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The American Journal of Physiology: Lung Cellular & Molecular Physiology is seeking papers on the topic of "Opioids and Respiratory Depression" in response to the opioid health crisis. The call for papers aims to address the dangerous side effect of respiratory depression caused by opioid overdose. The journal encourages researchers and healthcare professionals to submit articles and reviews related to breathing regulation and the detrimental consequences of opioids. The document is a list of references cited in an article discussing the prediction of opioid-induced respiratory depression using continuous capnography and oximetry, including studies on the effects of opioids on breathing, sleep-disordered breathing, risk factors, and the neurobiology of respiratory control. [Extracted from the article]

Purpose: Some teenagers with adolescent idiopathic scoliosis (AIS) display compromised lung function. However, the evidence regarding the relations between pulmonary impairments and various spinal deformity parameters in these patients remains unclear, which affects clinical management. This systematic review and meta-analysis aimed to summarize the associations between various lung function parameters and radiographic features in teenagers with AIS. Methods: A search of PubMed, Embase, PEDro, SPORTDiscus, CINAHL, Cochrane Library, and PsycINFO (from inception to March 14, 2022) without language restriction. Original studies reporting the associations between lung function and spinal deformity in patients with AIS were selected. Independent reviewers extracted data and evaluated the methodological quality of the included studies according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Pearson correlation and 95% confidence intervals were calculated using random-effects meta-analysis. Results: Twenty-seven studies involving 3162 participants were included. Limited-quality evidence supported that several spinal parameters were significantly related to lung function parameters (e.g., absolute value and percent of the predicted forced vital capacity (FVC; %FVC), forced expiratory volume in one second (FEV1; %FEV1), and total lung capacity (TLC; %TLC)) in AIS patients. Specifically, meta-analyses showed that main thoracic Cobb angles in the coronal plane were significantly and negatively related to FVC (r = − 0.245), %FVC (r = − 0.302), FEV1 (r = − 0.232), %FEV1 (r = − 0.348), FEV1/FVC ratio (r = − 0.166), TLC (r = − 0.302), %TLC (r = − 0.183), and percent predicted vital capacity (r = − 0.272) (p < 0.001). Similarly, thoracic apical vertebral rotation was negatively associated with %FVC (r = − 0.215) and %TLC (r = − 0.126) (p < 0.05). Conversely, thoracic kyphosis angles were positively related to %FVC (r = 0.180) and %FEV1 (r = 0.193) (p < 0.05). Conclusion: Larger thoracic Cobb angles, greater apical vertebral rotation angle, or hypokyphosis were significantly associated with greater pulmonary impairments in patients with AIS, although the evidence was limited. From a clinical perspective, the results highlight the importance of minimizing the three-dimensional spinal deformity in preserving lung function in these patients. More research is warranted to confirm these results. [ABSTRACT FROM AUTHOR]

This document is a letter to the editor of the British Journal of Neurosurgery commenting on a case report about intraventricular metastases from lung adenocarcinoma. The authors of the letter raise a few questions about the case report, specifically regarding the claim that no cases of intraventricular metastasis from lung adenocarcinoma have been reported in the literature. They point out a previous report from 1996 that described a case of intraventricular metastasis from primary pulmonary adenocarcinoma. The authors also question the completeness of the literature search conducted by the authors of the case report. They suggest that functional MRI could be used to differentiate intraventricular metastases from other conditions. The letter is written by Lihua Liu from the Department of Geriatrics at The Affiliated Nanchong Central Hospital of North Sichuan Medical College in China and Xueyun Deng from the Department of Neurosurgery at the same hospital. [Extracted from the article]

This document is a letter to the editor of the British Journal of Neurosurgery commenting on a previous article about intraventricular metastases from small cell carcinoma of the lung. The letter raises questions about the previous article's claims and presents a different case of intraventricular metastasis. It discusses possible mechanisms and challenges the assertion that small cell lung carcinoma usually metastasizes to basal cisterns and sulci rather than ventricles. The letter also mentions the use of functional MRI for differentiation and presents a new case of intraventricular metastasis. Overall, the document provides additional information and perspectives on the topic of intraventricular metastases from lung cancer. [Extracted from the article]

Membrane transport proteins are involved in the absorption, disposition, efficacy, and/or toxicity of many drugs. Numerous mechanisms (e.g., nuclear receptors, epigenetic gene regulation, microRNAs, alternative splicing, post‐translational modifications, and trafficking) regulate transport protein levels, localization, and function. Various factors associated with disease, medications, and dietary constituents, for example, may alter the regulation and activity of transport proteins in the intestine, liver, kidneys, brain, lungs, placenta, and other important sites, such as tumor tissue. This white paper reviews key mechanisms and regulatory factors that alter the function of clinically relevant transport proteins involved in drug disposition. Current considerations with in vitro and in vivo models that are used to investigate transporter regulation are discussed, including strengths, limitations, and the inherent challenges in predicting the impact of changes due to regulation of one transporter on compensatory pathways and overall drug disposition. In addition, translation and scaling of in vitro observations to in vivo outcomes are considered. The importance of incorporating altered transporter regulation in modeling and simulation approaches to predict the clinical impact on drug disposition is also discussed. Regulation of transporters is highly complex and, therefore, identification of knowledge gaps will aid in directing future research to expand our understanding of clinically relevant molecular mechanisms of transporter regulation. This information is critical to the development of tools and approaches to improve therapeutic outcomes by predicting more accurately the impact of regulation‐mediated changes in transporter function on drug disposition and response. [ABSTRACT FROM AUTHOR]

