetic study shoprolonged circulation time of FITC-dextran encapsulated in NT LPs compared to the free form. As expected, free FITC-dextran manifested no brain distribution. Further studies on the pharmacokinetics of RVG LPs are warranted, to establish the proof of concept for its application in brain-targeted drug delivery

An Empirical Analysis of On-demand Ride Sharing and Traffic Congestion

Sharing economy, which leverages information technology to re-distribute unused or underutilized assets to people who are willing to pay for the services, has received tremendous attention in the last few years. Its creative business model has disrupted many traditional industries (e.g., transportation, hotel) by fundamentally changing the mechanism to facilitate the matching of demand with supply in real time. In this research, we investigate how Uber, a peer-to-peer mobile ride-sharing platform, affects traffic congestion in the urban areas of the United States. Combining data from Uber and the Urban Mobility Report, we empirically examine whether and how the entry of Uber car services affect traffic congestion using a difference-in-difference framework. Findings from this research provide evidence on the potential effect of ride sharing services in the transportation industry, contributing to the understanding of the sharing economy and government policy decisions

An Empirical Analysis of the Impacts of the Sharing Economy Platforms on the U.S. Labor Market

Each generation of digital innovation has caused a dramatic change in the way people work. Sharing economy is the latest trend of digital innovation, and it has fundamentally changed the traditional business models. In this paper, we empirically examine the impacts of the sharing economy platforms (specifically, Uber) on the labor market in terms of labor force participation, unemployment rate, supply, and wage of low-skilled workers. Combining a data set of Uber entry time and several microdata sets, we utilize a difference-in-differences (DID) method to investigate whether the above measures before and after Uber entry are significantly different across the U.S. metropolitan areas. Our empirical findings show that sharing economy platforms such as Uber significantly decrease the unemployment rate and increase the labor force participation. We also find evidence of a shift in the supply of low skill workers and consequently a higher wage rate for such workers in the traditional industries

Efficient Task Offloading Algorithm for Digital Twin in Edge/Cloud Computing Environment

In the era of Internet of Things (IoT), Digital Twin (DT) is envisioned to empower various areas as a bridge between physical objects and the digital world. Through virtualization and simulation techniques, multiple functions can be achieved by leveraging computing resources. In this process, Mobile Cloud Computing (MCC) and Mobile Edge Computing (MEC) have become two of the key factors to achieve real-time feedback. However, current works only considered edge servers or cloud servers in the DT system models. Besides, The models ignore the DT with not only one data resource. In this paper, we propose a new DT system model considering a heterogeneous MEC/MCC environment. Each DT in the model is maintained in one of the servers via multiple data collection devices. The offloading decision-making problem is also considered and a new offloading scheme is proposed based on Distributed Deep Learning (DDL). Simulation results demonstrate that our proposed algorithm can effectively and efficiently decrease the system's average latency and energy consumption. Significant improvement is achieved compared with the baselines under the dynamic environment of DTs

Exploring Chain-of-Thought Style Prompting for Text-to-SQL

In-context learning with large language models (LLMs) has recently caught increasing attention due to its superior few-shot performance on various tasks. However, its performance on text-to-SQL parsing still has much room for improvement. In this paper, we hypothesize that a crucial aspect of LLMs to improve for text-to-SQL parsing is their multi-step reasoning ability. Thus, we systematically study how to enhance LLMs' reasoning ability through chain of thought (CoT) style prompting, including the original chain-of-thought prompting (Wei et al., 2022b) and least-to-most prompting (Zhou et al., 2023). Our experiments demonstrate that iterative prompting as in Zhou et al. (2023) may be unnecessary for text-to-SQL parsing, and using detailed reasoning steps tends to have more error propagation issues. Based on these findings, we propose a new CoT-style prompting method for text-to-SQL parsing. It brings 5.2 and 6.5 point absolute gains on the Spider development set and the Spider Realistic set, respectively, compared to the standard prompting method without reasoning steps; 2.4 and 1.5 point absolute gains, compared to the least-to-most prompting method.Comment: EMNLP 2023 main; long pape

Ghrelin contributes to protection of hepatocellular injury induced by ischaemia/reperfusion

Background & Aims Ghrelin, a gut hormone with pleiotropic effects, may act as a protective signal in parenchymal cells. We investigated the protective effects of ghrelin on hepatocytes after ischaemia/reperfusion (I/R). Methods Hepatic injury was assessed by measurement of plasma alanine aminotransferase ( ALT ) and lactate dehydrogenase ( LDH ), histological analysis, and TUNEL assay. Effects of exogenous ghrelin and ghrelin receptor gene deletion on I/R induced injury of liver were evaluated. Results Ischaemia/reperfusion induced a profound injury to hepatocytes. This was accompanied by elevations in plasma ALT and LDH . Pretreatment with ghrelin significantly reduced elevations in plasma ALT and LDH , and attenuated tissue damage induced by hepatic I/R in mice. Hepatic injury induced by I/R was more pronounced in ghrelin receptor gene null mice. Ghrelin administration blocked the up‐regulation of AMP ‐activated protein kinase ( AMPK ) activity induced by hepatic I/R. Conclusions This study demonstrates that ghrelin contributes to the cytoprotection during hepatic I/R.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106759/1/liv12286.pd