Identify treatment effect patterns for personalised decisions

In personalised decision making, evidence is required to determine suitable actions for individuals. Such evidence can be obtained by identifying treatment effect heterogeneity in different subgroups of the population. In this paper, we design a new type of pattern, treatment effect pattern to represent and discover treatment effect heterogeneity from data for determining whether a treatment will work for an individual or not. Our purpose is to use the computational power to find the most specific and relevant conditions for individuals with respect to a treatment or an action to assist with personalised decision making. Most existing work on identifying treatment effect heterogeneity takes a top-down or partitioning based approach to search for subgroups with heterogeneous treatment effects. We propose a bottom-up generalisation algorithm to obtain the most specific patterns that fit individual circumstances the best for personalised decision making. For the generalisation, we follow a consistency driven strategy to maintain inner-group homogeneity and inter-group heterogeneity of treatment effects. We also employ graphical causal modelling technique to identify adjustment variables for reliable treatment effect pattern discovery. Our method can find the treatment effect patterns reliably as validated by the experiments. The method is faster than the two existing machine learning methods for heterogeneous treatment effect identification and it produces subgroups with higher inner-group treatment effect homogeneity

Transcriptional activation of follistatin by Nrf2 protects pulmonary epithelial cells against silica nanoparticle-induced oxidative stress

Silica nanoparticles (SiO2 NPs) cause oxidative stress in respiratory system. Meanwhile, human cells launch adaptive responses to overcome SiO2 NP toxicity. However, besides a few examples, the regulation of SiO2 NP-responsive proteins and their functions in SiO2 NP response remain largely unknown. In this study, we demonstrated that SiO2 NP induced the expression of follistatin (FST), a stress responsive gene, in mouse lung tissue as well as in human lung epithelial cells (A549). The levels of Ac-H3(K9/18) and H3K4me2, two active gene markers, at FST promoter region were significantly increased during SiO2 NP treatment. The induction of FST transcription was mediated by the nuclear factor erythroid 2-related factor 2 (Nrf2), as evidenced by the decreased FST expression in Nrf2-deficient cells and the direct binding of Nrf2 to FST promoter region. Down-regulation of FST promoted SiO2 NP-induced apoptosis both in cultured cells and in mouse lung tissue. Furthermore, knockdown of FST increased while overexpression of FST decreased the expression level of NADPH oxidase 1 (NOX1) and NOX5 as well as the production of cellular reactive oxygen species (ROS). Taken together, these findings demonstrated a protective role of FST in SiO2 NP-induced oxidative stress and shed light on the interaction between SiO2 NPs and biological systems

Aggregation-Induced Emission (AIE), Life and Health

Light has profoundly impacted modern medicine and healthcare, with numerous luminescent agents and imaging techniques currently being used to assess health and treat diseases. As an emerging concept in luminescence, aggregation-induced emission (AIE) has shown great potential in biological applications due to its advantages in terms of brightness, biocompatibility, photostability, and positive correlation with concentration. This review provides a comprehensive summary of AIE luminogens applied in imaging of biological structure and dynamic physiological processes, disease diagnosis and treatment, and detection and monitoring of specific analytes, followed by representative works. Discussions on critical issues and perspectives on future directions are also included. This review aims to stimulate the interest of researchers from different fields, including chemistry, biology, materials science, medicine, etc., thus promoting the development of AIE in the fields of life and health

Failure Analysis of Hydraulic Expanding Assembled Camshafts Using BP Neural Network and Failure Tree Theory

Due to the complex and changeable working environment of assembled camshafts using tube hydroforming (THF) technology, the manifestations of failure, the causes of failure and the preventive measures for these failures are a major concern. Therefore, in view of this new connection technology for assembled camshafts, it is important to put forward a prediction and evaluation method of failure for hydraulic expanding assembled camshafts. In this study, an isometric-trilateral profile cam was used to complete the hydroforming connection with the hollow shaft (tube) under different hydraulic pressures. Orthogonal torsion experiment and laser measurement experiment were performed. Finite element analysis was carried out using ABAQUS 6.14 software, and relevant research data were obtained. A more accurate BP neural network model was constructed to predict the main failure factors of assembled camshafts. The failure manifestations of assembled camshafts are displayed by the experiment from the microscopic perspective. The causes of failure are analyzed by using the minimum cut set in the failure Tree (FT) theory. The effect of basic causes on the subsystems is analyzed, and the weight distribution of the main events in the FT is given. Finally, the specific measures to prevent failure are proposed from a macro perspective. The research is of great significance to study the failures of assembled camshafts in service to further the production, manufacturing, failure prevention, faults monitoring and performance improvement of assembled camshafts in the engine industry