Prognostic markers reflecting nutritional vulnerability in idiopathic pulmonary fibrosis (IPF) remain poorly defined.

In this prospective cohort study, 63 stable outpatients with IPF were followed for 3 years. Sarcopenia was defined according to the 2019 Asian Working Group for Sarcopenia criteria. Serum transthyretin levels were measured concurrently. Cox proportional hazards regression, binary logistic regression, and Kaplan-Meier survival analyses were performed.

During follow-up, 18 patients (29%) died and 21 (33%) experienced respiratory-related hospitalization. Serum transthyretin was an independent predictor of both 3-year mortality and respiratory-related hospitalization, even after adjusting for the Gender-Age-Physiology index. Conversely, sarcopenia and low appendicular skeletal muscle mass index (ASMI) were not independently associated with either outcome. Kaplan-Meier analysis demonstrated significant differences in both mortality and hospitalization according to serum transthyretin levels. Low ASMI evaluated using sex-specific cutoffs was associated with higher mortality in the unadjusted analysis, but not with hospitalization; sarcopenia was not significantly associated with either endpoint.

Serum transthyretin may serve as a practical biomarker of nutritional vulnerability, providing complementary prognostic information beyond muscle mass-based assessment in IPF.

Stroke-associated pneumonia (SAP) is a common complication after acute ischemic stroke and contributes to worse recovery and greater resource use. Nutritional and inflammatory dysregulation have been implicated in both SAP susceptibility and adverse prognosis.

To examine whether admission inflammatory and nutritional markers are associated with the development of SAP and with short-term functional prognosis.

We performed a retrospective single-centre cohort study of consecutive patients with acute ischemic stroke admitted between 1 January 2015 and 31 December 2024 (N=303;SAPn=108,non-SAPn=195). Admission laboratory indices (albumin, CRP, fibrinogen, WBC, PCT, and prealbumin) in the first 24 h and clinical variables were analysed. Multivariable logistic regression identified factors independently associated with SAP; the relationship between SAP and early functional recovery was assessed in adjusted outcome models. A nomogram integrating key predictors was developed and its apparent discrimination is reported.

SAP occurred in 35.6% of patients. Factors independently associated with SAP included nasogastric tube placement (OR: 7.02, 95% CI: 3.50-14.62), venous thromboembolism (OR: 3.20, 95% CI: 1.62-6.31), cognitive impairment (OR: 2.90, 95% CI: 1.32-6.36), and elevated inflammatory markers (WBC OR: 1.52, 95% CI: 1.28-1.80; fibrinogen OR: 1.37, 95% CI: 1.02-1.84; CRP OR: 1.01, 95% CI: 1.00-1.03). Higher admission serum albumin was associated with lower odds of SAP (OR: 0.92, 95% CI: 0.86-0.98). The nomogram showed strong apparent discrimination (AUC: 0.90, 95% CI: 0.86-0.94). After multivariable adjustment, SAP remained associated with poorer short-term functional improvement (adjusted OR: 6.99, 95% CI: 3.05-17.54) and greater healthcare utilization (median length of stay: 39.6 vs. 30.6 days; median cost: USD 12,836 vs. 6585).

In this retrospective cohort, admission markers of nutritional depletion and inflammatory activation were associated not only with increased likelihood of SAP, but also with adverse early functional outcomes. These association-based findings support early risk stratification using routine admission markers; prospective studies and external validation are required before clinical implementation.

Patients with end-stage lung diseases awaiting lung transplant frequently experience severe hypoxemia, dyspnea, and functional limitations that may compromise survival and transplant eligibility. Optimizing noninvasive respiratory support during the waiting period is crucial to preserve oxygenation, maintain physical conditioning, and avoid escalation to invasive mechanical ventilation, which is associated with poorer transplant outcomes. High-flow nasal cannula therapy has emerged as an important noninvasive respiratory support modality capable of providing physiological and clinical benefits such as precise fractions of inspired oxygen, a low level of positive end-expiratory pressure, dead-space washout, and reduced work of breathing. This review summarizes the pathophysiology of hypoxemia in lung transplant candidates, the mechanisms of action of high-flow nasal cannulas, and the current clinical evidence supporting its use in this population during the pre-transplant period. Available evidence suggests that the use of high-flow nasal cannulas improves oxygenation, relieves dyspnea, enhances exercise tolerance, facilitates participation in pulmonary rehabilitation programs, and may reduce the need for endotracheal intubation, thereby improving the likelihood of survival to transplantation. The review also discusses patient selection, the practical implementation of high-flow nasal cannula therapy, and comparisons with other respiratory support modalities. Although the current evidence is largely observational and heterogenous, high flow appears to be a valuable supportive and bridging therapy for selected patients awaiting lung transplant. Future prospective studies are needed to define standardized protocols and evaluate transplant-specific outcomes.

