Objective: Various studies have shown that diabetes mellitus (DM) increases the risk of thrombosis in the venous system as well as in the arterial system. In this study, it was aimed to evaluate the association between admission blood glucose levels and clinical severity, recurrence, and mortality in pulmonary embolism in patients with DM. Materials and methods: This study was designed as a retrospective cross-sectional study. Patients with DM who were admitted to a tertiary care hospital due to pulmonary [...] ABSTRACT Objective: Various studies have shown that diabetes mellitus (DM) increases the risk of thrombosis in the venous system as well as in the arterial system. In this study, it was aimed to evaluate the association between admission blood glucose levels and clinical severity, recurrence, and mortality in pulmonary embolism in patients with DM. Materials and methods: This study was designed as a retrospective cross-sectional study. Patients with DM who were admitted to a tertiary care hospital due to pulmonary

Although diagnostic and therapeutic advances in Lung cancer (LC) have increased the survival of patients, infection and its complications are still among the most important causes of mortality. The disruption of tissue caused by tumor mass, management of cancer therapy and alteration in the humoral/cellular immune systems due to both cancer itself and therapy considerably increase susceptibility to infection in cancer patients. Particularly, opportunistic microorganisms should be considered, then applying rapid and sensitive diagnostic methods for them. Thus, cancer patients who are already exposed to difficult, long-term and expensive treatments can be prevented from dying from complications related to infections.

Introduction: The purpose of this study is to determine how long patients who developed pneumothorax were followed up on in the emergency department, how many patients required chest tube placement, and what factors influenced the need for a chest tube in patients who underwent computed tomography (CT)-guided percutaneous trans -thoracic fine needle aspiration biopsy (PTFNAB).Materials and Methods: Patients who developed pneumothorax following CT-guided PTFNAB were analyzed retrospectively. In cases with pneumotho-rax, the relationship between chest tube placement and the size of the lesion, the lesion depth from the pleural surface, the presence of emphysema, and the needle entry angle were investigated. It was determined how long the patients were followed up in the emergency department, when a chest tube was pla-ced, and when patients who did not require chest tube placement were discharged.Results: CT-guided PTFNAB was performed in 3426 patients within two years. Pneumothorax developed in 314 (9%) cases and a chest tube was placed in 117 (37%). The risk factor for chest tube placement was found to be the lesion depth from the pleural surface. The lesion depth from the pleural surface of >24 mm increased the risk of chest tube placement by 4.8 times. Chest tubes were placed at an average of five hours (5.04 +/- 5.57).Conclusion: This study has shown that in cases with pneumothorax that required chest tube placement, the lesion depth from the pleural surface is a risk factor. Patients who developed pneumothorax on CT during the procedure had chest tubes placed after an average of five hours.

Introduction: Asthma is a chronic airway inflammatory disease characterized by variable airflow limitation and intermittent symptoms. In well-controlled asthma, auscultation and spirometry often appear normal, making diagnosis challenging. Moreover, bronchial provocation tests carry a risk of inducing acute bronchoconstriction. This study aimed to develop a non-invasive, objective, and reproducible diagnostic method using machine learning-based lung sound analysis for the early detection of asthma, even during stable periods. Methods: We designed a machine learning algorithm to classify controlled asthma patients and healthy individuals using respiratory sounds recorded with a digital stethoscope. We enrolled 120 participants (60 asthmatic, 60 healthy). Controlled asthma was defined according to Global Initiative for Asthma (GINA) criteria and was supported by normal spirometry, no pathological auscultation findings, and no exacerbations in the past three months. A total of 3600 respiratory sound segments (each 3 s long) were obtained by dividing 90 s recordings from 120 participants (60 asthmatic, 60 healthy) into non-overlapping clips. The samples were analyzed using Mel-Frequency Cepstral Coefficients (MFCCs) and Tunable Q-Factor Wavelet Transform (TQWT). Significant features selected with ReliefF were used to train Quadratic Support Vector Machine (SVM) and Narrow Neural Network (NNN) models. Results: In 120 participants, pulmonary function test (PFT) results in the asthma group showed lower FEV1 (86.9 ± 5.7%) and FEV1/FVC ratios (86.1 ± 8.8%) compared to controls, but remained within normal ranges. Quadratic SVM achieved 99.86% accuracy, correctly classifying 99.44% of controls and 99.89% of asthma cases. Narrow Neural Network achieved 99.63% accuracy. Sensitivity, specificity, and F1-scores exceeded 99%. Conclusion: This machine learning-based algorithm provides accurate asthma diagnosis, even in patients with normal spirometry and clinical findings, offering a non-invasive and efficient diagnostic tool.

