The cell refractive index has been proposed as a putative cancer biomarker of great potential, being correlated with cell content and morphology, cell division rate and membrane permeability. We used digital holographic microscopy to compare the refractive index and dry mass density of two B16 murine melanoma sublines of different metastatic potential. Using statistical methods, the distribution of phase shifts within the reconstructed quantitative phase images was analyzed by the method of bimodality coefficients. The observed correlation of refractive index, dry mass density and bimodality profile with the metastatic potential of the cells was validated by real time impedance‐based assay and clonogenic tests. We suggest that the refractive index and bimodality analysis of quantitative phase image histograms could be developed as optical biomarkers useful in label‐free detection and quantitative evaluation of cell metastatic potential.

Changes in optical and shape-related characteristics of B16F10 cells after electroporation were investigated using digital holographic microscopy (DHM). Bipolar rectangular pulses specific for electrochemotherapy were used. Electroporation was performed in an “off-axis” DHM set-up without using exogenous markers. Two types of cell parameters were monitored seconds and minutes after pulse train application: parameters addressing a specifically defined area of the cell (refractive index and cell height) and global cell parameters (projected area, optical phase shift profile and dry mass). The biphasic behavior of cellular parameters was explained by water and mannitol dynamics through the electropermeabilized cell membrane.

Gliomas are the most common type of cerebral tumors; they occur with increasing incidence in the last decade and have a high rate of mortality. For efficient treatment, fast accurate diagnostic and grading of tumors are imperative. Presently, the grading of tumors is established by histopathological evaluation, which is a time-consuming procedure and relies on the pathologists’ experience. Here we propose a supervised machine learning procedure for tumor grading which uses quantitative phase images of unstained tissue samples acquired by digital holographic microscopy. The algorithm is using an extensive set of statistical and texture parameters computed from these images. The procedure has been able to classify six classes of images (normal tissue and five glioma subtypes) and to distinguish between gliomas types from grades II to IV (with the highest sensitivity and specificity for grade II astrocytoma and grade III oligodendroglioma and very good scores in recognizing grade III anaplastic astrocytoma and grade IV glioblastoma). The procedure bolsters clinical diagnostic accuracy, offering a swift and reliable means of tumor characterization and grading, ultimately the enhancing treatment decision-making process.

Reactive oxygen species (ROS) regulate cellular signaling at physiological concentrations and drive tissue damage when their generation exceeds antioxidant defenses. The conceptual reframing of the field into oxidative eustress (low, controlled redox signaling) and oxidative distress (supraphysiological levels causing biomolecular damage), alongside parallel advances in detection chemistry and genetically encoded biosensors, has transformed how investigators measure ROS in living systems. This review provides a critical, methods-focused update covering the contemporary toolkit, with particular emphasis on advances from 2015 to 2026 while incorporating earlier foundational work where it remains indispensable to interpretation. Consistent with the title and reflecting both the maturity of the available chemistry and the weight of the recent literature, our emphasis falls on hydrogen peroxide and mammalian experimental systems; superoxide, the hydroxyl radical, and singlet oxygen are addressed primarily where their detection intersects with the platforms reviewed here, and non-mammalian models are considered only selectively. Readers seeking dedicated coverage of these other species or of plant, microbial, and invertebrate systems are directed to the specialized reviews cited throughout. Five complementary measurement platforms are evaluated: (i) electron paramagnetic resonance spectroscopy with classical nitrone spin traps and the newer cyclic hydroxylamine probes; (ii) small-molecule fluorescent probes, with particular emphasis on the boronate-based, activity-based sensing platform that has supplanted 2′,7′-dichlorofluorescin diacetate for hydrogen peroxide imaging; (iii) genetically encoded biosensors of the HyPer and roGFP families, which now permit ratiometric, organelle-resolved, and longitudinal measurements; (iv) mass-spectrometry-based quantification of oxidation products and radical adducts, including isoprostanes, 2-hydroxyethidium, and redox-modified cysteines via chemical proteomics; and (v) electrochemical and nanosensor approaches enabling real-time single-cell measurements. The selectivity, sensitivity, temporal resolution, spatial resolution, and quantitative capability of each platform are critically compared. Reliance on a single non-specific probe is no longer sufficient as the sole evidence base for quantitative or species-specific claims; contemporary investigators are expected to apply complementary approaches and to validate findings across modalities. Standardization of reporting, integration with single-cell omics, and clinical translation of validated mass-spectrometry biomarkers are identified as priorities for the coming decade.

An accurate detection of the spread of colorectal cancer to lymph nodes is a critical factor in determining the patient prognosis and treatment. Standard histological evaluation faces significant challenges that start from the intrinsic fragility of lymph node tissues, making them difficult to process and prepare for analysis, and from the subsequent manual review of slides, demanding extensive reading time from pathologists. There is a need of computer assisted diagnostic companion of the pathologist that provides objectivity and workflow efficacy. To address this, our study introduces an innovative approach that combines label-free imaging with machine learning (ML) for detection of metastatic invasions into lymph nodes. We utilized a Digital Holographic Microscope to obtain quantitative phase images (QPIs) of unstained samples. QPIs were cropped in superpixels that were classified with two supervised machine learning models (coarse and fine models) based on phase-shift parameters extracted from each superpixel. The ML-based procedure classified pixels with 97.91% Sensitivity and 98.62% Specificity into normal and invasion classes, and delineated invasion and areas with high suspicion grade from normal areas in segmented images. Our tool supports the clinicians in identifying malignant areas and focusing their expertise on the tumor boundaries, representing a step forward in computer-assisted cancer diagnosis.

In addition to information about cell morphology, hyperspectral images (HSIs) provide consistent spectral details across hundreds of wavelengths. Cytological analysis of samples extracted from serous cavity effusions is used to diagnose their benign or malignant origin but faces several challenges due to interference of various cell types. Therefore, cells segmentation from microscopy images is necessary to enable the extraction of specific quantitative biological characteristics. Here, we propose an automated semantic segmentation of cells into nucleus and cytoplasm classes using machine learning classifiers applied to HSIs. Moreover, the proposed procedure can be applied regardless of whether the cells are stained or unstained. We defined as input data multiple hyperspectral vectors derived from spectral profiles collected at the single-pixel scale from HSIs. The ensemble models achieving highest overall accuracy on the test sets were selected as demonstrators and subsequently applied under real conditions to the regions of interest of new, independent HSIs. The limitations associated with poor contrast in unstained samples were mitigated by introducing spectral shape transformations, along with a series of predefined post-processing functions, in order to improve the robustness of external evaluation. We achieved an overall accuracy of 0.98 for stained samples and 0.97 for unstained samples on the test sets. In the external evaluation, the Dice similarity coefficient was 0.94 for stained samples and 0.70 for unstained samples for the nucleus class, respectively, 0.88 and 0.82, for the cytoplasm class. This study advances the automatic segmentation methods for HSIs of stained and unstained samples. Future work will focus on integrating these findings into automated classification frameworks for cells harvested from serous cavity fluids.