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Photodynamic therapy (PDT) has shown potentially beneficial results in treating port-wine stain, but its benefit-risk profile remains undefined. This study aimed to evaluate the efficacy and safety of PDT conducted with hemoporfin and a 532 nm continuous wave laser to treat port-wine stain clinically. This randomized clinical trial was conducted in eight hospitals in China. Participants were adolescent and adult patients (age range: 14-65 years old) with port-wine stain. During stage 1 (day 1 to week 8) all patients were randomized at a 3:1 ratio to treatment (532 nm laser irradiation (96-120 J/cm2) with hemoporfin (5mg/kg; PDT-hemoporfin, n = 330)) or placebo groups (irradiation with placebo (PDT-placebo, n = 110)); during stage 2 (week 8 to 16) patients in both groups were offered treatment. Clinician-evaluators, who were blind to the study, classified each case on the following four-level scale according to assessment of before and after standardized pictures of the lesion area: no improvement: <20%; some improvement: 20-59%; great improvement: 60-89%; or nearly completely resolved: ≥90%. The primary efficacy endpoint was proportion of patients achieving at least some improvement at week 8. The secondary efficacy endpoints were proportion of patients achieving nearly completely resolved or at least great improvement at week 8, proportion of patients achieving early completely resolved, at least great improvement, or at least some improvement at week 16, and the corresponding satisfaction of the investigators and the patients (designated as 'excellent', 'good', 'moderate', or 'ineffective') at weeks 8 and 16. Compared to the PDT-placebo group, the PDT-hemoporfin group showed a significantly higher proportion of patients that achieved at least some improvement (89.7% [n = 295; 95% CI, 85.9%-92.5%] vs. 24.5% [n = 27; 95% CI, 17.4%-33.3%]) at week 8 (P < 0.0001) and higher improvements for all secondary efficacy endpoints. Treatment reactions occurred in 99.5% (n = 731; 95% CI, 98.7%-99.8%) of the PDT-hemoporfin treatments (n = 735). Hyperpigmentation occurred in 22.9 per 100 patient-treatments (n = 168; 95% CI, 20.0-26.0) in the PDT-hemoporfin treated patients. Hemoporfin-mediated PDT is an effective and safe treatment option for adolescent and adult patients with port-wine stain. Chinese Clinical Trial Registry ChiCTR-TRC-08000213.

Blood and semen stains are the most common biological stains encountered at crime scenes. The washing of biological stains is a common application that perpetrators use to spoil the crime scene. With a structured experiment approach, this study aims to investigate the effects of washing with various chemicals on the ATR-FTIR detection of blood and semen stains on cotton. On cotton pieces, a total of 78 blood and 78 semen stains were applied, and each group of six stains was immersed or mechanically cleaned in water, 40% methanol, 5% sodium hypochlorite solution, 5% hypochlorous acid solution, 5 g/L soap dissolved pure water, and 5 g/L dishwashing detergent dissolved water. ATR-FTIR spectra gathered from all stains and analyzed with chemometric tools. According to performance parameters of developed models, PLS-DA is a powerful tool for discrimination of washing chemical for both washed blood and semen stains. Results from this study show that FTIR is promising for use in detecting blood and semen stains that have become invisible to the naked eye due to washing of the findings. Our approach allows blood and semen to be detected on cotton pieces using FTIR combined with chemometrics, even though it is not visible to the naked eye. Washing chemicals also can be distinguished via FTIR spectra of stains. [Display omitted] •Blood and semen stains can be discriminated based on FTIR spectra with PLS-DA.•Washed blood and semen stains can be identified via FTIR.•Washing chemicals can be discriminated based on FTIR spectra of biological stains with PLS-DA.

Pathology is practiced by visual inspection of histochemically stained tissue slides. While the hematoxylin and eosin (H&E) stain is most commonly used, special stains can provide additional contrast to different tissue components. Here, we demonstrate the utility of supervised learning-based computational stain transformation from H&E to special stains (Masson’s Trichrome, periodic acid-Schiff and Jones silver stain) using kidney needle core biopsy tissue sections. Based on the evaluation by three renal pathologists, followed by adjudication by a fourth pathologist, we show that the generation of virtual special stains from existing H&E images improves the diagnosis of several non-neoplastic kidney diseases, sampled from 58 unique subjects (P = 0.0095). A second study found that the quality of the computationally generated special stains was statistically equivalent to those which were histochemically stained. This stain-to-stain transformation framework can improve preliminary diagnoses when additional special stains are needed, also providing significant savings in time and cost. Performing multiple histological stains on a biopsy can be costly and time consuming. Here the authors present a method for the digital transformation of H&E stained tissue into special stains (e.g., PAS, Masson’s Trichrome and Jones silver stain), and demonstrate that it improves diagnoses over the use of H&E only.

Color change was compared through artificial and outdoor weathering tests according to wood species and stain type. In the artificial weathering tests, the color change DE was the highest for the initial 200 hour, and teak solvent-based stain was the most effective in preventing color change. Outdoor weathering tests also showed a rapid color change until the initial 60 days, and the uncoated larch specimens exhibited graying after 120 days. Teak solvent-based stain had the highest preventing color effect, whereas water-based white semi-transparent stain had the highest contact angle. It is difficult to check the color change of wood due to the addition of pigment in teak, as its resistance to moisture is rapidly reduced and its surface protection effect is poor. Water-based white semi-transparent stain prevented color change and maintained a contact angle of 57.1° for up to 150 days, confirming the effect of moisture resistance. This study aimed to provide basic data on weather resistance by wood species and to suggest that the development direction of outdoor exposed wood is a water-based semi-transparent stain.

