Hospitalization, case fatality, comorbidities, and isolated pathogens of adult inpatients with pneumonia from 2013 to 2022: a real-world study in Guangzhou, China.

BACKGROUND: In the context of increasing population aging, ongoing drug-resistant pathogens and the COVID-19 epidemic, the changes in the epidemiological and clinical characteristics of patients with pneumonia remain unclear. This study aimed to assess the trends in hospitalization, case fatality, comorbidities, and isolated pathogens of pneumonia-related adult inpatients in Guangzhou during the last decade. METHODS: We retrospectively enrolled hospitalized adults who had doctor-diagnosed pneumonia in the First Affiliated Hospital of Guangzhou Medical University from January 1, 2013 to December 31, 2022. A natural language processing system was applied to automatically extract the clinical data from electronic health records. We evaluated the proportion of pneumonia-related hospitalizations in total hospitalizations, pneumonia-related in-hospital case fatality, comorbidities, and species of isolated pathogens during the last decade. Binary logistic regression analysis was used to assess predictors for patients with prolonged length of stay (LOS). RESULTS: A total of 38,870 cases were finally included in this study, with 70% males, median age of 64 (53, 73) years and median LOS of 7.9 (5.1, 12.8) days. Although the number of pneumonia-related hospitalizations showed an upward trend, the proportion of pneumonia-related hospitalizations decreased from 199.6 per 1000 inpatients in 2013 to 123.4 per 1000 in 2021, and the case fatality decreased from 50.2 per 1000 in 2013 to 23.9 per 1000 in 2022 (all P < 0.05). The most common comorbidities were chronic obstructive pulmonary disease, lung malignancy, cardiovascular diseases and diabetes. The most common pathogens were Pseudomonas aeruginosa, Candida albicans, Acinetobacter baumannii, Stenotrophomonas maltophilia, Klebsiella pneumoniae, and Staphylococcus aureus. Glucocorticoid use during hospitalization (Odd Ratio [OR] = 1.86, 95% Confidence Interval (CI): 1.14-3.06), immunosuppressant use during hospitalization (OR = 1.99, 1.14-3.46), ICU admission (OR = 16.23, 95%CI: 11.25-23.83), receiving mechanical ventilation (OR = 3.58, 95%CI: 2.60-4.97), presence of other underlying diseases (OR = 1.54, 95%CI: 1.15-2.06), and elevated procalcitonin (OR = 1.61, 95%CI: 1.19-2.19) were identified as independent predictors for prolonged LOS. CONCLUSION: The proportion of pneumonia-related hospitalizations and the in-hospital case fatality showed downward trends during the last decade. Pneumonia inpatients were often complicated by chronic underlying diseases and isolated with gram-negative bacteria. ICU admission was a significant predictor for prolonged LOS in pneumonia inpatients.

Supervised training models with or without manual lesion delineation outperform clinicians in distinguishing pulmonary cryptococcosis from lung adenocarcinoma on chest CT.

BACKGROUND: The role of artificial intelligence (AI) in the discrimination between pulmonary cryptococcosis (PC) and lung adenocarcinoma (LA) warrants further research. OBJECTIVES: To compare the performances of AI models with clinicians in distinguishing PC from LA on chest CT. METHODS: Patients diagnosed with confirmed PC or LA were retrospectively recruited from three tertiary hospitals in Guangzhou. A deep learning framework was employed to develop two models: an undelineated supervised training (UST) model utilising original CT images, and a delineated supervised training (DST) model utilising CT images with manual lesion annotations provided by physicians. A subset of 20 cases was randomly selected from the entire dataset and reviewed by clinicians through a network questionnaire. The sensitivity, specificity and accuracy of the models and the clinicians were calculated. RESULTS: A total of 395 PC cases and 249 LA cases were included in the final analysis. The internal validation results for the UST model showed a sensitivity of 85.3%, specificity of 81.0%, accuracy of 83.6% and an area under the curve (AUC) of 0.93. Similarly, the DST model exhibited a sensitivity of 88.2%, specificity of 88.1%, accuracy of 88.2% and an AUC of 0.94. The external validation of the two models yielded AUC values of 0.74 and 0.77, respectively. The average sensitivity, specificity and accuracy of 102 clinicians were determined to be 63.1%, 53.7% and 59.3%, respectively. CONCLUSIONS: Both models outperformed the clinicians in distinguishing between PC and LA on chest CT, with the UST model exhibiting comparable performance to the DST model.

H5N1 Virus Hemagglutinin Inhibition of cAMP-Dependent CFTR via TLR4-Mediated Janus Tyrosine Kinase 3 Activation Exacerbates Lung Inflammation.

