Pulmonary Infection In Patients Research Articles

Cardiac safety of ozanimod, a novel sphingosine-1-phosphate receptor ligand, in COVID-19 patients requiring oxygen

BackgroundOzanimod is a novel immune modulator that could be useful in viral pulmonary infections by reducing lung inflammation. It’s an S1P receptor ligand known to induce bradycardia and more serious adverse cardiac effects such as atrioventricular block (AVB) and QT prolongation. We present a sub-study of the COZI trial where ozanimod was administered in acute pulmonary infection patients, to assess cardiac safety. MethodsIn this pilot randomized open label trial, COVID-19 patients requiring oxygen support were randomized in 2 groups: standard of care + ozanimod (OZA) versus standard of care alone (SOC). All patients were monitored with a 14-day ECG monitor (CardioSTAT®) during their hospitalization. We evaluated the cardiac effects of ozanimod on heart rate (HR), PR length, and QT duration. ResultsA total of 42 patients were analyzed: 23 in SOC and 19 in OZA. Mean hourly HR over the first 10 days of treatment decrease in OZA compared with SOC (p<0.0001). The maximum decrease in HR occurred on day 3, The maximum decrease in heart rate occurred on day 3, without significant difference between groups: 49 bpm (42-59) in OZA and 54 bpm (48-60) in SOC, p=0.45. No high degree atrioventricular block was recorded. QT and PR median values were within normal in both groups without significant difference. ConclusionThe maximal reduction in HR occurred 3 days after the onset of ozanimod treatment in patients hospitalized for COVID-19 but it did not remain significant over the 10-day treatment. No relevant cardiac adverse event was observed.

A laboratory perspective on Mycobacterium abscessus biofilm culture, characterization and drug activity testing.

Mycobacterium abscessus is an emerging opportunistic pathogen causing severe pulmonary infections in patients with underlying lung disease and cystic fibrosis in particular. The rising prevalence of M. abscessus infections poses an alarming threat, as the success rates of available treatment options are limited. Central to this challenge is the absence of preclinical in vitro models that accurately mimic in vivo conditions and that can reliably predict treatment outcomes in patients. M. abscessus is notorious for its association with biofilm formation within the lung. Bacteria in biofilms are more recalcitrant to antibiotic treatment compared to planktonic bacteria, which likely contributes to the lack of correlation between preclinical drug activity testing (typically performed on planktonic bacteria) and treatment outcome. In recent years, there has been a growing interest in M. abscessus biofilm research. However, the absence of standardized methods for biofilm culture, biofilm characterization and drug activity testing has led to a wide spectrum of, sometimes inconsistent, findings across various studies. Factors such as strain selection, culture medium, and incubation time hugely impact biofilm development, phenotypical characteristics and antibiotic susceptibility. Additionally, a broad range of techniques are used to study M. abscessus biofilms, including quantification of colony-forming units, crystal violet staining and fluorescence microscopy. Yet, limitations of these techniques and the selected readouts for analysis affect study outcomes. Currently, research on the activity of conventional antibiotics, such as clarithromycin and amikacin, against M. abscessus biofilms yield ambiguous results, underscoring the substantial impact of experimental conditions on drug activity assessment. Beyond traditional drug activity testing, the exploration of novel anti-biofilm compounds and the improvement of in vitro biofilm models are ongoing. In this review, we outline the laboratory models, experimental variables and techniques that are used to study M. abscessus biofilms. We elaborate on the current insights of M. abscessus biofilm characteristics and describe the present understanding of the activity of traditional antibiotics, as well as potential novel compounds, against M. abscessus biofilms. Ultimately, this work contributes to the advancement of fundamental knowledge and practical applications of accurate preclinical M. abscessus models, thereby facilitating progress towards improved therapies for M. abscessus infections.

