Effects of Temperature and DC Electric Fields on Perfluorooctanoic Acid Sorption Kinetics to Activated Carbon.

Sorption to activated carbon is a common approach to reducing environmental risks of waterborne perfluorooctanoic acid (PFOA), while effective and flexible approaches to PFOA sorption are needed. Variations in temperature or the use of electrokinetic phenomena (electroosmosis and electromigration) in the presence of external DC electric fields have been shown to alter the contaminant sorption of contaminants. Their role in PFOA sorption, however, remains unclear. Here, we investigated the joint effects of DC electric fields and the temperature on the sorption of PFOA on activated carbon. Temperature-dependent batch and column sorption experiments were performed in the presence and absence of DC fields, and the results were evaluated by using different kinetic sorption models. We found an emerging interplay of DC and temperature on PFOA sorption, which was linked via the liquid viscosity (η) of the electrolyte. For instance, the combined presence of a DC field and low temperature increased the PFOA loading up to 38% in 48 h relative to DC-free controls. We further developed a model that allowed us to predict temperature- and DC field strength-dependent electrokinetic benefits on the drivers of PFOA sorption kinetics (i.e., intraparticle diffusivity and the film mass transfer coefficient). Our insights may give rise to future DC- and temperature-driven applications for PFOA sorption, for instance, in response to fluctuating PFOA concentrations in contaminated water streams.

Independent and Complementary Functions of Caf1b and Hir1 for Chromatin Assembly in Tetrahymena thermophila.

Histones and DNA associate to form the nucleosomes of eukaryotic chromatin. Chromatin assembly factor 1 (CAF-1) complex and histone regulatory protein A (HIRA) complex mediate replication-couple (RC) and replication-independent (RI) nucleosome assembly, respectively. CHAF1B and HIRA share a similar domain but play different roles in nucleosome assembly by binding to the different interactors. At present, there is limited understanding for the similarities and differences in their respective functions. Tetrahymena thermophila contains transcriptionally active polyploid macronuclei (MAC) and transcriptionally silent diploid micronuclei (MIC). Here, the distribution patterns of Caf1b and Hir1 exhibited both similarities and distinctions. Both proteins localized to the MAC and MIC during growth, and to the MIC during conjugation. However, Hir1 exhibited additional signaling on parental MAC and new MAC during sexual reproduction and displayed a punctate signal on developing anlagen. Caf1b and Hir1 only co-localized in the MIC with Pcna1 during conjugation. Knockdown of CAF1B impeded cellular growth and arrested sexual reproductive development. Loss of HIR1 led to MIC chromosome defects and aborted sexual development. Co-interference of CAF1B and HIR1 led to a more severe phenotype. Moreover, CAF1B knockdown led to the up-regulation of HIR1 expression, while knockdown of HIR1 also led to an increase in CAF1B expression. Furthermore, Caf1b and Hir1 interacted with different interactors. These results showed that CAF-1 and Hir1 have independent and complementary functions for chromatin assembly in T. thermophila.

STMS-YOLOv5: A Lightweight Algorithm for Gear Surface Defect Detection.

Most deep-learning-based object detection algorithms exhibit low speeds and accuracy in gear surface defect detection due to their high computational costs and complex structures. To solve this problem, a lightweight model for gear surface defect detection, namely STMS-YOLOv5, is proposed in this paper. Firstly, the ShuffleNetv2 module is employed as the backbone to reduce the giga floating-point operations per second and the number of parameters. Secondly, transposed convolution upsampling is used to enhance the learning capability of the network. Thirdly, the max efficient channel attention mechanism is embedded in the neck to compensate for the accuracy loss caused by the lightweight backbone. Finally, the SIOU_Loss is adopted as the bounding box regression loss function in the prediction part to speed up the model convergence. Experiments show that STMS-YOLOv5 achieves frames per second of 130.4 and 133.5 on the gear and NEU-DET steel surface defect datasets, respectively. The number of parameters and GFLOPs are reduced by 44.4% and 50.31%, respectively, while the mAP@0.5 reaches 98.6% and 73.5%, respectively. Extensive ablation and comparative experiments validate the effectiveness and generalization capability of the model in industrial defect detection.

