Effects of occupational exposure to metal fume PM2.5 on lung function and biomarkers among shipyard workers: a 3-year prospective cohort study.

OBJECTIVE: This study investigates the associations of α1-antitrypsin, inter-α-trypsin inhibitor heavy chain (ITIH4), and 8-isoprostane with lung function in shipyard workers exposed to occupational metal fume fine particulate matter (PM2.5), which is known to be associated with adverse respiratory outcomes. METHODS: A 3-year follow-up study was conducted on 180 shipyard workers with 262 measurements. Personal exposure to welding fume PM2.5 was collected for an 8-h working day. Pre-exposure, post-exposure, and delta (∆) levels of α1-antitrypsin, ITIH4, and 8-isoprostane were determined in urine using enzyme-linked immunosorbent assays. Post-exposure urinary metals were sampled at the beginning of the next working day and analyzed by inductively coupled plasma-mass spectrometry. Lung function measurements were also conducted the next working day for post-exposure. RESULTS: An IQR increase in PM2.5 was associated with decreases of 2.157% in FEV1, 2.806% in PEF, 4.328% in FEF25%, 5.047% in FEF50%, and 7.205% in FEF75%. An IQR increase in PM2.5 led to increases of 42.155 µg/g in ∆α1-antitrypsin and 16.273 µg/g in ∆ITIH4. Notably, IQR increases in various urinary metals were associated with increases in specific biomarkers, such as post-urinary α1-antitrypsin and ITIH4. Moreover, increases in ∆ α1-antitrypsin and ∆ITIH4 were associated with decreases in FEV1/FVC by 0.008% and 0.020%, respectively, and an increase in ∆8-isoprostane resulted in a 1.538% decline in FVC. CONCLUSION: Our study suggests that urinary α1-antitrypsin and ITIH4 could indicate early lung function decline in shipyard workers exposed to metal fume PM2.5, underscoring the need for better safety and health monitoring to reduce respiratory risks.

Citizen scientists' engagement in flood risk-related data collection: a case study in Bui River Basin, Vietnam.

Time constraints, financial limitations, and inadequate tools restrict the flood data collection in undeveloped countries, especially in the Asian and African regions. Engaging citizens in data collection and contribution has the potential to overcome these challenges. This research demonstrates the applicability of citizen science for gathering flood risk-related data on residential flooding, land use information, and flood damage to paddy fields for the Bui River Basin in Vietnam. Locals living in or around flood-affected areas participated in data collection campaigns as citizen scientists using self-investigation or investigation with a data collection app, a web form, and paper forms. We developed a community-based rainfall monitoring network in the study area using low-cost rain gauges to draw locals' attention to the citizen science program. Fifty-nine participants contributed 594 completed questionnaires and measurements for four investigated subjects in the first year of implementation. Five citizen scientists were active participants and contributed more than 50 completed questionnaires or measurements, while nearly 50% of citizen scientists participated only one time. We compared the flood risk-related data obtained from citizen scientists with other independent data sources and found that the agreement between the two datasets on flooding points, land use classification, and the flood damage rate to paddy fields was acceptable (overall agreement above 73%). Rainfall monitoring activities encouraged the participants to proactively update data on flood events and land use situations during the data collection campaign. The study's outcomes demonstrate that citizen science can help to fill the gap in flood data in data-scarce areas.

Chemically Recyclable Polymer System Based on Nucleophilic Aromatic Ring-Opening Polymerization.

The development of chemically recyclable polymers with desirable properties is a long-standing but challenging goal in polymer science. Central to this challenge is the need for reversible chemical reactions that can equilibrate at rapid rates and provide efficient polymerization and depolymerization cycles. Based on the dynamic chemistry of nucleophilic aromatic substitution (SNAr), we report a chemically recyclable polythioether system derived from readily accessible benzothiocane (BT) monomers. This system represents the first example of a well-defined monomer platform capable of chain-growth ring-opening polymerization through an SNAr manifold. The polymerizations reach completion in minutes, and the pendant functionalities are easily customized to tune material properties or render the polymers amenable to further functionalization. The resulting polythioether materials exhibit comparable performance to commercial thermoplastics and can be depolymerized to the original monomers in high yields.