Current real-world data on the utilization of atrial fibrillation (AF) catheter ablation (CA) are scarce, as is information on the impact of the COVID-19 pandemic on trends in interventional AF treatment. Aims of this study were to describe case characteristics and trends of CA management using a contemporary multicenter database.

In this retrospective, cross-sectional analysis, we investigated administrative data provided by 87 German hospitals from 01/01/2016 to 12/15/2022. Based on ICD-10 and OPS codes, inpatient cases with a main or secondary discharge diagnosis of AF who underwent CA were extracted. Incidence-rate ratios (IRR) for case numbers with 95% confidence intervals (CI) were calculated using negative binomial models. Trends based on regression analysis were adjusted for baseline variables.

Analyzing 29 144 CA cases (89.4% from high-volume centers), a significant increase in case numbers was observed throughout the study period (IRR 1.05, 95% CI 1.03-1.07, p < 0.001). There was no sustained impact on the overall trend from the COVID-19 pandemic, but a temporary drop in case numbers in 2020. Utilization of transesophageal echocardiography (OR 0.82, 95% CI 0.81-0.83, p < 0.001) and intensive care treatment declined (OR 0.92, 95% CI 0.89-0.94, p < 0.001) and there was a trend toward a reduced incidence of pericardial tamponade. The ratio of cryoablations to radiofrequency CA case numbers increased from 0.29 ± 0.06 in 2016 to 0.50 ± 0.07 in 2022.

We observed an increase in AF CA case numbers over the study period without a sustained influence of the COVID-19 pandemic on this long-term trend. Reported adaptations in CA management deserve further attention.

Observational studies suggest an association between insulin resistance (IR) and chronic obstructive pulmonary disease (COPD), but this link is susceptible to confounding and reverse causality. This study integrated cross-sectional analysis with Mendelian Randomization (MR) to systematically evaluate their potential causal relationship.

Using NHANES data, we employed complex sampling weighting and multivariable logistic regression to assess the observational association between IR (measured by HOMA-IR) and COPD. For genetic analysis, genetic variants strongly associated with IR were selected as instrumental variables from GWAS summary data. Two-sample MR analyses were conducted using inverse-variance weighted (IVW), weighted median, and MR-Egger regression, with rigorous testing for pleiotropy and heterogeneity.

Observational analysis showed no significant association before confounder adjustment (P=0.166). After adjustment, moderate IR levels (third quintile) were associated with increased COPD risk (OR=2.24, 95% CI: 1.15-4.37, P=0.018). MR analysis revealed inconsistent estimates: IVW suggested a weak risk effect (OR=1.009, P<0.001), while MR-Egger indicated a protective effect (OR=0.998, P=1.54e-05). The MR-Egger intercept test detected significant horizontal pleiotropy (P<2e-16), indicating that genetic instruments influence COPD through pathways independent of IR, violating a key MR assumption. The genetic effect sizes were extremely small and not clinically meaningful.

This integrated analysis does not support an independent causal role of IR in COPD. The observational association is confounded and non-linear, while genetic evidence is undermined by substantial pleiotropy. Therefore, IR should be regarded as a comorbid risk marker reflecting a systemic metabolic-inflammatory state rather than a direct causal target. For COPD patients with comorbid IR, clinical management should shift from targeting a single metabolic parameter toward a comprehensive strategy grounded in smoking cessation and pulmonary rehabilitation, alongside active management of obesity and dyslipidemia. Future research should prioritize elucidating the common upstream mechanisms linking metabolic dysregulation and lung function decline.

