Improving Enforcement Measures And Establishing Clear Criteria: A Content Analysis Of Tobacco-Brand-Owned Instagram Accounts.

INTRODUCTION: This study examines limitations of the current regulatory framework for tobacco advertising on Instagram. We first investigate compliance with FDA warning label requirements for posts by tobacco-owned accounts. Next, we examine the prevalence of content that has been restricted in broadcast or print for its youth appeal, followed by content meeting more expansive criteria for youth appeal set forth in the FDA's guidance document. METHODS: Posts by tobacco-brand-owned accounts between January 1, 2021, and February 14, 2022, were sampled from Mintel's Comperemedia Omni database. Instagram posts from 15 accounts were examined for violations of FDA warning label requirements and content that has been restricted on other mediums, including cartoons, sports branding, unauthorized claims, and young models (N=1243). Finally, a subsample of n=453 unambiguously branded posts was coded for themes that met the FDA's criteria of resonating with younger audiences, particularly that "adolescents rely on external information as they seek to shape their own identities". RESULTS: Only 12.8% of posts had fully compliant warning labels. Content that has been in some way regulated on other mediums, such as cartoons (1.6%), unauthorized health claims (<1%), sports branding (<1%), and young models (4.4%) were infrequent. However, a conservative analysis focusing only on branded posts found that posts frequently highlighted tech elements (45%), device customizability (24.5%), vaper identity (17.7%), stylized product photography (33.6%), social media engagement (32.2%) and memes (5.7%). CONCLUSIONS: Enforcement of existing regulations on Instagram is minimal. Explicit content restrictions applying evidence-based guidance on youth-appealing advertising are needed. IMPLICATIONS: This research has important implications for enforcing and expanding advertising regulations on social media. First, Instagram's self-imposed regulations are ineffective, permitting tobacco companies to post ads from brand-owned accounts despite claiming to restrict tobacco promotion on the platform. Second, policymakers should seek to apply FDA guidance on youth-appealing advertising informed by decades of research to create explicit enforceable content restrictions that extend beyond cartoons, sports figures, and young models to include content likely to situate tobacco use within the developing self-concept of vulnerable youth such as presenting e-cigarettes as hi-tech devices, highlighting vaper identity, or infiltrating online social media culture. Finally, greater resources for enforcement are needed given the only applicable regulation, warning labels, remains largely ignored.

Feasibility of measuring pericoronary fat from precontrast scans: Effect of iodinated contrast on pericoronary fat attenuation.

BACKGROUND: Pericoronary adipose tissue (PCAT) attenuation has been identified as a marker for cardiovascular risk. The effect of contrast enhancement on fat attenuation is unknown. We aim to compare precontrast coronary scans to postcontrast CCTA for quantification of pericoronary fat volume and attenuation. METHODS: Thin slice pre- and post-contrast studies obtained at 120 kVp, heart rate <60, with no plaque or artifact in the right coronary artery (RCA) were selected. Analysis was limited to pixels -30 Hounsfield units (HU) to -190 HU and from 10 mm to 50 mm distal to the RCA origin at a radial distance equal to the vessel diameter. A subgroup with no plaque across all coronaries was also analyzed. RESULTS: Of 119 study pairs, the average RCA diameter was highly correlated at 3.85 mm (postcontrast) and 3.84 mm (precontrast), r = 0.97, p < 0.0001. The mean attenuation of pre- and postcontrast images was also highly correlated at -87.02 ±â€¯7.15 HU and -82.74 ±â€¯6.54 HU, respectively (r = 0.65, p < 0.0001). Pericoronary fat volume in the -190 to -30 HU range was 396 mm³ lower in the post contrast versus pre-contrast, consistent with higher attenuation (less negative) voxels postcontrast (p < 0.0001). Inter- and intra-reader agreement ranged 95-100% and 90% for precontrast and 85-90% for postcontrast studies, respectively. Subgroup analysis revealed precontrast attenuation -85.59 ±â€¯7.53 HU and postcontrast -82.21 ±â€¯7.15 HU were highly correlated r = 0.67, p < 0.0001. CONCLUSION: Pericoronary fat enhances with iodinated contrast, potentially explaining some of its risk-predictive capabilities. Fat attenuation and volume can be reliably measured from precontrast calcium scans, with volume quantification showing particularly strong correlation. Excellent inter- and intra-reader agreement is also demonstrated.

