Humans ; Bronchiolitis Obliterans/etiology* ; Early Diagnosis ; Pneumonia, Mycoplasma/complications* ; Consensus ; Child ; Mycoplasma pneumoniae ; China/epidemiology* ; Mass Screening ; Adenoviridae Infections/complications*

Objectives To investigate interleukin 36γ (IL-36γ) expression, and analyze the influence of IL-36γ to CD8⁺ T cell activity in chronic obstructive pulmonary diseases (COPD) patients with secondary fungal pneumonia. Methods Peripheral blood was collected from 47 COPD patients, 39 COPD patients with secondary fungal pneumonia, and 20 controls. Bronchial alveolar lavage fluid (BALF) was isolated from 27 COPD patients with secondary fungal pneumonia. CD8⁺ T cells were purified. The levels of four IL-36 isoforms in plasma and BALF were measured by enzyme linked immunosorbent assay (ELISA). CD8⁺ T cells were stimulated with recombinant human IL-36γ. The levels of interferon γ(IFN-γ), tumor necrosis factor α(TNF-α), perforin and granzyme B in the cultured supernatants were measured by ELISA. Recombinant human IL-36γ-stimulated CD8⁺ T cells were co-cultured with NCI-H1882 cells in either direct cell-to-cell contact or Transwell^TM manner. The levels of IFN-γ, TNF-α, and lactate dehydrogenase in the cultured supernatants were assessed. The percentage of target cell death was calculated. Results Plasma IL-36α, IL-36β, and IL-36γ levels were significantly elevated in both COPD group and COPD with secondary fungal pneumonia group compared with those in control group. However, only plasma IL-36γ level was higher in COPD with secondary fungal pneumonia group than that in COPD group [(200.11±99.95)pg/mL vs (53.03±87.18)pg/mL, P=0.023]. There was no remarkable difference in plasma IL-36 receptor antagonist level among three groups. IL-36γ level in BALF from infectious site was higher than that from non-infectious site in COPD with secondary fungal pneumonia group [(305.82±59.60)pg/mL vs (251.93±76.01)pg/mL, P=0.011]. IL-36γ stimulation enhanced IFN-γ, TNF-α, perforin and granzyme B secreted by CD8⁺ T cells. When IL-36γ-stimulated CD8⁺ T cells were directly mixed with NCI-H1882 cells for co-culture, the percentage of cell death was increased [(16.06±3.67)% vs (11.47±2.36)%, P=0.002]. When using Transwell^TM plate for non-contact co-culture, IL-36γ-stimulated CD8⁺ T cell-mediated death of NCI-H1882 cells showed no significant difference compared to that without stimulation [(4.77±0.78)% vs (4.99±0.92)%, P=0.554]. Conclusion IL-36γ level in plasma and infectious site is elevated in COPD patients with secondary fungal pneumonia, which enhances the cytotoxicity of CD8⁺ T cells in peripheral blood and infectious microenviroment.
Humans ; Pulmonary Disease, Chronic Obstructive/complications* ; CD8-Positive T-Lymphocytes/metabolism* ; Male ; Female ; Aged ; Middle Aged ; Interferon-gamma/metabolism* ; Interleukin-1/metabolism* ; Tumor Necrosis Factor-alpha/metabolism* ; Lung Diseases, Fungal/complications* ; Bronchoalveolar Lavage Fluid/chemistry* ; Perforin/metabolism* ; Pneumonia/immunology* ; Granzymes/metabolism*

OBJECTIVES: To study the risk factors for hypoxemia in children with severe Mycoplasma pneumoniae pneumonia (SMPP). METHODS: A retrospective collection of clinical data from children diagnosed with SMPP at the Third Affiliated Hospital of Zhengzhou University from June to December 2023 was conducted. The patients were categorized into hypoxemia and non-hypoxemia groups. Logistic regression analysis was used to assess the risk factors for hypoxemia, and receiver operating characteristic (ROC) curve analysis was employed to analyze the diagnostic performance of various indicators. RESULTS: A total of 113 children with SMPP were included. Univariate logistic regression analysis showed that ferritin, aspartate aminotransferase, creatinine, creatine kinase isoenzyme, lactate dehydrogenase, alpha-hydroxybutyrate dehydrogenase, immunoglobulin G, complement C3, complement C4, age, extrapulmonary complications, and a chest computed tomography (CT) scan showing a bronchiolitis pattern were significant factors for hypoxemia in children with SMPP (P<0.05). Multivariate logistic regression analysis revealed that elevated ferritin levels, presence of extrapulmonary complications, and a bronchiolitis pattern on lung CT were independent risk factors for hypoxemia in these patients (P<0.05). The ROC curve analysis indicated that the combination of these three indicators for predicting hypoxemia had a sensitivity of 71.9%, a specificity of 95.1%, and an area under the curve of 0.888 (95%CI: 0.809-0.968). CONCLUSIONS In children with SMPP, when there are elevated ferritin levels, a bronchiolitis pattern on chest CT, and the presence of extrapulmonary complications, there should be a high level of vigilance for the potential development of hypoxemia.
Humans ; Pneumonia, Mycoplasma/complications* ; Male ; Female ; Risk Factors ; Child, Preschool ; Hypoxia/etiology* ; Retrospective Studies ; Child ; Logistic Models ; Infant ; ROC Curve ; Adolescent

