Iron plays a critical role in lung infections due to its function in the inflammatory immune response but also as an important factor for bacterial growth. Iron chelation represents a potential therapeutic approach to inhibit bacterial growth and pathologically increased pro-inflammatory mediator production. The present study was designed to investigate the impact of the iron chelator DIBI in murine lung infection induced by intratracheal Pseudomonas aeruginosa (strain PA14) administration. DIBI is a polymer with a polyvinylpyrrolidone backbone containing nine 3-hydroxy-1-(methacrylamidoethyl)-2-methyl-4(1H) pyridinone (MAHMP) residues per molecule and was given by intraperitoneal injection either as a single dose (80 mg/kg) immediately after PA14 administration or a double dose (second dose 4 h after PA14 administration). The results showed that lung NF-κBp65 levels, as well as levels of various inflammatory cytokines (TNFα, IL-1β, IL-6) both in lung tissue and bronchoalveolar lavage fluid (BALF), were significantly increased 24 h after PA14 administration. Single-dose DIBI did not affect the bacterial load or inflammatory response in the lungs or BALF. However, two doses of DIBI significantly decreased bacterial load, attenuated NF-κBp65 upregulation, reduced inflammatory cytokines production, and relieved lung tissue damage. Our findings support the conclusion that the iron chelator, DIBI, can reduce lung injury induced by P. aeruginosa, via its anti-bacterial and anti-inflammatory effects.

OBJECTIVE: Differentiating pulmonary lymphoma from lung infections using CT images is challenging. Existing deep neural network-based lung CT classification models rely on 2D slices, lacking comprehensive information and requiring manual selection. 3D models that involve chunking compromise image information and struggle with parameter reduction, limiting performance. These limitations must be addressed to improve accuracy and practicality.
METHODS: We propose a transformer sequential feature encoding structure to integrate multi-level information from complete CT images, inspired by the clinical practice of using a sequence of cross-sectional slices for diagnosis. We incorporate position encoding and cross-level long-range information fusion modules into the feature extraction CNN network for cross-sectional slices, ensuring high-precision feature extraction.
RESULTS: We conducted comprehensive experiments on a dataset of 124 patients, with respective sizes of 64, 20 and 40 for training, validation and testing. The results of ablation experiments and comparative experiments demonstrated the effectiveness of our approach. Our method outperforms existing state-of-the-art methods in the 3D CT image classification problem of distinguishing between lung infections and pulmonary lymphoma, achieving an accuracy of 0.875, AUC of 0.953 and F1 score of 0.889.
CONCLUSIONS: The experiments verified that our proposed position-enhanced transformer-based sequential feature encoding model is capable of effectively performing high-precision feature extraction and contextual feature fusion in the lungs. It enhances the ability of a standalone CNN network or transformer to extract features, thereby improving the classification performance. The source code is accessible at https://github.com/imchuyu/PTSFE .

Aim: Torquetenovirus (TTV) was a promising biomarker for immunity, while lung regional TTV for evaluating the opportunistic infection among immunocompromised hosts (ICH) was unclear. Materials & methods: In the ICH and non-ICH populations, we compared the susceptibility to opportunistic infections, clinical severity and the prognosis between subgroups, respectively. Results: ICH with detectable bronchoalveolar lavage fluid (BALF)-TTV were more susceptible to lung aspergillosis and Mycobacterium infections. Furthermore, our data demonstrated that the ICH cohort with detectable BALF-TTV represented a higher clinical severity and a worse prognosis, while the above findings were not found in the non-ICH population. Conclusion: Our findings demonstrated that the BALF-TTV could act as an effective predictor for opportunistic infection for ICH that complemented the CD4+ T cell counts.
[Box: see text].

OBJECTIVE: To predict the risk of in-hospital death in patients with chronic heart failure (CHF) complicated by lung infections using interpretable machine learning.
METHODS: The clinical data of 1415 patients diagnosed with CHF complicated by lung infections were obtained from the MIMIC-IV database. According to the pathogen type, the patients were categorized into bacterial pneumonia and non-bacterial pneumonia groups, and their risks of in-hospital death were compared using Kaplan-Meier survival curves. Univariate analysis and LASSO regression were used to select the features for constructing LR, AdaBoost, XGBoost, and LightGBM models, and their performance was compared in terms of accuracy, precision, F1 value, and AUC. External validation of the models was performed using the data from eICU-CRD database. SHAP algorithm was applied for interpretive analysis of XGBoost model.
RESULTS: Among the 4 constructed models, the XGBoost model showed the highest accuracy and F1 value for predicting the risk of in-hospital death in CHF patients with lung infections in the training set. In the external test set, the XGBoost model had an AUC of 0.691 (95% CI: 0.654-0.720) in bacterial pneumonia group and an AUC of 0.725 (95% CI: 0.577-0.782) in non-bacterial pneumonia group, and showed better predictive ability and stability than the other models.
CONCLUSIONS: The overall performance of the XGBoost model is superior to the other 3 models for predicting the risk of in-hospital death in CHF patients with lung infections. The SHAP algorithm provides a clear interpretation of the model to facilitate decision-making in clinical settings.