Automatic Detection of 2D and 3D Lung Nodules in Chest Spiral CT Scans.

Automatic detection of lung nodules is an important problem in computer analysis of chest radiographs. In this paper, we propose a novel algorithm for isolating lung abnormalities (nodules) from spiral chest low-dose CT (LDCT) scans. The proposed algorithm consists of three main steps. The first step isolates the lung nodules, arteries, veins, bronchi, and bronchioles from the surrounding anatomical structures. The second step detects lung nodules using deformable 3D and 2D templates describing typical geometry and gray-level distribution within the nodules of the same type. The detection combines the normalized cross-correlation template matching and a genetic optimization algorithm. The final step eliminates the false positive nodules (FPNs) using three features that robustly define the true lung nodules. Experiments with 200 CT data sets show that the proposed approach provided comparable results with respect to the experts.

3D shape analysis for early diagnosis of malignant lung nodules.

An alternative method of diagnosing malignant lung nodules by their shape, rather than conventional growth rate, is proposed. The 3D surfaces of the detected lung nodules are delineated by spherical harmonic analysis that represents a 3D surface of the lung nodule supported by the unit sphere with a linear combination of special basis functions, called Spherical Harmonics (SHs). The proposed 3D shape analysis is carried out in five steps: (i) 3D lung nodule segmentation with a deformable 3D boundary controlled by a new prior visual appearance model; (ii) 3D Delaunay triangulation to construct a 3D mesh model of the segmented lung nodule surface; (iii) mapping this model to the unit sphere; (iv) computing the SHs for the surface; and (v) determining the number of the SHs to delineate the lung nodule. We describe the lung nodule shape complexity with a new shape index, the estimated number of the SHs, and use it for the K-nearest classification into malignant and benign lung nodules. Preliminary experiments on 327 lung nodules (153 malignant and 174 benign) resulted in a classification accuracy of 93.6%, showing that the proposed method is a promising supplement to current technologies for the early diagnosis of lung cancer.

3D shape analysis for early diagnosis of malignant lung nodules.

An alternative method for diagnosing malignant lung nodules by their shape rather than conventional growth rate is proposed. The 3D surfaces of the detected lung nodules are delineated by spherical harmonic analysis, which represents a 3D surface of the lung nodule supported by the unit sphere with a linear combination of special basis functions, called spherical harmonics (SHs). The proposed 3D shape analysis is carried out in five steps: (i) 3D lung nodule segmentation with a deformable 3D boundary controlled by two probabilistic visual appearance models (the learned prior and the estimated current appearance one); (ii) 3D Delaunay triangulation to construct a 3D mesh model of the segmented lung nodule surface; (iii) mapping this model to the unit sphere; (iv) computing the SHs for the surface, and (v) determining the number of the SHs to delineate the lung nodule. We describe the lung nodule shape complexity with a new shape index, the estimated number of the SHs, and use it for the K-nearest classification to distinguish malignant and benign lung nodules. Preliminary experiments on 327 lung nodules (153 malignant and 174 benign) resulted in the 93.6% correct classification (for the 95% confidence interval), showing that the proposed method is a promising supplement to current technologies for the early diagnosis of lung cancer.

Elastic phantoms generated by microfluidics technology: validation of an imaged-based approach for accurate measurement of the growth rate of lung nodules.

This paper focuses on validating our approach for monitoring the development of lung nodules detected in successive chest low-dose computed tomography (LDCT) scans of a patient. Our methodology for monitoring detected lung nodules includes 3D LDCT data registration, which is a non-rigid technique and involves two steps: (i) global target-to-prototype alignment of one scan to another using the experience gained from a prior appearance model, followed by (ii) local alignment to correct for intricate relative deformations. We propose a new approach for validating the accuracy of our algorithm for elastic lung phantoms constructed with state-of-the-art microfluidics technology and in vivo data. Fabricated from a flexible transparent polymer, i.e. polydimethylsiloxane (PDMS), the phantoms mimic the contractions and expansions of the lung and nodules as seen during normal breathing. The in vivo data in our study had been collected from a small control group of four subjects and a larger test group of 27 subjects with known ground truth (biopsy diagnosis. The growth rate and diagnostic results for both phantoms and in vivo data confirm the high accuracy of our algorithm.

