CNN-O-ELMNet: Optimized Lightweight and Generalized Model for Lung Disease Classification and Severity Assessment.

The high burden of lung diseases on healthcare necessitates effective detection methods. Current Computer-aided design (CAD) systems are limited by their focus on specific diseases and computationally demanding deep learning models. To overcome these challenges, we introduce CNN-O-ELMNet, a lightweight classification model designed to efficiently detect various lung diseases, surpassing the limitations of disease-specific CAD systems and the complexity of deep learning models. This model combines a convolutional neural network for deep feature extraction with an optimized extreme learning machine, utilizing the imperialistic competitive algorithm for enhanced predictions. We then evaluated the effectiveness of CNN-O-ELMNet using benchmark datasets for lung diseases: distinguishing pneumothorax vs. non-pneumothorax, tuberculosis vs. normal, and lung cancer vs. healthy cases. Our findings demonstrate that CNN-O-ELMNet significantly outperformed (p < 0.05) state-of-the-art methods in binary classifications for tuberculosis and cancer, achieving accuracies of 97.85% and 97.70%, respectively, while maintaining low computational complexity with only 2481 trainable parameters. We also extended the model to categorize lung disease severity based on Brixia scores. Achieving a 96.20% accuracy in multi-class assessment for mild, moderate, and severe cases, makes it suitable for deployment in lightweight healthcare devices.

Developing a multivariate time series forecasting framework based on stacked autoencoders and multi-phase feature.

Time series forecasting across different domains has received massive attention as it eases intelligent decision-making activities. Recurrent neural networks and various deep learning algorithms have been applied to modeling and forecasting multivariate time series data. Due to intricate non-linear patterns and significant variations in the randomness of characteristics across various categories of real-world time series data, achieving effectiveness and robustness simultaneously poses a considerable challenge for specific deep-learning models. We have proposed a novel prediction framework with a multi-phase feature selection technique, a long short-term memory-based autoencoder, and a temporal convolution-based autoencoder to fill this gap. The multi-phase feature selection is applied to retrieve the optimal feature selection and optimal lag window length for different features. Moreover, the customized stacked autoencoder strategy is employed in the model. The first autoencoder is used to resolve the random weight initialization problem. Additionally, the second autoencoder models the temporal relation between non-linear correlated features with convolution networks and recurrent neural networks. Finally, the model's ability to generalize, predict accurately, and perform effectively is validated through experimentation with three distinct real-world time series datasets. In this study, we conducted experiments on three real-world datasets: Energy Appliances, Beijing PM2.5 Concentration, and Solar Radiation. The Energy Appliances dataset consists of 29 attributes with a training size of 15,464 instances and a testing size of 4239 instances. For the Beijing PM2.5 Concentration dataset, there are 18 attributes, with 34,952 instances in the training set and 8760 instances in the testing set. The Solar Radiation dataset comprises 11 attributes, with 22,857 instances in the training set and 9797 instances in the testing set. The experimental setup involved evaluating the performance of forecasting models using two distinct error measures: root mean square error and mean absolute error. To ensure robust evaluation, the errors were calculated at the identical scale of the data. The results of the experiments demonstrate the superiority of the proposed model compared to existing models, as evidenced by significant advantages in various metrics such as mean squared error and mean absolute error. For PM2.5 air quality data, the proposed model's mean absolute error is 7.51 over 12.45, about ∼40% improvement. Similarly, the mean square error for the dataset is improved from 23.75 to 11.62, which is ∼51%of improvement. For the solar radiation dataset, the proposed model resulted in ∼34.7% improvement in means squared error and ∼75% in mean absolute error. The recommended framework demonstrates outstanding capabilities in generalization and outperforms datasets spanning multiple indigenous domains.

MEMS Switch Realities: Addressing Challenges and Pioneering Solutions.

Micro-Electro-Mechanical System (MEMS) switches have emerged as pivotal components in the realm of miniature electronic devices, promising unprecedented advancements in size, power consumption, and versatility. This literature review paper meticulously examines the key issues and challenges encountered in the development and application of MEMS switches. The comprehensive survey encompasses critical aspects such as material selection, fabrication intricacies, performance metrics including switching time and reliability, and the impact of these switches on diverse technological domains. The review critically analyzes the influence of design parameters, actuation mechanisms, and material properties on the performance of MEMS switches. Additionally, it explores recent advancements, breakthroughs, and innovative solutions proposed by researchers to address these challenges. The synthesis of the existing literature not only elucidates the current state of MEMS switch technology but also paves the way for future research avenues. The findings presented herein serve as a valuable resource for researchers, engineers, and technologists engaged in advancing MEMS switch technology, offering insights into the current landscape and guiding future endeavors in this rapidly evolving field.

