Lung Disorders Research Articles

Curcumin’s therapeutic story: Facts findings in the light of clinical studies

Curcumin, a bright yellow phytochemical derived from Curcuma longa L., has been extensively studied for its health-promoting, disease-preventing, and treatment properties, and has been gaining popularity among medical researchers since its extraction. Unani and Ayurvedic medicine use it to treat a variety of conditions, including hepatic, lung, skin, and GIT disorders. It has diverse biological effects, including antimicrobial, antiproliferative, antioxidant, anti-inflammatory, antidiabetic, and neuroprotective properties, make it a promising lead compound for the development of new derivatives to treat diseases such as cancer, diabetes, and Alzheimer's disease. Despite its low cost, extensive potency, and its multitargeted approach of pathways, available data on its efficacy in multiple studies is still questioning, and more studies are required for this potential gap. Clinical trials assessing its biological effects revealed significant disproportionality however this review provides a comprehensive overview of recent clinical studies of curcumin, and summarizes its efficacy, biological properties, therapeutic potential, and safety. Moreover, this review provides a more special focus on its anti-inflammatory, antioxidant, anticancer, and neuroprotective properties.

CT Quantification of Interstitial Lung Abnormality and Interstitial Lung Disease: From Technical Challenges to Future Directions.

Interstitial lung disease (ILD) encompasses a variety of lung disorders with varying degrees of inflammation or fibrosis, requiring a combination of clinical, imaging, and pathologic data for evaluation. Imaging is essential for the noninvasive diagnosis of the disease, as well as for assessing disease severity, monitoring its progression, and evaluating treatment response. However, traditional visual assessments of ILD with computed tomography (CT) suffer from reader variability. Automated quantitative CT offers a more objective approach by using computer-based analysis to consistently evaluate and measure ILD. Advancements in technology have significantly improved the accuracy and reliability of these measurements. Recently, interstitial lung abnormalities (ILAs), which represent potential preclinical ILD incidentally found on CT scans and are characterized by abnormalities in over 5% of any lung zone, have gained attention and clinical importance. The challenge lies in the accurate and consistent identification of ILA, given that its definition relies on a subjective threshold, making quantitative tools crucial for precise ILA evaluation. This review highlights the state of CT quantification of ILD and ILA, addressing clinical and research disparities while emphasizing how machine learning or deep learning in quantitative imaging can improve diagnosis and management by providing more accurate assessments, and finally, suggests the future directions of quantitative CT in this area.

Four Transformer-Based Deep Learning Classifiers Embedded with an Attention U-Net-Based Lung Segmenter and Layer-Wise Relevance Propagation-Based Heatmaps for COVID-19 X-ray Scans.

Background: Diagnosing lung diseases accurately is crucial for proper treatment. Convolutional neural networks (CNNs) have advanced medical image processing, but challenges remain in their accurate explainability and reliability. This study combines U-Net with attention and Vision Transformers (ViTs) to enhance lung disease segmentation and classification. We hypothesize that Attention U-Net will enhance segmentation accuracy and that ViTs will improve classification performance. The explainability methodologies will shed light on model decision-making processes, aiding in clinical acceptance. Methodology: A comparative approach was used to evaluate deep learning models for segmenting and classifying lung illnesses using chest X-rays. The Attention U-Net model is used for segmentation, and architectures consisting of four CNNs and four ViTs were investigated for classification. Methods like Gradient-weighted Class Activation Mapping plus plus (Grad-CAM++) and Layer-wise Relevance Propagation (LRP) provide explainability by identifying crucial areas influencing model decisions. Results: The results support the conclusion that ViTs are outstanding in identifying lung disorders. Attention U-Net obtained a Dice Coefficient of 98.54% and a Jaccard Index of 97.12%. ViTs outperformed CNNs in classification tasks by 9.26%, reaching an accuracy of 98.52% with MobileViT. An 8.3% increase in accuracy was seen while moving from raw data classification to segmented image classification. Techniques like Grad-CAM++ and LRP provided insights into the decision-making processes of the models. Conclusions: This study highlights the benefits of integrating Attention U-Net and ViTs for analyzing lung diseases, demonstrating their importance in clinical settings. Emphasizing explainability clarifies deep learning processes, enhancing confidence in AI solutions and perhaps enhancing clinical acceptance for improved healthcare results.

