Engineering Dynamic 3D Models of Lung.

The lung parenchyma-consisting of gas-filled alveoli, vasculature, and connective tissue-is the site for gas exchange in the lung and plays a critical role in a number of chronic lung diseases. In vitro models of lung parenchyma can, therefore, provide valuable platforms for the study of lung biology in health and disease. Yet modeling such a complex tissue requires integrating multiple components, including biochemical cues from the extracellular environment, geometrically defined multicellular interactions, and dynamic mechanical inputs such as the cyclic stretch of breathing. In this chapter, we provide an overview of the broad spectrum of model systems that have been developed to recapitulate one or more features of lung parenchyma, and some of the scientific advances generated by those models. We discuss the use of both synthetic and naturally derived hydrogel materials, precision-cut lung slices, organoids, and lung-on-a-chip devices, with perspectives on the strengths, weaknesses, and potential future directions of these engineered systems.

B-line Elastography Measurement of Lung Parenchymal Elasticity.

This paper proposes a method to determine the elasticity of the lung parenchyma from the B-line Doppler signal observed using continuous shear wave elastography, which uses a small vibrator placed on the tissue surface to propagate continuous shear waves with a vibration frequency of approximately 100 Hz. Since the B-line is generated by multiple reflections in fluid-storing alveoli near the lung surface, the ultrasonic multiple-reflection signal from the B-line is affected by the Doppler shift due to shear waves propagating in the lung parenchyma. When multiple B-lines are observed, the propagation velocity can be estimated by measuring the difference in propagation time between the B-lines. Therefore, continuous shear wave elastography can be used to determine the elasticity of the lung parenchyma by measuring the phase difference of shear wave between the B-lines. In this study, three elastic sponges (soft, medium, and hard) with embedded glass beads were used to simulate fluid-storing alveoli. Shear wave velocity measured using the proposed method was compared with that calculated using Young's modulus obtained from compression measurement. Using the proposed method, the measured shear wave velocities (mean ± S.D.) were 3.78 ± 0.23, 4.24 ± 0.12, and 5.06 ± 0.05 m/s for soft, medium, and hard sponges, respectively, which deviated by a maximum of 5.37% from the values calculated using the measured Young's moduli. The shear wave velocities of the sponge phantom were in a velocity range similar to the mean shear wave velocities of healthy and diseased lungs reported by magnetic resonance elastography (3.25 and 4.54 m/s, respectively). B-line elastography may enable emergency diagnoses of acute lung disease using portable ultrasonic echo devices.

Intranasal Nanoparticle Vaccination Elicits a Persistent, Polyfunctional CD4 T Cell Response in the Murine Lung Specific for a Highly Conserved Influenza Virus Antigen That Is Sufficient To Mediate Protection from Influenza Virus Challenge.

Lung-localized CD4 T cells play a critical role in the control of influenza virus infection and can provide broadly protective immunity. However, current influenza vaccination strategies primarily target influenza hemagglutinin (HA) and are administered peripherally to induce neutralizing antibodies. We have used an intranasal vaccination strategy targeting the highly conserved influenza nucleoprotein (NP) to elicit broadly protective lung-localized CD4 T cell responses. The vaccine platform consists of a self-assembling nanolipoprotein particle (NLP) linked to NP with an adjuvant. We have evaluated the functionality, in vivo localization, and persistence of the T cells elicited. Our study revealed that intranasal vaccination elicits a polyfunctional subset of lung-localized CD4 T cells that persist long term. A subset of these lung CD4 T cells localize to the airway, where they can act as early responders following encounter with cognate antigen. Polyfunctional CD4 T cells isolated from airway and lung tissue produce significantly more effector cytokines IFN-γ and TNF-α, as well as cytotoxic functionality. When adoptively transferred to naive recipients, CD4 T cells from NLP:NP-immunized lung were sufficient to mediate 100% survival from lethal challenge with H1N1 influenza virus. IMPORTANCE Exploiting new, more efficacious strategies to potentiate influenza virus-specific immune responses is important, particularly for at-risk populations. We have demonstrated the promise of direct intranasal protein vaccination to establish long-lived immunity in the lung with CD4 T cells that possess features and positioning in the lung that are associated with both immediate and long-term immunity, as well as demonstrating direct protective potential.

