Journal of Biophotonics

PurposeTo evaluate the retinal safety of repeated low-level red-light (RLRL) therapy using the Eyerising Myopia Management Device (EMMD) by analysing exposure parameters relative to established thermal and photochemical retinal injury thresholds and empirical human exposure data. MethodsEmission characteristics of the EMMD were measured in an accredited laboratory under worst-case conditions. Parameters assessed included wavelength, intraocular power, corneal irradiance, and retinal image characteristics across accommodative states. These measurements were compared with international safety standards, maximum permissible exposure limits, and experimentally derived retinal injury thresholds from animal studies and validated computational models. The effects of repeated exposures from RLRL therapy using the EMMD were evaluated using photochemical additivity principles and repair kinetics, and further contextualised using human volunteer exposure data. ResultsThe EMMD emitted red laser radiation at 654-655 nm with a maximum intraocular power of approximately 1 mW through a 7 mm pupil, placing it within Class 3R and marginally above the Class 2 limit. Corneal irradiance was approximately 26 W m- 2, well below conservative photochemical exposure limits. Thermal injury modelling indicated retinal damage thresholds above device exposure, including under worst-case assumptions of minimal retinal image size and absence of eye movements. Accounting for repeated daily exposures and photochemical additivity, safety margins remained approximately 3-fold for a 7 mm pupil and approximately 8-fold for a more realistic 4 mm pupil. Human volunteer studies demonstrated no detectable structural or functional retinal injury at exposure levels approximately five times higher than those produced by the EMMD. ConclusionExposure parameters of RLRL therapy using the EMMD remain well below conservative retinal injury thresholds under prescribed use conditions. Integration of experimental, modelling, and human data indicates substantial safety margins, supporting its safe clinical use.

Stimulated emission depletion (STED) microscopy is a super-resolution fluorescence imaging technique that achieves high spatial and temporal resolution by exploiting stimulated emission to induce fluorescence depletion (FD) and is expected to have substantial utility for imaging applications using fluorescent proteins. However, the compatibility of fluorescent proteins with STED microscopy systems has been understood primarily through empirical observations, and there is no established methodology for the rational selection of fluorescent proteins for STED microscopy. In this study, we systematically evaluated the compatibility of commonly used fluorescent proteins with STED microscopy systems by measuring FD properties using transient absorption spectroscopy and fluorescence dip spectroscopy, both of which are classified as two-color spectroscopy (TCS). Fluorescent proteins identified as compatible with the STED microscopy system based on the TCS measurements were employed for three-dimensional STED imaging of cellular samples expressing each protein. In all samples, three-dimensional spatial resolution was improved relative to confocal laser microscopy, with particularly marked improvements in z-axis resolution. These findings demonstrate that measurements of FD properties via TCS provide a robust approach for evaluating the compatibility of fluorescent proteins with the STED microscopy system and for selecting suitable fluorescent proteins for STED imaging.

The spatial resolution of optical imaging systems is fundamentally restricted by the diffraction limit. However, in widefield live-cell microscopy, the achievable resolution is further constrained by the specimen motion, which indicates the existence of a fundamental spatio-temporal resolution trade-off between signal accumulation during the full frame integration and the resulting motion blur. To improve the fidelity with which moving objects can be imaged, a quantitative understanding of this spatio-temporal trade-off is necessary. Here, we present a systematic analysis of motion-induced resolution dynamics measured with spectral signal-to-noise ratio (SSNR). We developed a simulation framework which models the image formation of objects undergoing arbitrary motion, to evaluate the degradation of the spatial resolution under translational and rotational dynamics. Our results demonstrate that for translating objects, the spatial resolution is anisotropically reduced as a function of the orientation of the object relative to the motion vector, leading to the spectral signal-to-noise ratio degrading by up to 50% and the resolution by up to 40% for a 90{degrees} change in the motion direction. Furthermore, we show that for rotational motion, conventional radially averaged metrics such as the Fourier Ring Correlation are not able to quantify the effects of angular blur. On the other hand, the SSNR is able to accurately quantify this degradation. These findings underscore the necessity of an object-oriented imaging approach, in which acquisition parameters such as exposure time are tuned to specific biological spatio-temporal characteristics to optimize the trade-off between motion blur and spatial fidelity.

