This research was supported by the Clinical Research and Achievement Transformation Capacity Enhancement Pilot Project (DZMG-MLZY-23008) from the Dongzhimen Hospital of Beijing University of Chinese Medicine, and the Start-up Fund Project for New Teachers (2024-BUCMXJKY-052) from Beijing University of Chinese Medicine.

This study was approved by the Ethics Committee of Dongzhimen Hospital, affiliated with the Beijing University of Chinese Medicine (2024DZMEC-165-02). The patient was recruited from the Fever Outpatient Clinic, Dongzhimen Hospital. The patients provided informed consent for their diagnosis through digital modeling and authorized the use of their data for scientific research purposes. The model reconstruction function is derived from a commercially available software tool (see Table of Materials).
1. Data preparation and visualization
<ol>
	<li>Navigate to the folder containing the patient&#39;s CT scan DICOM data files.</li>
	<li>Generate a 3D volume matrix from the DICOM files using the following MATLAB code: 
	f=dir(&#39;*.dcm&#39;); 
	for i=1:length(f) 
	V(:,:,i)= dicomread(f(fidx(i)).name); 
	end</li>
	<li>Visualize the image sequence using MATLAB&#39;s sliceViewer function: (Figure 1) 
	figure; 
	H=sliceViewer(V); 
	colormap(gray(1024)); 
	&#8203;set(gcf, &#39;Toolbar&#39;, &#39;figure&#39;);</li>
	<li>Interact with the 3D volume visualization.
	<ol>
		<li>Use the scroll bar at the bottom of the graphical user interface (GUI) to browse through different slices in the CT sequence (Figure 1). Note the presence of a 22 mm malignant pulmonary nodule in the left lung at frame 325.</li>
		<li>Find the icons for zooming in, zooming out, and returning to the global view in the upper-right corner of the GUI in Figure 3. Use the Data Tip icon to mark the coordinates of the pixel selected. Use the Zoom function to observe the local features of the lesions and their relationships to surrounding tissues.</li>
		<li>The default color bar is the gray colormap, which means that blue to red represents values from low to high. Right-click the Color Bar in the pop-up menu to select the common gray colormap and reset the whole GUI.</li>
		<li>If the filter effect is not satisfied, use the left mouse button to drag up and down in the middle of the figure to adjust the window level. Drag left and right to adjust the window width, and the corresponding accurate filtering range will be displayed on the color bar. 
		NOTE: These interactive controls enable flexible inspection of CT-DICOM data characteristics across both intensity space and sequence location.</li>
	</ol>
	</li>
</ol>
2. Local 3D matrix visualization of pulmonary nodule lesions
NOTE: After locating the sequence position of the pulmonary nodule in the GUI shown in Figure 1, use the Data Tip tool to precisely delineate the nodule&#39;s position. This step is necessary before calculating the 3D matrix of the grayscale space for the lesion area.
<ol>
	<li>Use the Data Tip tool to precisely identify the pixel coordinates of the pulmonary nodule.
	<ol>
		<li>In the GUI shown in Figure 1, navigate to the slice containing the nodule (frame 325).</li>
		<li>Click the Data Tip icon in the upper-right corner of the GUI.</li>
		<li>Click on the edges of the nodule to mark its boundaries (Figure 2).</li>
		<li>Note the X and Y coordinates displayed in the data tip pop-up.</li>
	</ol>
	</li>
	<li>Extract the grayscale matrix of the pulmonary nodule.
	<ol>
		<li>Based on the coordinates obtained, define the region of interest (ROI) in the MATLAB command window: M = V (304:335, 309:336, 325); 
		NOTE: Adjust the coordinates (304:335, 309:336, 325) according to the specific nodule location in the image.</li>
	</ol>
	</li>
	<li>Visualize the local 3D matrix of the nodule:
	<ol>
		<li>Enter the following MATLAB command to create a 3D surface plot: figure; surf(M);</li>
		<li>Observe the resulting 3D visualization of the nodule&#39;s grayscale intensities (Figure 3).</li>
	</ol>
	</li>
	<li>Interact with the 3D visualization GUI. 
	NOTE: The X and Y axes represent the spatial dimensions of the nodule in pixels. The Z axis represents the grayscale intensity values.
	<ol>
		<li>Find the tools for zooming in, zooming out, rotating, and returning to the default initial view (Restore View) in the upper-right corner of the GUI. Use these tools for precise examination of the 3D digitized pulmonary nodule.</li>
	</ol>
	</li>
</ol>
3. Calculating the multifractal spectrum of pulmonary nodule
NOTE: The fractal dimension is not unique across different scales but rather forms a multifractal spectrum that varies with different computational scales.
<ol>
	<li>Call the function Pix_size, fractal_dimension = PN_fractal_feature(M) with the previously obtained M matrix as input. This will yield the fractal dimensions (fractal_dimension) at different scales (Pix_size).</li>
	<li>Visualize the Multifractal Spectrum (Figure 4) of the pulmonary nodule using the following code: 
	figure; 
	plot (Pix_size, fratal_dimention,&#39;linewidth&#39;,2); 
	xlabel(&#39;Fractal scale&#39;) 
	ylabel(&#39;Fractal Dimension&#39;)</li>
	<li>Using the same steps as in 1.1-3.2, calculate for another benign pulmonary nodule and plot it in the same coordinate system using a different color for comparison. This will produce Figure 5.</li>
	<li>To more precisely compare the multifractal spectra of different benign and malignant pulmonary nodules, use the Data Tip tool to mark the coordinates of key extrema points in Figure 5. 
	NOTE: The MATLAB code used for this protocol is available as Supplementary File 1.</li>
</ol>