Chemical Derivatization Leads to the Discovery Of Novel Analogs of Azotochelin, a Natural Siderophore, as Promising Anticancer Agents.

Siderophores are structurally unique medicinal natural products and exhibit considerable therapeutic potential. Herein, we report the design and synthesis of azotochelin, a natural siderophore, and an extensive library of azotochelin analogs and their anticancer properties. We modified the carboxylic acid and the aromatic ring of azotochelin using various chemical motifs. We evaluated the cytotoxicity of the compounds against six different cancer cell lines (KB-3-1, SNB-19, MCF-7, K-562, SW-620, and NCI-H460) and a non-cancerous cell line (HEK-293). Among the twenty compounds tested, the IC50 values of nine compounds (14, 32, 35-40, and 54) were between 0.7 and 2.0 µM against a lung cancer cell line (NCI-H460). Moreover, several compounds showed good cytotoxicity profile (IC50 <10 µM) against the tested cancer cell lines. The flow cytometry analysis showed that compounds 36 and 38 induced apoptosis in NCI-H460 in a dose-dependent manner. The cell cycle analysis indicated that compounds 36 and 38 significantly arrested the cell cycle at the S phase to block cancer cell proliferation in the NCI-H460 cell line. The study has produced various novel azotochelin analogs that are potentially effective anticancer agents and lead compounds for further synthetic and medicinal chemistry exploration.

One-Bone Forearm Procedure for Severe Recalcitrant Forearm Deformities in Masada IIb Hereditary Multiple Exostoses.

Introduction: Hereditary multiple exostoses (HME) Masada IIB has traditionally been treated by gradual ulnar lengthening with questionable efficacy in reducing the dislocated head. One-bone forearm (OBF) has been used as a reconstructive procedure in forearm deformities with very scarce literature for HME. The study aims to report short-term results of OBF as a definitive procedure for severe forearm deformities in Masada IIB patients with respect to clinical and radiological parameters. Materials and Methods: Four patients with HME Masada IIb were included in this retrospective study. All patients complained of forearm and wrist deformity with an abnormal bony protrusion restricting elbow motion. Indications for OBF were ulnar shortening > 3 cm, dysplastic proximal radius with convex radial head and restricted prono-supination. All patients were examined pre-operatively and post-operatively clinically and radiographically using the Peterson's outcome score. Results: The average age was 13 years (12-14 years). Pre-operative ulnar shortening, carpal slip percentage, and radial articular angle was 3.4 cm, 79.5%, and 47.5°, respectively. All radial heads were dislocated with convex articular surface restricting elbow extension and forearm prono-supination. At the latest follow-up, the mean elbow flexion was 110° with forearm in 10° supination. The mean carpal slip percentage, radial articular angle, and Peterson functional grade was 15%, 22.5°, and 8 points, respectively. The mean follow-up period was 30.25 months with no recurrence. Conclusion: We recommend one bone forearm as a definitive procedure in HME Masada IIB patients with severe forearm deformities with ulnar shortening > 3 cm and dysplastic proximal radius with a dislocated radial head, for faster return to function.

Pediatric Cancers: Insights and Novel Therapeutic Approaches.

Pediatric cancers cast a dark shadow over the lives of countless children and their families and represent a leading cause of mortality among children worldwide [...].

ACR Lung CT Screening Reporting and Data System, a Systematic Review and Meta-Analysis Before Change in US Preventative Services Taskforce Eligibility Criteria: 2014 to 2021.