Invertebrate vision relies on bistable visual pigments flipping upon photon absorption between rhodopsin and metarhodopsin states. In living butterflies, the UV-VIS absorption spectra of rhodopsin and metarhodopsin, respectively with 11-cis and all-trans isomers of 3-hydroxy-retinal (A3) chromophore, can be conveniently recorded from the eyeshine, the light reflected from the compound eye after passing twice through the light-guiding rhabdoms. * Here, a microscope coupled with a broadband LED source and a microspectrometer was used to record photorelaxations reported in eyeshine reflection spectra. Fitting temporal exponential relaxations to log-reflectance arrays yielded transient and baseline spectra that are analogous to absorbance difference and sum, respectively. Both types of spectra were subjected to singular value decomposition and to fitting of templated visual pigment absorption spectra. * The compound eye of the high brown fritillary Fabriciana adippe was exposed to a series of second-long broadband light pulses, causing photorelaxations with time constants between 40 and 120 ms that led to 80% metarhodopsin in equilibrium. The transient and baseline spectra were fitted with pigment templates, estimating the alpha peak wavelength 547-552 nm for rhodopsin and 496-501 nm for metarhodopsin. The metarhodopsin to rhodopsin alpha peak absorbance ratio 1.25-1.35 is consistent with the isosbestic wavelength at 530 nm. The second isosbestic wavelength indicates that rhodopsin beta (UV) peak absorbs more strongly than metarhodopsin below 405 nm. * Baseline spectra, which were not explicitly analysed in previous studies, enable concatenation of exposures, monitor long-term changes of pigment, and enhance the estimation of beta peak parameters. * The method can be directly used in many butterflies and could be adapted to other insects, particularly fruitflies, facilitating studies of the relation between the visual pigment spectra and the opsin sequences. Spectroscopic results can be complemented with physiologically measured photoreceptor spectral sensitivity datasets and analysed with the same global fitting procedure.

High-resolution microscopy techniques are used across research and industry to analyse biological systems, from biomolecules to subcellular organelles, multicellular models and tissues. As multimodal imaging workflows and quantitative analysis of bioimaging data become increasingly widespread, there is a growing need for materials and methods to calibrate imaging systems and evaluate the fidelity of generated image data. Here, we present three-dimensional microscopy phantoms fabricated using two-photon photolithography from transparent resins that exhibit both broadband visible autofluorescence and Raman scattering across the fingerprint and C-H stretching regions. Suitable for analysis using optical profilometry, the phantoms were dimensionally calibrated with SI traceability using a metrological confocal microscope. Immersible in air and common aqueous imaging media, the phantoms are compatible with a wide variety of optical microscopy techniques, including one and two-photon excited fluorescence and coherent Raman scattering microscopy. We employed a forked wedge design to validate image deconvolution results and a stacked lattice phantom to recover image distortion matrices under realistic biological imaging conditions. We demonstrate the impact of correcting chromatic offsets and axial scaling errors for a representative application: analysis of a cell seeded scaffold using confocal laser scanning fluorescence microscopy. These phantoms provide a versatile platform for calibration, quality control and validation of multimodal imaging pipelines and improved quantitative optical microscopy.