Fractal-Based CT Analysis for Lung Cancer Diagnosis and Staging

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	<li>Naik, A., Edla, D. R., Dharavath, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prediction+of+malignancy+in+lung+nodules+using+a+combination+of+deep,+fractal,+and+gray-level+co-occurrence+matrix+features.">Prediction of malignancy in lung nodules using a combination of deep, fractal, and gray-level co-occurrence matrix features.</a> Big Data. 9 (6), 480-498 (2021).</li><li>Al-Kadi, O. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prediction+of+FDG-PET+stage+and+uptake+for+non-small+cell+lung+cancer+on+non-contrast+enhanced+CT+scans+via+fractal+analysis.">Prediction of FDG-PET stage and uptake for non-small cell lung cancer on non-contrast enhanced CT scans via fractal analysis.</a> Clin Imaging. 65, 54-59 (2020).</li><li>Ferreira-Junior, J. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CT-based+radiomics+for+prediction+of+histologic+subtype+and+metastatic+disease+in+primary+malignant+lung+neoplasms.">CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms.</a> Int J Comput Assist Radiol Surg. 15 (1), 163-172 (2020).</li><li>Ryan, S. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Radiomic+measures+from+chest+high-resolution+computed+tomography+associated+with+lung+function+in+sarcoidosis.">Radiomic measures from chest high-resolution computed tomography associated with lung function in sarcoidosis.</a> Eur Respir J. 54 (2), 1900371 (2019).</li><li>Kravchenko, V. F., Ponomaryov, V. I., Pustovoit, V. 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K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Can+CT+radiomic+analysis+in+NSCLC+predict+histology+and+EGFR+mutation+status.">Can CT radiomic analysis in NSCLC predict histology and EGFR mutation status.</a> Medicine (Baltimore). 98 (1), e13963 (2019).</li><li>Xiao, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+automated+segmentation+method+for+lung+parenchyma+image+sequences+based+on+fractal+geometry+and+convex+hull+algorithm.">An automated segmentation method for lung parenchyma image sequences based on fractal geometry and convex hull algorithm.</a> Appl Sci (Basel). 8 (5), 832 (2018).</li><li>Xue, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Use+of+a+radiomics+model+to+predict+tumor+invasiveness+of+pulmonary+adenocarcinomas+appearing+as+pulmonary+ground-glass+nodules.">Use of a radiomics model to predict tumor invasiveness of pulmonary adenocarcinomas appearing as pulmonary ground-glass nodules.</a> Biomed Res Int. 2018, 6803971 (2018).</li><li>Kiryu, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impact+of+hepatocellular+carcinoma+heterogeneity+on+computed+tomography+as+a+prognostic+indicator.">Impact of hepatocellular carcinoma heterogeneity on computed tomography as a prognostic indicator.</a> Sci Rep. 7, 12689 (2017).</li><li>Bashir, U., Siddique, M. M., Mclean, E., Goh, V., Cook, G. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Imaging+heterogeneity+in+lung+cancer:+techniques,+applications,+and+challenges.">Imaging heterogeneity in lung cancer: techniques, applications, and challenges.</a> Am J Roentgenol. 207 (3), 534-543 (2016).</li><li>Niehaus, R., Raicu, D. 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F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantification+of+heterogeneity+as+a+biomarker+in+tumor+imaging:+a+systematic+review.">Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review.</a> PLoS One. 9 (10), e110300 (2014).</li><li>Miwa, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=FDG+uptake+heterogeneity+evaluated+by+fractal+analysis+improves+the+differential+diagnosis+of+pulmonary+nodules.">FDG uptake heterogeneity evaluated by fractal analysis improves the differential diagnosis of pulmonary nodules.</a> Eur J Radiol. 83 (4), 715-719 (2014).</li><li>Liu, G., Chen, N., Ou, C., Liao, Y., Yu, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Image+feature+extraction+based+on+multifractal+theory.">Image feature extraction based on multifractal theory.</a> , 1023-1026 (2014).</li><li>Lin, P., Huang, P., Lee, C., Wu, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automatic+classification+for+solitary+pulmonary+nodule+in+CT+image+by+fractal+analysis+based+on+fractional+Brownian+motion+model.">Automatic classification for solitary pulmonary nodule in CT image by fractal analysis based on fractional Brownian motion model.</a> Pattern Recognit. 46 (12), 3279-3287 (2013).</li><li>Ganeshan, B., Miles, K. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantifying+tumour+heterogeneity+with+CT.">Quantifying tumour heterogeneity with CT.</a> Cancer Imaging. 13 (1), 140-149 (2013).</li><li>Huang, P., Lin, P., Lee, C., Kuo, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+classification+system+of+lung+nodules+in+CT+images+based+on+fractional+Brownian+motion+model.">A classification system of lung nodules in CT images based on fractional Brownian motion model.</a> , 37-40 (2013).</li><li>Wang, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multilevel+binomial+logistic+prediction+model+for+malignant+pulmonary+nodules+based+on+texture+features+of+CT+image.">Multilevel binomial logistic prediction model for malignant pulmonary nodules based on texture features of CT image.</a> Eur J Radiol. 74 (1), 124-129 (2010).</li></ol>