Differentiating asthma from chronic obstructive pulmonary disease (COPD) remains challenging in clinical practice, and asthma-COPD overlap (ACO) lacks universally accepted diagnostic criteria. In this study, we propose a chest computed tomography (CT) image segmentation framework based on deep transfer learning to support imaging-assisted ACO-related classification as a proof-of-concept approach. Experiments were performed in a single-center cohort of patients with asthma, COPD, and ACO. Model performance was evaluated using classification accuracy and segmentation Dice similarity coefficient against expert-annotated reference masks. In addition, lung function parameters, inflammatory biomarkers, and ACT/CAT scores were summarized to characterize cohort profiles and assist clinical interpretation; these variables were not predicted by the AI model. The proposed approach achieved the highest ACO classification accuracy (93.21%), outperforming NUS-PSL (85.43%) and PRE-1000C (86.92%). These findings suggest potential utility for imaging-assisted ACO-related classification within this internal single-center evaluation. Further multi-center external validation and robustness analyses are warranted before conclusions regarding stability and generalizability can be made.

Candida species are frequently found in the lower respiratory tract (LRT) of patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD), but the clinical significance is uncertain. This study compared the clinical differences between AECOPD patients with and without Candida in their LRT and assessed the impact on disease outcomes.

We conducted a retrospective case-control study on AECOPD patients hospitalized at the First Affiliated Hospital of Guangxi Medical University. Demographic characteristics, clinical data, and follow-up data were compared between AECOPD patients with and without Candida isolated from their LRT. Univariate and multivariate logistic regression analyses were performed to identify risk factors for AECOPD. Survival curves for the patients with and without Candida-positive LRT samples were calculated using the Kaplan-Meier method.

A total of 225 hospitalized AECOPD patients were included in the study, 88 of whom had Candida isolated from their LRT, while 137 did not. The Candida-positive group had a greater pack-year history and higher COPD Assessment Test (CAT) scores compared to the Candida-negative group. The proportion of patients with Modified Medical Research Council (mMRC) grade 4, Global Initiative for Chronic Obstructive Pulmonary Disease (GOLD) grade 4 and hospitalizations for AECOPD in the past year were higher in the Candida-positive group. Peripheral blood lymphocytes, CD8⁺ T-lymphocytes and percent predicted forced expiratory volume in 1 s (FEV₁) were significantly lower in the Candida-positive group (P < 0.05). Patients without Candida survived significantly longer than those with Candida (P < 0.001). The presence of Candida and mMRC grade 4 were independent risk factors for both acute exacerbation and hospitalization in the past year.

Positive Candida isolation and mMRC grade 4 are independent risk factors for AECOPD. Candida in the LRT of COPD patients may predict more severe clinical symptoms, greater airflow limitation, and poorer survival outcomes.

This study investigated the distribution characteristics and antimicrobial resistance patterns of pathogens isolated from patients with oral and maxillofacial infections during and after the COVID-19 pandemic.

This retrospective study analyzed microbial cultures of specimens obtained from patients with odontogenic and non-odontogenic oral and maxillofacial infections during the pandemic period (January 2020-December 2022) and the post-pandemic period (January 2023-December 2024). Pathogen identification was performed using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Antimicrobial susceptibility testing was conducted using the VITEK 2 Compact automated analyzer, the Kirby-Bauer disk diffusion method, and the Etest. Data were analyzed using WHONET (version 5.6) and R software (version 4.2.1). This study was approved by the Medical Ethics Committee of West China Hospital of Stomatology, Sichuan University (Approval No.: WCHSIRB-D-2025-280).

While the microbial culture submission rate remained stable, the culture positivity rate decreased from 20.19 during the pandemic to 18.71% in the post-pandemic period. In odontogenic infections, anaerobic bacteria predominated, with Viridans streptococci, P. intermedia, and P. acnes being the most frequently isolated species. Notably, clindamycin resistance among Viridans streptococci significantly decreased from 93.81 to 82.35%. In contrast, non-odontogenic infections were predominantly caused by aerobic bacteria, most frequently involving K. pneumoniae, A. baumannii, S. aureus, and P. aeruginosa. Resistance to ceftriaxone in K. pneumoniae decreased significantly from 14.91 to 9%, whereas resistance to levofloxacin in P. aeruginosa markedly increased from 2.88 to 11.49%. In addition, the proportion of carbapenem-resistant A. baumannii (CR-AB) rose from 9.52 to 17.11%.

