Mapping multimorbidity progression among 190 diseases.

BACKGROUND: Current clustering of multimorbidity based on the frequency of common disease combinations is inadequate. We estimated the causal relationships among prevalent diseases and mapped out the clusters of multimorbidity progression among them. METHODS: In this cohort study, we examined the progression of multimorbidity among 190 diseases among over 500,000 UK Biobank participants over 12.7 years of follow-up. Using a machine learning method for causal inference, we analyzed patterns of how diseases influenced and were influenced by others in females and males. We used clustering analysis and visualization algorithms to identify multimorbidity progress constellations. RESULTS: We show the top influential and influenced diseases largely overlap between sexes in chronic diseases, with sex-specific ones tending to be acute diseases. Patterns of diseases that influence and are influenced by other diseases also emerged (clustering significance Pau > 0.87), with the top influential diseases affecting many clusters and the top influenced diseases concentrating on a few, suggesting that complex mechanisms are at play for the diseases that increase the development of other diseases while share underlying causes exist among the diseases whose development are increased by others. Bi-directional multimorbidity progress presents substantial clustering tendencies both within and across International Classification Disease chapters, compared to uni-directional ones, which can inform future studies for developing cross-specialty strategies for multimorbidity. Finally, we identify 10 multimorbidity progress constellations for females and 9 for males (clustering stability, adjusted Rand index >0.75), showing interesting differences between sexes. CONCLUSION: Our findings could inform the future development of targeted interventions and provide an essential foundation for future studies seeking to improve the prevention and management of multimorbidity.

Mapping out clusters of diseases is crucial to addressing the rising challenge of co-occurrence of multiple diseases, known as multimorbidity. However, the current way of grouping diseases based on their associations isn't enough to understand how they develop over time. We've come up with a new approach to map out how groups of diseases progress together based on the strength of their causal relationships. By looking at how each disease affects the development of others, we can get a better understanding of how they form clusters. Our research goes beyond just showing which diseases occur together, and it's a step toward improving how we prevent and manage multiple health conditions in the future.

The efficacy and safety of modified transbronchial cryobiopsy in the diagnosis of interstitial lung disease.

The objective of this study is to investigate the efficacy and safety of flexible transbronchial cryobiopsy (TBCB) in the diagnosis of diffuse parenchymal lung disease (DPLD) in a routine bronchoscopy examination room under analgesia and sedation, using neither endotracheal intubation or rigid bronchoscope nor fluoroscopy or general anesthesia. The data from 50 DPLD patients with unknown etiology who were treated in the Affiliated Hospital of Guilin Medical College from May 2018 to September 2020 were collected, and 43 were eventually included. The specimens obtained from these 43 patients were subjected to pathological examination, pathogenic microorganism culture, etc, and were analyzed in the clinical-radiological-pathological diagnosis mode to confirm the efficacy of TBCB in diagnosing the cause of DPLD. Subsequently, the intraoperative and postoperative complications of TBCB and their severity were closely observed and recorded to comprehensively evaluate the safety of TBCB. For the 43 patients included, a total of 85 TBCB biopsies were performed (1.98 [1, 4] times/case), and 82 valid tissue specimens were obtained (1.91 [1, 4] pieces/case), accounting for 96.5% (82/85) of the total sample. The average specimen size was 12.41 (1, 30) mm2. Eventually, 38 cases were diagnosed, including 11 cases of idiopathic pulmonary fibrosis, 5 cases of connective tissue-related interstitial lung disease, 5 cases of nonspecific interstitial pneumonia, 4 cases of tuberculosis, 4 cases of occupational lung injury, 3 cases of interstitial pneumonia with autoimmune characteristics, 1 case of lung cancer, 2 cases of interstitial lung disease (unclassified interstitial lung disease), 1 case of hypersensitivity pneumonitis, 1 case of pulmonary alveolar proteinosis, and 1 case of fungal infection. The remaining 5 cases were unclarified. For infectious diseases, the overall etiological diagnosis rate was 88.4% (38/43). With respect to complications, pneumothorax occurred in 4 cases (9.3%, 4/43, including 1 mild case and 3 moderate cases), of which 3 cases (75%) were closed by thoracic drainage and 1 case (25%) was absorbed without treatment. In addition, 22 cases experienced no bleeding (51.2%) and 21 cases suffered bleeding to varying degrees based on different severity assessment methods. TBCB is a minimally invasive, rapid, economical, effective, and safe diagnostic technique.

