Fluorescent imaging (FI) using indocyanine green (ICG) is a powerful tool in medical diagnostics and surgery. Although numerous studies have focused on optimizing injection protocols and suppressing excitation light leakage, tissue autofluorescence has not been widely recognized as a fundamental factor limiting sensitivity. We aim to quantitatively determine the sensitivity limit for ICG detection in biological tissues, accounting for background signals from both scattered excitation light and tissue autofluorescence. We combine experiments on tissue phantoms with varying ICG concentrations and Monte Carlo numerical simulation of light transport in media with different optical properties. Human skin autofluorescence was quantified in vivo using a nonfluorescent reference and a model medium with a known ICG concentration. It was established that skin autofluorescence is the dominant source of background, exceeding the scattered light by 4 to 25 times in the imaging system used. The determined ultimate sensitivity for ICG detection in biological tissue is to when accounting for the autofluorescence signal. Tissue autofluorescence is a fundamental factor limiting the sensitivity of ICG FI in the near-infrared range. The developed approach will allow for future optimization of imaging equipment and protocols for ICG and other contrast agents.

Although shortwave infrared (SWIR) imaging provides superior tissue penetration and reduced autofluorescence for preclinical applications, quantitative fluorescence analysis is hindered by the limited dynamic range (DR) of InGaAs cameras, forcing a focus on either bright or dim anatomical features. We develop a high dynamic range (HDR) imaging method specifically adapted for the high-noise characteristics of InGaAs detectors to enable quantitative fluorescence imaging across wide intensity ranges. We demonstrate that one-time camera calibration based on a series of images encompassing the range of radiance intensities enables all subsequent image processing. We modified classical HDR algorithms with exposure-time-dependent dark current subtraction, preprocessing to exclude saturated and noisy pixels before camera response function recovery, and dynamic weighting range adjustment to account for shrinking intensity ranges at longer exposures. High dynamic range image processing effects on preclinical imaging outcomes were analyzed using indocyanine green and SWIR-emitting PbS/CdS quantum dots in mouse models. High dynamic range imaging achieved a 22-dB improvement in DR over single exposures, enabling simultaneous quantification across more than three orders of magnitude of fluorophore concentration. In vivo studies showed improvements in contrast-to-noise ratios across all anatomical features, with improvements in vascular contrast while maintaining quantitative accuracy. After one-time camera calibrations, this approach enables rapid processing of subsequent datasets. This software-based HDR SWIR imaging approach eliminates exposure parameter optimization and enables comprehensive biodistribution analysis across all anatomical structures from a single acquisition sequence, significantly streamlining preclinical imaging workflows while preserving quantitative accuracy.

.SignificanceDissolving microneedles (MN) have emerged as a promising platform for drug delivery, providing a minimally invasive approach to bypass the skin’s natural barriers and enhance molecular penetration and diffusion. Their biocompatibility, user-friendly application, and ability to deliver precise therapeutic dosing make them particularly suitable for dermatological use. In addition to pharmacological benefits, dissolving MN possesses a geometric structure that enables optical waveguiding, thereby improving light penetration and distribution.AimWe address a key limitation of photodynamic therapy (PDT): the limited penetration of light into biological tissues. PDT relies on activating photosensitizing agents with specific wavelengths of light to generate cytotoxic species, selectively targeting abnormal or diseased cells while minimizing effects on surrounding healthy tissue.ApproachPyramidal dissolving MN arrays were fabricated from a biocompatible polymer and systematically characterized. Their light distribution profile under laser illumination was evaluated using image analysis.ResultsQuantitative analysis of light distribution demonstrates that MN can simultaneously facilitate drug delivery and light distribution.ConclusionsThis multifunctionality provides a synergistic therapeutic advantage, as localized drug release is complemented by optimized light delivery, thereby enhancing treatment outcomes. The dual-function platform has significant implications for PDT, enabling the design of integrated therapeutic systems that combine chemical and photonic modalities within a single, biodegradable device. Such systems may be particularly advantageous in resource-limited settings or outpatient care, where ease of use and effectiveness are essential. This strategy offers an approach to overcoming the limitations of conventional light-based therapies, supporting the development of more effective and accessible treatments for skin cancer and other dermatological conditions.