Introduction: There have been relatively few attempts to quantitatively assess if, and in which areas, the use of non-animal methods (NAMs) is increasing in biomedical research and importantly, how this compares to the use of live animals. Methods: We conducted a bibliometric analysis of the relative publication of papers reporting the use of NAMs-only compared to those reporting the use of animals, even if they also reported the use of NAMs, over the period 2003 to 2022 across seven research areas (breast cancer, lung disease, blood cancer, heart disease, neurodegenerative diseases, diabetes and toxicology) and five regions (USA, China, France, Germany, United Kingdom). Results: We found that the relative number of publications of research using NAMs-only has been higher than animal-based research for the last 20 years for all research areas and is growing. Research areas differed in their relative publication of NAMs-only based work, with breast cancer and lung disease having consistently the highest ratio of NAMs-only to animal-based publications and heart disease, diabetes and toxicology showing the greatest change over the time period. A key period of change was 2016--18. By 2022 the UK had the highest NAMs-only to animal-based research ratio than any other country for five of the seven research areas and China the lowest for six, accounting for publication rate. Tissue and in silico-based methods were the most common of all NAMs-only publications; lab-on-a-chip and stem cell models are increasing in their use but at much lower levels and rate of increase. Conclusion: We found that proportionately the reliance on animals in these research areas is decreasing, which will be encouraging to those that support the replacement of animal experiments. [ABSTRACT FROM AUTHOR]

Autologous cancer vaccines (ACV) are an emerging option for adjuvant cancer treatment in veterinary medicine. I The aim of this online cross-sectional, questionnaire-based survey by i Amie Wilson and co-workers I in the UK was to characterise current antimicrobial prescribing practices by equine veterinarians and to describe surveillance, audit processes and identification of antimicrobial resistance (AMR) i . There was a significant increase in the amount of I-lines (10.8 ± 8.7 vs. 15.28 ± 8.19), B-lines (3.2 ± 3.5 vs. 8.72 ± 4.86) and coalescent B-lines (0.04 ± 0.2 vs. 1.12 ± 1.45) after anaesthesia compared with before anaesthesia, and a significantly higher LUS score 2 h after anaesthesia (4.92 ± 8.40) than that before anaesthesia (0.9 ± 1.8). [Extracted from the article]

The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided diagnosis. However, in PET/CT (Positron Emission Tomography/Computed Tomography) lung images, the lesion shapes are complex, the edges are blurred, and the sample numbers are unbalanced. To solve these problems, this paper proposes a Multi-branch Cross-scale Interactive Feature fusion Transformer model (MCIF-Transformer Mask RCNN) for PET/CT lung tumor instance segmentation, The main innovative works of this paper are as follows: Firstly, the ResNet-Transformer backbone network is used to extract global feature and local feature in lung images. The pixel dependence relationship is established in local and non-local fields to improve the model perception ability. Secondly, the Cross-scale Interactive Feature Enhancement auxiliary network is designed to provide the shallow features to the deep features, and the cross-scale interactive feature enhancement module (CIFEM) is used to enhance the attention ability of the fine-grained features. Thirdly, the Cross-scale Interactive Feature fusion FPN network (CIF-FPN) is constructed to realize bidirectional interactive fusion between deep features and shallow features, and the low-level features are enhanced in deep semantic features. Finally, 4 ablation experiments, 3 comparison experiments of detection, 3 comparison experiments of segmentation and 6 comparison experiments with two-stage and single-stage instance segmentation networks are done on PET/CT lung medical image datasets. The results showed that APdet, APseg, ARdet and ARseg indexes are improved by 5.5%, 5.15%, 3.11% and 6.79% compared with Mask RCNN (resnet50). Based on the above research, the precise detection and segmentation of the lesion region are realized in this paper. This method has positive significance for the detection of lung tumors. [ABSTRACT FROM AUTHOR]

Objective. The percutaneous puncture lung mass biopsy procedure, which relies on preoperative CT (Computed Tomography) images, is considered the gold standard for determining the benign or malignant nature of lung masses. However, the traditional lung puncture procedure has several issues, including long operation times, a high probability of complications, and high exposure to CT radiation for the patient, as it relies heavily on the surgeon's clinical experience. Approach. To address these problems, a multi-constrained objective optimization model based on clinical criteria for the percutaneous puncture lung mass biopsy procedure has been proposed. Additionally, based on fuzzy optimization, a multidimensional spatial Pareto front algorithm has been developed for optimal path selection. The algorithm finds optimal paths, which are displayed on 3D images, and provides reference points for clinicians' surgical path planning. Main results. To evaluate the algorithm's performance, 25 data sets collected from the Second People's Hospital of Zigong were used for prospective and retrospective experiments. The results demonstrate that 92% of the optimal paths generated by the algorithm meet the clinicians' surgical needs. Significance. The algorithm proposed in this paper is innovative in the selection of mass target point, the integration of constraints based on clinical standards, and the utilization of multi-objective optimization algorithm. Comparison experiments have validated the better performance of the proposed algorithm. From a clinical standpoint, the algorithm proposed in this paper has a higher clinical feasibility of the proposed pathway than related studies, which reduces the dependency of the physician's expertise and clinical experience on pathway planning during the percutaneous puncture lung mass biopsy procedure. [ABSTRACT FROM AUTHOR]

Introduction: Uniportal video-assisted thoracoscopic surgery (uVATS) is becoming popular for major lung resections, even for more complex procedures. The technique initially described for minor procedures seems more difficult to reproduce and has a longer learning curve. This review aims to describe the evolution from multiportal to uVATS and to explore its feasibility and reproducibility by identifying its drawbacks and limitations. Methods: Research from PubMed was obtained with the terms [uniportal] AND [surgery] OR [single-port] AND [thoracic surgery] OR [VATS]. Papers concerning pediatric cases and non-English papers were excluded. Individual case reports were also excluded. Discussion: uVATS seems to be widely adopted and performed for minor procedures. The applicability of uVATS for different indications is discussed, even though practically all thoracic surgical interventions can be performed through a single incision. Conclusions: The transition from conventional three-port VATS to uVATS is described in this paper. An increasing number of thoracic surgeons worldwide have adopted this approach, even for major complex anatomical lung resections. Regarding the performance of minor thoracic interventions, we believe this technique is easily reproducible with a short learning curve because the instruments do not cross each other, and intraoperative movements remain intuitive. It is therefore a feasible, safe, and efficacious technique. For these reasons, we believe uVATS should be offered to all patients undergoing minor thoracoscopic procedures. [ABSTRACT FROM AUTHOR]