Analysis of segmentation of lung parenchyma based on deep learning methods.

Precise segmentation of lung parenchyma is essential for effective analysis of the lung. Due to the obvious contrast and large regional area compared to other tissues in the chest, lung tissue is less difficult to segment. Special attention to details of lung segmentation is also needed. To improve the quality and speed of segmentation of lung parenchyma based on computed tomography (CT) or computed tomography angiography (CTA) images, the 4th International Symposium on Image Computing and Digital Medicine (ISICDM 2020) provides interesting and valuable research ideas and approaches. For the work of lung parenchyma segmentation, 9 of the 12 participating teams used the U-Net network or its modified forms, and others used the methods to improve the segmentation accuracy include attention mechanism, multi-scale feature information fusion. Among them, U-Net achieves the best results including that the final dice coefficient of CT segmentation is 0.991 and the final dice coefficient of CTA segmentation is 0.984. In addition, attention U-Net and nnU-Net network also performs well. In this paper, the methods chosen by 12 teams from different research groups are evaluated and their segmentation results are analyzed for the study and references to those involved.

[Research progress in lung parenchyma segmentation based on computed tomography].

Lung diseases such as lung cancer and COVID-19 seriously endanger human health and life safety, so early screening and diagnosis are particularly important. computed tomography (CT) technology is one of the important ways to screen lung diseases, among which lung parenchyma segmentation based on CT images is the key step in screening lung diseases, and high-quality lung parenchyma segmentation can effectively improve the level of early diagnosis and treatment of lung diseases. Automatic, fast and accurate segmentation of lung parenchyma based on CT images can effectively compensate for the shortcomings of low efficiency and strong subjectivity of manual segmentation, and has become one of the research hotspots in this field. In this paper, the research progress in lung parenchyma segmentation is reviewed based on the related literatures published at domestic and abroad in recent years. The traditional machine learning methods and deep learning methods are compared and analyzed, and the research progress of improving the network structure of deep learning model is emphatically introduced. Some unsolved problems in lung parenchyma segmentation were discussed, and the development prospect was prospected, providing reference for researchers in related fields.

Does fissure status affect the outcome of thoracoscopic pulmonary lobectomy?

PURPOSE: We studied fissure status and devices used to divide lung parenchyma during thoracoscopic pulmonary lobectomy (TPL). METHODS: 52 consecutive TPL indicated for congenital pulmonary airway malformation or interlobar pulmonary sequestration performed between 2009 and 2019 were reviewed prospectively to compare patients with absent fissure and no visible interlobar pulmonary artery (IPA) treated by stapling (group A; n = 10), incomplete fissure with partially visible IPA treated with an Enseal® or LigaSure™ device (group I; n = 17), and complete fissure with fully visible IPA treated by electrocautery (group C; n = 25). RESULTS: Patient demographics were similar. Mean age at TPL was 2.82 years (range 0.03-9.81). Mean duration of follow-up was 4.77 years (range 0.33-10.19). Operative time and duration of chest tube insertion were similar (p = NS). Intraoperative blood loss was significantly lower in group C compared with group I (p = 0.028). Complications were minor bronchial artery hemorrhage during anterior-to-posterior bronchial dissection (group A: n = 1; group I: n = 1), problematic single lung ventilation (group I: n = 1), and persistent postoperative air leak (group I: n = 1). CONCLUSION: While fissure status does not appear to affect the outcome of TPL in children, the choice of device for dividing lung parenchyma relies specifically on fissure status.

[Radiological manifestations of pulmonary diseases in COVID-19]. / Radiologische Manifestationen von Lungenerkrankungen bei COVID-19.

