Pulmonary Conditions Research Articles

Epidemiology of chronic pulmonary aspergillosis: A nationwide descriptive study

BackgroundChronic pulmonary aspergillosis (CPA) has recently gained attention owing to its substantial health burden. However, the precise epidemiology and prognosis of the disease are still unclear due to the lack of a nationwide descriptive analysis. This study aimed to elucidate the epidemiology of patients with CPA and to investigate their prognosis. MethodsUsing a national administrative database covering >99% of the population in Japan, we calculated the nationwide incidence and prevalence of CPA from 2016 to 2022. Additionally, we clarified the survival rate of patients diagnosed with CPA and identified independent prognostic factors using multivariate Cox proportional hazard analysis. ResultsDuring the study period, while the prevalence of CPA remained stable at 9.0–9.5 per 100,000 persons, its incidence declined to 2.1 from 3.5 per 100,000 person-years. The 1-, 3-, and 5-year survival rates were 65%, 48%, and 41%, respectively. During the year of CPA onset, approximately 50% of patients received oral corticosteroids (OCS) at least once, while about 30% underwent frequent OCS treatment (≥4 times per year) within the same timeframe. Increased mortality was independently associated with older age (>65 years) (hazard ratio [HR], 2.65; 95% confidence interval (CI), 2.54–2.77), males (1.24; 1.20–1.29), a history of chronic obstructive pulmonary disease (1.05; 1.02–1.09), lung cancer (1.12; 1.06–1.18); and ILD (1.19; 1.14–1.24); and frequent OCS use (1.13; 1.09–1.17). Conversely, decreased mortality was associated with a history of tuberculosis (HR, 0.81; 95% CI, 0.76–0.86), non-tuberculous mycobacteria (0.91; 0.86–0.96), and other chronic pulmonary diseases (0.89; 0.85–0.92). ConclusionsThe incidence of CPA decreased over the past decade, although the prevalence was stable and much higher than that in European countries. Moreover, the patients’ prognosis was poor. Physicians should be vigilant about CPA onset in patients with specific high-risk underlying pulmonary conditions.

The involvement of HDAC3 in the pathogenesis of lung injury and pulmonary fibrosis.

Acute lung injury (ALI) and its severe counterpart, acute respiratory distress syndrome (ARDS), are critical respiratory conditions with high mortality rates due primarily to acute and intense pulmonary inflammation. Despite significant research advances, effective pharmacological treatments for ALI and ARDS remain unavailable, highlighting an urgent need for therapeutic innovation. Notably, idiopathic pulmonary fibrosis (IPF) is a chronic, progressive disease characterized by the irreversible progression of fibrosis, which is initiated by repeated damage to the alveolar epithelium and leads to excessive extracellular matrix deposition. This condition is further complicated by dysregulated tissue repair and fibroblast dysfunction, exacerbating tissue remodeling processes and promoting progression to terminal pulmonary fibrosis. Similar to that noted for ALI and ARDS, treatment options for IPF are currently limited, with no specific drug therapy providing a cure. Histone deacetylase 3 (HDAC3), a notable member of the HDAC family with four splice variants (HD3α, -β, -γ, and -δ), plays multiple roles. HDAC3 regulates gene transcription through histone acetylation and adjusts nonhistone proteins posttranslationally, affecting certain mitochondrial and cytoplasmic proteins. Given its unique structure, HDAC3 impacts various physiological processes, such as inflammation, apoptosis, mitochondrial homeostasis, and macrophage polarization. This article explores the intricate role of HDAC3 in ALI/ARDS and IPF and evaluates its therapeutic potential the treatment of these severe pulmonary conditions.