Histological staining is the gold standard for tissue examination in clinical pathology and life-science research, which visualizes the tissue and cellular structures using chromatic dyes or fluorescence labels to aid the microscopic assessment of tissue. However, the current histological staining workflow requires tedious sample preparation steps, specialized laboratory infrastructure, and trained histotechnologists, making it expensive, time-consuming, and not accessible in resource-limited settings. Deep learning techniques created new opportunities to revolutionize staining methods by digitally generating histological stains using trained neural networks, providing rapid, cost-effective, and accurate alternatives to standard chemical staining methods. These techniques, broadly referred to as virtual staining , were extensively explored by multiple research groups and demonstrated to be successful in generating various types of histological stains from label-free microscopic images of unstained samples; similar approaches were also used for transforming images of an already stained tissue sample into another type of stain, performing virtual stain-to-stain transformations. In this Review, we provide a comprehensive overview of the recent research advances in deep learning-enabled virtual histological staining techniques. The basic concepts and the typical workflow of virtual staining are introduced, followed by a discussion of representative works and their technical innovations. We also share our perspectives on the future of this emerging field, aiming to inspire readers from diverse scientific fields to further expand the scope of deep learning-enabled virtual histological staining techniques and their applications. Deep Learning enables virtual histological staining of biological samples.

Background Histological images show strong variance (e.g. illumination, color, staining quality) due to differences in image acquisition, tissue processing, staining, etc. This can impede downstream image analysis such as staining intensity evaluation or classification. Methods to reduce these variances are called image normalization techniques. Methods In this paper, we investigate the potential of CycleGAN (cycle consistent Generative Adversarial Network) for color normalization in hematoxylin-eosin stained histological images using daily clinical data with consideration of the variability of internal staining protocol variations. The network consists of a generator network G B that learns to map an image X from a source domain A to a target domain B , i.e. G B : X A → X B . In addition, a discriminator network D B is trained to distinguish whether an image from domain B is real or generated. The same process is applied to another generator-discriminator pair ( G A , D A ), for the inverse mapping G A : X B → X A . Cycle consistency ensures that a generated image is close to its original when being mapped backwards ( G A ( G B ( X A ))≈ X A and vice versa). We validate the CycleGAN approach on a breast cancer challenge and a follicular thyroid carcinoma data set for various stain variations. We evaluate the quality of the generated images compared to the original images using similarity measures. In addition, we apply stain normalization on pathological lymph node data from our institute and test the gain from normalization on a ResNet classifier pre-trained on the Camelyon16 data set. Results Qualitative results of the images generated by our network are compared to original color distributions. Our evaluation indicates that by mapping images to a target domain, the similarity training images from that domain improves up to 96%. We also achieve a high cycle consistency for the generator networks by obtaining similarity indices greater than 0.9. When applying the CycleGAN normalization to HE-stain images from our institute the kappa-value of the ResNet-model that is only trained on Camelyon16 data is increased more than 50%. Conclusions CycleGANs have proven to efficiently normalize HE-stained images. The approach compensates for deviations resulting from image acquisition (e.g. different scanning devices) as well as from tissue staining (e.g. different staining protocols), and thus overcomes the staining variations in images from various institutions.The code is publicly available at https://github.com/m4ln/stainTransfer_CycleGAN_pytorch . The data set supporting the solutions is available at https://doi.org/10.11588/data/8LKEZF .

Quantitative phase imaging has gained popularity in bioimaging because it can avoid the need for cell staining, which, in some cases, is difficult or impossible. However, as a result, quantitative phase imaging does not provide the labelling of various specific intracellular structures. Here we show a novel computational segmentation method based on statistical inference that makes it possible for quantitative phase imaging techniques to identify the cell nucleus. We demonstrate the approach with refractive index tomograms of stain-free cells reconstructed using tomographic phase microscopy in the flow cytometry mode. In particular, by means of numerical simulations and two cancer cell lines, we demonstrate that the nucleus can be accurately distinguished within the stain-free tomograms. We show that our experimental results are consistent with confocal fluorescence microscopy data and microfluidic cyto-fluorimeter outputs. This is a remarkable step towards directly extracting specific three-dimensional intracellular structures from the phase contrast data in a typical flow cytometry configuration.The accurate identification of the three-dimensional quantitative shape of a cell nucleus is now possible without fluorescent staining by applying computational segmentation to refractive index tomograms recorded in the flow cytometry mode.

Trypan blue has long been the gold standard for staining dead cell to determine cell viability. The dye is excluded from membrane-intact live cells, but can enter and concentrate in membrane-compromised dead cells, rendering the cells dark blue. Over the years, there has been an understanding that trypan blue is inaccurate for cell viability under 80% without scientific support. We previously showed that trypan blue can alter the morphology of dead cells to a diffuse shape, which can lead to over-estimation of viability. Here, we investigate the origin of the dim and diffuse objects after trypan blue staining. Utilizing image and video acquisition, we show real-time transformation of cells into diffuse objects when stained with trypan blue. The same phenomenon was not observed when staining cells with propidium iodide. We also demonstrate the co-localization of trypan blue and propidium iodide, confirming these diffuse objects as cells that contain nuclei. The videos clearly show immediate cell rupturing after trypan blue contact. The formation of these diffuse objects was monitored and counted over time as cells die outside of the incubator. We hypothesize and demonstrate that rapid water influx may have caused the cells to rupture and disappear. Since some dead cells disappear after trypan blue staining, the total can be under-counted, leading to over-estimation of cell viability. This inaccuracy could affect the outcomes of cellular therapies, which require accurate measurements of immune cells that will be infused back into patients.