Publications

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.

Precision and personalized medicine for disease management necessitates real-time, continuous monitoring of biomarkers and therapeutic drugs to adjust treatment regimens based on individual patient responses. This study introduces a wearable Microneedle-based Continuous Biomarker/Drug Monitoring (MCBM) system, designed for the simultaneous, in vivo pharmacokinetic and pharmacodynamic evaluation for diabetes. Utilizing a dual-sensor microneedle and a layer-by-layer nanoenzyme immobilization strategy, the MCBM system achieves high sensitivity and specificity in measuring glucose and metformin concentrations in skin interstitial fluid (ISF). Seamless integration with a smartphone application enables real-time data analysis and feedback, fostering a pharmacologically informed approach to diabetes management. The MCBM system’s validation and in vivo trials demonstrate its precise monitoring of glucose and metformin, offering a tool for personalized treatment adjustments. Its proven biocompatibility and safety suit long-term usage. This system advances personalized diabetes care, highlighting the move towards wearables that adjust drug dosages in real-time, enhancing precision and personalized medicine.

Background
Mutational signatures are increasingly used to understand the mechanisms causing cancer. However, their important applications in predicting prognosis and stratifying patients for therapy are hampered by inaccurate inference of the various featureless, dense trinucleotide mutational spectra, which are often confounded with one another. One of them is the homologous recombination deficiency (HRd)-associated signature SBS3, relevant because of its association with prognosis in ovarian and breast cancer and because of its potential as a biomarker for synthetic lethality therapies.

Methods
Here, we highlight strong benefits of a multimodal approach for mutational signature extraction, applied on top of standard bioinformatic pipelines. By jointly operating on single-base substitution (SBS) and indel (ID) spectra, this method enables accurate identification of various DNA repair deficiency signatures and patient survival prediction.

Results
Across four different cohorts of whole-genome sequenced high-grade serous ovarian cancers (HGSOC), the multimodal SBS + ID approach correctly distinguished the commonly confused signatures SBS3, SBS5, SBS8, SBS39, and SBS40. Importantly, we robustly identified two different multimodal SBS3 signatures, m-SBS3a and m-SBS3b, with distinct patterns in the indel spectrum. Multimodal SBS3b signature was strongly predictive of longer survival in ovarian cancer patients, replicating across four cohorts, with effect sizes greatly exceeding other genetic markers. Our m-SBS3 also predicted survival in platinum-treated patients with various cancer types, and moreover, the SBS + ID joint inference was successfully applied to mismatch repair-deficient colorectal cancer and immunotherapy response, supporting a general utility of the multimodal mutational signatures approach.

Conclusions
Overall, combining SBS and ID mutations improves detection of HR deficiency-associated signatures and reveals distinct SBS3 subtypes with prognostic value. This multimodal approach outperforms existing markers and is readily applicable to therapy stratification.

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.

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.

Biological Field Effect Transistors (Bio-FETs) are redefining the standard of biosensing by enabling label-free, real-time, and extremely sensitive detection of biomolecules. At the center of this innovation is the fundamental empowering role of advanced materials, such as graphene, molybdenum disulfide, carbon nanotubes, and silicon. These materials, when harnessed with the downstream biomolecular probes like aptamers, antibodies, and enzymes, allow Bio-FETs to offer unrivaled sensitivity and precision. This review is an exposition of how advancements in materials science have permitted Bio-FETs to detect biomarkers in extremely low concentrations, from femtomolar to attomolar levels, ensuring device stability and reliability. Specifically, the review examines how the incorporation of cutting-edge materials architectures, like flexible / stretchable and multiplexed designs, is expanding the frontiers of biosensing and contributing to the development of more adaptable and user-friendly Bio-FET platforms. A key focus is placed on the synergy of Bio-FETs with artificial intelligence (AI), the Internet of Things (IoT), and sustainable materials approaches as fast-tracking toward transition from research into practical healthcare applications. The review also explores current challenges such as material reproducibility, operational durability, and cost-effectiveness. It outlines targeted strategies to address these hurdles and facilitate scalable manufacturing. By emphasizing the transformative role played by advanced materials and their cementing position in Bio-FETs, this review positions Bio-FETs as a cornerstone technology for the future healthcare solution for precision applications. These advancements would lead to an era where material innovation would herald massive strides in biomedical diagnostics and subsume.

As part of the LUCIA project, funded under Horizon Europe, Yaghma facilitated Social Lab workshops to identify and address challenges in implementing novel lung cancer screening technologies including an AI-driven risk prediction algorithm. Engaging healthcare professionals, policymakers, and patient representatives, these participatory sessions helped uncover key barriers of implementing LUCIA technologies to lung cancer screening.

Through structured discussions, 80 actionable barriers have been identified across 10 key categories, including trust, feasibility, ethical considerations, and regulation. These insights were mapped in an Impact-Influence map through a prioritization survey, ensuring that the project focuses on the most critical and actionable challenges.

Yaghma’s role in this process underscores the importance of stakeholder engagement in responsible AI adoption, ensuring that AI technologies in cancer screening are practical, inclusive, and aligned with real-world healthcare and patient needs.

This review article explores the transformative potential of smart dust systems by examining how existing chemical sensing technologies can be adapted and advanced to realize their full capabilities. Smart dust, characterized by submillimeter-scale autonomous sensing platforms, offers unparalleled opportunities for real-time, spatiotemporal chemical mapping across diverse environments. This article introduces the technological advancements underpinning these systems, critically evaluates current limitations, and outlines new avenues for development. Key challenges, including multi-compound detection, system control, environmental impact, and cost, are discussed alongside potential solutions. By leveraging innovations in miniaturization, wireless communication, AI-driven data analysis, and sustainable materials, this review highlights the promise of smart dust to address critical challenges in environmental monitoring, healthcare, agriculture, and defense sectors. Through this lens, the article provides a strategic roadmap for advancing smart dust from concept to practical application, emphasizing its role in transforming the understanding and management of complex chemical systems.