High-resolution Microscopy Images Research Articles

Revisiting the taxonomy of Korean Ischnochiton species (Polyplacophora, Ischnochitonidae) based on a combined analysis of morphological and molecular data

Chiton species belonging to the genus Ischnochiton J. E. Gray, 1847 are commonly found in intertidal rocky shores worldwide, with the exception of the northern Atlantic and Arctic oceans. Ischnochiton species are characterised by imbricate girdle scales that are uniform in size, rounded, sculpted with striae or occasionally smooth. However, their species-level taxonomy is complicated due to the high variation in their shell microstructures. Despite more than a hundred species reported worldwide, taxonomic studies of this group remain relatively unexplored in Korean waters, with only a few species recorded to date. In this study, we compared the microstructural characteristics of tegmentum sculpture, girdle scales and radula amongst four Korean Ischnochiton species using high-resolution microscopic images and a scanning electron microscope (SEM). Along with mtDNA cox1 sequence comparison, a comprehensive analysis of their morphology revealed that I. hayamii Owada, 2018 was identified for the first time in Korean waters. This species is morphologically distinguished by its small body size of adults, smooth lateral areas on valves and small perinotum scales sculptured with weak longitudinal ribs. Phylogenetic analysis of the mtDNA cox1 sequence provides distinct resolution at the species level, but interrelationships amongst Ischnochiton species remain unresolved. Results from the morphological and molecular analyses presented in this study offer valuable taxonomic information for accurate species identification amongst closely-related Ischnochiton species.

From two segments and beyond: Investigating the onset of regeneration in Syllis malaquini.

Annelids feature a diverse range of regenerative abilities, but complete whole-body regeneration is less common, particularly in the context of the head and anterior body regeneration. This study provides a detailed morphological description of Syllis malaquini regenerative abilities. By replicating previous experiments and performing diverse surgical procedures, we explored the capacity of this species for whole-body regeneration. We detailed the precise timing of regeneration of particular structures such as the eyes, proventricle, pharyngeal tooth, nuchal organs, and body pigmentation after amputation. Our high-resolution scanning electron microscopy and confocal laser-scanning microscopy images provide details of the blastema region, revealing that while anal opening remains in connection to the exterior environment, oral opening is formed "de novo" during blastema differentiation. Additionally, we performed amputations to isolate fragments consisting of one, two, and three segments from the intestinal trunk region. We found that S. malaquini requires at least two to three segments to successfully regenerate the whole body. In addition, we verified a variable capacity to regenerate depending upon the gut region, with structures of the foregut greatly impairing some steps of the regenerative process. Our work notably addresses the gap in knowledge concerning gut formation and its impact on regenerative capabilities. Ongoing research is crucial to unravel the role of gut tissue specificity and plasticity during regeneration in annelids, and particularly in syllids.

Enhanced MALDI-2 Sensitivity with Reflecting Post-Ionization Laser for High-Resolution MS Imaging Combined with Real-Time Microscope Imaging.

Laser-induced matrix-assisted laser desorption/ionization post-ionization (MALDI-2) could improve the MALDI sensitivity of biological metabolites by over 1 order of magnitude. Herein, we demonstrate that MALDI-2 sensitivity can be further enhanced with reflecting post-ionization laser that multiplies the intersection times between laser and MALDI plume. This method, which we named MALDI-2+, typically brought over 2 times sensitivity improvement from conventional MALDI-2. Advancing in sensitivity thereby prompted us to pursue higher mass spectrometry imaging (MSI) spatial resolution. A dedicated T-shaped ion guide was designed to allow perpendicular incidence of ablation laser in reflection geometry MALDI. Although 8-10 μm pixel was used in MALDI imaging due to the limited precision of the motorized stage, the laser spot diameter could be down to 2.5 μm for potentially higher spatial resolution. In addition, this ion source enabled real-time and high-quality microscope imaging from backward of the sample plate. Beneficially, we were able to monitor the actual laser spot condition in real time as well as obtain high-resolution microscopic sample images that inherently register with MSI images. All of these benefits have been demonstrated by analyzing standard samples and imaging of cells. We believe that the enhancement in sensitivity, spatial resolution, and microscope capacity of our design could facilitate spatial omics studies.

