Single color virtual H&E staining with In-and-Out Net.

Virtual staining streamlines traditional staining procedures by digitally generating stained images from unstained or differently stained images. While conventional staining methods involve time-consuming chemical processes, virtual staining offers an efficient and low infrastructure alternative. Leveraging microscopy-based techniques, such as confocal microscopy, researchers can expedite tissue analysis without the need for physical sectioning. However, interpreting grayscale or pseudo-color microscopic images remains a challenge for pathologists and surgeons accustomed to traditional histologically stained images. To fill this gap, various studies explore digitally simulating staining to mimic targeted histological stains. This paper introduces a novel network, In-and-Out Net, specifically designed for virtual staining tasks. Based on Generative Adversarial Networks (GAN), our model efficiently transforms Reflectance Confocal Microscopy (RCM) images into Hematoxylin and Eosin (H&E) stained images. We enhance nuclei contrast in RCM images using aluminum chloride preprocessing for skin tissues. Training the model with virtual H\&E labels featuring two fluorescence channels eliminates the need for image registration and provides pixel-level ground truth. Our contributions include proposing an optimal training strategy, conducting a comparative analysis demonstrating state-of-the-art performance, validating the model through an ablation study, and collecting perfectly matched input and ground truth images without registration. In-and-Out Net showcases promising results, offering a valuable tool for virtual staining tasks and advancing the field of histological image analysis.

Erratum: Tissue harvest with a laser microbiopsy (Erratum).

[This corrects the article DOI: 10.1117/1.JBO.27.12.125001.].

Tissue harvest with a laser microbiopsy.

Significance: Traditional pathology workflow suffers from limitations including biopsy invasiveness, small fraction of large tissue samples being analyzed, and complex and time-consuming processing. Aim: We address limitations of conventional pathology workflow through development of a laser microbiopsy device for minimally invasive harvest of sub-microliter tissue volumes. Laser microbiopsy combined with rapid diagnostic methods, such as virtual hematoxylin and eosin (H&E) imaging has potential to provide rapid minimally invasive tissue diagnosis. Approach: Laser microbiopsies were harvested using an annular shaped Ho:YAG laser beam focused onto the tissue surface. As the annulus was ablated, the tissue section in the center of the annulus was ejected and collected directly onto a glass slide for analysis. Cryogen spray cooling was used before and after laser harvest to limit thermal damage. Microbiopsies were collected from porcine skin and kidney. Harvested microbiopsies were imaged with confocal microscopy and digitally false colored to provide virtual H&E images. Results: Microbiopsies were successfully harvested from porcine skin and kidney. Computational and experimental results show the benefit of cryogen pre- and post-cooling to limit thermal damage. Virtual H&E images of microbiopsies retained observable cellular features including cell nuclei. Conclusions: Laser microbiopsy with virtual H&E imaging shows promise as a potential rapid and minimally invasive tool for biopsy and diagnosis.

Rhamnan sulfate reduces atherosclerotic plaque formation and vascular inflammation.

OBJECTIVE: While lipid-lowering drugs have become a mainstay of clinical therapy these treatments only slow the progression of the disease and can have side effects. Thus, new treatment options are needed to supplement the effects of lipid lowering therapy for treating atherosclerosis. We examined the use of an inexpensive and widely available marine polysaccharide rhamnan sulfate as an oral therapeutic for limiting vascular inflammation and atherosclerosis. METHODS AND RESULTS: We found rhamnan sulfate enhanced the barrier function of endothelial cells, preventing the deposition of LDL and maintaining barrier function even in the presence of glycocalyx-degrading enzymes. Rhamnan sulfate was also found to bind directly to FGF-2, PDGF-BB and NF-κB subunits with high affinity. In addition, rhamnan sulfate was a potent inhibitor of NF-κB pathway activation in endothelial cells by TNF-α. We treated ApoE-/- mice with a high fat diet for 4 weeks and then an addition 9 weeks of high fat diet with or without rhamnan sulfate. Rhamnan sulfate reduced vascular inflammation and atherosclerosis in both sexes of ApoE-/- mice but had a stronger therapeutic effect in female mice. Oral consumption of rhamnan sulfate induced a significant decrease in cholesterol plasma levels in female mice but not in male mice. In addition, there was a marked reduction in inflammation for female mice in the liver and aortic root in comparison to male mice. CONCLUSIONS: Rhamnan sulfate has beneficial effects in reducing inflammation, binding growth factors and NF-κB, enhancing endothelial barrier function and reducing atherosclerotic plaque formation in ApoE-/- mice.

