Etiology and Prognosis of Pneumonia in Patients with Solid Tumors: A Prospective Cohort of Hospitalized Cases

Evaluation of the BioFire® FilmArray® Pneumonia plus Panel for Detecting Bacterial Etiological Agents of Lower Respiratory Tract Infections in an Oncologic Hospital. Comparison with Conventional Culture Method

Conventional methods used to determine pneumonia pathogens are characterized by low sensitivity and long turnaround times. Introducing new tests with better parameters in patients at higher risk of infections is highly anticipated. The results of the conventional quantitative culture method (CM) in determining the bacterial etiology of pneumonia were compared with the results of the Pneumonia plus Panel test (PNP; BioFire® Diagnostics, USA) in 79 samples of bronchoalveolar lavage (BAL). Materials were collected from 79 patients with suspected pneumonia treated in an oncologic hospital due to solid tumors. Only 16/79 BAL samples (20.3%) were true positive (TP) for bacterial etiology in CM vs. 27/79 samples (34.2%) true positive in the PNP test. The total agreement between methods of interpreting the result (positive or negative) was 84.8%. The most prevalent pathogens in both methods were Staphylococcus aureus, followed by Escherichia coli, Pseudomonas aeruginosa, and Haemophilus influenzae. The PNP test identified several respiratory pathogens that were not grown in culture. The semiquantitative value reported by the PNP test was higher than that reported by culture. The PNP test vs. combined test (PNP test and CM methods) demonstrated positive predictive value (PPV) and negative predictive value (NPV) values of 100.0% and 98.1%, and the sensitivity and specificity were 96.4% and 100.0%. The PNP test is a good tool for determining the etiology of bacterial pneumonia and may support the care of an oncologic patient. However, further large-sample studies are needed to research in strictly defined groups of oncologic patients.

A Prognostic Model for the Respiratory Function of Patients with Nonsevere Pulmonary Infection Based on Breathing Exercises and Acupuncture Therapy: Development and Validation

Objective. In this study, a prognostic model for the respiratory function was constructed based on the treatment methods of patients with nonsevere pulmonary infection, aiming to provide a reference for clinical decision-making. Method. A total of 500 patients with nonsevere pulmonary infection were included in this study. The patients were randomized into training set ( $n=350$ ) and validation set ( $n=150$ ), and the baseline characteristics were collected. All patients received breathing exercises or breathing exercises combined with acupuncture therapy for 3 months, and then the forced expiratory volume in one second (FEV1)/forced vital capacity (FVC) was assessed. Next, an ordinal multinomial logistic regression model was used to analyze prognostic factors affecting respiratory function of patients with nonsevere pulmonary infection. The Test of Parallel Lines was used to determine the accuracy (ACC) of the model and screen the influencing factors. The confusion matrix was drawn, and the ACC and harmonic mean (F1 score) were calculated to evaluate the feasibility of the model results. Results. Results of the ordinal multinomial logistic regression model showed that age ( $P=0.000$ ), treatment method ( $P=0.000$ ), underlying diseases ( $P<0.001$ ), and sex ( $P=0.389$ ) were independent factors affecting the respiratory function of patients in the training set. The ACC value of the training set was 88.86%, and that of the validation set was 91.33%, indicating a high accuracy and favorable predictive ability of the model. Besides, the F1 score was 62.38%, indicating a high reliability of the model. Conclusion. The prognostic model for respiratory function of patients with nonsevere pulmonary infection constructed in this study had favorable predictive performance, which is of great significance in the clinical nursing and treatment of patients with pulmonary infection.

Employing hypoxia characterization to predict tumour immune microenvironment, treatment sensitivity and prognosis in hepatocellular carcinoma

The hypoxic microenvironment was recognized as a major driving force of the malignant phenotype in hepatocellular carcinoma (HCC), which contributes to tumour immune microenvironment (TIM) remodeling and tumor progression. Dysregulated hypoxia-related genes (HRGs) result in treatment resistance and poor prognosis by reshaping tumor cellular activities and metabolism. Approaches to identify the relationship between hypoxia and tumor progression provided new sight for improving tumor treatment and prognosis. But, few practical tools, forecasting relationship between hypoxia, TIM, treatment sensitivity and prognosis in HCC were reported. Here, we pooled mRNA transcriptome and clinical pathology data from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), and later developed a hypoxia risk model including four HRGs (DCN, DDIT4, PRKCA and NDRG1). The high-risk group displayed poor clinical characteristics, a malignant phenotype with carcinogenesis/proliferation pathways activation (MTORC1 and E2F) and immunosuppressive TIM (decreased immune cell infiltrations and upregulated immunosuppressive cytokines). Meanwhile, activated B cells, effector memory CD8 T cells and EZH2 deregulation were associated with patient’s survival, which might be the core changes of HCC hypoxia. Finally, we validated the ability of the hypoxia risk model to predict treatment sensitivity and found high hypoxia risk patients had poor responses to HCC treatment, including surgical resection, Sorafenib, Transarterial Chemoembolization (TACE) and immunotherapy. In conclusion, based on 4 HRGs, we developed and validated a hypoxia risk model to reflect pathological features, evaluate TIM landscape, predict treatment sensitivity and compounds specific to hypoxia signatures in HCC patients.