An Extensive Review on Lung Cancer Detection Models

The categorization and identification of lung disorders in medical imageries are made easier by recent advances in deep learning (DL). As a result, various studies using DL to identify lung illnesses were developed. This study aims to analyze different publications that have been contributed to in order to recognize lung cancer. This literature review examines the many methods for detecting lung cancer. It analyzes several segmentation models that have been used and reviews different research papers. It examines several feature extraction methods, such as those using texture-based and other features. The investigation then concentrates on several cancer detection strategies, including “DL models” and machine learning (ML) models. It is possible to examine and analyze the performance metrics. Finally, research gaps are presented to encourage additional investigation of lung detection models.

Pulmonary vascular disease in chronic lung diseases: cause or comorbidity?

To provide timely and relevant insights into the complex relationship between pulmonary vascular disease (PVD) and chronic lung disease (CLD), focusing on the causative and consequential dynamics between these conditions. There are shared pathogenic mechanisms between pulmonary arterial hypertension (PAH) and group 3 pulmonary hypertension, including altered expression of mediators and growth factors implicated in both conditions. Factors such as hypoxia, hypoxemia, and hypercapnia also contribute to pulmonary vascular remodelling and endothelial dysfunction. However, the role of hypoxia as the sole driver of pulmonary hypertension in CLD is being reconsidered, particularly in chronic obstructive pulmonary disease (COPD), with evidence suggesting a potential role for cigarette smoke products in initiating pulmonary vascular impairment. On the other hand, interstitial lung disease (ILD) encompasses a group of heterogeneous lung disorders characterized by inflammation and fibrosis of the interstitium, leading to impaired gas exchange and progressive respiratory decline, which could also play a role as a cause of pulmonary hypertension. Understanding the intricate interplay between the pulmonary vascular compartment and the parenchymal and airway compartments in respiratory disease is crucial for developing effective diagnostic and therapeutic strategies for patients with PVD and CLD, with implications for both clinical practice and research.

Interstitial lung disease: diagnosis and management principles

Interstitial lung disease (ILD) encompasses a diverse group of chronic, often progressive, parenchymal lung disorders with varying aetiologies. Advanced clinical practitioners (ACPs) increasingly play a crucial role in the multidisciplinary management of ILD patients across a variety of healthcare settings, enabling timely access to specialised expertise, co-ordinating multidisciplinary care and enhancing patient understanding. An awareness of the principles of diagnosis and management are key to support patient care, as ACPs will need to consider the type of ILD, and its trajectory and prognosis, with and without treatment. Most importantly, the views and health beliefs of the patient must take centre stage, with careful assessment and counselling required prior to commencing treatment. Multimorbidity is common and may impact on both symptoms and treatment tolerance. Considering the holistic needs of the patient is essential and a focus on quality of life should remain a priority. This review examines the nature of ILDs and summarises their treatment principles, with a specific focus on symptom management.

Lung ultrasound in respiratory therapy: a global reflective survey.