The software tool for multifractal spectrum for assessing pulmonary nodules, Multifractal Spectrum V1.0, is a product of Beijing Intelligent Entropy Science &amp; Technology Co., Ltd. The intellectual property rights of this software tool belong to the company. The authors have no conflicts of interest to declare.

The multifractal spectrum analysis presented in this study represents a significant advancement in the non-invasive assessment of pulmonary nodule malignancy. This method offers important advantages and addresses key limitations in existing approaches to pulmonary nodule diagnosis and staging17.
Critical steps in the protocol include precise 3D reconstruction of CT-DICOM data (Figure 1), accurate delineation of the region of interest (<stro...

Lung cancer remains one of the leading causes of cancer-related deaths worldwide, with early detection and accurate diagnosis playing crucial roles in improving patient outcomes1. Pulmonary nodules, often detected incidentally or through screening programs, present a significant diagnostic challenge for clinicians. The ability to differentiate between benign and malignant nodules, particularly in their early stages, is paramount for timely intervention and appropriate management2.
Traditionally, the criterion standard for diagnosing pulmonary nodule malignancy has been histopathological examination through invasive procedures such as biopsy or surgical resection. While these methods provide definitive diagnoses, they carry inherent risks, including pneumothorax, bleeding, and infection3. Moreover, the invasive nature of these procedures can lead to patient discomfort and anxiety, as well as increased healthcare costs. Additionally, biopsy procedures themselves are subject to sampling accuracy issues, with the potential for obtaining non-representative tissue samples that may lead to misdiagnosis. Consequently, there is a pressing need for non-invasive diagnostic techniques that can accurately assess nodule malignancy without subjecting patients to unnecessary invasive procedures4.
Computed Tomography (CT) imaging has emerged as a powerful tool in the detection and characterization of pulmonary nodules5. However, the interpretation of CT images for nodule assessment remains challenging, with considerable inter-observer variability among radiologists. Current guidelines and expert consensus statements on CT-based nodule evaluation primarily rely on morphological features such as size, shape, and growth rate. While these criteria provide valuable information, they often lack the precision necessary for definitive diagnosis, particularly in cases of small or indeterminate nodules6.
In recent years, there has been growing interest in utilizing quantitative imaging features, often referred to as &#34;radiomics,&#34; to enhance the diagnostic accuracy of CT-based nodule assessment7. Among these approaches, fractal analysis has shown promise in capturing the complex structural characteristics of pulmonary nodules8. Fractal dimension, a measure of an object&#39;s complexity across different scales, has been applied to various medical imaging problems, including the characterization of pulmonary nodules9.
However, existing fractal-based methods for nodule analysis typically employ a single-scale approach, calculating a single fractal dimension for each nodule10. While this approach has shown some utility in differentiating between benign and malignant nodules, it often results in significant overlap between the two categories, limiting its diagnostic precision. The inherent limitation of single-scale fractal analysis lies in its inability to capture the full spectrum of structural complexities that may exist within a nodule across different spatial scales11.
To address these limitations, this study introduces a novel approach, multifractal spectrum analysis, for pulmonary nodule assessment. This method extends beyond traditional single-scale fractal analysis by computing fractal dimensions across multiple voxel scales, thereby generating a comprehensive spectrum that characterizes the nodule&#39;s structural complexity at various levels of detail12. This approach is rooted in the understanding that biological structures, including tumors, often exhibit different fractal properties at different scales, a characteristic that single-scale methods fail to capture13.
The development of this multifractal spectrum analysis is motivated by the need for more precise, quantitative, and non-invasive methods for assessing pulmonary nodule malignancy. By leveraging advanced image processing techniques and mathematical models, this approach aims to extract a richer set of features from CT images, potentially revealing subtle differences between benign and malignant nodules that may not be apparent through conventional analysis or single-scale fractal methods14.
The significance of this research lies in its potential to enhance the accuracy of early-stage lung cancer diagnosis and staging. By providing a more nuanced and comprehensive characterization of nodule structure, the multifractal spectrum analysis may enable clinicians to make more informed decisions about patient management, potentially reducing the need for unnecessary invasive procedures in cases of benign nodules while ensuring timely intervention for malignant ones15.
In summary, this research introduces multifractal spectrum analysis for assessing pulmonary nodule malignancy, addressing the limitations of current diagnostic approaches and single-scale fractal methods. By providing a more comprehensive and precise quantitative assessment of nodule characteristics, this non-invasive technique aims to improve early diagnosis and accurate staging of lung cancer, ultimately enhancing clinical decision-making in pulmonary oncology and contributing to improved patient outcomes16.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>MATLAB</td>
			<td>MathWorks</td>
			<td>2022B</td>
			<td>Computing and visualization</td>
		</tr>
		<tr>
			<td>Multifractal Spectrum software</td>
			<td>Intelligent Entropy, Beijing Intelligent Entropy Science &#38; Technology Co Ltd.</td>
			<td>V1.0</td>
			<td>Modeling for CT/MRI fusion</td>
		</tr>
	</tbody>
</table>