Beyond Symptoms: Radiologic identification of asymptomatic Mycobacterium avium complex pulmonary infections

BackgroundAlthough international nontuberculous mycobacterial pulmonary disease (NTM-PD) guidelines highlight symptom presence at diagnosis, the clinical characteristics of asymptomatic Mycobacterium avium complex pulmonary infection (MAC-PI) patients remain understudied. We clarified the clinical characteristics and course of asymptomatic MAC-PI patients. MethodsWe retrospectively analyzed 200 consecutive patients with MAC-PIs and adequate available data who newly met the microbiological and radiological criteria for NTM-PD at Fukujuji Hospital from January 2018 to June 2020. We compared the clinical characteristics and course of asymptomatic patients with symptomatic patients and evaluated factors influencing treatment initiation through multivariate analysis. Results111 patients were symptomatic and 89 were asymptomatic at diagnosis. While the proportion was significantly lower than that in the symptomatic group (28.8 %), 15.7 % of asymptomatic group patients had cavitary lesions (P = 0.042). In the asymptomatic group, treatments were initiated in 38 (42.7 %) patients, and cavitary lesions, a positive acid-fast bacilli smear, and younger age were independent risk factors for treatment initiation. Among 22 (57.9 %) patients who experienced disease progression necessitating treatment during follow-up, 13 (34.2 %) displayed radiological progression without any worsening of symptoms. Agents used for treatment were consistent across the groups, with no significant differences in culture conversion, microbiological recurrence rates, or spontaneous culture conversion rates. ConclusionRoutine health checkups and radiological examinations can detect clinically important MAC-PIs even in the absence of symptoms. Considering that the clinical course of asymptomatic MAC-PI patients is largely similar to that of symptomatic patients, timely and appropriate management and intervention are essential for all MAC-PI patients.

Novel Dry Hyaluronic Acid-Vancomycin Complex Powder for Inhalation, Useful in Pulmonary Infections Associated with Cystic Fibrosis.

Polyelectrolyte-drug complexes are interesting alternatives to improve unfavorable drug properties. Vancomycin (VAN) is an antimicrobial used in the treatment of methicillin-resistant Staphylococcus aureus pulmonary infections in patients with cystic fibrosis. It is generally administered intravenously with a high incidence of adverse side effects, which could be reduced by intrapulmonary administration. Currently, there are no commercially available inhalable formulations containing VAN. Thus, the present work focuses on the preparation and characterization of an ionic complex between hyaluronic acid (HA) and VAN with potential use in inhalable formulations. A particulate-solid HA-VAN25 complex was obtained by spray drying from an aqueous dispersion. FTIR spectroscopy and thermal analysis confirmed the ionic interaction between HA and VAN, while an amorphous diffraction pattern was observed by X-ray. The powder density, geometric size and morphology showed the suitable aerosolization and aerodynamic performance of the powder, indicating its capability of reaching the deep lung. An in vitro extended-release profile of VAN from the complex was obtained, exceeding 24 h. Microbiological assays against methicillin-resistant and -sensitive reference strains of Staphylococcus aureus showed that VAN preserves its antibacterial efficacy. In conclusion, HA-VAN25 exhibited interesting properties for the development of inhalable formulations with potential efficacy and safety advantages over conventional treatment.

Development of a self-microemulsifying drug delivery system to deliver delamanid via a pressurized metered dose inhaler for treatment of multi-drug resistant pulmonary tuberculosis

Tuberculosis (TB) is a serious health issue that contributes to millions of deaths throughout the world and increases the threat of serious pulmonary infections in patients with respiratory illness. Delamanid is a novel drug approved in 2014 to deal with multi-drug resistant TB (MDR-TB). Despite its high efficiency in TB treatment, delamanid poses delivery challenges due to poor water solubility leading to inadequate absorption upon oral administration. This study involves the development of novel formulation-based pressurized metered dose inhalers (pMDIs) containing self-microemulsifying mixtures of delamanid for efficient delivery to the lungs. To identify the appropriate self-microemulsifying formulations, ternary diagrams were plotted using different combinations of surfactant to co-surfactant ratios (1:1, 2:1, and 3:1). The combinations used Cremophor RH40, Poly Ethylene Glycol 400 (PEG 400), and peppermint oil, and those that showed the maximum microemulsion region and rapid and stable emulsification were selected for further characterization. The diluted self-microemulsifying mixtures underwent evaluation of dose uniformity, droplet size, zeta potential, and transmission electron microscopy. The selected formulations exhibited uniform delivery of the dose throughout the canister life, along with droplet sizes and zeta potentials that ranged from 24.74 to 88.99 nm and − 19.27 to − 10.00 mV, respectively. The aerosol performance of each self-microemulsifying drug delivery system (SMEDDS)-pMDI was assessed using the Next Generation Impactor, which indicated their capability to deliver the drug to the deeper areas of the lungs. In vitro cytotoxicity testing on A549 and NCI-H358 cells revealed no significant signs of toxicity up to a concentration of 1.56 µg/mL. The antimycobacterial activity of the formulations was evaluated against Mycobacterium bovis using flow cytometry analysis, which showed complete inhibition by day 5 with a minimum bactericidal concentration of 0.313 µg/mL. Moreover, the cellular uptake studies showed efficient delivery of the formulations inside macrophage cells, which indicated the potential for intracellular antimycobacterial activity. These findings demonstrated the potential of the Delamanid-SMEDDS-pMDI for efficient pulmonary delivery of delamanid to improve its effectiveness in the treatment of multi-drug resistant pulmonary TB.