Accelerated Scheme to Predict Ring-Opening Polymerization Enthalpy: Simulation-Experimental Data Fusion and Multitask Machine Learning.

Ring-opening enthalpy (ΔHROP) is a fundamental thermodynamic quantity controlling the polymerization and depolymerization of an important class of recyclable polymers, namely, those created from ring-opening polymerization (ROP). Highly accurate first-principles-based computational methods to compute ΔHROP are computationally too demanding to efficiently guide the design of depolymerizable polymers. In this work, we develop a generalizable machine-learning model that was trained on experimental measurements and reliably computed simulation results of ΔHROP (the latter provides a pathway to systematically increase the chemical diversity of the data). Predictions of ΔHROP using this machine-learning model require essentially no time while the prediction accuracy is about ∼8 kJ/mol, approaching the well-known chemical accuracy. We hope that this effort will contribute to the future development of new depolymerizable polymers.

Climate change and mortality rates of COPD and asthma: A global analysis from 2000 to 2018.

BACKGROUND: Climate change plays a significant role in global health threats, particularly with respiratory diseases such as chronic obstructive pulmonary disease (COPD) and asthma, but the long-term global-scale impact of climate change on these diseases' mortality remains unclear. OBJECTIVE: This study aims to investigate the impact of climate change on the age-standardized mortality rates (ASMR) of COPD and asthma at national levels. METHODS: We used Global Burden of Disease (GBD) data of ASMR of COPD and asthma from 2000 to 2018. The climate change index was represented as the deviance percentage of temperature (DPT) and relative humidity (DPRH), calculated based on 19-year temperature and humidity averages. Annual temperature, RH, and fine particulate matter (PM2.5) levels in 185 countries/regions were obtained from ERA5 and the OECD's environmental statistics database. General linear mixed-effect regression models were used to examine the associations between climate change with the log of ASMR (LASMR) of COPD and asthma. RESULTS: After adjusting for annual PM2.5, SDI level, smoking prevalence, and geographical regions, a 0.26% increase in DPT was associated with decreases of 0.016, 0.017, and 0.014 per 100,000 people in LASMR of COPD and 0.042, 0.046, and 0.040 per 100,000 people in LASMR of asthma for both genders, males, and females. A 2.68% increase in DPRH was associated with increases of 0.009 and 0.011 per 100,000 people in LASMR of COPD. We observed a negative association of DPT with LASMR for COPD in countries/regions with temperatures ranging from 3.8 to 29.9 °C and with LASMR for asthma ranging from -5.3-29.9 °C. However, we observed a positive association of DPRH with LASMR for both COPD and asthma in the RH range of 41.2-67.2%. CONCLUSION: Climate change adaptation and mitigation could be crucial in reducing the associated COPD and asthma mortality rates, particularly in regions most vulnerable to temperature and humidity fluctuations.

Novel high voltage polymer insulators using computational and data-driven techniques.

One of the key bottlenecks in the development of high voltage electrical systems is the identification of suitable insulating materials capable of supporting high voltages. Under high voltage scenarios, conventional polymer based insulators, which are one of the popular choices of insulators, suffer from the drawback of space charge accumulation, which leads to degradation in desirable electronic properties and facilitates dielectric breakdown. In this work, we aid the development of novel polymers for high voltage insulation applications by enabling the rapid prediction of properties that are correlated with dielectric breakdown, i.e.,the bandgap (Egap) of the polymer and electron injection barrier (Φe) at the electrode-insulator interface. To accomplish this, density functional theory based methods are used to develop large, chemically diverse datasets of Φe and Egap. The deviation of the computed properties from experimental observations is addressed using a statistical technique called Bayesian calibration. Furthermore, to enable rapid estimation of these properties for a large set of polymers, machine learning models are developed using the created dataset. These models are further used to predict Egap and Φe for a set of 13k previously known polymers. Polymers with high values of these properties are selected as potential high voltage insulators and are recommended for synthesis. Finally, the models developed here are deployed at www.polymergenome.org to enable the community use.

An Efficient Deep Learning Scheme To Predict the Electronic Structure of Materials and Molecules: The Example of Graphene-Derived Allotropes.