Use of Sodium-glucose cotransporter 2 (SGLT 2) inhibitor is associated with reduced emergency room visits and hospitalizations in patients with Chronic obstructive pulmonary disease (COPD) and type 2 Diabetes Mellitus

BackgroundThe Sodium-Glucose Cotransporter 2 inhibitors (SGLT2i) are a class of anti-diabetic medications that confer cardio-renal-metabolic (CRM) benefits. Emerging evidence also suggests that these agents provide better benefits for chronic pulmonary conditions, especially chronic obstructive pulmonary disease (COPD). Research questionWe aimed to assess the association between SGLT2i use and outcomes in patients with COPD and concomitant Type 2 Diabetes Mellitus (T2DM). Study design and methodsWe conducted a retrospective cohort study on adults with T2DM and COPD in a primary care clinic from January 01, 2019 to 01/01//2023. Patients were categorized into two groups based on SGLT2i use. We collected demographic information and outcomes such as emergency room (ER) visits, hospitalizations secondary to COPD exacerbation over the period of four years and time to hospitalization and ER visits. Chi-square analysis was used for categorical variables, whereas an unpaired t-test was used for continuous variables. Cox regression was performed to identify significant prognostic factors of hospitalization and ER visits. A Kaplan-Meir analysis was used to visualize the probability of non-hospitalization and the probability of not visiting the ER. Statistical significance was set at p-value <0.05. ResultsOf the 220 patients screened, 94 met the inclusion criteria, of which 20 patients (21.3 %) had SGLT2i use at admission, and 74 (78.7 %) did not. Baseline demographic information were well-matched between the two groups. SGLT2i use was associated with a significant reduction in ER visits (70 % vs. 97.3 %, p-0.001) and the number of hospitalizations (55 % vs 87.8 %, p-0.001). Further multivariate analysis showed lower hazards of hospitalization (adjusted HR-0.156; CI:0.073 to 0.331) and ER visits (HR)-0.232; CI:0.118 to 0.453) in patients on SGLT2i. InterpretationIn patients with T2DM with COPD, SGLT2i use was associated with reduced ER visits and hospitalizations related to COPD. This protective effect of SGLT2i could be explained by reduced systemic proinflammatory markers and increased anti-inflammatory markers via inhibition of Node like receptor protein 3(NLRP3) inflammasome activation in multiple tissues, including the lungs.

The Use of Point-of-Care Ultrasound (POCUS) in Anesthesiology: A Narrative Review.

Point-of-care ultrasound (POCUS) has become an essential tool in modern anesthesiology, offering real-time imaging that aids in diagnosis, treatment, and procedural guidance. Its applications have expanded significantly, from routine assessments to complex perioperative management, enhancing patient care across various domains. This study aimed to review and summarize the current literature on the applications of POCUS in anesthesiology, focusing on its utility in airway management, cardiac assessment, lung ultrasound (LUS), and abdominopelvic evaluations. Additionally, it discusses the challenges and strategies for overcoming barriers to its widespread adoption. A narrative literature review was conducted, encompassing studies on using POCUS in anesthesiology. Key databases were searched for relevant articles ("Point-of-care ultrasound,""Focused cardiac ultrasound,""Lung ultrasound"),and studies were selected based on their relevance to the main topics of interest: airway management, cardiac ultrasound, LUS, and abdominopelvic and gastric ultrasound. Both observational studies and randomized controlled trials were included. POCUS improves the safety and accuracy of regional anesthesia and airway procedures, enhancing patient outcomes by providing real-time visualization. Focused cardiac ultrasound is valuable in preoperative, intraoperative, and postoperative care, allowing for tailored anesthesia plans and timely interventions based on cardiac function. The US is an effective diagnostic tool for assessing pulmonary conditions, with specific signs and artifacts aiding in identifying pathologies such as pneumothorax, pleural effusions, and pulmonary congestion. Despite its benefits, POCUS faces barriers such as limited access to training, the need for standardized protocols, and the high equipment cost, especially in low-resource settings. While its integration into clinical practice continues to grow, overcoming challenges related to training, standardization, and equipment access is essential to realize its full potential. Continued efforts in education and research are needed to support the widespread adoption of POCUS in anesthesiology.

Imaging of ventilation and lung injury with low-frequency tomographic ultrasound.