Predicting the risk of pulmonary infection in patients with chronic kidney failure: A-C2GH2S risk score-a retrospective study.

The objective of this study is to investigate the associated risk factors of pulmonary infection in individuals diagnosed with chronic kidney disease (CKD). The primary goal is to develop a predictive model that can anticipate the likelihood of pulmonary infection during hospitalization among CKD patients. This retrospective cohort study was conducted at two prominent tertiary teaching hospitals. Three distinct models were formulated employing three different approaches: (1) the statistics-driven model, (2) the clinical knowledge-driven model, and (3) the decision tree model. The simplest and most efficient model was obtained by comparing their predictive power, stability, and practicability. This study involved a total of 971 patients, with 388 individuals comprising the modeling group and 583 individuals comprising the validation group. Three different models, namely Models A, B, and C, were utilized, resulting in the identification of seven, four, and eleven predictors, respectively. Ultimately, a statistical knowledge-driven model was selected, which exhibited a C-statistic of 0.891 (0.855-0.927) and a Brier score of 0.012. Furthermore, the Hosmer-Lemeshow test indicated that the model demonstrated good calibration. Additionally, Model A displayed a satisfactory C-statistic of 0.883 (0.856-0.911) during external validation. The statistical-driven model, known as the A-C2GH2S risk score (which incorporates factors such as albumin, C2 [previous COPD history, blood calcium], random venous blood glucose, H2 [hemoglobin, high-density lipoprotein], and smoking), was utilized to determine the risk score for the incidence rate of lung infection in patients with CKD. The findings revealed a gradual increase in the occurrence of pulmonary infections, ranging from 1.84% for individuals with an A-C2GH2S Risk Score ≤ 6, to 93.96% for those with an A-C2GH2S Risk Score ≥ 18.5. A predictive model comprising seven predictors was developed to forecast pulmonary infection in patients with CKD. This model is characterized by its simplicity, practicality, and it also has good specificity and sensitivity after verification.

Prediction of pulmonary infection in patients with severe myelitis by NPAR combined with spinal cord lesion segments.

To investigate the risk factors of pulmonary infection in patients with severe myelitis and construct a prediction model. The clinical data of 177 patients with severe myelitis at admission from the First Affiliated Hospital of Zhengzhou University from January 2020 to December 2022 were retrospectively analyzed. The predicting factors associated with pulmonary infection were screened by multivariate logistic regression analysis, and the nomogram model was constructed, and the predictive efficiency of the model was evaluated, which was verified by calibration curve, Hosmer-Lemeshow goodness-of-fit test and decision curve analysis. Of the 177 patients with severe myelitis, 38 (21.5%) had pulmonary infection. Multivariate logistic regression analysis showed that neutrophil percentage to albumin ratio (NPAR) (OR = 6.865, 95%CI:1.746-26.993, p = 0.006) and high cervical cord lesion (OR = 2.788, 95%CI:1.229-6.323, p = 0.014) were independent risk factors for pulmonary infection, and the combined nomogram could easily predict the occurrence of pulmonary infection, with a C-index of 0.766 (95% CI: 0.678-0.854). The calibration curve, Hosmer-Lemeshow goodness-of-fit test (χ2 = 9.539, p = 0.299) and decision curve analysis showed that the model had good consistency and clinical applicability. The nomogram model constructed based on NPAR combined with high cervical cord lesion at admission has good clinical application value in predicting pulmonary infection in patients with severe myelitis, which is conducive to clinicians' evaluation of patients.

Dynamic immune signatures of patients with advanced non-small-cell lung cancer for infection prediction after immunotherapy.