Ensemble learning-based applied research on heavy metals prediction in a soil-rice system.

Accurate prediction of heavy metal accumulation in soil ecosystems is crucial for maintaining healthy soil environments and ensuring high-quality agricultural products, as well as a challenging scientific task. In this study, we constructed a dataset containing 490 sets of multidimensional environmental covariate data and proposed prediction models for heavy metal concentrations (HMC) in a soil-rice system, EL-HMC (including RF-HMC and GBM-HMC), based on Random Forest (RF) and Gradient Boosting Machine (GBM) ensemble learning (EL) techniques. To reasonably evaluate the effectiveness of each model, Multiple linear and Bayesian regressions were selected as benchmark models (BM), and mean absolute error (MAE), root mean square error (RMSE), and determination coefficient R2 were selected as evaluation indicators. In addition, sensitivity and spatial autocorrelation (SAC) analyses were used to examine the robustness of the model. The results showed that the R2 values of RF-HMC and GBM-HMC for modeling available cadmium (Cd) concentrations in soil were 0.654 and 0.690, respectively, with an average increase of 48.0 % compared to the BMs. The R2 values of RF-HMC and GBM-HMC for predicting Cd, lead (Pb), chromium (Cr), and mercury (Hg) concentrations in rice ranged from 0.618 to 0.824 and 0.645 to 0.850, respectively, with an average increase of 58.2 % compared with the BMs. The corresponding MAEs and RMSEs of RF-HMC and GBM-HMC had low error levels. Sensitivity analysis of the input features and the SAC of the prediction bias showed that the EL-HMC models have excellent robustness. Therefore, the EL technology-based prediction models for HMCs proposed herein are practical and feasible, demonstrating better accuracy and stability than the traditional model. This study verifies the application potential of EL technology in pollution ecology and provides a new perspective and solution for sustainable management and precise prevention of heavy metal pollution in farmland soil at the regional scale.

Prediction of bronchodilation test in adults with chronic cough suspected of cough variant asthma.

Background: Many patients with cough variant asthma (CVA) are underdiagnosed and undertreated due to the atypical symptoms, low diagnostic sensitivity of bronchodilator response (BDR), and limited application of bronchial challenge test. Objective: To investigate whether airway reversibility in BDR can predict CVA diagnosis in patients with chronic cough and negative BDR. Methods: This open-label, prospective cohort study included patients with chronic cough, nearly normal chest CT scan, and negative BDR results. Inhaled corticosteroids and long-acting ß2 agonists were given for 4 weeks. The confirmed diagnosis of CVA was defined as improved symptoms and an increase of forced expiratory volume in 1 s (FEV1) by >12% and >200 mL after 4 weeks of treatment. Results: Of 155 patients recruited, 140 completed the study. Patients in the CVA positive diagnosis group had greater absolute (Δ) and percent (Δ%) improvements in FEV1 and forced expiratory flows (FEFs), and higher fractional exhaled nitric oxide (FENO) than in the CVA negative diagnosis group. The area under the receiver operating characteristic curves (AUCs) of ΔFEV1%, FEF25-75%pred (percentage of predicted forced expiratory flow at 25% to 75%) and FENO for CVA positive diagnosis was 0.825, 0.714, and 0.637, with cutoff values of 5.90%, 61.99% and 41.50 ppb, respectively. A joint model of ΔFEV1% combined with FEF25-75%pred or FENO increased the AUC to 0.848 and 0.847, respectively. Conclusion: ΔFEV1% in BDR can predict a CVA diagnosis and response to anti-asthma treatment in patients with chronic cough and negative BDR. Clinical trial registration: [http://www.chictr.org.cn/index.aspx], identifier [ChiCTR2000029065].

Driving effects and transfer prediction of heavy metal(loid)s in contaminated courtyard gardens using redundancy analysis and multilayer perceptron.

