Elevated serum PAR-1 levels as an emerging biomarker of inflammation to predict the dengue infection severity.

The present study was designed to check the serum levels of protease-activated receptor (PAR-1) in patients during different phases of dengue severity. Moreover, a correlation between serum PAR-1 levels and hematological parameters, inflammatory cytokine levels, and liver functional changes was also determined. Based on the World Health Organization criteria, the study population was divided into: nonsevere dengue fever (DF; n = 30), severe dengue hemorrhagic fever (DHF; n = 19), and severe dengue shock syndrome (DSS; n = 11). The platelet count (PLT) and hematocrit (HCT) were analyzed using an automated hematology analyzer and liver function enzymes aspartate transaminase (AST), alanine transaminase (ALT), and alkaline phosphate (ALP), bilirubin were checked by auto-analyzer using diagnostic kits. Moreover, the levels of inflammatory mediators C-reactive protein (CRP), tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), interleukin-17 (IL-17), and PAR-1 were determined using respective ELISA kits. The HCT levels were elevated and platelet count decreased significantly during dengue complications (DHF and DSS) compared to the DF patients, while the levels of liver functional biomarkers AST, ALT, ALP, and bilirubin remained elevated in DHF and DSS groups than in the corresponding DF group. Similarly, the inflammatory cytokine levels of CRP, TNF-α, IL-6, and IL-17 in DHF and DSS subjects were markedly increased when observed against DF subjects. Notably, the PAR-1 levels were significantly elevated in DHF and DSS groups than in the DF group and positively correlated with changes in HCT levels, inflammatory biomarkers, and liver enzymes. Our findings conclude that PAR-1 levels persistently increased with the severity of the dengue infection and are strongly associated with various clinical manifestations. Thus, PAR-1 levels can be used as a diagnostic marker for assessing dengue severity.

All-digital histopathology by infrared-optical hybrid microscopy.

Optical microscopy for biomedical samples requires expertise in staining to visualize structure and composition. Midinfrared (mid-IR) spectroscopic imaging offers label-free molecular recording and virtual staining by probing fundamental vibrational modes of molecular components. This quantitative signal can be combined with machine learning to enable microscopy in diverse fields from cancer diagnoses to forensics. However, absorption of IR light by common optical imaging components makes mid-IR light incompatible with modern optical microscopy and almost all biomedical research and clinical workflows. Here we conceptualize an IR-optical hybrid (IR-OH) approach that sensitively measures molecular composition based on an optical microscope with wide-field interferometric detection of absorption-induced sample expansion. We demonstrate that IR-OH exceeds state-of-the-art IR microscopy in coverage (10-fold), spatial resolution (fourfold), and spectral consistency (by mitigating the effects of scattering). The combined impact of these advances allows full slide infrared absorption images of unstained breast tissue sections on a visible microscope platform. We further show that automated histopathologic segmentation and generation of computationally stained (stainless) images is possible, resolving morphological features in both color and spatial detail comparable to current pathology protocols but without stains or human interpretation. IR-OH is compatible with clinical and research pathology practice and could make for a cost-effective alternative to conventional stain-based protocols for stainless, all-digital pathology.

Probe-Sample Interaction-Independent Atomic Force Microscopy-Infrared Spectroscopy: Toward Robust Nanoscale Compositional Mapping.

Nanoscale topological imaging using atomic force microscopy (AFM) combined with infrared (IR) spectroscopy (AFM-IR) is a rapidly emerging modality to record correlated structural and chemical images. Although the expectation is that the spectral data faithfully represents the underlying chemical composition, the sample mechanical properties affect the recorded data (known as the probe-sample-interaction effect). Although experts in the field are aware of this effect, the contribution is not fully understood. Further, when the sample properties are not well-known or when AFM-IR experiments are conducted by nonexperts, there is a chance that these nonmolecular properties may affect analytical measurements in an uncertain manner. Techniques such as resonance-enhanced imaging and normalization of the IR signal using ratios might improve fidelity of recorded data, but they are not universally effective. Here, we provide a fully analytical model that relates cantilever response to the local sample expansion which opens several avenues. We demonstrate a new method for removing probe-sample-interaction effects in AFM-IR images by measuring the cantilever responsivity using a mechanically induced, out-of-plane sample vibration. This method is then applied to model polymers and mammary epithelial cells to show improvements in sensitivity, accuracy, and repeatability for measuring soft matter when compared to the current state of the art (resonance-enhanced operation). Understanding of the sample-dependent cantilever responsivity is an essential addition to AFM-IR imaging if the identification of chemical features at nanoscale resolutions is to be realized for arbitrary samples.