Lung ultrasound (LUS) is a non-invasive point of care diagnostic tool used to assess the presence and severity of various lung disorders for more than two decades. Within the healthcare professionals are the respiratory therapists (RTs) who play a vital role in managing ventilated and other patients requiring respiratory support, but the incorporation of LUS into their scope of practice has not been well highlighted. This international cross-sectional survey was specifically designed to evaluate the knowledge, attitude, and practice of RTs with respect to LUS. This observational cross-sectional study was conducted among RTs from different parts of the world using a questionnaire-based study tool. 514 RTs responded to all the questions and were considered for statistical analysis. Descriptive statistics, Analysis of variance, Fisher's exact, Chi-square, Bonferroni post-hoc analysis, and Binomial logistic regression analyses were performed to identify the significance of the data. From 22 countries, 514 RTs responded to the survey, with the major share from the middle eastern countries. Out of the 514 responders, 44.9% of the responders were in the age group of 23-30 years; 67.1% were bachelor's degree holders; and 40.9% of participants had more than 10 years of experience. The knowledge-based questions revealed that RTs with higher experience and academic qualification provided more positive responses. While in the attitude-related domain it is observed that standardized training in LUS helps them to enhance the current practice and to add LUS to the academic curriculum of respiratory therapy schools; however there remains barriers to practice LUS based on their responses. The practice-based questions revealed that RTs expect some additional seminars/workshops/webinars to be done on LUS frequently. More than half of the participants are found to be knowledgeable with a positive attitude and working towards the inclusion of LUS in respiratory therapy profession. It is concluded that respiratory therapists have a positive attribute towards the inclusion of lung ultrasound in their clinical practice. Providing more structured training for professional respiratory therapists and including lung ultrasound modules in the respiratory therapy school curriculum may facilitate mastering their diagnostic skills, thereby expanding the scope of practice.

Advanced Delivery Strategies of Nintedanib for Lung Disorders and Beyond: A Comprehensive Review.

Nintedanib, a primary treatment for lung fibrosis, has gathered substantial attention due to its multifaceted potential. A tyrosine kinase inhibitor, nintedanib, inhibits multiple signalling receptors, including endothelial growth factor receptor (VEGFR), platelet-derived growth factor receptor (PDGFR), and fibroblast growth factor receptor (FGFR) and ultimately inhibits fibroblast proliferation and differentiation. Therefore, nintedanib has been studied widely for other ailments like cancers and hepatic fibrosis, apart from lung disorders. Commercially, nintedanib is available as soft gelatin capsules for treatment against idiopathic pulmonary fibrosis. Since it has very low oral bioavailability (4.7%), high doses of a drug, such as 100-150mg, are administered, which can cause problems of gastrointestinal irritation and hepatotoxicity. The article begins with exploring the mechanism of action of nintedanib, elucidating its complex interactions within cellular pathways that govern fibrotic processes. It also emphasizes the pharmacokinetics of nintedanib, clinical trial insights, and the limitations of conventional formulations. The article mainly focuses on the emerging landscape of nanoparticle-based carriers such as hybrid liposome-exosome, nano liquid crystals, discoidal polymeric, and magnetic systems, offering promising avenues to optimize drug targeting, address its efficacy issues and minimise adverse effects. However, none of these delivery systems are commercialised, and further research is required to ensure safety and effectiveness in clinical settings. Yet, as research progresses, these advanced delivery systems promise to revolutionise the treatment landscape for various fibrotic disorders and cancers, potentially improving patient outcomes and quality of life.

Comparative study between corticosteroids and colchicine effect on interleukin-6 and Krebs-von-den Lungen-6 in treatment of post covid pulmonary fibrosis

