Tissue Segmentation Research Articles

Tissue segmentation for traumatic brain injury based on multimodal MRI image fusion-semantic segmentation

Accurate segmentation of traumatic brain injury (TBI) has great significance for physicians to diagnose and assess a patient’s condition. The utilization of multimodal information plays a critical role in TBI segmentation. However, most of the existing methods mainly focus on direct extraction and selection of deep semantic features, whereas in this paper, we use image fusion as an auxiliary task for feature learning based on multimodal feature extraction to achieve more sufficient fusion of multimodal features. Therefore, we design a multimodal image fusion-semantic segmentation based framework. The proposed approach mainly consists of a semantic encoder module, a semantic segmentation module and an image fusion module. The semantic encoder compresses the input image into a smaller feature space to extract semantic features. The semantic segmentation module utilizes both the detailed information extracted by the encoder and the semantic information of high-level features extracted from the semantic segmentation module to generate the segmentation results. The image fusion module fuses semantic feature information from different modalities as an auxiliary task to semantic segmentation. Furthermore, to enhance the model’s performance even further, an uncertainty-based approach is employed, which dynamically adjusts the loss weights for the image fusion task and the semantic segmentation task during the model training process. The proposed method is evaluated on a private dataset, and compared with other widely recognized methods. It demonstrates outstanding performance in both Dice score and Recall metrics.

Brainchop: Providing an Edge Ecosystem for Deployment of Neuroimaging Artificial Intelligence Models.

Deep learning has proven highly effective in various medical imaging scenarios, yet the lack of an efficient distribution platform hinders developers from sharing models with end-users. Here, we describe brainchop, a fully functional web application that allows users to apply deep learning models developed with Python to local neuroimaging data from within their browser. While training artificial intelligence models is computationally expensive, applying existing models to neuroimaging data can be very fast; brainchop harnesses the end user's graphics card such that brain extraction, tissue segmentation, and regional parcellation require only seconds and avoids privacy issues that impact cloud-based solutions. The integrated visualization allows users to validate the inferences, and includes tools to annotate and edit the resulting segmentations. Our pure JavaScript implementation includes optimized helper functions for conforming volumes and filtering connected components with minimal dependencies. Brainchop provides a simple mechanism for distributing models for additional image processing tasks, including registration and identification of abnormal tissue, including tumors, lesions and hyperintensities. We discuss considerations for other AI model developers to leverage this open-source resource.

Quantitative CT analysis of idiopathic pulmonary fibrosis and correlation with lung function study

BackgroundIdiopathic Pulmonary Fibrosis (IPF) is a progressive fibrotic lung disease. However, the field of quantitative CT scan analysis in conjunction with pulmonary function test for IPF patients remains relatively understudied. In this study, we evaluated the diagnostic value of features derived high-resolution computed tomography (HRCT) for patients with IPF and correlated them with pulmonary function tests.MethodsWe retrospectively analyzed the chest HRCT images and pulmonary function test results of 52 patients with IPF during the same period (1 week) and selected 52 healthy individuals, matched for sex, age, and body mass index (BMI) and with normal chest HRCT as controls. HRCT scans were performed using a Philips 256-row Brilliance iCT scanner with standardized parameters. Lung function tests were performed using a Jaeger volumetric tracer for forced vital capacity (FVC), total lung capacity (TLC), forced expiratory volume in first second (FEV1), FEV1/FVC, carbon monoxide diffusing capacity (DLCO), and maximum ventilation volume (MVV) metrics. CT quantitative analysis, including tissue segmentation and threshold-based quantification of lung abnormalities, was performed using 3D-Slicer software to calculate the percentage of normal lung areas (NL%), percentage of ground-glass opacity areas (GGO%), percentage of fibrotic area (F%) and abnormal lesion area percentage (AA%). Semi-quantitative analyses were performed by two experienced radiologists to assess disease progression. The aortic-to-sternal distance (ASD) was measured on axial images as a standardized parameter. Spearman or Pearson correlation analysis and multivariate stepwise linear regression were used to analyze the relationship between the data in each group, and the ROC curve was used to determine the optimal quantitative CT metrics for identifying IPF and controls.ResultsROC curve analysis showed that F% distinguished the IPF patient group from the control group with the largest area under the curve (AUC) of 0.962 (95% confidence interval: 0.85–0.96). Additionally, with F% = 4.05% as the threshold, the Youden’s J statistic was 0.827, with a sensitivity of 92.3% and a specificity of 90.4%. The ASD was significantly lower in the late stage of progression than in the early stage (t = 5.691, P < 0.001), with a mean reduction of 2.45% per month. Quantitative CT indices correlated with all pulmonary function parameters except FEV1/FVC, with the highest correlation coefficients observed for F% and TLC%, FEV1%, FVC%, MVV% (r = − 0.571, − 0.520, − 0.521, − 0.555, respectively, all P-values < 0.001), and GGO% was significantly correlated with DLCO% (r = − 0.600, P < 0.001). Multiple stepwise linear regression analysis showed that F% was the best predictor of TLC%, FEV1%, FVC%, and MVV% (R2 = 0.301, 0.301, 0.300, and 0.302, respectively, all P-values < 0.001), and GGO% was the best predictor of DLCO% (R2 = 0.360, P < 0.001).ConclusionsQuantitative CT analysis can be used to diagnose IPF and assess lung function impairment. A decrease in the ASD may indicate disease progression.

