Rational Design of Lipid-Based Vectors for Advanced Therapeutic Vaccines.

Recent advancements in vaccine delivery systems have seen the utilization of various materials, including lipids, polymers, peptides, metals, and inorganic substances, for constructing non-viral vectors. Among these, lipid-based nanoparticles, composed of natural, synthetic, or physiological lipid/phospholipid materials, offer significant advantages such as biocompatibility, biodegradability, and safety, making them ideal for vaccine delivery. These lipid-based vectors can protect encapsulated antigens and/or mRNA from degradation, precisely tune chemical and physical properties to mimic viruses, facilitate targeted delivery to specific immune cells, and enable efficient endosomal escape for robust immune activation. Notably, lipid-based vaccines, exemplified by those developed by BioNTech/Pfizer and Moderna against COVID-19, have gained approval for human use. This review highlights rational design strategies for vaccine delivery, emphasizing lymphoid organ targeting and effective endosomal escape. It also discusses the importance of rational formulation design and structure-activity relationships, along with reviewing components and potential applications of lipid-based vectors. Additionally, it addresses current challenges and future prospects in translating lipid-based vaccine therapies for cancer and infectious diseases into clinical practice.

Global PM2.5 Prediction and Associated Mortality to 2100 under Different Climate Change Scenarios.

Ambient fine particulate matter (PM2.5) has severe adverse health impacts, making it crucial to reduce PM2.5 exposure for public health. Meteorological and emissions factors, which considerably affect the PM2.5 concentrations in the atmosphere, vary substantially under different climate change scenarios. In this work, global PM2.5 concentrations from 2021 to 2100 were generated by combining the deep learning technique, reanalysis data, emission data, and bias-corrected CMIP6 future climate scenario data. Based on the estimated PM2.5 concentrations, the future premature mortality burden was assessed using the Global Exposure Mortality Model. Our results reveal that SSP3-7.0 scenario is associated with the highest PM2.5 exposure, with a global concentration of 34.5 µg/m3 in 2100, while SSP1-2.6 scenario has the lowest exposure, with an estimated of 15.7 µg/m3 in 2100. PM2.5-related deaths for individuals under 75 years will decrease by 16.3 and 10.5% under SSP1-2.6 and SSP5-8.5, respectively, from 2030s to 2090s. However, premature mortality for elderly individuals (>75 years) will increase, causing the contrary trends of improved air quality and increased total PM2.5-related deaths in the four SSPs. Our results emphasize the need for stronger air pollution mitigation measures to offset the future burden posed by population age.

14, 15-EET alleviates neurological impairment through maintaining mitochondrial dynamics equilibrium via AMPK/SIRT1/FoxO1 signal pathways in mice with cerebral ischemia reperfusion.

AIM: To explore whether 14, 15-EET regulates mitochondrial dynamics to exert neuroprotective effects after cerebral ischemia-reperfusion and its underlying mechanisms. METHODS: The mouse middle cerebral artery occlusion reperfusion model was used to observe brain infarct volume and neuronal apoptosis by TTC staining and Tunel assay, modified neurological severity score to detect neurological impairment, HE staining and Nissl staining to observe neuron damage, western blot and immunofluorescence methods to detect the expression of mitochondrial dynamics-related proteins, transmission electron microscopy, and Golgi-Cox staining to detect mitochondrial morphology and neuronal dendritic spines. RESULTS: 14, 15-EET reduced the neuronal apoptosis and cerebral infarction volume induced by middle cerebral artery occlusion reperfusion (MCAO/R), inhibited the degradation of dendritic spines, maintained the structural integrity of neurons, and alleviated neurological impairment. Cerebral ischemia-reperfusion induces mitochondrial dynamics disorders, upregulates the expression of the mitochondrial division protein Fis 1, and inhibits the expression of mitochondrial fusion proteins MFN1, MFN2, and OPA1, while 14, 15-EET treatment reverses this process. Mechanistic studies have shown that 14, 15-EET promotes the phosphorylation of AMPK, upregulates the expression of SIRT1 and phosphorylation of FoxO1, thereby inhibiting mitochondrial division and promoting mitochondrial fusion, preserving mitochondrial dynamics, maintaining neuronal morphological and structural integrity, and alleviating neurological impairment induced by middle cerebral artery occlusion reperfusion. Compound C treatment diminishes the neuroprotective effect of 14, 15-EET following MCAO/R in mice. CONCLUSION: This study elucidates the novel neuroprotective mechanism of 14, 15-EET, providing a novel approach for the development of drugs based on mitochondrial dynamics.