Following the COVID-19 pandemic, shifts in both the pathogen spectrum and antimicrobial resistance patterns were observed in odontogenic and non-odontogenic infections. These findings reflect an evolving microbial landscape and highlight the need for tailored, etiology-specific antimicrobial strategies in the management of oral and maxillofacial infections. However, as these changes may be attributable to multiple confounding factors, further studies are warranted to establish causality.

Nontuberculous mycobacteria (NTM) are rare but emerging pathogens in pediatric populations, particularly in countries where BCG vaccination has been discontinued. Their diagnosis is often delayed due to nonspecific symptoms and limited microbiological sensitivity.

We conducted a retrospective cohort study of all pediatric patients with confirmed or clinically probable NTM disease, diagnosed at the National Institute for Pediatric Tuberculosis and Respiratory Diseases in Slovakia between 2017 and 2024. Medical records from 2012 to 2016 were screened, but no cases fulfilling inclusion criteria were identified. Clinical characteristics, diagnostic approaches, therapeutic strategies, and patient outcomes were systematically evaluated.

In total, 30 patients were diagnosed. The majority of cases (27/30) involved cervical lymphadenitis (including two with concurrent pulmonary involvement), most commonly affecting children between 1 and 2 years. In addition, one patient was diagnosed with a pulmonary form of NTM disease, one with NTM-associated osteomyelitis, and one with a skin and soft tissue infection. Mycobacterium avium complex was the most frequently identified species, detected in 8 patients. Bacteriological confirmation was achieved in 53.3% of cases, while histological evidence of granulomatous inflammation was found in 86.7%. The median time to diagnosis was 45 days (IQR: 31-109.5), with longer delays in culture-negative patients (p = 0.0648). Surgical excision was performed in 25 of 27 patients with lymphadenitis, 60% received adjunctive antibiotic therapy. Follow-up data were available for 19 patients: 15 had full recovery, 3 experienced recurrent upper respiratory tract infections, and 1 immunocompromised patient died from miliary tuberculosis.

This is the first national study on pediatric NTM disease in a post-BCG vaccination era in Slovakia. Despite centralization of care, diagnostic delays were common, particularly in bacteriologically negative cases. These findings underscore the need for early tissue sampling, comprehensive microbiological evaluation, and interdisciplinary collaboration to improve diagnostic efficiency.

Influenza A outbreaks in Saudi Arabia exhibit different seasonal patterns, influenced by significant changes, including a near-total halt during the COVID-19 pandemic (2020-2021) and a substantial rebound, as evidenced by national surveillance data, until the end of 2023. Traditional time-series models rely on stationarity and stable seasonal patterns; however, these assumptions are significantly undermined by regime shifts. This study introduces a forecasting method that uses light gradient boost machine (LightGBM) regression, along with Seasonal-Trend decomposition using LOESS (STL), to better track influenza in changing contexts. The proposed method adapts to the evolving epidemiological dynamics shaped by policy and behavioral changes by decomposing the incidence series into long-term trends, stable annual seasonal components, and irregular residual fluctuations prior to nonlinear learning. Exploratory analysis supports strong winter seasonality, linear correlations with meteorological variables, and major structural disruptions linked to pandemic-related interventions. shows how standard SARIMAX and seasonal baseline models cannot be used across all epidemiological regimes. The hybrid model, when evaluated during the test window, shows strong out-of-sample performance, substantially outperforming the benchmark models (R ² = 0.831, MAE = 89.0). In-sample fitting throughout the study period indicates a high degree of representational capacity (R ² = 0.987). The framework is further extended to probabilistic forecasting via quantile regression, resulting in accurately calibrated 95% prediction intervals. The uncertainty in the predictions increases appropriately during periods of epidemiological disruption, highlighting the importance of uncertainty-aware prediction under structural change. The proposed STL-LightGBM architecture is a resilient and comprehensible instrument for monitoring influenza in post-pandemic contexts, facilitating early warning systems and expeditious public health decision-making in Saudi Arabia and analogous regions.