Comparative Morphology of Skeletal Development in Homo sapiens and Raja asterias: Divergent Stiffening Patterns Due to Different Matrix Calcification Processes.

Before calcification begins, the early embryonic and fetal skeletal development of both mammalian Homo sapiens and the chondrichthyan fish Raja asterias consists exclusively of cartilage. This cartilage is formed and shaped through processes involving tissue segmentation and the frequency, distribution, and orientation of chondrocyte mitoses. In the subsequent developmental phase, mineral deposition in the cartilage matrix conditions the development further. The stiffness and structural layout of the mineralized cartilage have a significant impact on the shape of the anlagen (early formative structure of a tissue, a scaffold on which the new bone is formed) and the mechanical properties of the skeletal segments. The fundamental difference between the two studied species lies in how calcified cartilage serves as a scaffold for osteoblasts to deposit bone matrix, which is then remodeled. In contrast, chondrichthyans retain the calcified cartilage as the definitive skeletal structure. This study documents the distinct mineral deposition pattern in the cartilage of the chondrichthyan R. asterias, in which calcification progresses with the formation of focal calcification nuclei or "tesserae". These are arranged on the flat surface of the endo-skeleton (crustal pattern) or aligned in columns (catenated pattern) in the radials of the appendicular skeleton. This anatomical structure is well adapted to meet the mechanical requirements of locomotion in the water column. Conversely, in terrestrial mammals, endochondral ossification (associated with the remodeling of the calcified matrix) provides limb bones with the necessary stiffness to withstand the strong bending and twisting stresses of terrestrial locomotion. In this study, radiographs of marine mammals (reproduced from previously published studies) document how the endochondral ossification in dolphin flippers adapts to the mechanical demands of aquatic locomotion. This adaptation includes the reduction in the length of the stylopodium and zeugopodium and an increase in the number of elements in the autopodium's central rays.

SEEG4D: a tool for 4D visualization of stereoelectroencephalography data.

Epilepsy is a prevalent and serious neurological condition which impacts millions of people worldwide. Stereoelectroencephalography (sEEG) is used in cases of drug resistant epilepsy to aid in surgical resection planning due to its high spatial resolution and ability to visualize seizure onset zones. For accurate localization of the seizure focus, sEEG studies combine pre-implantation magnetic resonance imaging, post-implant computed tomography to visualize electrodes, and temporally recorded sEEG electrophysiological data. Many tools exist to assist in merging multimodal spatial information; however, few allow for an integrated spatiotemporal view of the electrical activity. In the current work, we present SEEG4D, an automated tool to merge spatial and temporal data into a complete, four-dimensional virtual reality (VR) object with temporal electrophysiology that enables the simultaneous viewing of anatomy and seizure activity for seizure localization and presurgical planning. We developed an automated, containerized pipeline to segment tissues and electrode contacts. Contacts are aligned with electrical activity and then animated based on relative power. SEEG4D generates models which can be loaded into VR platforms for viewing and planning with the surgical team. Automated contact segmentation locations are within 1 mm of trained raters and models generated show signal propagation along electrodes. Critically, spatial-temporal information communicated through our models in a VR space have potential to enhance sEEG pre-surgical planning.

