İhsan Topaloğlu

Background: Hypoxia triggers pulmonary inflammation and oxidative stress, leading to endothelial dysfunction, apoptosis and lung injury. Although the flavonoid orientin exhibits potent antioxidant and anti-inflammatory properties, its pharmacokinetic behavior remains incompletely characterized. Nanoparticle-based delivery systems may enhance efficacy. This study investigated whether Orientin delivered via chitosan nanoparticles (CNP–orientin) protects against hypoxia-induced pulmonary inflammation, oxidative stress, and tissue injury in rats. Methods: A total of 48 female Sprague–Dawley rats were randomized into four normoxic and four hypoxic groups (n = 6 per group). Rats in the hypoxic groups were exposed to intermittent hypoxia (7% O₂, 8 h/day for 7 days). Hypoxic rats were allocated to experimental groups, including an untreated hypoxia group and groups receiving orientin alone, chitosan nanoparticles alone or orientin-loaded chitosan nanoparticles (CNP–orientin), while normoxic rats served as controls. Hypoxic exposure was conducted using a controlled glove box system. At the end of the protocol, blood samples were collected for serum inflammatory marker analysis, and lung tissues were harvested to assess oxidative stress parameters, hypoxia- and inflammation-related signaling pathways, apoptosis-related gene expression, and histopathological lung injury and fibrosis. Results: Intermittent hypoxia significantly increased pulmonary HIF-1α and iNOS expression, oxidative stress markers (increased malondialdehyde and decreased superoxide dismutase and glutathione), pro-inflammatory cytokines (TNF-α, IL-1β), apoptotic signaling and histopathological lung injury compared with normoxic controls (p < 0.05). Treatment with orientin or chitosan nanoparticles alone attenuated hypoxia-associated biochemical and molecular alterations compared to untreated hypoxic rats (p < 0.05). Notably, CNP–orientin treatment significantly attenuated multiple oxidative stress, inflammatory, and apoptosis-related alterations compared with untreated hypoxic rats (p < 0.05). These protective effects were observed despite the substantially lower orientin-equivalent dose in the CNP–orientin formulation compared with free orientin. NF-κB and TNF-α expression, caspase-3 levels, antioxidant markers, and Bcl-2 expression differed significantly among hypoxic groups (p < 0.05). Histopathological lung injury and Modified Ashcroft fibrosis scores were significantly lower in the CNP–orientin group than in the untreated hypoxic group (p < 0.01 and p < 0.001, respectively). Conclusions: Orientin-loaded chitosan nanoparticles effectively mitigate hypoxia-induced pulmonary inflammation, oxidative stress, apoptosis and fibrotic injury. These findings identify CNP–orientin as a promising nanotherapeutic strategy for hypoxia-associated lung diseases and support further translational investigations.

Acute asthma exacerbations are a common cause of emergency department visits and require rapid risk stratification to guide disposition decisions. Although spirometry is the primary objective method used to assess clinical severity, it may not reliably predict clinical outcomes. In this context, machine learning–based approaches have gained increasing attention for improving prognostic assessment in emergency settings. To evaluate the prognostic relevance of spirometric parameters, tracheal respiratory sound–derived acoustic features, and machine learning-based classification models for short-term outcomes in adults presenting to the emergency department with acute asthma exacerbations. In this prospective cohort study, adults with acute asthma exacerbations underwent spirometry and tracheal respiratory sound recording before and after emergency department treatment. Short-term prognosis was defined as hospitalization during the index visit or emergency department re-presentation within seven days. Changes in spirometric and acoustic features were analyzed, and machine learning models incorporating demographic, spirometric, acoustic, and combined feature sets were developed and compared. Baseline airflow limitation at presentation was the strongest determinant of short-term prognosis. Patients with poor outcomes had significantly lower pre-treatment and post-treatment FEV₁ and PEF values. Acoustic feature changes showed minimal correlation with ΔFEV₁ and ΔPEF; however, selected frequency-domain features differed between prognosis groups, indicating complementary physiological information. Machine learning models incorporating spirometric variables achieved the highest performance, with accuracy up to 84.4% and balanced classification metrics. Sound-based models demonstrated moderate but clinically meaningful performance (accuracy approximately 67–77%), while multimodal models did not outperform spirometry-only models. In acute asthma exacerbations, baseline spirometric impairment remains the most reliable predictor of short-term outcomes. Tracheal respiratory sound analysis combined with machine learning may provide complementary prognostic information, particularly when spirometry is unavailable or unreliable, supporting its role as a clinical decision-support tool rather than a replacement for conventional assessment.