OBJECTIVES: To investigate the epidemiological characteristics of human metapneumovirus (hMPV) and the risk factors for severe pneumonia in hospitalized children. METHODS: The epidemiological characteristics of hMPV in hospitalized children at Hebei Children's Hospital from January 2019 to December 2023 were retrospectively analyzed. The clinical data of hospitalized children with hMPV infection from April to December 2023 were included, and independent risk factors for severe pneumonia were identified through logistic regression. RESULTS: A total of 44 092 children were tested, with an hMPV positive rate of 7.30% (3 220/44 092). Children aged 3-6 years constituted the largest proportion (40.93%, 1 318/3 220) among hMPV-positive cases. The detection rate varied significantly by year (P<0.001), peaking in 2022 (12.35%, 978/7 919). The peak season of the epidemic was winter and spring from 2019 to 2021, but shifted to spring and summer from 2022 to 2023. The proportion of co-infection was 38.70% (1 246/3 220), primarily with rhinovirus (600/1 246, 48.15%), Mycoplasma pneumoniae (217/1 246, 17.42%), and respiratory syncytial virus (182/1 246, 14.61%). The main manifestations of hMPV pneumonia were cough, expectoration, and fever. Children with severe pneumonia were significantly younger (P<0.05). Wheezing, underlying diseases, co-infection, and younger age were identified as independent risk factors for severe pneumonia (P<0.05). CONCLUSIONS There are significant annual and seasonal differences in the epidemiological characteristics of hMPV in hospitalized children. Young age, underlying diseases, wheezing, and co-infection are independent risk factors for severe pneumonia.
Humans ; Risk Factors ; Metapneumovirus ; Child, Preschool ; Child ; Male ; Female ; Paramyxoviridae Infections/complications* ; Pneumonia/epidemiology* ; Retrospective Studies ; Child, Hospitalized ; Infant ; Logistic Models ; Seasons ; Hospitalization

OBJECTIVES: To investigate the predictive factors for plastic bronchitis (PB) in children with Mycoplasma pneumoniae pneumonia (MPP) and to establish a nomogram prediction model for PB occurrence. METHODS: A retrospective analysis was conducted on children with MPP hospitalized at The Affiliated Hospital of Xuzhou Medical University from January 2023 to June 2024. The patients were randomly divided into a training set (n=562) and a validation set (n=240) at a ratio of 7:3 using simple random sampling. In the training set, patients were categorized into a PB group (n=70) and a non-PB group (n=492) based on the occurrence of PB. Spearman correlation analysis was performed to exclude collinearity among variables, followed by univariate analysis and LASSO regression to identify predictive factors. A nomogram prediction model for PB in children with MPP was constructed. The discriminative ability of the model was assessed using receiver operating characteristic (ROC) curve analysis, model calibration was evaluated with calibration curves, and clinical utility was appraised through decision curve analysis. RESULTS: Compared with the non-PB group, the PB group exhibited significantly longer disease duration prior to bronchoscopy, prolonged fever duration, higher fever peaks, higher proportions of patients with a family history of allergy and personal allergy history, and a higher proportion of patients with pleural effusion, as well as significantly elevated levels of white blood cell count, neutrophil percentage, C-reactive protein, procalcitonin, fibrinogen, D-dimer, aspartate aminotransferase, alanine aminotransferase, creatine kinase, lactate dehydrogenase, immunoglobulin A, and interleukin-6, along with a significantly lower lymphocyte percentage (all P<0.05). LASSO regression analysis identified pleural effusion, procalcitonin, D-dimer, and lactate dehydrogenase as major predictive factors for PB occurrence in children with MPP. The nomogram model based on these factors demonstrated good discriminative ability (area under the ROC curve: 0.852 in the training set and 0.830 in the validation set), with satisfactory calibration and clinical benefit. CONCLUSIONS The nomogram prediction model based on pleural effusion, procalcitonin, D-dimer, and lactate dehydrogenase provides effective predictive performance for the occurrence of PB in children with MPP.
Humans ; Pneumonia, Mycoplasma/complications* ; Nomograms ; Male ; Female ; Child ; Child, Preschool ; Retrospective Studies ; Bronchitis/etiology* ; Infant ; ROC Curve ; Adolescent