Combination of androgen and estrogen improves asthma by mediating Runx3 expression.

Objective: Asthma is a chronic heterogeneous airway disease, and imbalanced T-helper type 1 (Th1) and Th2 cell-mediated inflammation contribute to its pathogenesis. Although it has been suggested that androgen and estrogen were involved in development of asthma, the underlying mechanisms remained largely unclear. Studies have demonstrated that Runx3 could promote naive CD4+ T cells to differentiate into Th1 cells. Hence, our study aimed to explore the potential regulatory mechanism of androgen and estrogen on asthma via modulating Runx3. Methods: First, clinical assessments and pulmonary function tests were conducted on 35 asthma patients and 24 healthy controls. The concentrations of androgen, estrogen, and androgen estrogen ratios were assessed in peripheral blood samples of asthma patients and healthy controls. Then, a murine asthma model was established to explore the effects of estrogen and androgen (alone or in combination) on asthma. Third, an in vitro assay was used to explore the mechanism of combination of androgen and estrogen in asthma. Results: We observed decreased androgen and increased estrogen levels in asthma patients compared with healthy controls. In mice with experimental asthma, there were increased serum concentrations of estrogen and decreased serum concentrations of androgen, intervention with combination of androgen and estrogen alleviated airway inflammations, increased Runx3 expressions and elevated Th1 differentiation. In CD4+ T cells co-cultured with bronchial epithelial cells (BECs), treatment with androgen plus estrogen combination promoted Th1 differentiation, which was mitigated by Runx3 knockdown in BECs and enhanced by Runx3 overexpression. Conclusion: These findings suggest that androgen estrogen combination modulate the Th1/Th2 balance via regulating the expression of Runx3 in BECs, thereby providing experimental evidence supporting androgen and estrogen combination as a novel therapy for asthma.

Thiosemicarbazone Mixed-Valence Cu(I/II) Complex against Lung Adenocarcinoma Cells through Multiple Pathways Involving Cuproptosis.

Induction of cuproptosis and targeting of multiple signaling pathways show promising applications in tumor therapy. In this study, we synthesized two thiosemicarbazone-copper complexes ([CuII(L)Cl] 1 and [CuII2CuI(L)2Cl3] 2, where HL is the (E)-N-methyl-2-(phenyl(pyridin-2-yl)methylene ligand), to assess their antilung cancer activities. Both copper complexes showed better anticancer activity than cisplatin and exhibited hemolysis comparable to that of cisplatin. In vivo experiments showed that complex 2 retarded the A549 cell growth in a mouse xenograft model with low systemic toxicity. Primarily, complex 2 kills lung cancer cells in vitro and in vivo by triggering multiple pathways, including cuproptosis. Complex 2 is the first mixed-valent Cu(I/II) complex to induce cellular events consistent with cuproptosis in cancer cells, which may stimulate the development of mixed-valent copper complexes and provide effective cancer therapy.

METTL3 mediates SOX5 m6A methylation in bronchial epithelial cells to attenuate Th2 cell differentiation in T2 asthma.