OBJECTIVE: To review Lung CT Screening Reporting and Data System (Lung-RADS) scores from 2014 to 2021, before changes in eligibility criteria proposed by the US Preventative Services Taskforce. METHODS: A registered systematic review and meta-analysis was conducted in MEDLINE, Embase, CINAHL, and Web of Science in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines; eligible studies examined low-dose CT (LDCT) lung cancer screening at institutions in the United States and reported Lung-RADS from 2014 to 2021. Patient and study characteristics, including age, gender, smoking status, pack-years, screening timeline, number of individual patients, number of unique studies, Lung-RADS scores, and positive predictive value (PPV) were extracted. Meta-analysis estimates were derived from generalized linear mixed modeling. RESULTS: The meta-analysis included 24 studies yielding 36,211 LDCT examinations for 32,817 patient encounters. The meta-analysis Lung-RADS 1-2 scores were lower than anticipated by ACR guidelines, at 84.4 (95% confidence interval [CI] 83.3-85.6) versus 90% respectively (P < .001). Lung-RADS 3 and 4 scores were both higher than anticipated by the ACR, at 8.7% (95% CI 7.6-10.1) and 6.5% (95% CI 5.707.4), compared with 5% and 4%, respectively (P < .001). The ACR's minimum estimate of PPV for Lung-RADS 3 to 4 is 21% or higher; we observed a rate of 13.1% (95% CI 10.1-16.8). However, our estimated PPV rate for Lung-RADS 4 was 28.6% (95% CI 21.6-36.8). CONCLUSION: Lung-RADS scores and PPV rates in the literature are not aligned with the ACR's own estimates, suggesting that perhaps Lung-RADS categorization needs to be reexamined for better concordance with real-world screening populations. In addition to serving as a benchmark before screening guideline broadening, this study provides guidance for future reporting of lung cancer screening and Lung-RADS data.

Can incorrect artificial intelligence (AI) results impact radiologists, and if so, what can we do about it? A multi-reader pilot study of lung cancer detection with chest radiography.

OBJECTIVE: To examine whether incorrect AI results impact radiologist performance, and if so, whether human factors can be optimized to reduce error. METHODS: Multi-reader design, 6 radiologists interpreted 90 identical chest radiographs (follow-up CT needed: yes/no) on four occasions (09/20-01/22). No AI result was provided for session 1. Sham AI results were provided for sessions 2-4, and AI for 12 cases were manipulated to be incorrect (8 false positives (FP), 4 false negatives (FN)) (0.87 ROC-AUC). In the Delete AI (No Box) condition, radiologists were told AI results would not be saved for the evaluation. In Keep AI (No Box) and Keep AI (Box), radiologists were told results would be saved. In Keep AI (Box), the ostensible AI program visually outlined the region of suspicion. AI results were constant between conditions. RESULTS: Relative to the No AI condition (FN = 2.7%, FP = 51.4%), FN and FPs were higher in the Keep AI (No Box) (FN = 33.0%, FP = 86.0%), Delete AI (No Box) (FN = 26.7%, FP = 80.5%), and Keep AI (Box) (FN = to 20.7%, FP = 80.5%) conditions (all ps < 0.05). FNs were higher in the Keep AI (No Box) condition (33.0%) than in the Keep AI (Box) condition (20.7%) (p = 0.04). FPs were higher in the Keep AI (No Box) (86.0%) condition than in the Delete AI (No Box) condition (80.5%) (p = 0.03). CONCLUSION: Incorrect AI causes radiologists to make incorrect follow-up decisions when they were correct without AI. This effect is mitigated when radiologists believe AI will be deleted from the patient's file or a box is provided around the region of interest. CLINICAL RELEVANCE STATEMENT: When AI is wrong, radiologists make more errors than they would have without AI. Based on human factors psychology, our manuscript provides evidence for two AI implementation strategies that reduce the deleterious effects of incorrect AI. KEY POINTS: • When AI provided incorrect results, false negative and false positive rates among the radiologists increased. • False positives decreased when AI results were deleted, versus kept, in the patient's record. • False negatives and false positives decreased when AI visually outlined the region of suspicion.

Extracellular vesicles in neurodegenerative diseases: A systematic review.