Background Interstitial lung diseases (ILDs) involve a diverse range of over 200 lung disorders, constituting a vast group of diseases. This study aimed to compare Corticosteroids and Colchicine in the treatment of post-covid pulmonary fibrosis (PC19-PF) through the assessment of effects of these drugs on some biomarkers as interleukin-6 (IL6) and Krebs-von-den Lungen-6 (KL6). Method This randomized controlled study was carried out 30 patients with PC19-PF and 10 healthy volunteers. All patients were randomly classified into four equal groups: group 1: received corticosteroids in the dose of 0.5 mg/kg/day. Group 2: received colchicine in the dose of 500 µg/12 h for 1 month. Group 3: received both corticosteroids in the dose of 0.5 mg/kg/day and colchicine in the dose of 500µg/12 h for 1 month. Group 4: healthy volunteers. Results A 45 pg/ml was the cut off value used to demonstrate the effect of treatment on serum IL6 in studied PC19-PF patients, with a sensitivity of 77% and 70% specificity. 180 U/ml was the cut off value used to demonstrate the effect of treatment on serum KL6, serum KL6 levels provide sensitivity of 67% and 70% specificity for the effect of treatment on serum KL6. The decrease in serum IL6 and KL6 after treatment was in group 3, followed by group 1 then group 2. Conclusion Prednisolone and colchicine were better than prednisolone alone in treatment of PC19-PF and colchicine alone showed slight improvement.

Deficiency of Secreted Phosphoprotein 1 Alleviates Hyperoxia-induced Bronchopulmonary Dysplasia in Neonatal Mice.

Bronchopulmonary dysplasia (BPD) is a common chronic lung disorder characterized by impaired proximal airway and bronchoalveolar development in premature births. Secreted phosphoprotein 1 (SPP1) is involved in lung development and lung injury events, while its role was not explored in BPD. For establishing the in vivo models of BPD, a mouse model of hyperoxia-induced lung injury was generated by exposing neonatal mice to hyperoxia for 7days after birth. Alveolar myofibroblasts (AMYFs) were treated with hyperoxia to establish the in vitro models of BPD. Based on the scRNA-seq analysis of lungs of mice housed under normoxia or hyperoxia conditions, mouse macrophages and fibroblasts were main different cell clusters between the two groups, and differentially expressed genes in fibroblasts were screened. Further GO and KEGG enrichment analysis revealed that these differentially expressed genes were mainly enriched in the pathways related to cell proliferation, apoptosis as well as the PI3K-AKT and ERK/MAPK pathways. SPP1 was found up-regulated in the lung tissues of hyperoxia mice. We also demonstrated the up-regulation of SPP1 in the BPD patients, the mouse model of hyperoxia-induced lung injury, and hyperoxia-induced cells. SPP1 deficiency was revealed to reduce the hyperoxia-induced apoptosis, oxidative stress and inflammation and increase the viability of AMYFs. In the mouse model of hyperoxia induced lung injury, SPP1 deficiency was demonstrated to reverse the hyperoxia-induced alveolar growth disruption, oxidative stress and inflammation. Overall, SPP1 exacerbates BPD progression in vitro and in vivo by regulating oxidative stress and inflammatory response via the PI3K-AKT and ERK/MAPK pathways, which might provide novel therapeutic target for BPD therapy.

Vascular syk-ness: A new role for an old immunological favorite

Acute respiratory distress syndrome (ARDS) is a deadly clinical presentation in sepsis, COVID, and other lung disorders where vascular fluid leakage is a severe problem. Recent findings by Shadab et al in the JBC show that a well-known player in immune function, Syk, also regulates vascular leakage in response to sepsis. An existing FDA-approved inhibitor of Syk, fostamatinib, prevents the vascular leakage and improves survival in a mouse sepsis model, providing promise for ARDS treatment in the clinic.

Cell type-specific expression of angiotensin receptors in the human lung with implications for health, aging, and chronic disease.

