Impact of anthropogenic emission control in reducing future PM2.5 concentrations and the related oxidative potential across different regions of China.

Affected by both future anthropogenic emissions and climate change, future prediction of PM2.5 and its Oxidative Potential (OP) distribution is a significant challenge, especially in developing countries like China. To overcome this challenge, we estimated historical and future PM2.5 concentrations and associated OP using the Danish Eulerian Hemispheric Model (DEHM) system with meteorological input from WRF weather forecast model. Considering different future socio-economic pathways and emission scenario assumptions, we quantified how the contribution from various anthropogenic emission sectors will change under these scenarios. Results show that compared to the CESM_SSP2-4.5_CLE scenario (based on moderate radiative forcing and Current Legislation Emission), the CESM_SSP1-2.6_MFR scenario (based on sustainability development and Maximum Feasible Reductions) is projected to yield greater environmental and health benefits in the future. Under the CESM_SSP1-2.6_MFR scenario, annual average PM2.5 concentrations (OP) are expected to decrease to 30 (0.8 nmolmin-1m-3) in almost all regions by 2030, which will be 65 % (67 %) lower than that in 2010. From a long-term perspective, it is anticipated that OP in the Fen-Wei Plain region will experience the maximum reduction (82.6 %) from 2010 to 2049. Largely benefiting from the effective control of PM2.5 in the region, it has decreased by 82.1 %. Crucially, once emission reduction measures reach a certain level (in 2040), further reductions become less significant. This study also emphasized the significant role of secondary aerosol formation and biomass-burning sources in influencing OP during both historical and future periods. In different scenarios, the reduction range of OP from 2010 to 2049 is estimated to be between 71 % and 85 % by controlling precursor emissions involved in secondary aerosol formation and emissions from biomass burning. Results indicate that strengthening the control of anthropogenic emissions in various regions are key to achieving air quality targets and safeguarding human health in the future.

Chemoproteomics-based profiling reveals potential antimalarial mechanism of Celastrol by disrupting spermidine and protein synthesis.

BACKGROUND: Malaria remains a global health burden, and the emergence and increasing spread of drug resistance to current antimalarials poses a major challenge to malaria control. There is an urgent need to find new drugs or strategies to alleviate this predicament. Celastrol (Cel) is an extensively studied natural bioactive compound that has shown potentially promising antimalarial activity, but its antimalarial mechanism remains largely elusive. METHODS: We first established the Plasmodium berghei ANKA-infected C57BL/6 mouse model and systematically evaluated the antimalarial effects of Cel in conjunction with in vitro culture of Plasmodium falciparum. The potential antimalarial targets of Cel were then identified using a Cel activity probe based on the activity-based protein profiling (ABPP) technology. Subsequently, the antimalarial mechanism was analyzed by integrating with proteomics and transcriptomics. The binding of Cel to the identified key target proteins was verified by a series of biochemical experiments and functional assays. RESULTS: The results of the pharmacodynamic assay showed that Cel has favorable antimalarial activity both in vivo and in vitro. The ABPP-based target profiling showed that Cel can bind to a number of proteins in the parasite. Among the 31 identified potential target proteins of Cel, PfSpdsyn and PfEGF1-α were verified to be two critical target proteins, suggesting the role of Cel in interfering with the de novo synthesis of spermidine and proteins of the parasite, thus exerting its antimalarial effects. CONCLUSIONS: In conclusion, this study reports for the first time the potential antimalarial targets and mechanism of action of Cel using the ABPP strategy. Our work not only support the expansion of Cel as a potential antimalarial agent or adjuvant, but also establishes the necessary theoretical basis for the development of potential antimalarial drugs with pentacyclic triterpenoid structures, as represented by Cel. Video Abstract.

Investigating the cumulative lag effects of environmental exposure under urban differences on COVID-19.

Although all walks of life are paying less attention to COVID-19, the spread of COVID-19 has never stopped. As an infectious disease, its transmission speed is closely related to the atmosphere environment, particularly the temperature (T) and PM2.5 concentrations. However, How T and PM2.5 concentrations are related to the spread of SARS-CoV-2 and how much their cumulative lag effect differ across cities is unclear. To identify the characteristics of cumulative lag effects of environmental exposure under city differences, this study used a generalized additive model to investigate the associations between T/PM2.5 concentrations and the daily number of new confirmed COVID-19 cases (NNCC) during the outbreak period in the second half of 2021 in Shaoxing, Shijiazhuang, and Dalian. The results showed that except for PM2.5 concentrations in Shaoxing, the NNCC in the three cities generally increased with the unit increase of T and PM2.5 concentrations. In addition, the cumulative lag effects of T/PM2.5 concentrations on NNCC in the three cities reached a peak at lag 26/25, lag 10/26, and lag 18/13 days, respectively, indicating that the response of NNCC to T and PM2.5 concentrations varies among different regions. Therefore, combining local meteorological and air quality conditions to adopt responsive measures is an important way to prevent and control the spread of SARS-CoV-2.