OBJECTIVES: To identify risk factors for recurrent plastic bronchitis (PB) among children with Mycoplasma pneumoniae pneumonia (MPP). METHODS: The clinical data of children with MPP complicated by PB who underwent bronchoscopy at Gansu Province Maternity and Child Health Hospital between July 2023 and January 2025 were retrospectively analyzed. Patients were grouped into a single-episode PB group and a recurrent PB group according to the number of PB episodes. Multivariable logistic regression was used to identify risk factors for recurrent PB. Receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic performance of individual and combined predictors. RESULTS: A total of 264 children were included; 188 (71.2%) had a single episode of PB and 76 (28.8%) had recurrent PB. Multivariable logistic regression analysis showed that decreased serum albumin, atelectasis, and fever persisting beyond 72 hours after the initial bronchoscopy were significantly associated with recurrent PB (all P<0.05). The combination of these predictors yielded a sensitivity of 82.9%, specificity of 61.7%, and an area under the ROC curve of 0.777 (95%CI: 0.714-0.839), outperforming any single predictor (P<0.05). CONCLUSIONS In children with MPP complicated by PB, decreased serum albumin, the presence of atelectasis, and fever persisting beyond 72 hours after the initial bronchoscopy are associated with an increased risk of PB recurrence. In such cases, early repeat or multiple bronchoscopic interventions should be considered.
Humans ; Pneumonia, Mycoplasma/complications* ; Male ; Female ; Risk Factors ; Recurrence ; Child, Preschool ; Bronchitis/etiology* ; Child ; Retrospective Studies ; Logistic Models ; Infant ; ROC Curve ; Adolescent

OBJECTIVE: To investigate the application value of bronchoalveolar lavage fluid (BALF) metagenomic next-generation sequencing (mNGS) in etiological diagnosis of children with hematological malignancies complicated with pneumonia. METHODS: We retrospectively analyzed the clinical data of children with hematological malignancies who underwent BALF mNGS pathogenic detection due to pneumonia. All patients underwent mNGS detection of bronchoalveolar lavage fluid as well as traditional methods(including sputum culture, bronchoalveolar lavage fluid culture, blood culture, serological detection of pathogens, etc.). By analyzing the results of mNGS and traditional methods, we compared key indicators such as the positive rate, etiological distribution. RESULTS: A total of 26 children with hematological malignancies enrolled in the study, including 12 males and 14 females, with a median age of 4.9 (1.8-14.9) years, underwent bronchoalveolar lavage (BAL) 35 times. A total of 17 pathogenic microorganisms were detected in BALF mNGS, including 9 cases of bacterial infection, 10 cases of viral infection, 3 cases of fungal infection, 2 cases of mycoplasma infection and 8 cases of mixed infection, and the most commonly detected bacteria, viruses and fungi were streptococcus pneumoniae, cytomegalovirus and pneumocystis jirovecii, respectively. The positive rate of mNGS detection (91.43%) was significantly higher than that of traditional methods detection (20%, P <0.001). A total of 25 cases were adjusted according to BALF mNGS results. CONCLUSION The application of BALF mNGS technology can improve the detection rate of the pathogens in children with hematological malignancies complicated with pneumonia, initially revealed the pathogen spectrum of pulmonary infection in this group, and effectively guide clinical medication, improve treatment outcomes.
Humans ; Bronchoalveolar Lavage Fluid/microbiology* ; Hematologic Neoplasms/complications* ; Child ; Child, Preschool ; Infant ; Retrospective Studies ; Male ; Female ; Adolescent ; High-Throughput Nucleotide Sequencing ; Pneumonia/microbiology*