Objective: Asthma, a chronic inflammatory disease in which type 2 T helper cells (Th2) play a causative role in the development of T2 asthma. N6-methyladenosine (m6A) modification, an mRNA modification, and methyltransferase-like 3 (METTL3) is involved in the development of T2 asthma by inhibiting Th2 cell differentiation. Sex determining region Y-box protein 5 (SOX5) is involved in regulating T cell differentiation, but its role in T2 asthma was unclear. The objective of this study was to explore the role of METTL3 and SOX5 in T2 asthma and whether there is an interaction between the two. Materials and methods: Adults diagnosed with T2 asthma (n = 14) underwent clinical information collection and pulmonary function tests. In vivo and in vitro T2 asthma models were established using female C57BL/6 mice and human bronchial epithelial cells (HBE). The expressions of METTL3 and SOX5 were detected by Western blot and qRT-PCR and Western blot. Th2 cell differentiation was determined by flow cytometry and IL-4 level was detected by ELISA. m6A methylation level was determined by m6A quantitative assay. The relationship between METTL3 expression and clinical parameters was determined by Spearman rank correlation analysis. The function of METTL3 and SOX5 genes in asthma was investigated in vitro and in vivo. The RNA immunoprecipitation assay detected the specific interaction between METTL3 and SOX5. Results: Patients with T2 asthma displayed lower METTL3 levels compared to healthy controls. Within this group, a negative correlation was observed between METTL3 and Th2 cells, while a positive correlation was noted between METTL3 and clinical parameters as well as Th1 cells. In both in vitro and in vivo models representing T2 asthma, METTL3 levels decreased significantly, while SOX5 levels showed the opposite trend. Overexpression of METTL3 gene in HBE cells significantly inhibited Th2 cell differentiation and increased m6A methylation activity. From a mechanism perspective, low METTL3 negatively regulates SOX5 expression through m6A modification dependence, while high SOX5 expression is positively associated with T2 asthma severity. Cell transfection experiments confirmed that METTL3 regulates Th2 cell differentiation and IL-4 release through SOX5. Conclusions: Overall, our results indicate that METTL3 alleviates Th2 cell differentiation in T2 asthma by modulating the m6A methylation activity of SOX5 in bronchial epithelial cells. This mechanism could potentially serve as a target for the prevention and management of T2 asthma.

HMGB1/RAGE Signaling Regulates Th17/IL-17 and Its Role in Bronchial Epithelial-Mesenchymal Transformation.

BACKGROUND: Airway remodeling is one of the reasons for severe steroidresistant asthma related to HMGB1/RAGE signaling or Th17 immunity. OBJECTIVE: Our study aims to investigate the relationship between the HMGB1/RAGE signaling and the Th17/IL-17 signaling in epithelial-mesenchymal transformation (EMT) of airway remodeling. METHODS: CD4+ T lymphocytes were collected from C57 mice. CD4+ T cell and Th17 cell ratio was analyzed by flow cytometry. IL-17 level was detected by ELISA. The Ecadherin and α-SMA were analyzed by RT-qPCR and immunohistochemistry. The Ecadherin, α-SMA, and p-Smad3 expression were analyzed by western blot. RESULTS: The HMGB1/RAGE signaling promoted the differentiation and maturation of Th17 cells in a dose-dependent manner in vitro. The HMGB1/RAGE signaling also promoted the occurrence of bronchial EMT. The EMT of bronchial epithelial cells was promoted by Th17/IL-17 and the HMGB1 treatment in a synergic manner. Silencing of RAGE reduced the signaling transduction of HMGB1 and progression of bronchial EMT. CONCLUSION: HMGB1/RAGE signaling synergistically enhanced TGF-ß1-induced bronchial EMT by promoting the differentiation of Th17 cells and the secretion of IL-17.

SAR model for accurate detection of multi-label arrhythmias from electrocardiograms.

Objective: Arrhythmias are prevalent symptoms of cardiovascular disease, necessitating accurate and timely detection to mitigate associated risks. Detecting arrhythmias from ECGs quickly and accurately holds great significance in preventing heart disease and reducing mortality. This research endeavors to outperform previous studies by developing a scientific neural network model capable of training and predicting ECG signals for 11 categories of arrhythmias, accounting for up to 5 co-existing labels. Methods: In this study, we initially address the issue of imbalanced datasets by employing Borderline SMOTE and Cluster Centroids techniques during preprocessing. Subsequently, we propose a novel SAR model that combines attention and resnet mechanisms. The dataset is subjected to a 10-fold validation process to train and evaluate the model. Finally, several metrics such as HammingLoss, RankingLoss, F1-score, AUC and Coverage are used to evaluate the model. Results: By evaluating the results of the tests, the average Hamming Loss is 1.12 %, the average Ranking Loss is 1.17 %, the average Micro F1-score is 98.46 %, the average Micro AUC is 98.76 %, and the average Coverage is 3.2762. The results show that the SAR model outperforms previous related studies on the task of classifying arrhythmia signals with multiple categories and labels. Conclusion: The SAR model demonstrated excellent performance in accurately classifying multi-category and multi-label arrhythmia signals, affirming its scientific validity. Compared with previous studies, the model achieves a certain improvement in performance, which can help cardiologists to achieve scientific and accurate diagnosis of arrhythmia diseases.

