Search Results (1,448)

Mucociliary clearance, the ability of the respiratory tract to protect the integrity of the airways through the mechanical removal of potentially harmful substances, is of enormous importance during intensive care treatment. The present study aimed to evaluate the influence of clinically relevant inotropic agents on mucociliary clearance. The particle transport velocity (PTV) of isolated murine tracheae was measured as a surrogate for mucociliary clearance in the presence of dobutamine, epinephrine, and milrinone. Inhibitory substances were applied to elucidate the signal transduction cascades and the value and origin of calcium ions which provoke alterations in mucociliary clearance function. Dobutamine, epinephrine, and milrinone increased the PTV in a dose-dependent manner with half maximal effective concentrations of 75.7 nM, 87.0 nM, and 13.7 µM, respectively. After the depletion of intracellular calcium stores, no increase in PTV was observed after administering any of the three inotropic agents. While dobutamine and epinephrine activated β-adrenergic receptors, epinephrine used both the phospholipase C (PLC) and protein kinase A (PKA) pathway to promote the release of intracellular Ca²⁺. However, dobutamine primarily acted on the PKA pathway, having only a minor influence on the PLC pathway. The induced changes in PTV following milrinone administration required both the PKA and PLC pathway, although the PKA pathway was responsible for most of the induced changes. In conclusion, the common inotropic agents dobutamine, epinephrine, and milrinone increase murine PTV in a concentration-dependent manner and ultimately release Ca²⁺ from intracellular calcium stores, suggesting the function of changes in mucociliary clearance in the respiratory tract. Full article

Background/Objectives: 5DCT was first proposed in 2005 as a motion-compensated CT simulation approach for radiotherapy treatment planning to avoid sorting artifacts that arise in 4DCT when patients breathe irregularly. Since March 2019, 5DCT has been clinically implemented for routine use at our institution to leverage this technological advantage. The clinical workflow includes a quality assurance report that describes the output of primary workflow steps. This study reports on the challenges and quality of the clinical 5DCT workflow using these quality assurance reports. Methods: We evaluated all thoracic 5DCT simulation datasets consecutively acquired at our institution between March 2019 and December 2022 for thoracic radiotherapy treatment planning. The 5DCT datasets utilized motion models constructed from 25 fast-helical free-breathing computed tomography (FHFBCTs) with simultaneous respiratory bellows signal monitoring to reconstruct individual, user-specified breathing-phase images (termed 5DCT phase images) for internal target volume contouring. Each 5DCT dataset was accompanied by a structured quality assurance report composed of qualitative and quantitative measures of the breathing pattern, image quality, DIR quality, model fitting accuracy, and a validation process by which the original FHFBCT scans were regenerated with the 5DCT model. Measures of breathing irregularity, image quality, and DIR quality were retrospectively categorized on a grading scale from 1 (regular breathing and accurate registration/modeling) to 4 (irregular breathing and inaccurate registration/modeling). The validation process was graded according to the same scale, and this grade was termed the suitability-for-treatment-planning (STP) grade. We correlated the graded variables to the STP grade. In addition to the quality assurance reports, we reviewed the contour sessions to determine how often 5DCT phase images were used for treatment planning and delivery. Results: There were 169 5DCT simulation datasets available from 156 patients for analysis. The STP was moderately correlated with breathing irregularity, image quality, and DIR quality (Spearman coefficients: 0.26, 0.30, and 0.50, respectively). Multiple linear regression analysis demonstrated that STP was correlated with regular breathing patterns (p = 0.008), image quality (p < 0.001), and better DIR quality (p < 0.001). 5DCT datasets were used for treatment planning in 82% of cases, while in 12% of cases, a backup image process was used. In total, 6% of image datasets were not used for treatment planning due to factors unrelated to the 5DCT workflow quality. Conclusions: The strongest association with STP was with DIR quality grades, as indicated by both Spearman and multiple linear regression analysis, implying that improvements to DIR accuracy and evaluation may be the best route for further improvement to 5DCT. The high rate of 5DCT phase image use for treatment planning showed that the workflow was reliable, and this has encouraged us to continue to develop and improve the workflow steps. Full article