CLINICAL ISSUE: Since its emergence in late 2019, the disease caused by the novel coronavirus, termed COVID-19, has been declared a pandemic by the World Health Organization. Reference standard for the diagnosis of COVID-19 is a positive reverse transcription polymerase chain reaction (RT-PCR) test. While the RT-PCR shows a high specificity, its sensitivity depends on the duration of symptoms, viral load, quality of the sample, and the assay used. STANDARD RADIOLOGICAL METHODS: Chest radiography and computed tomography (CT) of the chest are the imaging modalities primarily used for assessment of the lung manifestations, extent, and complications of COVID-19 pneumonia. PERFORMANCE: Sensitivity and specificity of chest radiography is low. While sensitivity of CT for detecting COVID-19 pneumonia is high-averaging around 90%-its specificity is low-between 25 and 33%. PRACTICAL RECOMMENDATIONS: Indications for imaging in patients with suspected or diagnosed COVID-19 infection should be carefully considered to minimize the risk of infection for medical personnel and other patients. Imaging, particularly CT, can assess disease extent, complications, and differential diagnoses. COVID-19 pneumonia typically presents with bilateral, subpleural areas of ground glass opacifications with or without consolidations. During the course of the disease features resembling organizing pneumonia can occur. Follow-up examinations after recovery from COVID-19 pneumonia should focus on fibrotic changes of the lung parenchyma.

Segmentation of lung parenchyma in CT images using CNN trained with the clustering algorithm generated dataset.

BACKGROUND: Lung segmentation constitutes a critical procedure for any clinical-decision supporting system aimed to improve the early diagnosis and treatment of lung diseases. Abnormal lungs mainly include lung parenchyma with commonalities on CT images across subjects, diseases and CT scanners, and lung lesions presenting various appearances. Segmentation of lung parenchyma can help locate and analyze the neighboring lesions, but is not well studied in the framework of machine learning. METHODS: We proposed to segment lung parenchyma using a convolutional neural network (CNN) model. To reduce the workload of manually preparing the dataset for training the CNN, one clustering algorithm based method is proposed firstly. Specifically, after splitting CT slices into image patches, the k-means clustering algorithm with two categories is performed twice using the mean and minimum intensity of image patch, respectively. A cross-shaped verification, a volume intersection, a connected component analysis and a patch expansion are followed to generate final dataset. Secondly, we design a CNN architecture consisting of only one convolutional layer with six kernels, followed by one maximum pooling layer and two fully connected layers. Using the generated dataset, a variety of CNN models are trained and optimized, and their performances are evaluated by eightfold cross-validation. A separate validation experiment is further conducted using a dataset of 201 subjects (4.62 billion patches) with lung cancer or chronic obstructive pulmonary disease, scanned by CT or PET/CT. The segmentation results by our method are compared with those yielded by manual segmentation and some available methods. RESULTS: A total of 121,728 patches are generated to train and validate the CNN models. After the parameter optimization, our CNN model achieves an average F-score of 0.9917 and an area of curve up to 0.9991 for classification of lung parenchyma and non-lung-parenchyma. The obtain model can segment the lung parenchyma accurately for 201 subjects with heterogeneous lung diseases and CT scanners. The overlap ratio between the manual segmentation and the one by our method reaches 0.96. CONCLUSIONS: The results demonstrated that the proposed clustering algorithm based method can generate the training dataset for CNN models. The obtained CNN model can segment lung parenchyma with very satisfactory performance and have the potential to locate and analyze lung lesions.

Robotic sleeve resection for pulmonary disease.

