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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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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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

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193 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

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 ...

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 ...

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 ...

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. ...

Precision Cut Lung Slices as an Efficient Tool for Ex vivo Pulmonary Vessel Structure and Contractility Studies

The visualization of murine lung tissue provides valuable structural and cellular information regarding the underlying airway and vasculature. However, the preservation of pulmonary vessels that truly represents physiological conditions still presents challenges. In addition, the delicate configuration of murine lungs result in technical challenges preparing samples for high-quality images that preserve both cellular composition and architecture. Similarly, cellular contractility assays can be performed to study the potential of cells to respond to vasoconstrictors in vitro, but these assays do not reproduce the complex environment of the intact lung. In contrast to these technical issues, the precision-cut lung slice (PCLS) method can be applied as an efficient alternative to visualize lung tissue in three dimensions without regional bias and serve as a live surrogate contractility model for up to 10 days. Tissue prepared using PCLS has preserved structure and spatial orientation, making it ideal to study disease processes ex vivo. The location of endogenous tdTomato-labeled cells in PCLS harvested from an inducible tdTomato reporter murine model can be successfully visualized by confocal microscopy. After exposure to vasoconstrictors, PCLS demonstrates the preservation of both vessel contractility and lung structure, which can be captured by a time-lapse module. In combination with the other procedures, such as western blot and RNA analysis, PCLS can contribute to the comprehensive understanding of signaling cascades that underlie a wide variety of disorders and lead to a better understanding of the pathophysiology in pulmonary vascular diseases.

Precision Cut Lung Slices as an Efficient Tool for Ex vivo Pulmonary Vessel Structure and Contractility Studies

Presented here is a protocol for preserving the vascular contractility of PCLS murine lung tissue, resulting in a sophisticated three-dimensional image of the pulmonary vasculature and airway, which can be preserved for up to 10 days that is susceptible to numerous procedures.

The visualization of murine lung tissue provides valuable structural and cellular information regarding the underlying airway and vasculature. However, ...

Chronic Ovine Model of Right Ventricular Failure and Functional Tricuspid Regurgitation

The pathophysiology of severe functional tricuspid regurgitation (FTR) associated with right ventricular dysfunction is poorly understood, leading to suboptimal clinical results. We set out to establish a chronic ovine model of FTR and right heart failure to investigate the mechanisms of FTR. Twenty adult male sheep (6-12 months old, 62 &#177; 7 kg) underwent a left thoracotomy and baseline echocardiography. A pulmonary artery band (PAB) was placed and cinched around the main pulmonary artery (PA) to at least double the systolic pulmonary artery pressure (SPAP), inducing right ventricular (RV) pressure overload and signs of RV dilatation. PAB acutely increased the SPAP from 21 &#177; 2 mmHg to 62 &#177; 2 mmHg. The animals were followed for 8 weeks, symptoms of heart failure were treated with diuretics, and surveillance echocardiography was used to assess for pleural and abdominal fluid collection. Three animals died during the follow-up period due to stroke, hemorrhage, and acute heart failure. After 2 months, a median sternotomy and epicardial echocardiography were performed. Of the surviving 17 animals, 3 developed mild tricuspid regurgitation, 3 developed moderate tricuspid regurgitation, and 11 developed severe tricuspid regurgitation. Eight weeks of pulmonary artery banding resulted in a stable chronic ovine model of right ventricular dysfunction and significant FTR. This large animal platform can be used to further investigate the structural and molecular basis of RV failure and functional tricuspid regurgitation.

Right ventricular failure and functional tricuspid regurgitation are associated with left-sided heart disease and pulmonary hypertension, which contribute significantly to morbidity and mortality in patients. Establishing a chronic ovine model to study right ventricular failure and functional tricuspid regurgitation will help in understanding their mechanisms, progression, and possible treatments.

The pathophysiology of severe functional tricuspid regurgitation (FTR) associated with right ventricular dysfunction is poorly understood, leading to ...

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

In recent years, the incidence of thyroid cancer has been increasing. Thyroid nodule detection is critical for both the detection and treatment of thyroid cancer. Convolutional neural networks (CNNs) have achieved good results in thyroid ultrasound image analysis tasks. However, due to the limited valid receptive field of convolutional layers, CNNs fail to capture long-range contextual dependencies, which are important for identifying thyroid nodules in ultrasound images. Transformer networks are effective in capturing long-range contextual information. Inspired by this, we propose a novel thyroid nodule detection method that combines the Swin Transformer backbone and Faster R-CNN. Specifically, an ultrasound image is first projected into a 1D sequence of embeddings, which are then fed into a hierarchical Swin Transformer. 
The Swin Transformer backbone extracts features at five different scales by utilizing shifted windows for the computation of self-attention. Subsequently, a feature pyramid network (FPN) is used to fuse the features from different scales. Finally, a detection head is used to predict bounding boxes and the corresponding confidence scores. Data collected from 2,680 patients were used to conduct the experiments, and the results showed that this method achieved the best mAP score of 44.8%, outperforming CNN-based baselines. In addition, we gained better sensitivity (90.5%) than the competitors. This indicates that context modeling in this model is effective for thyroid nodule detection.

Here, a new model for thyroid nodule detection in ultrasound images is proposed, which uses Swin Transformer as the backbone to perform long-range context modeling. Experiments prove that it performs well in terms of sensitivity and accuracy.

In recent years, the incidence of thyroid cancer has been increasing. Thyroid nodule detection is critical for both the detection and treatment of thyroid ...

3D Reconstruction for the Diagnosis and Treatment of Pulmonary Nodules

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

The three-dimensional (3D) reconstruction of pulmonary nodules using medical images has introduced new technical approaches for diagnosing and treating pulmonary nodules, and these approaches are progressively being acknowledged and adopted by physicians and patients. Nonetheless, constructing a relatively universal 3D digital model of pulmonary nodules for diagnosis and treatment is challenging due to device differences, shooting times, and nodule types. The objective of this study is to propose a new 3D digital model of pulmonary nodules that serves as a bridge between physicians and patients and is also a cutting-edge tool for pre-diagnosis and prognostic evaluation. Many AI-driven pulmonary nodule detection and recognition methods employ deep learning techniques to capture the radiological features of pulmonary nodules, and these methods can achieve a good area under-the-curve (AUC) performance. However, false positives and false negatives remain a challenge for radiologists and clinicians. The interpretation and expression of features from the perspective of pulmonary nodule classification and examination are still unsatisfactory. In this study, a method of continuous 3D reconstruction of the whole lung in horizontal and coronal positions is proposed by combining existing medical image processing technologies. Compared with other applicable methods, this method allows users to rapidly locate pulmonary nodules and identify their fundamental properties while also observing pulmonary nodules from multiple perspectives, thereby providing a more effective clinical tool for diagnosing and treating pulmonary nodules.

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules  

The objective of this study is to develop a novel 3D digital model of pulmonary nodules that serves as a communication bridge between physicians and patients and is also a cutting-edge tool for pre-diagnosis and prognostic evaluation.

The three-dimensional (3D) reconstruction of pulmonary nodules using medical images has introduced new technical approaches for diagnosing and treating ...