The editorial highlights articles in a JBO special section, as well as emerging trends in small-animal molecular imaging.

White blood cells (WBC) are hematopoietic cells of the immune system that protect the body by recognizing and eliminating infectious agents. Abnormalities in WBC production, maturation, or function can lead to disease and associated morphologic changes that, when systematically characterized, support diagnostic classification and clinical decision-making. We aim to investigate polarized hyperspectral imaging (PHSI) and polarized light imaging (PLI) microscopy for the visualization of WBCs. We developed a dual-modality microscopic imaging system that performs both polarized hyperspectral imaging and polarized light imaging. In the dual imaging setup, we used a snapscan hyperspectral camera and an RGB camera to acquire images separately and further calculate four Stokes parameters (S0, S1, S2, and S3) as well as three Stokes vector-derived parameters, namely, the degree of polarization, degree of linear polarization, and degree of circular polarization. Synthetic RGB images of Stokes vectors and Stokes vector-derived parameters were generated for the visualization of cellular components with PHSI images. The spectral signatures of representative WBCs, e.g., granulocytes and lymphocytes, were extracted for qualitative comparison. The preliminary results demonstrate that Stokes vector parameters can enhance the visualization of granules in granulocytes, the visualization of surface structures of lymphocytes, and the morphologic visualization of the monocyte nucleus. Furthermore, the results also reveal that the measured spectra of Stokes vector parameters could enhance the differentiation of WBCs in the spectral dimension, represented by the qualitative comparison between granulocytes and lymphocytes. Utilizing the spatial and spectral information from the Stokes vector data, our customized polarized hyperspectral microscopic imaging system enhances the visualization of WBCs and may provide a tool for the diagnosis of disorders related to white blood cells.

Intermittent hypoxia (IH) is common in preterm neonates and can cause hypoxic-ischemic brain injury. Simultaneous monitoring of cerebral blood flow (CBF) and oxygenation is essential to detect oxygen delivery-extraction mismatches and guide intervention. We aimed to adapt and test an innovative diffuse speckle contrast flow oximetry (DSCFO) system for continuous monitoring of cerebral hemodynamics during IH in neonatal rats, a model approximating human neonates. Two compact laser diodes and a miniature CMOS camera were integrated into a fiber-free probe for continuous monitoring of changes in relative CBF (rCBF) and oxy- and deoxy-hemoglobin concentrations (Δ[HbO2] and Δ[Hb]) in 8-day-old neonatal rats. Sham rats ( ) underwent 10min of normoxia, whereas IH rats ( ) experienced 10 cycles of sequential 2-min hypoxia (8% O2) and 2-min hyperoxia (100% ). Sham rats maintained stable cerebral hemodynamics under normoxia, whereas IH rats exhibited pronounced periodic fluctuations during IH. Hypoxic episodes caused instantaneous decreases in rCBF and and increases in , whereas hyperoxic episodes reversed these effects, reducing hypoxic stress. However, the pronounced cerebral hemodynamic fluctuations during IH may still contribute to brain injury. We demonstrate an affordable, noninvasive, wearable, fiber-free DSCFO system for continuous monitoring of rCBF, , and during IH in neonatal rats. The system captured hypoxia-induced cerebral deoxygenation and hyperoxia-driven recovery, revealing episode-dependent protective and disruptive mechanisms. Future work will correlate DSCFO findings with neurological and histological outcomes to guide IH interventions in large neonatal animal models and human infants.