The host tolerance mechanisms to avian influenza virus (H5N1) infection that limit tissue injury remain unknown. Emerging evidence indicates that cystic fibrosis transmembrane conductance regulator (CFTR), a cAMP-dependent Cl(-) channel, modulates airway inflammation. Janus tyrosine kinase (JAK) 3, a JAK family member that plays a central role in inflammatory responses, prominently contributes to the dysregulated innate immune response upon H5N1 attachment; therefore, this study aims to elucidate whether JAK3 activation induced by H5N1 hemagglutinin (HA) inhibits cAMP-dependent CFTR channels. We performed short-circuit current, immunohistochemistry and molecular analyses of the airway epithelium in Jak3(+/+) and Jak3(+/-) mice. We demonstrate that H5N1 HA attachment inhibits cAMP-dependent CFTR Cl(-) channels via JAK3-mediated adenylyl cyclase (AC) suppression, which reduces cAMP production. This inhibition leads to increased nuclear factor-kappa B (NF-κB) signaling and inflammatory responses. H5N1 HA is detected by TLR4 expressed on respiratory epithelial cells, facilitating JAK3 activation. This activation induces the interaction between TLR4 and Gαi protein, which blocks ACs. Our findings provide novel insight into the pathogenesis of acute lung injury via the inhibition of cAMP-dependent CFTR channels, indicating that the administration of cAMP-elevating agents and targeting JAK3 may activate host tolerance to infection for the management of influenza virus-induced fatal pneumonia.

Deep Learning-Based Analytic Models Based on Flow-Volume Curves for Identifying Ventilatory Patterns.

INTRODUCTION: Spirometry, a pulmonary function test, is being increasingly applied across healthcare tiers, particularly in primary care settings. According to the guidelines set by the American Thoracic Society (ATS) and the European Respiratory Society (ERS), identifying normal, obstructive, restrictive, and mixed ventilatory patterns requires spirometry and lung volume assessments. The aim of the present study was to explore the accuracy of deep learning-based analytic models based on flow-volume curves in identifying the ventilatory patterns. Further, the performance of the best model was compared with that of physicians working in lung function laboratories. METHODS: The gold standard for identifying ventilatory patterns was the rules of ATS/ERS guidelines. One physician chosen from each hospital evaluated the ventilatory patterns according to the international guidelines. Ten deep learning models (ResNet18, ResNet34, ResNet18_vd, ResNet34_vd, ResNet50_vd, ResNet50_vc, SE_ResNet18_vd, VGG11, VGG13, and VGG16) were developed to identify patterns from the flow-volume curves. The patterns obtained by the best-performing model were cross-checked with those obtained by the physicians. RESULTS: A total of 18,909 subjects were used to develop the models. The ratio of the training, validation, and test sets of the models was 7:2:1. On the test set, the best-performing model VGG13 exhibited an accuracy of 95.6%. Ninety physicians independently interpreted 100 other cases. The average accuracy achieved by the physicians was 76.9 ± 18.4% (interquartile range: 70.5-88.5%) with a moderate agreement (κ = 0.46), physicians from primary care settings achieved a lower accuracy (56.2%), while the VGG13 model accurately identified the ventilatory pattern in 92.0% of the 100 cases (P < 0.0001). CONCLUSIONS: The VGG13 model identified ventilatory patterns with a high accuracy using the flow-volume curves without requiring any other parameter. The model can assist physicians, particularly those in primary care settings, in minimizing errors and variations in ventilatory patterns.

Regularity and mechanism of fake crackle noise in an electronic stethoscope.

Background: Electronic stethoscopes are widely used for cardiopulmonary auscultation; their audio recordings are used for the intelligent recognition of cardiopulmonary sounds. However, they generate noise similar to a crackle during use, significantly interfering with clinical diagnosis. This paper will discuss the causes, characteristics, and occurrence rules of the fake crackle and establish a reference for improving the reliability of the electronic stethoscope in lung auscultation. Methods: A total of 56 participants with healthy lungs (no underlying pulmonary disease, no recent respiratory symptoms, and no adventitious lung sound, as confirmed by an acoustic stethoscope) were enrolled in this study. A 30-s audio recording was recorded from each of the nine locations of the larynx and lungs of each participant with a 3M Littmann 3200 electronic stethoscope, and the audio was output in diaphragm mode and auscultated by the clinician. The doctor identified the fake crackles and analyzed their frequency spectrum. High-pass and low-pass filters were used to detect the frequency distribution of the fake crackles. Finally, the fake crackle was artificially regenerated to explore its causes. Results: A total of 500 audio recordings were included in the study, with 61 fake crackle audio recordings. Fake crackles were found predominantly in the lower lung. There were significant differences between lower lung and larynx (p < 0.001), lower lung and upper lung (p = 0.005), lower lung and middle lung (p = 0.005), and lower lung and infrascapular region (p = 0.027). Furthermore, more than 90% of fake crackles appeared in the inspiratory phase, similar to fine crackles, significantly interfering with clinical diagnosis. The spectral analysis revealed that the frequency range of fake crackles was approximately 250-1950 Hz. The fake crackle was generated when the diaphragm of the electronic stethoscope left the skin slightly but not completely. Conclusion: Fake crackles are most likely to be heard when using an electronic stethoscope to auscultate bilateral lower lungs, and the frequency of a fake crackle is close to that of a crackle, likely affecting the clinician's diagnosis.

