Environmental pollution is escalating due to rapid global development that often prioritizes human needs over planetary health. Despite global efforts to mitigate legacy pollutants, the continuous introduction of new substances remains a major threat to both people and the planet. In response, global initiatives are focusing on risk assessment and regulation of emerging contaminants, as demonstrated by the ongoing efforts to establish the UN's Intergovernmental Science-Policy Panel on Chemicals, Waste, and Pollution Prevention. This review identifies the sources and impacts of emerging contaminants on planetary health, emphasizing the importance of adopting a One Health approach. Strategies for monitoring and addressing these pollutants are discussed, underscoring the need for robust and socially equitable environmental policies at both regional and international levels. Urgent actions are needed to transition toward sustainable pollution management practices to safeguard our planet for future generations. 

Emerging Contaminants: A One Health Perspective

Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation.

<h2>Summary</h2><h3>Background</h3> It remains unknown how ambient temperature impact pneumonia of various infectious causes. <h3>Methods</h3> Based on the national death registry covering all counties in Chinese mainland, we conducted an individual-level case-crossover study in China from 2013 to 2019. Exposures were assigned at residential addresses for each decedent. Conditional logistic regression model combined with distributed lag non-linear models were used to estimate the exposure-response associations. The attributable fractions due to non-optimum temperature were calculated after accounting for spatial and temporal patterns for the excess risks. <h3>Findings</h3> The exposure-response curves were inversely J-shaped with both low and high temperature increasing the risks, and the effect of low temperature was stronger. Extremely low temperature was associated with higher magnitude of influenza-related pneumonia [relative risk (RR): 2.46, 95% confidence interval (CI): 1.62–3.74], than viral pneumonia (RR: 1.89, 95% CI: 1.55–2.30) and bacterial pneumonia (RR: 1.81, 95% CI: 1.56–2.09). The magnitudes of RRs associated with extremely high temperature were similar among the three categories of pneumonia. The mortality attributable fraction for influenza-related pneumonia (29.78%) was the highest. The effects were stronger in people of low education level or residence in the north. <h3>Interpretation</h3> This nationwide study presents findings on the varied risk and burden of pneumonia mortality of various infectious causes, and highlights the susceptibility of influenza-related pneumonia to ambient low temperature. <h3>Funding</h3> This study is supported by the National Key Research and Development Program (2022YFC3702701), the Shanghai Municipal Science and Technology Commission (21TQ015) and Shanghai International Science and Technology Partnership Project (21230780200). 

Differentiating the impacts of ambient temperature on pneumonia mortality of various infectious causes: a nationwide, individual-level, case-crossover study

Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating the design of novel materials, and reducing the need for time-consuming and labour-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials and nanostructures is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarised luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, we provide an analysis of machine learning methods used in studying achiral nanomaterials, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. We present an overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships and provide insights on how to leverage machine learning for the study of these highly complex relationships. We also review and discuss some key recent publications on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field. This article is protected by copyright. All rights reserved.

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures.

Frequent outbreaks of viral diseases have brought substantial negative impacts on society and the economy, and they are very difficult to detect, as the concentration of viral aerosols in the air is low and the composition is complex. The traditional detection method is manually collection and re-detection, being cumbersome and time-consuming. Here we propose a virus aerosol detection method based on microfluidic inertial separation and spectroscopic analysis technology to rapidly and accurately detect aerosol particles in the air. The microfluidic chip is designed based on the principles of inertial separation and laminar flow characteristics, resulting in an average separation efficiency of 95.99% for 2 μm particles. We build a microfluidic chip composite spectrometer detection platform to capture the spectral information on aerosol particles dynamically. By employing machine-learning techniques, we can accurately classify different types of aerosol particles. The entire experiment took less than 30 min as compared with hours by PCR detection. Furthermore, our model achieves an accuracy of 97.87% in identifying virus aerosols, which is comparable to the results obtained from PCR detection.