Pulmonary infections are a crucial health concern for patients with advanced non-small-cell lung cancer (NSCLC). Whether the clinical outcome of pulmonary infection is influenced by immunotherapy(IO) remains unclear. By evaluating immune signatures, this study investigated the post-immunotherapy risk of pulmonary infection in patients with lung cancer and identified circulating biomarkers that predict post-immunotherapy infection. Blood specimens were prospectively collected from patients with NSCLC before and after chemotherapy(C/T) and/or IO to explore dynamic changes in immune signatures. Real-world clinical data were extracted from medical records for outcome evaluation. Mass cytometry and ELISA were employed to analyze immune signatures and cytokine profiles to reveal potential correlations between immune profiles and the risk of infection. The retrospective cohort included 283 patients with advanced NSCLC. IO was associated with a lower risk of pneumonia (odds ratio=0.46, p=0.012). Patients receiving IO and remained pneumonia-free exhibited the most favorable survival outcomes compared with those who received C/T or developed pneumonia (p<0.001). The prospective cohort enrolled 30 patients. The proportion of circulating NK cells significantly increased after treatment in IO alone (p<0.001) and C/T+IO group (p<0.01). An increase in cell densities of circulating PD-1+CD8+(cytotoxic) T cells (p<0.01) and PD-1+CD4+ T cells (p<0.01) were observed in C/T alone group after treatment. In IO alone group, a decrease in cell densities of TIM-3+ and PD-1+ cytotoxic T cells (p<0.05), and PD-1+CD4+ T cells (p<0.01) were observed after treatment. In C/T alone and C/T+IO groups, cell densities of circulating PD-1+ cytotoxic T cells significantly increased in patients with pneumonia after treatment(p<0.05). However, in IO alone group, cell density of PD-1+ cytotoxic T cells significantly decreased in patients without pneumonia after treatment (p<0.05). TNF-α significantly increased after treatment with IO alone (p<0.05) but decreased after C/T alone (p<0.01). Our results indicate that the incorporation of immunotherapy into treatment regimens may potentially offer protective effects against pulmonary infection. Protective effects are associated with reduction of exhausted T-cells and augmentation of TNF-α and NK cells. Exhausted T cells, NK cells, and TNF-α may play crucial roles in immune responses against infections. These observations highlight the potential utility of certain circulating biomarkers, particularly exhausted T cells, for predicting post-treatment infections.

Development and validation of a nomogram for predicting pulmonary infection in patients receiving immunosuppressive drugs.

Objective: Pulmonary infection (PI), a severe complication of immunosuppressive therapy, affects patients' prognosis. As part of this study, we aimed to construct a pulmonary infection prediction (PIP) model and validate it in patients receiving immunosuppressive drugs (ISDs). Methods: Totally, 7,977 patients being treated with ISDs were randomised 7:3 to the developing (n = 5,583) versus validation datasets (n = 2,394). Our predictive nomogram was established using the least absolute shrinkage and selection operator (LASSO) and multivariate COX regression analyses. With the use of the concordance index (C-index) and calibration curve, the prediction performance of the final model was evaluated. Results: Among the patients taking immunosuppressive medication, PI was observed in 548 (6.9%). The median time of PI occurrence after immunosuppressive therapy was 123.0 (interquartile range: 63.0, 436.0) days. Thirteen statistically significant independent predictors (sex, age, hypertension, DM, malignant tumour, use of biologics, use of CNIs, use of methylprednisolone at 500mg, use of methylprednisolone at 40mg, use of methylprednisolone at 40mg total dose, use of oral glucocorticoids, albumin level, and haemoglobin level) were screened using the LASSO algorithm and multivariate COX regression analysis. The PIP model built on these features performed reasonably well, with the developing C-index of 0.87 (sensitivity: 85.4%; specificity: 81.0%) and validation C-indices of 0.837, 0.829, 0.832 and 0.830 for predicting 90-, 180-, 270- and 360-day PI probability, respectively. The decision curve analysis (DCA) and calibration curves displayed excellent clinical utility and calibration performance of the nomogram. Conclusion: The PIP model presented herein could aid in the prediction of PI risk in individual patients who receive immunosuppressive treatment and help personalise clinical decision-making.

The Use of Low-Dose Chest Computed Tomography for the Diagnosis and Monitoring of Pulmonary Infections in Patients with Hematologic Malignancies.