Revolutionizing Colon Histopathology Glandular Segmentation Using an Ensemble Network With Watershed Algorithm

ABSTRACTColorectal adenocarcinoma, the most prevalent form of colon cancer, originates in the glandular structures of the intestines, presenting histopathological abnormalities in affected tissues. Accurate gland segmentation is crucial for identifying these potentially fatal abnormalities. While recent methodologies have shown success in segmenting glands in benign tissues, their efficacy diminishes when applied to malignant tissue segmentation. This study aims to develop a robust learning algorithm using a convolutional neural network (CNN) to segment glandular structures in colon histology images. The methodology employs a CNN based on the U‐Net architecture, augmented by a weighted ensemble network that integrates DenseNet 169, Inception V3, and Efficientnet B3 as backbone models. Additionally, the segmented gland boundaries are refined using the watershed algorithm. Evaluation on the Warwick‐QU dataset demonstrates promising results for the ensemble model, by achieving an F1 score of 0.928 and 0.913, object dice coefficient of 0.923 and 0.911, and Hausdorff distances of 38.97 and 33.76 on test sets A and B, respectively. These results are compared with outcomes from the GlaS challenge (MICCAI 2015) and existing research findings. Furthermore, our model is validated with a publicly available dataset named LC25000, and visual inspection reveals promising results, further validating the efficacy of our approach. The proposed ensemble methodology underscores the advantages of amalgamating diverse models, highlighting the potential of ensemble techniques to enhance segmentation tasks beyond individual model capabilities.

Research on hybrid segmentation technologies for postprocessing the lung and trachea CT images

This paper introduces a systematic method for segmenting the main trachea and bronchioles in lung computed tomography scans. It begins with a stack-based three-dimensional region growth algorithm to outline the main trachea, which is then refined using morphological techniques to improve accuracy. The segmentation of bronchioles is achieved through domain labeling, lung tissue segmentation, adaptive binarization, and inner product analysis. The main trachea and bronchioles are integrated using an operating room (OR) operation and a novel splicing algorithm to form a complete tracheal tree. The method’s accuracy is validated against manual labeling, showing a Dice coefficient of about 0.99, on average, in lung parenchyma segmentation and a segmentation overlap with expert results ranging from 79.89% to 93.31% in lung trachea tree segmentation. This robust methodology is thoroughly tested and validated.

Detailed delineation of the fetal brain in diffusion MRI via multi-task learning.

Diffusion-weighted MRI is increasingly used to study the normal and abnormal development of fetal brain inutero. Recent studies have shown that dMRI can offer invaluable insights into the neurodevelopmental processes in the fetal stage. However, because of the low data quality and rapid brain development, reliable analysis of fetal dMRI data requires dedicated computational methods that are currently unavailable. The lack of automated methods for fast, accurate, and reproducible data analysis has seriously limited our ability to tap the potential of fetal brain dMRI for medical and scientific applications. In this work, we developed and validated a unified computational framework to (1) segment the brain tissue into white matter, cortical/subcortical gray matter, and cerebrospinal fluid, (2) segment 31 distinct white matter tracts, and (3) parcellate the brain's cortex and delineate the deep gray nuclei and white matter structures into 96 anatomically meaningful regions. We utilized a set of manual, semi-automatic, and automatic approaches to annotate 97 fetal brains. Using these labels, we developed and validated a multi-task deep learning method to perform the three computations. Our evaluations show that the new method can accurately carry out all three tasks, achieving a mean Dice similarity coefficient of 0.865 on tissue segmentation, 0.825 on white matter tract segmentation, and 0.819 on parcellation. The proposed method can greatly advance the field of fetal neuroimaging as it can lead to substantial improvements in fetal brain tractography, tract-specific analysis, and structural connectivity assessment.

Deep Learning Based Automatic Fibroglandular Tissue Segmentation in Breast Magnetic Resonance Imaging Screening.