The distribution and migration of heavy metal(loid)s in the soil-vegetable systems of courtyard gardens near mining areas have rarely been investigated, leading to potential food safety risks for residents. Moreover, the existing research is mainly focused on the total content of heavy metal(loid)s (tMetals) rather than the bioavailable contents (aMetals). In this study, 26 and 28 pairs of soil and vegetable samples were collected from the courtyard gardens near the Realgar mine in Baiyun Town and the lead-zinc (Pb-Zn) mine in Shuikoushan Town, respectively. The tMetal and aMetal of cadmium (Cd), mercury (Hg), arsenic (As), Pb, chromium (Cr), nickel (Ni), copper (Cu), Zn, manganese (Mn), iron (Fe), and calcium (Ca) in the samples were analyzed in this study. The results showed that courtyard gardens were polluted by various heavy metal(loid)s at varying degrees. The bioavailabilities of different metals varied significantly, among which Cd has the highest bioavailability (> 30%). In the transfer process of heavy metal(loid)s, the transfer rate (Tf) was ranked as soil-roots (1.50) > stems-leaves (1.07) > roots-stems (0.46) > stems-fruits (0.33). Redundancy analysis was used to evaluate the driving effects, and the results revealed that aCa, aZn, and aFe in soil could inhibit the absorption of aCd by plant roots. Soil organic matter was the inhibiting factor regarding the transfer of aAs and aCu, whereas it was also the promoting factor for transferring aPb, aNi, and aCr. Furthermore, the multilayer perceptron (MLP) could effectively predict the Tf of heavy metal(loid)s based on the aMetal. The R2 values of the MLP were ranked as follows: 0.91 for As, 0.88 for Zn, 0.85 for Hg, 0.83 for Cu, 0.79 for Cr, 0.66 for Cd, 0.65 for Pb, and 0.52 for Ni. This study emphasizes the aMetal-based ecological characteristics and prediction ability. The study results are significant for guiding residents to strategize appropriate crop planting and ensure the safe production and consumption of vegetables.

Intelligent Detection Method of Gearbox Based on Adaptive Hierarchical Clustering and Subset.

Deep learning uses mechanical time-frequency signals to train deep neural networks, which realizes automatic feature extraction and intelligent diagnosis of fault features and gets rid of the dependence on a large number of signal processing technology and experience. Aiming at the problem of misclassification of similar samples, a fault diagnosis algorithm based on adaptive hierarchical clustering and subset (AHC-SFD) is proposed to extract features and applied to gearbox fault diagnosis. Firstly, the adaptive hierarchical clustering algorithm is used to analyze the characteristics of different data, and then the data set is clustered into multiple feature groups; finally, according to the feature group, the SubCNN model is established for multiscale feature extraction, so as to carry out fault diagnosis. The test results show that the fault recognition rate achieved by the proposed method is more than 99.7% on the gearbox dataset, and the method has better generalization ability.

Adipose-derived mesenchymal stem cells suppress ozone-mediated airway inflammation in a mouse model of chronic obstructive pulmonary disease.

OBJECTIVE AND DESIGN: Ozone exposure is an important risk factor for Chronic Obstructive Pulmonary Disease (COPD) which is a global public health concern. Until now, there is no effective approach to reverse airflow limitation and accelerated loss of lung function completely. Here, we delineate the efficacy of mouse allogeneic adipose-derived mesenchymal stem cells (mASCs) in the treatment of COPD mice by intratracheal and intravenous administration. METHODS: In this study, we established ozone-exposed COPD model in mice and were administered intratracheally or intravenously with mASCs which were extracted, cultured, and identified in vitro. RESULTS: We observed that exposure to ozone resulted in a marked lung neutrophilia with high levels of inflammatory cell counts, enhanced expression of cytokines IL-1ß and TNF-α, reduced expression of IL-10, lung function and airspace enlargement. mASCs intratracheal administration rescured the lung neutrophilia, lung function and emphysema-like phenotype. Similar results were observed in mice with mASCs intravenous administration. But the altered levels of serum cytokines in mice with mASCs intratracheal administration appears more robust than those in mice with mASCs intravenous administration. CONCLUSIONS: Collectively, these data indicate that intratracheal administration of mASCs appears more effective in treating ozone-induced COPD compared to intravenous administration of mASCs, although the two approaches can be comparable in safety. mASCs are expected to become a new potential intervention strategy for COPD.

