Theranostics is the emerging field of medicine that uniquely combines diagnostic techniques and active agents to diagnose and treat medical conditions simultaneously or sequentially. Finding a theranostic agent capable to cure the affected cells and being safe for the healthy ones is the key for successful treatment. Here, we demonstrate that agglomerated single-walled carbon nanotubes (SWCNTs) are promising theranostic agent that enables photo-activated ‘cold’ destruction of the cancer cells keeping their environment alive. The absorption of picosecond pulses by SWCNT agglomerates results in the mechanical (due to photoacoustic effect) rather than photothermal cancer cell destruction, which was visualized by micro-Raman and ultrafast near-infrared CARS. The developed theoretical model allows us to distinguish photothermal, photoacoustic, and photothermoacoustic regimes of the cancer cell destruction, and also to optimize SWCNT-based theranostics recipe.

Papillary thyroid carcinoma (PTC) is one of the most common, well-differentiated carcinomas of the thyroid gland. PTC nodules are often surrounded by a collagen capsule that prevents the spread of cancer cells. However, as the malignant tumor progresses, the integrity of this protective barrier is compromised, and cancer cells invade the surroundings. The detection of capsular invasion is, therefore, crucial for the diagnosis and the choice of treatment and the development of new approaches aimed at the increase of diagnostic performance are of great importance. In the present study, we exploited the wide-field second harmonic generation (SHG) microscopy in combination with texture analysis and unsupervised machine learning (ML) to explore the possibility of quantitative characterization of collagen structure in the capsule and designation of different capsule areas as either intact, disrupted by invasion, or apt to invasion. Two-step k-means clustering showed that the collagen capsules in all analyzed tissue sections were highly heterogeneous and exhibited distinct segments described by characteristic ML parameter sets. The latter allowed a structural interpretation of the collagen fibers at the sites of overt invasion as fragmented and curled fibers with rarely formed distributed networks. Clustering analysis also distinguished areas in the PTC capsule that were not categorized as invasion sites by the initial histopathological analysis but could be recognized as prospective micro-invasions after additional inspection. The characteristic features of suspicious and invasive sites identified by the proposed unsupervised ML approach can become a reliable complement to existing methods for diagnosing encapsulated PTC, increase the reliability of diagnosis, simplify decision making, and prevent human-related diagnostic errors. In addition, the proposed automated ML-based selection of collagen capsule images and exclusion of non-informative regions can greatly accelerate and simplify the development of reliable methods for fully automated ML diagnosis that can be integrated into clinical practice.

The shape of coherent anti-Stokes Raman scattering (CARS) spectral line depends on the ratio of the vibrational and electronic contributions to the third-order susceptibility of the material. The G-mode (1590 cm⁻¹) of graphene and carbon nanotubes (CNTs) exhibits opposite features in the CARS spectrum, showing “dip” and “peak,” respectively. Here, we consider the CARS spectra of graphene and carbon nanotubes in terms of Fano formalism describing the line shapes of CARS resonances. We show that imaging at only 1590 cm⁻¹ is not sufficient to separate the constituents of a composite material consisting of both graphene and CNTs. We propose an algorithm to map the graphene and CNTs in a composite material.

The identification of tumor capsular invasion as a sign of malignancy is currently employed in traditional histopathology routines for thyroid nodules. However, its limitations are associated with the assessment criteria for invasion, which often lead to disagreements among observers. The aim of this paper is to introduce a widefield imaging technique combined with quantitative collagen analysis to identify areas of capsular invasion in thyroid neoplasms. In this study, we introduce the application of widefield polarization-resolved second harmonic generation microscopy for imaging entire thyroid nodule sections on histological slides. We employ a cylindrical collagen model to extract parameters associated with the ultrastructure and orientation of collagen within the entire capsule of the thyroid nodule. We showcase the effectiveness of these parameters in distinguishing between areas of nodule capsule invasion and unaffected regions of the capsule through statistical analysis of individual parameters and employing a machine learning technique that involves generating maps via cluster analysis. Our results suggest that quantitative analysis facilitated by polarization-resolved widefield second harmonic generation microscopy could prove beneficial for the automated evaluation of capsular invasion sites in thyroid pathology.