With the increasing resistance of Acinetobacter baumannii (A. baumannii) to antibiotics, researchers have turned their attention to the development of new antimicrobial agents. Among them, coumarin-based heterocycles have attracted much attention due to their unique biological activities, especially in the field of antibacterial infection. In this study, a series of coumarin derivatives were synthesized and screened for their bactericidal activities (Ren et al. 2018; Salehian et al. 2021). The inhibitory activities of these compounds on bacterial strains were evaluated, and the related mechanism of the new compounds was explored. Firstly, the MIC values and bacterial growth curves were measured after compound treatment to evaluate the antibacterial activity in vitro. Then, the in vivo antibacterial activities of the new compounds were assessed on A. baumannii-infected mice by determining the mice survival rates, counting bacterial CFU numbers, measuring inflammatory cytokine levels, and histopathology analysis. In addition, the ROS levels in the bacterial cells were measured with DCFH-DA detection kit. Furthermore, the potential target and detailed mechanism of the new compounds during infection disease therapy were predicted and evidenced with molecular docking. After that, ADMET characteristic prediction was completed, and novel, synthesizable, drug-effective molecules were optimized with reinforcement learning study based on the probed compound as a training template. The interaction between the selected structures and target proteins was further evidenced with molecular docking. This series of innovative studies provides important theoretical and experimental data for the development of new anti-A. baumannii infection drugs.

Objective: To analyze the comprehensive blood inflammation index of the patients with stage I pneumoconiosis complicated with pulmonary infection, and to explore its value in predicting the patients\' disease. Methods: In September 2023, 83 patients with stage I pneumoconiosis who were treated in Tianjin Occupational Diseases Precaution and Therapeutic Hospital from November 2021 to August 2023 were selected and divided into non-infected group (56 cases) and infected group (27 cases) according to whether they were combined with lung infection. Workers with a history of dust exposure but diagnosed without pneumoconiosis during the same period were selected as the control group (65 cases) . By referring to medical records and collecting clinical data such as gender, age, occupational history, past medical history, hematology testing, the differences in the comprehensive blood inflammation indexes among the three groups were compared, ROC curve was drawn, and the relationship between comprehensive blood inflammation indexes and stage I pneumoconiosis and its combined lung infection was analyzed. Results: There were significtant differences in the number of neutrophils (N) , the number of lymphocytes (L) , the number of monocytes (M) , C-reactive protein (CRP) , the neutrophil to lymphocyte ratio (NLR) , the monocyte to lymphocyte ratio (MLR) , the platelet to lymphocyte ratio (PLR) , the systemic immune-inflammatory index (SII) , the systemic inflammation response index (SIRI) , the aggregate index of systemic inflammation (AISI) , the derived neutrophil to lymphocyte ratio (dNLR) , the neutrophil to lymphocyte and platelet ratio (NLPR) , and the C-reactive protein to lymphocyte ratio (CLR) (P<0.05) . Compared with the control group, MLR, SIRI and AISI in the non-infected group were significantly increased (P<0.05) . NLR, MLR, PLR, SII, SIRI, AISI, dNLR, NLPR, CLR were significantly increased (P<0.05) . Compared with the non-infected group, NLR, PLR, SII, SIRI, AISI, dNLR, NLPR and CLR were significantly increased in the infected group (P<0.05) . ROC analysis showed that NLR, MLR, PLR, SII, SIRI and AISI had a certain predictive capability for stage I pneumoconiosis (P<0.05) , among which MLR had the highest efficacy, with an AUC of 0.791 (95% CI: 0.710-0.873) , the cut-off value was 0.18, the sensitivity was 71.4%, and the specificity was 78.5%. NLR, MLR, PLR, SII, SIRI, AISI, dNLR, NLPR and CLR all had a certain predictive capability forstage I pneumoconiosis combined lung infection (P<0.05) , among which CLR had the highest efficacy, with an AUC of 0.904 (95%CI: 0.824~0.985) , the cut-off value was 5.33, sensitivity was 77.8%, specificity was 98.2%. Conclusion: The comprehensive blood inflammation index may be an auxiliary predictor of stage I pneumoconiosis and its combined lung infections.
目的： 分析壹期尘肺病合并肺部感染患者的综合血液炎症指数，探讨其对患者病情的预测价值。 方法： 于2023年9月，选取2021年11月至2023年8月在天津市职业病防治院就诊的83例壹期尘肺病患者，根据是否合并肺部感染分为非感染组（56例）和感染组（27例），选取同期有接尘史但未诊断为尘肺病的65例工人为对照组。通过查阅病案，收集性别、年龄、职业史、既往病史、血液学化验检测等临床资料，比较各组人员综合血液炎症指数的差异，绘制ROC曲线，分析综合血液炎症指数与壹期尘肺病及其合并肺部感染的关系。 结果： 各组患者的中性粒细胞数目（N）、淋巴细胞数目（L）、单核细胞数目（M）、C反应蛋白（CRP）、中性粒细胞/淋巴细胞比值（NLR）、单核细胞/淋巴细胞比值（MLR）、血小板/淋巴细胞比值（PLR）、全身免疫炎症反应指数（SII）、全身炎症反应指数（SIRI）、系统炎症综合指数（AISI）、衍生中性粒细胞与淋巴细胞比值（dNLR）、中性粒细胞与淋巴细胞和血小板比值（NLPR）、C反应蛋白与淋巴细胞比值（CLR）差异均有统计学意义（P<0.05），与对照组比较，非感染组患者MLR、SIRI、AISI均明显升高（P<0.05），感染组NLR、MLR、PLR、SII、SIRI、AISI、dNLR、NLPR、CLR均明显升高（P<0.05）；与非感染组比较，感染组患者NLR、PLR、SII、SIRI、AISI、dNLR、NLPR、CLR明显升高（P<0.05）。ROC分析显示，NLR、MLR、PLR、SII、SIRI、AISI对壹期尘肺有一定预测能力（P<0.05），其中MLR的效能最高，其AUC为0.791（95%CI：0.710~0.873），截断值为0.18，灵敏度为71.4%，特异度为78.5%；NLR、MLR、PLR、SII、SIRI、AISI、dNLR、NLPR、CLR对壹期尘肺病合并肺部感染均有一定预测能力（P<0.05），其中CLR的效能最高，其AUC为0.904（95%CI：0.824~0.985），截断值为5.33，灵敏度为77.8%，特异度为98.2%。 结论： 壹期尘肺病患者的综合血液炎症指数可能是其合并肺部感染的辅助预测指标。.