BACKGROUND: Few studies have described robotic sleeve resection with pulmonary resection. Here, we report the successful implementation of a completely portal robotic sleeve resection with or without pulmonary resection using a modified suture mode. METHODS: In total, 339 patients underwent curative robotic pulmonary surgery at Ruijin Hospital between May 2015 and September 2017. Three of these patients underwent robotic sleeve resection (right upper lobe, one; left upper lobe, one; and lingular segmental bronchus, one). Five port incisions were utilized, and a simple continuous running suture combined with two interrupted sutures of the membranous and cartilaginous junction portion was preferred for the anastomosis. RESULTS: The postoperative course was uneventful for two patients with squamous cell carcinoma. The lingular segmental bronchus patient without pulmonary resection (a salivary gland tumor) underwent short-term atelectasis. The median operation time was 155 (range 132-230) minutes. The median anastomosis time was 25 (range 23-32) minutes. The median length of postoperative hospital stay was 7 (range 6-10) days. There was no mortality or conversion to thoracotomy for any of the patients. All patients were followed for 3-6 months, and there is no tumour recurrence. CONCLUSIONS: Our limited experience suggested that robotic sleeve resection for pulmonary disease with or without pulmonary resection may be safe and effective. The anastomosis time can be shortened with more robotic surgery experiences and the modified suture mode.

Three-dimensional T1 and T2* mapping of human lung parenchyma using interleaved saturation recovery with dual echo ultrashort echo time imaging (ITSR-DUTE).

PURPOSE: To develop and assess a new technique for three-dimensional (3D) full lung T1 and T2* mapping using a single free breathing scan during a clinically feasible time. MATERIALS AND METHODS: A 3D stack of dual-echo ultrashort echo time (UTE) radial acquisition interleaved with and without a WET (water suppression enhanced through T1 effects) saturation pulse was used to map T1 and T2* simultaneously in a single scan. Correction for modulation due to multiple views per segment was derived. Bloch simulations were performed to study saturation pulse excitation profile on lung tissue. Optimization of the saturation delay time (for T1 mapping) and echo time (for T2* mapping) was performed. Monte Carlo simulation was done to predict accuracy and precision of the sequence with signal-to-noise ratio of in vivo images used in the simulation. A phantom study was carried out using the 3D interleaved saturation recovery with dual echo ultrashort echo time imaging (ITSR-DUTE) sequence and reference standard inversion recovery spin echo sequence (IR-SE) to compare accuracy of the sequence. Nine healthy volunteers were imaged and mean (SD) of T1 and T2* in lung parenchyma at 3T were estimated through manually assisted segmentation. 3D lung coverage with a resolution of 2.5 × 2.5 × 6 mm3 was performed and nominal scan time was recorded for the scans. Repeatability was assessed in three of the volunteers. Regional differences in T1/T2* values were also assessed. RESULTS: The phantom study showed accuracy of T1 values to be within 2.3% of values obtained from IR-SE. Mean T1 value in lung parenchyma was 1002 ± 82 ms while T2* was 0.85 ± 0.1 ms. Scan time was ∼10 min for volunteer scans. Mean coefficient of variation (CV) across slices was 0.057 and 0.09, respectively. Regional variation along the gravitational direction and between right and left lung were not significant (P = 0.25 and P = 0.06, respectively) for T1. T2* showed significant variation (P = 0.03) along the gravitational direction. Repeatability for three volunteers was within 0.7% for T1 and 1.9% for T2*. CONCLUSION: 3D T1 and T2* maps of the entire lung can be obtained in a single scan of ∼10 min with a resolution of 2.5 × 2.5 × 6 mm3 . LEVEL OF EVIDENCE: 2 J. Magn. Reson. Imaging 2017;45:1097-1104.

Study of differential expression of miRNAs in lung tissue of mice submitted to experimental infection by Paracoccidioides brasiliensis.

MicroRNAs (miRNAs) are small single stranded RNA sequences involved in post-transcriptional regulation of different biological and physiological processes. Paracoccidioidomycosis (PCM) is an infection caused by Paracoccidioides brasiliensis, and it is a major cause of mortality due to systemic mycoses in Brazil. To date, there have been few reports on the role of miRNAs in the immune response against fungi, especially PCM. The objective of this study was to evaluate the differential expression of miRNAs related to the inflammatory response associated with pulmonary infection by P. brasiliensis. For this purpose, lungs from BALB/c mice, intravenously infected with P. brasiliensis (2.7×107 yeast cells/ml, n = 12) and noninfected BALB/c mice (n = 8), were collected at the 28 and 56 day after infection. The lung parenchyma presented a great number of yeast cells, granulomas, and edema at 28 days and a framework of resolution of the inflammatory process after 56 days. The mRNAs gata-3, ror-γt, foxp3, and IL-6 were positively regulated at the moment at the 56 day, while the TGF-ß1 mRNA was positively regulated at both moments. The miRNAs 126a-5p, 340-5p, 30b-5p, 19b-3p, 221-3p, 20a-5p, 130a-3p, and 301a-3p, 466k presented the greatest increase in expression levels 28 days after infection, and the miRNAs let-7f-5p, let-7a-5p, 5p-26b, let-7e-5p and 369-3p, 466k presented a greater increase in levels of expression 56 days after infection. This study shows a set of differentially expressed miRNAs possibly involved in the immune response in mice during pulmonary infection by P. brasiliensis.