Computations based on density functional theory (DFT) are transforming various aspects of materials research and discovery. However, the effort required to solve the central equation of DFT, namely the Kohn-Sham equation, which remains a major obstacle for studying large systems with hundreds of atoms in a practical amount of time with routine computational resources. Here, we propose a deep learning architecture that systematically learns the input-output behavior of the Kohn-Sham equation and predicts the electronic density of states, a primary output of DFT calculations, with unprecedented speed and chemical accuracy. The algorithm also adapts and progressively improves in predictive power and versatility as it is exposed to new diverse atomic configurations. We demonstrate this capability for a diverse set of carbon allotropes spanning a large configurational and phase space. The electronic density of states, along with the electronic charge density, may be used downstream to predict a variety of materials properties, bypassing the Kohn-Sham equation, leading to an ultrafast and high-fidelity DFT emulator.

Atomistic mechanisms for chemical defects formation in polyethylene.

Chemical defects can progressively degrade the electronic structure of polymer dielectrics, ultimately leading to their failure. Because the polymer degradation and breakdown related processes are notably complicated in nature, they remain far from being understood both experimentally and computationally. Using a combination of density functional theory calculations and classical molecular dynamics simulations, we propose seven atomistic mechanisms for the formation of common chemical defects in polyethylene using which a variety of defect-related experimental observations can be explained. This work provides a comprehensive connection among the experiments related to polyethylene defects and aging, laying the groundwork for an understanding of polymer degradation and breakdown.

Unraveling the luminescence signatures of chemical defects in polyethylene.

Chemical defects in polyethylene (PE) can deleteriously downgrade its electrical properties and performance. Although these defects usually leave spectroscopic signatures in terms of characteristic luminescence peaks, it is nontrivial to make unambiguous assignments of the peaks to specific defect types. In this work, we go beyond traditional density functional theory calculations to determine intra-defect state transition and charge recombination process derived emission and absorption energies in PE. By calculating the total energy differences of the neutral defect at excited and ground states, the emission energies from intra-defect state transition are obtained, reasonably explaining the photoluminescence peaks in PE. In order to study the luminescence emitted in charge recombination processes, we characterize PE defect levels in terms of thermodynamic and optical charge transition levels that involve total energy calculations of neutral and charged defects. Calculations are performed at several levels of theory including those involving (semi)local and hybrid electron exchange-correlation functionals, and many-body perturbation theory. With these critical elements, the emission energies are computed and further used to clarify and confirm the origins of the observed electroluminescence and thermoluminescence peaks.

First-principles predicted low-energy structures of NaSc(BH4)4.

According to previous interpretations of experimental data, sodium-scandium double-cation borohydride NaSc(BH4)4 crystallizes in the crystallographic space group Cmcm where each sodium (scandium) atom is surrounded by six scandium (sodium) atoms. A careful investigation of this phase based on ab initio calculations indicates that the structure is dynamically unstable and gives rise to an energetically and dynamically more favorable phase with C2221 symmetry and nearly identical x-ray diffraction pattern. By additionally performing extensive structural searches with the minima-hopping method we discover a class of new low-energy structures exhibiting a novel structural motif in which each sodium (scandium) atom is surrounded by four scandium (sodium) atoms arranged at the corners of either a rectangle with nearly equal sides or a tetrahedron. These new phases are all predicted to be insulators with band gaps of 7.9-8.2 eV. Finally, we estimate the influence of these structures on the hydrogen-storage performance of NaSc(BH4)4.

Short-term mediating effects of PM2.5 on climate-associated COPD severity.