OBJECTIVE: To study the etiological characteristics of community-acquired pneumonia (CAP) combined with type 2 diabetes (T2D), providing a reference for early clinical diagnosis and treatment of the disease.
METHODS: We selected a total of 93 patients with CAP and analyzed their metagenomics nextgeneration sequencing (mNGS) data. The case group comprised 46 patients with combined CAP/T2D, and the control group comprised 47 patients without diabetes. We analyzed the pathogenic findings of the two groups.
RESULTS: There were statistically significant differences in age between the two groups (P = 0.001). Leukocytes (P = 0.012), blood platelets (P = 0.034), fibrinogen (P = 0.037), D-dimer (P = 0.000), calcitonin ogen (P = 0.015), ultrasensitive C-reactive protein or C-reactive protein (CRP) (P = 0.000), serum amyloid A (P = 0.000), and erythrocyte sedimentation rate (P = 0.003) were higher in the case group than in the control group. Albumin was lower in the case group than in the control group. All differences were statistically significant. The infection rates of Klebsiella pneumoniae (P = 0.030), Pseudomonas aeruginosa (P = 0.043), and Candida albicans (P = 0.032) were significantly different between the two groups.
CONCLUSIONS: Compared with those without diabetes, the infection rates of Klebsiella pneumoniae, Pseudomonas aeruginosa, and Candida albicans were higher in patients with combined CAP/T2D.

UNASSIGNED: To evaluate the antibacterial effect of Tanreqing (TRQ) against K. pneumoniae and its inhibition activity on bacterial biofilm formation in vitro and in vivo, and to explore the mechanism of the inhibitory effects of TRQ on K. pneumoniae biofilm formation.
UNASSIGNED: An in vitro biofilm model of K. pneumoniae was established, and the impact of TRQ on biofilm formation was evaluated using crystal violet staining and scanning electron microscopy (SEM). Furthermore, the clearance effect of TRQ against K. pneumoniae in the biofilm was assessed using the viable plate counting method; q-RT PCR was used to evaluate the inhibitory effect of different concentrations of TRQ on the expression of biofilm-related genes in Klebsiella pneumoniae; The activity of quorum sensing signal molecule AI-2 was detected by Vibrio harveyi bioluminescence assay; Meanwhile, a guinea pig lung infection model of Klebsiella pneumoniae was constructed, and after treated with drugs, pathological analysis of lung tissue and determination of bacterial load in lung tissue were performed. The treatment groups included TRQ group, imipenem(IPM) group, TRQ+IPM group, and sterile saline group as the control.
UNASSIGNED: The formation of K. pneumoniae biofilm was significantly inhibited by TRQ in vitro experiments. Furthermore, when combined with IPM, the clearance of K. pneumoniae in the biofilm was notably increased compared to the TRQ group and IPM group alone. q-RT PCR analysis revealed that TRQ down-regulated the expression of genes related to biofilm formation in K. pneumoniae, specifically luxS, wbbm, wzm, and lsrK, and also inhibited the activity of AI-2 molecules in the bacterium. In vivo experiments demonstrated that TRQ effectively treated guinea pig lung infections, resulting in reduced lung inflammation. Additionally, when combined with IPM, there was a significant reduction in the bacterial load in lung tissue.
UNASSIGNED: TRQ as a potential therapeutic agent plays a great role in the treatment of K. pneumoniae infections, particularly in combination with conventional antibiotics. And TRQ can enhanced the clearance effect on the bacterium by inhibiting the K. pneumoniae biofilm formation, which provided experimental evidence in support of clinical treatment of TRQ against K. pneumoniae infections.