Introduction: Extracellular vesicles (EVs) are known to have a significant role in the central nervous system (CNS) and neurodegenerative disease. Methods: PubMed, Scopus, ISI Web of Science, EMBASE, and Google Scholar were used to identify published articles about EV modifications (2012 to Feb 2022). Results: In total, 1,435 published papers were identified among the searched articles, with 1,128 non-duplicate publications being identified. Following the screening of titles and abstracts, 214 publications were excluded; following the full-text screening of 93 published articles, another 33 publications were excluded. The remaining 60 studies were considered. The kappa statistic of 0.868 indicated that the raters were highly reliable. Furthermore, the inter-reliability and intra-reliability coefficients were found to be 0.931 and 0.908, respectively, indicating strong reliability and consistency between the eligible studies identified by the raters. A total of 27 relevant studies demonstrated the role of EVs as therapeutic and diagnostic biomarkers in neurodegenerative diseases. Of note, 19 and 14 studies, respectively, found EVs to be pioneering in diagnostic and therapeutic roles. Discussion: EVs play an important role in the central nervous system (CNS), aiding in cell-to-cell communication and serving as a diagnostic marker and therapeutic target in a variety of neurodegenerative diseases. EVs are the home of several proteins [including-synuclein (-syn) and tau proteins], lipids, and genetic materials such as DNA and RNA. The presence of novel miRNAs in EVs suggests biomarkers for the diagnosis and screening of neurodegenerative disorders. Furthermore, EVs play an important role in the pathogenesis of such disorders. This systematic review discussed the current state of EVs' role in neurological diseases, as well as some preclinical studies on the therapeutic and diagnostic potential of EVs.

Protective role of engineered extracellular vesicles loaded quercetin nanoparticles as anti-viral therapy against SARS-CoV-2 infection: A prospective review.

Quercetin (QCT) is a naturally occurring phenolic flavonoid compound with inbuilt characteristics of antioxidant, anti-inflammatory, and immune protection. Several recent studies have shown that QCT and QCTits nanoparticles have therapeutic potential against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. Novel therapeutics also include the implication of extracellular vesicles (EVs) to protect from SARS-CoV-2 viral infection. This article highlighted the therapeutic/prophylactic potential of engineered EVs loaded with QCT against SARS-CoV-2 infection. Several biotechnological engineering approaches are available to deliver EVs loaded with QCT nanoparticles. Among these biotechnological advances, a specific approach with significantly higher efficiency and yield has to be opted to fabricate such drug delivery of nano molecules, especially to combat SARS-CoV-2 infection. The current treatment regime protects the human body from virus infection but has some limitations including drugs and long-term steroid side effects. However, the vaccine strategy is somehow effective in inhibiting the spread of coronavirus disease-19 (COVID-19) infection. Moreover, the proposed exosomal therapy met the current need to repair the damaged tissue along with inhibition of COVID-19-associated complications at the tissue level. These scientific findings expand the possibilities and predictability of developing a novel and cost-effective therapeutic approach that combines the dual molecule, EVs and QCT nanoparticles, to treat SARS-CoV-2 infection. Therefore, the most suitable engineering method to fabricate such a drug delivery system should be better understood before developing novel therapeutics for clinical purposes.

Electronic Cigarettes for Smoking Cessation: The Gap Between Behavior in Smokers and Medical Education.

INTRODUCTION: E-cigarettes have engendered a great deal of controversy within the public health and medical communities.  Methods: Two cross-sectional surveys were administered. First, patients at an annual lung cancer screening appointment who self-identified as former smokers were asked about strategies for achieving and maintaining smoking cessation with open-ended questions. Second, medical students at a single university reported their opinion and knowledge of combustible cigarettes and e-cigarettes. RESULTS: Among the n=102 in the patient survey indicating that they used e-cigarettes or over-the-counter (OTC) nicotine replacement products for smoking cessation, 34.3% (35/102) vaped e-cigarettes, making it the second most common next to patches (47.1% {48/102}). By comparison, n=48 reported using medication. Medical student participants (n=168) were mixed regarding whether a patient should switch from traditional to electronic cigarettes (56.0% yes; 44.0% no) and reported receiving education about traditional cigarettes (92.3%) at a much higher rate than for e-cigarettes (46.4%), p<.001. CONCLUSION: Many former heavy smokers undergoing a lung cancer screen used e-cigarettes to achieve smoking cessation. However, nearly half of medical students surveyed do not think patients should switch from traditional to e-cigarettes.

New horizons of biomaterials in treatment of nerve damage in diabetes mellitus: A translational prospective review.