Zebrafish (Danio rerio) are an important vertebrate model for vision and neuroscience research. In the larval stages, the aquatic species begins to elicit the optomotor response (OMR) to stabilize themselves in water -- a behaviour that may be exploited in the laboratory to measure visual acuity. However, up to now, the measurement of the OMR in juvenile and adult zebrafish has been limited due to their behavioural complexity. Here, we optimize a protocol to assay zebrafish aged between 4 and 9 weeks-post-fertilization, by displaying sinusoidal gratings parallel to the zebrafish eye to elicit a robust OMR. We assessed the visual spatial-frequency tuning function of an environmentally induced myopia model to confirm the sensitivity and robustness of the protocol. Additionally, we show the OMR is sensitive to the contrast and temporal resolution of the sinusoidal gratings. Furthermore, we found that the time between stimulus presentations impact the spatial-frequency tuning function likely as time is required for zebrafish to return to baseline swimming after eliciting the OMR. Finally, we found that the OMR after ten versus twenty seconds of stimulus onset appears comparable; indicating that robust OMR responses in zebrafish can be elicited through relatively short stimulus presentations. Through the experiments conducted, we present an optimized protocol specific to zebrafish. The protocol may be used to follow the progression or treatment efficacy of progressive neurological disorders including specific visual disorders and higher brain functions with visual endophenotypes. Ultimately, this protocol allows for high-throughput robust measures of visual and neural function in zebrafish.

We find that multi-site temporal control of optogenetic photostimulation in peripheral nerves can enhance firing rates by overcoming the intrinsic limitation of opsin photophysics. The benefits of multi-site optogenetic stimulation were demonstrated with three approaches: (1) in silico modeling, (2) ex vivo in the sciatic nerve, and (3) in vivo in the vagus nerve. An in silico model of multi-site optogenetic stimulation was developed in two Hodgkin and Huxley type neuron models, that supported our hypothesis. The ex vivo sciatic nerve showed an increase in firing frequency that is physiologically relevant for functional control. The technique was then applied in vivo for optogenetic vagus nerve stimulation resulting in significant changes in heart rate compared with standard methods of single-site stimulation. Improving the control of optogenetically induced neural firing will have broad impacts for future developments in optical nerve interfaces and brain-machine interfaces.

Light sheet fluorescence microscopy enables volumetric imaging with high imaging speed, optical sectioning capability, and reduced photobleaching and phototoxicity, and has become a workhorse in bioimaging. However, widely adopted Gaussian light sheets face an inherent trade-off between axial resolution and field-of-view due to diffraction. State-of-the-art nondiffracting light sheets--including Bessel beam, Airy beam, and lattice light sheet--alleviate this trade-off but suffer from optical aberrations that compromise performance with increasing imaging depth. While the integration of adaptive optics offers a promising solution, such integrated systems are typically complex, expensive, and slow due to the need for serial mapping and correction of spatially varying aberrations across the specimen. Here, we present polarization-engineered aberration-resilient light sheet (PEARLS), a new class of monochromatic nondiffracting light sheet with temporally invariant profile and robustness to optical aberrations. In comparison with existing light sheets, PEARLS showed significantly reduced photobleaching and enhanced aberration-resilience, permitting imaging of three-dimensional subcellular dynamics in optically complex environments. We applied PEARLS for noninvasive observations of biological dynamics in various living systems, revealing phenotypic diversity across spatial and temporal scales--from rapid membrane dynamics and organelle interactions in cultured cells to coordinated mitosis and cell migrations in developing embryos.

We introduce a workflow to identify oligomeric structures that are recorded with single-molecule localization microscopy (SMLM) under cryogenic conditions. Typically, these oligomers are assumed to consist of protomers arranged as equilateral two-dimensional polygons and every protomer is labeled with a dye molecule for visualization. Unlike previous work, we consider scenarios in which the sample plane has an unknown orientation relative to the focal plane. Our contribution is a high-precision plane-fitting algorithm to determine the sample plane, combined with geometrical transformations and two circle-fitting algorithms to identify the oligomeric structures. Our simulations on synthetic data demonstrate that the proposed workflow achieves high accuracy in estimating both the unknown tilted plane and the oligomer size.