Impact and costs of targeted recruitment of minorities to the National Lung Screening Trial.

BACKGROUND: To promote results in the National Lung Screening Trial (NLST) that are generalizable across the entire US population, a subset of NLST sites developed dedicated strategies for minority recruitment. PURPOSE: To report the effects of targeted strategies on the accrual of underrepresented groups, to describe participant characteristics, and to estimate the costs of targeted enrollment. METHODS: The 2002-2004 Tobacco Use Supplement was used to estimate eligible proportions of racial and ethnic categories. Strategic planning included meetings/conferences with key stakeholders and minority organizations. Potential institutions were selected based upon regional racial/ethnic diversity and proven success in recruitment of underrepresented groups. Seven institutions submitted targeted recruitment strategies with budgets. Accrual by racial/ethnic category was tracked for each institution. Cost estimates were based on itemized receipts for minority strategies relative to minority accrual. RESULTS: Of 18,842 participants enrolled, 1576 (8.4%) were minority participants. The seven institutions with targeted recruitment strategies accounted for 1223 (77.6%) of all minority participants enrolled. While there was a significant increase in the rate of minority accrual pre-implementation to post-implementation for the institutions with targeted recruitment (9.3% vs. 15.2%, p < 0.0001), there was no significant difference for the institutions without (3.5% vs. 3.8%, p = 0.46). Minority enrollees at the seven institutions tended to have less than a high school education, be economically disadvantaged, and were more often uninsured. These socio-demographic differences persisted at the seven institutions even after adjusting for race and ethnicity. The success of different strategies varied by institution, and no one strategy was successful across all institutions. Costs for implementation were also highly variable, ranging from $146 to $749 per minority enrollee. LIMITATIONS: Data on minority recruitment processes were not consistently kept at the individual institutions. In addition, participant responses via newspaper advertisements and the efforts of minority staff hired by the institutions could not be coded on Case Report Forms. CONCLUSIONS: Strategic efforts were associated with significant increases in minority enrollment. The greatest successes require that a priori goals be established based on eligible racial/ethnic proportions; the historical performance of sites in minority accrual should factor into the selection of sites; recruitment planning must begin well in advance of trial launch; and there must be endorsement by prominent representatives of the racial groups of interest.

Modeling of the lung nodules for detection in LDCT scans.

A novel approach is proposed for generating data driven models of the lung nodules appearing in low dose CT (LDCT) scans of the human chest. Four types of common lung nodules are analyzed using Active Appearance Model methods to create descriptive lung nodule models. The proposed approach is also applicable for automatic classification of nodules into pathologies given a descriptive database. This approach is a major step forward for early diagnosis of lung cancer. We show the performance of the new nodule models on clinical datasets which illustrates significant improvements in both sensitivity and specificity.

Toward precise pulmonary nodule descriptors for nodule type classification.

A framework for nodule feature-based extraction is presented to classify lung nodules in low-dose CT slices (LDCT) into four categories: juxta, well-circumscribed, vascularized and pleural-tail, based on the extracted information. The Scale Invariant Feature Transform (SIFT) and an adaptation to Daugman's Iris Recognition algorithm are used for analysis. The SIFT descriptor results are projected to lower-dimensional subspaces using PCA and LDA. Complex Gabor wavelet nodule response obtained from an adopted Daugman Iris Recognition algorithm revealed improvements from the original Daugman binary iris code. This showed that binarized nodule responses (codes) are inadequate for classification since nodules lack texture concentration as seen in the iris, while the SIFT algorithm projected using PCA showed robustness and precision in classification.

Toward early diagnosis of lung cancer.