Mechanical ventilation in the intensive care unit (ICU) is a life-saving technique for patients with acute respiratory failure, but is also associated with a high incidence of complications in the injured lung. Currently, there is no widely used monitoring technique to guide the ventilator setting to facilitate a precision medicine approach or to provide a real-time alert for developing adverse pulmonary conditions. Conventional ultrasound has been used as a thoracic bedside technology, but the lack of signal penetration into lung tissue results in images that often contain more information in their artifacts than in the images themselves. Perhaps the greatest obstacle to using traditional ultrasound in the ICU is the need for highly skilled technicians to perform the data collection. In contrast, low-frequency ultrasound (50-500 kHz) has been shown to penetrate the lung, and can detect air trapping in patients with chronic obstructive pulmonary disease (COPD). Here, we present a method of collecting low-frequency ultrasound computed tomographic (USCT) data in vivo on a mechanically ventilated porcine model and computing tomographic reconstructions of airflow during tidal breathing and induced lung injuries. We evaluate the ability of the novel low-frequency USCT system to image regional changes in sound speed in the thorax due to changes in airflow during tidal breathing and induced lung injuries. This represents the first study of low-frequency tomographic ultrasound imaging in vivo and the first to produce tomographic images of ventilatory changes invivo. USCT and computed tomography (CT) scan data were collected alternately on a mechanically ventilated Landrace pig weighing approximately 75 kg during tidal breathing, induced pneumothorax, atelectasis, and pleural effusion. The pneumothorax was induced by injecting air through a 5 mm thick intrathoracic tube inserted in the 8th posterior intercostal space. After removing the air, atelectasis was induced by ventilating the animal with a high concentration of oxygen and low tidal volumes. The pleural effusion was induced by injecting a saline solution through the tube. The USCT data were collected at 125 kHz using the USCT low-frequency ultrasound tomography (LUFT) system on a transducer belt placed around the animal's thorax. Tomographic reconstructions were computed from the USCT data using a regularized refraction-corrected Gauss-Newton-based time-of-flight reconstructionalgorithm. Cyclic changes in computed lung area during tidal breathing were demonstrated to agree with the respiratory rate on the mechanical ventilator. Reconstructed images computed at time steps during the procedure demonstrate regional changes consistent with what would be expected during the induced lung injury. No ground truth was available for images during the procedures since CT scans could only be taken before and after each established lunginjury. In this work, we have demonstrated in the first in vivo study using a mechanically ventilated porcine animal model that low-frequency ultrasound tomography has the ability to image regional changes in sound speed in the thorax corresponding to changes in airflow during tidal breathing and induced lung injury. The results show promise for using low-frequency USCT as a bedside imaging technique in the future for patients with acute respiratory distresssyndrome.

Current status, trends, and predictions in the burden of coal worker's pneumoconiosis in 204 countries and territories from 1990 to 2019

Coal worker's pneumoconiosis (CWP), a pulmonary condition resulting from prolonged exposure to coal mining environments, not only leads to a high prevalence of morbidity and mortality among miners but also incurs substantial economic burdens and diminishes the labor force within society. In light of evolving trends in the global coal industry, the ramifications of CWP are anticipated to manifest in new patterns and variations. This study seeks to comprehensively assess the present landscape, trend dynamics, and future projections of the global CWP burden from 1990 to 2019. The objective is to provide a scientific framework for nations to develop and enhance pertinent policies and preventative strategies, thereby promoting the health and occupational safety of coal miners.

Rosa roxburghii fermented juice mitigates LPS-induced acute lung injury by modulation of intestinal flora and metabolites.

Acute lung injury (ALI) is a severe pulmonary condition with high mortality and morbidity, lacking effective pharmacotherapeutic options. Rosa roxburghii Tratt, a unique fruit from southwestern China, is valued for its rich nutritional content and functional properties. Fermentation is known to enhance the nutritional value, flavor, and shelf life of foods. In this study, we investigated the effects of fermented Rosa roxburghii juice (RRFJ) on gut microbiota, metabolites, and the levels of short-chain fatty acids in the intestines, as well as its impact on lung tissue and intestine tissue injury, inflammation, and oxidative stress in murine models. The results showed that RRFJ modulated gut microbiota and metabolites, increased short-chain fatty acid levels, and consequently reduced lung tissue injury, inflammation, and oxidative stress in mice with ALI. These findings suggest that RRFJ has the potential to serve as a functional dietary adjunct in the management of acute lung injury, providing a scientific basis for its therapeutic role.