Development and validation of a clinical predictive model for 1-year prognosis in coronary heart disease patients combine with acute heart failure.

Background: The risk factors for acute heart failure (AHF) vary, reducing the accuracy and convenience of AHF prediction. The most common causes of AHF are coronary heart disease (CHD). A short-term clinical predictive model is needed to predict the outcome of AHF, which can help guide early therapeutic intervention. This study aimed to develop a clinical predictive model for 1-year prognosis in CHD patients combined with AHF. Materials and methods: A retrospective analysis was performed on data of 692 patients CHD combined with AHF admitted between January 2020 and December 2020 at a single center. After systemic treatment, patients were discharged and followed up for 1-year for major adverse cardiovascular events (MACE). The clinical characteristics of all patients were collected. Patients were randomly divided into the training (n = 484) and validation cohort (n = 208). Step-wise regression using the Akaike information criterion was performed to select predictors associated with 1-year MACE prognosis. A clinical predictive model was constructed based on the selected predictors. The predictive performance and discriminative ability of the predictive model were determined using the area under the curve, calibration curve, and clinical usefulness. Results: On step-wise regression analysis of the training cohort, predictors for MACE of CHD patients combined with AHF were diabetes, NYHA ≥ 3, HF history, Hcy, Lp-PLA2, and NT-proBNP, which were incorporated into the predictive model. The AUC of the predictive model was 0.847 [95% confidence interval (CI): 0.811-0.882] in the training cohort and 0.839 (95% CI: 0.780-0.893) in the validation cohort. The calibration curve indicated good agreement between prediction by nomogram and actual observation. Decision curve analysis showed that the nomogram was clinically useful. Conclusion: The proposed clinical prediction model we have established is effective, which can accurately predict the occurrence of early MACE in CHD patients combined with AHF.

Predictive Value of Non-high-Density Lipoprotein Cholesterol and Neutrophil-Lymphocyte Ratio for Coronary Artery Vulnerable Plaques in Type 2 Diabetes Mellitus.

Background: Patients with diabetes have an increased risk of developing vulnerable plaques (VPs), in which dyslipidemia and chronic inflammation play important roles. Non-high-density lipoprotein cholesterol (non-HDL-C) and neutrophil-lymphocyte ratio (NLR) have emerged as potential markers of both coronary artery VPs and cardiovascular prognosis. This study aimed to investigate the predictive value of non-HDL-C and NLR for coronary artery VPs in patients with type 2 diabetes mellitus (T2DM). Methods: We retrospectively enrolled 204 patients with T2DM who underwent coronary computed tomography angiography between January 2018 and June 2020. Clinical data including age, sex, hypertension, smoking, total cholesterol, low-density lipoprotein cholesterol, HDL-C, triglyceride, non-HDL-C, glycated hemoglobin, neutrophil count, lymphocyte count, NLR, and platelet count were analyzed. Multivariate logistic regression was used to estimate the association between non-HDL-C, NLR, and coronary artery VPs. Receiver operating curve analysis was performed to evaluate the value of non-HDL-C, NLR, and their combination in predicting coronary artery VPs. Results: In our study, 67 patients (32.84%) were diagnosed with VPs, 75 (36.77%) with non-VP, and 62 (30.39%) with no plaque. Non-HDL-C and NLR were independent risk factors for coronary artery VPs in patients with T2DM. The areas under the ROC curve of non-HDL-C, NLR, and their combination were 0.748 [95% confidence interval (CI): 0.676-0.818], 0.729 (95% CI: 0.650-0.800), and 0.825 (95% CI: 0.757-0.887), respectively. Conclusion: Either non-HDL-C or NLR could be used as a predictor of coronary artery VPs in patients with T2DM, but the predictive efficiency and sensitivity of their combination would be better.

Research on Multi-Terminal's AC Offloading Scheme and Multi-Server's AC Selection Scheme in IoT.

