Publications

Background
Electronic cigarettes (e-cigarettes) have gained popularity in recent years. While initially introduced as a safe alternative for tobacco and a bridge for smoking cessation, subsequent studies found that they contain toxic substances. We aimed to assess the acute effect of a single session of e-cigarette smoking on cardiorespiratory parameters, exhaled volatile organic compounds (VOCs) and markers of inflammation.

Methods
A prospective single-centre study was carried out. Participants (healthy volunteers, former e-cigarette users) were assessed before and after a 30-min session of e-cigarette smoking. Evaluations included vital signs, pulmonary functions – spirometry and fractional exhaled nitric oxide (FeNO) – blood and exhaled breath condensate (EBC) cytokines and electronic nose (e-nose) for analysis of exhaled VOCs profile.

Results
30 participants aged 27.9±4.4 years were enrolled in the study. Post-smoking observations revealed a significant increase in heart rate (77.5±10.9 to 85.5±12.1 beats·min−1, p=0.002), respiratory rate (15.4±2.2 to 17.1±1.8 breaths·min−1, p=0.002) and blood pressure (systolic 118±8.1 to 123.5±11.9, p=0.017; diastolic 73.9±8.4 to 78.5±6.3 mmHg, p=0.011). FeNO decreased significantly (median of 11 (7.5–15.5) to 9.7 (7.3–17.3) ppb, p=0.024). Analysis of e-nose found a significant change of exhaled VOC pattern after e-cigarette smoking. No significant changes were found in spirometry and cytokine levels in blood or EBC.

Conclusions
A single session of 30 min of e-cigarette smoking caused significant cardiorespiratory effects, decreased FeNO and altered exhaled VOC pattern, similar to the effect seen with cigarette and water-pipe smoking.‏ The observed acute effects, together with the well-known chronic risks, highlight the importance of effective regulation of e-cigarettes.

Breath analysis offers a non-invasive approach to modern diagnostics by capturing volatile organic compounds (VOCs) in exhaled breath. However, current breath analysis technologies face challenges like humidity sensitivity, high costs, and biodegradable solutions, limiting their scalability and environmental sustainability. This study presents a paper-based, biodegradable, humidity-insensitive electronic nose (e-nose) sensor array integrated into a face mask for real-time breath analysis. The sensors, coated with hydrophobic polymer coating, ensure robust insensitivity to humidity, enabling reliable detection of VOCs even in high-moisture environments. The mask-integrated e-nose facilitates real-time breath monitoring for applications such as alcohol consumption tracking and respiratory health assessment. For the latter, Tuberculosis (TB) detection is selected as a representative use case, achieving 89% accuracy in disease diagnosis and recovery monitoring using a pre-trained deep-learning model. The fully-biodegradable paper-based sensor naturally degrades in soil within months, underscoring its eco-friendly design and suitability for disposable health monitoring. This work introduces a sustainable, user-friendly approach to breath analysis with potential applications in non-invasive disease detection and personalized healthcare monitoring.

Organoids mimic human organ function, offering insights into development and disease. However, non-destructive, real-time monitoring is lacking, as traditional methods are often costly, destructive, and low-throughput. In this article, a non-destructive chemical tomographic strategy is presented for decoding cyto-proteo-genomics of organoid using volatile signaling molecules, hereby, Volatile Organic Compounds (VOCs), to indicate metabolic activity and development of organoids. Combining a hierarchical design of graphene-based sensor arrays with AI-driven analysis, this method maps VOC spatiotemporal distribution and generate detailed digital profiles of organoid morphology and proteo-genomic features. Lens- and label-free, it avoids phototoxicity, distortion, and environmental disruption. Results from testing organoids with the reported chemical tomography approach demonstrate effective differentiation between cyto-proteo-genomic profiles of normal and diseased states, particularly during dynamic transitions such as epithelial-mesenchymal transition (EMT). Additionally, the reported approach identifies key VOC-related biochemical pathways, metabolic markers, and pathways associated with cancerous transformations such as aromatic acid degradation and lipid metabolism. This real-time, non-destructive approach captures subtle genetic and structural variations with high sensitivity and specificity, providing a robust platform for multi-omics integration and advancing cancer biomarker discovery.

Lung cancer remains one of the deadliest cancers and a major public health concern. Although numerous studies have identified various risk factors, further research is essential, particularly in the biological domain. Existing data sources compile biological information on lung cancer and its subtypes but differ in structure and format, complicating data extraction and integration for artificial intelligence (AI) models. Ontologies and semantic technologies address this challenge by enabling the construction of unified knowledge graphs that promote interoperability. Lung-CABO is an ontology specifically designed for lung cancer, supporting the creation of a knowledge graph for risk factor identification and AI applications. Its modular design allows expansion to integrate additional data, such as environmental factors, further enhancing its utility and reusability.