Visual prostheses face a critical miniaturisation challenge: converting photoreceptor signals to biologically appropriate retinal ganglion cell (RGC) stimulation patterns within the spatial constraints of intraocular implants. Existing systems rely on external microcontrollers for signal processing, limiting scalability for high-density pixel arrays. This paper presents an integrated per-pixel circuit architecture that directly converts photocurrent into frequency-modulated current pulses that match RGC activation thresholds. The design targets are established through NEURON computational modelling of red-green colour-opponent midget RGCs, identifying stimulation thresholds of +0.1nA to +3.5nA for depolarisation and -0.1nA for repolarisation. The proposed circuit combines a transimpedance amplifier, a voltage-controlled oscillator with a Schmitt trigger, and a current-controlled output stage to generate biphasic pulses within these thresholds. A complementary output provides lateral inhibition, reducing crosstalk between adjacent RGC stimulation sites. Photoreceptor integration is achieved using P3HT:PCBM organic photodiodes for cone-associated RGCs and phototransistors for rod-associated RGCs, validated through OghmaNano finite element simulations. The photodiode circuit produces output frequencies of 2.5Hz (dark) to 600Hz (100 W/m2), matching reported RGC response ranges. This architecture eliminates external processing requirements, enabling scalable high-density retinal prostheses design.

Millimeter-wave radar can quietly monitor health and behavior at home, which is vital for supporting people living with dementia. Most studies, however, remain limited to short-term testing in controlled spaces. Real-world deployment requires robust activity classification as a prerequisite: vital-sign and behavioral sensing require fundamentally different processing pipelines, and absent periods need to be reliably distinguished from stationary states. Bridging the critical gap between controlled laboratory demonstrations and continuous home monitoring, this paper introduces a self-adapting radar framework that extracts meaningful behavioral segments from massive, unconstrained real-world data. The system performs continuous real-time activity classification (stationary, walking, and absent) and target localization, selectively directing downstream processing to the most informative segments. It addresses key real-world deployment challenges including adaptive thresholding across subjects and environments, and walking detection under naturalistic activity conditions. Prior to integration with the Minder platform, the system was validated in a fully instrumented studio apartment against ground truth. Across 12 subjects, the system achieved an overall classification accuracy of 0.98, with F1 scores of 0.99 for absence and stationary states, and 0.95 for walking. Event-based evaluation yielded a per-subject walking sensitivity of 0.916{+/-} 0.058 and F1 score of 0.935 {+/-}0.030. Localization root mean square error during movement was 0.40 m. The results demonstrate reliable performance suitable for transitioning to long-term real-world home deployment.

This study presents a proof-of-concept cyber-physical architecture integrating a SEIR epidemiological model (Susceptible-Exposed-Infectious-Recovered), implemented in MATLAB, with a simulated Internet of Things (IoT) acquisition and transmission stage based on the ESP32 microcontroller and the ThingSpeak platform. The system generates synthetic biomedical signals of body temperature and peripheral oxygen saturation (SpO2), structured across three levels: circadian variation, scheduled pathological episodes, and Gaussian noise. These signals feed a dual parametric coupling function that dynamically updates the SEIR transmission parameter as a combined function of body temperature and oxygen saturation deviations from their clinical reference values. The proposed architecture is organized into four functional phases: measurement, communication, computational processing, and feedback. Five simulated clinical scenarios were evaluated, ranging from normal conditions (T = 36.5 {degrees}C, SpO2 = 97%) to fever with severe hypoxia (T = 38.5 {degrees}C, SpO2 = 88%), yielding basic reproduction number (R0) values between 4.20 and 5.38, and peak infected proportions between 29.9% and 35.2% of the simulated population (N = 1,000). A sensitivity analysis on the coupling coefficients, with {+/-}50% variation from nominal values, showed that the oxygen saturation coefficient is the most influential parameter on R0 (range = 0.76) compared to the thermal coefficient (range = 0.42), with monotonic and predictable behavior across the entire evaluated parametric space. The primary contribution of this work is system integration: we propose a reproducible platform connecting biomedical simulation, IoT communication, and epidemiological modeling through parametric coupling in a controlled environment. All data used are entirely synthetic; a retrospective calibration with real Colombian data from the first epidemic wave of 2020 confirmed the epidemiological consistency of the model, with a calibrated R0 of 1.85 and a Pearson correlation of 0.930. Results should be interpreted as evidence of architectural feasibility, not as clinical or epidemiological validation. Author SummaryThe COVID-19 pandemic made it clear that epidemiological surveillance systems need tools that combine accessible technology with mathematical models capable of anticipating disease spread. In this work, we built a proof-of-concept platform connecting three elements: a low-cost electronic sensor based on the ESP32 microcontroller, a cloud communication platform (ThingSpeak), and a mathematical model that simulates how an epidemic spreads through a population. The sensor generates synthetic data on body temperature and oxygen saturation that, through a mathematical formula we designed, dynamically modify the rate of contagion in the model. We evaluated five clinical scenarios, ranging from normal conditions to fever with severe hypoxia, and analyzed how sensitive the results are to changes in the system parameters. We found that oxygen saturation has a greater influence on the estimated contagion potential than body temperature. Although all data are synthetic, this platform demonstrates that it is possible to integrate low-cost sensors with epidemiological models in real time, opening a viable pathway for early warning systems in resource-limited settings.

