The airway remodeling in asthma is closely associated with the abnormal differentiation of Th17 cells and epithelial-mesenchymal transition (EMT) of bronchial epithelial cells. This study aims to elucidate the regulatory mechanisms of histone lactylation in Th17 cell differentiation and the EMT of bronchial epithelial cells. An asthma mouse model was constructed, pathological changes, immune cell subsets, and histone lactylation levels were analyzed. CD4⁺ T cells from normal and asthmatic mice were treated with a glycolysis activator (Nala) or inhibitor (2-deoxy-glucose (2-DG)). ChIP-qPCR and dual-luciferase assay were performed to verify the regulation of DPP4 promoter by H3K18la. DPP4 inhibitor (K579) was used to intervene in Th17 cell differentiation. Finally, bronchial epithelial cells were induced to undergo EMT by TGF-β1, and co-cultured with CD4+ T cells to evaluate EMT markers. The asthma mouse model showed lung inflammation, airway remodeling, and imbalance in immune cell subsets. H3K18la and DPP4 levels were upregulated, correlating positively with IL-17. Inhibiting glycolysis reduced H3K18la, inhibited Th17 cell differentiation, and decreased IL-17 secretion. H3K18la activated DPP4 transcription by enriching in DPP4 promoter region. K579 blocked Th17 cell differentiation mediated by H3K18la. Additionally, DPP4 significantly promoted the EMT of bronchial epithelial cells by promoting Th17 cell differentiation, as evidenced by downregulation of E-cadherin, upregulation of α-SMA, and changes in cell morphology. This process was partially inhibited by 2-DG treatment. H3K18la promoted Th17 cell differentiation by activating DPP4, thereby driving the EMT of bronchial epithelial cells. Targeting H3K18la-DPP4-Th17 axis may be a potential asthma therapy.

BackgroundTuberculosis-diabetes mellitus (TB-DM) multimorbidity significantly increases the risk of multidrug-resistant/rifampicin-resistant tuberculosis (MDR/RR-TB). Early risk stratification tools for this high-risk population remain lacking.ObjectiveTo develop and validate an interpretable machine learning (ML) model for predicting MDR/RR-TB in patients with TB-DM multimorbidity, and to identify key predictive factors using explainable artificial intelligence.MethodsThis dual-center retrospective study enrolled 245 patients with TB-DM multimorbidity from January 2019 to December 2022. Seven machine learning algorithms were constructed and validated with 10-fold cross-validation. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC-ROC), accuracy, precision, recall, F1-score, calibration curve, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) was applied to identify critical predictive factors.ResultsThe random forest (RF) model achieved the optimal performance, with an AUC-ROC of 0.818, accuracy of 0.806, precision of 0.688, recall of 0.611, and F1-score of 0.647; the moderate recall indicates a considerable false-negative rate (FNR) , supporting its use as a triage tool rather than a stand-alone diagnostic test. Calibration and DCA confirmed robust predictive reliability and substantial clinical net benefit within a clinically relevant threshold range of 0.06-0.80. SHAP analysis identified the symptom-to-diagnosis interval, tuberculosis (TB) treatment history, treatment adherence, pulmonary cavitation, and smoking history as the top five critical predictors.ConclusionThe interpretable RF model accurately and reliably predicts the risk of MDR/RR-TB in patients with TB-DM multimorbidity. The symptom-to-diagnosis interval is the most crucial risk factor. This model can assist clinical triage, early intervention, and personalized management.

Metal-based complexes have demonstrated significant potential in modulating physiological behaviors. In this study, three hydrazone copper(II) complexes ([Cu(HL1)Ind]NO3 & sdot;CH3OH 1, [Cu(HL2)Ind]NO3 2 and [Cu(L3)Ind] CH3OH 3) were synthesized and evaluated for their impact on the physiological behavior of airway smooth muscle cells (ASMCs). The structures of these complexes were characterized using techniques such as X-ray single-crystal diffraction, mass, elemental analysis, and theoretical calculations. ASMCs were derived from rat specimens. The CCK-8 assay revealed that these Cu(II) complexes significantly suppressed the proliferation of rat ASMCs. The wound healing assay demonstrated that complex 1 exhibits good anti-migratory activity against ASMCs. Flow cytometry, western blotting, and fluorescence imaging analyses demonstrated that complex 1 induces G2 phase cell cycle arrest and reactive oxygen species (ROS)-mediated apoptosis in ASMCs. In addition, in the three-dimensional (3D) model of ASMCs, complex 1 also exerts an inhibitory effect. These results reveal the potential role of copper hydrazone complexes in the treatment of airway remodeling in asthma.