multifractal spectrum analysis for assessing pulmonary nodule malignancy

Non-invasive assessment of pulmonary nodule malignancy remains a critical challenge in lung cancer diagnosis. Traditional methods often lack precision in differentiating benign from malignant nodules, particularly in the early stages. This study introduces an approach using multifractal spectrum analysis to quantitatively evaluate pulmonary nodule characteristics.
A fractal-based protocol was developed to process computed tomography (CT)-digital imaging and communications in medicine (DICOM) data, enabling three-dimensional (3D) visualization and analysis of pulmonary nodule's multifractal spectrum. The method involves 3D volume reconstruction, precise ROI delineation, and calculation of fractal dimensions across multiple scales. Multifractal spectra were computed for both early-stage and late-stage lung adenocarcinoma nodules, with comparative analysis performed using data tip tool quantification.
Analysis revealed that the fractal dimension of a pulmonary nodule's 3D digital matrix varies continuously with different voxel scales, forming a distinctive multifractal spectrum. Significant differences were observed between early-stage and late-stage nodules. Late-stage nodules demonstrated a wider scale range (longer X-axis) and higher extreme points in their multifractal spectra. These distinctions were quantitatively confirmed, indicating the method's potential for precise staging.
The multifractal spectrum analysis provides a highly significant and precise quantitative method for staging pulmonary nodules, effectively differentiating between benign and malignant cases. This non-invasive technique shows promise for improving early diagnosis and accurate staging of lung cancer, potentially enhancing clinical decision-making in pulmonary oncology.

Figure 1&#160;uses a 3D-volume reconstruction of the patient&#39;s thoracic CT sequence, allowing for convenient viewing and localization of the subject&#39;s pulmonary nodules. The Data Tip tool can effectively outline the Region of Interest (ROI) of the nodule of interest (Figure 2). Figure 3 provides a digitized structure of the nodule&#39;s 3D intensity space.
This study found through practical experi...

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy

This study introduces multifractal spectrum analysis for assessing pulmonary nodule malignancy. Using CT-DICOM data, the method calculates fractal dimensions across multiple voxel scales, revealing significant differences between early-stage and late-stage pulmonary nodules.

Non-invasive assessment of pulmonary nodule malignancy remains a critical challenge in lung cancer diagnosis. Traditional methods often lack precision in ...

multifractal-spectrum-analysis-for-assessing-pulmonary-nodule

Research

JoVE Journal

Medicine

235 Views.  Affiliated to Beijing University of Chinese Medicine. This study introduces multifractal spectrum analysis for assessing pulmonary nodule malignancy. Using CT-DICOM data, the method calculates fractal dimensions across multiple voxel scales, revealing significant differences between early-stage and late-stage pulmonary nodules.