Effectiveness of Intercostal Stretch Technique on Pulmonary Conditions: A Narrative Review

Background: The Intercostal Stretch Technique is one of the physiotherapy techniques that is used to solve different respiratory problems. It is used for increasing chest expansion and diaphragm excursion improvement and also intra-thoracic lung volume. Aim: The study aims to evaluate the effectiveness of the intercostal stretch technique. Methodology: A narrative review. In this study, an RCT study was used to review the intervention. Results: Several articles are used to discuss the effectiveness of the intercostal stretch technique. In stable COPD, diaphragmatic breathing and the intercostal stretch technique both work equally well to improve chest expansion and functional capacity while lowering dyspnea. Research has shown that IC stretch outperforms the anterior basal lift technique in lowering heart and respiratory rates while increasing oxygen saturation. According to this study, IC stretching in conjunction with breathing control may improve dynamic lung parameters, particularly FEV1/FVC%, more than breathing control alone. Patients with COPD have an increased functional exercise capacity and reduced dyspnea when aerobic training and respiratory muscle stretching are combined. Conclusion: Different articles demonstrated that IC stretch improved lung function, expired tidal volume, decreased dyspnea, and increased chest expansion.

Smoking: Profile of an Elderly Sample in the City of Porto Alegre-RS/Brazil

Objective: The objective of this study is to determine the profile of smokers in a sample of elderly people served by the Family Health Strategy (ESF) in the city of Porto Alegre/RS. Theoretical Framework: Smoking is one of the main risk factors for several chronic diseases, including cardiovascular disease, cancer, chronic obstructive pulmonary disease (COPD) and several other conditions. Although the number of smokers is declining in many countries, the impact of smoking remains a significant public health concern. Method: Cross-sectional study with prospective data collection involving a sample of 567 elderly people. 30 ESF teams from different Health Districts were chosen through a stratified random draw and 36 elderly people were selected from each team. Results and Discussion: The prevalence of smoking was 21.5%, with a higher prevalence in men (26%). Widowed elderly people had less history of smoking (47.8%), as did retired elderly people (54.7%), religious practitioners (50.7%) and those with higher incomes. An increase in prevalence was observed in elderly people who stopped smoking, with an increase in age groups. Research Implications: The research can provide specific data on the profile of elderly smokers in Porto Alegre, allowing public policies to be created or adjusted to more effectively serve this population. Originality/Value: While many studies on smoking focus on younger populations or middle-aged adults, the choice to specifically study smoking among older adults is less common and therefore fills a significant gap in the literature.

Optimizing Lung Condition Categorization through a Deep Learning Approach to Chest X-ray Image Analysis

Background: Evaluating chest X-rays is a complex and high-demand task due to the intrinsic challenges associated with diagnosing a wide range of pulmonary conditions. Therefore, advanced methodologies are required to categorize multiple conditions from chest X-ray images accurately. Methods: This study introduces an optimized deep learning approach designed for the multi-label categorization of chest X-ray images, covering a broad spectrum of conditions, including lung opacity, normative pulmonary states, COVID-19, bacterial pneumonia, viral pneumonia, and tuberculosis. An optimized deep learning model based on the modified VGG16 architecture with SE blocks was developed and applied to a large dataset of chest X-ray images. The model was evaluated against state-of-the-art techniques using metrics such as accuracy, F1-score, precision, recall, and area under the curve (AUC). Results: The modified VGG16-SE model demonstrated superior performance across all evaluated metrics. The model achieved an accuracy of 98.49%, an F1-score of 98.23%, a precision of 98.41%, a recall of 98.07% and an AUC of 98.86%. Conclusion: This study provides an effective deep learning approach for categorizing chest X-rays. The model’s high performance across various lung conditions suggests its potential for integration into clinical workflows, enhancing the accuracy and speed of pulmonary disease diagnosis.