In light of the global increase in breast cancer cases and the crucial importance of the density of fibroglandular tissue (FGT) in assessing risk and predicting the course of the disease, the accurate measurement of FGT emerges as a significant challenge in diagnostic imaging. The current study focuses on the automatic segmentation of breast glandular tissue in MRI scans using a deep learning model. The aim is to establish a solid foundation for the development of methods for the precise quantification of fibroglandular tissue. For this purpose, the publicly available 'Duke Breast Cancer MRI' dataset was systematically processed to train a deep neural network model utilizing the nnU-Net ('no-new-Net') framework, which was then subjected to a quantitative evaluation. The results show the following macro-averaged metrics with standard deviation: Dice Similarity Coefficient 0.827 ± 0.152, accuracy 0.997 ± 0.003, sensitivity 0.825 ± 0.158, and specificity 0.999 ± 0.001. The effectiveness of our model in segmenting FGT is underscored by the high values of the Dice coefficient, Accuracy, Sensitivity, and Specificity, which reflect the precision and reliability of our results. The findings of this study lay a solid foundation for developing automated methods to quantify FGT. Our research efforts, especially driven by clinical studies at the University Hospital Augsburg, are focused on further exploring and validating these potentials.

ACSwinNet: A Deep Learning-Based Rigid Registration Method for Head-Neck CT-CBCT Images in Image-Guided Radiotherapy.

Accurate and precise rigid registration between head-neck computed tomography (CT) and cone-beam computed tomography (CBCT) images is crucial for correcting setup errors in image-guided radiotherapy (IGRT) for head and neck tumors. However, conventional registration methods that treat the head and neck as a single entity may not achieve the necessary accuracy for the head region, which is particularly sensitive to radiation in radiotherapy. We propose ACSwinNet, a deep learning-based method for head-neck CT-CBCT rigid registration, which aims to enhance the registration precision in the head region. Our approach integrates an anatomical constraint encoder with anatomical segmentations of tissues and organs to enhance the accuracy of rigid registration in the head region. We also employ a Swin Transformer-based network for registration in cases with large initial misalignment and a perceptual similarity metric network to address intensity discrepancies and artifacts between the CT and CBCT images. We validate the proposed method using a head-neck CT-CBCT dataset acquired from clinical patients. Compared with the conventional rigid method, our method exhibits lower target registration error (TRE) for landmarks in the head region (reduced from 2.14 ± 0.45 mm to 1.82 ± 0.39 mm), higher dice similarity coefficient (DSC) (increased from 0.743 ± 0.051 to 0.755 ± 0.053), and higher structural similarity index (increased from 0.854 ± 0.044 to 0.870 ± 0.043). Our proposed method effectively addresses the challenge of low registration accuracy in the head region, which has been a limitation of conventional methods. This demonstrates significant potential in improving the accuracy of IGRT for head and neck tumors.

Next-generation lung cancer pathology: Development and validation of diagnostic and prognostic algorithms

Non-small cell lung cancer (NSCLC) is one of the most common malignant tumors. In this study, we develop a clinically useful computational pathology platform for NSCLC that can be a foundation for multiple downstream applications and provide immediate value for patient care optimization and individualization. We train the primary multi-class tissue segmentation algorithm on a substantial, high-quality, manually annotated dataset of whole-slide images with lung adenocarcinoma and squamous cell carcinomas. We investigate two downstream applications. NSCLC subtyping algorithm is trained and validated using a large, multi-institutional (n= 6), multi-scanner (n= 5), international cohort of NSCLC cases (slides/patients 4,097/1,527). Moreover, we develop four AI-derived, fully explainable, quantitative, prognostic parameters (based on tertiary lymphoid structure and necrosis assessment) and validate them for different clinical endpoints. The computational platform enables the high-precision, quantitative analysis of H&E-stained slides. The developed prognostic parameters facilitate robust and independent risk stratification of patients with NSCLC.

Subject-specific atlas for automatic brain tissue segmentation of neonatal magnetic resonance images

Developing advanced systems for 3D brain tissue segmentation from neonatal magnetic resonance (MR) images is vital for newborn structural analysis. However, automatic segmentation of neonatal brain tissues is challenging due to smaller head size and inverted T1/T2 tissue contrast compared to adults. In this work, a subject-specific atlas based technique is presented for segmentation of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) from neonatal MR images. It involves atlas selection, subject-specific atlas creation using random forest (RF) classifier, and brain tissue segmentation using the expectation maximization-Markov random field (EM-MRF) method. To increase the segmentation accuracy, different tissue intensity- and gradient-based features were used. Evaluation on 40 neonatal MR images (gestational age of 37–44 weeks) demonstrated an overall accuracy of 94.3% and an average Dice similarity coefficient (DSC) of 0.945 (GM), 0.947 (WM), and 0.912 (CSF). Compared to multi-atlas segmentation methods like SEGMA and EM-MRF with multiple atlases, our method improved accuracy by up to 4%, particularly in complex tissue regions. Our proposed method allows accurate brain tissue segmentation, a crucial step in brain magnetic resonance imaging (MRI) applications including brain surface reconstruction and realistic head model creation in neonates.