Pressure injuries represent a significant healthcare challenge requiring early detection to prevent severe complications. While thermal imaging shows promise for detecting early pressure-related temperature changes, its robustness across varying imaging conditions and diverse patient populations remains unclear. This study systematically evaluated how imaging protocol variations (lighting, distance, positioning, camera type) and participant skin tone influence classification model performance for thermal cooling detection. Using a controlled cooling protocol to simulate early pressure injury temperature changes, we collected 1,680 images from 35 diverse participants across 12 imaging protocol variations. We compared two approaches: three deep learning models (MobileNetV2, InceptionNetV3, ResNet50) and a threshold-based approach using an optimal fixed threshold temperature differential. Deep learning models outperformed the threshold-based approach, achieving 98.6-99.6% accuracy compared to 95.6%, with superior performance across all imaging protocols and skin tone groups. Threshold-based approach showed camera-dependent misclassification patterns across skin tones. On the high-resolution FLIR E8XT, the MST 7-10 group had 8 of 11 misclassifications. This pattern shifted on the low-resolution FLIR ONE Pro, where the intermediate skin tone group (MST 6) had 22 of 44 total misclassifications.In contrast, deep learning models maintained consistent performance across all skin tone groups and imaging protocols. Visualization analysis of the deep learning models suggested that these models focused on thermal gradients at cooling region boundaries, suggesting that spatial temperature gradients, not single-value thresholds, are critical for accurate detection. These findings suggest the potential of deep learning-based approaches to maintain robust, equitable performance across diverse skin tones and imaging conditions.

Alzheimers disease (AD) is a neurodegenerative disorder affecting more than 55 million people worldwide, with a diagnosis that remains predominantly clinical and frequently delayed. The electroretinogram (ERG) offers a non-invasive electrophysiological method for detecting retinal dysfunction associated with neurodegeneration; however, it remains unclear whether robust and reliable candidate biomarkers can be extracted from ERG signals beyond conventional amplitude- and latency-based parameters. Here we present a pilot study of a multi-domain signal processing framework applied to ERGs recorded from 46 participants (20 AD patients, 26 controls) with a handheld device (RETeval, LKC Technologies) using sinusoidal (1-50 Hz) and photopic ISCEV protocols. Five complementary techniques were implemented: (i) multiscale fuzzy entropy (MSFuzzyEn); (ii) FFT harmonic analysis; (iii) stimulus-response wavelet time-frequency coherence (WTC); (iv) a novel inter-cycle lag variant of sample entropy (SampEnT), introduced to isolate cycle-to-cycle retinal response consistency independently of stimulus periodicity; and (v) discrete wavelet transform (DWT) for energetic extraction of oscillatory potentials (OPs). Univariate comparisons (Mann-Whitney, Cliffs{delta} , Benjamini-Hochberg FDR) identified seven significant candidate biomarkers (q < 0.05), five with large effect size: AUCfast (|{delta}| = 0.546, q = 0.009), Slopevery-slow (|{delta}| = 0.554, q = 0.007), R14f (|{delta}| = 0.515, q = 0.031), SampEnT (|{delta}| = 0.504, q = 0.019) and WTCR,mean (|{delta}| = 0.531, q = 0.023); and two with medium effect size (OP_amp_sum, band_snr). A logistic regression classifier combining three candidate biomarkers, validated by leave-one-out cross-validation, achieved ROC-AUC = 0.858, sensitivity = 70.0% and specificity = 88.5% (n = 46). These proof-of-concept results demonstrate that multi-domain ERG analysis captures retinal temporal dysfunction signatures in AD that are inaccessible to standard clinical analysis, supporting further investigation of portable ERG devices as a source of non-invasive candidate biomarkers for early AD detection.