Influenza Epidemic Trend Surveillance and Prediction Based on Search Engine Data: Deep Learning Model Study.

BACKGROUND: Influenza outbreaks pose a significant threat to global public health. Traditional surveillance systems and simple algorithms often struggle to predict influenza outbreaks in an accurate and timely manner. Big data and modern technology have offered new modalities for disease surveillance and prediction. Influenza-like illness can serve as a valuable surveillance tool for emerging respiratory infectious diseases like influenza and COVID-19, especially when reported case data may not fully reflect the actual epidemic curve. OBJECTIVE: This study aimed to develop a predictive model for influenza outbreaks by combining Baidu search query data with traditional virological surveillance data. The goal was to improve early detection and preparedness for influenza outbreaks in both northern and southern China, providing evidence for supplementing modern intelligence epidemic surveillance methods. METHODS: We collected virological data from the National Influenza Surveillance Network and Baidu search query data from January 2011 to July 2018, totaling 3,691,865 and 1,563,361 respective samples. Relevant search terms related to influenza were identified and analyzed for their correlation with influenza-positive rates using Pearson correlation analysis. A distributed lag nonlinear model was used to assess the lag correlation of the search terms with influenza activity. Subsequently, a predictive model based on the gated recurrent unit and multiple attention mechanisms was developed to forecast the influenza-positive trend. RESULTS: This study revealed a high correlation between specific Baidu search terms and influenza-positive rates in both northern and southern China, except for 1 term. The search terms were categorized into 4 groups: essential facts on influenza, influenza symptoms, influenza treatment and medicine, and influenza prevention, all of which showed correlation with the influenza-positive rate. The influenza prevention and influenza symptom groups had a lag correlation of 1.4-3.2 and 5.0-8.0 days, respectively. The Baidu search terms could help predict the influenza-positive rate 14-22 days in advance in southern China but interfered with influenza surveillance in northern China. CONCLUSIONS: Complementing traditional disease surveillance systems with information from web-based data sources can aid in detecting warning signs of influenza outbreaks earlier. However, supplementation of modern surveillance with search engine information should be approached cautiously. This approach provides valuable insights for digital epidemiology and has the potential for broader application in respiratory infectious disease surveillance. Further research should explore the optimization and customization of search terms for different regions and languages to improve the accuracy of influenza prediction models.

Inhibition of METTL3 alleviated LPS-induced alveolar epithelial cell apoptosis and acute lung injury via restoring neprilysin expression.

AIMS: To investigate the role and mechanisms of methyltransferase-like 3 (METTL3) in the pathogenesis of lipopolysaccharide (LPS)-induced acute lung injury (ALI). MAIN METHODS: LPS intratracheally instillation was applied in alveolar epithelial cell METTL3 conditional knockout (METTL3-CKO) mice and their wild-type littermates. In addition, METTL3 inhibitor STM2457 was used. LPS treatment on mouse lung epithelial 12 (MLE-12) cell was applied to establish an in vitro model of LPS-induced ALI. H&E staining, lung wet-to-dry ratio, and total broncho-alveolar lavage fluid (BALF) concentrations were used to evaluate lung injury. Overall, the m6A level was determined with the m6A RNA Methylation Quantification Kit and dot blot assay. Expression of METTL3 and neprilysin were measured with immunohistochemistry, immunofluorescence, immunofluorescence-fluorescence in situ hybridization, and western blot. Apoptosis was detected with TUNEL, western blot, and flow cytometry. The interaction of METTL3 and neprilysin was determined with RIP-qPCR and MeRIP. KEY FINDINGS: METTL3 expression and apoptosis were increased in alveolar epithelial cells of mice treated with LPS, and METTL3-CKO or METTL3 inhibitor STM2457 could alleviate apoptosis and LPS-induced ALI. In MLE-12 cells, LPS-Induced METTL3 expression and apoptosis. Knockdown of METTL3 alleviated, while overexpression of METTL3 exacerbated LPS-induced apoptosis. LPS treatment reduced neprilysin expression, the intervention of neprilysin expression negatively regulated apoptosis without affecting METTL3 expression, and mitigated the promoting effect of METTL3 on LPS-induced apoptosis. Additionally, METTL3 could bind to the mRNA of neprilysin, and reduce its expression. SIGNIFICANCE: Our findings revealed that inhibition of METTL3 could exert anti-apoptosis and ALI-protective effects via restoring neprilysin expression.