This deliverable has been conceived in the frame of T4.3 “General population screening”, T4.4 “Diagnosis and precision follow-up and stratification” and T4.5 “Contextual-empirical investigations to evaluate the realization of identified values”. These tasks are devoted to the recruitment of a prospective cohort of around 3,000 volunteers that will be followed up for 2 years and on the diagnosis, including Indeterminate Pulmonary Nodules (IPN) characterization, with an accentuation on the Never Smokers and Reduced Smokers (NSRS) patients, incorporating Breath Analyser (BAN), Wide-biomarker-spectrum Multi-Use Sensing Patch (WBSP) and spectrometry-on-card (SPOC) into clinical studies.

Regarding Task 4.5, it examines whether identified socio-technical values (in WP1-3) (e.g., transparency, bias, accountability, explanability) are realized when the technology is used. To achieve this goal, the different contexts of the use of technology are to be analysed as different contextual variables come into play to impact the way values are understood.

Volunteers have been recruited from different clinical centers (“Servicio Andaluz de Salud” (SAS) and “Osakidetza Servicio Vasco de Salud” (OSA), in Spain; “Centre Hospitalier Universitaire de Liège” (CHUL) in Belgium, and “Centre for Tuberculosis and Lung Diseases (CTLD) of Riga East University Hospital (REUH)” in Latvia). Non-invasive devices such as Breath Analyzer (BAN), Multiomics (MO) and spectrometry-on-card (SPOC) are monitoring these participants.

The entire study cohort is currently being followed up. For 2 years, participants of the study will attend to 4 visits: baseline, month 6, month 12 and month 24. During these visits, the following tests and procedures will be carried out:

– Baseline visit: blood test, spirometry, lifestyle questionnaires, sociodemographic data, medical record data, exposure to harmful agents data, physical exploration, LDCT scan and new lung cancer screening devices testing (breath analyzer and spectrometry-on-card)
– 6 months visit: remote visit where sociodemographic data, medical record data and exposure to harmful agents data will be recorded.
– 12 months visit: spirometry, lifestyle questionnaires, sociodemographic data, medical record data, exposure to harmful agents’ data and physical exploration.
– 24 months visit: spirometry, ifestyle questionnaires, sociodemographic data, medical record data, exposure to harmful agents’ data, physical exploration, low-dose computed tomography (LDCT) scan and new lung cancer screening devices testing (breath analyzer and spectrometry-on-card).

(See the following of the Executive Summary in file).

BACKGROUND
The US Preventive Services Task Force (USPSTF) recommends lung cancer screening for individuals aged 50–80 with ≥20 pack-years of smoking and ≤15 quit-years. This implies that former heavy smokers with ≥20 pack-years would either be offered screening from age 50 onwards or not at all, depending on a dichotomous classification by time since cessation. An alternative strategy that would better match individual risks could be to define risk-adapted starting ages of screening, according to time since cessation.

METHODS
Based on data from the UK Biobank cohort, we assessed the relationship between smoking cessation time, pack-years, age, and lung cancer risk among ever heavy smokers using multivariable Cox proportional hazards models. We estimated “risk postponement periods” (RPPs) from the regression coefficients for the time since cessation and age and used these RPP estimates to derive risk-adapted starting ages for lung cancer screening among former heavy smokers, using 50 years as the reference starting age for current heavy smokers.

RESULTS
The RPPs for smoking cessation ranged from 3.1 (95% CI 1.7, 4.5) years for former heavy smokers who quit 6-10 years ago to 14.6 (95% CI 13.0, 16.3) years for former heavy smokers who quit more than 15 years ago, which translate to risk-adapted starting ages of screening between 53 and 65 years.

CONCLUSIONS
Our analysis provides an empirical basis for risk-adapted starting ages of lung cancer screening among former heavy smokers.

Somatic cells can transform into tumors due to mutations, and the tumors further evolve towards increased aggressiveness and therapy resistance. We develop DiffInvex, a framework for identifying changes in selection acting on individual genes in somatic genomes, drawing on an empirical mutation rate baseline derived from non-coding DNA that accounts for shifts in neutral mutagenesis during cancer evolution. We apply DiffInvex to >11,000 somatic whole-genome sequences from ~30 cancer types or healthy tissues, identifying genes where point mutations are under conditional positive or negative selection during exposure to specific chemotherapeutics, suggesting drug resistance mechanisms occurring via point mutation. DiffInvex identifies 11 genes exhibiting treatment-associated selection for different classes of chemotherapies, linking selected mutations in PIK3CA, APC, MAP2K4, SMAD4, STK11 and MAP3K1 with drug exposure. Various gene-chemotherapy associations are further supported by differential functional impact of mutations pre- versus post-therapy, and are also replicated in independent studies. In addition to nominating drug resistance genes, we contrast the genomes of healthy versus cancerous cells of matched human tissues. We identify noncancerous expansion-specific drivers, including NOTCH1 and ARID1A. DiffInvex can also be applied to diverse analyses in cancer evolution to identify changes in driver gene repertoires across time or space.

How is the LUCIA Project Study Conducted in Latvia

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