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.

Monitoring of tiny intracell temperature variations is of high importance to understand the mechanisms of exothermic/endothermic processes inside the living cells. Small shifts in thermal balance may drastically influence cell functioning and induce pathological conditions. By using biocompatible diamond single‐crystal microneedles enriched with nitrogen‐vacancy (NV)/silicon‐vacancy (SiV) color centers, this study demonstrates all‐optical in vitro temperature monitoring in the physiologically significant range (25–55 °C). Zero‐phonon line (ZPL) of SiV centers belonging to the “therapeutic window” is used to improve measurement precision via suppression of the tissue autofluorescence. The simultaneous detection of the NV and SiV fluorescence enables two‐band visualization of the living cells combined with the temperature sensing. This study demonstrates experimentally that temperature can be measured by lifetime, full‐width at half maximum, and peak position of SiV ZPL, while accuracy can be further improved by normalizing the photoluminescence (PL) ZPL peak intensity on the PL signal measured at the wavelength where it is temperature independent. According to performed numerical simulations diamond microneedles enable real‐time temperature measurements because their characteristic heating time is less than 10 ns. The results open a way toward accurate, noninvasive, precise, and real‐time monitoring of temperature variations accompanying intracellular biochemical reactions and processes on the single‐cell level.

A systematic spectroscopic characterization of highly homogeneous water suspensions of ‘buckydiamonds’ comprising sp³ cubic nanodiamond (ND) core covered with disordered sp² shell densely decorated with oxygen-containing groups demonstrates the excitation-wavelength-dependent photoluminescence (PL) given by at least four types of specific structures on the ND surface (hydroxyl, C=O containing ketones, carboxylic anhydrides, and carboxyl groups). PL properties of NDs suspensions possess concentration-dependent behavior revealing tendency of NDs to agglomerate. PL of NDs has been found to be strongly sensitive to pH of the environment in wide range of pH values, i.e. 2–11. We disclosed the mechanisms of pH sensitivity of the ‘buckydiamond’ and proved that it can serve as all-optical sensor of tiny pH variations suitable for further exploitation for pH sensing locally in the area where NDs have been delivered for any purpose, e.g. bioimaging or therapeutic needs.

We propose a simple, fast, and low-cost method for producing Au-coated black Si-based SERS-active substrates with a proven enhancement factor of 10⁶. Room temperature reactive ion etching of silicon wafer followed by nanometer-thin gold sputtering allows the formation of a highly developed lace-type Si surface covered with homogeneously distributed gold islands. The mosaic structure of deposited gold allows the use of Au-uncovered Si domains for Raman peak intensity normalization. The fabricated SERS substrates have prominent uniformity (with less than 6% SERS signal variations over large areas, 100 × 100 μm²). It has been found that the storage of SERS-active substrates in an ambient environment reduces the SERS signal by less than 3% in 1 month and not more than 40% in 20 months. We showed that Au-coated black Si-based SERS-active substrates can be reused after oxygen plasma cleaning and developed relevant protocols for removing covalently bonded and electrostatically attached molecules. Experiments revealed that the Raman signal of 4-MBA molecules covalently bonded to the Au coating measured after the 10th cycle was just 4 times lower than that observed for the virgin substrate. A case study of the reusability of the black Si-based substrate was conducted for the subsequent detection of 10^-5 M doxorubicin, a widely used anticancer drug, after the reuse cycle. The obtained SERS spectra of doxorubicin were highly reproducible. We demonstrated that the fabricated substrate permits not only qualitative but also quantitative monitoring of analytes and is suitable for the determination of concentrations of doxorubicin in the range of 10^-9-10^-4 M. Reusable, stable, reliable, durable, low-cost Au-coated black Si-based SERS-active substrates are promising tools for routine laboratory research in different areas of science and healthcare.

We demonstrated that wide-field second harmonic generation (SHG) microscopy of lung tissue in combination with quantitative analysis of SHG images is a powerful tool for fast and label-free visualization of the fibrosis pathogenesis in pulmonary arterial hypertension (PAH). Statistical analysis of the SHG images revealed changes of the collagen content and morphology in the lung tissue during the monocrotaline-induced PAH progression in rats. First order statistics disclosed the dependence of the collagen overproduction on time, the second order statistics indicated tightening of collagen fiber network around blood vessels and their spreading into the alveolar region. Fourier analysis revealed that enhancement of the fiber orientation in the collagen network with PAH progression was followed with its subsequent reduction at the terminating phase of the disease. Proposed approach has potential for assessing pulmonary fibrosis in interstitial lung disease, after lung(s) transplantation, cancer, etc.

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.