Video: Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy

Bronchial Thermoplasty:  A Novel Therapeutic Approach to Severe Asthma

Bronchial thermoplasty is a non-drug procedure for severe persistent asthma that delivers thermal energy to the airway wall in a precisely controlled manner to reduce excessive airway smooth muscle. Reducing airway smooth muscle decreases the ability of the airways to constrict, thereby reducing the frequency of asthma attacks. Bronchial thermoplasty is delivered by the Alair System and is performed in three outpatient procedure visits, each scheduled approximately three weeks apart. The first procedure treats the airways of the right lower lobe, the second treats the airways of the left lower lobe and the third and final procedure treats the airways in both upper lobes. After all three procedures are performed the bronchial thermoplasty treatment is complete.
Bronchial thermoplasty is performed during bronchoscopy with the patient under moderate sedation. All accessible airways distal to the mainstem bronchi between 3 and 10 mm in diameter, with the exception of the right middle lobe, are treated under bronchoscopic visualization. Contiguous and non-overlapping activations of the device are used, moving from distal to proximal along the length of the airway, and systematically from airway to airway as described previously. Although conceptually straightforward, the actual execution of bronchial thermoplasty is quite intricate and procedural duration for the treatment of a single lobe is often substantially longer than encountered during routine bronchoscopy. As such, bronchial thermoplasty should be considered a complex interventional bronchoscopy and is intended for the experienced bronchoscopist. Optimal patient management is critical in any such complex and longer duration bronchoscopic procedure. This article discusses the importance of careful patient selection, patient preparation, patient management, procedure duration, postoperative care and follow-up to ensure that bronchial thermoplasty is performed safely.
Bronchial thermoplasty is expected to complement asthma maintenance medications by providing long-lasting asthma control and improving asthma-related quality of life of patients with severe asthma. In addition, bronchial thermoplasty has been demonstrated to reduce severe exacerbations (asthma attacks) emergency rooms visits for respiratory symptoms, and time lost from work, school and other daily activities due to asthma.

Bronchial thermoplasty is a non-drug procedure for severe persistent asthma that delivers thermal energy to the airway wall in a precisely controlled manner to reduce excessive airway smooth muscle. Reducing airway smooth muscle decreases the ability of the airways to constrict, thereby reducing the frequency of asthma attacks.

Bronchial thermoplasty is a non-drug procedure for severe persistent asthma that delivers thermal energy to the airway wall in a precisely controlled ...

Videomorphometric Analysis of Hypoxic Pulmonary Vasoconstriction of Intra-pulmonary Arteries Using Murine Precision Cut Lung Slices

Acute alveolar hypoxia causes pulmonary vasoconstriction (HPV) - also known as von Euler-Liljestrand mechanism - which serves to match lung perfusion to ventilation. Up to now, the underlying mechanisms are not fully understood. The major vascular segment contributing to HPV is the intra-acinar artery. This vessel section is responsible for the blood supply of an individual acinus, which is defined as the portion of lung distal to a terminal bronchiole. Intra-acinar arteries are mostly located in that part of the lung that cannot be selectively reached by a number of commonly used techniques such as measurement of the pulmonary artery pressure in isolated perfused lungs or force recordings from dissected proximal pulmonary artery segments1,2. The analysis of subpleural vessels by real-time confocal laser scanning luminescence microscopy is limited to vessels with up to 50 &#181;m in diameter3.
We provide a technique to study HPV of murine intra-pulmonary arteries in the range of 20-100 &#181;m inner diameters. It is based on the videomorphometric analysis of cross-sectioned arteries in precision cut lung slices (PCLS). This method allows the quantitative measurement of vasoreactivity of small intra-acinar arteries with inner diameter between 20-40 &#181;m which are located at gussets of alveolar septa next to alveolar ducts and of larger pre-acinar arteries with inner diameters between 40-100 &#181;m which run adjacent to bronchi and bronchioles. In contrast to real-time imaging of subpleural vessels in anesthetized and ventilated mice, videomorphometric analysis of PCLS occurs under conditions free of shear stress. In our experimental model both arterial segments exhibit a monophasic HPV when exposed to medium gassed with 1% O2 and the response fades after 30-40 min at hypoxia.

Hypoxic pulmonary vasoconstriction (HPV) is an important physiological phenomenon by which at alveolar hypoxia lung perfusion is matched to ventilation. The major vascular segment contributing to HPV is the intra-acinar artery. Here, we describe our protocol for the analysis of HPV of murine pulmonary vessels with diameters of 20-100 &mu;m.

Acute alveolar hypoxia causes pulmonary vasoconstriction (HPV) - also known as von Euler-Liljestrand mechanism - which serves to match lung perfusion to ...