Intraoperative margin assessment with near real time pathology during partial gland ablation of prostate cancer: A feasibility study

BackgroundIn-field or in-margin recurrence after partial gland cryosurgical ablation (PGCA) of prostate cancer (PCa) remains a limitation of the paradigm. Stimulated Raman histology (SRH) is a novel microscopic technique allowing real time, label-free, high-resolution microscopic images of unprocessed, un-sectioned tissue which can be interpreted by humans or artificial intelligence (AI). We evaluated surgical team and AI interpretation of SRH for real-time pathologic feedback in the planning and treatment of PCa with PGCA. MethodsAbout 12 participants underwent prostate mapping biopsies during PGCA of their PCa between January and June 2022. Prostate biopsies were immediately scanned in a SRH microscope at 20 microns depth using 2 Raman shifts to create SRH images which were interpreted by the surgical team intraoperatively to guide PGCA, and retrospectively assessed by AI. The cores were then processed, hematoxylin and eosin stained as per normal pathologic protocols and used for ground truth pathologic assessment. ResultsSurgical team interpretation of SRH intraoperatively revealed 98.1% accuracy, 100% sensitivity, 97.3% specificity for identification of PCa, while AI showed a 97.9% accuracy, 100% sensitivity and 97.5% specificity for identification of clinically significant PCa. 3 participants’ PGCA treatments were modified after SRH visualized PCa adjacent to an expected MRI predicted tumor margin or at an untreated cryosurgical margin. ConclusionSRH allows for accurate rapid identification of PCa in PB by a surgical team interpretation or AI. PCa tumor mapping and margin assessment during PGCA appears to be feasible and accurate. Further studies evaluating impact on clinical outcomes are warranted.

Deep Learning Assisted High Resolution Microscopy Image Processing for Phase Segmentation in Functional Composite Materials

In the domain of battery research, the processing of high-resolution microscopy images is a challenging task, as it involves dealing with complex images and requires a prior understanding of the components involved. The utilization of deep learning methodologies for image analysis has attracted considerable interest in recent years, with multiple investigations employing such techniques for image segmentation and analysis within the realm of battery research. However, the automated analysis of high-resolution microscopy images for detecting phases and components in composite materials is still an underexplored area.The presentation discusses the formulation of a sophisticated deep learning-assisted workflow tailored for the meticulous processing of high throughput, high-resolution microscopy images to detect, map and analyze the evolution of periodic components from a stack of high-resolution Transmission Electron Microscopy (TEM) image. The developed methodology employs deep learning techniques and utilizes Fast Fourier Transform (FFT) patterns extracted from TEM images to analyze feature positions and ascertain periodic components. The approach incorporates innovative strategies to process high-resolution images, capitalizing on the symmetry within FFT patterns to enhance model performance. This methodology streamlines the detection and mapping of components while the intensity profiles derived from the frames provide valuable insights into the evolution of periodic components within the sample during the imaging period.High-resolution images obtained through a beam damage analysis investigation of lithium fluoride (LiF), a prevalent constituent within the solid-electrolyte interphase (SEI) of cycled lithium metal anodes, were employed to assess the program's scalability and robustness. The study elucidated decomposition products and their spatial distribution. Furthermore, the intensity profile of periodic components detected across multiple frames aided in evaluating the beam damage mechanism of the LiF component.Our research has resulted in an open-source Python GUI program that the research community can freely access and experiment with. Figure 1

Chemical imaging unveils mitochondria as the major site of medicinal biguanide accumulation within cells

Metformin (MET), a commonly prescribed medication for managing type 2 diabetes, has demonstrated various beneficial effects beyond its primary anti-diabetic efficacy. However, the mechanism underlying MET activity and its distribution within organelles remain largely unknown. In this study, we integrate multiple technologies, including chemical labeling, immunostaining, and high-resolution microscopy imaging, to visualize the accumulation of MET in organelles of cultured cells. To achieve this objective, an alkynylated MET probe is developed that preserves biological activity similar to biguanide drugs. As determined by biorthogonal chemical labeling and imaging, the MET probe selectively localizes to substructures within cells, contrasting with its probe control. Furthermore, the MET probe can be competitively and efficiently washed out through biguanide administration, demonstrating the specific activity of this probe in monitoring the cellular dynamics of biguanide drugs. Our results indicate that the MET probe can reach near-saturated concentrations within 2 h and is rapidly eliminated within an additional 2 h once the exogenous source of the drug is removed. Furthermore, we reveal that the MET probe primarily accumulates in mitochondria, particularly within the mitochondrial matrix, and has a minor presence in other organelles, such as lysosomes and endosomes. Together, this study provides the first view of the subcellular localization of MET and lays the foundation for future investigations on its molecular targets and mechanisms of action in promoting human health.