The study aimed to assess the image quality and diagnostic performance of low-dose Chest Computed Tomography (LDCCT) in detecting pulmonary infections in patients with hematologic malignancies. A total of 164 neutropenic patients underwent 256 consecutive CT examinations, comparing 149 LDCCT and 107 Standard-Dose Chest CT (SDCCT) between May 2015 and June 2019. LDCCT demonstrated a 47% reduction in radiation dose while maintaining acceptable image noise and quality compared to SDCCT. However, LDCCT exhibited lower sensitivity in detecting consolidation (27.5%) and ground glass opacity (64.4%) compared to SDCCT (45.8% and 82.2%, respectively) with all the respective p-values from unadjusted and adjusted for sex, age, and BMI analyses being lower than 0.006 and the corresponding Odds Ratios of detection ranging from 0.30 to 0.34. Similar trends were observed for nodules ≥3 mm and ground glass halo in nodules but were not affected by sex, age and BMI. No significant differences were found for cavitation in nodules, diffuse interlobular septal thickening, pleural effusion, pericardial effusion, and lymphadenopathy. In conclusion, LDCCT achieved substantial dose reduction with satisfactory image quality but showed limitations in detecting specific radiologic findings associated with pulmonary infections in neutropenic patients compared to SDCCT.

Stenotrophomonas maltophilia: An Urgent Threat with Increasing Antibiotic Resistance.

Stenotrophomonas maltophilia is a Gram-negative opportunistic pathogen that can cause many infections, such as chronic pulmonary infections in patients with cystic fibrosis and infections in immunocompromised patients with hematology-oncology diseases. Because of its remarkable and increasing antimicrobial resistance, the treatment of S. maltophilia infections is quite challenging. Meanwhile, the prevalence of S. maltophilia infections is increasing in recent decades. S. maltophilia is usually considered to be of low virulence but has numerous virulence factors involved in the pathogenesis of infections caused by S. maltophilia. By revealing its pathogenesis associated with virulence factors and molecular mechanisms of antimicrobial resistance, many existing or potential therapeutic strategies have been developed. However, because of the limited treatment options, new strategies are urgently needed. Here, we review the recent progresses in research on S. maltophilia which may help to develop more effective treatments against this increasing threat.

Pathogen distribution in pulmonary infection in chinese patients with lung cancer: a systematic review and meta-analysis

BackgroundThe immunity of patients with lung cancer decreases after treatment; thus, they are easily infected with pathogenic bacteria that causes pulmonary infections. Understanding the distribution characteristics of pathogenic bacteria in pulmonary infection in patients with lung cancer after treatment can provide a basis to effectively prevent infection and rationally use antibacterial drugs. However, no meta-analyses have assessed the distribution characteristics of pathogenic bacteria in mainland China. Therefore, our meta-analysis aimed to investigate the pathogen distribution in pulmonary infection in Chinese patients with lung cancer.MethodsA literature search was conducted to study the pathogen distribution in pulmonary infection in Chinese patients with lung cancer between January 1, 2020 and December 31, 2022, using English and Chinese databases. The relevant data were extracted. The meta-analysis was performed using a random-effects model ( I2 > 50%) with 95% confidence intervals for forest plots. Data were processed using RevMan 5.3.ResultsFifteen studies (2,683 strains in 2,129 patients with pulmonary infection were cultured) met the evaluation criteria. The results showed that Gram-negative bacteria had the highest detection rate (63%), followed by Gram-positive bacteria (23%), and fungi (12%). Among the Gram-negative bacteria detected, the distribution of the main pathogenic bacteria was Klebsiella pneumonia (17%), Pseudomonas aeruginosa (14%), Escherichia coli (13%), Acinetobacter baumannii (7%), Enterobacter cloacae (4%), and Hemophilus influenza (4%). Moreover, the prevalence of pulmonary infections after chemotherapy (53%) was significantly higher than that after surgery (10%), P < 0.05.ConclusionsThe prevalence of pulmonary infections after treatment, especially after chemotherapy, is high in Chinese patients with lung cancer, and Gram-negative bacteria are the predominant pathogens. Further studies are needed to monitor the prevalence of pulmonary infections and pathogen distribution in lung cancer patients in mainland China.

Risk Factors for Pulmonary Infection and Nursing Interventions Post-Tracheostomy in Patients with Spinal Cord Injury.