Murine Heterotopic Heart Transplant Technique

It is now over forty years since this technique was first reported by Corry, Wynn and Russell. Although it took some years for other labs to become proficient in and utilize this technique, it is now widely used by many laboratories around the world. A significant refinement to the original technique was developed and reported in 2001 by Niimi. Described here are the techniques that have evolved over more than a decade in the hands of three surgeons (Plenter, Grazia, Pietra) in our center. These techniques are now being passed on to a younger generation of surgeons and researchers.
Based largely on the Niimi experience, the procedures used have evolved in the fine details - details which we will endeavor to relate here in such a way that others may be able to use this very useful model. Like Niimi, we have found that a video aid to learning is a priceless resource for the beginner.

The manuscript describes the steps required to perform the heterotopic heart transplant in the mouse.

It is now over forty years since this technique was first reported by Corry, Wynn and Russell. Although it took some years for other labs to become ...

Increasing Pulmonary Artery Pulsatile Flow Improves Hypoxic Pulmonary Hypertension in Piglets

Pulmonary arterial hypertension (PAH) is a disease affecting distal pulmonary arteries (PA). These arteries are deformed, leading to right ventricular failure. Current treatments are limited. Physiologically, pulsatile blood flow is detrimental to the vasculature. In response to sustained pulsatile stress, vessels release nitric oxide (NO) to induce vasodilation for self-protection. Based on this observation, this study developed a protocol to assess whether an artificial pulmonary pulsatile blood flow could induce an NO-dependent decrease in pulmonary artery pressure. One group of piglets was exposed to chronic hypoxia for 3 weeks and compared to a control group of piglets. Once a week, the piglets underwent echocardiography to assess PAH severity. At the end of hypoxia exposure, the piglets were subjected to a pulsatile protocol using a pulsatile catheter. After being anesthetized and prepared for surgery, the jugular vein of the piglet was isolated and the catheter was introduced through the right atrium, the right ventricle and the pulmonary artery, under radioscopic control. Pulmonary artery pressure (PAP) was measured before (T0), immediately after (T1) and 30 min after (T2) the pulsatile protocol. It was demonstrated that this pulsatile protocol is a safe and efficient method of inducing a significant reduction in mean PAP via an NO-dependent mechanism. These data open up new avenues for the clinical management of PAH.

Pulmonary hypertension is associated with a significant reduction in pulmonary artery pulsatility, contractility and elasticity, contributing to an increase in pulmonary artery pressure and pulmonary resistance. Using a hypoxic piglet model, this study demonstrated that improving pulmonary artery plasticity using a newly developed pulsatile catheter improves hypoxic pulmonary hypertension.

Pulmonary arterial hypertension (PAH) is a disease affecting distal pulmonary arteries (PA). These arteries are deformed, leading to right ventricular ...

Use of Electromagnetic Navigational Transthoracic Needle Aspiration (E-TTNA) for Sampling of Lung Nodules

Lung nodule evaluation represents a clinical challenge especially in patients with intermediate risk for malignancy. Multiple technologies are presently available to sample nodules for pathological diagnosis. Those technologies can be divided into bronchoscopic and non-bronchoscopic interventions. Electromagnetic navigational bronchoscopy is being extensively used for the endobronchial approach to peripheral lung nodules but has been hindered by anatomic challenges resulting in a 70% diagnostic yield. Electromagnetic navigational guided transthoracic needle lung biopsy is novel non-bronchoscopic method that uses a percutaneous electromagnetic tip tracked needle to obtain core biopsy specimens. Electromagnetic navigational transthoracic needle aspiration complements bronchoscopic techniques potentially allowing the provider to maximize the diagnostic yield during one single procedure. This article describes a novel integrated diagnostic approach to pulmonary lung nodules. We propose the use of endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA) for mediastinal staging; radial EBUS, navigational bronchoscopy and E-TTNA during one single procedure to maximize diagnostic yield and minimize the number of invasive procedures needed to obtain a diagnosis. This manuscript describes in detail how the navigation transthoracic procedure is performed. Additional clinical studies are needed to determine the clinical utility of this novel technology.

Use of Electromagnetic Navigational Transthoracic Needle Aspiration E-TTNA for Sampling of Lung Nodules

We describe the novel use of electromagnetic navigational guided transthoracic needle aspiration for the pathologic assessment of human lung nodules.

Lung nodule evaluation represents a clinical challenge especially in patients with intermediate risk for malignancy. Multiple technologies are presently ...