Deep learning on reflectance confocal microscopy improves Raman spectral diagnosis of basal cell carcinoma.

SIGNIFICANCE: Raman spectroscopy (RS) provides an automated approach for assisting Mohs micrographic surgery for skin cancer diagnosis; however, the specificity of RS is limited by the high spectral similarity between tumors and normal tissues structures. Reflectance confocal microscopy (RCM) provides morphological and cytological details by which many features of epidermis and hair follicles can be readily identified. Combining RS with deep-learning-aided RCM has the potential to improve the diagnostic accuracy of RS in an automated fashion, without requiring additional input from the clinician. AIM: The aim of this study is to improve the specificity of RS for detecting basal cell carcinoma (BCC) using an artificial neural network trained on RCM images to identify false positive normal skin structures (hair follicles and epidermis). APPROACH: Our approach was to build a two-step classification model. In the first step, a Raman biophysical model that was used in prior work classified BCC tumors from normal tissue structures with high sensitivity. In the second step, 191 RCM images were collected from the same site as the Raman data and served as inputs for two ResNet50 networks. The networks selected the hair structure and epidermis images, respectively, within all images corresponding to the positive predictions of the Raman biophysical model with high specificity. The specificity of the BCC biophysical model was improved by moving the Raman spectra corresponding to these selected images from false positive to true negative. RESULTS: Deep-learning trained on RCM images removed 52% of false positive predictions from the Raman biophysical model result while maintaining a sensitivity of 100%. The specificity was improved from 84.2% using Raman spectra alone to 92.4% by integrating Raman spectra with RCM images. CONCLUSIONS: Combining RS with deep-learning-aided RCM imaging is a promising tool for guiding tumor resection surgery.

Design of smart nanomedicines for effective cancer treatment.

Nanomedicine is a novel field of study that involves the use of nanomaterials to address challenges and issues that are associated with conventional therapeutics for cancer treatment including, but not limited to, low bioavailability, low water-solubility, narrow therapeutic window, nonspecific distribution, and multiple side effects of the drugs. Multiple strategies have been exploited to reduce the nonspecific distribution, and thus the side effect of the active pharmaceutical ingredients (API), including active and passive targeting strategies and externally controllable release of the therapeutic cargo. Site-specific release of the drug prevents it from impacting healthy cells, thereby significantly reducing side effects. API release triggers can be either externally applied, as in ultrasound-mediated activation, or induced by the tumor. To rationally design such nanomedicines, a thorough understanding of the differences between the tumor microenvironment versus that of healthy tissues must be paired with extensive knowledge of stimuli-responsive biomaterials. Herein, we describe the characteristics that differentiate tumor tissues from normal tissues. Then, we introduce smart materials that are commonly used for the development of smart nanomedicines to be triggered by stimuli such as changes in pH, temperature, and enzymatic activity. The most recent advances and their impact on the field of cancer therapy are further discussed.

Simultaneous Wide-Field Planar Strain-Fiber Orientation Distribution Measurement Using Polarized Spatial Domain Imaging.

