Morphological and Chemical Diversity within Japanese Laurencia Complex (Rhodomelaceae, Ceramiales, Rhodophyta).

Seaweeds of the red algal genus Laurencia are widely distributed worldwide in tropical, subtropical to temperate zones, and grow in Japan from Hokkaido to Okinawa. Laurencia is one of the most studied seaweeds by organic chemists because it produces a variety of compounds with unique structures. In Japan, various halogenated compounds have been found in Laurencia, while some species do not produce any halogenated compounds. Laurencia is one of the most difficult seaweeds to classify morphologically; however, the major halogenated secondary metabolites produced tend to be species-specific, and these compounds can be used as chemical markers for chemical systematics (chemotaxonomy). Similarly, it has been confirmed that domestic Laurencia species produce species-specific halogenated compounds of certain types. Laurencia is one of the "weedy seaweeds" that have not been effectively utilized at present, but it produces a wide variety of metabolites, so there is a good possibility that compounds with specific activity may be found. Thus, it can be seen that the secondary metabolites in Laurencia have many interesting aspects. In this review, we reported significant morphological features to distinguish species in this genus, and the morphological features, habitat, distribution, and chemical composition that help discriminate Japanese Laurencia species.

Morphological Features of the Vertebrobasilar System Predict Ischemic Stroke Risk in Spontaneous Vertebral Artery Dissection.

The vertebral artery's morphological characteristics are crucial in spontaneous vertebral artery dissection (sVAD). We aimed to investigate morphologic features related to ischemic stroke (IS) and develop a novel prediction model. Out of 126 patients, 93 were finally analyzed. We constructed 3D models and morphological analyses. Patients were randomly classified into training and validation cohorts (3:1 ratio). Variables selected by LASSO - including five morphological features and five clinical characteristics - were used to develop prediction model in the training cohort. The model exhibited a high area under the curve (AUC) of 0.944 (95%CI, 0.862-0.984), with internal validation confirming its consistency (AUC = 0.818, 95%CI, 0.597-0.948). Decision curve analysis (DCA) indicated clinical usefulness. Morphological features significantly contribute to risk stratification in sVAD patients. Our novel developed model, combining interdisciplinary parameters, is clinically useful for predicting IS risk. Further validation and in-depth research into the hemodynamics related to sVAD are necessary.

Clinical and morphological features of eccrine acrospiroma: analysis of literature data and case from practice.

Eccrine acrospiroma is a rare benign tumor of the skin arising from the epithelial cells of eccrine sweat ducts. The clinical picture is characterized by its variability, so a detailed morphological study of the operative material is necessary to establish a diagnosis. Differential diagnosis must be carried out with hemangioma, melanoma, infected sebaceous cyst, metastatic skin lesion, and other tumors from elements of the sweat gland. In the article the authors presented the clinical and morphological analysis of own case from practice of large eccrine acrospiroma on the back surface of the left thigh which was diagnosed in a 56-year-old man.

Anatomical Changes during Chestnut (Castanea mollissima BL.) Gall Development Stages Induced by the Gall Wasp Dryocosmus kuriphilus (Hymenoptera: Cynipidae).

This study delved into the larval development and the morphological and anatomical transformations that occur in the galls of chestnut trees (Castanea mollissima BL.) and are induced by the chestnut gall wasp Dryocosmus kuriphilus Yasumatsu (GWDK) across various stages: initial, growth, differentiation, maturity, and lignification. Chestnut galls in the five development stages were collected. Gall structural characteristics were observed with an anatomical stereomicroscope, and anatomical changes in galls were analyzed with staining and scanning electron microscope techniques. The chestnut gall wasp laid its eggs on young leaves and buds. Chestnut gall wasp parasitism caused plant tissues to form a gall chamber, with parenchyma, protective, and epidermal layers. The development of the gall structure caused by the infestation of the GWDK gall led to the weakening of the reactive oxygen species (ROS) elimination ability of the host. The accumulation of ROS led to cell wall peroxidation, resulting in structural damage and diminished host resistance, and the parenchyma layer exhibited significant nutrient supply and thickening. The thickness of the protective and epidermal layers varied notably across different growth stages. The oviposition of the chestnut gall wasp induced modifications in the original plant tissues, with gall formation being most favorable in young tissues, correlating with the maturity level of the host plant tissues. Variances in the internal structures of the galls primarily stemmed from nutrient supplementation, while those in the external structure were attributed to defensive characteristics. This research contributes a foundational understanding of gall development induced by the chestnut gall wasp in Chinese chestnut, offering valuable insights into the intricate interplay between insect infestation and plant physiology.