Patient-facing large language models (LLMs) hold potential to streamline inefficient transitions from primary to specialist care. We developed the preassessment (PreA), an LLM chatbot co-designed with local stakeholders, to perform the general medical consultations for history-taking, preliminary diagnoses, and test ordering that would normally be performed by primary care providers and to generate referral reports for specialists. PreA was tested in a randomized controlled trial involving 111 specialists from 24 medical disciplines across two health centers, where 2,069 patients (1,141 women; 928 men) were randomly assigned to use PreA independently (PreA-only), use it with staff support (PreA-human), or not use it (No-PreA) before specialist consultation. The trial met its primary end points with the PreA-only group showing significantly reduced physician consultation duration (28.7 ChiCTR2400094159 . In a trial involving 2,069 patients and 111 clinicians across 24 disciplines, patients performing a preconsultation session with an LLM-powered chatbot had a significantly lower consultation time and both patients and clinicians reported improved communication and satisfaction.

ETHNOPHARMACOLOGICAL RELEVANCE:The flavonoid scutellarin (Scu) is a principal bioactive component of Erigeron breviscapus, a herb long utilized in the traditional medicine of the Miao and Yi peoples of China for the treatment of cardiovascular, inflammatory, and respiratory disorders. Despite its established use in ethnomedicine, the protective mechanisms of Scu in chronic obstructive pulmonary disease (COPD) remain poorly elucidated. AIM OF THE STUDY:This study aimed to investigate the therapeutic effects of Scu on COPD, with a specific focus on its role in regulating ferroptosis and elucidating the underlying molecular mechanisms. MATERIALS AND METHODS:An integrated strategy of computational and experimental approaches was employed. Network pharmacology and molecular docking identified Scu's core targets and pathways against COPD, which were subsequently verified in vitro in CSE-induced BEAS-2B cells and in vivo in a murine COPD model induced by CS exposure. RESULTS:Network pharmacology and molecular docking identified the PI3K/AKT pathway as the central mechanism and predicted high-affinity binding between Scu and core targets, including PIK3CA. Experimentally, Scu demonstrated potent anti-ferroptotic effects in vitro. In vivo, Scu ameliorated CS-induced lung function impairment and alveolar destruction, with suppressing ferroptosis in lung tissues. Pharmacological inhibition of either PI3K or Nrf2 validated the essential role of the PI3K/AKT/Nrf2 axis in mediating Scu's protection. CONCLUSION:Our study demonstrates that Scu alleviates CS-induced COPD by inhibiting ferroptosis, primarily through activation of the PI3K/AKT/Nrf2 pathway, providing a comprehensive pharmacological basis for Scu as a promising therapeutic candidate for COPD.

BACKGROUND:Airway management during the perioperative period is a vital component of perioperative care. However, there is a lack of consensus on the selection of medications, timing of administration, and the management of airway complications. This consensus aimed to promote a more rational and standardized application of airway management medications. METHODS:Clinical medical and pharmaceutical experts were invited to participate in this study using the modified Delphi method. Participants completed two rounds of online surveys, with the second round based on the responses from the first round. RESULTS:Participants (n = 42) reached a consensus on 11 clinical issues and formed 11 recommendations for clinical practice, each with a consensus degree of more than 80%. The recommendations covered aspects of preoperative, intraoperative, and postoperative risk factors evaluation, along with crucial points of medication monitoring in preventing and treating perioperative pulmonary complications. CONCLUSIONS:The modified Delphi method resulted in consensus recommendations for the perioperative physician-pharmacist airway co-management. We hope this consensus will prevent pulmonary complications and improve patient outcomes through collaborative discussions between physicians and pharmacists.