In the present study, we demonstrate that soft tissue fiber architectural maps captured using polarized spatial frequency domain imaging (pSFDI) can be utilized as an effective texture source for DIC-based planar surface strain analyses. Experimental planar biaxial mechanical studies were conducted using pericardium as the exemplar tissue, with simultaneous pSFDI measurements taken. From these measurements, the collagen fiber preferred direction [Formula: see text] was determined at the pixel level over the entire strain range using established methods ( https://doi.org/10.1007/s10439-019-02233-0 ). We then utilized these pixel-level [Formula: see text] maps as a texture source to quantify the deformation gradient tensor [Formula: see text] as a function of spatial position [Formula: see text] within the specimen at time t. Results indicted that that the pSFDI approach produced accurate deformation maps, as validated using both physical markers and conventional particle based method derived from the DIC analysis of the same specimens. We then extended the pSFDI technique to extract the fiber orientation distribution [Formula: see text] as a function of [Formula: see text] from the pSFDI intensity signal. This was accomplished by developing a calibration procedure to account for the optical behavior of the constituent fibers for the soft tissue being studied. We then demonstrated that the extracted [Formula: see text] was accurately computed in both the referential (i.e. unloaded) and deformed states. Moreover, we noted that the measured [Formula: see text] agreed well with affine kinematic deformation predictions. We also demonstrated this calibration approach could also be effectively used on electrospun biomaterials, underscoring the general utility of the approach. In a final step, using the ability to simultaneously quantify [Formula: see text] and [Formula: see text], we examined the effect of deformation and collagen structural measurements on the measurement region size. For pericardial tissues, we determined a critical length of [Formula: see text] 8 mm wherein the regional variations sufficiently dissipated. This result has immediate potential in the identification of optimal length scales for meso-scale strain measurement in soft tissues and fibrous biomaterials.

Dual Photothermal/Chemotherapy of Melanoma Cells with Albumin Nanoparticles Carrying Indocyanine Green and Doxorubicin Leads to Immunogenic Cell Death.

Recent focus on cancer immunotherapies has led to significant interest in the development of therapeutic strategies that can lead to immunogenic cell death (ICD), which can cause activation of an immune response against tumor cells and improve immunotherapy outcomes by enhancing the immunogenicity of the tumor microenvironment. In this work, a nanomedicine-mediated combination therapy is used to deliver the ICD inducers doxorubicin (Dox), a chemotherapeutic agent, and indocyanine green (ICG), a photothermal agent. These agents are loaded into nanoparticles (NPs) of bovine serum albumin (BSA) that are prepared through a desolvation process. The formulation of BSA NPs is optimized to achieve NPs of 102.6  nm in size and loadings of 8.55 % and 5.69 % (w/w) for ICG and Dox, respectively. The controlled release of these agents from the BSA NPs is confirmed. Upon laser irradiation for 2.5 min, NPs at a dose of 62.5 µg mL-1 are able to increase the temperature of the cells by 7 °C and thereby inhibit the growth of B16F10 melanoma cells in vitro. Surface presentation of heat shock proteins and calreticulin from the cells after treatment confirmed the ability of the Dox/ICG loaded BSA NPs to induce ICD in the melanoma cells.

Mechanisms of Pulse Modulated Holmium:YAG Lithotripsy.

Introduction: This study aimed at answering three research questions: (1) Under the experimental conditions studied, what is the dominant mechanism of Holmium:YAG lithotripsy with or without pulse modulation? (2) Under what circumstances can laser pulse modulation increase crater volume of stone ablation per joule of emitted radiant energy? (3) Are BegoStone phantoms a suitable model for laser lithotripsy studies? Materials and Methods: The research questions were addressed by ablation experiments with BegoStone phantoms and native stones. Experiments were performed under three stone conditions: dry stones in air, hydrated stones in air, and hydrated stones in water. Single pulses with and without pulse modulation were applied. For each pulse mode, temporal profile, transmission through 1 mm water, and cavitation bubble collapse pressures were measured and compared. For each stone condition and pulse mode, stones were ablated with a fiber separation distance of 1 mm and crater volumes were measured using optical coherence tomography. Results: Pulses with and without pulse modulation had high (>80%) transmission through 1 mm of water. Pulses without pulse modulation generated much higher peak pressures than those with pulse modulation (62.3 vs 11.4 bar). Pulse modulation resulted in similar or larger craters than without pulse modulation. Trends in BegoStone crater volumes differed from trends in native stones. Conclusions: This results of this study suggest that the dominant mechanism is photothermal with possible photoacoustic contributions for some stone compositions. Pulse modulation can increase ablation volume per joule of emitted radiant energy, but the effect may be composition specific. BegoStones showed unique infrared ablation characteristics compared with native stones and are not a suitable model for laser lithotripsy studies.

Imaging sub-diffuse optical properties of cancerous and normal skin tissue using machine learning-aided spatial frequency domain imaging.