New Assessment Method of Alveolar Bone Grafting Using Automatic Registration and AI-based Segmentation.

The aim of this Technical note is to present a new assessment method of alveolar bone grafts after secondary alveolar bone grafting using automatic registration and artificial intelligence (AI)-based segmentation. A total of 7 Japanese patients (4 men and 3 women) with a unilateral cleft lip, alveolus, and/or palate, who underwent secondary alveolar bone grafting between March 2021 and August 2022 were evaluated. Computed tomography (CT) analyses were performed about 1 month before the surgery, and 1 day and 6 months after the surgery. All CT images were imported into a 3-dimensional analysis workstation. CT images from after the surgery were superimposed onto CT images from before the surgery, by automatic rigid image registration. The segmentation of bone tissues was automatically performed by the AI-based function. Grafted bone was extracted by subtraction of the bone tissue after the surgery from the bone tissue before the surgery. The volumes and Hounsfield units (HUs) of the grafted bones were calculated. The intraclass correlation coefficient (ICC) was reviewed to assess inter-rater reliability. The ICCs (2,1) of the volumes and HUs measured by the observers immediately after the surgery were 0.95 and 0.99, respectively. On the other hand, the ICCs (2,1) of the volumes and HUs measured by the observers 6 months after the surgery were 0.81 and 0.57, respectively. Our new assessment method enables simple and quick evaluation of residual grafted bone after secondary alveolar bone grafting and demonstrated relatively high inter-rater reliability.

Dominant hippocampus segmentation with brain atrophy analysis-based AD subtype classification using KLW-RU-Net and T1FL

A progressive neurodegenerative disorder that leads to cognitive decline and memory loss is known as Alzheimer's Disease (AD). Researchers propose an integrated framework centered on brain atrophy analysis and hippocampus segmentation to predict AD and its subtypes. This approach augments the accurate prediction of AD subtype classification, thus addressing the lack of emphasis in prevailing works. Primarily, the T1-weighted brain Magnetic Resonance Imaging (MRI) images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database undergo preprocessing. After that, for spatiotemporal evaluation, region segmentation, tau accumulation analysis, and Feature Extraction (FE), the image is processed. By employing the Fisher-Kolmogorov (FK) model, the essential parameters are extracted in spatio-temporal evaluation. In the meantime, for tissue segmentation, the Knowledge Partitioned Clustering (KPC) is utilized, and for hippocampus segmentation, the Kullback-Leibler Within-layer Regularized UNet (KLW-RU-Net) is employed. Moreover, by binarizing the image, the tau accumulation is analyzed; in addition, Principal Component Analysis (PCA) is utilized for FE. After PCA, the Adaptive Step-sized Gauss Rabbits Optimization (ASGRO) is employed for feature selection. Subsequently, to classify AD, the Dilated Derivative Sigmoid-Weighted DenseNet (DD-SWDN) is employed. Additionally, by employing Type-1 Fuzzy Logic (T1FL), the subtype of AD is classified, thus achieving higher accuracy for AD subtype classification. Therefore, as per these outcomes, the proposed work performs better than the other conventional approaches.

A potential treatment for erectile dysfunction: Effect of platelet-rich plasma administration on axon and collagen regeneration in cavernous nerve injury.