The impact of short-term exposure to environmental factors such as temperature, relative humidity (RH), and fine particulate matter (PM2.5) on chronic obstructive pulmonary disease (COPD) remains unclear. The objective of this study is to investigate PM2.5 as a mediator in the relationship between short-term variations in RH and temperature and COPD severity. A cross-sectional study was conducted on 930 COPD patients in Taiwan from 2017 to 2022. Lung function, COPD Assessment Test (CAT) score, and modified Medical Research Council (mMRC) dyspnea scale were assessed. The mean and differences in 1-day, 7-day, and 30-day individual-level exposure to ambient RH, temperature, and PM2.5 were estimated. The associations between these factors and clinical outcomes were analyzed using linear regression models and generalized additive mixed models, adjusting for age, sex, smoking, and body mass index. In the total season, increases in RH difference were associated with increases in forced expiratory volume in 1 s (FEV1) / forced vital capacity (FVC), while increases in temperature difference were associated with decreases in FEV1 and FEV1/FVC. Increases in PM2.5 mean were associated with declines in FEV1. In the cold season, increases in temperature mean were associated with decreases in CAT and mMRC scores, while increases in PM2.5 mean were associated with declines in FEV1, FVC, and FEV1/FVC. In the warm season, increases in temperature difference were associated with decreases in FEV1 and FEV1/FVC, while increases in RH difference and PM2.5 mean were associated with decreases in CAT score. PM2.5 fully mediated the associations of temperature mean with FEV1/FVC in the cold season. In conclusion, PM2.5 mediates the effects of temperature and RH on clinical outcomes. Monitoring patients during low RH, extreme temperature, and high PM2.5 levels is crucial. Capsule of findings The significance of this study is that an increase in ambient RH and temperature, as well as PM2.5 exposure, were significantly associated with changes in lung function, and clinical symptoms in these patients. The novelty of this study is that PM2.5 plays a mediating role in the association of RH and temperature with COPD clinical outcomes in the short term.

The impact of air pollution on respiratory diseases in an era of climate change: A review of the current evidence.

The impacts of climate change and air pollution on respiratory diseases present significant global health challenges. This review aims to investigate the effects of the interactions between these challenges focusing on respiratory diseases. Climate change is predicted to increase the frequency and intensity of extreme weather events amplifying air pollution levels and exacerbating respiratory diseases. Air pollution levels are projected to rise due to ongoing economic growth and population expansion in many areas worldwide, resulting in a greater burden of respiratory diseases. This is especially true among vulnerable populations like children, older adults, and those with pre-existing respiratory disorders. These challenges induce inflammation, create oxidative stress, and impair the immune system function of the lungs. Consequently, public health measures are required to mitigate the effects of climate change and air pollution on respiratory health. The review proposes that reducing greenhouse gas emissions contribute to slowing down climate change and lessening the severity of extreme weather events. Enhancing air quality through regulatory and technological innovations also helps reduce the morbidity of respiratory diseases. Moreover, policies and interventions aimed at improving healthcare access and social support can assist in decreasing the vulnerability of populations to the adverse health effects of air pollution and climate change. In conclusion, there is an urgent need for continuous research, establishment of policies, and public health efforts to tackle the complex and multi-dimensional challenges of climate change, air pollution, and respiratory health. Practical and comprehensive interventions can protect respiratory health and enhance public health outcomes for all.

Prediction value of neutrophil and eosinophil count at risk of COPD exacerbation.

INTRODUCTION: Predicting acute exacerbations (AEs) in chronic obstructive pulmonary disease (COPD) is crucial. This study aimed to identify blood biomarkers for predicting COPD exacerbations by inflammatory phenotypes. MATERIALS AND METHODS: We analyzed blood cell counts and clinical outcomes in 340 COPD patients aged 20-90 years. Patients were categorized into eosinophilic inflammation (EOCOPD) and non-eosinophilic inflammation (N-EOCOPD) groups. Blood cell counts, eosinophil-to-lymphocyte ratio (ELR), neutrophil-to-lymphocyte ratio (NLR) and neutrophil-to-eosinophil ratio (NER) were calculated. Linear and logistic regression models assessed relationships between health outcomes and blood cell counts. RESULTS: EOCOPD patients had distinct characteristics compared to N-EOCOPD patients. Increased neutrophil % and decreased lymphocyte % were associated with reduced pulmonary function, worse quality of life and more exacerbations, but they did not show statistical significance after adjusting by age, sex, BMI, smoking status, FEV1% and patient's medication. Subgroup analysis revealed a 1.372-fold increase in the OR of AE for every 1 unit increase in NLR in EOCOPD patients (p < .05). In N-EOCOPD patients, every 1% increase in blood eosinophil decreased the risk of exacerbation by 59.6%. CONCLUSIONS: Our study indicates that distinct white blood cell profiles in COPD patients, with or without eosinophilic inflammation, can help assess the risk of AE in clinical settings.

Safe and effective profile of the VSTENT bioresorbable polymer sirolimus-eluting stent in the treatment of patients with de novo coronary artery lesions: a prospective, cohort, multicenter study.