Correlations of Computed Tomography Measurement of Distal Pulmonary Vascular Pruning with Airflow Limitation and Emphysema in COPD Patients.

Background: Pulmonary vascular alteration is an important feature of chronic obstructive pulmonary disease (COPD), which is characterized by distal pulmonary vascular pruning in angiography. We aimed to further investigate the clinical relevance of pulmonary vasculature in COPD patients using non-contrast computed tomography (CT). Methods: Seventy-one control subjects and 216 COPD patients completed the questionnaires, spirometry, and computed tomography (CT) scans within 1 month and were included in the study. Small pulmonary vessels represented by percentage of cross-sectional area of pulmonary vessels smaller than 5 mm2 or 5-10 mm2 to the total lung fields (%CSA<5 or %CSA5-10, respectively) were measured using ImageJ software. Spearman correlation was used to investigate the relationship between %CSA<5 and airflow limitation. A receiver operating characteristic (ROC) curve was built to evaluate the value of %CSA<5 in discriminating COPD patients from healthy control subjects. Segmented regression was used to analyze the relationship between %CSA<5 and %LAA-950 (percentage of low-attenuation areas less than -950 HU). Results: We found a significant correlation between %CSA<5 and forced expiratory volume in one second (FEV1) percentage of predicted value (%pred) (r = 0.564, P < 0.001). The area under the ROC curve for the value of %CSA<5 in distinguishing COPD was 0.816, with a cut-off value of 0.537 (Youden index J, 0.501; sensitivity, 78.24%; specificity, 71.83%). Since the relationship between %CSA<5 and %LAA-950 was not constant, performance of segmented regression was better than ordinary linear regression (adjusted R2, 0.474 vs 0.332, P < 0.001 and P < 0.001, respectively). As %CSA<5 decreased, %LAA-950 slightly increased until an inflection point (%CSA<5 = 0.524) was reached, after which the %LAA-950 increased apparently with a decrease in %CSA<5. Conclusion: %CSA<5 was significantly correlated with both airflow limitation and emphysema, and we identified an inflection point for the relationship between %CSA<5 and %LAA-950.

Cytokine-induced killer cell therapy for modulating regulatory T cells in patients with non-small cell lung cancer.

Previous studies have reported that regulatory T cells (Tregs), which are physiologically engaged in the maintenance of immunological self-tolerance, have a critical role in the regulation of the antitumor immune response. Targeting Tregs has the potential to augment cancer vaccine approaches. The current study therefore aimed to evaluate the role of cytokine-induced killer (CIK) cell infusion in modulating Tregs in patients with non-small cell lung cancer (NSCLC). A total of 15 patients with advanced NSCLC were treated by an infusion of CIK cells derived from autologous peripheral blood mononuclear cells (PBMCs). By using flow cytometry and liquid chip analysis, subsets of T cells and natural killer (NK) cells in peripheral blood, and plasma cytokine profiles in the treated patients, were analyzed at 2 and 4 weeks after CIK cell infusion. Cytotoxicity of PBMCs (n=15) and NK cells (n=6) isolated from NSCLC patients was evaluated before and after CIK cell therapy. Progression-free survival (PFS) and overall survival (OS) were also assessed. Analysis of the immune cell populations before and after treatment showed a significant increase in NK cells (P<0.05) concomitant with a significant decrease in Tregs (P<0.01) at 2 weeks post-infusion of CIK cells compared with the baseline. NK group 2D receptor (NKG2D) expression on NK cells was also significantly increased at 2 weeks post-infusion compared with the baseline (P<0.05). There was a positive correlation between NKG2D expression and the infusion number of CIK cells (P<0.05). When evaluated at 2 weeks after CIK cell therapy, the cytotoxicity of PBMCs and isolated NK cells was significantly increased compared with the baseline (P<0.01 and P<0.05). Correspondingly, plasma cytokine profiles showed significant enhancement of the following antitumor cytokines: Interferon (IFN)-γ (P<0.05), IFN-γ-inducible protein 10 (P<0.01), tumor necrosis factor-α (P<0.001), granulocyte-macrophage colony-stimulating factor (P<0.01), monocyte chemotactic protein-3 (P<0.01) and interleukin-21 (P<0.05) at 2 weeks post-infusion, compared with the baseline. At the same time, the expression of transforming growth factor-ß1, which is primarily produced by Tregs, was significantly decreased compared with the baseline (P<0.05). Median PFS and OS in the CIK cell treatment group were significantly increased compared with the control group (PFS, 9.98 vs. 5.44 months, P=0.038; OS, 24.17 vs. 20.19 months, P=0.048). No severe side-effects were observed during the treatment period. In conclusion, CIK cell therapy was able to suppress Tregs and enhance the antitumor immunity of NK cells in advanced NSCLC patients. Therefore, CIK cell treatment may improve PFS and OS in patients with advanced NSCLC. CIK cell infusion may have therapeutic value for patients with advanced NSCLC, as a treatment that can be combined with chemotherapy and radiotherapy.