Dynamic changes of respiratory microbiota associated with treatment outcome in drug-sensitive and drug-resistant pulmonary tuberculosis

BackgroundRespiratory microbiota is closely related to tuberculosis (TB) initiation and progression. However, the dynamic changes of respiratory microbiota during treatment and its association with TB progression remains unclear.MethodsA total of 16 healthy individuals and 16 TB patients (10 drug-sensitive TB (DS-TB) and 6 drug-resistant TB (DR-TB)) were recruited. Sputum samples were collected at baseline for all anticipants and after anti-TB treatment at Month-6 for TB patients. High throughput 16 S RNA sequencing was used to characterize the respiratory microbiota composition.ResultsCompared to the healthy individuals, TB patients exhibited lower respiratory microbiota diversity (p < 0.05). This disruption was alleviated after anti-TB treatment, especially for DS-TB patients. Parvimonas spp. numbers significantly increased after six months of anti-TB treatment in both DS-TB and DR-TB patients (p < 0.05). Rothia spp. increase during treatment was associated with longer sputum-culture conversion time and worse pulmonary lesion absorption (p < 0.05). Besides, Moraxella spp. prevalence was associated with longer sputum-culture conversion time, while Gemella spp. increase was associated with worsening resolving of pulmonary lesions (p < 0.05).ConclusionDynamic changes of respiratory microbiota during anti-TB treatment is closely related to TB progression. The involvement of critical microorganisms, such as Parvimonas spp., Rothia spp., Moraxella, and Gemella spp., appears to be associated with pulmonary inflammatory conditions, particularly among DR-TB. These microorganisms could potentially serve as biomarkers or even as targets for therapeutic intervention to enhance the prognosis of tuberculosis patients.

Multimodal Disease Classification and Severity analysis Approaches using Machine and Deep Learning

Chest X-rays are a common diagnostic tool for pulmonary and cardiac conditions in hospitals because they provide a clear picture of the patient's thorax. With the use of image-to-text radiology report production, medical imaging results may be automatically described in radiology reports. There are a lot of different pieces of patient data that radiologists may access, but most current systems only use the picture data. the objective of developing AI systems with a focus on humans, with the ability to learn radiologists' search habits via their eye movements, with the hope of enhancing DL system categorisation. The goal of this research is to evaluate several multimodal DL architectures in collaboration with trained radiologists to see which ones work best. In particular, this study aims to build strong DL models for medical picture analysis by investigating the integration of several data modalities, such as eye tracking data and patients' clinical data. A multimodal DL model integrating clinical data and chest X-rays (CXRs) was suggested by us. Findings demonstrated that baseline performance was unaffected by directly supplying fixation masks of radiologists' gaze patterns as input. Confine Areas Using R-CNN (Recurrent Neural Networks).

451. PREDICTIVE FACTORS FOR PNEUMONIA AND ANASTOMOTIC LEAK AFTER TRANSTHORACIC ESOPHAGECTOMY FOR CANCER AND FAILURE TO RESCUE ANALYSIS ON 1355 PATIENTS