Pneumocystis jirovecii pneumonia (PJP) is an opportunistic pulmonary infection that commonly occurs in immunocompromised children. We report a case of infantile leukemia complicated by PJP and review the relevant literature. A summary and analysis of 10 infantile leukemia patients with PJP infection (9 cases reported in the literature and 1 case from our center) showed that PJP mostly occurred in the early stages of chemotherapy (80%, 8/10). The main clinical manifestations were dyspnea (100%, 10/10) and hypoxemia (50%, 5/10), while pulmonary imaging findings lacked specificity. In most cases (50%, 5/10), diagnosis was established by identifying pathogens in bronchoalveolar lavage fluid under microscopy. In our case, diagnosis was confirmed using targeted next-generation sequencing (tNGS) of bronchoalveolar lavage fluid. Treatment with intravenous sulfamethoxazole complex was administered in 8 patients, all of whom eventually recovered. PJP may occur in the early stages of chemotherapy for infantile leukemia, thus early prevention is necessary. tNGS facilitates early diagnosis of PJP, and sulfamethoxazole complex remains an effective therapeutic option.
Humans ; Infant ; Bronchoalveolar Lavage Fluid/microbiology* ; Immunocompromised Host ; Leukemia/complications* ; Pneumocystis carinii/isolation & purification* ; Pneumonia, Pneumocystis/diagnosis* ; Trimethoprim, Sulfamethoxazole Drug Combination/therapeutic use*

OBJECTIVE: To identify early potential risk factors for hospital-acquired pneumonia (HAP) in patients with mild traumatic brain injury (mTBI), construct a risk prediction model, and evaluate its predictive efficacy. METHODS: A case-control study was conducted using clinical data from mTBI patients admitted to the neurosurgery department of Changzhou Second People's Hospital from September 2021 to September 2023. The patients were divided into two groups based on whether they developed HAP. Clinical data within 48 hours of admission were statistically analyzed to identify factors influencing HAP occurrence through univariate analysis. Least absolute shrinkage and selection operator (LASSO) regression analysis was employed for feature selection to identify the most influential variables. The dataset was divided into training and validation sets in a 7:3 ratio. A multivariate Logistic regression analysis was then performed using the training set to construct the prediction model, exploring the risk factors for HAP in mTBI patients and conducting internal validation in the validation set. Receiver operator characteristic curve (ROC curve), decision curve analysis (DCA), and calibration curve were utilized to assess the sensitivity, specificity, decision value, and predictive accuracy of the prediction model. RESULTS: A total of 677 mTBI patients were included, with 257 in the HAP group and 420 in the non-HAP group. The significant differences were found between the two groups in terms of age, maximum body temperature (MaxT), maximum heart rate (MaxHR), maximum systolic blood pressure (MaxSBP), minimum systolic blood pressure (MinSBP), maximum respiratory rate (MaxRR), cause of injury, and laboratory indicators [C-reactive protein (CRP), procalcitonin (PCT), neutrophil count (NEUT), erythrocyte sedimentation rate (ESR), fibrinogen (FBG), fibrinogen equivalent units (FEU), prothrombin time (PT), activated partial thromboplastin time (APTT), total cholesterol (TC), lactate dehydrogenase (LDH), prealbumin (PAB), albumin (Alb), blood urea nitrogen (BUN), serum creatinine (SCr), hematocrit (HCT), hemoglobin (Hb), platelet count (PLT), glucose (Glu), K⁺, Na⁺], suggesting they could be potential risk factors for HAP in mTBI patients. After LASSO regression analysis, the key risk factors were enrolled in the multivariate Logistic regression analysis. The results revealed that the cause of injury being a traffic accident [odds ratio (OR) = 2.199, 95% confidence interval (95%CI) was 1.124-4.398, P = 0.023], NEUT (OR = 1.330, 95%CI was 1.214-1.469, P < 0.001), ESR (OR = 1.053, 95%CI was 1.019-1.090, P = 0.003), FBG (OR = 0.272, 95%CI was 0.158-0.445, P < 0.001), PT (OR = 0.253, 95%CI was 0.144-0.422, P < 0.001), APTT (OR = 0.689, 95%CI was 0.578-0.811, P < 0.001), Alb (OR = 0.734, 95%CI was 0.654-0.815, P < 0.001), BUN (OR = 0.720, 95%CI was 0.547-0.934, P = 0.016), and Na⁺ (OR = 0.756, 95%CI was 0.670-0.843, P < 0.001) could serve as main risk factors for constructing the prediction model. Calibration curves demonstrated good calibration of the prediction model in both training and validation sets with no evident over fitting. ROC curve analysis showed that the area under the ROC curve (AUC) of the prediction model in the training set was 0.943 (95%CI was 0.921-0.965, P < 0.001), with a sensitivity of 83.6% and a specificity of 91.5%. In the validation set, the AUC was 0.917 (95%CI was 0.878-0.957, P < 0.001), with a sensitivity of 90.1% and a specificity of 85.0%. DCA indicated that the prediction model had a high net benefit, suggesting practical clinical applicability. CONCLUSIONS The cause of injury being a traffic accident, NEUT, ESR, FBG, PT, APTT, Alb, BUN, and Na⁺ are identified as major risk factors influencing the occurrence of HAP in mTBI patients. The prediction model constructed using these parameters effectively assesses the likelihood of HAP in mTBI patients.
Humans ; Risk Factors ; Case-Control Studies ; Logistic Models ; Healthcare-Associated Pneumonia/epidemiology* ; Brain Injuries, Traumatic/complications* ; Male ; Female ; ROC Curve ; Pneumonia/etiology* ; Middle Aged ; Adult