Ground-glass nodules (GGN) are the main manifestation of early lung cancer, and accurate and efficient identification of ground-glass pulmonary nodules is of great significance for the treatment of lung diseases. In response to the problem of traditional machine learning requiring manual feature extraction, and most deep learning models applied to 2D image classification, this paper proposes a Transformer-based recognition model for ground-glass nodules from the view of global 3D asymmetry feature representation. Firstly, a 3D convolutional neural network is used as the backbone to extract the features of the three-dimensional CT-image block of pulmonary nodules automatically; secondly, positional encoding information is added to the extracted feature map and input into the Transformer encoder layer for further extraction of global 3D asymmetry features, which can preserve more spatial information and obtain higher-order asymmetry feature representation; finally, the extracted asymmetry features are entered into a support vector machine or ELM-KNN model to further improve the recognition ability of the model. The experimental results show that the recognition accuracy of the proposed method reaches 95.89%, which is 4.79, 2.05, 4.11, and 2.74 percentage points higher than the common deep learning models of AlexNet, DenseNet121, GoogLeNet, and VGG19, respectively; compared with the latest models proposed in the field of pulmonary nodule classification, the accuracy has been improved by 2.05, 2.05, and 0.68 percentage points, respectively, which can effectively improve the recognition accuracy of ground-glass nodules. [ABSTRACT FROM AUTHOR]

We developed a new interactive web-based teaching material to improve lung auscultation skills. Our objective was to investigate the effectiveness of the web-based teaching material on nurses with less than one-year work experience, using a prospective, open-label, stratified block randomized controlled trial. Of the 69 participants, 23, 22, and 24 participants were assigned to the web-based, paper-based, and control (with no intervention) groups, respectively. Using a simulator, a discrimination test on seven lung sounds, such as “normal,” “wheeze,” “rhonchi,” “coarse crackles,” “fine crackles,” “left lung diminish,” and “right lung absent,” was conducted. Next, a post-test was conducted after one-week of training. Answers with formal names were considered “correct”; those with common names, misspellings, and without left and right parts were considered “insufficient”; and wrong answers were considered “incorrect.” The control group showed no significant difference between the pre-test and post-test for any lung sounds. The paperbased group showed significant improvement in performance for “wheeze” (p=0.004) and “coarse crackles” (p=0.035). The web-based group showed a significant improvement in performance for “fine crackles” (p=0.026). The number of correct answers in the post-test was higher in the paper- and web-based groups than the control group (p=0.023). The web-based teaching materials that we had developed effectively improved the ability of new graduate nurses to auscultate lung sounds. Additionally, the results suggest that the combined use of web- and paper-based teaching materials may be more effective since the sounds that each method enhanced their ability to auscultate different lung sounds. [ABSTRACT FROM AUTHOR]

Background and Aims: Journal impact factor has historically been taken as a proxy for quality. However, this is open to significant manipulation and bias. There is currently not widely adopted, robust journal and paper ranking metric which is focused solely on risk of bias. Methods: Risk of bias data was extracted from all Cochrane database systematic reviews in Child Health, Lungs, and Airways for the years 2017–2019. A novel paper quality score, the Clinical Research Bias Index (CRBI), was applied. Individual paper data were pooled for each journal. A comparison was made to journal impact factors, individual paper citations, reads, and altmetric scores. Results: 927 papers were analyzed for risk of bias. 119 (12·8%) scored a CRBI of 100%, with a mean score of 70%. A journal's overall CRBI risk of bias score was poorly correlated with impact factor (r 0.25). Citations (r 0.02), and reads (r 0.01) of individual papers showed very little association with the paper's risk of bias. Likewise, reads were not correlated with citations (r 0.03). H‐index and Altmetric scores were similarly poorly correlated with CRBI. Conclusion: The novel research quality tool CRBI demonstrates the poor correlation between journal impact factor, citations, and risk of bias. Journal and paper ranking metrics should ensure that they are fit for purpose, and enable the dissemination of high‐quality research for the benefit of patients. We propose the CRBI as a potential solution which is resistant to manipulation and will reward the creation and publication of bias‐free research. [ABSTRACT FROM AUTHOR]

aims to perform a systematic assessment of the influence of physical exercise on asthma patients and discuss the intervention effects of different exercises on the lung function FEV1 (%pred) and quality of life among asthma patients so as to lay a scientific foundation for improving asthma symptoms. Methods: Both Chinese and English databases were retrieved, including PubMed, Web of Science, Embase, The Cochrane Library, CBM, CNKI, Wan Fang Data, and VIP, whose retrieval period started from the founding date of each database to 1st, November 2021. Randomized controlled trials (RCT) studying the symptom indicators of asthma patients were collected. Those collected papers were screened according to the Inclusion Criteria and Exclusion Criteria. Then, methodological quality assessments were conducted on the included papers, and combined effect sizes were analyzed by using software ReMan 5.3.5. Results: The meta analysis showed that physical exercise could significantly improve lung function FEV1 (%pred) and quality of life score. Trails containing breathing exercise are the main source of heterogeneity, and the subgroup of breathing exercise may have better performance than the subgroup of aerobic exercise in improving FEV1 (%pred). Conclusion: Physical exercise can significantly improve the symptoms and quality of life of asthma patients. Except the breathing exercise that showed heterogeneity, the subgroup of aerobic exercise could improve the capacity of FEV1 (%pred) more effectively, which led to a significant difference in the influence of quality of life. However, with regard to the gymnastic exercise including breathing exercise, there are limited same intervention methods and insufficient same outcome indicators. Therefore, more precise and high-quality researches are needed to make deeper verification in the future. [ABSTRACT FROM AUTHOR]