Background: The drug-eluting stent was a significant stride forward in the development of enhanced therapeutic therapy for coronary intervention, with three generations of increased advancement. VSTENT is a newly developed stent manufactured in Vietnam that aims to provide coronary artery patients with a safe, effective, and cost-efficient option. The purpose of this trial was to determine the efficacy and safety of a new bioresorbable polymer sirolimus-eluting stent called VSTENT. Methods: This is a prospective, cohort, multicenter research in 5 centers of Vietnam. A prespecified subgroup received intravascular ultrasound (IVUS) or optical coherence tomography (OCT) imaging. We determined procedure success and complications during index hospitalization. We monitored all participants for a year. Six-month and 12-month rates of major cardiovascular events were reported. All patients had coronary angiography after 6 months to detect late lumen loss (LLL). Prespecified patients also had IVUS or OCT performed. Results: The rate of device success was 100% (95% CI: 98.3-100%; P<0.001). Major cardiovascular events were 4.7% (95% CI: 1.9-9.4%; P<0.001). The LLL over quantitative coronary angiography (QCA) was 0.08±0.19 mm (95% CI: 0.05-0.10; P<0.001) in the in-stent segment and 0.07±0.31 mm (95% CI: 0.03-0.11; P=0.002) in 5 mm within the two ends of the stent segment. The LLL recorded by IVUS and OCT at 6 months was 0.12±0.35 mm (95% CI: 0.01-0.22; P=0.028) and 0.15±0.24 mm (95% CI: 0.02-0.28; P=0.024), respectively. Conclusions: This study's device success rates were perfect. IVUS and OCT findings on LLL were favorable at 6-month follow-up. One-year follow-up showed low in-stent restenosis (ISR) and target lesion revascularization (TLR) rates, reflecting few significant cardiovascular events. VSTENT's safety and efficacy make it a promising percutaneous intervention option in developing nations.

Toward Recyclable Polymers: Ring-Opening Polymerization Enthalpy from First-Principles.

Ring-opening polymerization (ROP) enthalpy ΔHROP is an important thermodynamic property controlling the polymerization of cyclic monomers. While ΔHROP can be measured, computing ΔHROP for realistic polymer systems with an error of ≃5-10 kJ/mol is critical for designing new monomer systems for depolymerizable polymers. We have developed a first-principles computational scheme in which multiple challenges in computing ΔHROP are resolved definitively including extensive exploration of conformational states and adequately addressing finite size effects. This scheme is validated on a diverse benchmark set of 42 ROP polymers for which reliable experimental values of ΔHROP are available. For this set, the ΔHROP root-mean-square error is ≃7 kJ/mol, about 3-times smaller than conventional approaches. This development opens up new pathways to build up a high-quality database of ΔHROP for downstream predictive machine-learning models and ultimately to accelerate the design of depolymerizable polymers with desired properties.

Polymer Structure Predictor (PSP): A Python Toolkit for Predicting Atomic-Level Structural Models for a Range of Polymer Geometries.

Three-dimensional atomic-level models of polymers are the starting points for physics-based simulation studies. A capability to generate reasonable initial structural models is highly desired for this purpose. We have developed a python toolkit, namely, polymer structure predictor (psp), to generate a hierarchy of polymer models, ranging from oligomers to infinite chains to crystals to amorphous models, using a simplified molecular-input line-entry system (SMILES) string of the polymer repeat unit as the primary input. This toolkit allows users to tune several parameters to manage the quality and scale of models and computational cost. The output structures and accompanying force field (GAFF2/OPLS-AA) parameter files can be used for downstream ab initio and molecular dynamics simulations. The psp package includes a Colab notebook where users can go through several examples, building their own models, visualizing them, and downloading them for later use. The psp toolkit, being a first of its kind, will facilitate automation in polymer property prediction and design.

High dielectric constant and high breakdown strength polyimide via tin complexation of the polyamide acid precursor.