Early prediction of COVID-19 severity and mortality is crucial for optimizing clinical care and patient outcomes, but remains challenging. We aim to develop a screening tool combining label-free Raman spectroscopy and machine learning modeling to predict COVID-19 severity and mortality. Patients infected by SARS-CoV-2 ( ) were recruited during the first wave of COVID-19 and stratified based on respiratory support. Blood samples were collected during hospitalization and analyzed using Raman spectroscopy and metabolomics. Machine learning models based on Raman spectra were developed to classify (1)survivors versus nonsurvivors, (2)critical patients with noninvasive versus invasive ventilation, and (3)noncritical (no respiratory support or oxygen via nasal cannula) versus critical patients. Raman peaks assigned to proteins, glucose, lactic acid, fatty acids, urea, and lipids were extracted by the models. Area under the receiver operating characteristic curve ranged between 0.83 and 0.94, with sensitivities and specificities ranging between 80% and 83% and 75% and 92%, respectively. Accuracy for detecting mortality, invasive ventilation, and critical disease was 90%, 87%, and 78%. A complementary metabolomic analysis confirmed some molecular differences between groups. These results suggest the potential of Raman spectroscopy and machine learning modeling to stratify COVID-19 patients at admission, individualize care, and improve survival rates.

Non-invasive optical measures for evaluating rheumatoid arthritis (RA) remain limited. Clinical scores such as the 28-joint Disease Activity Score (DAS28) and ultrasound are operator-dependent and do not directly quantify optical tissue changes. Short-wave infrared (SWIR) hyperspectral imaging (HSI) provides sensitivity to inflammation-associated spectral changes in biological tissues. The aim is to develop and evaluate a quantitative HSI soft abundance scoring (HSISAS) method for assessing treatment response in RA patients using SWIR-HSI. Eleven RA patients who met the 2010 American College of Rheumatology/European Alliance of Associations for Rheumatology criteria underwent SWIR-HSI (900 to 1700nm) of the wrist joints before and after 12 weeks of biologic therapy. The HSISAS algorithm used pixel-wise reflectance spectra together with spectral correlation structure estimation and constrained energy minimization-based abundance scoring to compute intra- and inter-subject scores. These scores were statistically compared with conventional clinical indices, including DAS28, erythrocyte sedimentation rate, C-reactive protein, and power Doppler ultrasound findings. Both intra- and inter-HSISAS significantly increased after treatment ( ) and were positively associated with DAS28 improvement. Reflectance spectra showed visible pre/post-treatment differences, particularly near 1250nm. The abundance maps visually differentiated pre- and post-treatment states consistent with clinical improvement. The HSISAS framework provides a quantitative, non-contact optical measure of treatment-related spectral changes in RA. This method may offer translational potential for disease monitoring and may complement existing ultrasound and laboratory assessments.

.SignificanceAtrial fibrillation is treated with thermal ablation to isolate ectopic signals. Although this is the current standard of care, recurrence occurs in up to 40% of cases. Clinicians have no reliable way to predict treatment durability intraoperatively. Adding the capability of direct optical measurement of the tissue to an ablation catheter could help better guide the treatment.AimA radiofrequency ablation catheter was developed with polarization-sensitive optical coherence tomography (PSOCT) and near-infrared spectroscopy (NIRS) to demonstrate, for the first time, simultaneous monitoring of thermal lesion formation.ApproachWe fabricated the multimodal PSOCT-NIRS ablation catheter and validated optical metrics using known targets of tissue-like phantom, deoxygenated blood, and atrial tissue. We then demonstrated recording PSOCT and NIRS data during ex vivo ablation of swine atria.ResultsPSOCT-NIRS metrics showed expected values in known targets. Measurements during ablation also exhibited previously reported patterns—OCT scattering increases, PSOCT birefringence decreases, and NIRS lesion optical index increases. Furthermore, simultaneous measurement revealed varying rates of change and magnitudes of response to different powers of thermal ablation.ConclusionsPSOCT-NIRS can measure tissue response to thermal energy delivery, and the optical metrics are complementary. By collecting more information during thermal energy delivery, PSOCT-NIRS metrics could contribute to understanding treatment durability in future investigations.