Tracking gastrointestinal (GI) transit in preclinical models is essential for assessing gut motility and drug delivery. Current preclinical methods rely on end-to-end transit measurements or emptying studies that require terminal endpoints and organ explanation. Clinically, radiopaque "Sitz" markers are administered orally and their position in the GI tract is assessed through radiography. Sitz markers have been in use since 1969 and are typically mass-produced using industrial molding or extrusion, resulting in a single, fixed geometry with limited tunability. We present a stereolithography (SLA)-based method to fabricate customizable radiopaque markers using additive manufacturing with a barium sulfate (BaSO4)-doped resin. We demonstrate precise control over marker geometry, a key advantage over existing markers. Furthermore, we apply this method in vivo, tracking markers in a live rat model from ingestion to excretion using serial CT imaging. We systematically investigate how changes in marker geometry impact GI residency and transit time. Our results show that 3D printed markers provide a flexible and tunable platform for radiopaque marker fabrication and enable investigation of the fundamental relationship between a markers physical properties and its performance in a dynamic biological environment. This work establishes a novel, tunable platform for GI motility evaluation and drug delivery studies.

This paper presents experimental evidence that alpha-band EEG signals can be reliably detected from an in-ear electrode during physical activity, enabling fatigue monitoring in dynamic, real-world conditions such as sports. We collected an EEG dataset using a custom-designed, compact wearable system measuring only 20 mm in diameter, integrated inside the earphone. It supports five channels, four head electrodes (T3, C3, C4, T4) and one in-ear electrode, allowing simultaneous multi-site recordings. Recordings were made while a participant engaged in a controlled cycling protocol designed to induce physical fatigue. We demonstrated a direct relationship between alpha power and entropy in EEG data recorded from both the head and ear, during both activity and rest. To our knowledge, this is the first study to demonstrate in-ear alpha power tracking during active physical movement for sports-related fatigue monitoring. These findings open new possibilities for compact, wearable EEG systems in athletic and high-performance settings, where traditional EEG setups are impractical

For decades, the photon counting histogram (PCH) was used as the sole method to quantify fluorophore numbers in a diffraction-limited focal volume. This technique combines spatial excitation profiles, and the distribution of photon counts to register the photon emission statistics of individual fluorophores. However, this approach has not yet been transferred to widefield fluorescent imaging due to the lack of fast and single photon sensitive camera sensors which can capture the photon emission statistics of a single fluorophore. Here, we explore avenues towards quantitative analysis of the active fluorophore number by leveraging recent advancements in single photon avalanche diode (SPAD) array technology. Binary exposures of a SPAD array can be synchronized with picosecond laser pulses to measure the PCH in a widefield setting. Then, by modeling the statistical relationship between the active fluorophore number and the PCH in a region of interest following a laser pulse, we can perform Bayesian inference of this number. The model is demonstrated experimentally by counting quantum dots and various numbers of fluorescent dye molecules bound to DNA origamis. We find that this method has several important applications in widefield microscopy, including enhanced localization microscopy and constrained fitting of multiple unresolvable fluorescent emitters.