Polymer dielectrics with ultra-high charge-discharge rates are significant for advanced electrical and electronic systems. Despite the fact that polymers possess high breakdown strength, the low dielectric constant (k) of polymers gives rise to low energy densities. Incorporating metal into polyimides (PI) at the polyamic acid (PAA) precursor stage of the synthetic process is a cheap and versatile way to improve the dielectric constant of the hybrid system while maintaining a high breakdown strength. Here, we explore inclusion of different percentages of Sn as a coordinated complex in a polyimide matrix to achieve metal homogeneity within the dielectric film to boost dielectric constant. Sn-O bonds with high atomic polarizability are intended to enhance the ionic polarization without sacrificing bandgap, a measurable property of the material to assess intrinsic breakdown strength. Enhancements of k from ca. 3.7 to 5.7 were achieved in going from the pure PI film to films containing 10 mol% tin.

Modulating Polymerization Thermodynamics of Thiolactones Through Substituent and Heteroatom Incorporation.

A central challenge in the development of next-generation sustainable materials is to design polymers that can easily revert back to their monomeric starting material through chemical recycling to monomer (CRM). An emerging monomer class that displays efficient CRM are thiolactones, which exhibit rapid rates of polymerization and depolymerization. This report details the polymerization thermodynamics for a series of thiolactone monomers through systematic changes to substitution patterns and sulfur heteroatom incorporation. Additionally, computational studies highlight the importance of conformation in modulating the enthalpy of polymerization, leading to monomers that display high conversions to polymer at near-ambient temperatures, while maintaining low ceiling temperatures (Tc). Specifically, the combination of a highly negative enthalpy (-19.3 kJ/mol) and entropy (-58.4 J/(mol·K)) of polymerization allows for a monomer whose equilibrium polymerization conversion is very sensitive to temperature.

Climate-mediated air pollution associated with COPD severity.

Air pollution has been reported to be associated with chronic obstructive pulmonary disease (COPD). Our study aim was to examine the mediating effects of air pollution on climate-associated health outcomes of COPD patients. A cross-sectional study of 117 COPD patients was conducted in a hospital in Taiwan. We measured the lung function, 6-min walking distance, oxygen desaturation, white blood cell count, and percent emphysema (low attenuation area, LAA) and linked these to 0-1-, 0-3-, and 0-5-year lags of individual-level exposure to relative humidity (RH), temperature, and air pollution. Linear regression models were conducted to examine associations of temperature, RH, and air pollution with severity of health outcomes. A mediation analysis was conducted to examine the mediating effects of air pollution on the associations of RH and temperature with health outcomes. We observed that a 1 % increase in the RH was associated with increases in forced expiratory volume in 1 s (FEV1), eosinophils, and lymphocytes, and a decrease in the total-lobe LAA. A 1 °C increase in temperature was associated with decreases in oxygen desaturation, and right-, left-, and upper-lobe LAA values. Also, a 1 µg/m3 increase in PM2.5 was associated with a decrease in the FEV1 and an increase in oxygen desaturation. A 1 µg/m3 increases in PM10 and PM2.5 was associated with increases in the total-, right-, left, upper-, and lower-lobe (PM2.5 only) LAA. A one part per billion increase in NO2 was associated with a decrease in the FEV1 and an increase in the upper-lobe LAA. Next, we found that NO2 fully mediated the association between RH and FEV1. We found PM2.5 fully mediated associations of temperature with oxygen saturation and total-, right-, left-, and upper-lobe LAA. In conclusion, climate-mediated air pollution increased the risk of decreasing FEV1 and oxygen saturation and increasing emphysema severity among COPD patients. Climate change-related air pollution is an important public health issue, especially with regards to respiratory disease.

Polymer informatics with multi-task learning.

Modern data-driven tools are transforming application-specific polymer development cycles. Surrogate models that can be trained to predict properties of polymers are becoming commonplace. Nevertheless, these models do not utilize the full breadth of the knowledge available in datasets, which are oftentimes sparse; inherent correlations between different property datasets are disregarded. Here, we demonstrate the potency of multi-task learning approaches that exploit such inherent correlations effectively. Data pertaining to 36 different properties of over 13,000 polymers are supplied to deep-learning multi-task architectures. Compared to conventional single-task learning models, the multi-task approach is accurate, efficient, scalable, and amenable to transfer learning as more data on the same or different properties become available. Moreover, these models are interpretable. Chemical rules, that explain how certain features control trends in property values, emerge from the present work, paving the way for the rational design of application specific polymers meeting desired property or performance objectives.