Background: Simnotrelvir has demonstrated potent anti-viral activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In a Phase II/III study, Simnotrelvir plus ritonavir (S/R, co-packaged) shortened the time to the resolution of symptoms in adult COVID-19 patients. However, real-world data on effectiveness of simnotrelvir/ritonavir against SARS-CoV-2 during XBB variant surges are still limited. Study design and methods: This was a nationwide, multicenter, prospective, observational real-world study at 42 sites in China. Adult patients with mild to moderate COVID-19 and at disease onset were eligible for participation. Patients were grouped in S/R group (treated with S/R) and control group (not receiving oral antivirals for COVID-19). The primary endpoint was the COVID-19-related hospitalization or all-cause mortality within 28 days. Secondary endpoints included the time from confirmed SARS-CoV-2 infection to negative conversion, and the time to resolution of COVID-19 symptoms. Besides, serious adverse events (SAE), adverse drug reactions (ADR) and combined medication were reported. Propensity Score-Matched (PSM) analysis (1:1) was performed for adjustment for baseline variables. Hazard ratios (HR) and adjusted risk ratios (aRR) were estimated using the Cox and Modified Poisson regression, respectively. Results: Between June 6, 2023, and December 27, 2023, 3522 patients were enrolled. S/R was associated with a reduced incidence of COVID-19-related hospitalization (6/1896 [0.3%] vs. 43/1408 [3.1%]; HR: 0.110, 95% confidence interval [CI]: 0.043 to 0.283, p < 0.001 vs control), consistently with the results after PSM (4/1381 [0.3%] in S/R vs. 40/1381 patients [2.9%]; aRR: 0.12; 95% CI: 0.05, 0.29; P < 0.001). No deaths occurred in both S/R and control groups. Matched Patients over 65 and patients with risk factors who received S/R achieved significantly reduced risk of COVID-19-related hospitalization (aRR: 0.032; 95% CI: 0.004, 0.268; aRR: 0.034; 95% CI: 0.005, 0.252, respectively; all P < 0.001). Furthermore, S/R shortened the median time to viral clearance by 1 day (6.0 vs 7.0 days; 95% CI: -2.0 to -1.0; P < 0.001) and reduced the median time to symptom resolution by 2 days (8.0 days vs 10.0 days; 95% CI: -2.0 to -1.0; P < 0.001). Besides, the proportion of patients in the S/R group using combined medication was significantly lower than that in the control group (30.2% vs 49.4%). Subgroup analysis showed potential protective effect of S/R in the elderly and patients with more than 1 risk factor. Conclusion: In real world, S/R significantly reduced the incidence of COVID-19-related hospitalization, demonstrated favorable safety profiles, and less use of combined medication. (c) 2024 The Author(s). Published by Elsevier Ltd on behalf of The British Infection Association. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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.

The prediction capacity of the Clinical COPD Questionnaire (CCQ) and its functional, symptom, and mental subdomain for COPD hospitalized exacerbation were rarely studied. To examine the prognostic capacity of the total CCQ and its three subdomains for 3-year COPD hospitalized exacerbations. We analyzed the predictive ability of total CCQ score and its subdomains for hospitalized exacerbations in an observational cohort of 987 subjects with stable COPD from the RealDTC, an ongoing multicenter prospective study. Hospitalized exacerbations were prospectively collected every 6 month for a maximum of 3 years. The total CCQ and its functional and symptom domain, but not the mental domain, were significantly associated with 3-year hospitalized exacerbations by multivariate Cox regression analysis. The predictive capacity of functional domain was similar to that of the total CCQ, but significantly stronger than the symptom and mental domain by ROC analysis (P < 0.05). ROC curves also showed that the AUC of exacerbation history combined with CCQ functional domain was larger than that of exacerbation history alone (P < 0.0001). Additionally, the predictive value of multivariable models that contains CCQ functional domain was significantly better than the corresponding model without CCQ functional domain (P < 0.05). The total CCQ and its functional and symptom domain were independent risk factors of 3-year hospitalized exacerbations. The prognostic capacity of the functional domain was similar to that of total CCQ, and was significantly stronger than the symptom and mental domain. The CCQ functional domain was able to increase the predictive power of exacerbation history and other multivariable prediction models, indicating it may have an important role in the multivariable prediction tool for hospitalized exacerbation, and its combination with other clinical variables might be used as a low-cost approach for assessments of the disease severity and severe exacerbation in COPD patients in the future.