Phosphodiesterase 4 (PDE4) catalyzes cyclic adenosine monophosphate (cAMP) hydrolysis, playing a crucial role in the cAMP signaling pathway. cAMP is a secondary messenger involved in numerous physiological functions, such as inflammatory responses, immune responses, neural activity, learning, and memory. PDE4 inhibition is important for controlling anti-inflammatory and neuroprotective effects. In this review, we provide a comprehensive overview of the molecular functions and properties of human PDE4s. The study presents detailed sequence information for the PDE4 isoforms and the structural properties of the catalytic domain in members of the PDE4 family. We also review the inhibitory effects of the PDE4 inhibitors roflumilast and cilomilast related to respiratory diseases in PDE4. The crystal structures of PDE4 in complex with roflumilast and cilomilast are also analyzed. This review provides useful information for the future design of novel PDE4 inhibitors. Full article

Influenza A virus (IAV) is a widespread human respiratory pathogen that contributes significantly to morbidity and mortality worldwide. The adsorption of the virus into the cell surface is the earliest stage of its replication cycle. The key role of N-linked sialic acids (SIAs) as receptors for binding to IAV’s hemagglutinin (HA) has long been acknowledged. The molecular specificity of this interaction is a key factor in host range, pathogenicity, and transmissibility of various IAV subtypes. Along with this, a number of recent studies have introduced significant complexity into the picture of IAV adsorption and revealed a multitude of new molecules on host cell surfaces to serve as receptors and/or co-receptors for IAV attachment. For successful internalization of the adsorbed virus, downstream signal transduction is necessary to activate effector endocytosis mechanisms. In recent years, our understanding of the sophistication and variability of signal transduction pathways in the virus attachment site has significantly expanded, with the help of research techniques like fluorescence imaging of individual viruses in real-time, dominant-negative mutants, siRNA knockdowns, protein kinase selective inhibitors, phosphoproteome profiling, and others. These approaches deepen our knowledge of the molecules involved in the early stages of the IAV life cycle and also serve as the basis for the development of new effective antiviral drugs. In our review, we analyze recent publications on the mechanisms of IAV adsorption, newly discovered receptors for virus attachment, and signal transmission in the site of the adsorbed virion. Besides this, we consider new data on the development of selective inhibitors as antiviral drugs aimed at both viral and cellular factors of IAV adsorption. Full article

To facilitate the effective assessment of respiratory entropy during poultry breeding, a novel oxygen (O₂) and carbon dioxide (CO₂) sensor was developed based on the off−axis integrated cavity output spectroscopy technique, featuring effective absorption optical paths of 15.5 m and 8.5 m, respectively. The sensor employs integrated environmental control technology, substantially enhancing detection precision. To improve the instrument’s response speed, the miniaturization of the cavity and structural optimization were implemented, achieving a rapid response time of merely 6.22 s, addressing the stringent requirements for quick responsiveness in poultry respiration thermometry research. A signal processing model tailored for on−site applications was designed, boosting the system’s signal−to−noise ratio by 4.7 times under complex environmental noise conditions. Utilizing Allan variance analysis, the sensor’s detection limits for O₂ and CO₂ were ascertained to be 2.9 ppm and 7.4 ppb, respectively. A 24−h field application test conducted in Gongzhuling demonstrated that the sensor’s results align with the respiratory characteristics of poultry under normal physiological conditions, validating its extensive potential for application in respiratory analysis, environmental monitoring, and industrial sectors. Full article

Regional anesthesia has gained increasing attention in pediatric surgery as a valuable tool for managing perioperative pain and improving surgical outcomes. This narrative review highlights the numerous advantages of regional anesthesia in pediatric populations, including superior pain control, reduced reliance on systemic opioids, fewer anesthetic-related complications, and enhanced recovery profiles. Using ultrasound-guided techniques has further expanded the safety and precision of regional blocks in children. Regional anesthesia also addresses critical concerns about the potential neurotoxicity of general anesthetics in developing brains, offering a safer alternative or complement for specific procedures. Reducing systemic anesthetic and opioid exposure minimizes the risk of adverse effects such as respiratory depression, nausea, and sedation, which are particularly significant in medically fragile or younger patients. Furthermore, regional techniques contribute to faster recovery times, better preservation of neurophysiological monitoring signals during surgery, and attenuation of the stress response. The integration of adjuvants like clonidine, dexmedetomidine, and dexamethasone further enhances the efficacy and duration of regional blocks while improving safety profiles. Despite these benefits, implementing regional anesthesia in pediatric populations requires specialized expertise and an understanding of children’s unique anatomical and physiological differences. This review underscores the growing role of regional anesthesia in modern pediatric perioperative care. It highlights its potential to optimize outcomes, reduce complications, and address emerging concerns about the safety of general anesthesia in children undergoing surgery. Full article