[Morphology of sternum for formation of the biological profile of unidentified individual]. / Morfologiya grudiny dlya formirovaniya biologicheskogo profilya neopoznannogo individa.

Identification of a person by general group characteristics does not lose its relevance over a long period. An analysis of publications (2000-2023) devoted to the possibilities of using the sternum to determine gender and age showed a fairly large amount of work on this topic, with very promising results. The trend in the development of this area is the use of modern methods of medical imaging. This becomes the starting point for conducting such studies on the territory of the Russian Federation and developing a methodology that includes the Russian population, taking into account their population characteristics.

Morphological and physiological features in small ruminants: an adaptation strategy for survival under changing climatic conditions.

Climate change due to natural human activity is a significant global phenomenon affecting the sustainability of most countries' livestock industries. Climate change factors such as ambient temperature, relative humidity, direct and indirect sun radiation, and wind have significant consequences on feed, water, pasture availability, and the re-emergence of diseases in livestock. All these variables have a considerable impact on livestock production and welfare. However, animals' ability to respond and adapt to changes in climate differs within species and breeds. Comparatively, small ruminants are more adaptive to the adverse effects of climate change than large ruminants in terms of reproduction performance, survival, production yield, and resistance to re-emerging diseases. This is mainly due to their morphological features against harsh climate effects. Tropical breeds are more adaptive to the adverse effects of climate change than small temperate ruminants. However, the difference in morphological characteristics towards adaptation to the impact of climate change will guide the development of suitable policies on the selection of breeding stock suitable for different regions in the world. The choice of breeds based on morphological features and traits is an essential strategy in mitigating and minimizing the effects of climate change on small ruminants' production and welfare. This review highlights the adaptive morphological features within and among breeds of small ruminants toward adaptation to climate change.

Application of imaging and spectroscopy techniques for grading of bovine embryos - a review.

Although embryo transfers have grown considerably in the cattle industry, the selection of embryos required for successful pregnancies remains a challenging task. Visual inspection of 7th-day embryos using a stereomicroscope, followed by classification based on morphological features is the most commonly practiced procedure. However, there are inaccuracies and inconsistencies in the manual grading of bovine embryos. The objective of this review was to evaluate the potential of imaging and spectroscopic techniques in the selection of bovine embryos. Digital analysis of microscopic images through extracting visual features in the embryo region, and classification using machine learning methods have yielded about 88-96% success in pregnancies. The Raman spectral pattern provides valuable information regarding developmental stages and quality of the embryo. The Raman spectroscopy approach has also been successfully used to determine various parameters of bovine oocytes. Besides, Fourier Transform Infrared (FTIR) spectroscopy has the ability to assess embryo quality through analyzing embryo composition, including nucleic acid and amides present. Hyperspectral Imaging has also been used to characterize metabolite production during embryo growth. Although the time-lapse imaging approach is beneficial for morphokinetics evaluation of embryo development, optimized protocols are required for successful implementation in bovine embryo transfers. Most imaging and spectroscopic findings are still only at an experimental stage. Further research is warranted to improve the repeatability and practicality to implement in commercial facilities.

Cause and Manner of Death of a Skeletonized Cadaver: Meeting Some Challenges.

Cause of death is defined as a natural disease or injury that led to physiologic changes resulting in death. Manner of death refers to the circumstances surrounding death. Decomposition, especially in advanced stages, creates difficulties in post-mortem examination for it encompasses the processes that lead to the loss of important observable findings and features. Traumatic injuries observed in decomposed cadavers might be analyzed by their vital features and significance for the occurrence of fatal outcomes that help determine the cause and manner of death. An almost fully skeletonized cadaver was admitted to the Department of Forensic Medicine and Deontology at The Medical University, Sofia, Bulgaria. Along with the obligation to answer the post-mortem interval, what were the anthropological and biological features, the cause and manner of death had to be determined in order to classify the case as criminal or not. The cause of death was established by the morphological finding - gunshot injury of the head, passing the brain. The manner of death remained undetermined because of the absence of soft tissues in the areas of the injuries. It was concluded that there was no sufficient forensic data to answer if it was suicide, homicide, or even an accident.

