Lung adenocarcinoma promotion by air pollutants

A complete understanding of how exposure to environmental substances promotes cancer formation is lacking. More than 70 years ago, tumorigenesis was proposed to occur in a two-step process: an initiating step that induces mutations in healthy cells, followed by a promoter step that triggers cancer development1. Here we propose that environmental particulate matter measuring ≤2.5 μm (PM2.5), known to be associated with lung cancer risk, promotes lung cancer by acting on cells that harbour pre-existing oncogenic mutations in healthy lung tissue. Focusing on EGFR-driven lung cancer, which is more common in never-smokers or light smokers, we found a significant association between PM2.5 levels and the incidence of lung cancer for 32,957 EGFR-driven lung cancer cases in four within-country cohorts. Functional mouse models revealed that air pollutants cause an influx of macrophages into the lung and release of interleukin-1β. This process results in a progenitor-like cell state within EGFR mutant lung alveolar type II epithelial cells that fuels tumorigenesis. Ultradeep mutational profiling of histologically normal lung tissue from 295 individuals across 3 clinical cohorts revealed oncogenic EGFR and KRAS driver mutations in 18% and 53% of healthy tissue samples, respectively. These findings collectively support a tumour-promoting role for PM2.5 air pollutants and provide impetus for public health policy initiatives to address air pollution to reduce disease burden

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Determining the preprocessing clustering algorithm in radial basis function neural network

Radial Basis Function Networks have been widely used to approximate and classify data. In the common model for radial basis function, the centres and spreads are fixed while the weights are adjusted until it manages to approximate the data. There exist some problems in finding the best centres for the hidden layer of Radial Basis Function. Even though some clustering methods like K-means or K-median are used in finding the centres, there are no consistent results that show which one is better. The main objective in this study is to determine the better method to be used to find the centres in the Radial Basis Functional Link Nets for data classification. Three types of method used in this study to find the centres include random selections, K-means clustering algorithm and also K-median clustering algorithm. The effects of K-means and K-median clustering algorithms on centres selection for Radial Basis Functional Link Nets in terms of accuracy and speed are shown in this study. To determine which clustering method is better, we calculate the preliminary Mardia’s skewness. Therefore the skewness of the data is calculated to choose between the K-means or K-median clustering method in finding the centre of Radial Basis Function Network. Besides, the initial selection criterion using Mardia’s skewness is able to show the improvement of efficiency in data classification. We use two sets of real data to demonstrate our resul

Calculation of effectiveness factors for spherical shells using shooting technique

Journal of Environmental Engineering1176859-864JOEE

Reliability of domestic-waste biofilm reactors

10.1061/(ASCE)0733-9372(1995)121:11(785)Journal of Environmental Engineering12111785-790JOEE

Free vibration analysis of cylindrical liquid storage tanks

International Journal of Mechanical Sciences24147-59IMSC

Phase-selective synthesis of copper sulfide nanocrystals

10.1021/cm061686iChemistry of Materials18266170-6177CMAT

Biofeedback device for patients on axillary crutches

Archives of Physical Medicine and Rehabilitation708644-647APMH

Chronic impairment of prospective memory after mild traumatic brain injury

10.1089/neu.2009.1074Journal of Neurotrauma27177-83JNEU