The renin-angiotensin system is a highly characterized integrative pathway in mammalian homeostasis whose clinical spectrum has been expanded to lung disorders such as chronic obstructive pulmonary disease (COPD)-emphysema, idiopathic pulmonary fibrosis (IPF), and COVID pathogenesis. Despite this widespread interest, specific localization of this receptor family in the mammalian lung is limited, partially due to the imprecision of available antibody reagents. In this study, we establish the expression pattern of the two predominant angiotensin receptors in the human lung, AGTR1 and AGTR2, using complementary and comprehensive bulk and single-cell RNA-sequence datasets that are publicly available. We show these two receptors have distinct localization patterns and developmental trajectories in the human lung, pericytes for AGTR1 and a subtype of alveolar epithelial type 2 cells for AGTR2. In the context of disease, we further pinpoint AGTR2 localization to the COPD-associated subpopulation of alveolar epithelial type 2 (AT2B) and AGTR1 localization to fibroblasts, where their expression is upregulated in individuals with COPD, but not in individuals with IPF. Finally, we examine the genetic variation of the angiotensin receptors, finding AGTR2 associated with lung phenotype (i.e., cystic fibrosis) via rs1403543. Together, our findings provide a critical foundation for delineating this pathway's role in lung homeostasis and constructing rational approaches for targeting specific lung disorders.

3D mask based lung function monitoring system using machine learning for early identification of lung disorders

AbstractEarly identification of illness can aid in lowering the death rate related to lung illnesses. Asthma, Chronic Obstructive Pulmonary Disease (COPD), and bronchiectasis are all chronic respiratory illnesses that cause irritation and oedema of the airway due to increased mucus discharge. Monitoring the asthmatic patient's physiological state is vital to avoiding dangerous circumstances. This study offers a regular lung function monitoring system that employs Machine Learning (ML) approach to aids in the prompt detection of symptoms of illness and the prevention of significant epidemics of the lung condition. A collection of sensors are coupled to the microcontroller in a 3D mask created using 3D printing technology. When a person wearing a face mask breathes in and out, the sensor values are instantly retrieved. The sensor data is sent to the cloud via a Wi‐Fi module for additional evaluation, and categorisation is performed using genetic algorithms, Support Vector Machine (SVM), and Principal Component Analysis (PCA). The GA, SWM, and PCA algorithms identify lung sickness using data from sensors obtained from the 3D masks through the web interface. There were 250 participants in total, comprising persons from all ages, smoker and those who do not smoke as well as asthmatics. The classifiers are trained utilising a set of pretrained values obtained from freely accessible datasets. Furthermore, patients are alerted when physiological indicators deviate from normal and when favourable atmospheric circumstances change.

Designing a Novel Learning Approach for Infectious Lung Disease Prediction using Bi-Directional Recurrent Neural Network (BRNN)

The principal goal of this research is to use regular imaging to diagnose and classify several lung disorders, including pneumonia, COVID-19 and tuberculosis.A novel Bi-directional Recurrent Neural Network (BRNN) model is implemented in this work to categorize the disease according to the input samples given. With the help of the feature extractor, prediction model, and classification model, the suggested model performs feature analysis. The E2E model that has been suggested carries out knowledge acquisition using an efficient computational and memory approach. The benchmark dataset, made accessible online, was used to test the proposed model. When networks are trained to extract characteristics, the developments in deep learning techniques yield encouraging results and offer higher potential efficiency than biomedical applications. The main goal is to evaluate the significance of current methods and validate the BRNN model to address the concerns that are currently present. The suggested model offers better efficiency and performance for better detection. The results reveal that the model with a recurrent network works better than the current methods, with an accuracy of 71% for all diseases, whereas the traditional network models perform worse. The suggested model has less computational overhead and fewer training parameters. When compared to alternative methods, the model offers a superior trade-off.

COVID-19 та його наслідки для емоційно-вольової сфериосіб похилого і старечого віку