Our long term research goal is to develop a fully automated, image-based diagnostic system for early diagnosis of pulmonary nodules that may lead to lung cancer. In this paper, we focus on generating new probabilistic models for the estimated growth rate of the detected lung nodules from Low Dose Computed Tomography (LDCT). We propose a new methodology for 3D LDCT data registration which is non-rigid and involves two steps: (i) global target-to-prototype alignment of one scan to another using the learned prior appearance model followed by (ii) local alignment in order to correct for intricate relative deformations. Visual appearance of these chest images is described using a Markov-Gibbs random field (MGRF) model with multiple pairwise interaction. An affine transformation that globally registers a target to a prototype is estimated by the gradient ascent-based maximization of a special Gibbs energy function. To handle local deformations, we displace each voxel of the target over evolving closed equi-spaced surfaces (iso-surfaces) to closely match the prototype. The evolution of the iso-surfaces is guided by a speed function in the directions that minimize distances between the corresponding voxel pairs on the iso-surfaces in both the data sets. Preliminary results show that the proposed accurate registration could lead to precise diagnosis and identification of the development of the detected pulmonary nodules.

A novel 3D joint Markov-Gibbs model for extracting blood vessels from PC-MRA images.

New techniques for more accurate segmentation of a 3D cerebrovascular system from phase contrast (PC) magnetic resonance angiography (MRA) data are proposed. In this paper, we describe PC-MRA images and desired maps of regions by a joint Markov-Gibbs random field model (MGRF) of independent image signals and interdependent region labels but focus on most accurate model identification. To better specify region borders, each empirical distribution of signals is precisely approximated by a Linear Combination of Discrete Gaussians (LCDG) with positive and negative components. We modified the conventional Expectation-Maximization (EM) algorithm to deal with the LCDG. The initial segmentation based on the LCDG-models is then iteratively refined using a MGRF model with analytically estimated potentials. Experiments with both the phantoms and real data sets confirm high accuracy of the proposed approach.

A new stochastic framework for accurate lung segmentation.

New techniques for more accurate unsupervised segmentation of lung tissues from Low Dose Computed Tomography (LDCT) are proposed. In this paper we describe LDCT images and desired maps of regions (lung and the other chest tissues) by a joint Markov-Gibbs random field model (MGRF) of independent image signals and interdependent region labels but focus on most accurate model identification. To better specify region borders, each empirical distribution of signals is precisely approximated by a Linear Combination of Discrete Gaussians (LCDG) with positive and negative components. We modify a conventional Expectation-Maximization (EM) algorithm to deal with the LCDG and develop a sequential EM-based technique to get an initial LCDG-approximation for the modified EM algorithm. The initial segmentation based on the LCDG-models is then iteratively refined using a MGRF model with analytically estimated potentials. Experiments on real data sets confirm high accuracy of the proposed approach.

Advanced imaging programs: maximizing a multislice CT investment.

Advanced image processing has moved from a luxury to a necessity in the practice of medicine. A hospital's adoption of sophisticated 3D imaging entails several important steps with many factors to consider in order to be successful. Like any new hospital program, 3D post-processing should be introduced through a strategic planning process that includes administrators, physicians, and technologists to design, implement, and market a program that is scalable-one that minimizes up front costs while providing top level service. This article outlines the steps for planning, implementation, and growth of an advanced imaging program.

Whistleblowers: there ain't no cure, but there is prevention.

Under federal and an increasing number of state laws, disgruntled and former employees are given significant financial incentives to report fraud to federal and state regulators. This article addresses strategies for reducing the likelihood that your practice will be the target of a whistleblower suit.

Virtual fly-over: a new visualization technique for virtual colonoscopy.

In this paper, we propose a new visualization technique for virtual colonoscopy (VC). The proposed method is called Virtual Fly-Over, which splits the entire colon anatomy into exactly two halves. Then, it assigns a virtual camera to each half to perform fly-over navigation, which has several advantages over both traditional fly-through and related methods. First, by controlling the elevation of the camera, there is no restriction on its field of view (FOV) angle (e.g., >90 degrees) to maximize visualized surface areas, and hence no perspective distortion. Second, the camera viewing volume is perpendicular to each colon half, so potential polyps that are hidden behind haustral folds are easily found. Finally, because the orientation of the splitting surface is controllable, the navigation can be repeated at a different split orientation to overcome the problem of having a polyp that is divided between the two halves of the colon. Quantitative experimental results on 15 clinical datasets have shown that the average surface visibility coverage is 99.59 +/- 0.2%.