Development of an integrated machine-learning and data assimilation framework for NOx emission inversion.

As major air pollutants, nitrogen oxides (NOx, mainly comprising NO and NO2) not only have adverse effects on human health but also contribute to the formation of secondary pollutants, such as ozone and particulate nitrate. To acquire reasonable NOx simulation results for further analysis, a reasonable emission inventory is needed for three-dimensional chemical transport models (3D-CTMs). In this study, a comprehensive emission adjustment framework for NOx emission, which integrates the simulation results of the 3D-CTM, surface NO2 measurements, the three-dimensional variational data assimilation method, and an ensemble back propagation neural network, was proposed and applied to correct NOx emissions over China for the summers of 2015 and 2020. Compared with the simulation using prior NOx emissions, the root-mean-square error, normalized mean error, and normalized mean bias decreased by approximately 40 %, 40 %, and 60 % in NO2 simulation using posterior NOx emissions corrected by the framework proposed in this work. Compared with the emissions for 2015, the NOx emission generally decreased by an average of 5 % in the simulation domain for 2020, especially in Henan and Anhui provinces, where the percentage reductions reached 24 % and 19 %, respectively. The proposed framework is sufficiently flexible to correct emissions in other periods and regions. The framework can provide reliable and up-to-date emission information and can thus contribute to both scientific research and policy development relating to NOx pollution.

The Role of CCL2/CCR2 Axis in Cerebral Ischemia-Reperfusion Injury and Treatment: From Animal Experiments to Clinical Trials.

C-C motif chemokine ligand 2 (CCL2) is a member of the monocyte chemokine protein family, which binds to its receptor CCR2 to induce monocyte infiltration and mediate inflammation. The CCL2/CCR2 signaling pathway participates in the transduction of neuroinflammatory information between all types of cells in the central nervous system. Animal studies and clinical trials have shown that CCL2/CCR2 mediate the pathological process of ischemic stroke, and a higher CCL2 level in serum is associated with a higher risk of any form of stroke. In the acute phase of cerebral ischemia-reperfusion, the expression of CCL2/CCR2 is increased in the ischemic penumbra, which promotes neuroinflammation and enhances brain injury. In the later phase, it participates in the migration of neuroblasts to the ischemic area and promotes the recovery of neurological function. CCL2/CCR2 gene knockout or activity inhibition can reduce the nerve inflammation and brain injury induced by cerebral ischemia-reperfusion, suggesting that the development of drugs regulating the activity of the CCL2/CCR2 signaling pathway could be used to prevent and treat the cell injury in the acute phase and promote the recovery of neurological function in the chronic phase in ischemic stroke patients.

Estimation and variation analysis of secondary inorganic aerosols across the Greater Bay Area in 2005 and 2015.

As the concentrations of primary components of fine particulate matter (PM2.5) have substantially decreased, the contribution of secondary inorganic aerosols to PM2.5 pollution has become more prominent. Therefore, understanding the variations in and characteristics of secondary inorganic aerosols is vital to further reducing PM2.5 concentrations in the future. In this study, an ensemble back-propagation neural network model was built by combining 3D numerical models, observation data, and machine learning methods, to estimate the concentrations of secondary inorganic aerosols (SO2-4, NO-3, and NH+4) across the Greater Bay Area (GBA) in 2005 and 2015. The ensemble model provided a better estimation than the 3D numerical air quality model, with higher correlation coefficients (approximately 0.85) and lower root mean square errors. The model revealed that the concentrations of the SO2-4, NO-3, and NH+4 decreased by 1.91, 0.20, and 0.49 µg/m3, respectively, from 2005 to 2015. To investigate the oxidation and acidy of sulfate, the sulfur oxidation ratio (SOR), degree of sulfate neutralization (DSN), and particle neutralization ratio (PNR) were calculated and analyzed for 2005 and 2015 across the GBA region. The SOR slightly increased in summer, but decreased in other seasons in 2015, indicating the overall weaker sulfate chemical formation due to sulfur emission control measures. The increasing DSN and PNR indicated that more sulfate was neutralized due to reduced sulfur emission and increased ammonia availability. Our study suggests that more effort is needed to control ammonia emission to further reduce the concentrations of SO2-4, NO-3, and NH+4 across the GBA region in the future.