The presence of a variety of symptoms that last longer than the acute phase of COVID-19 is called post-COVID syndrome (PCS). Musculoskeletal, digestive (i.e., diarrhea), and neurological symptoms, including depression (according to the Zung scale), were most commonly observed in patients with PCS. Fatigue/muscle weakness, dyspnea, pain and discomfort, anxiety/depression, and impaired concentration were observed in more than 20% of patients. PCS is mainly characterized by musculoskeletal, lung, digestive and neurological disorders, including depression. PCS is independent of the severity of the acute illness and the humoral response. During and after infection with COVID-19, patients are at increased risk of developing depression and anxiety. Many people with COVID-19 are at risk of long-term impairment and disability, especially in critical cases. Symptoms after acute COVID-19 are highly variable and varied. Not only the stressors associated with the consequences of the infection of COVID-19, but also the measures taken against the pandemic can lead to the development of new mental illnesses or the worsening of existing mental health disorders. Depressive disorders affect physical, mental, and social functioning and increase the risk of premature death. People who survived COVID-19 had a high prevalence of acute psychiatric complications. Common neuropsychiatric sequelae following SARS-CoV-2 infection included fatigue, sleep problems, anxiety, depression, post-traumatic stress disorder, headache, raving, and cognitive impairment. Additional diverse manifestations of COVID-19 have been noted, such as delirium, peripheral neuropathy, dizziness, and mood disorders.

Self‐Learning e‐Skin Respirometer for Pulmonary Disease Detection

AbstractAmid the landscape of respiratory health, lung disorders stand out as the primary contributors to pulmonary intricacies and respiratory diseases. Timely precautions through accurate diagnosis hold the key to mitigating their impact. Nevertheless, the existing conventional methods of lungs monitoring exhibit limitations due to bulky instruments, intrusive techniques, manual data recording, and discomfort in continuous measurements. In this context, an unintrusive organic wearable piezoelectric electronic‐skin respirometer (eSR) exhibiting a high‐sensitivity (385 mV N−1), precise conversion factor (12 mL mV−1), high signal‐to‐noise ratio (58 dB), and a low limit of detection down to 100 mL is demonstrated, which is perfectly suitable to record diverse breathing signals. To empower the eSR with early diagnosis functionality, self‐learning capability is further added by integrating the respirometer with the machine learning algorithms. Among various tested algorithms, gradient boosting regression emerges as the most suitable, leveraging sequential model refinement to achieve an accuracy exceeding 95% in detection of chronic obstructive pulmonary diseases (COPD). From conception to validation, the approach not only provides an alternative pathway for tracking the progression of lung diseases but also has the capability to replace the conventional techniques, with the conformable AI‐empowered respirometer.

Anesthetic management of broncho- alveolar lavage in pulmonary alveolar proteinosis: A case report

: Pulmonary alveolar proteinosis (PAP) is a very rare, life threatening, noninflammatory, diffuse lung disorder characterized by dense accumulation of lipo-proteinaceous material within the alveoli causing hypoxemia, restrictive lung disease leading to respiratory failure. Whole lung lavage (WLL) is considered as the treatment of choice which includes infusing warm saline in the lungs and draining it out with the lipo-porteinaceous materials thereby clearing the obstruction. WLL requires good team co-ordination between the anaesthesiologists, pulmonologists and physiotherapists. The procedural course is challenging in many aspects like hypoxaemia and hemodynamic fluctuations. Post procedure short term mechanical ventilation is commonly required.: A 45 year old female, diagnosed case of PAP presented with dyspnea at rest with increasing oxygen requirement. After clinical examination and investigations, due to severity of the disease, broncho-alveolar lavage (BAL) of left lung was planned with subsequent right lung BAL after few days. During the procedure, desaturation upto 78% was noted. With meticulous corrective measures, saturation picked up and the procedure was completed uneventfully. Post-operatively patient was mechanically ventilated and extubated after 32 hours.: WLL is the treatment of choice for PAP which involves multidisciplinary approach with mulitple challenges. Pre-oxygenation, adequate lung isolation with left sided double lumen tube (DLT), one lung ventilation with positive end expiratory pressure (PEEP), vigilant intra-operative monitoring, cautious use of positional manoeuvres and recruitment manoeuvres with a good teamwork is the key for successful outcome.