Evaluation of optimized breath-hold and free-breathing 3D ultrashort echo time contrast agent-free MRI of the human lung.

PURPOSE: To evaluate an optimized stack of radials ultrashort echo time (UTE) 3D magnetic resonance imaging (MRI) sequence for breath-hold and free-breathing imaging of the human lung. MATERIALS AND METHODS: A 3D stack of ultrashort echo time radials trajectory was optimized for coronal and axial lower-resolution breath-hold and higher-resolution free-breathing scans using Bloch simulations. The sequence was evaluated in 10 volunteers, without the use of contrast agents. Signal-to-noise ratio (SNR) mean and 95% confidence interval (CI) were determined from separate signal and noise images in a semiautomated fashion. The four scanning schemes were evaluated for significant differences in image quality using Student's t-test. Ten clinical patients were scanned with the sequence and findings were compared with concomitant computed tomography (CT) in nine patients. Breath-hold 3D spokes images were compared with 3D stack of radials in five volunteers. A Mann-Whitney U-test was performed to test significance in both cases. RESULTS: Breath-hold imaging of the entire lung in volunteers was performed with SNR (mean = 42.5 [CI]: 35.5-49.5; mean = 34.3 [CI]: 28.6-40) in lung parenchyma for coronal and axial scans, respectively, which can be used as a quick scout scan. Longer respiratory triggered free-breathing scan enabled high-resolution UTE scanning with mean SNR of 14.2 ([CI]: 12.9-15.5) and 9.2 ([CI]: 8.2-10.2) for coronal and axial scans, respectively. Axial free-breathing scans showed significantly higher image quality (P = 0.008) than the three other scanning schemes. The mean score for comparison with CT was 1.67 (score 0: n = 0; 1: n = 3; 2: n = 6). There was no significant difference between CT and MRI (P = 0.25). 3D stack of radials images were significantly better than 3D spokes images (P < 0.001). CONCLUSION: The optimized 3D stack of radials trajectory was shown to provide high-quality MR images of the lung parenchyma without the use of MRI contrast agents. The sequence may offer the possibility of breath-hold imaging and provides greater flexibility in trading off slice thickness and parallel imaging for scan time.

Investigations of initial airtightness after non-anatomic resection of lung parenchyma using a thulium-doped laser with different optical fibres.

Lung metastases in healthy patients should be removed non-anatomically whenever possible. This can be done with a laser. Lung parenchyma can be cut very well, because of its high energy absorption at a wavelength of 1940 nm. A coagulation layer is created on the resected surface. It is not clear, whether this surface also needs to be sutured to ensure that it remains airtight even at higher ventilation pressures. It would be helpful, if suturing could be avoided, because the lung can become too puckered, especially with multiple resections, resulting in considerable restriction. We carried out our experiments on isolated and ventilated paracardiac lung lobes of pigs. Non-anatomic resection was carried out reproducibly using three different thulium laser fibres (230, 365 and 600 µm) at two different laser power levels (10 W, 30 W) and three different resection depths (0.5, 1.0 and 2.0 cm). Initial airtightness was investigated while ventilating at normal frequency. We also investigated the bursting pressures of the resected areas by increasing the inspiratory pressure. When 230- and 365-µm fibres were used with a power of 10 W, 70 % of samples were initially airtight up to a resection depth of 1 cm. This rate fell at depths of up to 2 cm. All resected surfaces remained airtight during ventilation when 600-µm fibres were used at both laser power levels (10 and 30 W). The bursting pressures achieved with 600-µm fibres were higher than with the other fibres used: 0.5 cm, 41.6 ± 3.2 mbar; 1 cm, 38.2 ± 2.5 mbar; 2 cm, 33.7 ± 4.8 mbar. As laser power and thickness of laser fibre increased, so the coagulation zone became thicker. With a 600-µm fibre, it measured 145.0 ± 8.2 µm with 10 W power and 315.5 ± 6.4 µm with 30 W power. Closure with sutures after non-anatomic resection of lung parenchyma is not necessary when a thulium laser is used provided a 600-µm fibre and adequate laser power (30 W) are employed. At deeper resection levels, the risk of cutting small segmental bronchi is considerably increased. They must always be closed with sutures.