We analyzed the characteristics and risk factors for pulmonary infection in patients with spinal cord injury who underwent tracheostomy and propose measures to help in early detection and intervention to reduce mortality and improve prognosis. We collected data retrospectively from January 1, 2018, to December 31, 2022. The inclusion criteria were: Patients aged 18 years or more with a spinal cord injury who underwent tracheostomy, were treated with mechanical ventilation for over 48 hours, and were diagnosed as having a pulmonary infection. Sputum samples were cultured and analyzed. 101 cases of pulmonary infection were analyzed, and the incidence was 32.17%. Diabetes (OR 2.302, 95% CI 1.285-3.972), hypoproteinemia (OR 1.992, 95% CI 1.125-3.101), administration of glucocorticoids (OR 2.934, 95% CI 1.412-4.661), ASIA grade A (OR 3.672, 95% CI 1.988-5.046), mechanical ventilation for ≥ 6 days (OR 2.108, 95% CI 1.385-4.751), and length of hospital stay for ≥ 20 days (OR 2.137, 95% CI 1.092-3.842) were risk factors for pulmonary infection in patients with spinal cord injury post-tracheostomy. Among 213 pathogenic bacteria, 52 (51.48%) were Gram-negative and 24 (23.76%) were Gram-positive. Klebsiella pneumoniae (15.84%) and Staphylococcus aureus (8.91%) were the most common pathogenic bacteria. The mortality rate of patients with gram-positive infection was higher than that of patients with gram-negative infection. K. pneumoniae and S. aureus were sensitive to cefoperazone, meropenem, and levofloxacin. Pulmonary infection is a complication post-tracheostomy in patients with spinal cord injury. Diabetes, hypoproteinemia, administration of glucocorticoids, mechanical ventilation for ≥ 6 days, length of hospital stay for ≥ 20 days were risk factors for pulmonary infection. Pulmonary infection was mainly caused by gram-negative bacteria. Timely and effective measures for managing risk factors are essential for improving the prognosis of pulmonary infection post-tracheostomy in patients with spinal cord injuries.

A-242 Integrating Respiratory Metagenomics and Metatranscriptomics for Diagnosis of Lung Cancer and Infection in Patients with Pulmonary Diseases

Abstract Background Advances in unbiased metagenomic next generation sequencing (mNGS) technologies have enabled the study of microbial and host genetic material (DNA and RNA) in one test. In this study, we aimed to develop machine learning-based differential diagnostic models (MLBDDMs) using the metagenomic and human transcriptomic data generated by an affordable bronchoalveolar lavage fluid (BLAF) mNGS assay and investigated their clinical utility for early differential diagnosis of lung cancer and pulmonary infection in patients with pulmonary diseases. Methods We recruited 775 patients with respiratory disease, including 160 pathologically diagnosed lung cancer and clinically diagnosed 615 infectious causes (131 tuberculosis, 172 fungal pneumonia and 312 bacterial pneumonia). An affordable mNGS assay on BALF samples collected from these patients on admission were performed. Using the generated mNGS data, we compared the differences in microbial diversity and host gene expression between lung cancer patients and pulmonary infection patients. The BLAF mNGS datasets of lung cancer group and each infection group were then randomly divided into a training dataset and a validation dataset at a ratio of approximately 3:1 for developing optimal MLBDDMs that can be used to distinguish lung cancer from various pulmonary infections. Results By comparing the BALF mNGS data of lung cancer (n = 160) and pulmonary infection (n = 615), we found that the infection group had higher microbial diversity than lung cancer group (P-value &amp;lt; 0.05). Respiratory colonizing microorganisms (e.g., Corynebacterium propinquum and Bacteroides uniformis) and pathogen (Mycobacterium tuberculosis and Cryptococcus neoformans) were found as differential microbes (adjusted p-value &amp;lt; 0.05, LDA score &amp;gt; 2). From BALF gene expression data, we detected 175 genes enriched in NOD-like receptor signaling pathway and chemokine signaling pathway differentially expressed between lung cancer and pulmonary infection groups (False Discovery Rate, FDR &amp;lt; 0.05). Cell composition analysis revealed that macrophage M1 was higher in lung infection group (P-value &amp;lt; 0.001), whereas mast cell activated and DCs activated were higher in lung cancer group (P-value &amp;lt; 0.001, P-value = 0.016). We integrated the metagenomic (microbial composition and human copy number variation) and transcriptomic data (host differentially expressed genes and cell composition) generated by the BALF mNGS assay with eleven machine learning classifiers to establish diagnosis models for distinguishing lung cancer from pulmonary infection (we named LC/PI model). The results showed that a Random Forest diagnostic model (the RF-LC/PI model) had optimal performance, with a sensitivity and specificity of 86.7% and 87.8%, respectively, in distinguishing lung cancer from pulmonary infection (area under the receiver operating characteristic curve [AUC] = 0.838 in the training dataset; AUC = 0.79 in a held-out validation dataset). Similar to the establishment of the LC/PI model, we further developed three diagnostic models for distinguishing lung cancer and tuberculosis (LC/TB model), lung cancer and fungal pneumonia (LC/FP model), and lung cancer and bacterial pneumonia (LC/BP model), respectively. The AUC of these three models were 0.91, 0.88, 0.91, respectively, showing a high differential diagnosis accuracy. Conclusions We have established MLBDDMs using BALF metagenomic and metatranscriptomic data and achieved superior accuracy for differentiating lung cancer and pulmonary infections, which could promote early diagnosis of pulmonary diseases and benefit more patients with one test.