Aerosol dosimetry in the whole conducting zone of a murine left-lung using CF-PD and LSFM images

Aerosol dosimetry in respiratory airways is relevant for pulmonary drug delivery and inhalation toxicology. Consequently, computational fluid-particle dynamics (CF-PD) modelling of pulmonary aerosol delivery is an active research field. Additionally, mice are the most commonly used animals in medical research. Technological advances have provided information on whole mice lung morphologies with unprecedented high resolution. Therefore, in this study, we used high-resolution light sheet fluorescent microscopy (LSFM) images of a healthy C57BL/6 mouse lung with a constant air flow rate of 72 ml/min, to extract an anatomical 3-dimensional (3D) geometry of the entire airway tree of the left lung from the primary bronchi to the most distal bronchioles excluding the trachea. The airways were segmented based on an order- and generation-based method. Also, to compare the morphological data and regional deposition, a generation-based investigation including 25 generations was employed in the present model. One-way coupling of CF-PD modeling was applied to model an intubated and mechanically-ventilated mouse. Maximum values of the velocity and vorticity magnitude of 3.2 m/s and 200,000 1/s were reached in the second order, respectively, and maximum pressure and wall shear stress levels were 30 Pa and 3.5 Pa, respectively. Finally, order- and generation-based particle deposition efficiency and dose per lung area were obtained for the particle size range of 1 μm ≤ dp ≤ 10 μm yielding pronounced hotspot deposition patterns mainly near the proximal bifurcations. The results showed a positive correlation between deposition efficiency and particle size due to a size-dependent increase in inertial and gravitational effects. Maximum regional deposition and normalized dose was seen for 10 μm particles in the 1st order of the murine left lung. Smaller peak sizes of deposition efficiency were seen in the third and fourth orders of the mouse left lung due to almost complete loss of the largest particles in lower order airways. It also justifies the close to zero deposition efficiency in the highest orders (fifth to sixth). Both lung morphology as well as total and regional aerosol deposition showed reasonably good agreement with empirical data from the literature. The present CF-PD model with accurate realistic lung morphology, improves our knowledge of airway aerosol deposition hotspots. The obtained modeling method and the qualitative results can be implemented on human airways.

Fast and robust feature-based stitching algorithm for microscopic images

The limited field of view of high-resolution microscopic images hinders the study of biological samples in a single shot. Stitching of microscope images (tiles) captured by the whole-slide imaging (WSI) technique solves this problem. However, stitching is challenging due to the repetitive textures of tissues, the non-informative background part of the slide, and the large number of tiles that impact performance and computational time. To address these challenges, we proposed the Fast and Robust Microscopic Image Stitching (FRMIS) algorithm, which relies on pairwise and global alignment. The speeded up robust features (SURF) were extracted and matched within a small part of the overlapping region to compute the transformation and align two neighboring tiles. In cases where the transformation could not be computed due to an insufficient number of matched features, features were extracted from the entire overlapping region. This enhances the efficiency of the algorithm since most of the computational load is related to pairwise registration and reduces misalignment that may occur by matching duplicated features in tiles with repetitive textures. Then, global alignment was achieved by constructing a weighted graph where the weight of each edge is determined by the normalized inverse of the number of matched features between two tiles. FRMIS has been evaluated on experimental and synthetic datasets from different modalities with different numbers of tiles and overlaps, demonstrating faster stitching time compared to existing algorithms such as the Microscopy Image Stitching Tool (MIST) toolbox. FRMIS outperforms MIST by 481% for bright-field, 259% for phase-contrast, and 282% for fluorescence modalities, while also being robust to uneven illumination.