This work presents an innovative method for detecting the respiratory patterns of subjects walking on a treadmill, by leveraging the capabilities of deep learning (DL) technologies and a dual-radar setup. The study aims to overcome the challenge of accurately capturing respiratory rates in subjects performing body movements, a scenario less addressed in prior studies. By employing two radars operating at $5.8$ GHz for motion mitigation, this study compares the efficacy of dual-radar configurations against a single-radar setup. The study employs DL algorithms based on convolutional autoencoders to mitigate the low-quality demodulated radar signals by reconstructing the respiratory signal. The models are trained with data from a single subject and data from 15 subjects, attaining average absolute errors of $0.29$ and $4.59$ Respiration Per Minute (RPM), respectively, allowing to conclude that the use of DL algorithms enhances the accuracy of respiratory signal detection when compared with arctangent demodulation, even in cases where radar data contain minimal information regarding vital signals. Full article

Respiratory motion serves as a major challenge during treatment of lung cancer patients using radiotherapy. In this work, an image-based method is presented to extract a respiratory signal directly from Cone Beam CT (CBCT) projections. A dense optical-flow method is used to acquire motion vectors between successive projections in each dataset, followed by the extraction of the dominant motion pattern by application of linear kernel Principal Component Analysis (PCA). The effectiveness of the method was tested on three patient datasets and the extracted breathing signal was compared to a ground-truth signal. The average phase shift was observed to be 1.936 ± 0.734 for patient 1, 1.185 ± 0.781 for patient 2 and 1.537 ± 0.93 for patient 3. Moreover, a 4D CBCT image was reconstructed, considering the respiratory signal extracted, using the proposed method, and compared to that reconstructed considering the ground-truth respiratory signal. Results showed that a minimal difference was found between the image reconstructed using the proposed method and the ground-truth in terms of clarity, motion artifacts and edge sharpness. Full article

Reduced respiratory sinus arrhythmia, quantified as expiration-triggered sinus arrhythmia (ETA) from simultaneous electrocardiogram and respiration recordings, is a strong long-term mortality predictor in myocardial infarction survivors. Here, we investigated whether ETA also predicts mortality risk in the general elderly population. ETA was quantified from 30-min electrocardiogram and respiration recordings in 1788 general population subjects aged ≥60 years, who were then followed for a median of 4.0 years (median age 72 years, 58% female). Four-year all-cause mortality was 4.6%. Abnormal ETA using a predefined cutoff (≤0.19 ms) was associated with a 4-year mortality of 6.9%, compared to 3.7% in the remaining participants (p = 0.0022). ETA remained a significant mortality predictor in multivariable Cox analysis, also considering a modified Framingham score incorporating sex, age, smoking, cholesterol, blood pressure, antihypertensive medication, family history, diabetes and clinical atherosclerosis (multivariable hazard ratio 1.81; 95% confidence interval 1.17–2.81; p = 0.008). Combined risk prediction by ETA (using an optimized cutoff of ≤0.86 ms) and the Framingham score stratified patients into a low-risk (both parameters normal), an intermediate-risk (one parameter abnormal) and a high-risk group (both parameters abnormal), with 4-year mortality rates of 1.9%, 4.4% and 10.1%, respectively. We conclude that in elderly general population subjects, ETA is a mortality risk predictor that complements classical clinical risk stratification. Full article