SIGNIFICANCE: Sub-diffuse optical properties may serve as useful cancer biomarkers, and wide-field heatmaps of these properties could aid physicians in identifying cancerous tissue. Sub-diffuse spatial frequency domain imaging (sd-SFDI) can reveal such wide-field maps, but the current time cost of experimentally validated methods for rendering these heatmaps precludes this technology from potential real-time applications. AIM: Our study renders heatmaps of sub-diffuse optical properties from experimental sd-SFDI images in real time and reports these properties for cancerous and normal skin tissue subtypes. APPROACH: A phase function sampling method was used to simulate sd-SFDI spectra over a wide range of optical properties. A machine learning model trained on these simulations and tested on tissue phantoms was used to render sub-diffuse optical property heatmaps from sd-SFDI images of cancerous and normal skin tissue. RESULTS: The model accurately rendered heatmaps from experimental sd-SFDI images in real time. In addition, heatmaps of a small number of tissue samples are presented to inform hypotheses on sub-diffuse optical property differences across skin tissue subtypes. CONCLUSION: These results bring the overall process of sd-SFDI a fundamental step closer to real-time speeds and set a foundation for future real-time medical applications of sd-SFDI such as image guided surgery.

Introduction to the Biophotonics Congress 2020 feature issue.

The guest editors introduce a feature issue containing papers based on research presented at the OSA Biophotonics Congress (the former BIOMED) 20-23 April 2020, in the first all virtual, web conference format undertaken by OSA.

Toward automated assessment of mole similarity on dermoscopic images.

Purpose: Current skin cancer detection relies on dermatologists' visual assessments of moles directly or dermoscopically. Our goal is to show that our similarity assessment algorithm on dermoscopic images can perform as well as a dermatologist's assessment. Approach: Given one target mole and two other moles from the same patient, our model determines which mole is more similar to the target mole. Similarity was quantified as the Euclidean distance in a feature space designed to capture mole properties such as size, shape, and color. We tested our model on 18 patients, each of whom had at least five moles, and compared the model assessments of mole similarity with that of three dermatologists. Fleiss' Kappa agreement coefficients and iteration tests were used to evaluate the agreement in similarity assessment among dermatologists and our model. Results: With the selected features of size, entropy (color variation), and cluster prominence (asymmetry), our algorithm's similarity assessments agreed moderately with the similarity assessments of dermatologists. The mean Kappa of 1000 iteration tests was 0.49 ( confidence interval ( CI ) = [ 0.23 , 0.74 ] ) when comparing three dermatologists and our model, which is comparable to the agreement in similarity assessment among the dermatologists themselves (the mean Kappa of 1000 iteration tests for three dermatologists was 0.48, CI = [ 0.19 , 0.77 ] .) By contrast, the mean Kappa was 0.22 ( CI = [ - 0.00 , 0.43 ] ) when comparing the similarity assessments of three dermatologists and random guesses. Conclusions: Our study showed that our image feature-engineering-based algorithm can effectively assess the similarity of moles as dermatologists do. Such a similarity assessment could serve as the foundation for computer-assisted intra-patient evaluation of moles.

Laser nanobubbles induce immunogenic cell death in breast cancer.

Recent advances in immunotherapy have highlighted a need for therapeutics that initiate immunogenic cell death in tumors to stimulate the body's immune response to cancer. This study examines whether laser-generated bubbles surrounding nanoparticles ("nanobubbles") induce an immunogenic response for cancer treatment. A single nanosecond laser pulse at 1064 nm generates micron-sized bubbles surrounding gold nanorods in the cytoplasm of breast cancer cells. Cell death occurred in cells treated with nanorods and irradiated, but not in cells with irradiation treatment alone. Cells treated with nanorods and irradiation had increased damage-associated molecular patterns (DAMPs), including increased expression of chaperone proteins human high mobility group box 1 (HMGB1), adenosine triphosphate (ATP), and heat shock protein 70 (HSP70). This enhanced expression of DAMPs led to the activation of dendritic cells. Overall, this treatment approach is a rapid and highly specific method to eradicate tumor cells with simultaneous immunogenic cell death signaling, showing potential as a combination strategy for immunotherapy.

Induction of immunogenic cell death of cancer cells through nanoparticle-mediated dual chemotherapy and photothermal therapy.