Appearance models for robust segmentation of pulmonary nodules in 3D LDCT chest images.

To more accurately separate each pulmonary nodule from its background in a low dose computer tomography (LDCT) chest image, two new adaptive probability models of visual appearance of small 2D and large 3D pulmonary nodules are used to control evolution of deformable boundaries. The appearance prior is modeled with a translation and rotation invariant Markov-Gibbs random field of voxel intensities with pairwise interaction analytically identified from a set of training nodules. Appearance of the nodules and their background in a current multi-modal chest image is also represented with a marginal probability distribution of voxel intensities. The nodule appearance model is isolated from the mixed distribution using its close approximation with a linear combination of discrete Gaussians. Experiments with real LDCT chest images confirm high accuracy of the proposed approach.

Physicians heal thy laws: ten steps to becoming an effective citizen lobbyist.

By bringing information on medical issues to the attention of their representatives at the national and local levels, physicians can play an important role in molding policies that will affect their patients and their modes of practice. This article outlines the specific steps by which practitioners may engage law makers and their staffers to express their viewpoints and impact the legislative process.

A patient, an internet article, and thou: challenges of the Internet in clinical practice.

This article addresses issues a physician should consider when responding to medical research gathered by a patient from the Internet, discussing both potential medical malpractice liability and offering specific, recommended responses for physicians whose patients conduct online medical research.

Cellular factors required for protection from hyperoxia toxicity in Saccharomyces cerevisiae.

Prolonged exposure to hyperoxia represents a serious danger to cells, yet little is known about the specific cellular factors that affect hyperoxia stress. By screening the yeast deletion library, we have identified genes that protect against high-O2 damage. Out of approx. 4800 mutants, 84 were identified as hyperoxia-sensitive, representing genes with diverse cellular functions, including transcription and translation, vacuole function, NADPH production, and superoxide detoxification. Superoxide plays a significant role, since the majority of hyperoxia-sensitive mutants displayed cross-sensitivity to superoxide-generating agents, and mutants with compromised SOD (superoxide dismutase) activity were particularly vulnerable to hyperoxia. By comparison, factors known to guard against H2O2 toxicity were poorly represented amongst hyperoxia-sensitive mutants. Although many cellular components are potential targets, our studies indicate that mitochondrial glutathione is particularly vulnerable to hyperoxia damage. During hyperoxia stress, mitochondrial glutathione is more susceptible to oxidation than cytosolic glutathione. Furthermore, two factors that help maintain mitochondrial GSH in the reduced form, namely the NADH kinase Pos5p and the mitochondrial glutathione reductase (Glr1p), are critical for hyperoxia resistance, whereas their cytosolic counterparts are not. Our findings are consistent with a model in which hyperoxia toxicity is manifested by superoxide-related damage and changes in the mitochondrial redox state.

Differential fly-throughs (DFT): a general framework for computing flight paths.

In this paper, we propose a new variational framework based on distance transform and gradient vector flow, to compute flight paths through tubular and non-tubular structures, for virtual endoscopy. The proposed framework propagates two wave fronts of different speeds from a point source voxel, which belongs to the medial curves of the anatomical structure. The first wave traverses the 3D structure with a moderate speed that is a function of the distance field to extract its topology, while the second wave propagates with a higher speed that is a function of the magnitude of the gradient vector flow to extract the flight paths. The motion of the fronts are governed by a nonlinear partial equation, whose solution is computed efficiently using the higher accuracy fast marching level set method (HAFMM). The framework is robust, fully automatic, and computes flight paths that are centered, connected, thin, and less sensitive to boundary noise. We have validated the robustness of the proposed method both quantitatively and qualitatively against synthetic and clinical datasets.