Path Planning and Trajectory Tracking for Automatic Guided Vehicles.

Automated guided vehicle technology has become a hot area of scientific research due to its increasing use in manufacturing and logistics. Its main features are programming and control, remote computer eye tracking, command receiving and execution, autonomous route planning, and autonomous driving execution of tasks, with the advantages of high intelligence and flexibility. In this work, a simple vehicle model is used to study the route planning and tracking control of automatic guided vehicles. This paper uses wireless communication to find the optimal route planning problem. Using geometric methods, we develop a model of the working environment of the mobile automatic guided vehicle and develop a route finding algorithm. Based on the kinematic model, an advanced routing controller is designed to conduct experimental simulation of two trajectories and verify the effectiveness of the trajectory tracking controller. When the time is after 2 s, the position error is almost completely zero. In the path planning, when the number of iterations is greater than 10, the path length remains constant, verifying the effectiveness of the method in this paper.

Convolutional neural networks-based health risk modelling of some heavy metals in a soil-rice system.

The long-term consumption of heavy metal-rich rice can cause serious harm to human health. However, the existing health risk assessment (HRA) can only be performed after the rice has been harvested, and this approach belongs to a passive and lagging pattern. This study is the first to explore the feasibility of health risk (HR) prediction by proposing the indirect model CNNHR-IND and the direct model CNNHR-DIR based on the convolutional neural network (CNN) technology. The dataset included 390 pairs of soil-rice samples collected from You County, China, with 17 environmental covariates. The R2 values for CNNHR-IND for non-carcinogenic and carcinogenic risks were 0.578 and 0.554, respectively, and those for CNNHR-DIR were 0.647 and 0.574, respectively. The results demonstrated that both models performed well, especially CNNHR-DIR had a higher estimation accuracy. The spatial autocorrelation analysis indicated that CNNHR-DIR exerted no systematic bias in the prediction results for health risks, confirming the rationality of the CNNHR-DIR model. The sensitivity analysis further confirmed the generalizability and robustness of CNNHR-DIR. This study proved the feasibility of HR prediction and the potential of CNN technology in HRA, and is significant regarding early risk warnings of rice planting and the sustainable development of public health.

Probabilistic health risk assessment for residents exposed to potentially toxic elements near typical mining areas in China.

Public health problems caused by toxic elements in mining areas have always been an important topic worldwide. However, existing studies have focused on single exposure routes and common toxic elements, which might underestimate the risks faced by residents. In this study, three typical mining areas in central China were selected to assess the health risks of 14 potentially toxic elements through five exposure routes using Monte Carlo simulations. The results indicated that the 95th percentile non-carcinogenic risk values to humans via rice and vegetable ingestion ranged from 9.8 to 26.0 and 6.2 to 19.0. The corresponding carcinogenic risks ranged from 1.4E-2 to 6.3E-2 and from 2.9E-3 to 2.3E-2, respectively. Therefore, residents face serious health risks. Multi-element analysis showed that cadmium (Cd), boron (B), and arsenic (As) were the main contributors to rice non-carcinogenicity, whereas Cd and nickel (Ni) were the main elements of rice carcinogenicity. B and lead (Pb) played an essential role in the non-carcinogenesis of vegetables, and B, Ni, and Cd played an essential role in carcinogenesis. Accidental ingestion is the main route of soil exposure. In these three areas, the probability of non-carcinogenic risk faced by adults was 40%, 0%, and 1%, respectively, while the probabilities for children were 100%, 62%, and 83%, respectively. Regarding carcinogenicity, the risk for both adults and children was up to 100%. This study emphasizes the overall health risks in polluted areas via multi-route and multi-element analysis. This conclusion is helpful to comprehensively assess the potential health risks faced by residents in mining areas and provide baseline data support and a scientific basis for formulating reasonable risk control measures.

