Dose-Dependent Effect on Plant Growth of Exposure to Metal-Organic Framework MIL-101(Cr).

With the increasing use of metal-organic frameworks (MOFs), they will inevitably enter the environment intentionally or unintentionally. However, the effects of MOFs on plant growth are poorly understood. Here, we investigated the effects of exposure of the rhizosphere to MOFs on plant growth. MIL-101(Cr) was selected as a research model due to its commercial availability and wide use. Soybean plants at the two-leaf stage were subjected to various durations (1-7 days) and concentrations (0-1000 mg/L) of exposure in hydroculture with a control group treated with ultrapure water. We found that MIL-101(Cr) had a positive effect on soybean growth at a lower dose (i.e., 200 mg/L); however, at higher doses (i.e., 500 and 1000 mg/L), it exhibited significant toxicity to plant growth, which is evidenced by leaf damage. To investigate the mechanism of this effect, we used Cr as an indicator to quantify, track, and image MIL-101(Cr) in the plant with laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). Results indicated that MIL-101(Cr) primarily accumulated in the cortex of roots (up to 40 times higher than that in stems), with limited translocation to stems and negligible presence in leaves and cotyledons. In addition, metabolomic analysis of soybeans indicated that low-dose MIL-101(Cr) could increase the sucrose content of soybean roots to promote plant growth, while a high dose could induce lipid oxidation in roots. This study provides valuable insights into the ecological toxicology of MOFs and underscores the importance of assessing their environmental impact for sustainable agricultural practices.

A new ChatGPT-empowered, easy-to-use machine learning paradigm for environmental science.

The quantity and complexity of environmental data show exponential growth in recent years. High-quality big data analysis is critical for performing a sophisticated characterization of the complex network of environmental pollution. Machine learning (ML) has been employed as a powerful tool for decoupling the complexities of environmental big data based on its remarkable fitting ability. Yet, due to the knowledge gap across different subjects, ML concepts and algorithms have not been well-popularized among researchers in environmental sustainability. In this context, we introduce a new research paradigm-"ChatGPT + ML + Environment", providing an unprecedented chance for environmental researchers to reduce the difficulty of using ML models. For instance, each step involved in applying ML models to environmental sustainability, including data preparation, model selection and construction, model training and evaluation, and hyper-parameter optimization, can be easily performed with guidance from ChatGPT. We also discuss the challenges and limitations of using this research paradigm in the field of environmental sustainability. Furthermore, we highlight the importance of "secondary training" for future application of "ChatGPT + ML + Environment".

Chronic Ambient Ozone Exposure Aggravates Autism-Like Symptoms in a Susceptible Mouse Model.

Although there is evidence that exposure to ground-level ozone (O3) may cause an increased risk of neurological disorders (e.g., autistic spectrum disorder), low-dose chronic ozone exposure and its adverse effects on the nervous system have not been fully understood. Here, we evaluated the potential neurotoxic effects of long-term exposure to environmentally relevant O3 concentration (200 µg/m3 via a whole-body inhalation system, 12 h/day for 5 days/week) using a susceptible mouse model of autism induced by valproic acid. Various indicators of oxidative stress, mitochondria, and synapse in the brain tissues were then measured to determine the overall damage of O3 to the mouse brain. The results showed an aggravated risk of autism in mice offspring, which was embodied in decreased antioxidant contents, disturbed energy generation in mitochondria, as well as reduced expressions of protein kinase Mζ (PKMζ) and synaptic proteins [e.g., Synapsin 1 (SYN 1), postsynaptic density protein-95 (PSD-95)]. Overall, our study indicates that prenatal exposure to environmentally relevant O3 may exacerbate the symptoms of autism, shedding light on possible molecular mechanisms and providing valuable insights into the pathogenesis of autism, especially concerning low-dose levels of those pollutants.

Machine learning-aided metallomic profiling in serum and urine of thyroid cancer patients and its environmental implications.