Background/Objectives: Spread through air spaces (STAS) represents an aggressive invasion pattern in lung cancer and is associated with unfavorable oncologic outcomes. As STAS is currently identifiable only on postoperative pathology, reliable preoperative, noninvasive prediction remains a clinical challenge. This study aimed to evaluate the feasibility of predicting STAS using ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT)-derived radiomic and clinicoradiomic models. Methods: In this retrospective study, patients who underwent surgical resection for lung cancer with available preoperative ¹⁸F-FDG PET/CT imaging were analyzed. Radiomic features were extracted from intratumoral and peritumoral regions. Clinical, radiomic-only, and combined clinicoradiomic models were developed using LASSO-based feature selection and multivariable logistic regression. Model performance was evaluated using nested cross-validation, receiver operating characteristic analysis, calibration assessment, and decision curve analysis. Results: Radiomic features reflecting intratumoral metabolic characteristics and peritumoral tissue heterogeneity were significantly associated with STAS. The combined clinicoradiomic model demonstrated superior discriminative performance compared with the clinical and radiomic-only models (mean AUC ≈ 0.75), along with favorable calibration (Brier score = 0.20) and improved clinical net benefit across relevant threshold probabilities. Lower eosinophil count, lower SUVmin_tumor, and lower intratumoral SUV skewness emerged as independent predictors of STAS. Conclusions: Preoperative prediction of STAS in lung cancer is feasible using PET/CT-based radiomic analysis integrating intratumoral, peritumoral, and clinical features. This noninvasive approach provides biologically relevant information beyond conventional anatomical assessment and warrants further validation in prospective, multicenter cohorts.

Determining factors affecting the acceptability of spirometry: A survey study in a tertiary chest diseases center Introduction: Unlike other laboratory tests, spirometry requires the participant's full compliance with the maneuvers in the test for an acceptable test result. In this study, we aimed to determine the suitability of spirometric tests regarding acceptability and the factors associated with acceptability. Materials and Methods: Before the test, our 15-scale questionnaire, prepared by us in the respiratory function laboratory, was applied to the participants who requested spirometric examination in our hospital. Afterwards, patients were subjected to spirometric analysis. Spirogram results of the participants were evaluated by four clinicians who were experts in the field based on the acceptability criteria in the American Thoracic Society and European Respiratory Society Spirometry Standardization Guidelines. Participants were divided into two groups as those who met the acceptability criteria and those who did not. Both groups were compared regarding demographic data, comorbidities, education levels, and questions in the questionnaire. Results: The acceptability spirometry rate was 71.2%. The most common error among those who could not perform an acceptable test was the inability to complete the expiratory time to the time that would create a plateau, with 37.3%. Education level and acceptability of spirometry were not related (p= 0.228). Asthma was statistically significantly higher in the group that per-formed acceptable spirometry (p= 0.049). Acceptable spirometry rate was statistically significantly higher in the participants who had previously per-formed spirometric tests compared to the other group (p< 0.001). The test success of the participants who did not have success anxiety about the test was significantly higher than the other group (p= 0.033). Conclusion: Reduction of participants' anxiety and repetitive testing increases test acceptability. For this reason, in our clinical practice, we recommend that people who want a spirometry test relieve their anxiety about the test and repeat the test in unacceptable tests.