A field study to estimate heavy metal concentrations in a soil-rice system: Application of graph neural networks.

Accurate prediction of the concentration of heavy metals is of great significance for assessing the quality of agricultural products and reducing health risks. However, the complexity and interconnectivity of the farmland ecosystem restricts the improvement of the prediction accuracy of traditional methods. This research explored the application potential of graph neural network (GNN) technology, which can extract and learn information in large-scale networks in detail, in the field of heavy metal prediction for the first time. In this study, a heavy metal prediction model for rice, CoNet-GNN, was proposed with 17 environmental factors as input variables using the co-occurrence network and GNN. Experimental results using a dataset from a field study showed that the R2 of CoNet-GNN for predicting Cd, Pb, Cr, As, and Hg had outstanding values of 0.872, 0.711, 0.683, 0.489, and 0.824, respectively. Sensitivity analysis further indicated that CoNet-GNN had good stability and robustness. Compared with random forest, gradient boosting, and multilayer perceptron, CoNet-GNN made a remarkable improvement to the prediction accuracy of all studied heavy metals. Therefore, CoNet-GNN can effectively simulate the rich relationships and laws between various factors in the soil-rice system and effectively characterize the influence diffusion path. Furthermore, it provides new ideas for heavy metal prediction based on network research methods and expands the technical scope of heavy metal evaluation.

Convolutional neural network-based applied research on the enrichment of heavy metals in the soil-rice system in China.

The enrichment of heavy metals in the soil-rice system is affected by various factors, which hampers the prediction of heavy metal concentrations. In this research, a prediction model (CNN-HM) of heavy metal concentrations in rice was constructed based on convolutional neural network (CNN) technology and 17 environmental factors. For comparison, other machine learning models, such as multiple linear regression, Bayesian ridge regression, support vector machine, and backpropagation neural networks, were applied. Furthermore, the LH-OAT method was used to evaluate the sensitivity of CNN-HM to each environmental factor. The results showed that the R2 values of CNN-HM for Cd, Pb, Cr, As, and Hg were 0.818, 0.709, 0.688, 0.462, and 0.816, respectively, and both the MAE and RMAE values were acceptable. The sensitivity analysis showed that the concentrations of Cd and Pb, mechanical composition, soil pH, and altitude were the main sensitive features for CNN-HM. Compared with CNN-HM based on all input features, the performance of the quick prediction model that was based on the sensitive features did not degrade significantly, thereby indicating that CNN-HM has stronger stability and robustness. The quick prediction model has extensive application value for timely prediction of the enrichment of heavy metals in emergencies. This study demonstrated the effectiveness and practicability of CNNs in predicting heavy metal enrichment in the soil-rice system and provided a new perspective and solution for heavy metal prediction.

Histone Chaperone Nrp1 Mutation Affects the Acetylation of H3K56 in Tetrahymena thermophila.

Histone modification and nucleosome assembly are mainly regulated by various histone-modifying enzymes and chaperones. The roles of histone-modification enzymes have been well analyzed, but the molecular mechanism of histone chaperones in histone modification and nucleosome assembly is incompletely understood. We previously found that the histone chaperone Nrp1 is localized in the micronucleus (MIC) and the macronucleus (MAC) and involved in the chromatin stability and nuclear division of Tetrahymena thermophila. In the present work, we found that truncated C-terminal mutant HA-Nrp1TrC abnormally localizes in the cytoplasm. The truncated-signal-peptide mutants HA-Nrp1TrNLS1 and HA-Nrp1TrNLS2 are localized in the MIC and MAC. Overexpression of Nrp1TrNLS1 inhibited cellular proliferation and disrupted micronuclear mitosis during the vegetative growth stage. During sexual development, Nrp1TrNLS1 overexpression led to abnormal bouquet structures and meiosis arrest. Furthermore, Histone H3 was not transported into the nucleus; instead, it formed an abnormal speckled cytoplastic distribution in the Nrp1TrNLS1 mutants. The acetylation level of H3K56 in the mutants also decreased, leading to significant changes in the transcription of the genome of the Nrp1TrNLS1 mutants. The histone chaperone Nrp1 regulates the H3 nuclear import and acetylation modification of H3K56 and affects chromatin stability and genome transcription in Tetrahymena.