Simultaneous Pore Detection and Morphological Features Extraction in Laser Powder Bed Fusion with Image Processing.

Internal pore defects are inevitable during laser powder bed fusion (LPBF), which have a significant impact on the mechanical properties of the parts. Therefore, detecting pores and obtaining their morphology will contribute to the quality of LPBF parts. Currently, supervised models are used for defect image detection, which requires a large amount of LPBF sample data, image labeling, and computing power equipment during the training process, resulting in high detection costs. This study extensively collected LPBF sample data and proposed a method for pore defect classification by obtaining its morphological features while detecting pore defects in optical microscopy (OM) images under various conditions. Compared with other advanced models, the proposed method achieves better detection accuracy on pore defect datasets with limited data. In addition, quickly detecting pore defects in a large number of labeling ground truth images will also contribute to the development of deep learning. In terms of image segmentation, the average accuracy scores of this method in the test images exceed 85%. The research results indicate that the algorithm proposed in this paper is suitable for quickly and accurately identifying pore defects from optical microscopy images.

Evaluation of some garden flowers as specialty cut flowers in Eskisehir province-Türkiye.

Specialty cut flowers are in demand, especially in the domestic market as they can be grown with low production costs without the need for specially equipped greenhouses and offer diversity in terms of form, texture, and colour. These products, which are widely cultivated in the USA, are not well known in Türkiye. One of the main problems of the Turkish cut flower sector is its dependence on foreign inputs and the lack of product diversity. Therefore, specialty cut flower production can be an alternative crop for Türkiye, which has climatic advantages. The cut flower potential of plants such as Zinnia elegans, Tagates erecta, Helianthus annuus, Gomphrena globosa, Centaurea cyanus, and Cleome spinosa that are commonly grown in gardens has been evaluated. After harvesting these flowers grown in an open field in June-October 2020, the stem length (cm), stem thickness (mm), flower length (cm), flower diameter (cm), flower weight (g), and vase life (days) were measured. As a result of the evaluations, considering the phenological findings such as flowering and harvesting period, Zinnia elegans, Tagates erecta, Helianthus annuus, Gomphrena globosa, and Centaurea cyanus were found to be suitable for Eskisehir climatic conditions due to their long flowering periods. On the other hand, if the stem length value, which is one of the most important parameters for cut flowers, is taken as a reference, the minimum stem length value of 30 cm and above is met by Zinnia elegans, Tagates erecta, Helianthus annuus, and Cleome spinosa while the vase life value of 6 days and above is met by Zinnia elegans, Tagates erecta, Helianthus annuus, Gomphrena globosa, and Cleome spinosa. However, Cleome spinosa was not found to be suitable for the region due to its low yield value and short flowering period, while Zinnia elegans, Helianthus annuus, Tagates erecta, and Gomphrena globosa were found to be plants that could be evaluated for the region. In addition, it is believed that the cultivation of specialty cut flowers, with the selection of suitable species, will be an alternative production in regions without climatic advantages.

Blood cell image segmentation and classification: a systematic review.