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Accurate segmentation of the pulmonary airway tree is crucial for diagnosing lung diseases. To tackle the issues of low segmentation accuracy and frequent leaks in existing methods, this paper proposes a precise segmentation method using quasi-spherical region-constrained wavefront propagation with tracheal wall gap sealing. Based on the characteristic that the surface formed by seed points approximates the airway cross-section, the width of the unsegmented airway is calculated, determining the initial quasi-spherical constraint region. Using the wavefront propagation method, seed points are continuously propagated and segmented along the tracheal wall within the quasi-spherical constraint region, thus overcoming the need to determine complex segmentation directions. To seal tracheal wall gaps, a morphological closing operation is utilized to extract the characteristics of small holes and locate low-brightness tracheal wall gaps. By filling the CT values at these gaps, the method seals the tracheal wall gaps. Extensive experiments on the EXACT09 dataset demonstrate that our algorithm ranks third in segmentation completeness. Moreover, its performance in preventing airway leaks is significantly better than the top-two algorithms, effectively preventing large-scale leak-induced spread. [ABSTRACT FROM AUTHOR]

Globally, lung cancer is the primary cause of cancer-related mortality. Higher chance of survival depends on the early diagnosis of lung nodules. Manual lung cancer screenings depends on the human factor. The variability in size, texture, and shape of lung nodules may pose a challenge for developing accurate automatic detection systems. This article proposes an ensemble approach to tackle the challenge of lung nodule detection. The goal was to improve prediction accuracy by exploring the performance of multiple transfer learning models instead of relying solely on deep learning models. An extensive dataset of CT scans was gathered to train the built deep learning models. This research paper is focused on the Convolutional Neural Networks' (CNNs') ability to automatically learn and adapt to discernible features in the lung images which is particularly beneficial for accurate classification, aiding in identifying true and false labels, and ultimately enhancing lung cancer diagnostic accuracy. This paper provides a comparative analysis of the performance of CNN, VGG-16, and VGG-19. Notably, the built transfer learning model VGG-16 achieved a remarkable accuracy of 95%, surpassing the baseline method. [ABSTRACT FROM AUTHOR]

Lung is the most important gas exchange organ of human, and the smooth airway is the basis of lung function. The condition of the trachea is associated with a variety of diseases. In this paper several methods of tracheal simulation based on CT-based data since 2003 are reviewed. Reasonable algorithms and image processing methods are important development directions for airway scanning reconstruction. The development of airway reconstruction needs to be closely integrated with mathematical modelling to improve the accuracy and precision of reconstruction. [ABSTRACT FROM AUTHOR]

COVID-19 is a highly contagious epidemic, and detection in the incipient phase is essential to curb the expansion of the disease. Chest Xrays are used in detecting COVID-19 infection. Lung's images and CT -Scan photos are available for coronavirus analysis. This paper is composed of deep learning techniques and methods used to detect COVID-19 contamination in the lung images. The methods employed and collected datasets used for testing metrics are summed up. The Analytical metrics utilized by the methods which are completely comparable. Through this work, we have taken a perspective on COVID-19 affected chest x-ray scanners and healthy patients. After sorting and pre-processing the images and implementing the data addition, we applied deep-learning-based CNN models to compare their performance with other models. The aim is to provide a helping hand to the most distressed medical professionals who are analyzing images with two eyes, detect COVID-19. According to this analysis we provide a proposed methodology that uses deep learning, dropout technique with python language on Google Colab platform for reduces over-fitting by this deep learning technology. During the testing phase, I got 98.1% accuracy by increasing convolution layer and dropout layer. Our proposed methodology gives better accuracy than other compared models. The primary goal of this paper is to present research on medical image processing and define and implement the proposed CNN model. [ABSTRACT FROM AUTHOR]

Lung disease is one of the most harmful diseases in traditional days and is the same nowadays. Early detection is one of the most crucial ways to prevent a human from developing these types of diseases. Many researchers are involved in finding various techniques for predicting the accuracy of the diseases. On the basis of the machine learning algorithm, it was not possible to predict the better accuracy when compared to the deep learning technique; this work has proposed enhanced artificial neural network approaches for the accuracy of lung diseases. Here, the discrete Fourier transform and the Burg auto-regression techniques are used for extracting the computed tomography (CT) scan images, and feature reduction takes place by using principle component analysis (PCA). This proposed work has used the 120 subjective datasets from public landmarks with and without lung diseases. The given dataset is trained by using an enhanced artificial neural network (ANN). The preprocessing techniques are handled by using a Gaussian filter; thus, our proposed approach provides enhanced classification accuracy. Finally, our proposed method is compared with the existing machine learning approach based on its accuracy. [ABSTRACT FROM AUTHOR]

Lung ultrasound has great application value in the differential diagnosis of pulmonary exudative lesions. It has good sensitivity and specificity for the diagnosis of various pulmonary diseases in neonates and children. It is believed that it can replace chest CT examination. It is routinely used for the diagnosis of pulmonary diseases in emergency critical care medicine. However, the interpretation of the impact of ultrasound on the human lungs relies heavily on experienced physicians, which greatly restricts the interpretation efficiency of the impact of ultrasound. In order to improve the efficiency of monitoring and interpretation of the impact of ultrasound, this paper proposes an intelligent detection algorithm for human lung clinical ultrasound images based on recurrent neural network. Transfer learning is used to replace the fully connected layer of the VGG16 model and improve the loss function, so that the same the Euclidean distance between category images can be reduced, and the Euclidean distance between different categories of images can be increased, enhancing the resolution of the entire model, thereby achieving better image feature extraction results. The experimental results show that the algorithm proposed in this paper can surpass the doctor's level in the identification of various lung diseases. [ABSTRACT FROM AUTHOR]