Minimum Minutes Machine-Learning Microfluidic Microbe Monitoring Method (M7).

Mounting data suggest that environmental pollution due to airborne fine particles (AFPs) increases the occurrence and severity of respiratory virus infection in humans. However, it is unclear whether and how interactions with AFPs alter viral infection and distribution. We report synergetic effects between various AFPs and the H1N1 virus, regulated by physicochemical properties of the AFPs. Unlike infection caused by virus alone, AFPs facilitated the internalization of virus through a receptor-independent pathway. Moreover, AFPs promoted the budding and dispersal of progeny virions, likely mediated by lipid rafts in the host plasma membrane. Infected animal models demonstrated that AFPs favored penetration of the H1N1 virus into the distal lung, and its translocation into extrapulmonary organs including the liver, spleen, and kidney, thus causing severe local and systemic disorders. Our findings revealed a key role of AFPs in driving viral infection throughout the respiratory tract and beyond. These insights entail stronger air quality management and air pollution reduction policies.

Airborne fine particles drive H1N1 viruses deep into the lower respiratory tract and distant organs

Adjuvants stimulate the immune system to vigorously respond to a vaccine. While current adjuvants such as aluminum salts and oil-in-water emulsions have been used for decades, they do not generate broad and long-lasting responses in many vaccines. Consequently, more potent adjuvants are needed. Here, using computer-aided molecule design and machine learning, we discovered 2 new, broad-spectrum adjuvants that can boost vaccine responses. Our library containing 46 toll-like receptor (TLR)-targeting agonist ligands were assembled on Au nanoparticles. Comprehensive in vitro, ex vivo and in vivo studies showed both leads promoted dendritic cell activation via multiple TLRs and enhanced antigen presentation to T cells. When used together with tumor-specific antigens to immunize mice against B16-OVA melanoma and 4T1-PD1 breast cancer, both adjuvants unleashed strong immune responses that suppressed tumor growth and lung metastases. Our results show computer-aided design and screening can rapidly uncover potent adjuvants for tackling waning immunity in current vaccines.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/anie.202301059

Computer-aided discovery of potent broad-spectrum vaccine adjuvants.

The heterogeneity of pulmonary cells affects their response to inhaled fine particles. However, the most critical responder cells in particle exposure still remain controversial, and their functional heterogeneity warrants detailed exploration. Herein, a gold nanoparticle model dual-labeled with sulfo-cyanine3 and Tag RNAs is developed, aiming to capture prominent fine particle-responsive cell subpopulations using fluorescence-activated cell sorting and single-cell RNA sequencing. Alveolar macrophages (AMs), recruited macrophages (recMacs), and interstitial macrophages (IMs) exhibited the strongest responsiveness to particles, among which 14 subsets are identified with partially overlapping yet distinct functions. Notably, AMs_3, AMs_7, and recMacs_4 subsets are highlighted in the particle-responsive single-cell atlas, as evinced from their functional scoring and Tag RNA levels. AMs_3 and AMs_7 showed function enrichment for acute-phase responses to inhaled particles, while recMacs_4 exhibited enrichment for cell chemotaxis and phagocytosis. Fine particle engulfment led to enhanced outgoing interactions between these three macrophage subsets and other cell populations within particle-responsive cellular communication networks. This study reveals that specific macrophage subpopulations act as the primary responsive cell subpopulations in engulfing inhaled particles and link their transcriptional heterogeneity to functional diversity, thereby opening a new avenue to explore the heterogeneity of immune cells in pulmonary disorders.

Deciphering the Heterogeneity of Pulmonary Macrophages in Response to Fine Particles.

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Airborne fine particles drive H1N1 viruses deep into the lower respiratory tract and distant organs

Computer-aided discovery of potent broad-spectrum vaccine adjuvants.

Deciphering the Heterogeneity of Pulmonary Macrophages in Response to Fine Particles.