Background: Blood diseases such as leukemia, anemia, lymphoma, and thalassemia are hematological disorders that relate to abnormalities in the morphology and concentration of blood elements, specifically white blood cells (WBC) and red blood cells (RBC). Accurate and efficient diagnosis of these conditions significantly depends on the expertise of hematologists and pathologists. To assist the pathologist in the diagnostic process, there has been growing interest in utilizing computer-aided diagnostic (CAD) techniques, particularly those using medical image processing and machine learning algorithms. Previous surveys in this domain have been narrowly focused, often only addressing specific areas like segmentation or classification but lacking a holistic view like segmentation, classification, feature extraction, dataset utilization, evaluation matrices, etc. Methodology: This survey aims to provide a comprehensive and systematic review of existing literature and research work in the field of blood image analysis using deep learning techniques. It particularly focuses on medical image processing techniques and deep learning algorithms that excel in the morphological characterization of WBCs and RBCs. The review is structured to cover four main areas: segmentation techniques, classification methodologies, descriptive feature selection, evaluation parameters, and dataset selection for the analysis of WBCs and RBCs. Results: Our analysis reveals several interesting trends and preferences among researchers. Regarding dataset selection, approximately 50% of research related to WBC segmentation and 60% for RBC segmentation opted for manually obtaining images rather than using a predefined dataset. When it comes to classification, 45% of the previous work on WBCs chose the ALL-IDB dataset, while a significant 73% of researchers focused on RBC classification decided to manually obtain images from medical institutions instead of utilizing predefined datasets. In terms of feature selection for classification, morphological features were the most popular, being chosen in 55% and 80% of studies related to WBC and RBC classification, respectively. Conclusion: The diagnostic accuracy for blood-related diseases like leukemia, anemia, lymphoma, and thalassemia can be significantly enhanced through the effective use of CAD techniques, which have evolved considerably in recent years. This survey provides a broad and in-depth review of the techniques being employed, from image segmentation to classification, feature selection, utilization of evaluation matrices, and dataset selection. The inconsistency in dataset selection suggests a need for standardized, high-quality datasets to strengthen the diagnostic capabilities of these techniques further. Additionally, the popularity of morphological features indicates that future research could further explore and innovate in this direction.

Chromosome Doubling Enhances Biomass and Carotenoid Content in Lycium chinense.

Lycium chinense, a type of medicinal and edible plant, is rich in bioactive compounds beneficial to human health. In order to meet the market requirements for the yield and quality of L. chinense, polyploid induction is usually an effective way to increase plant biomass and improve the content of bioactive components. This study established the most effective tetraploid induction protocol by assessing various preculture durations, colchicine concentrations, and exposure times. The peak tetraploid induction efficacy, 18.2%, was achieved with a 12-day preculture and 24-h exposure to 50 mg L-1 colchicine. Compared to diploids, tetraploids exhibited potentially advantageous characteristics such as larger leaves, more robust stems, and faster growth rates. Physiologically, tetraploids demonstrated increased stomatal size and chloroplast count in stomata but reduced stomatal density. Nutrient analysis revealed a substantial increase in polysaccharides, calcium, iron, and zinc in tetraploid leaves. In addition, seventeen carotenoids were identified in the leaves of L. chinense. Compared to the diploid, lutein, ß-carotene, neoxanthin, violaxanthin, and (E/Z)-phytoene exhibited higher levels in tetraploid strains T39 and T1, with T39 demonstrating a greater accumulation than T1. The findings suggest that the generated tetraploids harbor potential for further exploitation and lay the foundation for the selection and breeding of novel genetic resources of Lycium.

Decreased posterior tibial slope is a risk factor for primary posterior cruciate ligament rupture and posterior cruciate ligament reconstruction failure: A systematic review.

PURPOSE: To determine bony knee morphological factors associated with primary posterior cruciate ligament (PCL) rupture or PCL graft failure after PCL reconstruction. METHODS: Three databases, namely MEDLINE, PubMed and EMBASE, were searched on 30th May 2023. The authors adhered to the PRISMA and R-AMSTAR guidelines as well as the Cochrane Handbook for Systematic Reviews of Interventions. Data such as receiver operating characteristic curve parameters, as well as p-values for comparisons of values between patients with PCL pathology and control patients, were recorded. RESULTS: Nine studies comprising 1054 patients were included. Four studies reported that patients with PCL injury had flatter medial posterior tibial slopes (MTS) than controls, with mean values of 4.3 (range: 3.0-7.0) and 6.5 (range: 5.0-9.2) degrees, respectively. Two studies reported an MTS cutoff value ranging below 3.90-3.93° being a significant risk factor for primary PCL rupture or PCL graft failure. Two studies reported that shallow medial tibial depths were associated with primary PCL rupture, with mean values of 2.1 (range: 2.0-2.2) and 2.6 (range: 2.4-2.7) mm in PCL injury and control groups, respectively. Stenotic intercondylar notches and femoral condylar width were not consistently associated with PCL injuries. CONCLUSION: Decreased MTS is associated with primary PCL rupture and graft failure after PCL reconstruction with values below 3.93° being considered as a significant risk factor. Less common risk factors include shallow medial tibial depth, while femoral condylar width and parameters with regards to the intercondylar notch, such as notch width, notch width index and intercondylar notch volume, demonstrated conflicting associations with primary or secondary PCL injuries. LEVEL OF EVIDENCE: Level III.