Background: Peripheral nerve injury is a serious concern that leads to loss of neuronal communication that impairs the quality of life and, in adverse conditions, causes permanent disability. The limited availability of autografts with associated demerits shifts the paradigm of researchers to use biomaterials as an alternative treatment approach to recover nerve damage. Purpose: The purpose of this study is to explore the role of biomaterials in translational treatment approaches in diabetic neuropathy. Study design: The present study is a prospective review study. Methods: Published literature on the role of biomaterials in therapeutics was searched for. Results: Biomaterials can be implemented with desired characteristics to overcome the problem of nerve regeneration. Biomaterials can be further exploited in the treatment of nerve damage especially associated with PDN. These can be modified, customized, and engineered as scaffolds with the potential of mimicking the extracellular matrix of nerve tissue along with axonal regeneration. Due to their beneficial biological deeds, they can expedite tissue repair and serve as carriers of cellular and pharmacological treatments. Therefore, the emerging research area of biomaterials-mediated treatment of nerve damage provides opportunities to explore them as translational biomedical treatment approaches. Conclusions: Pre-clinical and clinical trials in this direction are needed to establish the effective role of several biomaterials in the treatment of other human diseases.

ERK Inhibitor Ulixertinib Inhibits High-Risk Neuroblastoma Growth In Vitro and In Vivo.

Neuroblastoma (NB) is a pediatric tumor of the peripheral nervous system. Approximately 80% of relapsed NB show RAS-MAPK pathway mutations that activate ERK, resulting in the promotion of cell proliferation and drug resistance. Ulixertinib, a first-in-class ERK-specific inhibitor, has shown promising antitumor activity in phase 1 clinical trials for advanced solid tumors. Here, we show that ulixertinib significantly and dose-dependently inhibits cell proliferation and colony formation in different NB cell lines, including PDX cells. Transcriptomic analysis revealed that ulixertinib extensively inhibits different oncogenic and neuronal developmental pathways, including EGFR, VEGF, WNT, MAPK, NGF, and NTRK1. The proteomic analysis further revealed that ulixertinib inhibits the cell cycle and promotes apoptosis in NB cells. Additionally, ulixertinib treatment significantly sensitized NB cells to the conventional chemotherapeutic agent doxorubicin. Furthermore, ulixertinib potently inhibited NB tumor growth and prolonged the overall survival of the treated mice in two different NB mice models. Our preclinical study demonstrates that ulixertinib, either as a single agent or in combination with current therapies, is a novel and practical therapeutic approach for NB.

Exploring Amodiaquine's Repurposing Potential in Breast Cancer Treatment-Assessment of In-Vitro Efficacy & Mechanism of Action.

Due to the heterogeneity of breast cancer, current available treatment options are moderately effective at best. Hence, it is highly recommended to comprehend different subtypes, understand pathogenic mechanisms involved, and develop treatment modalities. The repurposing of an old FDA approved anti-malarial drug, amodiaquine (AQ) presents an outstanding opportunity to explore its efficacy in treating majority of breast cancer subtypes. Cytotoxicity, scratch assay, vasculogenic mimicry study, and clonogenic assay were employed to determine AQ's ability to inhibit cell viability, cell migration, vascular formation, and colony growth. 3D Spheroid cell culture studies were performed to identify tumor growth inhibition potential of AQ in MCF-7 and MDAMB-231 cell lines. Apoptosis assays, cell cycle analysis, RT-qPCR assays, and Western blot studies were performed to determine AQ's ability to induce apoptosis, cell cycle changes, gene expression changes, and induction of autophagy marker proteins. The results from in-vitro studies confirmed the potential of AQ as an anti-cancer drug. In different breast cancer cell lines tested, AQ significantly induces cytotoxicity, inhibit colony formation, inhibit cell migration, reduces 3D spheroid volume, induces apoptosis, blocks cell cycle progression, inhibit expression of cancer related genes, and induces LC3BII protein to inhibit autophagy. Our results demonstrate that amodiaquine is a promising drug to repurpose for breast cancer treatment, which needs numerous efforts from further studies.

Direct Targeting of the Raf-MEK-ERK Signaling Cascade Inhibits Neuroblastoma Growth.