In this paper, the Scale-Invariant Feature Transform (SIFT) and Fast Library for Approximate Nearest Neighbors (FLANN) based algorithm is used to detect the abnormalities in the National Lung Screening Trial (NLST) CT scans as the exact clinical nodule locations are not provided in the dataset. These identified nodules on NLST CT Scans are then annotated using LabelImg tool. This process consumes time and so furthermore, the automatic nodule detection, You Only Look Once version 4 (YOLOv4) object detection model is implemented. The YOLOv4 object detection model is provided with total of 4187 labelled images in form of training (70%), validation (20%), and test (10%) datasets. Our YOLOv4 model achieves precision of 95%, sensitivity of 81% and mean Average Precision (mAP) of 89.1%. [ABSTRACT FROM AUTHOR]

The in-depth combination and application of AI technology and medical imaging, especially high- definition CT imaging technology, make accurate diagnosis and treatment possible. Retrieving similar CT image(CI)s to an input one from the large-scale CI database of labeled diseases is helpful to realize a precise computer-aided diagnosis. In this paper, we take lung CI as an example and propose progressive content-based similarity retrieval(CBSR) method of the lung CIs based on a Weakly Supervised Similarity LearningNetwork (WSSLN) model. Two enabling techniques (i.e., the WSSLN model and the distance- based pruning scheme) are proposed to facilitate the CBSR processing of the large lung CIs. The main result of our paper is that, our approach is about 45% more effective than the state-of-the-art methods in terms of the mean average precision(mAP). Moreover, for the retrieval efficiency, the WSSLN-based CBSR method is about 150% more efficient than the sequential scan. [ABSTRACT FROM AUTHOR]

The lung is one of the most vital organs in the human body, and its condition is closely correlated with overall health. Electrical impedance tomography (EIT), as a biomedical imaging technique, often produces low-quality reconstructed images due to its inherent ill-posedness in solving the inverse problem. To address this issue, this paper proposes a soft-threshold region segmentation algorithm with a relaxation factor. This algorithm segments the reconstructed lung images into internal regions, edge regions, and background regions, resulting in clearer boundaries in the reconstructed images. This facilitates the intuitive identification of regions of interest by healthcare professionals. Additionally, this segmentation algorithm is suitably combined with a dimension-reduced Tikhonov regularization algorithm. By utilizing the joint capabilities of these algorithms, the partition points belonging to the background region can be excluded from the sought grayscale vector, thereby improving the ill-posedness of the image reconstruction process and enhancing the quality of image reconstruction. Finally, a 16-electrode human lung EIT simulation model is established for the thoracic region and verified through simulation. Experimental validation is conducted using a human lung tank simulation platform to further demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Gastroparesis is a gastric disease characterized by delayed gastric emptying. "Vomiting" is the main clinical manifestation of this disease, while "atrophy" indicates dysfunction of the stomach. Based on the theory of "atrophy, dyspnea and vomiting are ascribed to the upper part", originating from the Chapter of "Zhi Zhen Yao Da Lun" from the Suwen(Basic Questions) section this paper explored the pathogenesis and treatment idea of gastroparesis from the function of the lung. It is put forward that failure of lung qi in dispersion, so the lung qi cannot maintain the circulation of harmonizing qi and blood, leading to malnutrition of stomach and loss of stomach receiving food and drink function. Melancholy impairing lung, so lung cannot govern diffusion, leading to stomach governing the disfunction of dredging and descending. Impaired depurative descending of lung qi, so the large intestine and stomach are lost in the alternating operation of deficiency and excess, leading to stomach governing the disfunction of transportation and transformation. Therefore, the treatment approach is proposed to improve the stomach's function to receive food and drink, transport and transform and promote the recovery of gastric motility by replenishing and restoring lung qi, regulating emotions and catharsis and smoothing lung. Analyzing the effect of function of the lung on gastric motility is of great value for expanding the application of traditional Chinese medicine in gastrointestinal motility disorders, and digging new connotations of classic theories in modern clinical practice. [ABSTRACT FROM AUTHOR]

The article titled "[Retracted] MicroRNA-106a-5p promotes the proliferation, autophagy and migration of lung adenocarcinoma cells by targeting LKB1/AMPK" has been retracted by the journal Experimental & Therapeutic Medicine. Concerns were raised by a reader regarding the similarity of certain data in the article to data published in other articles by different authors at different research institutes. The editor of the journal decided to retract the paper due to the apparent prior publication of the data. The authors were asked for an explanation but did not respond. The journal apologizes for any inconvenience caused. [Extracted from the article]

The segmentation accuracy of the lung images is affected by the occlusion of the front background objects. To address this problem, we propose a full-scale lung image segmentation algorithm based on hybrid skip connection and attention mechanism (HAFS). The algorithm uses yolov8 as the underlying network and enhancement of multi-layer feature fusion by incorporating dense and sparse skip connections into the network structure, and increased weighting of important features through attention gates. Finally the proposed algorithm was applied to the lung datasets Montgomery County chest X-ray and Shenzhen chest X-ray. The experimental results show that the proposed algorithm improves the precision, recall, pixel accuracy, Dice, mIoU, mAP and GFLOPs metrics compared to the comparison algorithms, which proves the advancement and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper introduces a systematic method for segmenting the main trachea and bronchioles in lung computed tomography scans. It begins with a stack-based three-dimensional region growth algorithm to outline the main trachea, which is then refined using morphological techniques to improve accuracy. The segmentation of bronchioles is achieved through domain labeling, lung tissue segmentation, adaptive binarization, and inner product analysis. The main trachea and bronchioles are integrated using an operating room (OR) operation and a novel splicing algorithm to form a complete tracheal tree. The method's accuracy is validated against manual labeling, showing a Dice coefficient of about 0.99, on average, in lung parenchyma segmentation and a segmentation overlap with expert results ranging from 79.89% to 93.31% in lung trachea tree segmentation. This robust methodology is thoroughly tested and validated. [ABSTRACT FROM AUTHOR]

