Deposition potential of 0.003-10 mu m ambient particles in the humidified human respiratory tract : Contribution of new particle formation events in Beijing

Ultrafine particles (UFPs) usually explosive growth during new particle formation (NPF) events. However, the risk of exposure to UFPs on NPF days has been ignored due to the prevalence of mass-based air quality standards. In this study, the daily deposited doses, i.e., the daily deposited particle number dose (D-PNd), mass dose (D-PMd), and surface area dose (D-PSd), of ambient particles in the human respiratory tract in Beijing were evaluated based on the particle number size distribution (3 nm-10 mu m) from June 2018 to May 2019 utilizing a Multiple-Path Particle Dosimetry Model (MPPD) after the hygroscopic growth of particles in the respiratory tract had been accounted for. Our observations showed a high frequency (72.6%) of NPF on excellent air quality days, with daily mean PM2.5 concentrations less than 35 mu g m(-3). The daily D-PNd on excellent air quality days was com-parable with that on polluted days, although the D-PMd on excellent air quality days was as low as 15.6% of that on polluted days. The D-PNd on NPF days was similar to 1.3 times that on non-NPF days. The D-PNd in respiratory tract regions decreased in the order: tracheobronchial (TB) > pulmonary (PUL) > extrathoracic (ET) on NPF days, while it was PUL > TB > ET on non-NPF days. The number of deposited nucleation mode particles, which were deposited mainly in the TB region (45%), was 2 times higher on NPF days than that on non-NPF days. Our results demonstrated that the deposition potential due to UFPs in terms of particle number concentrations is high in Beijing regardless of the aerosol mass concentration. More toxicological studies related to UFPs on NPF days, especially those targeting tracheobronchial and pulmonary impairment, are required in the future.Peer reviewe

Transcription factor MEF2D regulates aberrant expression of ACSL3 and enhances sorafenib resistance by inhibiting ferroptosis in HCC

BackgroundSorafenib is a first-line treatment for hepatocellular carcinoma (HCC); however, acquired resistance often results in a poor prognosis, indicating a need for more effective therapies. Sorafenib induces cell death through an iron-dependent mechanism known as ferroptosis, which is closely associated with the onset and progression of HCC.MethodsThis study investigated the role of ACSL3 in sorafenib resistance and ferroptosis in HCC. The expression of ACSL3 was analyzed in HCC tissues and cell lines. Ferroptosis levels and cell viability were assessed in ACSL3-silenced HCC cells treated with sorafenib. The regulatory relationship between the transcription factor MEF2D and ACSL3 was evaluated using promoter binding assays and gene expression analysis.ResultsACSL3 was aberrantly expressed in HCC and promoted the progression of non-alcoholic fatty liver disease (NAFLD) to HCC. Elevated ACSL3 expression inhibited ferroptosis and enhanced resistance to sorafenib. The transcription factor MEF2D directly regulated the upregulation of ACSL3 expression. MEF2D bound to the promoter regions of ACSL3 to enhance its transcription and negatively regulate ferroptosis in HCC.ConclusionThis study demonstrated for the first time that MEF2D regulated ACSL3 expression and mediated sorafenib resistance by inhibiting ferroptosis in HCC, providing a potential therapeutic target for improving HCC outcomes

Continuous and comprehensive atmospheric observations in Beijing : a station to understand the complex urban atmospheric environment

Peer reviewe

DeePMD-kit v2: A software package for Deep Potential models

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments.Comment: 51 pages, 2 figure