A deep learning-based model for detecting Leishmania amastigotes in microscopic slides: a new approach to telemedicine

BackgroundLeishmaniasis, an illness caused by protozoa, accounts for a substantial number of human fatalities globally, thereby emerging as one of the most fatal parasitic diseases. The conventional methods employed for detecting the Leishmania parasite through microscopy are not only time-consuming but also susceptible to errors. Therefore, the main objective of this study is to develop a model based on deep learning, a subfield of artificial intelligence, that could facilitate automated diagnosis of leishmaniasis.MethodsIn this research, we introduce LeishFuNet, a deep learning framework designed for detecting Leishmania parasites in microscopic images. To enhance the performance of our model through same-domain transfer learning, we initially train four distinct models: VGG19, ResNet50, MobileNetV2, and DenseNet 169 on a dataset related to another infectious disease, COVID-19. These trained models are then utilized as new pre-trained models and fine-tuned on a set of 292 self-collected high-resolution microscopic images, consisting of 138 positive cases and 154 negative cases. The final prediction is generated through the fusion of information analyzed by these pre-trained models. Grad-CAM, an explainable artificial intelligence technique, is implemented to demonstrate the model’s interpretability.ResultsThe final results of utilizing our model for detecting amastigotes in microscopic images are as follows: accuracy of 98.95 1.4%, specificity of 98 2.67%, sensitivity of 100%, precision of 97.91 2.77%, F1-score of 98.92 1.43%, and Area Under Receiver Operating Characteristic Curve of 99 1.33.ConclusionThe newly devised system is precise, swift, user-friendly, and economical, thus indicating the potential of deep learning as a substitute for the prevailing leishmanial diagnostic techniques.

MicronuclAI: Automated quantification of micronuclei for assessment of chromosomal instability.

Chromosomal instability (CIN) is a hallmark of cancer that drives metastasis, immune evasion and treatment resistance. CIN results from chromosome mis-segregation events during anaphase, as excessive chromatin is packaged in micronuclei (MN), that can be enumerated to quantify CIN. Despite recent advancements in automation through computer vision and machine learning, the assessment of CIN remains a predominantly manual and time-consuming task, thus hampering important work in the field. Here, we present micronuclAI , a novel pipeline for automated and reliable quantification of MN of varying size, morphology and location from DNA-only stained images. In micronucleAI , single-cell crops are extracted from high-resolution microscopy images with the help of segmentation masks, which are then used to train a convolutional neural network (CNN) to output the number of MN associated with each cell. The pipeline was evaluated against manual single-cell level counts by experts and against routinely used MN ratio within the complete image. The classifier was able to achieve a weighted F1 score of 0.937 on the test dataset and the complete pipeline can achieve close to human-level performance on various datasets derived from multiple human and murine cancer cell lines. The pipeline achieved a root-mean-square deviation (RMSE) value of 0.0041, an R 2 of 0.87 and a Pearson's correlation of 0.938 on images obtained at 10X magnification. We tested the approach in otherwise isogenic cell lines in which we genetically dialed up or down CIN rates, and also on a publicly available image data set (obtained at 100X) and achieved an RMSE value of 0.0159, an R 2 of 0.90, and a Pearson's correlation of 0.951. Given the increasing interest in developing therapies for CIN-driven cancers, this method provides an important, scalable, and rapid approach to quantifying CIN on routinely obtained images. We release a GUI-implementation for easy access and utilization of the pipeline.

Microscopic Image Dataset with Segmentation and Detection Labels for Microplastic Analysis in Sewage: Enhancing Research and Environmental Monitoring

We introduce a novel microscopic image dataset augmented with segmentation and detection labels specifically designed for microplastic analysis in sewage environments. Recognizing the increasing concern over microplastics—particles of synthetic polymers smaller than 5 mm—and their detrimental effects on marine ecosystems and human health, our research focuses on enhancing detection and analytical methodologies through advanced computer vision and deep learning techniques. The dataset comprises high-resolution microscopic images of microplastics collected from sewage, meticulously labeled for both segmentation and detection tasks, aiming to facilitate accurate and efficient identification and quantification of microplastic pollution. In addition to dataset development, we present example deep learning models optimized for segmentation and detection of microplastics within complex sewage samples. The models demonstrate significant potential in automating the analysis of microplastic contamination, offering a scalable solution to environmental monitoring challenges. Furthermore, we ensure the accessibility and reproducibility 12 of our research by making the dataset and model codes publicly available, accompanied by detailed 13 documentation on GitHub and LabelBox.

Synergizing Deep Learning-Enabled Preprocessing and Human-AI Integration for Efficient Automatic Ground Truth Generation.