Spatial lipidomics reveals biased phospholipid remodeling in acute Pseudomonas lung infection

Pseudomonas aeruginosa (Pa) is a pathogen causing chronic pulmonary infections in patients with cystic fibrosis (CF). Manipulation of lipids is an important feature of Pa infection and on a tissue-level scale is poorly understood. Using a mouse model of acute Pa pulmonary infection, we explored the whole-lung phospholipid response using mass spectrometry imaging (MSI) and spatial lipidomics. Using a histology-driven analysis, we isolated airways and parenchyma from both mock- and Pa-infected lungs and used systems biology tools to identify enriched metabolic pathways from the differential phospholipid identities. Infection was associated with a set of 26 ions, with 11 unique to parenchyma and 6 unique to airways. Acyl remodeling was differentially enriched in infected parenchyma as the predominant biological function. These functions correlated with markers of polymorphonuclear (PMN) cell influx, a defining feature of the lung response to Pa infection, implicating enzymes active in phospholipid remodeling.

Retracted: Establishment of a Risk Prediction Model for Pulmonary Infection in Patients with Advanced Cancer.

[This retracts the article DOI: 10.1155/2022/6149884.].

Risk Factors for Post-Operative Pulmonary Infection in Patients With Brain Tumors: A Systematic Review and Meta-Analysis.

Background: This study aims to analyze the risk factors for post-operative pulmonary infection in patients with brain tumors by meta-analysis to provide a reference for its prevention. Methods: PubMed, Embase, Web of Science, Cochrane Library, Ovid, and four Chinese databases (CNKI, SinoMed, VIP, and Wanfang databases) were searched for studies covering risk factors of pulmonary infection in patients with brain tumors, limited to the duration from the dates of inception of the respective databases to December 31, 2022. The Newcastle-Ottawa scale was used to assess the evidence. A meta-analysis of the factors affecting the incidence of pulmonary infection was performed using Revman 5.4 software. Results: Twelve studies were selected, covering 35,615 patients with brain tumors, among whom pulmonary infection occurred in 1,635 cases with an accumulated incidence of 4.6%, including 38 related risk factors. Meta-analysis results indicated: history of chronic pulmonary disease (odds ratio [OR], 5.74; 95% confidence interval [CI], 1.34-24.51; p = 0.02], diabetes mellitus (OR, 1.58; 95% CI, 1.29-1.95; p < 0.0001), history of cardiovascular disease (OR, 3.97; 95% CI, 2.18-7.24; p < 0.00001), age ≥60 years (OR, 1.55; 95% CI, 1.12-2.15; p = 0.009)], operation time ≥3 hours (OR, 1.03; 95% CI, 1.00-1.05; p = 0.03], Glasgow Coma Scale (GCS) score <13 (OR, 3.5; 95% CI, 1.90-6.46; p < 0.0001), and the American Society of Anesthesiologists classification (ASA) ≥3 (OR, 2.03; 95% CI, 1.68-2.46; p < 0.00001) as independent risk factors. Conclusions: History of chronic pulmonary disease, diabetes mellitus, history of cardiovascular disease, age ≥60 years, operation time ≥3 hours, GCS score <13, and the ASA grade ≥3 are independent risk factors for post-operative pulmonary infection in patients with brain tumors, which nursing staff should be aware of.