Recent studies highlighted the role of platelet-rich plasma (PRP) in progenitor cell homing, migration, and nerve cell regeneration while also inhibiting fibrosis and apoptosis in cavernous nerve injury (CNI). The aim of this study was to investigate the effect of PRP administration on axon and collagen regeneration in CNI. A true experimental study using a post-test-only control group design was conducted. Twenty-five male Wistar rats (Rattus norvegicus), weighing 200-300 grams, were divided into five groups: two control groups (sham procedure and negative control), and three experimental groups receiving local PRP, intraperitoneal PRP, and a combination of local and intraperitoneal PRP. The cavernous nerve was injured with a hemostasis clamp for one minute before 200 µL of 200 PRP was injected locally, intraperitoneally, or both, depending on the group. After four weeks, the rats were euthanized, tissue segments (2 mm) from each cavernous nerve and mid-penis were collected and analyzed for collagen density, axon diameter, and number of myelinated axons. Our study found that collagen growth was slower in CNI group without PRP (sham procedure) compared to all PRP groups (local, intraperitoneal, and combination). The intraperitoneal PRP group had the highest collagen density at 5.62 µm; however, no significant difference was observed in collagen density among all groups (p=0.056). Similar axon diameter was found across the groups, with no statistically significant difference observed (p=0.856). In the number of myelinated axons, a significant difference was found among all groups with significantly more axons in local PRP and combined local and intraperitoneal PRP groups compared to others (p=0.026). In conclusion, PRP administration improved the number of myelinated axons in CNI, suggesting PRP role in CNI regeneration and the potential for an innovative approach to treating erectile dysfunction associated with CNI.

Blood Vessels

Abstract: One of the primary target tissues for inflammatory and allergy mediators is the blood vessel, and cytokines have a variety of impacts on it. We describe the usefulness of isolated blood vessels immersed in organ baths as a valuable source of pharmacological data. Contractility assays provide strong potency and selectivity data on agonists, partial agonists, and competitive or noncompetitive antagonists; however, their use in the bioassay of vasoactive drugs is gradually being replaced by more advanced analytical techniques. The human umbilical vein, for instance, has been widely utilized to identify bradykinin B2 receptor ligands. Isolated segments of vascular tissue are highly reactive living tissues, particularly in terms of regulating gene products associated with inflammation (e.g., kinin B1 receptor). The venule’s extremely thin walls have the potential to burst due to their high volume. Venules allow blood to move into larger veins. Both veins and arteries have three layers in their walls. On the other hand, venous pressure is modest. Veins are less elastic and have thin walls, allowing them to maintain a high blood circulation rate. The venous system’s high capacitance enables it to store a significant amount of blood at comparatively low pressures, containing around 75% of the circulating blood at any given time. The veins’ one-way valves permit blood to move forward toward the heart through muscle contractions.

Method of muscle tissue segmentation in computed tomography images based on preprocessed three-channel images

Method of muscle tissue segmentation in computed tomography images based on preprocessed three-channel images

A hierarchical algorithm with randomized learning for robust tissue segmentation and classification in digital pathology

Highly detailed and accurate segmentation and classification of images constitutes an important class of tasks in computer vision. Typical “universal” domain-agnostic methods are known to suffer from instabilities and are prone to adversarial perturbations. Natural heterogeneity inherent in biological tissue structures complicates the interpretation of images even by trained physicians. Yet, algorithms in the medical domain require a high level of stability and interpretability to ensure their adoption by clinical experts and acceptance in clinical decision-making. In this work, we propose a novel method for segmentation and classification to address these challenges. The method is based on a hierarchical approach and biologically-informed feature extraction. The method's technical pipeline includes the automatic extraction of key biologically-informed features typically considered by physicians. This is followed by image classification using these features. Both stages rely on randomized ML techniques. The proposed hierarchical biomedically-informed approach significantly improved the image classification quality compared to the baseline solution of image classification in the task of colorectal cancer (CRC) analysis. The average F1-score for the four tissue types increased from 0.737 to 0.956. Using tumor tissue classification task as an example, we showed that the proposed algorithm offers an effective and practical avenue to solve these challenging issues.

Modeling Pressure Effect of Circular Tourniquet Based on Digital Arm

Background: This study aims to investigate displacement deformation of human tissue in the force region subjected to annular pressure. Methods: In this patent, 727 images of a Chinese digital human arm, captured from shoulder to fingertip, were used as the reconstruction data. The geometric entities of tissue structure were obtained after tissue segmentation, three-dimensional modeling, and reverse engineering to establish the working mechanism model of the tourniquet of the human forearm in the finite element simulation software (COMSOL Multiphysics 5.5). By setting different parameter models (tourniquet pressure and width models), we analyzed the force conduction mechanism and the displacement deformation mechanism of the viscoelastic and rigid tissues of the forearm when subjected to annular pressure. Results: Modeling analysis showed that when a pressure of 800 kPa was applied on a width of 40 mm, the annular pressure on the viscoelastic tissues was converted into displacement deformation, thus changing the tissue structure in the body and realizing the hemostatic effect of the tourniquet. In the case of fixed tourniquet width but variable tourniquet pressure, with the gradual increase of the pressure, displacement deformation showed an increasing trend. When the externally applied pressure was fixed and the tourniquet width was different, with the gradual increase of the tourniquet width, the displacement deformation showed a decreasing trend. Conclusion: This patent study demonstrates that both the amount of externally applied pressure and the width of the tourniquet affect the hemostatic effect of the tourniquet. The hemostatic effect on the damaged body will be more obvious under a small tourniquet width and large pressure.