Automated Lung Segmentation from HRCT Scans with Diffuse Parenchymal Lung Diseases.

Performing accurate and fully automated lung segmentation of high-resolution computed tomography (HRCT) images affected by dense abnormalities is a challenging problem. This paper presents a novel algorithm for automated segmentation of lungs based on modified convex hull algorithm and mathematical morphology techniques. Sixty randomly selected lung HRCT scans with different abnormalities are used to test the proposed algorithm, and experimental results show that the proposed approach can accurately segment the lungs even in the presence of disease patterns, with some limitations in the apices and bases of lungs. The algorithm demonstrates a high segmentation accuracy (dice similarity coefficient = 98.62 and shape differentiation metrics dmean = 1.39 mm, and drms = 2.76 mm). Therefore, the developed automated lung segmentation algorithm is a good candidate for the first stage of a computer-aided diagnosis system for diffuse lung diseases.

Three-dimensional assessment of lung tissue density using a clinical ultrashort echo time at 3 tesla: a feasibility study in healthy subjects.

PURPOSE: To implement and assess the performance of three-dimensional (3D) ultra-short echo (UTE) time for evaluating lung tissue density changes induced by gravity dependence and lung inflation. MATERIALS AND METHODS: Twelve healthy volunteers were imaged by 3D UTE at 3 Tesla, during free-breathing and breathholding of the subjects. MR signal intensities were measured in lung tissue and muscle regions. The variations of MR lung signal intensity and lung water content were evaluated as a function of lung inflation and anterior/posterior position. RESULTS: SNR in lung tissue ranged between 35 for free-breathing acquisitions and 7 for breathhold acquisitions at functional residual capacity. Lung-to-muscle signal ratios decreased from 0.58 in posterior areas to 0.34 in anterior areas. The average water content measured in lungs was equal to 34% and 58% in gravitationally nondependent and dependent regions of interest. CONCLUSION: The 3D UTE lung MRI provides signal within lung parenchyma and can be used to assess lung tissue density.

A rare case of solitary fibrous tumor of the lung parenchyma: case report.

Solitary fibrous tumor (SFT) of the lung is a rare neoplasm, usually originating from lung pleura. We present a case report of a 57-year-old male with no significant medical history who was incidentally diagnosed with an SFT of lung parenchyma on chest computed tomography scan. Radiological imaging revealed a well-defined mass in the left lower lobe of the lung. Biopsy and histopathological examination confirmed the diagnosis of solitary fibrous tumor. This case highlights the importance of considering SFT in the differential diagnosis of lung masses, as its clinical presentation and radiological features can mimic those of more common pulmonary malignancies.

Fully automatic deep learning-based lung parenchyma segmentation and boundary correction in thoracic CT scans.