Mobile Edge Computing (MEC) technology and Simultaneous Wireless Information and Power Transfer (SWIPT) technology are important ones to improve the computing rate and the sustainability of devices in the Internet of things (IoT). However, the system models of most relevant papers only considered multi-terminal, excluding multi-server. Therefore, this paper aims at the scenario of IoT with multi-terminal, multi-server and multi-relay, in which can optimize the computing rate and computing cost by using deep reinforcement learning (DRL) algorithm. Firstly, the formulas of computing rate and computing cost in proposed scenario are derived. Secondly, by introducing the modified Actor-Critic (AC) algorithm and convex optimization algorithm, we get the offloading scheme and time allocation that maximize the computing rate. Finally, the selection scheme of minimizing the computing cost is obtained by AC algorithm. The simulation results verify the theoretical analysis. The algorithm proposed in this paper not only achieves a near-optimal computing rate and computing cost while significantly reducing the program execution delay, but also makes full use of the energy collected by the SWIPT technology to improve energy utilization.

CircPan3 Promotes the Ghrelin System and Chondrocyte Autophagy by Sponging miR-667-5p During Rat Osteoarthritis Pathogenesis.

This study aimed to investigate the potential roles of circRNAs in regulating osteoarthritis (OA)-related ghrelin synthesis, autophagy induction, and the relevant molecular mechanisms. Results showed that Col2a1, Acan, ghrelin, and autophagy-related markers expression were downregulated, while matrix metalloproteinase 13 (MMP13) and a disintegrin and metalloproteinase with thrombospondin motifs 5 (ADAMTS5) expressions increased in both IL-1ß-induced rat chondrocytes and cartilage tissues of OA rats. A total of 130 circRNAs and 731 mRNAs were differentially expressed in IL-1ß-induced rat chondrocytes. Among them, we found that circPan3 expression was significantly decreased in both cellular and animal OA models. CircPan3 directly targeted miR-667-5p. CircPan3 overexpression promoted Col2a1, Acan, ghrelin, beclin 1, and LC3-II expression but reduced MMP13 and ADAMTS5 expression in rat chondrocytes, whereas overexpression of miR-667-5p exhibited opposite effects on the above markers. Furthermore, we found that miR-667-5p bound directly to the 3'-UTR sequence of ghrelin gene. Moreover, the circPan3-induced alterations in chondrocytes were antagonized by miR-667-5p overexpression. Taken together, our findings demonstrate that circPan3 promotes ghrelin synthesis and chondrocyte autophagy via targeting miR-667-5p, protecting against OA injury. This study provided experimental evidence that circPan3/miR-667-5p/ghrelin axis might serve as targets of drug development for the treatment of OA.

Synergistic Effect of Combined Mirror Therapy on Upper Extremity in Patients With Stroke: A Systematic Review and Meta-Analysis.

Background: There is an increasing trend for researchers to combine mirror therapy with another rehabilitation therapy when treating the upper extremity of patients with stroke. Objective: To evaluate the synergistic effect of combined mirror therapy (MT) on the upper extremity in patients with stroke and to judge efficacies of four combined mirror therapy subgroups [EMGBF group: electromyographic biofeedback (EMGBF) + MT; MG group: mesh glove (MG) + MT; AT group: acupuncture (AT) + MT; ES group: EMG-triggered electrical stimulation (ES) + MT]. Methods: CNKI, Wan Fang, VIP, Web of Science, ScienceDirect, PubMed, OVID LWW, and Cochrane were used. We searched these databases for randomized controlled trials published from January 2013 to August 2019, which presented results of combining mirror therapy with other rehabilitation therapies. Quality assessments were performed using the Cochrane Handbook criteria in order to accurately review interventions. The primary outcomes were measured by the Fugl-Meyer Assessment-upper extremity (FMA-UE). Results: Ten trials, with a total of 444 patients whose upper limb functions were damaged after stroke, were included in the meta-analysis. Compared with the control group, a remarkable effect of combined mirror therapy [all: weight mean difference in random effects model (WMD): 8.07, 95% confidence interval (CI) 5.87, 10.26] on functional recovery of the upper limb was detected. However, a high value of heterogeneity (χ2 = 20.09, df = 9; I 2 = 55%) was found. The subgroup analysis (EMGBF group: WMD = 8.95, 95% CI 6.33, 11.58; ES group: WMD = 10.14, 95% CI: 5.67, 15.01) showed moderate improvement in functional recovery of the upper extremity in patients with stroke when mirror therapy was combined with conventional therapy. Furthermore, no difference in efficacy on upper extremity in patients with stroke was observed between the EMGBF group and the ES group. Conclusion: Despite the heterogeneity, the results indicate that combining mirror therapy with another rehabilitation therapy on the upper extremity in patients with stroke is better than single rehabilitation therapy. However, more randomized controlled clinical trials and larger sample sizes are required for an in-depth meta-analysis.