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 P-au > 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.

Introduction: Alanyl-glutamine (Ala-Gln) is a compound known for its protective effects in various tissue injuries. However, its role in asthma-related lung injuries remains underexplored. This study investigates the mechanisms by which Ala-Gln modulates sDPP4-induced airway epithelial-mesenchymal transition and ovalbumin (OVA)-induced asthma in a mouse model. Methods: An asthma model was established in female C57BL/6 J mice by using OVA. CD4+ T cells and bronchial epithelial cells (BECs) were isolated from the spleen and bronchi of the mice, respectively. Interventions included recombinant sCD26/sDPP4 protein, Ala-Gln, and EX527 (a SIRT1 inhibitor). Flow cytometry was used to assess Th17 and Treg cell populations. Mice were treated with Ala-Gln, EX527, and budesonide (BUD). Histopathological changes in lung tissues were evaluated using hematoxylin-eosin and Masson staining. White blood cell counts were measured with a hematology analyzer. The expression levels of DPP4, IL-17, SIRT1, SMAD2/3, N-cadherin, E-cadherin, MMP9, and α-SMA proteins were analyzed. Results: Treatment with recombinant sCD26/sDPP4 resulted in decreased E-cadherin expression in BECs and increased levels of α-SMA, MMP9, and N-cadherin, effects that were mitigated by Ala-Gln. Ala-Gln also prevented the reduction in SIRT1 expression in BECs and the increase in Th17 cell differentiation induced by recombinant sCD26/sDPP4. EX527 administration alongside Ala-Gln reversed these changes and enhanced the phosphorylation of SMAD2/3 through SIRT1 signaling. BUD alone reduced inflammation and fibrosis in bronchial tissue and lowered the Th17/Treg ratio in peribronchial lymph nodes. The therapeutic effect of BUD was further improved with concurrent Ala-Gln treatment. Conclusion: Ala-Gln can inhibit BEC fibrosis and Th17 cell differentiation mediated by recombinant sCD26/sDPP4 through the SIRT1 pathway. Combined with BUD, Ala-Gln enhanced therapeutic efficacy in OVA-induced asthma in mice, which could offer improved outcomes for asthmatic patients with elevated DPP4 levels.