Is small airway dysfunction an abnormal phenomenon for patients with normal forced expiratory volume in 1 second and the ratio of forced expiratory volume in 1 second to forced vital capacity?

BACKGROUND: The clinical significance of small airway dysfunction (SAD) determined with spirometry in patients with normal forced expiratory volume in 1 second (FEV1) and the ratio of FEV1 to forced vital capacity (FVC) is controversial. OBJECTIVE: To determine whether SAD presents histologic abnormalities in the setting of normal computed tomography (CT) imaging and FEV1 and FEV1/FVC. METHODS: A cross-sectional study was performed in 64 patients undergoing thoracotomy for pulmonary nodules. Thoracic high-resolution CT (HRCT), bronchodilation test, and fractional exhaled nitric oxide (FENO) and its alveolar component (nitric oxide alveolar concentration [CANO]) were obtained before surgery. Lung pathology and levels of cytokines in lung tissue were measured. The patients were divided into SAD and small airway normal function groups according to forced expiratory flow at 75% and 50% of the FVC (maximal expiratory flow [MEF] 25, MEF50) and maximum midexpiratory flow. RESULTS: The MEF50, MEF25, and maximum midexpiratory flow were strongly negatively correlated with CANO (r, -0.42, -0.42, -0.40, respectively; P ≤ .001 for all). The MEFs were mildly negatively correlated with interleukin (IL)-6 and macrophages in lung tissue (r < -0.25, P < .001 for all). The CANO (P < .001), airspace size (mean linear intercept) (P = .02), macrophages (P = .003), IL-6 (P = .003), and IL-8 (P = .008) in lung tissue were higher in patients with SAD (n = 35) than those with small airway normal function (n = 29). A total of 8 patients (22.86%) with SAD and 2 (6.90%) without SAD had pneumatoceles (P = .10). CONCLUSION: Patients with pulmonary nodules and SAD were more likely to have abnormal inflammation and emphysematous destruction than patients without SAD. Thus, SAD indicates histologic abnormalities in patients with normal CT imaging and FEV1 and FEV1/FVC.

Probabilistic health risk assessment of inorganic arsenic and some heavy metals in rice produced from a typical multi-mining county, China.

The potential impact of exposure to toxic elements in rice on human health has become a global public health issue. This study analyzed the pollution characteristics and probabilistic health risks of exposure to iAs, Pb, Cd, Cr, and Hg in rice produced in a typical multi-mining county using Monte Carlo simulation, a geographic information system, and bioavailability analysis. The results showed that the enrichment of As and Cd was prominent in rice, with mean tAs, iAs, and Cd contents of 0.34 ± 0.20, 0.15 ± 0.09, and 0.48 ± 0.50 mg·kg-1, respectively. The probability of non-carcinogenic risk via rice consumption in adults and children exceeding the threshold was 72% and 78%, respectively, and that of carcinogenic risk was as high as 100%. Among toxic elements, Cd and iAs were the main risk factors for health risks. The high-level health-risk areas mainly occurred in the north-eastern and central parts of the study area. Sensitivity analysis highlighted that the top three influential parameters for non-carcinogenic risk in adults were Content(Cd), Content(iAs), and Bioaccessibility(Cd), whereas those in children were ingestion rate of rice, Content(Cd), and Content(iAs). The Content(Cd) was the decisive factor for carcinogenic risk, with a sensitivity coefficient of 78.0% in adults and 64.7% in children. Considering the high risk of ingestion of local rice in this area, it is imperative to place strict supervision and take control measures.