The use of nanomedicines to induce immunogenic cell death is a new strategy that aims to increase tumor immunogenicity and thereby prime tumors for further immunotherapies. In this study, we developed a nanoparticle formulation for combinatory chemotherapy and photothermal therapy based only on materials previously used in FDA-approved products and investigated the effect of the combinatory therapy on the growth inhibition and induction of immunogenic cell death in human MDA-MB-231 breast cancer cells. The formulation consists of ~108-nm nanoparticles made of poly(lactic acid)-b-methoxy poly(ethylene glycol) which carry doxorubicin for chemotherapy and indocyanine green for photothermal therapy. A 0.3 mg/mL suspension of NPs increased the medium temperature up to 10 °C upon irradiation with an 808-nm diode laser. In vitro studies showed that combination of laser assisted indocyanine green-mediated photothermal therapy and doxorubicin-mediated chemotherapy effectively eradicated cancer cells and resulted in the highest level of damage-associated molecular pattern presentation (calreticulin, high mobility group box 1, and adenosine triphosphate) compared to the individual treatments alone. These results demonstrate that our nanoparticle-mediated combinatory approach led to the most intense immunogenic cell death when compared to individual chemotherapy or photothermal therapy, making it a potent option for future in vivo studies in combination with cancer immunotherapies.

Assessment of Raman Spectroscopy for Reducing Unnecessary Biopsies for Melanoma Screening.

A key challenge in melanoma diagnosis is the large number of unnecessary biopsies on benign nevi, which requires significant amounts of time and money. To reduce unnecessary biopsies while still accurately detecting melanoma lesions, we propose using Raman spectroscopy as a non-invasive, fast, and inexpensive method for generating a "second opinion" for lesions being considered for biopsy. We collected in vivo Raman spectral data in the clinical skin screening setting from 52 patients, including 53 pigmented lesions and 7 melanomas. All lesions underwent biopsies based on clinical evaluation. Principal component analysis and logistic regression models with leave one lesion out cross validation were applied to classify melanoma and pigmented lesions for biopsy recommendations. Our model achieved an area under the receiver operating characteristic (ROC) curve (AUROC) of 0.903 and a specificity of 58.5% at perfect sensitivity. The number needed to treat for melanoma could have been decreased from 8.6 (60/7) to 4.1 (29/7). This study in a clinical skin screening setting shows the potential of Raman spectroscopy for reducing unnecessary skin biopsies with in vivo Raman data and is a significant step toward the application of Raman spectroscopy for melanoma screening in the clinic.

Superpixel Raman spectroscopy for rapid skin cancer margin assessment.

Spontaneous Raman micro-spectroscopy has been demonstrated great potential in delineating tumor margins; however, it is limited by slow acquisition speed. We describe a superpixel acquisition approach that can expedite acquisition between ~×100 and ×10 000, as compared to point-by-point scanning by trading off spatial resolution. We present the first demonstration of superpixel acquisition on rapid discrimination of basal cell carcinoma tumor from eight patients undergoing Mohs micrographic surgery. Results have been demonstrated high discriminant power for tumor vs normal skin based on the biochemical differences between nucleus, collagen, keratin and ceramide. We further perform raster-scanned superpixel Raman imaging on positive and negative margin samples. Our results indicate superpixel acquisition can facilitate the use of Raman microspectroscopy as a rapid and specific tool for tumor margin assessment.

Physiological model using diffuse reflectance spectroscopy for nonmelanoma skin cancer diagnosis.

Diffuse reflectance spectroscopy (DRS) is a noninvasive, fast, and low-cost technology with potential to assist cancer diagnosis. The goal of this study was to test the capability of our physiological model, a computational Monte Carlo lookup table inverse model, for nonmelanoma skin cancer diagnosis. We applied this model on a clinical DRS dataset to extract scattering parameters, blood volume fraction, oxygen saturation and vessel radius. We found that the model was able to capture physiological information relevant to skin cancer. We used the extracted parameters to classify (basal cell carcinoma [BCC], squamous cell carcinoma [SCC]) vs actinic keratosis (AK) and (BCC, SCC, AK) vs normal. The area under the receiver operating characteristic curve achieved by the classifiers trained on the parameters extracted using the physiological model is comparable to that of classifiers trained on features extracted via Principal Component Analysis. Our findings suggest that DRS can reveal physiologic characteristics of skin and this physiologic model offers greater flexibility for diagnosing skin cancer than a pure statistical analysis. Physiological parameters extracted from diffuse reflectance spectra data for nonmelanoma skin cancer diagnosis.