Integrating artificial intelligence (AI) into lung sound classification has markedly improved respiratory disease diagnosis by analysing intricate patterns within audio data. This study is driven by the widespread issue of lung diseases, which affect around 500 million people globally. Early detection of respiratory diseases is crucial for delivering timely and effective treatment. Our study consists of a comprehensive survey of lung sound classification methodologies, exploring the advancements made in leveraging AI to identify and classify respiratory diseases. This survey thoroughly investigates lung sound classification models, along with data augmentation, feature extraction, explainable techniques and support tools to improve systems for diagnosing respiratory conditions. Our goal is to provide meaningful insights for healthcare professionals, researchers and technologists who are dedicated to developing methodologies for the early detection of pulmonary diseases. The paper provides a summary of the current status of lung sound classification research, highlighting both advancements and challenges in the use of AI for more accurate and efficient diagnostic methods in respiratory healthcare. [ABSTRACT FROM AUTHOR]

During acute pancreatitis, intestinal permeability increases due to intestinal motility dysfunction, microcirculatory disorders, and ischemia-reperfusion injury, and disturbances in the intestinal flora make bacterial translocation easier, which consequently leads to local or systemic complications such as pancreatic and peripancreatic necrotic infections, acute lung injury, systemic inflammatory response syndrome, and multiple organ dysfunction syndrome. Therefore, adjusting intestinal ecosystem balance may be a promising approach to control local and systemic complications of acute pancreatitis. In this paper, we reviewed the causes and manifestations of intestinal flora disorders during acute pancreatitis and their complications, focused on the reduction of acute pancreatitis and its complications by adjusting the intestinal microbial balance, and innovatively proposed the treatment of acute pancreatitis and its complications by gut microbiota-derived extracellular vesicles. [ABSTRACT FROM AUTHOR]

Purpose: Studies have recently shown a correlation between the elemental components of coal dusts, including potassium aluminosilicates, and the lung response as a potential driver for lung diseases associated with inhalation of coal dust. Here we aimed to systematically review the evidence for the association between exposure to potassium bearing dusts and lung outcomes in human studies as well as adverse cellular response in animal/cell culture studies. Methods: A systematic search of the National Library of Medicine (PubMed), Embase and Web of Science was conducted in May 2024. Studies regarding exposure to potassium bearing dusts and aluminosilicates and respiratory consequence were included in the search. Articles were screened, with a secondary assessment for relevance and extraction of key information using a systematic review process. Results: We identified 64 studies, of which 35 were human studies, 22 involved animal experiments and 12 were in vitro studies with 5 papers having both in vivo and in vitro studies. Our review found some evidence that a range of potassium rich dusts increased incidence of respiratory symptoms and lung diseases, although the effect on radiological changes in the lung and pulmonary function loss was not consistent. The in vivo animal and in vitro studies consistently demonstrated an inflammatory response and cytotoxic effect after exposure to these dusts. Conclusion: Given complexity in the dust compositions and lack of direct evidence attributing these specific responses to potassium, we are not able to conclude the effect of potassium minerals in coal on development of respiratory diseases, warranting further research. [ABSTRACT FROM AUTHOR]

Pulmonary fibrosis is a fatal lung disease affecting approximately 5 million people worldwide, with a 5-year survival rate of less than 50%. Currently, the only available treatments are palliative care and lung transplantation, as there is no curative drug for this condition. The disease involves the excessive synthesis of the extracellular matrix (ECM) due to alveolar epithelial cell damage, leading to scarring and stiffening of the lung tissue and ultimately causing respiratory failure. Although multiple factors contribute to the disease, the exact causes remain unclear. The mechanical properties of lung tissue, including elasticity, viscoelasticity, and surface tension, are not only affected by fibrosis but also contribute to its progression. This paper reviews the alteration in these mechanical properties as pulmonary fibrosis progresses and how cells in the lung, including alveolar epithelial cells, fibroblasts, and macrophages, respond to these changes, contributing to disease exacerbation. Furthermore, it highlights the importance of developing advanced in vitro models, based on hydrogels and 3D bioprinting, which can accurately replicate the mechanical and structural properties of fibrotic lungs and are conducive to studying the effects of mechanical stimuli on cellular responses. This review aims to summarize the current understanding of the interaction between the progression of pulmonary fibrosis and the alterations in mechanical properties, which could aid in the development of novel therapeutic strategies for the disease. [ABSTRACT FROM AUTHOR]