To the Editor: Influenza viruses are constantly evolving and have the ability to infect a wide range of hosts, leading to recurrent infections and ongoing morbidity.[1] In China, the surveillance for respiratory infectious diseases has been specifically performed for influenza and other respiratory infectious diseases. However, the current surveillance system relies heavily on the analysis of clinically confirmed influenza cases, which has lagged behind the times.[2] It is very important to establish a more accurate influenza prediction model, particularly in densely populated megacities. Our research aims to explore and develop more accurate and sensitive models for predicting influenza outbreaks. The data of this study were provided by Beijing infectious disease surveillance and early warning system for medical institutions and have been anonymized. We selected the influenza-like illness (ILI) patients and influenza polymerase chain reaction-positivity patients from the 26th week of 2010 to the 25th week of 2019 for modeling. We considered ILI%, influenza positive rate, and the product of ILI% and influenza positive rate (ILI% × influenza positive rate) as independent variables. We established seasonal autoregressive integrated moving average (SARIMA) models for ILI%, influenza positive rate, and ILI% × influenza positive rate respectively, starting from the summer point, the peak bottom point, and the peak rising point [Supplementary Method, https://links.lww.com/CM9/C91]. After selecting the best prediction point by R square (R2) and Akaike information criterion (AIC), we compared the model performances of different parameters at that point using the SARIMA model. We established a hybrid model based on the long short-term memory (LSTM) architecture. For supervised learning, we implemented a "slider" mechanism with a period of 52 and a step size of 1 along the time axis. As the "slider" progressed along the timeline, features and labels were dynamically defined. In Path 1, the data directly interfaced with a Self-Attention block, enabling the model to focus on pivotal features while disregarding less influential ones. Path 2 involved routing the data through ZeroPadding, Convolution, and Globalpooling layers before linking with a ReSNet block. Path 3 incorporated data into an LSTM layer, then merged the outcomes of Path 1 and Path 2. The data then passed through the Dense, Dropout, and GlobalPooling layers after concatenation. A Linear activation function was applied to the final Dense layer to yield prediction results [Supplementary Figure 1, https://links.lww.com/CM9/C91]. Finally, R-square (R2) and expected variance were applied to compare the hybrid LSTM prediction model with the best model selected by SARIMA. Between the 26th week of 2010 and the 25th week of 2019, there were 542,602,473 outpatient and emergency department visits, of which 6,753,116 met the ILI criteria, and the ILI% was 1.24%. Of all the ILI patients, 94,813 samples were tested and 15,883 (16.75%) were positive for influenza [Supplementary Table 1, https://links.lww.com/CM9/C91]. Time series decomposition of ILI%, influenza positive rate, and ILI% × influenza positive rate revealed seasonal and periodic patterns [Supplementary Figure 2, https://links.lww.com/CM9/C91]. The trends and peak times exhibited a consistent alignment across ILI%, influenza positive rate, and ILI% × influenza positive rate [Supplementary Figure 3, https://links.lww.com/CM9/C91]. SARIMA models were initially constructed for the summer point, the peak bottom point, and the peak rising point. R2 and AIC were used to compare the fitting effects of the models. The results suggested that modeling at the peak rising point was better than those at the other two forecast points, and the prediction model of influenza positive rate showed the best effect [Supplementary Figure 4, https://links.lww.com/CM9/C91]. According to the comparison of models at different forecast points, the peak rising point was finally selected to establish the SARIMA model. The data were stable after the first order difference [Supplementary Figure 5, https://links.lww.com/CM9/C91]. The best SARIMA model for ILI% was (1,1,1) (1,1,1)52 with the R2 of 0.590 and the expected variance of 0.596. The model for influenza positive rate was (2,1,3) (1,1,1)52 with the R2 of 0.836 and the expected variance of 0.840. The model for ILI% × influenza positive rate was (5,1,7) (1,1,1)52, the R2 was 0.754, and the expected variance was 0.760. We further constructed hybrid LSTM model for ILI% (R2 was 0.781 and expected variance was 0.782), influenza positive rate (R2 was 0.945 and expected variance was 0.945), and ILI% × influenza positive rate (R2 was 0.868 and expected variance was 0.868). The results showed that the hybrid LSTM model performed better than the SARIMA model in terms of ILI%, influenza positive rate, and ILI% × influenza positive rate [Figure 1].Figure 1: Comparison of the SARIMA and LSTM models. The peak rising point was set as the 52nd week of 2018 (abbreviated as 201852). Modeling was conducted from 201026 to 201851 to predict the influenza trend from 201852 to 201925. The vertical coordinates of LSTM method were normalized. CI: Confidence interval. EX: Expected variance; ILI: Influenza-like illness; LSTM: Long short-term memory; SARIMA: Seasonal autoregressive integrated moving average.To further compare the two models for different prediction periods, we incrementally extended the prediction window from the 1st week to the 26th week. The values for R2 and expected variance of the hybrid LSTM model were higher than those of SARIMA model for ILI%, influenza positive rate, and ILI% × influenza positive rate. This suggested