PURPOSE: The proposed work aims to develop an algorithm to precisely segment the lung parenchyma in thoracic CT scans. To achieve this goal, the proposed technique utilized a combination of deep learning and traditional image processing algorithms. The initial step utilized a trained convolutional neural network (CNN) to generate preliminary lung masks, followed by the proposed post-processing algorithm for lung boundary correction. METHODS: First, the proposed method trained an improved 2D U-Net CNN model with Inception-ResNet-v2 as its backbone. The model was trained on 32 CT scans from two different sources: one from the VESSEL12 grand challenge and the other from AIIMS Delhi. Further, the model's performance was evaluated on a test dataset of 16 CT scans with juxta-pleural nodules obtained from AIIMS Delhi and the LUNA16 challenge. The model's performance was assessed using evaluation metrics such as average volumetric dice coefficient (DSCavg), average IoU score (IoUavg), and average F1 score (F1avg). Finally, the proposed post-processing algorithm was implemented to eliminate false positives from the model's prediction and to include juxta-pleural nodules in the final lung masks. RESULTS: The trained model reported a DSCavg of 0.9791 ± 0.008, IoUavg of 0.9624 ± 0.007, and F1avg of 0.9792 ± 0.004 on the test dataset. Applying the post-processing algorithm to the predicted lung masks obtained a DSCavg of 0.9713 ± 0.007, IoUavg of 0.9486 ± 0.007, and F1avg of 0.9701 ± 0.008. The post-processing algorithm successfully included juxta-pleural nodules in the final lung mask. CONCLUSIONS: Using a CNN model, the proposed method for lung parenchyma segmentation produced precise segmentation results. Furthermore, the post-processing algorithm addressed false positives and negatives in the model's predictions. Overall, the proposed approach demonstrated promising results for lung parenchyma segmentation. The method has the potential to be valuable in the advancement of computer-aided diagnosis (CAD) systems for automatic nodule detection.

On the generality of the finite element modeling physical fields in biological systems by the multiscale smeared concept (Kojic transport model).

The biomechanical and biochemical processes in the biological systems of living organisms are extremely complex. Advances in understanding these processes are mainly achieved by laboratory and clinical investigations, but in recent decades they are supported by computational modeling. Besides enormous efforts and achievements in this modeling, there still is a need for new methods that can be used in everyday research and medical practice. In this report, we give a view of the generality of the finite element methodology introduced by the first author and supported by his collaborators. It is based on the multiscale smeared physical fields, termed as Kojic Transport Model (KTM), published in several journal papers and summarized in a recent book (Kojic et al., 2022) [1]. We review relevant literature to demonstrate the distinctions and advantages of our methodology and indicate possible further applications. We refer to our published results by a selection of a few examples which include modeling of partitioning, blood flow, molecular transport within the pancreas, multiscale-multiphysics model of coupling electrical field and ion concentration, and a model of convective-diffusive transport within the lung parenchyma. Two new examples include a model of convective-diffusive transport within a growing tumor, and drug release from nanofibers with fiber degradation.

Electromagnetic Navigational Bronchoscopy Learning Curve Regarding Pneumothorax Rate and Diagnostic Yield.

Electromagnetic navigational bronchoscopy (ENB) has emerged as an innovative technique for diagnosing peripheral and central nodules, offering an improved diagnostic yield compared to conventional bronchoscopy with fewer complications. That being said, pneumothorax remains a frequent complication. This retrospective study conducted at Castle Hill Hospital, UK, analysed ENB procedures over four years to assess the diagnostic yield and pneumothorax rates, exploring learning curves and procedural improvements specifically focusing on the diagnostic yield and pneumothorax rate as markers of change. A total of 246 patients underwent 358 peripheral lung biopsies, revealing an overall diagnostic yield of 61.3%. The diagnostic yield increased from 58.2% in 2020-2021 to 66.0% in 2022-2023 while the pneumothorax rate decreased significantly from 9.8% to 3.4% (p = 0.021*). The majority of pneumothorax cases occurred following upper lobe procedures. The study depicts the importance of procedural experience in improving outcomes, suggesting a learning curve effect. Additionally, it emphasizes the potential for technological advancements, such as robotic assistance, to mitigate operator-dependent variability and improve reproducibility in ENB procedures. These findings contribute to optimizing diagnostic pathways for lung lesions and improving patient safety in ENB interventions.