Wide-field imaging (WFI) is a mesoscopic approach for monitoring cortex-wide activity with high temporal resolution and a broad field of view. Owing to its simple optical configuration and compatibility with chronic preparations, WFI has become an important tool in systems neuroscience and disease-model research. In this chapter, we describe practical protocols for chronic transcranial WFI in mice using two complementary optical signals: genetically encoded calcium indicators (GCaMP) and endogenous flavoprotein autofluorescence. Calcium imaging provides a robust readout of neuronal population activity, whereas flavoprotein imaging reflects mitochondrial redox dynamics and cellular metabolic demand. We detail procedures for animal preparation, skull clearing, headplate implantation, macroscope assembly, synchronized sensory stimulation, triggered image acquisition, and MATLAB-based data analysis. The analysis workflow includes {Delta}F/F normalization, reference-based signal correction, and artifact reduction, followed by trial averaging, atlas registration, and region-of-interest analysis. Because imaging is performed through the intact skull, the protocol enables repeated longitudinal measurements in the same animal over extended periods. This approach is reproducible, cost-effective, and adaptable to studies of cortical physiology and neurological disorders.

Background: Quantitative lipid assessment is central to identifying rupture-prone coronary plaques and represents a therapeutic target for lipid-lowering therapy. Near-infrared spectroscopy (NIRS)-derived lipid core burden index (LCBI) is well validated and widely used for detecting lipid-rich lesions. Optical frequency domain imaging (OFDI) is increasingly adopted for guiding percutaneous coronary intervention (PCI) due to its high-resolution structural imaging capabilities. Depolarization-sensitive OFDI (depOFDI) provides intrinsic lipid contrast and may enable combined structural and compositional plaque characterization within a single OFDI-based platform. Objective: To define an OFDI-derived lipid metric and evaluate its agreement with NIRS-derived LCBI. Methods: Thirty-three patients underwent both polarization-sensitive OFDI and NIRS-intravascular ultrasound imaging during PCI. After exclusion of 4 datasets, 29 co-registered pullbacks were analyzed. A signal-to-noise-corrected depolarization metric was used to identify lipid-rich regions and generate depOFDI chemograms. maxLCBI4mm value and location, as well as total LCBI, were computed and compared with NIRS. Results: depOFDI demonstrated strong agreement with NIRS, showing high correlation for maxLCBI4mm (r^2 = 0.862) and total LCBI (r^2 = 0.867), along with strong spatial concordance for the location of the maxLCBI4mm (r^2 = 0.900). Bland-Altman analysis of LCBI4mm showed minimal bias (10.7) with 95% limits of agreement of [81.4 to 102.8]. Conclusions: depOFDI enables accurate quantification of lipid burden alongside the high-resolution structural information inherently provided by OFDI. Because depolarization metrics can be derived from polarization-diverse detection available in many commercial OFDI systems, this approach provides a practical pathway toward comprehensive plaque characterization within existing PCI workflows, without the need for additional imaging modalities.

Due to recent technological advances, in situ structural cell biology is becoming a high throughput microscopy technique as all the steps of the workflow, from sample preparation to data analysis, are executed faster, more reliable and more reproducible. Sample thinning by cryoFIB-SEM is an essential tool in preparing electron transparent lamellae of biological specimens suitable for further characterization by cryoET. Modern cryoFIB-SEM instruments can be operated remotely and are capable of automated and unsupervised lamellae preparation. To take full advantage of these developments they need a constant supply of LN2 to maintain cryogenic conditions inside the microscope chamber. Here, we introduce a custom automated LN2 refill system that is compatible with gas cooled cryostages, supports long-term cryoFIB-SEM operations and liberates the user from highly repetitive and manual work. We believe this solution can be utilized with other cryoSEM or cryoFIB-SEM devices requiring N2 gas-flow cooling.