Factors Associated with PCV13 Vaccine Hesitancy in Parents under an Innovative Immunization Strategy: a Cross-Sectional Study - Weifang City, Shandong Province, China, 2021.

What is already known on this topic?: Pneumococcal diseases (PDs) are serious threats to child health. Although vaccination is one of the most effective ways to prevent these diseases, the pneumococcal vaccination coverage rate is still relatively low in China. What is added by this report?: This study investigated the factors associated with 13-valent pneumococcal conjugate vaccine (PCV13) vaccine hesitancy in parents under an innovative immunization strategy. This study found that 29.7% of the participants hesitated to vaccinate their children against PCV13 and the main reasons for vaccine hesitancy were individual and group influences. What are the implications for public health practice?: This study can provide scientific evidence for further improving children's PCV13 vaccination rate and improving the prevention and control strategy for PDs.

Oxidative stress-induced TET1 upregulation mediates active DNA demethylation in human gastric epithelial cells.

The gastrointestinal (GI) tract is more vulnerable to effects by the outside environment, and experiences oxidative stress. A wide diversity of GI disorders can be partially attributed to oxidative stress. However, the mechanism of oxidative stress-caused GI pathological changes is not clear. In the present study, human gastric epithelial cells (hGECs) were treated with hydrogen peroxide (H2O2), and oxidative stress was determined. The effect of oxidative stress on the levels of some antioxidative enzymes, proliferation, nuclear DNA damage, apoptosis, expression of ten-eleven translocation (TET), and level of DNA methylation was determined in these cells. The results showed that H2O2 treatment caused oxidative stress, increased the levels of superoxide dismutase (SOD), catalase (CAT), and malondialdehyde (MDA), decreased the level of glutathione (GSH), inhibited proliferation, caused nuclear DNA damage and apoptosis, upregulated the expression of TET1 gene, and ultimately led to active DNA demethylation in hGECs. The present study presents a mechanism by which oxidative stress induces active DNA demethylation in hGECs. We propose that TET inhibitors can be used to restore the oxidative stress-induced DNA demethylation, and thus inhibit possible malignant transformation of GI cells.

FOXO signaling pathway participates in oxidative stress-induced histone deacetylation.

High concentrations of antioxidants can exert pro-oxidative effects, elevate the level of intracellular reactive oxygen species (ROS), and cause oxidative stress in cells. We previously found that high concentrations of curcumin, a natural polyphenol antioxidant, elevated ROS levels and upregulated the expression of histone deacetylase 1 (HDAC1) in human gastric cancer cells (hGCCs); however, its potential mechanisms and subsequent functions have not been elucidated. In the present study, we treated hGCCs with high concentrations of curcumin, detected several indicators of oxidative stress, and investigated the mechanism of curcumin-treatment-mediated HDAC1 upregulation and its effect on histone acetylation. The results showed that curcumin treatment caused oxidative stress in hGCCs and upregulated HDAC1/2 expression via the forkhead box O (FOXO) signaling pathway, ultimately leading to the deacetylation of histones in hGCCs. Moreover, HDAC1/2 mediates the deacetylation of FOXOs and promotes their transcription activities, implying a positive feedback loop between FOXOs and HDAC1/2. These findings present a mechanism by which oxidative stress induces histone deacetylation in hGCCs.

Clinical-functional characteristics and risk of exacerbation and mortality among more symptomatic patients with chronic obstructive pulmonary disease: a retrospective cohort study.