The incidence rate of thyroid cancer has been growing worldwide. Thyroid health is closely related with multiple trace metals, and the nutrients are essential in maintaining thyroid function while the contaminants can disturb thyroid morphology and homeostasis. In this study, we conducted metallomic analysis in thyroid cancer patients (n = 40) and control subjects (n = 40) recruited in Shenzhen, China with a high incidence of thyroid cancer. We found significant alterations in serumal and urinary metallomic profiling (including Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Sr, Cd, I, Ba, Tl, and Pb) and elemental correlative patterns between thyroid cancer patients and controls. Additionally, we also measured the serum Cu isotopic composition and found a multifaceted disturbance in Cu metabolism in thyroid disease patients. Based on the metallome variations, we built and assessed the thyroid cancer-predictive performance of seven machine learning algorithms. Among them, the Random Forest model performed the best with the accuracy of 1.000, 0.858, and 0.813 on the training, 5-fold cross-validation, and test set, respectively. The high performance of machine learning has demonstrated the great promise of metallomic analysis in the identification of thyroid cancer. Then, the Shapley Additive exPlanations approach was used to further interpret the variable contributions of the model and it showed that serum Pb contributed the most in the identification process. To the best of our knowledge, this is the first study that combines machine learning and metallome data for cancer identification, and it supports the indication of environmental heavy metal-related thyroid cancer etiology.

Metallomic profiling and natural copper isotopic signatures of childhood autism in serum and red blood cells.

Excessive exposure to metals directly threatens human health, including neurodeve lopment. Autism spectrum disorder (ASD) is a neurodevelopmental disorder, leaving great harms to children themselves, their families, and even society. In view of this, it is critical to develop reliable biomarkers for ASD in early childhood. Here we used inductively coupled plasma mass spectrometry (ICP-MS) to identify the abnormalities in ASD-associated metal elements in children blood. Multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS) was applied to detect isotopic differences in copper (Cu) for further assessment on account of its core role in the brain. We also developed a machine learning classification method for unknown samples based on a support vector machine (SVM) algorithm. The results indicated significant differences in the blood metallome (chromium (Cr), manganese (Mn), cobalt (Co), magnesium (Mg), and arsenic (As)) between cases and controls, and a significantly lower Zn/Cu ratio was observed in the ASD cases. Interestingly, we found a strong association of serum copper isotopic composition (δ65Cu) with autistic serum. SVM was successfully applied to discriminate cases and controls based on the two-dimensional Cu signatures (Cu concentration and δ65Cu) with a high accuracy (94.4%). Overall, our findings revealed a new biomarker for potential early diagnosis and screening of ASD, and the significant alterations in the blood metallome also helped to understand the potential pathogenesis of ASD in terms of metallomics.

Internal Exposure and Distribution of Airborne Fine Particles in the Human Body: Methodology, Current Understandings, and Research Needs.

Exposure to airborne fine particles (PM2.5, particulate matter with aerodynamic diameter <2.5 µm) severely threatens global human health. Understanding the distribution and processes of inhaled PM2.5 in the human body is crucial to clarify the causal links between PM2.5 pollution and diseases. In contrast to extensive research on the emission and formation of PM2.5 in the ambient environment, reports about the occurrence and fate of PM2.5 in humans are still limited, although many studies have focused on the exposure and adverse effects of PM2.5 with animal models. It has been shown that PM2.5, especially ultrafine particles (UFPs), have the potential to go across different biological barriers and translocate into different human organs (i.e., blood circulation, brain, heart, pleural cavity, and placenta). In this Perspective, we summarize the factors affecting the internal exposure of PM2.5 and the relevant analytical methodology and review current knowledge about the exposure pathways and distribution of PM2.5 in humans. We also discuss the research challenges and call for more studies on the identification and characterization of key toxic species of PM2.5, quantification of internal exposure doses in the general population, and further clarification of translocation, metabolism, and clearance pathways of PM2.5 in the human body. In this way, it is possible to develop toxicity-based air quality standards instead of the currently used mass-based standards.

COVID-19-Induced Lockdowns Indicate the Short-Term Control Effect of Air Pollutant Emission in 174 Cities in China.

The contradiction between the regional imbalance and an one-size-fits-all policy is one of the biggest challenges in current air pollution control in China. With the recent implementation of first-level public health emergency response (FLPHER) in response to the COVID-19 pandemic in China (a total of 77 041 confirmed cases by February 22, 2020), human activities were extremely decreased nationwide and almost all economic activities were suspended. Here, we show that this scenario represents an unprecedented "base period" to probe the short-term emission control effect of air pollution at a city level. We quantify the FLPHER-induced changes of NO2, SO2, PM2.5, and PM10 levels in 174 cities in China. A machine learning prediction model for air pollution is established by coupling a generalized additive model, random effects meta-analysis, and weather research and forecasting model with chemistry analysis. The short-term control effect under the current energy structure in each city is estimated by comparing the predicted and observed results during the FLPHER period. We found that the short-term emission control effect ranges within 53.0%-98.3% for all cities, and southern cities show a significantly stronger effect than northern cities (P < 0.01). Compared with megacities, small-medium cities show a similar control effect on NO2 and SO2 but a larger effect on PM2.5 and PM10.