The progress of incorporating deep learning in the field of medical image interpretation has been greatly hindered due to the tremendous cost and time associated with generating ground truth for supervised machine learning, alongside concerns about the inconsistent quality of images acquired. Active learning offers a potential solution to these problems of expanding dataset ground truth by algorithmically choosing the most informative samples for ground truth labeling. Still, this effort incurs the costs of human labeling, which needs minimization. Furthermore, automatic labeling approaches employing active learning often exhibit overfitting tendencies while selecting samples closely aligned with the training set distribution and excluding out-of-distribution samples, which could potentially improve the model's effectiveness. We propose that the majority of out-of-distribution instances can be attributed to inconsistent cross images. Since the FDA approved the first whole-slide image system for medical diagnosis in 2017, whole-slide images have provided enriched critical information to advance the field of automated histopathology. Here, we exemplify the benefits of a novel deep learning strategy that utilizes high-resolution whole-slide microscopic images. We quantitatively assess and visually highlight the inconsistencies within the whole-slide image dataset employed in this study. Accordingly, we introduce a deep learning-based preprocessing algorithm designed to normalize unknown samples to the training set distribution, effectively mitigating the overfitting issue. Consequently, our approach significantly increases the amount of automatic region-of-interest ground truth labeling on high-resolution whole-slide images using active deep learning. We accept 92% of the automatic labels generated for our unlabeled data cohort, expanding the labeled dataset by 845%. Additionally, we demonstrate expert time savings of 96% relative to manual expert ground-truth labeling.

Intravital observation of high-scattering and dense-labeling hepatic tissues using multi-photon fluorescence microscopy.

Achieving high-resolution and large-depth microscopic imaging in vivo under conditions characterized by high-scattering and dense-labeling, as commonly encountered in the liver, poses a formidable challenge. Here, through the optimization of multi-photon fluorescence excitation window, tailored to the unique optical properties of the liver, intravital microscopic imaging of hepatocytes and hepatic blood vessels with high spatial resolution was attained. It's worth noting that resolution degradation caused by tissue scattering of excitation light was mitigated by accounting for moderate tissue self-absorption. Leveraging high-quality multi-photon fluorescence microscopy, we discerned structural and functional alterations in hepatocytes during drug-induced acute liver failure. Furthermore, a reduction in indocyanine green metabolism rates associated with acute liver failure was observed using NIR-II fluorescence macroscopic imaging.

Increasing the Q-factor of resonant cantilevers in magnetic force microscopy through helium gas flow

To obtain high-resolution magnetic force microscopy (MFM) images, it is essential to have a cantilever with a high-quality factor. However, conventional vibrating cantilevers typically have quality factor values in the range of a few hundred, which limits their sensitivity for MFM measurements. To address this limitation, numerous studies have explored methods to enhance the quality factor in different environments, including vacuum, air, and liquid. This study introduces a novel approach for improving the quality factor using flowing helium gas. By selecting helium gas with a low viscosity coefficient, we successfully achieved a higher quality factor (Q-factor) of MFM microcantilever oscillations at room temperature in one atmosphere compared with the Q-factor in air. This provides a potential approach for achieving high-resolution MFM measurements under room temperature conditions. By optimizing the gas flow rate at room temperature in one atmosphere, we successfully obtained a higher MFM cantilever oscillation Q-factor and clearer MFM images compared with the air. The experimental results revealed a long and narrow resonant curve, and the quality factor significantly increased to 778.2, which is 3.8 times higher than that observed in air 205.4. Furthermore, systematic investigations demonstrated the capability of this approach to produce high-resolution MFM images of videotape track patterns under the optimized helium gas flow rate of 60 mm/s.

Deep learning for automated segmentation and counting of hypocotyl and cotyledon regions in mature Pinus radiata D. Don. somatic embryo images.