OBJECTIVE: To analyze the risk factors for ventilator-associated pneumonia (VAP) and its prognosis in patients with severe craniocerebral injury. METHODS: A prospective observational study was conducted. Patients with severe craniocerebral injury admitted to the Second Affiliated Hospital of Xingtai Medical College from January 2020 to December 2022 were enrolled as the study subjects. Patients were divided into VAP group and non-VAP group based on the occurrence of VAP. VAP patients were further stratified into low-risk group [sequential organ failure assessment (SOFA) score 0-5], moderate-risk group (SOFA score 6-8), and high-risk group (SOFA score ≥ 9). General data, serological indicators [interleukin-6 (IL-6), interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), and signal transducer and activator of transcription 3 (STAT3)], and 28-day prognosis (with mortality as the endpoint event) were compared. Multivariate Logistic regression was used to identify risk factors for VAP and 28-day mortality. Linear regression was applied to analyze the correlations between risk factors and outcomes. RESULTS: A total of 140 patients with severe craniocerebral injury were enrolled, including 49 in the VAP group and 91 in the non-VAP group. The primary cause of injury was traffic accidents, followed by falls and heavy object impacts. Among VAP patients, 38 survived and 11 died within 28 days; 112 were classified as low-risk, 25 as moderate-risk, and 12 as high-risk. Significant differences were observed in age, body mass index (BMI), smoking history, hypertension, diabetes, hyperlipidemia, length of hospital stay, duration of mechanical ventilation, serum albumin levels, and frequency of sputum suction among different subgroups. Serologically, IL-1β, TNF-α, IL-6, and STAT3 mRNA expression levels in the VAP group were significantly higher than those in the non-VAP group. Deceased VAP patients exhibited higher IL-1β, TNF-α, IL-6, and STAT3 mRNA levels compared to survivors. These biomarkers progressively increased from low-risk to high-risk subgroups. Multivariate Logistic regression identified age [odds ratio (OR) were 0.328 and 0.318], BMI (OR were 0.340 and 0.268), hypertension (OR were 0.275 and 0.245), diabetes (OR were 0.319 and 0.307), hyperlipidemia (OR were 0.228 and 0.235), smoking history (OR were 0.255 and 0.240), length of hospital stay (OR were 0.306 and 0.230), duration of mechanical ventilation (OR were 0.247 and 0.219), frequency of sputum suction (OR were 0.325 and 0.228), IL-1β (OR were 0.231 and 0.259), TNF-α (OR were 0.308 and 0.235), IL-6 (OR were 0.298 and 0.277), and STAT3 (OR were 0.259 and 0.265) as independent risk factors for both VAP occurrence and 28-day mortality (all P < 0.05). Correlation analysis revealed that serum albumin levels were negatively correlated with VAP occurrence and mortality (all P < 0.01), while other factors showed positive correlations (all P < 0.01). CONCLUSIONS Age, BMI, length of hospital stay, duration of mechanical ventilation, frequency of sputum suction, hypertension, diabetes, hyperlipidemia, smoking history, IL-1β, TNF-α, and IL-6/STAT3 signaling pathway activation are significantly associated with VAP development and poor prognosis in patients with severe craniocerebral injury, providing a scientific basis for targeted clinical interventions.
Humans ; Risk Factors ; Pneumonia, Ventilator-Associated ; Prognosis ; Prospective Studies ; Craniocerebral Trauma/complications* ; Interleukin-6/blood* ; Male ; Female ; STAT3 Transcription Factor/blood* ; Interleukin-1beta/blood* ; Tumor Necrosis Factor-alpha/blood* ; Middle Aged ; Adult ; Logistic Models