Abstract Background Pneumonia (PN) and anastomotic leak (AL) represent the most feared complications after transthoracic esophagectomy for cancer, the former more commonly observed after open and the latter after minimally-invasive (MI) procedures. Studies comparing these two complications with focus on predictive risk factors and procedure-related failure-to-rescue (FTR) are sparse. Aim of the study was to explore such aspects with a large multicentric dataset. Methods All patients who underwent an Ivor Lewis transthoracic esophagectomy for cancer between 2008 and 2022 in four tertiary centers were queried. Predictive risk factors for pneumonia, anastomotic leak and in-hospital mortality were analyzed with univariable models. FTR was defined as in-hospital death following a major complication. Patients were then classified and compared according to the type of operation [open, hybrid-laparoscopic, hybrid-robotic, standard minimally-invasive (MIE) or robotic minimally-invasive (RAMIE)]. Pneumonia and anastomotic leak associated FTR was calculated and compared across the groups. Machine-learning models (neuronal nets, support vector machines and random forests classifier) were applied to predict complications and FTR. Results In total, 1,355 patients were included. Significant (p&amp;lt;0.05) predictors for PN (n=211, 15.6%) were ASA=4, preexisting cardiac and pulmonary conditions, conversion (thoracotomy), neoadjuvant therapy and age. Those for AL (n=160, 11.8%) were preexisting cardiac condition, conversion (thoracotomy) and operation time. Overall FTR-rate was 3.6%. Age&amp;gt;65y, preexisting cardiac and pulmonary condition, AL, PN, postoperative bleeding, sepsis, pulmonary embolism, myocardial infarction, arrhythmia and need for blood transfusion were the risk factors significantly associated with in-hospital mortality. PN- and AL- associated FTR rates did not significantly differ among the different operation types (Figure 1). Machine-learning models were unable to predict complications or FTR properly. Conclusions Our study showed that there is a greater number of factors associated with pneumonia rather than anastomotic leak, probably making the prevention of this complication more challenging to achieve. However, pneumonia- and leak-associated FTR-rates did not significantly differ among the groups, demonstrating that the implementation of minimally-invasive procedures can be safely carried out, if experience is provided.

Comparing Different Methodologies to Quantify Particulate Matter Accumulation on Plant Leaves

Urban air pollution poses a significant threat to human health, with metropolitan areas particularly affected due to high emissions from human activities. Particulate matter (PMx) is among the most harmful pollutants to human health, being composed of a complex mixture of substances related to severe pulmonary conditions. Urban green spaces play a vital role in mitigating air pollution by capturing PMx, and it is essential to select plant species with a high capacity for PMx accumulation to effectively enhance air quality. This study aimed to evaluate and compare the accuracy of two PMx quantification methods—light microscopy and filtration—which demonstrated a high correlation (R2 = 0.72), suggesting that both methods are reliable for assessing PMx accumulation on leaves. Light microscopy allowed for the visualization of PMx deposition, revealing the species warranting further analysis using the filtration method. Among the species analyzed, Euonymus japonicus, Ligustrum lucidum, Alnus glutinosa, Rubus ulmifolius, and Laurus nobilis demonstrated the highest total PMx accumulation, exceeding 50 µg cm−2, making them particularly valuable for air pollution mitigation. This study examined the correlation between leaf traits such as specific leaf area (SLA), leaf area (LA), leaf dissection index (LDI), and leaf roundness and PMx accumulation across the 30 different plant species. A multiple linear regression analysis indicated that these leaf traits significantly influenced PMx accumulation, with SLA and LA showing negative correlations and leaf roundness exhibiting a positive correlation with PMx deposition. In conclusion, this study highlights the importance of selecting plant species with specific leaf traits for effective air quality improvement in urban environments particularly in highly polluted areas, to enhance air quality and public health.

Innovative integration of lung ultrasound and wearable monitoring for predicting pulmonary complications in colorectal surgery: A prospective study.

Postoperative pulmonary complications (PPCs) are common in patients who undergo colorectal surgery. Studies have focused on how to accurately diagnose and reduce the incidence of PPCs. Lung ultrasound has been proven to be useful in preoperative monitoring and postoperative care after cardiopulmonary surgery. However, lung ultrasound has not been studied in abdominal surgeries and has not been used with wearable devices to evaluate the influence of postoperative ambulation on the incidence of PPCs. To investigate the relationship between lung ultrasound scores, PPCs, and postoperative physical activity levels in patients who underwent colorectal surgery. In this prospective observational study conducted from November 1, 2019 to August 1, 2020, patients who underwent colorectal surgery underwent daily bedside ultrasonography from the day before surgery to postoperative day (POD) 5. Lung ultrasound scores and PPCs were recorded and analyzed to investigate their relationship. Pedometer bracelets measured the daily movement distance for 5 days post-surgery, and the correlation between postoperative activity levels and lung ultrasound scores was examined. Thirteen cases of PPCs was observed in the cohort of 101 patients. The mean (standard deviation) peak lung ultrasound score was 5.32 (2.52). Patients with a lung ultrasound score of ≥ 6 constituted the high-risk group. High-risk lung ultrasound scores were associated with an increased incidence of PPCs after colorectal surgery (logistic regression coefficient, 1.715; odds ratio, 5.556). Postoperative movement distance was negatively associated with the lung ultrasound scores [Spearman's rank correlation coefficient (r), -0.356, P < 0.05]. Lung ultrasound effectively evaluates pulmonary condition post-colorectal surgery. Early ambulation and respiratory exercises in the initial two PODs will reduce PPCs and optimize postoperative care in patients undergoing colorectal surgery.