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Lung cancer is the leading cause of cancer-related mortality worldwide, largely due to the limited efficacy of anticancer drugs, which is primarily attributed to insufficient doses reaching the lungs. Additionally, patients undergoing treatment experience severe systemic adverse effects due to the distribution of anticancer drugs to non-targeted sites. In light of these challenges, there has been a growing interest in pulmonary administration of drugs for the treatment of lung cancer. This route allows drugs to be delivered directly to the lungs, resulting in high local concentrations that can enhance antitumor efficacy while mitigating systemic toxic effects. However, pulmonary administration poses the challenge of overcoming the mechanical, chemical, and immunological defenses of the respiratory tract that prevent the inhaled drug from properly penetrating the lungs. To overcome these drawbacks, the use of nanoparticles in inhaler formulations may be a promising strategy. Nanoparticles can assist in minimizing drug clearance, increasing penetration into the lung epithelium, and enhancing cellular uptake. They can also facilitate increased drug stability, promote controlled drug release, and delivery to target sites, such as the tumor environment. Among them, chitosan-based nanoparticles demonstrate advantages over other polymeric nanocarriers due to their unique biological properties, including antitumor activity and mucoadhesive capacity. These properties have the potential to enhance the efficacy of the drug when administered via the pulmonary route. In view of the above, this paper provides an overview of the research conducted on the delivery of anticancer drug-loaded chitosan-based nanoparticles incorporated into inhaled drug delivery devices for the treatment of lung cancer. Furthermore, the article addresses the use of emerging technologies, such as siRNA (small interfering RNA), in the context of lung cancer therapy. Particularly, recent studies employing chitosan-based nanoparticles for siRNA delivery via the pulmonary route are described. [ABSTRACT FROM AUTHOR]

Chronic respiratory illnesses, such as chronic obstructive pulmonary disease and asthma, pose significant challenges to public health worldwide. Mounting evidence suggests a bidirectional relationship between respiratory health and oral health, with periodontal disease emerging as a potential risk factor for the development and exacerbation of respiratory conditions. This review paper explores the complex interplay between periodontal disease and chronic respiratory illness, highlighting the importance of oral health maintenance in promoting respiratory well‑being. By examining recent advancements in the prevention and diagnosis of periodontal disease, particularly in the context of chronic respiratory conditions, this paper aims to elucidate novel solutions and strategies for improving oral health outcomes and respiratory outcomes simultaneously. Through a comprehensive analysis of the literature and emerging research trends, this review sheds light on the potential benefits and challenges of integrating oral health into the management of chronic respiratory illnesses and identifies opportunities for future research and clinical practice. [ABSTRACT FROM AUTHOR]

Bronchiectasis is defined as a permanent dilation of the bronchi that can cause pulmonary ventilation dysfunction. CT examination is an important means of diagnosing bronchiectasis. It can also be used in severity scoring. Current studies on bronchiectasis have focused on high-resolution CT (HRCT), ignoring the more common low-dose CT (LDCT). Methodologically, existing studies have not adopted an authoritative standard to classify the severity of bronchiectasis. In effect, the accuracy of detection and classification needs to be improved for practical application. In this paper, the ACER image enhancement method, RDU-Net lung lobe segmentation method and HDC Mask R-CNN model were proposed to detect and classify bronchiectasis. Moreover, a Python-based system was developed: after inputing an LDCT image of a patient's lung, it can automatically perform a series of processing, then call on the trained deep learning model for detection and classification, and automatically obtain the patient's bronchiectasis final score according to the Reiff and BRICS scoring criteria. In this paper, the mapping relationship between original lung CT image data and bronchiectasis scoring system was established. The accuracy of the method proposed in this paper was 91.4%; the IOU, sensitivity and specificity were 88.8%, 88.6% and 85.4%, respectively; and the recognition speed of one picture was about 1 s. Compared to a human doctor, the system can process large amounts of data simultaneously, quickly and efficiently, with the same judgment accuracy as a human doctor. Doctors only need to judge the uncertain cases, which significantly reduces the burden of doctors and provides a useful reference for doctors to diagnose the disease. [ABSTRACT FROM AUTHOR]

This correction notice, published in Molecular Systems Biology, addresses errors in Figure 3B and Dataset EV5 of the original paper titled "Bacterial expression of a designed single-chain IL-10 prevents severe lung inflammation." The authors discovered mistakes in two mutations for MutSC2 in Figure 3B and have corrected them. Additionally, Dataset EV5 has been updated to remove an erroneously inserted molecule name. The errors do not impact the original paper's conclusions, and the authors apologize for the mistakes. Supplementary information and appendices are available for further reference. [Extracted from the article]

With advance in technology, rapidly growing medical treatment and healthcare which can cure or pre-detect the diagnosis. Lung segmentation (LS) is prerequisite step for lung image analysis to provide accurate lung image. Doctors usually detect diagnosis by checking X-ray which is very time consuming and tedious. Here, we demonstrate LS in using CXR images and evaluate which contents of the image influenced the most. Semantic segmentation (SS) was performed using a U-Net CNN architecture, and the classification using three CNN architectures. Segmentation with deep learning (DL) is having very similar accuracy as detecting diagnosis by doctors. Here, we demonstrate LS by using chest X-ray and segmentation was performed using U-net architecture. In this project we have connected this model which can easily separating Lung. The paper is detailed analysis and discussion of U-Net results and implementation of UNet in LS using X-ray. [ABSTRACT FROM AUTHOR]

Deep learning has emerged as a powerful tool in the medical imaging field that aims to improve accuracy and speed of diagnosis. It can automatically learn feature representations from images which are then used to classify them as normal or abnormal. Several studies have shown the potential of deep learning in detecting lung diseases with high accuracy, even outperforming human experts in some cases. Deep learning models can also be used to predict disease progression and treatment outcomes, and to guide personalized treatment plans. This paper presents a study of the major deep learning techniques applied to medical imaging, focusing on pulmonary medical images, datasets, and benchmarks. The techniques include classification, detection, and segmentation tasks for various lung diseases, such as tuberculosis, lung cancer, pulmonary nodule diseases, pneumonia, asthma, COVID-19 and interstitial lung disease. The paper also discusses the challenges and potential directions for the future application of deep learning techniques in detecting lung disease. Deep learning has been successful in several domains, including acoustics, images, and natural language processing, and has the potential to significantly improve disease screening and diagnosis in the medical field. [ABSTRACT FROM AUTHOR]