First report of leaf spot disease caused by Pseudopithomyces chartarum on Lonicera caerulea L. in Heilongjiang Province, China.

Blue honeysuckle (Lonicera caerulea L.) cultivation has gradually expanded in China but continues to be limited by challenges such as leaf spot disease. Between September 2022 and September 2023, a leaf spot disease was observed on approximately 30% of 'Lanjingling' blue honeysuckles grown in a 2.66 ha field (a total of about 11,000 plants) in Jiamusi city (130.47°E, 46.16°N), Heilongjiang Province, China. Affected plants displayed brown necrotic lesions on their leaves that gradually expanded in area until the leaves fell off the plant entirely. Small, 3 to 4 mm segments of infected tissue from 50 randomly selected leaves were surface sterilized with 75% ethanol for 30 s and 5% sodium hypochlorite (NaOCl) for 3 min, rinsed three times with sterile distilled water, dried on paper towels, and plated in 9 cm Petri dishes containing potato dextrose agar (PDA) (Yan et al. 2022). Five pathogens (LD-232, LD-233, LD-234, LD-235, and LD-236) were isolated on PDA and displayed a conidia morphology consistent with Pseudopithomyces spp. (Perelló et al. 2017). The fungal colonies on PDA were villiform, white, and whorled and had sparse aerial mycelium on the surface with black conidiomata. The conidia were obpyriform and dark brown, had 0 to 3 transverse and 0 to 1 longitudinal septa, and measured 9.00 to 15.30 μm × 5.70 to 9.30 μm in size (n = 50). Genomic DNA was extracted from a representative isolate, LD-232, for molecular verification and PCR amplification was performed with ITS1/ITS4 (White et al. 1990), LROR/LR7 (Carbone and Kohn 1999), and RPB2-5F2/RPB2-7CR (Liu et al. 1999) primers. Sequences of LD-232 ITS (OR835654), LSU (OR835652), and RPB2 (OR859769) revealed 99.8% (530/531 nt), 98.8% (639/647 nt), and 99.8% (1015/1017 nt) shared identity with Pseudopithomyces chartarum sequences (OP269600, OP237014, and MK434892), respectively (Wu et al. 2023). Bayesian inference (BI) was used to construct the phylogenies using Mr. Bayes v. 3.2.7 to confirm the identity of the isolates (Ariyawansa et al. 2015). Phylogenetic trees cannot be constructed based on the genes' concatenated sequences because selective strains do not have complete rDNA-ITS, LSU, and RPB2 sequences. Therefore, based on the morphological characteristics and molecular phylogeny, LD-232 was identified as P. chartarum (Perelló et al. 2017; Wu et al. 2023). A pathogenicity test was performed with six healthy, two-year-old 'Lanjingling' blue honeysuckle plants. Three plants were inoculated by spraying the LD-232 conidial suspension (1 × 106 spores/ml) or clean water as an experimental control condition (Wu et al. 2023; Yan et al. 2023). All plants were cultured in a greenhouse at 28℃ under a 12-h light/dark cycle, and each experiment was replicated three times. Typical leaf spot symptoms were observed on inoculated leaves after 10 days. The same pathogens were reisolated from infected leaves, displayed the same morphological and molecular traits, and were again identified as P. chartarum, confirming Koch's postulate. P. chartarum previously caused leaf spot disease on Tetrapanax papyrifer in China (Wu et al. 2023). To our knowledge, this is the first report of blue honeysuckle leaf spot caused by P. chartarum in China. Identification of P. chartarum as a disease agent on blue honeysuckle will help guide future management of leaf diseases for this economically important small fruit tree.