STAT3 and IL-6 Contribute to Corticosteroid Resistance in an OVA and Ozone-induced Asthma Model with Neutrophil Infiltration.

Exposure to high levels of ozone contributes to insensitivity to glucocorticoids in asthma treatment, but the underlying mechanisms are not known. We built two asthma models: a "T2-high" asthma model was established by ovalbumin (OVA) sensitization/challenge and OVA sensitization/challenge combined with ozone exposure (OVA + ozone) was used to induce airway inflammation with increased numbers of neutrophils to simulate "T2-low" asthma. The expression of T-helper (Th)1/2/17-related cytokines was measured by cytokine antibody arrays. Bronchial provocation tests were carried out to evaluate the lung resistance of mice. Hematoxylin and eosin staining, periodic acid-Schiff staining, and immunohistochemical (IHC) analyses of alpha-smooth muscle actin were undertaken to observe morphology changes in lungs. The expression of glucocorticoid receptors (GRs) and phosphorylated-GR (p-GR) was measured by western blotting. Nr3c1 mRNA was quantified by RT-qPCR. Protein expression of proinflammatory cytokines, signal transducer and activator of transcription 3 (STAT3), suppressor of cytokine signaling 3 (SOCS3), and CXCL1 was measured through ELISAs, western blotting, or IHC analyses. Resected lung tissue from seven asthma patients and 10 healthy controls undergoing thoracotomy for pulmonary nodules was evaluated by IHC analyses and ELISAs. In both asthma models, mucus hypersecretion, as well as inflammation, hyperresponsiveness, and remodeling of the airways, was present compared with the control group, whereas the OVA + ozone group showed severe neutrophil infiltration. The expression of Th17-related cytokines (interleukin (IL)-6, IL-17A, IL-21), GR protein, and CXCL1 increased in the OVA + ozone group, whereas the expression of p-GR decreased. Dexamethasone (Dex) could not totally reverse the expression of p-GR and histone deacetylase-2 in the OVA + ozone group. STAT3 expression increased in the OVA + ozone group and could not be completely reversed by Dex, and nor could IL-6 expression. A positive correlation between IL-6 or IL-17A and STAT3 and negative correlation between SOCS3 and STAT3 were shown, suggesting that the IL-6/STAT3 pathway may be involved in OVA + ozone-induced corticosteroid-resistant airway inflammation. In clinical samples, IL-17A expression in lung tissue was positively correlated with percent STAT3-positive area and negatively correlated with SOCS3 expression. The IL-6/STAT3 pathway may contribute to corticosteroid insensitivity in OVA + ozone-induced neutrophilic airway inflammation through regulation of Th17 cells and could provide new targets for individual treatment of corticosteroid resistance in asthma.

Early Fever Is Associated With Clinical Outcomes in Patients With Coronavirus Disease.

Fever is one of the typical symptoms of coronavirus disease (COVID-19). We aimed to investigate the association between early fever (EF) and clinical outcomes in COVID-19 patients. A total of 1,014 COVID-19 patients at the Leishenshan Hospital were enrolled and classified into the EF and non-EF groups based on whether they had fever within 5 days of symptom onset. Risk factors for clinical outcomes in patients with different levels of disease severity were analyzed using multivariable analyses. Time from symptom onset to symptom alleviation, CT image improvement, and discharge were longer for patients with moderate and severe disease in the EF group than in the non-EF group. Multivariable analysis showed that sex, EF, eosinophil number, C-reactive protein, and IL-6 levels were positively correlated with the time from symptom onset to hospital discharge in moderate cases. The EF patients showed no significant differences in the development of acute respiratory distress syndrome, compared with the non-EF patients. The Kaplan-Meier curve showed no obvious differences in survival between the EF and non-EF patients. However, EF patients with increased temperature showed markedly lower survival than the non-EF patients with increased temperature. EF had no significant impact on the survival of critically ill patients, while an increase in temperature was identified as an independent risk factor. EF appears to be a predictor of longer recovery time in moderate/severe COVID-19 infections. However, its value in predicting mortality needs to be considered for critically ill patients with EF showing increasing temperature.