Biophysical basis of skin cancer margin assessment using Raman spectroscopy.

Achieving adequate margins during tumor margin resection is critical to minimize the recurrence rate and maximize positive patient outcomes during skin cancer surgery. Although Mohs micrographic surgery is by far the most effective method to treat nonmelanoma skin cancer, it can be limited by its inherent required infrastructure, including time-consuming and expensive on-site histopathology. Previous studies have demonstrated that Raman spectroscopy can accurately detect basal cell carcinoma (BCC) from surrounding normal tissue; however, the biophysical basis of the detection remained unclear. Therefore, we aim to explore the relevant Raman biomarkers to guide BCC margin resection. Raman imaging was performed on skin tissue samples from 30 patients undergoing Mohs surgery. High correlations were found between the histopathology and Raman images for BCC and primary normal structures (including epidermis, dermis, inflamed dermis, hair follicle, hair shaft, sebaceous gland and fat). A previously developed model was used to extract the biochemical changes associated with malignancy. Our results showed that BCC had a significantly different concentration of nucleus, keratin, collagen, triolein and ceramide compared to normal structures. The nucleus accounted for most of the discriminant power (90% sensitivity, 92% specificity - balanced approach). Our findings suggest that Raman spectroscopy is a promising surgical guidance tool for identifying tumors in the resection margins.

Non-Destructive Reflectance Mapping of Collagen Fiber Alignment in Heart Valve Leaflets.

Collagen fibers are the primary structural elements that define many soft-tissue structure and mechanical function relationships, so that quantification of collagen organization is essential to many disciplines. Current tissue-level collagen fiber imaging techniques remain limited in their ability to quantify fiber organization at macroscopic spatial scales and multiple time points, especially in a non-contacting manner, requiring no modifications to the tissue, and in near real-time. Our group has previously developed polarized spatial frequency domain imaging (pSFDI), a reflectance imaging technique that rapidly and non-destructively quantifies planar collagen fiber orientation in superficial layers of soft tissues over large fields-of-view. In this current work, we extend the light scattering models and image processing techniques to extract a critical measure of the degree of collagen fiber alignment, the normalized orientation index (NOI), directly from pSFDI data. Electrospun fiber samples with architectures similar to many collagenous soft tissues and known NOI were used for validation. An inverse model was then used to extract NOI from pSFDI measurements of aortic heart valve leaflets and clearly demonstrated changes in degree of fiber alignment between opposing sides of the sample. These results show that our model was capable of extracting absolute measures of degree of fiber alignment in superficial layers of heart valve leaflets with only general a priori knowledge of fiber properties, providing a novel approach to rapid, non-destructive study of microstructure in heart valve leaflets using a reflectance geometry.

Raman biophysical markers in skin cancer diagnosis.

Raman spectroscopy (RS) has demonstrated great potential for in vivo cancer screening; however, the biophysical changes that occur for specific diagnoses remain unclear. We recently developed an inverse biophysical skin cancer model to address this issue. Here, we presented the first demonstration of in vivo melanoma and nonmelanoma skin cancer (NMSC) detection based on this model. We fit the model to our previous clinical dataset and extracted the concentration of eight Raman active components in 100 lesions in 65 patients diagnosed with malignant melanoma (MM), dysplastic nevi (DN), basal cell carcinoma, squamous cell carcinoma, and actinic keratosis. We then used logistic regression and leave-one-lesion-out cross validation to determine the diagnostically relevant model components. Our results showed that the biophysical model captures the diagnostic power of the previously used statistical classification model while also providing the skin's biophysical composition. In addition, collagen and triolein were the most relevant biomarkers to represent the spectral variances between MM and DN, and between NMSC and normal tissue. Our work demonstrates the ability of RS to reveal the biophysical basis for accurate diagnosis of different skin cancers, which may eventually lead to a reduction in the number of unnecessary excisional skin biopsies performed.