Patient factors associated with positive respiratory cultures following tracheostomy in pediatric patients

ObjectivesAn increasing number of pediatric patients undergo tracheostomy placement annually. Despite advances in care, these patients remain at high risk for postoperative complications, including respiratory infections. The risk factors for positive respiratory cultures and the impact of culture positivity on overall morbidity and mortality in this population is not fully characterized. Study design and settingRetrospective cross-sectional analysis of all patients within a single tertiary care institution who underwent tracheostomy placement from January 1, 2019 to 12/31/2021. MethodsWe collected patient demographic information, comorbidities, primary indication for tracheostomy placement, and respiratory culture information preoperatively and postoperatively. The primary outcome measure was positive bacterial respiratory culture with speciation within 1, 3, or 6 months of tracheostomy placement. The secondary outcome measure was all-cause mortality within the study period. ResultsA total of 180 patients with median (IQR) age of 5.2 (3.4–31.0) months at time of tracheostomy placement were included in the study. Nearly half of patients had a positive culture within 1 month (n = 81,47.4 %) while 63.7 % of patients were positive within 6 months of tracheostomy placement (n = 109). Risk factors included respiratory and/or cardiac comorbidities. Positive respiratory cultures within 6 months of surgery and cardiac and/or neurologic comorbidities were associated with increased all-cause mortality following tracheostomy placement. ConclusionsPositive respiratory cultures are common following tracheostomy placement, more so in patients with cardiac and pulmonary comorbid conditions. Positive cultures are associated with increased all-cause mortality. More work is necessary to determine optimal screening frequency and treatment protocols for positive cultures in this population.

Enhancing Pulmonary Diagnosis in Chest X-rays through Generative AI Techniques

Chest X-ray imaging is an essential tool in the diagnostic procedure for pulmonary conditions, providing healthcare professionals with the capability to immediately and accurately determine lung anomalies. This imaging modality is fundamental in assessing and confirming the presence of various lung issues, allowing for timely and effective medical intervention. In response to the widespread prevalence of pulmonary infections globally, there is a growing imperative to adopt automated systems that leverage deep learning (DL) algorithms. These systems are particularly adept at handling large radiological datasets and providing high precision. This study introduces an advanced identification model that utilizes the VGG16 architecture, specifically adapted for identifying various lung anomalies such as opacity, COVID-19 pneumonia, normal appearance of the lungs, and viral pneumonia. Furthermore, we address the issue of model generalizability, which is of prime significance in our work. We employed the data augmentation technique through CycleGAN, which, through experimental outcomes, has proven effective in enhancing the robustness of our model. The combined performance of our advanced VGG model with the CycleGAN augmentation technique demonstrates remarkable outcomes in several evaluation metrics, including recall, F1-score, accuracy, precision, and area under the curve (AUC). The results of the advanced VGG16 model showcased remarkable accuracy, achieving 98.58%. This study contributes to advancing generative artificial intelligence (AI) in medical imaging analysis and establishes a solid foundation for ongoing developments in computer vision technologies within the healthcare sector.

