Risk Assessment

The impacts of air pollution on public health have become a great concern worldwide. Ambient particulate matter (PM) is a major air pollution that comprises a heterogeneous mixture of different particle sizes and chemical components. The chemical composition and physicochemical properties of PM change with space and time, which may cause different impairments. However, the mechanisms of the adverse effects of PM on various systems have not been fully elucidated and systematically integrated. The Adverse Outcome Pathway (AOP) framework was used to comprehensively illustrate the molecular mechanism of adverse effects of PM and its components, so as to clarify the causal mechanistic relationships of PM-triggered toxicity on various systems. The main conclusions and new insights of the correlation between public health and PM were discussed, especially at low concentrations, which points out the direction for further research in the future. With the deepening of the study on its toxicity mechanism, it was found that PM can still induce adverse health effects with low-dose exposure. And the recommended Air Quality Guideline level of PM2.5 was adjusted to 5 μg/m3 by World Health Organization, which meant that deeper and more complex mechanisms needed to be explored. Traditionally, oxidative stress, inflammation, autophagy and apoptosis were considered the main mechanisms of harmful effects of PM. However, recent studies have identified several emerging mechanisms involved in the toxicity of PM, including pyroptosis, ferroptosis and epigenetic modifications. This review summarized the comprehensive evidence on the health effects of PM and the chemical components of it, as well as the combined toxicity of PM with other air pollutants. Based on the AOP Wiki and the mechanisms of PM-induced toxicity at different levels, we first constructed the PM-related AOP frameworks on various systems.

The long-term investment in new approach methodologies (NAMs) within the EU and other parts of the world is beginning to result in an emerging consensus of how to use information from in silico, in vitro and targeted in vivo sources to assess the safety of chemicals. However, this methodology is being adopted very slowly for regulatory purposes. Here, we have developed a framework incorporating in silico, in vitro and in vivo methods designed to meet the requirements of REACH in which both hazard and exposure can be assessed using a tiered approach. The outputs from each tier are classification categories, safe doses, and risk assessments, and progress through the tiers depends on the output from previous tiers. We have exemplified the use of the framework with three examples. The outputs were the same or more conservative than parallel assessments based on conventional studies. The framework allows a transparent and phased introduction of NAMs in chemical safety assessment and enables science-based safety decisions which provide the same level of public health protection using fewer animals, taking less time, and using less financial and expert resource. Furthermore, it would also allow new methods to be incorporated as they develop through continuous selective evolution rather than periodic revolution.

BackgroundLung infection is a common cause of sepsis, and patients with sepsis and lung infection are more ill and have a higher mortality rate than sepsis patients without lung infection. We constructed a nomogram prediction model to accurately evaluate the prognosis of and provide treatment advice for patients with sepsis and lung infection.MethodsData were retrospectively extracted from the Medical Information Mart for Intensive Care (MIMIC-III) open-source clinical database. The definition of Sepsis 3.0 [10] was used, which includes patients with life-threatening organ dysfunction caused by an uncontrolled host response to infection, and SOFA score ≥ 2. The nomogram prediction model was constructed from the training set using logistic regression analysis, and was then internally validated and underwent sensitivity analysis.ResultsThe risk factors of age, lactate, temperature, oxygenation index, BUN, lactate, Glasgow Coma Score (GCS), liver disease, cancer, organ transplantation, Troponin T(TnT), neutrophil-to-lymphocyte ratio (NLR), and CRRT, MV, and vasopressor use were included in the nomogram. We compared our nomogram with the Sequential Organ Failure Assessment (SOFA) score and Simplified Acute Physiology Score II (SAPSII), the nomogram had better discrimination ability, with areas under the receiver operating characteristic curve (AUROC) of 0.743 (95% C.I.: 0.713-0.773) and 0.746 (95% C.I.: 0.699-0.790) in the training and validation sets, respectively. The calibration plot indicated that the nomogram was adequate for predicting the in-hospital mortality risk in both sets. The decision-curve analysis (DCA) of the nomogram revealed that it provided net benefits for clinical use over using the SOFA score and SAPSII in both sets.ConclusionOur new nomogram is a convenient tool for accurate predictions of in-hospital mortality among ICU patients with sepsis and lung infection. Treatment strategies that improve the factors considered relevant in the model could increase in-hospital survival for these ICU patients.

This paper examines why robust causality assessment is central to toxicology yet increasingly difficult in networked biological systems, including Adverse Outcome Pathway (AOP) networks and multi-omics datasets. Classical causal frameworks (e.g., Koch/Dale reasoning and Bradford Hill-type considerations) remain useful for scoping, but they offer limited operational guidance for multifactorial, nonlinear, and feedback-dominated mechanisms. We therefore synthesize a modern toolbox for causality analysis in toxicology spanning directed acyclic graphs for explicit causal assumptions, quantitative and probabilistic approaches for integrating evidence across key events, and network/dynamical methods that help identify influential hubs and points of vulnerability in AOP networks. Building on evidence-based toxicology, we propose an operational workflow that links (i) structured scoping and model specification, (ii) protocolized evidence retrieval across in vivo, in vitro, in silico and human data streams, (iii) risk-of-bias appraisal and quantitative synthesis of effect sizes and dose-response, (iv) mechanistic integration and targeted perturbation assays, and (v) translation via dual-strand certainty rating (mechanistic vs difference-making evidence) and Evidence-to-Decision tables. We discuss how explainable AI can support scalable integration and transparency. A worked developmental neurotoxicity example illustrates how this pipeline can support regulatory recommendations while explicitly documenting uncertainty.