In commercial forestry and large-scale plant propagation, the utilization of artificial intelligence techniques for automated somatic embryo analysis has emerged as a highly valuable tool. Notably, image segmentation plays a key role in the automated assessment of mature somatic embryos. However, to date, the application of Convolutional Neural Networks (CNNs) for segmentation of mature somatic embryos remains unexplored. In this study, we present a novel application of CNNs for delineating mature somatic conifer embryos from background and residual proliferating embryogenic tissue and differentiating various morphological regions within the embryos. A semantic segmentation CNN was trained to assign pixels to cotyledon, hypocotyl, and background regions, while an instance segmentation network was trained to detect individual cotyledons for automated counting. The main dataset comprised 275 high-resolution microscopic images of mature Pinus radiata somatic embryos, with 42 images reserved for testing and validation sets. The evaluation of different segmentation methods revealed that semantic segmentation achieved the highest performance averaged across classes, achieving F1 scores of 0.929 and 0.932, with IoU scores of 0.867 and 0.872 for the cotyledon and hypocotyl regions respectively. The instance segmentation approach demonstrated proficiency in accurate detection and counting of the number of cotyledons, as indicated by a mean squared error (MSE) of 0.79 and mean absolute error (MAE) of 0.60. The findings highlight the efficacy of neural network-based methods in accurately segmenting somatic embryos and delineating individual morphological parts, providing additional information compared to previous segmentation techniques. This opens avenues for further analysis, including quantification of morphological characteristics in each region, enabling the identification of features of desirable embryos in large-scale production systems. These advancements contribute to the improvement of automated somatic embryogenesis systems, facilitating efficient and reliable plant propagation for commercial forestry applications.

Vascularized organoid-on-a-chip: design, imaging, and analysis.

Vascularized organoid-on-a-chip (VOoC) models achieve substance exchange in deep layers of organoids and provide a more physiologically relevant system in vitro. Common designs for VOoC primarily involve two categories: self-assembly of endothelial cells (ECs) to form microvessels and pre-patterned vessel lumens, both of which include the hydrogel region for EC growth and allow for controlled fluid perfusion on the chip. Characterizing the vasculature of VOoC often relies on high-resolution microscopic imaging. However, the high scattering of turbid tissues can limit optical imaging depth. To overcome this limitation, tissue optical clearing (TOC) techniques have emerged, allowing for 3D visualization of VOoC in conjunction with optical imaging techniques. The acquisition of large-scale imaging data, coupled with high-resolution imaging in whole-mount preparations, necessitates the development of highly efficient analysis methods. In this review, we provide an overview of the chip designs and culturing strategies employed for VOoC, as well as the applicable optical imaging and TOC methods. Furthermore, we summarize the vascular analysis techniques employed in VOoC, including deep learning. Finally, we discuss the existing challenges in VOoC and vascular analysis methods and provide an outlook for future development.

(005) Immunohistochemical Staining of the 12:00 Vestibule in NPV Patients: First Ever Assessment

Abstract Introduction In 2004, Bornstein and colleagues performed immunohistochemical (IHC) staining of excised vestibular specimens in patients with vestibular pain. They demonstrated that these specimens contained > 8 CD117 immunopositive stained cells per high-power field (HPF), consistent with mast cells, and excess S100 stained immunopositive cells, consistent with nerves-far more compared to control tissue. However, Bornstein et al. only studied the 1:00-11:00 vestibule in this condition, now referred to as neuroproliferative vestibulodynia (NPV). To the best of our knowledge, the 12:00 region of the vestibule, below the clitoral frenulae and above the urethral meatus, has not been examined with IHC staining. Pain in the 12:00 region can be particularly bothersome for patients and poses several challenges for successful treatment. Objective To report IHC staining findings in the excised 12:00 vestibule specimen and associated clinical characteristics of NPV patients. Methods Retrospective chart review performed. Slides of IHC-stained vestibular tissue specimens from the 12:00 vestibule were examined and high-resolution digital microscopy images were captured under 100x and 200x magnification. A least 2 photomicrographs at both magnifications were taken for each slide that included representative areas of epithelial basement membrane and the adjacent subepithelial regions. Fractional area of positive immunostaining in all photomicrographs was assessed and mean values calculated using computer-assisted histometry by ImageJ. Results A total of 50 patients had complete vestibulectomy including 12:00 and 3 had regional vestibulectomy for only the 12:00 area. Using manual counting as applied by Bornstein et al., 53 excised 12:00 specimens stained for CD117 had a median immunopositive cell count, consistent with mast cells, of 29.5, greater than the described 8 mast cells per HPF. Using computer-assisted histometry, tissue stained for CD117 had a median fractional area of 0.73 in the 12:00 region. Tissues stained for PGP9.5 had a median fractional area of 0.31 in the 12:00 region. Overall, there was no difference between lifelong NPV (LNPV) and acquired NPV (ANPV) patients in the cell count or median fractional area for CD117-immunopositive or PGP9.5-immunopositive staining in the 12:00 region. In LNPV patients who had excision of both the 1:00-11:00 and 12:00 regions, the 12:00 region had significantly less PGP9.5 immunopositive fractional area than the 1:00-11:00 region of the vestibule (p = 0.001). Their H&E pathology findings, including severity grading of the subepithelial inflammatory infiltrate, did not predict the IHC assessment of either CD117 or PGP9.5 in the 12:00 region. The median cotton-tipped swab test pain score was 5 out of 10 for the 12:00 region vs 7 for the rest of the vestibule. Conclusions Current surgical practices often do not include the 12:00 region, likely due to lack of data demonstrating excess mast cells and nerves in this specific region. Our results suggest that there are, in fact, a high density of immunopositive histochemical stains consistent with mast cells and nerves in the 12:00 region and high reported pain scores in both 12:00 and 1:00-11:00 vestibule. Thus, NPV surgical treatments should include, when appropriate, the 12:00 vestibule, as NPV is a field disease involving the entire vestibule. Disclosure No.