Clinical features of pulmonary hypertension in lymphangioleiomyomatosis

Objective: To describe the prevalence of lymphangioleiomyomatosis-pulmonary hypertension (LAM-PH), to explore the clinical features of patients with LAM-PH, and to evaluate the role of pulmonary artery optical coherence tomography (OCT) in the diagnosis of LAM-PH. Methods: Among 234 patients diagnosed with LAM in our center from June 2017 to August 2023, echocardiography was performed in 167 patients, 15 patients with PH indicated by echocardiography were selected as the LAM-PH group. From the remaining 152 patients, 32 patients were randomly selected as the control group. We compared the demographic data, clinical manifestations, pulmonary function, blood gas analysis, and serum vascular endothelial growth factor D (VEGF-D) levels between the two groups. We also evaluated the data from right heart catheterization in five patients and the images from optical coherence tomography of pulmonary arteries in two patients. Results: Echocardiography showed pulmonary hypertension in 15 patients (8.98%). Compared with the control group, LAM-PH group had a higher proportion of patients receiving oxygen therapy. Patients with PH had lower FEV1, FVC, FEV1/FVC, DLCO and higher serum VEGF-D levels compared with those without PH. Right heart catheterization was performed in five patients. Five patients had a pre-capillary pattern and three of these patients had severe pulmonary hypertension. Optical coherence tomography showed that there were no obvious abnormalities in the intima and lumen of the blood vessels in the two patients. Conclusions: The prevalence of PH in LAM is low. Patients in the LAM-PH group had more severe hypoxemia, worse lung function and higher serum VEGF-D levels than those in the control group. PH in LAM is mainly pre-capillary. Optical coherence tomography can evaluate the pulmonary artery vascular condition and help to identify the cause of pulmonary hypertension in LAM patients.

Exploring the Role of Traditional Chinese Medicine in Pulmonary Conditions

Exploring the Role of Traditional Chinese Medicine in Pulmonary Conditions

Enhancing Heart Failure Diagnosis Accuracy and Distinguishing It From Other Pulmonary Conditions: A Retrospective Case Series Study Leveraging the HeartLogic Parameters.

Introduction Heart failure (HF) poses a substantial and escalating medical and economic challenge, marked by significant morbidity and mortality. It stands as the primary cause of hospital admissions among the elderly, contributing significantly to healthcare expenditures in developed nations. Evaluating cardiac and pulmonary function remains challenging, necessitating careful interpretation to mitigate misdiagnosis and inappropriate treatment. Remote monitoring has emerged as a preventive strategy to curb HF-related hospitalizations, emphasizing the importance of early detection of impending acute HF decompensation. Implantable cardiac defibrillators (ICDs) capture various parameters, including heart rhythm, pacing percentages, thoracic impedance, and physical activity. Objective In this study, we aim to investigate the effectiveness of HeartLogic (Boston Scientific, Marlborough, Massachusetts) parameters in accurately distinguishing HF patients from individuals with alternative diagnoses. Methods This cross-sectional study was conducted at Cabell Huntington Hospital, St. Mary's Medical Center in Huntington, West Virginia, between 2021 and 2022. The study involved a retrospective chart review of electronic medical records, approved by the institutional review board, encompassing patients aged >18 admitted with Heartlogic-capable devices. The analysis included demographic variables, admission and discharge diagnoses, length of hospital stays, health literacy index, and thoracic impedance. Results Of the initially included 26 patients, 19 met all inclusion criteria. The demographic profile highlighted a predominantly older population with a male preponderance and a notable incidence of congestive heart failure (CHF). Physiological changes, particularly in thoracic impedance and the HeartLogic Index, demonstrated significant alterations. Logistic regression analysis revealed that changes in health literacy index and thoracic impedance significantly contributed to predicting the change in CHF diagnosis. Conclusion This study, conducted in a rural setting, demonstrates the capability of the HeartLogic algorithm in predicting HF events, providing valuable insights into its utility in diverse clinical environments. The findings emphasize the potential of this technology to enhance diagnostic accuracy and improve patient outcomes.Despite inherent limitations, this analysis contributes unique perspectives, particularly in the context of a specific and underexplored rural population in West Virginia.