The Raf-MEK-ERK signaling network has been the subject of intense research due to its role in the development of human cancers, including pediatric neuroblastoma (NB). MEK and ERK are the central components of this signaling pathway and are attractive targets for cancer therapy. Approximately 3-5% of the primary NB samples and about 80% of relapsed samples contain mutations in the Raf-MEK-ERK pathway. In the present study, we analyzed the NB patient datasets and revealed that high RAF and MEK expression leads to poor overall survival and directly correlates with cancer progression and relapse. Further, we repurposed a specific small-molecule MEK inhibitor CI-1040 to inhibit the Raf-MEK-ERK pathway in NB. Our results show that CI-1040 potently inhibits NB cell proliferation and clonogenic growth in a dose-dependent manner. Inhibition of the Raf-MEK-ERK pathway by CI-1040 significantly enhances apoptosis, blocks cell cycle progression at the S phase, inhibits expression of the cell cycle-related genes, and significantly inhibits phosphorylation and activation of the ERK1/2 protein. Furthermore, CI-1040 significantly inhibits tumor growth in different NB 3D spheroidal tumor models in a dose-dependent manner and by directly inhibiting spheroidal tumor cells. Overall, our findings highlight that direct inhibition of the Raf-MEK-ERK pathway is a novel therapeutic approach for NB, and further developing repurposing strategies using CI-1040 is a clinically tractable strategy for effectively treating NB.

Repurposing clofazimine for malignant pleural mesothelioma treatment - In-vitro assessment of efficacy and mechanism of action.

AIMS: Malignant pleural mesothelioma (MPM) is a rare cancer of lungs' pleural cavity, with minimally effective therapies available. Thus, there exists a necessity for drug repurposing which is an attractive strategy for drug development in MPM. Repurposing of an old FDA-approved anti-leprotic drug, Clofazimine (CFZ), presents an outstanding opportunity to explore its efficacy in treating MPM. MAIN METHODS: Cytotoxicity, scratch assay, and clonogenic assays were employed to determine CFZ's ability to inhibit cell viability, cell migration, and colony growth. 3D Spheroid cell culture studies were performed to identify tumor growth inhibition potential of CFZ in MSTO-211H cell line. Gene expression analysis was performed using RT-qPCR assays to determine the CFZ's effect of key genes. Western blot studies were performed to determine CFZ's ability to induce apoptosis its effect to induce autophagy marker. KEY FINDINGS: CFZ showed significant cytotoxicity against both immortalized and primary patient-derived cell lines with IC50 values ranging from 3.4 µM (MSTO-211H) to 7.1 µM (HAY). CFZ significantly impaired MPM cell cloning efficiency, migration, and tumor spheroid formation. 3D Spheroid model showed that CFZ resulted in reduction in spheroid volume. RT-qPCR data showed downregulation of genes ß-catenin, BCL-9, and PRDX1; and upregulation of apoptosis markers such as PARP, Cleaved caspase 3, and AXIN2. Additionally, immunoblot analysis showed that CFZ down-regulates the expression of ß-catenin (apoptosis induction) and up-regulates p62, LC3B protein II (autophagy inhibition). SIGNIFICANCE: It can be concluded that CFZ could be a promising molecule to repurpose for MPM treatment which needs numerous efforts from further studies.

Heavy Smoking Patients Receiving a Lung Cancer Screen Want to Quit: A Call for Tailored Cessation Interventions.

Background: Lung cancer screening for current or former heavy smokers is now recommended among all asymptomatic adults 50-80 years old with a 20 pack-year history of smoking. However, little is known about the smoking-related attitudes of this population. Method: An assessment was conducted among 1,472 current smokers who presented for an annual lung cancer screen at one of 12 diagnostic imaging sites in Rhode Island between April 2019 and May 2020. Patients were asked about their use of smoking products, interest in quitting, and smoking-related attitudes. Results: Patients smoked a median of 16 cigarettes per day; 86.6% were daily cigarette smokers and 30.1% were daily cigar smokers. In total, 91.4% of patients were, to some degree, interested in quitting smoking and 71.4% were seriously thinking about quitting in the next 6 months or sooner. Patients planned on smoking less regardless of whether their lung screen was positive or negative for cancer, though they were more likely to plan on smoking less if negative (on 0-3 pt Likert scale: 0.31, 95% CI [0.27, 0.34] vs. 0.77, 95% CI [0.72, 0.81]). Confidence in quitting and belief in one's inherent ability to quit smoking varied substantially within the sample. Conclusion: Nearly all current smokers receiving a lung cancer screen have some interest in smoking cessation. Due to the heterogeneity in some smoking-related attitudes, tailored interventions for this population should be tested.