Thank you for forwarding the external comment regarding our published article (Machine Learning-Driven Lung Sound Analysis: Novel Methodology for Asthma Diagnosis)

Aim: This study evaluated the prognostic value of the Haemoglobin, Albumin, Lymphocyte, and Platelet (HALP) score for six-month mortality in acute pulmonary thromboembolism (APTE) and its added value over established risk tools.Materials and Methods: In this retrospective cohort study, 556 patients with APTE confirmed by computed tomography pulmonary angiography (CTPA) were analysed between 2017 and 2024. HALP was calculated as (haemoglobin × albumin × lymphocytes) / platelets. Non-normally distributed variables are given as median (interquartile range) and compared with the Mann–Whitney U test. Independent predictors were identified by logistic regression and discrimination by ROC analysis. The incremental value of HALP over sPESI was assessed with the integrated discrimination improvement (IDI) and continuous net reclassification improvement (NRI), and internal validity by bootstrap optimism correction.Results: Mean age was 55 ± 17 years, 42.3% were male, and six-month mortality was 12.2% (n = 68). HALP was lower in non-survivors (median 0.98 vs 3.01; p < 0.001). The AUC was 0.767 (95% CI 0.713–0.822), with an optimal cut-off of 1.91 (sensitivity 79.4%, specificity 65.2%); HALP discriminated mortality better than each of its components (DeLong p < 0.05). In multivariable analysis, HALP (OR 0.66; 95% CI 0.55–0.79; p < 0.001), lactate, pulmonary arterial pressure and age were independent predictors. Adding HALP to sPESI raised the AUC from 0.612 to 0.791 (IDI 0.098; NRI 0.862), and the optimism-corrected AUC was 0.815. HALP remained independent in the malignancy subgroup (OR 0.66; p = 0.008).Conclusions: A lower HALP score is independently associated with six-month mortality in APTE and may add prognostic information to sPESI. As a simple, low-cost index it could complement guideline-based models. Given its limited specificity and the lack of external validation, these findings are hypothesis-generating pending prospective multicentre confirmation. The proposed HALP cut-off should not be applied in routine clinical practice until it has been externally validated in prospective multicentre cohorts.

Ejmi Eurasian Journal of Medical Investigation

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.

Recent studies have shown that Gene Xpert MTB/RIF (Xpert) and Gene Xpert MTB/RIF ultra (Xpert-U) tests outperform traditional microbiological methods in detecting tuberculosis (TB) and identifying rifampicin resistance. This research aimed to examine their diagnostic accuracy and clinical relevance. This retrospective cohort study included patients aged 18 years and older with pulmonary or extrapulmonary samples analyzed by Xpert, Xpert-U, and conventional diagnostic methods between January 2016 and June 2020. The diagnostic performance of Xpert and Xpert-U was compared across four patient groups: A) Microscopy-culture-positive pulmonary TB, B) Microscopy-negative, culture-positive pulmonary TB, C) Pulmonary TB cases, and D) Extrapulmonary TB cases. A total of 1.366 samples (1.280 pulmonary, 86 extrapulmonary) were analyzed using Xpert and Xpert-U, including 1.042 males (76.3%) and 324 females (23.7%), with an average age of 54 years. For pulmonary TB, Xpert showed an overall sensitivity of 99.29% and specificity of 49.23%. Xpert-U demonstrated similar sensitivity at 98.56%, with an improved specificity of 89.66%. In extrapulmonary TB, Xpert-U achieved 100% sensitivity with a specificity of 70.59%. Xpert and Xpert-U tests offer high sensitivity for detecting Mycobacterium tuberculosis, greatly improving the timely diagnosis and management of TB, especially in cases with low bacterial loads or drug resistance.

This cross-sectional study aimed to evaluate the persistence of immunity provided by hepatitis B (Hep B) vaccine 18–26 years after primary vaccination among 628 participants. Blood samples were collected for Hep B surface antigen (HBsAg), anti-HBs antibody (anti-HBs), and core antibody (anti-HBc) analyses. For participants with anti-HBs levels of < 10 mIU/mL, booster dose was administered and anti-HBs titers were monitored 4–6 weeks after vaccination was completed. If the response to Hep B vaccine was inadequate, two additional Hep B vaccine doses were administered. Of the 628 participants, 269 were excluded based on the exclusion criteria, and 359 were included in the final analysis. Three participants were found to be HBsAg-positive, resulting in an HBsAg carrier rate of 0.83%. Moreover, 55.4% participants had anti-HBs levels of ≥ 10 mIU/mL, with a geometric mean concentration (GMC) of 82.59 mIU/mL (95% confidence interval [CI]). Among 125 participants with anti-HBs levels of < 10 mIU/mL, 93.1% reached anti-HBs levels of ≥ 10 mIU/mL after booster dose, with a GMC of 493.47 mIU/mL. A prebooster anti-HBs cutoff level of 1.28 mIU/mL was predictive for an immune response, with a sensitivity and specificity of 71.2% and 71.4%, respectively (p = 0.001, 95% CI, likelihood ratio: 2.49). Among 14 participants unresponsive to the booster dose, 78.5% responded to the second vaccine series with a GMC of 670.82 mIU/mL. Approximately 44.6% of participants required a booster dose 18–26 years after primary vaccination. Prebooster anti-HBs titration may predict the response to booster doses. Further studies with larger groups are needed to confirm these findings.