OBJECTIVES: The Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2017 classified chronic obstructive pulmonary disease (COPD) patients into more and less symptomatic groups. This study aimed to analyze the clinical characteristics, risk of future exacerbation and mortality among patients in more symptomatic group. DESIGN: A retrospective cohort study. SETTING: Data were obtained from patients enrolled in a database setup by Second Xiangya Hospital of Central South University. PARTICIPANTS: 1729 stable COPD patients listed from September 2017 to December 2019 in the database. The patients were classified into more and less symptomatic groups based on GOLD 2017 report. OUTCOMES: All patients were followed up for 18 months. We collected baseline data and recorded the number of exacerbations and mortality during follow-up. RESULTS: The more symptomatic patients were older, had higher Clinical COPD Questionnaire (CCQ) scores, more severe airflow limitation and higher number of exacerbations and hospitalizations in the past year (P < 0.05). Logistic regression showed that having more symptoms correlated with the CCQ scores and exacerbations in the past year (P < 0.05). After patients were followed up, there were higher numbers of exacerbations, hospitalizations and mortality rates in more symptomatic patients (P < 0.05). The multivariate model showed that age more than 65 years (OR = 2.047, 95% CI = 1.020-4.107) and COPD assessment test scores more than 30 (OR = 2.609, 95% CI = 1.339-5.085) were independent risk factors for mortality, whereas current smoker (OR = 1.565, 95% CI = 1.052-2.328), modified Medical Research Council scores (OR = 1.274, 95% CI = 1.073-1.512) and exacerbations in the past year (OR = 1.061, 95% CI = 1.013-1.112) were independent risk factors for exacerbation in more symptomatic patients (P < 0.05). CONCLUSIONS: More symptomatic COPD patients have worse outcomes. In addition, several independent risk factors for exacerbation and mortality were identified. Therefore, clinicians should be aware of these risk factors and take them into account during interventions.

Methyl-cpg-binding Domain Protein 2 Silencing Inhibits Th17 Differentiation of CD4+T cells Induced by Ovalbumin.

Background: Little is known about MBD2's epigenetic regulation in the immune pathogenesis of CD4+T cell differentiation. Objective: This study attempted to explore the mechanism of methyl-cpg-binding domain protein 2 (MBD2) in CD4+T cell differentiation stimulated by environmental allergen ovalbumin (OVA). Methods: Mononuclear cells were separated from the spleen tissues of male C57BL/6 mice. The OVA interfered with the differentiation of splenic mononuclear cells and CD4+T cells. The CD4+T cells were obtained by magnetic beads and identified by CD4 labeled antibody. CD4+T cells were transfected with lentivirus to silence MBD2 gene. A methylation quantification kit was used to detect 5-mC levels. Results: The purity of CD4+T cells reached 95.99% after magnetic beads sorting. Treatment with 200 µg/mL OVA stimulated the CD4+T cells differentiation to Th17 cells and promoted the secretion of IL-17. After being induced, the Th17 cell ratio increased. 5-Aza inhibited the Th17 cell differentiation and the IL-17 level in a dose-dependent manner. Under the intervention of the Th17 induction and 5-Aza, MBD2 silencing inhibited the differentiation of Th17 cell, and decreased the IL-17 and 5-mC levels in the cell supernatants. MBD2 silencing reduced the scale of the Th17 cell and IL-17 levels in the OVA-treated CD4+T cells. Conclusion: MBD2 affected IL-17 and 5-mC levels by mediating the Th17 cell differentiation in splenic CD4+T cells that were interfered with 5-Aza. OVA induced Th17 differentiation and increased IL-17 levels, inhibited by MBD2 silencing.

miR-146a-3p as a potential novel therapeutic by targeting MBD2 to mediate Th17 differentiation in Th17 predominant neutrophilic severe asthma.