Development and application of a hybrid long-short term memory - three dimensional variational technique for the improvement of PM2.5 forecasting.

The current state-of-the-art three-dimensional (3D) numerical model for air quality forecasting is restricted by the uncertainty from the emission inventory, physical/chemical parameterization, and meteorological prediction. Forecasting performance can be improved by using the 3D-variational (3D-VAR) technique for assimilating the observation data, which corrects the initial concentration field. However, errors from the prognostic model cause the correction effects at the first hour to be erased, and the bias of the forecast increases relatively fast as the simulation progresses. As an emerging alternative technique, long short-term memory (LSTM) shows promising performance in air quality forecasting for individual stations and outperforms the traditional persistent statistical models. In this study, a new method was developed to combine a 3D numerical model with 3D-VAR and LSTM techniques. This method integrates the advantage of LSTM, namely its high-accuracy forecasting for a single station and that of the 3D-VAR technique, namely its ability to extend improvement to the whole simulation domain. This hybrid method can effectively improve PM2.5 forecasting for the next 24 h, relative to forecasting with the 3D-VAR technique which uses the initial hour concentration correction. Results showed that the root-mean-square error and normalized mean error were decreased by 29.3% and 33.3% in the validation stations, respectively. The LSTM-3D-VAR method developed in this study can be further applied in other regions to improve the forecasting of PM2.5 and other ambient pollutants.

Impacts of urbanization and long-term meteorological variations on global PM2.5 and its associated health burden.

PM2.5 pollution has adverse health effects on humans. Urbanization and long-term meteorological variations play important roles in influencing the PM2.5 concentration and its associated health effects. Our results indicate that the urbanization process can enhance the PM2.5 concentration globally. The PM2.5-caused mortality density (deaths/100 km2) is also positively correlated with the urbanization degree in both developed and developing countries. The results from machine learning technique revealed that the meteorology-driven variation in PM2.5-caused health burden has increased with the increase in the urbanization degree from 1980 to 2018, suggesting that residents living in urban areas are more vulnerable to experiencing unfavorable meteorological conditions (e.g. low wind speed and planetary boundary layer height). The maximum difference in PM2.5-caused mortality due to the variation in annual meteorological conditions (between 2013 and 1986) was 270 600 (196 800-317 900). Our findings indicate an urgent need to understand the driving force behind the appearance of unfavorable meteorological situations and propose suitable climate mitigation measures.

Estimations of Long-Term nss-SO42- and NO3- Wet Depositions over East Asia by Use of Ensemble Machine-Learning Method.

Wet deposition of non-sea-salt sulfate (nss-SO42-) and nitrate (NO3-), derived from anthropogenic emissions of SO2 and NOx, exerts adverse effects on ecosystems. In this work, an ensemble back-propagation neural network was proposed to estimate the long-term wet depositions of nss-SO42- (2005-2017) and NO3- (2001-2014) over East Asia in 10 km resolution. The R2 values for the 10-fold cross-validation of annual wet depositions of nss-SO42- and NO3- were 0.90 and 0.85, respectively. The hotspots of the wet deposition of these two acidic species span southwestern, central, and eastern China. The molar ratio of NO3- to nss-SO42- increased in 10 out of 12 analyzed East Asian countries from 2005 to 2014, which indicates that the acidity in rainwater shifts from the sulfur type to nitrogen type over most of the regions. The wet deposition on the four ecosystems (forest, grassland, cropland, and freshwater body) was also analyzed. Results showed that the nss-SO42- wet deposition on 25.5% of freshwater bodies in 2015 and NO3- wet deposition on 21.7% of grassland in 2014 exceeded the ecosystem empirical critical loads (25 kg/ha sulfate and 2 kg N/ha) in East Asia. Thus, more stringent and regionally collaborative sulfur and nitrogen emission-control measures are urgently needed to protect the ecosystem of East Asia.

Source and exposure apportionments of ambient PM2.5 under different synoptic patterns in the Pearl River Delta region.