Streptomyces, the largest genus in the Streptomycetaceae family and a prolific producer of antibacterial drugs, is a saprophytic soil organism that rarely causes invasive infections. Here we report a case of necrotic pneumonia caused by Streptomyces albireticuli in a 75-year-old man who presented with progressive chest tightness and dyspnea. Streptomyces albireticuli was isolated from his bronchoalveolar lavage fluid and identified through whole-genome sequencing (WGS) and phylogenetic analysis. The patient responded satisfactorily to clarithromycin therapy. The findings of this study may enhance our vigilance in identifying visceral infections caused by Streptomyces.

UNASSIGNED: To investigate the performance of using lung dynamic compliance (Cdyn) and airway resistance (RAW) levels to predict lung infection in elderly esophageal cancer patients who have undergone radiotherapy.
UNASSIGNED: A total of 298 elderly esophageal cancer patients who received radiotherapy at Shanxi Fenyang Hospital between October 2017 and July 2022 were retrospectively enrolled and their clinical data were collected. The patients were divided into an infection group (124 cases) and a non-infection group (174 cases) according to their status of lung infection. Then, in the infection group, CURB-65 score was used to assess the severity of the patients\' lung infection and the patients were further divided into subgroups accordingly, with 36 cases in the mild infection subgroup, 58 cases in the moderate infection subgroup, and 30 cases in the severe infection subgroup. The levels of Cdyn, RAW, and infection indicators, including serum procalcitonin (PCT), interleukin-6 (IL-6), and angiotensin Ⅱ (Ang Ⅱ), were measured in both groups of patients and the differences in the findings were compared between the infection and the non-infection groups and among patients with infection of varying degrees of severity. The correlation between Cdyn and RAW and the levels of PCT, IL-6, and Ang Ⅱ was analyzed. Receiver operating characteristic (ROC) curve was used to evaluate the performance of predicting infection with Cdyn and RAW.
UNASSIGNED: The Cdyn level of patients in the infection group was lower than that of patients in the non-infection group, while the RAW level of the infection group was higher than that of the non-infection group (P<0.05). Among the infection subgroup, the level of Cdyn of the mild infection subgroup was higher than those of the moderate and severe infection subgroups, while the levels of RAW, PCT, IL-6, and Ang Ⅱ of the mild infection subgroup were lower than those of the moderate severe subgroups. The level of Cdyn of the moderate infection subgroup was higher than that of the severe infection subgroup, while the RAW, PCT, IL-6, and Ang Ⅱ levels of the moderate infection subgroup were lower than those of the severe infection subgroup, with all difference being statistically significant (P<0.05). The Cdyn level of patients with lung infection was negatively correlated with PCT, IL-6, and Ang Ⅱ levels and the severity of infection (r=-0.501, -0.430, -0.367, and -0.484, respectively, P<0.05), while RAW was positively correlated with PCT, IL-6, and Ang Ⅱ levels and the severity of infection (r=0.483, 0.395, 0.374, and 0.423, respectively, P<0.05). The area under the curve (AUC) of Cdyn and RAW for predicting lung infection in elderly patients with esophageal cancer after radiotherapy were 0.898 (95% confidence interval [CI]: 0.857-0.930) and 0.823 (95% CI: 0.775-0.865), respectively, and the AUC of combined evaluation of Cdyn and RAW was 0.959 (95% CI: 0.930-0.979), which suggested that the predictive performance of combined evaluation was better than evaluation with Cdyn or RAW alone.
UNASSIGNED: When elderly esophageal cancer patients develop lung infection after radiotherapy, their Cdyn level is decreased, while the levels of RAW, PCT, IL-6, and Ang Ⅱ are increased. In addition, the levels of Cdyn and RAW are correlated with the PCT, IL-6, and Ang Ⅱ levels. The combined use of Cdyn and RAW shows good performance for predicting lung infection in patients.