This study aimed to explore the therapeutic potential and mechanisms of Artemisia argyi essential oil (AAEO) in managing feline herpesvirus type 1 (FeHV-1) infections. FeHV-1, the causative agent of feline viral rhinotracheitis (FVR), leads to severe respiratory and systemic complications in cats. In this study, 35 cats were divided into blank, FeHV-1 infection, and AAEO treatment groups (high, medium, and low doses). In vivo experiments demonstrated that AAEO alleviated clinical symptoms, reduced tissue damage, and modulated immune responses. The AAEO-treated groups showed higher survival rates, stabilized body temperatures, and less severe weight loss compared to the FeHV-1 group. Histopathological analysis revealed improved integrity in nasal, tracheal, and bronchial tissues. Transcriptomic and proteomic analyses identified critical pathways, such as IL-17 signaling, influenced by AAEO treatment, highlighting its role in suppressing inflammation and protecting tissue integrity. In vitro assays revealed that AAEO has concentration-dependent cytotoxicity in feline kidney cells (F81) and provides protective effects when used as a pre-treatment. These findings suggest that AAEO enhances host immune defenses and mitigates FeHV-1-induced damage through immune modulation and tissue protection. Full article

Exposure to air pollution, especially fine particulate matter (PM2.5), is closely linked to various adverse health effects, particularly in the respiratory system. The present study was designed to investigate the lncRNA–mRNA interactions in PM2.5-induced lung cell injury using weighted gene co-expression network analysis (WGCNA). We downloaded the gene expression data of GSE138870 from the Gene Expression Omnibus (GEO) database and screened for differentially expressed lncRNAs and mRNAs. We constructed co-expression modules with WGCNA. Furthermore, functional enrichment analysis was also performed. We also constructed lncRNA–mRNA co-expression networks and lncRNA–mRNA-pathway networks to identify key regulatory relationships. The results revealed several modules significantly correlated with PM2.5-induced lung injury, such as the turquoise and blue modules. Genes within these modules were enriched in pathways related to signal transduction, metabolism, and cancer. Hub lncRNAs in the turquoise module, including LOC100129034 and CROCCP2, were found to be co-expressed with mRNAs involved in apoptosis and proliferation regulation. In the blue module, lnc-CLVS2-2 and GARS1-DT were connected to genes related to cell migration, invasion, and lung injury. These findings contribute novel perspectives to the molecular mechanisms involved in PM2.5-induced lung injury and suggest that WGCNA could be a valuable tool for predicting and understanding this disease process. Full article

Background/Objectives: Umckalin, a coumarin derivative abundantly present in the root extract of Pelargonium sidoides, is a key bioactive compound known for its antimicrobial, antiviral, antitubercular, and immunomodulatory properties. Its therapeutic potential has been extensively studied, particularly in the context of respiratory diseases. This study aimed to evaluate the potential of umckalin as a therapeutic agent for chronic inflammatory diseases and to elucidate its underlying mechanisms of action. Methods: Using lipopolysaccharide (LPS)-stimulated RAW 264.7 macrophages as an experimental model, we investigated the inhibitory effects of umckalin on inflammatory mediators and cytokine production. We measured levels of nitric oxide (NO), prostaglandin E₂ (PGE₂), tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and interleukin-1 beta (IL-1β), and assessed the expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2). Additionally, the regulation of MAPK signaling pathways, including JNK, p38 MAPK, and ERK, was analyzed. Results: The results demonstrated that umckalin significantly reduced the levels of NO, PGE₂, TNF-α, IL-6, and IL-1β in LPS-stimulated RAW 264.7 cells. Umckalin also suppressed the expression of iNOS and COX-2, leading to decreased NO and PGE₂ production. Furthermore, umckalin effectively regulated inflammatory responses by reducing the phosphorylation of MAPK signaling pathways, including JNK, p38 MAPK, and ERK. Conclusions: These findings indicate that umckalin inhibits the production of TNF-α, IL-6, IL-1β, and NO, while regulating MAPK signaling pathways, thereby suppressing the expression of iNOS and COX-2. This study highlights the potent anti-inflammatory effects of umckalin and suggests its potential as a promising candidate for the treatment of chronic inflammatory diseases. Full article