Pulmonary infections in patients with acute myeloid leukemia receiving frontline treatment with hypomethylating agents

Pulmonary infections (PIs) are a major complication of Acute Myeloid Leukemia (AML) treated with hypomethylating agents (HMA). We retrospectively evaluated 147 AML patients treated frontline with HMA in 2 Centers. Total number of HMA cycles was 1397. There were 88 episodes of PI in 64 patients (43.5%). Thirty-five/147 patients at risk (23.8%) developed at least 1 episode of early PI (during cycles 1–2). Median OS in patients who developed early PI was 3.3 months (95% CI 0.8 − 5.8) versus 10.5 months (95% CI 8.4 − 12.7) in patients without PI or with PI beyond the 2nd cycle (p < .001). Early PIs were an independent factor predicting lower survival (OR 1.94, 95% CI 1.28 − 2.93; p = .002). In conclusion, early PIs are common in AML patients receiving HMA and are associated with an unfavorable outcome. The results of our study raise the issue of a tailored infection prevention strategy.

Bacterial Biofilm Formation on Biomaterials and Approaches to Its Treatment and Prevention.

Bacterial biofilms can cause widespread infection. In addition to causing urinary tract infections and pulmonary infections in patients with cystic fibrosis, biofilms can help microorganisms adhere to the surfaces of various medical devices, causing biofilm-associated infections on the surfaces of biomaterials such as venous ducts, joint prostheses, mechanical heart valves, and catheters. Biofilms provide a protective barrier for bacteria and provide resistance to antimicrobial agents, which increases the morbidity and mortality of patients. This review summarizes biofilm formation processes and resistance mechanisms, as well as the main features of clinically persistent infections caused by biofilms. Considering the various infections caused by clinical medical devices, we introduce two main methods to prevent and treat biomaterial-related biofilm infection: antibacterial coatings and the surface modification of biomaterials. Antibacterial coatings depend on the covalent immobilization of antimicrobial agents on the coating surface and drug release to prevent and combat infection, while the surface modification of biomaterials affects the adhesion behavior of cells on the surfaces of implants and the subsequent biofilm formation process by altering the physical and chemical properties of the implant material surface. The advantages of each strategy in terms of their antibacterial effect, biocompatibility, limitations, and application prospects are analyzed, providing ideas and research directions for the development of novel biofilm infection strategies related to therapeutic materials.

Development and validation of a postoperative pulmonary infection prediction model for patients with primary hepatic carcinoma.

There are factors that significantly increase the risk of postoperative pulmonary infections in patients with primary hepatic carcinoma (PHC). Previous reports have shown that over 10% of patients with PHC experience postoperative pulmonary infections. Thus, it is crucial to prioritize the prevention and treatment of postoperative pulmonary infections in patients with PHC. To identify the risk factors for postoperative pulmonary infection in patients with PHC and develop a prediction model to aid in postoperative management. We retrospectively collected data from 505 patients who underwent hepatobiliary surgery between January 2015 and February 2023 in the Department of Hepatobiliary and Pancreaticospleen Surgery. Radiomics data were selected for statistical analysis, and clinical pathological parameters and imaging data were included in the screening database as candidate predictive variables. We then developed a pulmonary infection prediction model using three different models: An artificial neural network model; a random forest model; and a generalized linear regression model. Finally, we evaluated the accuracy and robustness of the prediction model using the receiver operating characteristic curve and decision curve analyses. Among the 505 patients, 86 developed a postoperative pulmonary infection, resulting in an incidence rate of 17.03%. Based on the gray-level co-occurrence matrix, we identified 14 categories of radiomic data for variable screening of pulmonary infection prediction models. Among these, energy, contrast, the sum of squares (SOS), the inverse difference (IND), mean sum (MES), sum variance (SUV), sum entropy (SUE), and entropy were independent risk factors for pulmonary infection after hepatectomy and were listed as candidate variables of machine learning prediction models. The random forest model algorithm, in combination with IND, SOS, MES, SUE, SUV, and entropy, demonstrated the highest prediction efficiency in both the training and internal verification sets, with areas under the curve of 0.823 and 0.801 and a 95% confidence interval of 0.766-0.880 and 0.744-0.858, respectively. The other two types of prediction models had prediction efficiencies between areas under the curve of 0.734 and 0.815 and 95% confidence intervals of 0.677-0.791 and 0.766-0.864, respectively. Postoperative pulmonary infection in patients undergoing hepatectomy may be related to risk factors such as IND, SOS, MES, SUE, SUV, energy, and entropy. The prediction model in this study based on diffusion-weighted images, especially the random forest model algorithm, can better predict and estimate the risk of pulmonary infection in patients undergoing hepatectomy, providing valuable guidance for postoperative management.