Th17 (T-helper 17) cells subtype of non-T2 (non-type 2) asthma is related to neutrophilic infiltration and resistance to inhaled corticosteroids (ICS), so is also known as severe asthma. Methyl-CpG binding domain protein 2 (MBD2) regulates the differentiation of the Th17 cells, tending to show a therapeutic target in severe asthma. miR-146a-3p is associated with anti-inflammatory characteristics and immunity. Moreover, bioinformatic analysis showed that MBD2 may be a target gene of miR-146a-3p. However, the role of miR-146a-3p in the differentiation of Th17 cells via MBD2 in severe asthma remains unknown. Here, we aimed to explore how miR-146a-3p interacts with MBD2 and affects the differentiation of Th17 cells in severe asthma. First, we recruited 30 eligible healthy people and 30 patients with severe asthma to detect the expression of miR-146a-3p in peripheral blood mononuclear cells (PBMCs) by qRT-PCR. Then, we established a HDM/LPS (house dust mite/lipopolysaccharide) exposure model of bronchial epithelial cells (BECs) to evaluate the expression of miR-146a-3p, the interaction between miR-146a-3p and MBD2 using western blot and luciferase reporter analysis and the effect of miR-146a-3p regulated Th17 cells differentiation by flow cytometry in BECs in vitro. Finally, we constructed a mouse model of Th17 predominant neutrophilic severe asthma to assess the therapeutic potential of miR-146a-3p in severe asthma and the effect of miR-146a-3p regulated Th17 cells differentiation via MBD2 in vivo. Decreased miR-146a-3p expression was noted in severe asthma patients, in the BECs and in the animal severe asthma models. Moreover, we demonstrated that miR-146a-3p suppressed Th17 cells differentiation by targeting the MBD2. miR-146a-3p overexpression significantly reduced airway hyperresponsiveness, airway inflammation and airway mucus secretion, while also inhibiting Th17 cells response in vivo, which relieved severe asthma. By targeting MBD2 to suppress Th17 cells differentiation, miR-146a-3p provides a potential novel therapeutic for Th17 predominant neutrophilic severe asthma.

NCR negative group 3 innate lymphoid cell (NCR- ILC3) participates in abnormal pathology of lung in cigarette smoking-induced COPD mice.

BACKGROUND: Natural cytotoxicity receptor negative innate lymphoid cell (NCR- ILC3) involves into mucosal homeostasis, inflammation regulation and tissue remodeling. The proportion of NCR- ILC3 is increased in the lung of smokers with chronic obstructive pulmonary disease (COPD). However, there's still few understandings on the role of NCR- ILC3 in COPD pathogenesis. METHODS: COPD mice were induced by cigarette smoking. The pathology in lung was detected in histology. The frequency of NCR- ILC3 (CD3-CD45+RORγt+NkP46-) from murine lung was detected using flow cytometry. IL-17+RORγt+ double positive cells in lung were assessed by double immunofluorescence staining. The protein expressions of epithelial-to-mesenchymal transition (EMT) markers, namely E-cadherin and Vimentin, were assessed using immunohistochemistry staining and western blotting. RESULTS: The frequency of NCR- ILC3 in lung was higher in COPD group than controls. The IL-17+RORγt+ cells in lung from COPD mice were more than controls. E-cadherin expression was decreased but Vimentin expression was increased in lung of COPD mice, when compared with controls. The frequency of NCR- ILC3 in lung tissues were positively correlated with mean linear intercept in lung, destructive index in lung and EMT, respectively. CONCLUSIONS: NCR- ILC3 could contribute to emphysema and EMT in lung of cigarette smoking-induced COPD, which will provide further understanding on COPD pathogenesis of immune response.

Deep-Learning Model for Influenza Prediction From Multisource Heterogeneous Data in a Megacity: Model Development and Evaluation.

BACKGROUND: In megacities, there is an urgent need to establish more sensitive forecasting and early warning methods for acute respiratory infectious diseases. Existing prediction and early warning models for influenza and other acute respiratory infectious diseases have limitations and therefore there is room for improvement. OBJECTIVE: The aim of this study was to explore a new and better-performing deep-learning model to predict influenza trends from multisource heterogeneous data in a megacity. METHODS: We collected multisource heterogeneous data from the 26th week of 2012 to the 25th week of 2019, including influenza-like illness (ILI) cases and virological surveillance, data of climate and demography, and search engines data. To avoid collinearity, we selected the best predictor according to the weight and correlation of each factor. We established a new multiattention-long short-term memory (LSTM) deep-learning model (MAL model), which was used to predict the percentage of ILI (ILI%) cases and the product of ILI% and the influenza-positive rate (ILI%×positive%), respectively. We also combined the data in different forms and added several machine-learning and deep-learning models commonly used in the past to predict influenza trends for comparison. The R2 value, explained variance scores, mean absolute error, and mean square error were used to evaluate the quality of the models. RESULTS: The highest correlation coefficients were found for the Baidu search data for ILI% and for air quality for ILI%×positive%. We first used the MAL model to calculate the ILI%, and then combined ILI% with climate, demographic, and Baidu data in different forms. The ILI%+climate+demography+Baidu model had the best prediction effect, with the explained variance score reaching 0.78, R2 reaching 0.76, mean absolute error of 0.08, and mean squared error of 0.01. Similarly, we used the MAL model to calculate the ILI%×positive% and combined this prediction with different data forms. The ILI%×positive%+climate+demography+Baidu model had the best prediction effect, with an explained variance score reaching 0.74, R2 reaching 0.70, mean absolute error of 0.02, and mean squared error of 0.02. Comparisons with random forest, extreme gradient boosting, LSTM, and gated current unit models showed that the MAL model had the best prediction effect. CONCLUSIONS: The newly established MAL model outperformed existing models. Natural factors and search engine query data were more helpful in forecasting ILI patterns in megacities. With more timely and effective prediction of influenza and other respiratory infectious diseases and the epidemic intensity, early and better preparedness can be achieved to reduce the health damage to the population.