Assessing Specificity of Anticancer Drugs In Vitro

A procedure for assessing specificity of anticancer drugs in vitro using cultures containing both tumor and non-tumor cells is demonstrated. The key element is the quantitative determination of a tumor-specific genetic alteration in relation to a universal sequence using a dual-probe digital PCR assay and the subsequent calculation of the proportion of tumor cells. The assay is carried out on a culture containing tumor cells of an established line and spiked-in non-tumor cells. The mixed culture is treated with a test drug at various concentrations. After the treatment, DNA is prepared directly from the survived adhesive cells in wells of 96-well plates using a simple and inexpensive method, and subjected to a dual-probe digital PCR assay for measuring a tumor-specific genetic alteration and a reference universal sequence. In the present demonstration, a heterozygous deletion of the NF1 gene is used as the tumor-specific genetic alteration and a RPP30 gene as the reference gene. Using the ratio NF1/RPP30, the proportion of tumor cells was calculated. Since the dose-dependent change of the proportion of tumor cells provides an in vitro indication for specificity of the drug, this genetic and cell-based in vitro assay will likely have application potential in drug discovery. Furthermore, for personalized cancer-care, this genetic- and cell-based tool may contribute to optimizing adjuvant chemotherapy by means of testing efficacy and specificity of candidate drugs using primary cultures of individual tumors.

Assessing Specificity of Anticancer Drugs In Vitro

The goal of this protocol is to assess specificity of anticancer drugs in vitro using mixed cultures containing both tumor and non-tumor cells.

A procedure for assessing specificity of anticancer drugs in vitro using cultures containing both tumor and non-tumor cells is demonstrated. The key ...

Electromagnetic Navigation Transthoracic Nodule Localization for Minimally Invasive Thoracic Surgery

The increased use of chest computed tomography (CT) has led to an increased detection of pulmonary nodules requiring diagnostic evaluation and/or excision. Many of these nodules are identified and excised via minimally invasive thoracic surgery; however, subcentimeter and subsolid nodules are frequently difficult to identify intra-operatively. This can be mitigated by the use of electromagnetic transthoracic needle localization. This protocol delineates the step-by-step process of electromagnetic localization from the pre-operative period to the postoperative period and is an adaptation of the electromagnetically guided percutaneous biopsy previously described by Arias et al. Pre-operative steps include obtaining a same day CT followed by the generation of a three-dimensional virtual map of the lung. From this map, the target lesion(s) and an entry site are chosen. In the operating room, the virtual reconstruction of the lung is then calibrated with the patient and the electromagnetic navigation platform. The patient is then sedated, intubated, and placed in the lateral decubitus position. Using a sterile technique and visualization from multiple views, the needle is inserted into the chest wall at the prechosen skin entry site and driven down to the target lesion. Dye is then injected into the lesion and, then, continuously during needle withdrawal, creating a tract for visualization intra-operatively. This method has many potential benefits when compared to the CT-guided localization, including a decreased radiation exposure and decreased time between the dye injection and the surgery. Dye diffusion from the pathway occurs over time, thereby limiting intra-operative nodule identification. By decreasing the time to surgery, there is a decrease in wait time for the patient, and less time for dye diffusion to occur, resulting in an improvement in nodule localization. When compared to electromagnetic bronchoscopy, airway architecture is no longer a limitation as the target nodule is accessed via a transparenchymal approach. Details of this procedure are described in a step-by-step fashion.

Presented here is a protocol for lung nodule localization using dye marking via electromagnetically navigated transthoracic needle access. The technique described here can be accomplished in the peri-operative period to optimize nodule localization and to successful resection when performing minimally invasive thoracic surgery.

The increased use of chest computed tomography (CT) has led to an increased detection of pulmonary nodules requiring diagnostic evaluation and/or ...

Left Atrial Stenosis Induced Pulmonary Venous Arterialization and Group 2 Pulmonary Hypertension in Rat

The mechanism of mitral stenosis-induced pulmonary venous arterialization and group 2 pulmonary hypertension (PH) is unclear. There is no rodent model of group 2 PH, due to mitral stenosis (MS), to facilitate the investigation of disease mechanisms and potential therapeutic strategies. We present a novel rat model of pulmonary venous congestion-induced pulmonary venous arterialization and group 2 PH caused by left atrial stenosis (LAS). LAS is achieved by constricting the left atrium using a half-closed titanium clip. After the LAS surgery, a rat model with a transmitral inflow velocity greater than or equal to 2.0 m/s on echocardiography gradually develops pulmonary venous arterialization and group 2 PH over an 8- to 10-week period. In this protocol, we provide the step-by-step procedure of how to perform the LAS surgery. The presented LAS rat model mimics MS in humans and is useful for studying the underlying molecular mechanism of pulmonary venous arterialization and for the preclinical evaluation of therapies for group 2 PH.

Left atrial stenosis (LAS) is a novel surgical technique used for studying group 2 pulmonary hypertension (PH) and mechanisms underlying pulmonary venous arterialization. Here, we present a protocol to constrict the left atrium using a titanium clip to cause pulmonary venous arterialization and moderate PH in a rat.