THE EFFECT OF NUTRITIONAL STATUS AND TNF ALPHA TO LUNG FUNCTION ON MARBLE CRAFTSMAN

Marble craftsmen are potentially exposed to marble dust which contains about 60% silica resulting in marble craftsmen facing this hazard and possibly physiological lung disorders. In addition, this hazard will increase Tumor Necrosis Factor-a (TNF- α) levels. This study aims to analyze the effect of working period, nutritional status, and TNF-α on physiological lung disorders. study is a quantitative study with observational research type and cross sectional research design In this study, the data collection techniques used were: (1) measurement of personal river stone dust levels using a personal dust sampler of CCZG2A type; (2) measurement of serum TNF-α levels using the ELISA technique, (3) examination of lung function with MIR spirolab III; (4) collection of data on respiratory complaints using the ATS standard questionnaire as well as to obtain data on age, tenure, length of work and smoking habits; (5) measurement of nutritional status by weighing body weight and measuring height. The data obtained were then presented in the form of tables, narratives, analyzed using statistical programs. The subjects of this study were all marble craftsmen who worked in the marble household industry in Tulungagung Regency as many as 30 people with the number of samples taken as many as 26 craftsmen. The average value of %FVC in obese respondents was lower than that of respondents with normal nutritional status. This indicates a decrease in lung function conditions in obese workers.

A multistage framework for respiratory disease detection and assessing severity in chest X-ray images

Chest Radiography is a non-invasive imaging modality for diagnosing and managing chronic lung disorders, encompassing conditions such as pneumonia, tuberculosis, and COVID-19. While it is crucial for disease localization and severity assessment, existing computer-aided diagnosis (CAD) systems primarily focus on classification tasks, often overlooking these aspects. Additionally, prevalent approaches rely on class activation or saliency maps, providing only a rough localization. This research endeavors to address these limitations by proposing a comprehensive multi-stage framework. Initially, the framework identifies relevant lung areas by filtering out extraneous regions. Subsequently, an advanced fuzzy-based ensemble approach is employed to categorize images into specific classes. In the final stage, the framework identifies infected areas and quantifies the extent of infection in COVID-19 cases, assigning severity scores ranging from 0 to 3 based on the infection’s severity. Specifically, COVID-19 images are classified into distinct severity levels, such as mild, moderate, severe, and critical, determined by the modified RALE scoring system. The study utilizes publicly available datasets, surpassing previous state-of-the-art works. Incorporating lung segmentation into the proposed ensemble-based classification approach enhances the overall classification process. This solution can be a valuable alternative for clinicians and radiologists, serving as a secondary reader for chest X-rays, reducing reporting turnaround times, aiding clinical decision-making, and alleviating the workload on hospital staff.

Thorium Alters Lung Surfactant Protein Expression in Alveolar Epithelial Cells: In Vitro and In Vivo Investigation.

Thorium-232 (Th), the most abundant naturally occurring nuclear fuel, has been identified as a sustainable source of energy. In view of its large-scale utilization and human evidence of lung disorders and carcinogenicity, it is imperative to understand the effect of Th exposure on lung cells. The present study investigated the effect of Th-dioxide (1-100 μg/mL, 24-48 h) on expression of surfactant proteins (SPs) (SP-A, SP-B, SP-C, and SP-D, which are essential to maintain lung's surface tension and host-defense) in human lung cells (WI26 and A549), representative of alveolar cell type-I and type-II, respectively. Results demonstrated the inhibitory effect of Th on transcriptional expression of SP-A, SP-B, and SP-C. However, Th promoted the mRNA expression of SP-D in A549 and reduced its expression in WI26. To a significant extent, the effect of Th on SPs was found to be in accordance with their protein levels. Moreover, Th exposure altered the extracellular release of SP-D/A from A549, which remained unaltered in WI26. Our results suggested the differential role of oxidative stress and ATM and HSP90 signaling in Th-induced alterations of SPs. These effects of Th were found to be consistent in lung tissues of mice exposed to Th aerosols, suggesting a potential role of SPs in Th-associated lung disorders.