The histone chaperone Nrp1 is required for chromatin stability and nuclear division in Tetrahymena thermophila.

Histone chaperones facilitate DNA replication and repair by promoting chromatin assembly, disassembly and histone exchange. Following histones synthesis and nucleosome assembly, the histones undergo posttranslational modification by different enzymes and are deposited onto chromatins by various histone chaperones. In Tetrahymena thermophila, histones from macronucleus (MAC) and micronucleus (MIC) have been comprehensively investigated, but the function of histone chaperones remains unclear. Histone chaperone Nrp1 in Tetrahymena contains four conserved tetratricopepeptide repeat (TPR) domains and one C-terminal nuclear localization signal. TPR2 is typically interrupted by a large acidic motif. Immunofluorescence staining showed that Nrp1 is located in the MAC and MICs, but disappeared in the apoptotic parental MAC and the degraded MICs during the conjugation stage. Nrp1 was also colocalized with α-tubulin around the spindle structure. NRP1 knockdown inhibited cellular proliferation and led to the loss of chromosome, abnormal macronuclear amitosis, and disorganized micronuclear mitosis during the vegetative growth stage. During sexual developmental stage, the gametic nuclei failed to be selected and abnormally degraded in NRP1 knockdown mutants. Affinity purification combined with mass spectrometry analysis indicated that Nrp1 is co-purified with core histones, heat shock proteins, histone chaperones, and DNA damage repair proteins. The physical direct interaction of Nrp1 and Asf1 was also confirmed by pull-down analysis in vitro. The results show that histone chaperone Nrp1 is involved in micronuclear mitosis and macronuclear amitosis in the vegetative growth stage and maintains gametic nuclei formation during the sexual developmental stage. Nrp1 is required for chromatin stability and nuclear division in Tetrahymena thermophila.

Small-Airway Dysfunction is Involved in the Pathogenesis of Asthma: Evidence from Two Mouse Models.

BACKGROUND: There has been growing evidence of small-airway dysfunction in patients with asthma. Few studies have evaluated the mechanism of small-airway dysfunction in mouse models of asthma. PURPOSE: We explored the correlation between small-airway spirometric variables and large-airway function or inflammation in different endotypes of asthma. METHODS: Ovalbumin (OVA) sensitization/challenge was used to produce a type 2 (T2)-high asthma model, and OVA combined with ozone exposure (OVA + ozone) was used for the T2-low asthma model with increased neutrophils. Spirometry, airway responsiveness, cytokine levels in bronchoalveolar lavage fluid (BALF), and pathological analyses of lung slices stained with hematoxylin-eosin, periodic acid-Schiff, and Masson's trichrome stain were all determined. Muc5ac expression in lung tissue was evaluated by the reverse transcription-polymerase chain reaction (RT-PCR), and alpha-smooth muscle actin was measured by immunohistochemistry. RESULTS: Inflammatory cells infiltrated the lung tissue and inflammatory cytokines were increased in the BALF of both the OVA and OVA + ozone groups, compared with the control group. Peribronchial hypersecretion and collagen deposition were evident in the models. The OVA + ozone group showed greater neutrophilic infiltration and peribronchial smooth muscle proliferation than the OVA group. Large-airway obstruction, small-airway dysfunction, and airway hyperresponsiveness were confirmed in both models. Small-airway functional variables, such as MMEF (mean midexpiratory flow, average flow from 25 to 75% forced vital capacity [FVC]) and FEF50 (forced expiratory flow at 50% of FVC), were positively correlated with large-airway function and had a stronger negative correlation with airway inflammation, mucus secretion, and responsiveness than large-airway function. CONCLUSION: Small-airway dysfunction was evident in the two endotypes of asthma and was correlated with severe airway inflammation, mucus hypersecretion, and airway hyperresponsiveness. The small airways may be an important target in asthma treatment, and further research in the role of small-airway variables in the pathogenesis of asthma is warranted.