Anatomical characteristics and resprouting capacity of the underground organs of Bohemian knotweed (Polygonum ×bohemicum)

AbstractThe hybrid Bohemian knotweed [Polygonum ×bohemicum (J. Chrtek & Chrtková) Zika & Jacobson [cuspidatum × sachalinense]; syn.: Reynoutria ×bohemica Chrtek & Chrtková] is part of the worldwide problematic rhizomatous invasive plants that impact (semi-)natural and agricultural systems. In this context, precise knowledge about the morpho-anatomy and resprouting capacity of the underground organs is key information for developing efficient eradication measures. In the present study, we aimed at (1) clarifying existing differences in the morpho-anatomical characteristics of rhizomes and roots, (2) developing an easy-to-apply field identification method for the underground organs, and (3) identifying the main morpho-anatomical features enhancing the rhizomes’ resprouting ability. For this purpose, we collected the underground organs of two wild populations of P. ×bohemicum in Canton Ticino (southern Switzerland) and analyzed the morpho-anatomical differences between rhizomes and roots, using high-resolution microscope images and microtome sections. Collected material was then used for a resprouting capacity test after assessing rhizome characteristics such as weight, total diameter, pith diameter, pith brightness, and pith color. In contrast to roots, rhizomes are characterized by pith tissue in the center and display nodes with peripheral dormant buds that enable them to resprout. Resprouting ability of rhizomes was high (87.1% on average) and depended on the ontogenetic developmental stage of the organs (peak values of 97% for young and clearer-colored organs, 50% for old and dark ones). In conclusion, the smooth pith tissue of rhizomes represents a key discriminating feature between rhizomes and roots, whereas relating existing nodes to the corresponding rhizome pith color allows assessment of the resprouting potential of a knotweed population.

Use of deep learning for the classification of hyperplastic lymph node and common subtypes of canine lymphomas: a preliminary study.

Artificial Intelligence has observed significant growth in its ability to classify different types of tumors in humans due to advancements in digital pathology technology. Among these tumors, lymphomas are quite common in dogs, despite studies on the application of AI in domestic species are scarce. This research aims to employ deep learning (DL) through convolutional neural networks (CNNs) to distinguish between normal lymph nodes and 3 WHO common subtypes of canine lymphomas. To train and validate the CNN, 1,530 high-resolution microscopic images derived from whole slide scans (WSIs) were used, including those of background areas, hyperplastic lymph nodes (n = 4), and three different lymphoma subtypes: diffuse large B cell lymphoma (DLBCL; n = 5), lymphoblastic (LBL; n = 5), and marginal zone lymphoma (MZL; n = 3). The CNN was able to correctly identify 456 images of the possible 457 test sets, achieving a maximum accuracy of 99.34%. The results of this study have demonstrated the feasibility of using deep learning to differentiate between hyperplastic lymph nodes and lymphomas, as well as to classify common WHO subtypes. Further research is required to explore the implications of these findings and validate the ability of the network to classify a broader range of lymphomas.

Optical measurement instrument for detection of powdery mildew and grey mould in protected crops

This study presents an initiative aimed at developing a real-time optical measurement system for non-contact measurement of airborne fungal spores in protected crops such as strawberries, tomatoes, and cucumbers. The system is based on a customized microscope and an automatic system for trapping fungal spores. It has been tested in the field under real conditions, and image processing algorithms have been developed to identify fungal spores in high-resolution microscope images.