The mechanism of mitral stenosis-induced pulmonary venous arterialization and group 2 pulmonary hypertension (PH) is unclear. There is no rodent model of ...

Quantitative Mapping of Specific Ventilation in the Human Lung using Proton Magnetic Resonance Imaging and Oxygen as a Contrast Agent

Specific ventilation imaging (SVI) is a functional magnetic resonance imaging technique capable of quantifying specific ventilation &#8213; the ratio of the fresh gas entering a lung region divided by the region&#8217;s end-expiratory volume &#8213; in the human lung, using only inhaled oxygen as a contrast agent. Regional quantification of specific ventilation has the potential to help identify areas of pathologic lung function. Oxygen in solution in tissue shortens the tissue&#8217;s longitudinal relaxation time (T1), and thus a change in tissue oxygenation can be detected as a change in T1-weighted signal with an inversion recovery acquired image. Following an abrupt change between two concentrations of inspired oxygen, the rate at which lung tissue within a voxel equilibrates to a new steady-state reflects the rate at which resident gas is being replaced by inhaled gas. This rate is determined by specific ventilation. To elicit this sudden change in oxygenation, subjects alternately breathe 20-breath blocks of air (21% oxygen) and 100% oxygen while in the MRI scanner. A stepwise change in inspired oxygen fraction is achieved through use of a custom three-dimensional (3D)-printed flow bypass system with a manual switch during a short end-expiratory breath hold. To detect the corresponding change in T1, a global inversion pulse followed by a single shot fast spin echo sequence was used to acquire two-dimensional T1-weighted images in a 1.5&#160;T MRI scanner, using an eight-element torso coil. Both single slice and multi-slice imaging are possible, with slightly different imaging parameters. Quantification of specific ventilation is achieved by correlating the time-course of signal intensity for each lung voxel with a library of simulated responses to the air/oxygen stimulus. SVI estimations of specific ventilation heterogeneity have been validated against multiple breath washout and proved to accurately determine the heterogeneity of the specific ventilation distribution.

Specific ventilation imaging is a functional magnetic resonance imaging technique that allows for quantification of regional specific ventilation in the human lung, using inhaled oxygen as a contrast agent. Here, we present a protocol to collect and analyze specific ventilation imaging data.

Specific ventilation imaging (SVI) is a functional magnetic resonance imaging technique capable of quantifying specific ventilation &#8213; the ratio of ...

Validated LC-MS/MS Panel for Quantifying 11 Drug-Resistant TB Medications in Small Hair Samples

Drug resistant-tuberculosis (DR-TB) is a growing public health threat, and assessment of therapeutic drug levels may have important clinical benefits. Plasma drug levels are the current gold standard assessment, but require phlebotomy and a cold chain, and capture only very recent adherence. Our method uses hair, a matrix that is easily collected and reflective of long-term adherence, to test for 11 anti-TB medications. Previous work by our group shows that antiretroviral drug levels in hair are associated with HIV outcomes. Our method for DR-TB drugs uses 2 mg of hair (3 cm proximal to the root), which is pulverized and extracted in methanol. Samples are analyzed with a single LC-MS/MS method, quantifying 11 drugs in a 16 min run. Lower limits of quantification (LLOQs) for the 11 drugs range from 0.01 ng/mg to 1 ng/mg. Drug presence is confirmed by comparing ratios of two mass spectrometry transitions. Samples are quantified using the area ratio of the drug to the deuterated, 15N-, or 13C-labeled drug isotopologue. We used a calibration curve ranging from 0.001-100 ng/mg. Application of the method to a convenience sample of hair samples collected from DR-TB patients on directly observed therapy (DOT) indicated drug levels in hair within the linear dynamic range of nine of the eleven drugs (isoniazid, pyrazinamide, ethambutol, linezolid, levofloxacin, moxifloxacin, clofazimine, bedaquiline, pretomanid). No patient was on prothionamide, and the measured levels for ethionamide were close to its LLOQ (with further work instead examining the suitability of ethionamide&#8217;s metabolite for monitoring exposure). In summary, we describe the development of a multi-analyte panel for DR-TB drugs in hair as a technique for therapeutic drug monitoring during drug-resistant TB treatment.

Current methods of analyzing patients&#8217; adherence to complex drug resistant-tuberculosis (DR-TB) regimens can be inaccurate and resource-intensive. Our method analyzes hair, an easily collected and stored matrix, for concentrations of 11 DR-TB medications. Using LC-MS/MS, we can determine sub-nanogram drug levels that can be utilized to better understand drug adherence.

Drug resistant-tuberculosis (DR-TB) is a growing public health threat, and assessment of therapeutic drug levels may have important clinical benefits. ...