Background and Objectives: Persistent exposure to airborne fine dust (FD) particles contributing to air pollution has been linked to various human health issues, including respiratory inflammation, allergies, and skin diseases. We aimed to identify potential seaweed anti-inflammatory bioactive reagents and determine their effects on systemic inflammatory responses induced by FD particles. Materials and Methods: While exploring anti-inflammatory bioactive reagents, we purified compounds with potential anti-inflammatory effects from the seaweed extracts of Ecklonia cava, Ecklonia stolonifera, and Codium fragile. Structural analyses of the purified compounds siphonaxanthin (Sx), fucoxanthin (Fx), dieckol (Dk), and phlorofucofuroeckol-A (PFF-A) were performed using NMR and LC-MS/MS. Results: Notably, these compounds, especially PFF-A, showed significant protective effects against FD-induced inflammatory responses in RAW 264.7 cells without cytotoxicity. Further investigation of inflammatory-associated signaling demonstrated that PFF-A inhibited IL-1β expression by modulating the NF-κB/MAPK signal pathway in FD-induced RAW 264.7 cells. Additionally, gene profiling revealed the early activation of various signature genes involved in the production of inflammatory cytokines and chemokines using gene profiling. Treatment with PFF-A markedly reduced the expression levels of pro-inflammatory and apoptosis-related genes and even elevated the Bmp gene families. Conclusions: These results suggested that PFF-A is a potential natural therapeutic candidate for managing FD-induced inflammatory response. Full article

Background: Obstructive sleep apnea (OSA) is a prevalent yet underdiagnosed condition associated with a major healthcare burden. Current diagnostic tools, such as full-night polysomnography (PSG), pose a limited accessibility to diagnosis due to their elevated costs. Recent advances in Artificial Intelligence (AI), including Machine Learning (ML) and deep learning (DL) algorithms, offer novel potential tools for an accurate OSA screening and diagnosis. This systematic review evaluates articles employing AI-powered models for OSA screening and diagnosis in the last decade. Methods: A comprehensive electronic search was performed on PubMed/MEDLINE, Google Scholar, and SCOPUS databases. The included studies were original articles written in English, reporting the use of ML algorithms to diagnose and predict OSA in suspected patients. The last search was performed in June 2024. This systematic review is registered in PROSPERO (Registration ID: CRD42024563059). Results: Sixty-five articles, involving data from 109,046 patients, met the inclusion criteria. Due to the heterogeneity of the algorithms, outcomes were analyzed into six sections (anthropometric indexes, imaging, electrocardiographic signals, respiratory signals, and oximetry and miscellaneous signals). AI algorithms demonstrated significant improvements in OSA detection, with accuracy, sensitivity, and specificity often exceeding traditional tools. In particular, anthropometric indexes were most widely used, especially in logistic regression-powered algorithms. Conclusions: The application of AI algorithms to OSA diagnosis and screening has great potential to improve patient outcomes, increase early detection, and lessen the load on healthcare systems. However, rigorous validation and standardization efforts must be made to standardize datasets. Full article

Background: Italy, particularly the northern region of Lombardy, has experienced very high rates of COVID-19 cases and deaths. Several indicators, i.e., the number of new positive cases, deaths and hospitalizations, have been used to monitor virus spread, but all suffer from biases. The aim of this study was to evaluate an alternative data source from Emergency Medical Service (EMS) activities for COVID-19 monitoring. Methods: Calls to the emergency number (112) in Lombardy (years 2015–2022) were studied and their overlap with the COVID-19 pandemic, influenza and official mortality peaks were evaluated. Modeling it as a counting process, a specific cause contribution (i.e., COVID-19 symptoms, the “signal”) was identified and enucleated from all other contributions (the “background”), and the latter was subtracted from the total observed number of calls using statistical methods for excess event estimation. Results: A total of 6,094,502 records were analyzed and filtered for respiratory and cardiological symptoms to identify potential COVID-19 patients, yielding 742,852 relevant records. Results show that EMS data mirrored the time series of cases or deaths in Lombardy, with good agreement also being found with seasonal flu outbreaks. Conclusions: This novel approach, combined with a machine learning predictive approach, could be a powerful public health tool to signal the start of disease outbreaks and monitor the spread of infectious diseases. Full article