This study explores the potential of conventional Raman spectroscopy and commonly used spectral analysis pipelines for rapid and straightforward assessment of degradation in serum samples resulting from storage delays. Serum samples from 18 volunteers were processed within 2 h of extraction, which later on were analyzed via Raman spectroscopy over 4 days, while the corresponding serum vials were kept at room temperature. The resulting spectra were processed, including silicon normalization and a newly proposed outlier detection ensemble method. Next, baseline correction was performed, and spectral unmixing along with Principal Component Analysis (PCA) were applied. Several classification models (KNN, RF, and SVM) were trained and evaluated on three distinct balanced datasets: one including all data, one excluding low signal-to-noise ratio (SNR) data, and one excluding low-SNR data with baseline correction. Feature importance, assessed through random permutations, was used for explainability. Spectral unmixing and PCA indicated limited spectral changes directly attributable to analyte degradation, with inter- and intra-sample variability dominating. Classification results showed that while removing the baseline led to inconclusive results, models trained on datasets retaining the baseline effectively identified non-degraded samples. These findings suggest that while conventional Raman spectroscopy may not be optimally sensitive to subtle analyte variations in serum stored at room temperature, the auto-fluorescence background holds promise as a potential biomarker for monitoring serum storage quality.

The visualization of 2D clinical data often relies on color-coded images, but different colormaps can introduce cognitive biases, impacting result interpretation. Moreover, when using color for diagnosis with multiple biomarkers, the application of distinct colormaps for each parameter can hinder comparisons. Our aim was to introduce a visualization technique that utilizes the hue (H), saturation (S), and value (V) in a single image to convey multi-parametric data on various optical properties in an effective manner. To achieve this, we conducted a study involving two datasets, one comprising multi-modality measurements of the human aorta and the other featuring multiple parameters of dystrophic mice muscles. Through this analysis, we determined that H is best suited to emphasize differences related to pathology, while V highlights high-spatial-resolution data disparities, and color alterations effectively indicate changes in chemical component concentrations. Furthermore, encoding structural information as S and V within the same image assists in pinpointing the specific locations of these variations. In cases where all data are of a high resolution, H remains the optimal indicator of pathology, ensuring results’ interpretability. This approach simplifies the selection of an appropriate colormap and enhances the ability to grasp a sample’s characteristics at a single glance.

SignificanceMueller matrix imaging (MMI) is a comprehensive form of polarization imaging useful for assessing structural changes. However, there is limited literature on the polarimetric properties of brain specimens, especially with multispectral analysis.AimWe aim to employ multispectral MMI for an exhaustive polarimetric analysis of brain structures, providing a reference dataset for future studies and enhancing the understanding of brain anatomy for clinicians and researchers.ApproachA multispectral wide-field MMI system was used to measure six fresh lamb brain specimens. Multiple decomposition methods (forward polar, symmetric, and differential) and polarization invariants (indices of polarimetric purity and anisotropy coefficients) have been calculated to obtain a complete polarimetric description of the samples. A total of 16 labels based on major brain structures, including grey matter (GM) and white matter (WM), were identified. K-nearest neighbors classification was used to distinguish between GM and WM and validate the feasibility of MMI for WM identification.ResultsAs the wavelength increases, both depolarization and retardance increase, suggesting enhanced tissue penetration into deeper layers. Moreover, utilizing multiple wavelengths allowed us to track dynamic shifts in the optical axis of retardance within the brain tissue, providing insights into morphological changes in WM beneath the cortical surface. The use of multispectral data for classification outperformed all results obtained with single-wavelength data and provided over 95% accuracy for the test dataset.ConclusionsThe consistency of these observations highlights the potential of multispectral wide-field MMI as a non-invasive and effective technique for investigating the brain’s architecture.

Cardiac fibrosis is a key characteristic of heart failure, with no effective treatment available. Using three-dimensional human models and cutting-edge biotechnology to evaluate new therapies offers a significant advancement. CTPR390, an experimental anti-fibrotic inhibitor targeting Hsp90, has shown success in animal models, but remains unexplored in human cardiac models. This study evaluated a cardiac three-dimensional engineered connective tissue (ECT) model treated with CTPR390, focusing on changes in the extracellular matrix and fibroblasts. Results showed that CTPR390 prevented architectural changes in TGFβ1-activated ECT, preserving tissue perimeter, collagen fibers alignment while reducing percentage of structured areas and degree of collagen structuration. Additionally, the treatment reduced cell area of elongated fibroblasts under tension, without changes in the internal rounded cells devoid of tension. Fibroblast recruitment to tension areas was diminished, showing biomechanical behavior similar to control ECT. This treatment also lowered the gene and protein expression of key pro-fibrotic markers. For the first time, advanced biotechnology was employed to detect the detailed structure of tissue fibrosis reduction after administering CTPR390, representing a significant advancement toward clinical application for cardiac fibrosis treatment. Graphical