that the hybrid LSTM model could maintain a good prediction effect with the extension of the prediction period. On the contrary, the prediction effect of SARIMA model decreased with the extension of the prediction period [Supplementary Figure 6, https://links.lww.com/CM9/C91]. In megacities, characterized by dense populations and numerous public spaces, local or regional outbreaks and epidemics of influenza can occur with ease. This study applies time series forecasting and deep learning techniques to construct prediction models for influenza during non-pandemic periods in such settings. By comparing the model effects at different prediction points, we found optimal predictive efficacy when models were developed during the peak rise period of influenza. Both the SARIMA and the hybrid LSTM models showed good prediction effects in terms of ILI%, influenza positive rate, and ILI% × influenza positive rate. Furthermore, our findings highlight the superior predictive performance of the influenza positivity rate. Notably, the hybrid LSTM model, boasting higher R2 and expected variance values, outperformed the SARIMA model, indicating its suitability for long-term forecasting. Previous studies have extensively documented the effectiveness of SARIMA models in influenza prediction.[3] However, these studies have often overlooked a crucial aspect: determining the most accurate prediction time point. Therefore, we conducted a comprehensive comparison of results across various prediction points. Our results showed that the model at the peak rising point performed better than those at the summer point and the peak bottom point for ILI%, influenza positive rate, and ILI% × influenza positive rate. This suggested that the SARIMA model displayed the best prediction effect at the peak rising point when predicting the trend of influenza in megacities. While the application of deep learning models in infectious disease research is gaining attention, its utilization remains relatively limited.[4] In our study, we incorporated parallel Self-Attention block and RESNET block based on LSTM, resulting in a novel hybrid LSTM model. This enhanced model demonstrated greater robustness and comprehensive data feature extraction capabilities. The results showed that the effect of the hybrid LSTM model was better than that of the SARIMA model. Additionally, we evaluated both models across various prediction periods ranging from 1st week to 26th weeks. Remarkably, the hybrid LSTM model maintained strong predictive efficacy even with extended prediction periods, whereas the performance of the SARIMA model notably declined with longer forecast horizons. This indicated that the hybrid LSTM model had a good prediction effect and was more suitable for long-term prediction of influenza, while SARIMA performed well in short-term prediction. Despite the increased complexity of the hybrid LSTM model, measures were taken to mitigate potential issues such as gradient disappearance. The implementation of short-circuit connections effectively addressed this concern, while the utilization of high-performance graphics processing units facilitated efficient model training, overcoming challenges associated with increased computational demand. In conclusion, enhancing the timeliness and sensitivity of influenza and other respiratory infectious disease predictions holds significant importance. Our findings highlight the efficacy of combining ILI% data with the hybrid LSTM model to accurately forecast influenza epidemic trends. Influenza-like cases and influenza positive rate played complementary roles in the prediction process. In addition, the hybrid LSTM model also showed good prediction effect for ILI% × influenza positive rate, which was generally used to estimate the activity intensity of influenza. Acknowledgement We thank staff members at the Beijing Centre for Disease Prevention and Control, and staff members at the Chinese Center for Disease Control and Prevention. Fundings This work was supported by grants from the Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (No.2021-I2M-1-044); the Special Fund for Health Development Research of Beijing (No. 2021-1G-3013); and the Postdoctoral Fellowship Program of CPSF (No.GZC20231052). Conflicts of interest None.

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.

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.

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.

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-β1-induced bronchial EMT by promoting the differentiation of Th17 cells and the secretion of IL-17.

Activating multiple anti-cancer pathways has great potential for tumor treatment. Herein, we synthesized two binuclear Cu(II) hydrazone complexes ([Cu2(HL1)2Cl2] 1 and [Cu2(HL1)2Br2] 2) and two mononuclear hydrazone-Cu(II) complexes ([Cu(HL2)Cl]·CH3OH 3 and [Cu(HL2)(H2O)Br]·2H2O 4), to evaluate their anti-lung cancer activities. MTT assays revealed that the Cu(II) complexes demonstrate superior anticancer activity compared to cisplatin. Among them, complex 3 exhibited selective toxicity towards A549 cancer cells in comparison to normal cells and demonstrated hemolytic activity comparable to cisplatin. The low toxicity and effective antitumor capabilities of complex 3 have been confirmed in xenograft experiments using A549 tumor-bearing mice. Interestingly, complex 3 eradicates lung tumor cells both in vivo and in vitro by initiating multiple anticancer pathways, including cuproptosis. Our research extends the study of hydrazone copper complexes and provides strategies for the treatment of lung cancer.

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.

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.