Different clinical characteristics of current smokers and former smokers with asthma: a cross-sectional study of adult asthma patients in China.

Smoking is a trigger for asthma, which has led to an increase in asthma incidence in China. In smokers, asthma management starts with smoking cessation. Data on predictors of smoking cessation in Chinese patients with asthma are scarce. The objective of this study was to find the differences in clinical characteristics between current smokers and former smokers with asthma in order to identify factors associated with smoking cessation. Eligible adults with diagnosed asthma and smoking from the hospital outpatient clinics (n = 2312) were enrolled and underwent a clinical evaluation, asthma control test (ACT), and pulmonary function test. Information on demographic and sociological data, lung function, laboratory tests, ACT and asthma control questionnaire (ACQ) scores was recorded. Patients were divided into a current smokers group and a former smokers group based on whether they had quit smoking. Logistic regression analysis was used to analyze the factors associated with smoking cessation. Of all patients with asthma, 34.6% were smokers and 65.4% were former smokers, and the mean age was 54.5 ± 11.5 years. Compared with current smokers, the former smokers were older, had longer duration of asthma, had higher ICS dose, had more partially controlled and uncontrolled asthma, had more pack-years, had smoked for longer, and had worse asthma control. The logistic regression model showed that smoking cessation was positively correlated with age, female sex, pack-years, years of smoking, partially controlled asthma, uncontrolled asthma, and body mass index (BMI), but was negatively correlated with ACT, FEV1, FEV1%predicted, and widowed status. More than 30% of asthma patients in the study were still smoking. Among those who quit smoking, many quit late, often not realizing they need to quit until they have significant breathing difficulties. The related factors of smoking cessation identified in this study indicate that there are still differences between continuing smokers and former smokers, and these factors should be focused on in asthma smoking cessation interventions to improve the prognosis of patients with asthma.

Exploring meteorological impacts based on Köppen-Geiger climate classification after reviewing China's response to COVID-19.

More than 30 months into the novel coronavirus 2019 (COVID-19) pandemic, efforts to bring this prevalence under control have achieved tentative achievements in China. However, the continuing increase in confirmed cases worldwide and the novel variants imply a severe risk of imported viruses. High-intensity non-pharmaceutical interventions (NPIs) are the mainly used measures of China's early response to COVID-19, which enabled effective control in the first wave of the epidemic. However, their efficiency is relatively low across China at the current stage. Therefore, this study focuses on whether measurable meteorological variables be found through global data to learn more about COVID-19 and explore flexible controls. This study first examines the control measures, such as NPIs and vaccination, on COVID-19 transmission across 189 countries, especially in China. Subsequently, we estimate the association between meteorological factors and time-varying reproduction numbers based on the global data by meta-population epidemic model, eliminating the aforementioned anthropogenic factors. According to this study, we find that the basic reproduction number of COVID-19 transmission varied wildly among Köppen-Geiger climate classifications, which is of great significance for the flexible adjustment of China's control protocols. We obtain that in southeast China, Köppen-Geiger climate sub-classifications, Cwb, Cfa, and Cfb, are more likely to spread COVID-19. In August, the RSIM of Cwb climate subclassification is about three times that of Dwc in April, which implies that the intensity of control efforts in different sub-regions may differ three times under the same imported risk. However, BSk and BWk, the most widely distributed in northwest China, have smaller basic reproduction numbers than Cfa, distributed in southeast coastal areas. It indicates that northwest China's control intensity could be appropriately weaker than southeast China under the same prevention objectives.