The theory of "lung governing skin and hair" has always occupied an important position in the clinical practice of traditional Chinese medicine. However, in western medicine, the close correlation between the lungs and the skin in health and disease has not been established. The difference between these two medical views has triggered an urgent need for the scientific interpretation and clinical application of the theory of "lung governing skin and hair" in western medicine. Therefore, the purpose of this paper is to discuss the application of "lung governing skin and hair" in chinese medicine and the correlation between the lung and the skin in western medicine from the connotation and history of " lung governing skin and hair" in Chinese medicine. In addition, we will discuss the common pathologic biomarkers of lung and skin diseases in western medicine and the co-morbidities between lung and skin, with the aim to provide new ideas for the clinical diagnosis and treatment of lung and skin diseases and modern research, as well as providing a direction for the integration of the theory of "lung governing skin and hair" into the clinical practice of western medicine. [ABSTRACT FROM AUTHOR]

Lung segmentation is an essential step in a computer‐aided diagnosis system for chest radiographs. The lung parenchyma is first segmented in pulmonary computer‐aided diagnosis systems to remove the interference of non‐lung regions while increasing the effectiveness of the subsequent work. Nevertheless, most medical image segmentation methods nowadays use U‐Net and its variants. These variant networks perform poorly in segmentation to detect smaller structures and cannot accurately segment boundary regions. A multi‐interaction feature fusion network model based on Kiu‐Net is presented in this paper to address this problem. Specifically, U‐Net and Ki‐Net are first utilized to extract high‐level and detailed features of chest images, respectively. Then, cross‐residual fusion modules are employed in the network encoding stage to obtain complementary features from these two networks. Second, the global information module is introduced to guarantee the segmented region's integrity. Finally, in the network decoding stage, the multi‐interaction module is presented, which allows to interact with multiple kinds of information, such as global contextual information, branching features, and fused features, to obtain more practical information. The performance of the proposed model was assessed on both the Montgomery County (MC) and Shenzhen datasets, demonstrating its superiority over existing methods according to the experimental results. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed a hybrid bio-inspired algorithm which takes the merits of whale optimization algorithm (WOA) and adaptive particle swarm optimization (APSO). The proposed algorithm is referred as the hybrid WOA_APSO algorithm. We utilize a convolutional neural network (CNN) for classification purposes. Extensive experiments are performed to evaluate the performance of the proposed model. Here, pre-processing and segmentation are performed on 120 lung CT images for obtaining the segmented tumored and non-tumored region nodule. The statistical, texture, geometrical and structural features are extracted from the processed image using different techniques. The optimized feature selection plays a crucial role in determining the accuracy of the classification algorithm. The novel variant of whale optimization algorithm and adaptive particle swarm optimization, hybrid bio-inspired WOA_APSO, is proposed for selecting optimized features. The feature selection grouping is applied by embedding linear discriminant analysis which helps in determining the reduced dimensions of subsets. Twofold performance comparisons are done. First, we compare the performance against the different classification techniques such as support vector machine, artificial neural network (ANN) and CNN. Second, the computational cost of the hybrid WOA_APSO is compared with the standard WOA and APSO algorithms. The experimental result reveals that the proposed algorithm is capable of automatic lung tumor detection and it outperforms the other state-of-the-art methods on standard quality measures such as accuracy (97.18%), sensitivity (97%) and specificity (98.66%). The results reported in this paper are encouraging; hence, these results will motivate other researchers to explore more in this direction. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) image reconstruction of tumors has been one of the most effective techniques for accurately visualizing tumor structures and treatment with high resolution, which requires a set of two-dimensional medical images such as CT slices. In this paper we propose a novel method based on generative adversarial networks (GANs) for 3D lung tumor reconstruction by CT images. The proposed method consists of three stages: lung segmentation, tumor segmentation and 3D lung tumor reconstruction. Lung and tumor segmentation are performed using snake optimization and Gustafson-Kessel (GK) clustering. In the 3D reconstruction part first, features are extracted using the pre-trained VGG model from the tumors that detected in 2D CT slices. Then, a sequence of extracted features is fed into an LSTM to output compressed features. Finally, the compressed feature is used as input for GAN, where the generator is responsible for high-level reconstructing the 3D image of the lung tumor. The main novelty of this paper is the use of GAN to reconstruct a 3D lung tumor model for the first time, to the best of our knowledge. Also, we used knowledge transfer to extract features from 2D images to speed up the training process. The results obtained from the proposed model on the LUNA dataset showed better results than state of the art. According to HD and ED metrics, the proposed method has the lowest values ​​of 3.02 and 1.06, respectively, as compared to those of other methods. The experimental results show that the proposed method performs better than previous similar methods and it is useful to help practitioners in the treatment process. [ABSTRACT FROM AUTHOR]

In the diagnosis and treatment of non-small cell lung cancer (NSCLC), the histological type may change from lung adenocarcinoma to lung squamous cell cancer or small cell lung cancer (SCLC). Pancreatic metastasis is extremely rare in advanced lung cancer, and pancreatitis characterized by lung cancer metastasis-induced acute pancreatitis (MIAP) is more rare. This paper reports in detail the clinical diagnosis and treatment of a female patient with lung adenocarcinoma who relapsed after radical surgery and progressed after multiple treatments. A second pathological biopsy revealed SCLC transformation, and the patient developed pancreatic metastasis and lung cancer MIAP during follow-up treatment. This paper mainly suggests that clinicians should pay attention to the possibility of pathological type transformation in the progression of advanced NSCLC, closely observe the dynamic changes of tumor markers and pay attention to the re-biopsy pathological analysis. In addition, it provides clinical experience and scientific reference for the discovery, diagnosis and treatment of transforming SCLC and lung cancer MIAP. [ABSTRACT FROM AUTHOR]