PM2.5 is one of the most notorious ambient pollutants in the Pearl River Delta (PRD) region during episodic conditions. In this work, the Comprehensive Air Quality Model with extension (CAMx) was used together with the Particulate Source Apportionment Technology (PSAT) module to analyze the influences of different sources on PM2.5 concentration in the PRD region under different synoptic patterns (sea high pressure, sub-tropical high pressure and equalizing pressure field). The result shows that the PM2.5 concentration increases to different degrees under the three synoptic patterns. The emissions outside the PRD region contribute more than 54% under episodic conditions. The source category contribution varies little under different synoptic patterns. Area (46%), mobile (21%) and industry point source (16%) are the major contributors over the three episodic cases. The regional source contributions (from other cities within the PRD) to Foshan, Zhongshan and Zhaoqing are larger and can reach up to 33%. People living in the PRD region are more exposed to pollutants produced from the area and mobile sources. About 80% of the population is exposed to PM2.5 levels exceeding the IT-3 standard during the pollution episodes.

Differences in concentration and source apportionment of PM2.5 between 2006 and 2015 over the PRD region in southern China.

During China's 11th Five Year Plan (FYP) and 12th FYP (2006-2015), a series of air pollution control measures was implemented in the Pearl River Delta (PRD) region. Therefore, it is vital to determine how the concentration and sources of fine particulate matter (PM2.5) in this region changed between 2006 and 2015. In this work, using 2006 and 2015 emission inventories, the concentration and source apportionment of PM2.5 were simulated using the Weather Research and Forecast - Comprehensive Air Quality Model with Extensions (WRF-CAMx) for January, April, July and October in the PRD region. The PM2.5 in 10 cities and the contributions made by sources in six major categories were tracked using the Particulate Source Apportionment Technology (PSAT) module. The results showed that the PM2.5 concentration was lower across the entire PRD region in the 2015 emission scenario than in the 2006 scenario, and that the degree of this reduction exceeded 40 µg/m3 in some places. The PM2.5 contributed by mobile emissions decreased the most, especially in Guangzhou, Foshan and Shenzhen, where mobile contributions decreased from 15.0, 17.9 and 13.0 µg/m3 in 2006 to 2.6, 3.1 and 4.1 µg/m3 in 2015, respectively. The PM2.5 contributed by power plants also decreased, and in Dongguan and Guangzhou, the extent of this reduction reached 2.5 and 3.4 µg/m3 respectively. However, due to an increase in industrial production and population size, the PM2.5 from industrial point sources and area sources also increased between 2006 and 2015 in some of the cities. Investigation of the source apportionment for city centers yielded similar results. In addition to emissions within the PRD region, outside-PRD non-local contribution is still an important PM2.5 contributor. Hence, more stringent policies for controlling industrial and area sources and deepening province-to-province cooperation are urgently needed as the next step in PM2.5 control.

Analysis of the adverse health effects of PM2.5 from 2001 to 2017 in China and the role of urbanization in aggravating the health burden.

In this study, the trend of PM2.5 concentrations and its adverse health effects in China from 2001 to 2017 are estimated utilizing 1-km high-resolution annual satellite-retrieved PM2.5 data. PM2.5 concentrations for most of the provinces/cities remained stable from 2001 to 2012; however, following the issue of the Air Pollution Prevention and Control Action Plan (APPCAP) by the central government of China, a dramatic decrease in PM2.5 concentrations from 2013 to 2017 occurred. Premature mortality caused by PM2.5 dropped from 1,078,800 in 2014 to 962,900 in 2017. The PM2.5 caused 17-year average mortality ranges from 3800 in Hainan Province to 124,800 in Henan Province. The health cost benefits gained by the reduction of PM2.5 pollution amounted to US $193,800 in 2017 (compared to the costs due to PM2.5 concentrations in 2013), amounting to 1.58% of the total national GDP. The impacts of urbanization on PM2.5 concentration and mortality are analyzed. The PM2.5 concentration and its induced mortality density in dense urban areas are much higher than those in rural areas. The aggravation of PM2.5 associated premature mortality in urban areas is mainly due to the larger amount of emissions and to urban migration, and 6500 deaths in 2014 could have been avoided were the population ratios in dense-urban/normal-urban/rural areas to be reversed to the ones in 2001. It is recommended that people with respiratory-related diseases live in rural areas, where the pollutant concentration is relatively low.