Extraction and characterization of cellulose nanoparticles from palm kernel meal for potential application in active food packaging.

The research aimed to explore the potential of palm kernel meal (PKM) as a sustainable source of cellulose nanoparticles (CNPs) for active food packaging. The CNPs were isolated using a combination of chemical techniques, such as alkaline treatment, bleaching, and acid hydrolysis. The characterization of the CNPs was analysed using various techniques, including scanning electron microscopy, Fourier-transform infrared spectroscopy, X-ray diffraction, and UV-visible spectroscopy. The findings revealed that chemical processing effectively removed lignin and hemicellulose from PKM. The SEM morphology confirmed the separation of the CNPs, resulting in the production of 40-100 nm spherical cellulose nanoparticles, while XRD and FTIR analyses confirmed their purity and composition. Moreover, the UV-visible spectroscopy exhibited high transmittance rates, indicating the potential of CNPs as reinforcing agents for polymer matrices. The significance of utilising PKM as a valuable fibre source for extracting CNPs can be recommended for developing active food packaging.

Technical note: ShinyAnimalCV: open-source cloud-based web application for object detection, segmentation, and three-dimensional visualization of animals using computer vision.

Computer vision (CV), a non-intrusive and cost-effective technology, has furthered the development of precision livestock farming by enabling optimized decision-making through timely and individualized animal care. The availability of affordable two- and three-dimensional camera sensors, combined with various machine learning and deep learning algorithms, has provided a valuable opportunity to improve livestock production systems. However, despite the availability of various CV tools in the public domain, applying these tools to animal data can be challenging, often requiring users to have programming and data analysis skills, as well as access to computing resources. Moreover, the rapid expansion of precision livestock farming is creating a growing need to educate and train animal science students in CV. This presents educators with the challenge of efficiently demonstrating the complex algorithms involved in CV. Thus, the objective of this study was to develop ShinyAnimalCV, an open-source cloud-based web application designed to facilitate CV teaching in animal science. This application provides a user-friendly interface for performing CV tasks, including object segmentation, detection, three-dimensional surface visualization, and extraction of two- and three-dimensional morphological features. Nine pre-trained CV models using top-view animal data are included in the application. ShinyAnimalCV has been deployed online using cloud computing platforms. The source code of ShinyAnimalCV is available on GitHub, along with detailed documentation on training CV models using custom data and deploying ShinyAnimalCV locally to allow users to fully leverage the capabilities of the application. ShinyAnimalCV can help to support the teaching of CV, thereby laying the groundwork to promote the adoption of CV in the animal science community.

The integration of cameras and data science has great potential to revolutionize livestock production systems, making them more efficient and sustainable by replacing human-based management with real-time individualized animal care. However, applying these digital tools to animal data presents challenges that require computer programming and data analysis skills, as well as access to computing resources. Additionally, there is a growing need to train animal science students to analyze image or video data using data science algorithms. However, teaching computer programming to all types of students from the ground up can prove complicated and challenging. Therefore, the objective of this study was to develop ShinyAnimalCV, a user-friendly online web application that supports users to learn the application of data science to analyze animal digital video data, without the need for complex coding. The application includes nine pre-trained models for detecting and segmenting animals in image data and can be easily accessed through a web browser. We have also made the source code and detailed documentation available online for advanced users who wish to use the application locally. This software tool facilitates the teaching of digital animal data analysis in the animal science community, with potential benefits to livestock production systems.

Reconstructing Spatial Transcriptomics at the Single-cell Resolution with BayesDeep.

Spatially resolved transcriptomics (SRT) techniques have revolutionized the characterization of molecular profiles while preserving spatial and morphological context. However, most next-generation sequencing-based SRT techniques are limited to measuring gene expression in a confined array of spots, capturing only a fraction of the spatial domain. Typically, these spots encompass gene expression from a few to hundreds of cells, underscoring a critical need for more detailed, single-cell resolution SRT data to enhance our understanding of biological functions within the tissue context. Addressing this challenge, we introduce BayesDeep, a novel Bayesian hierarchical model that leverages cellular morphological data from histology images, commonly paired with SRT data, to reconstruct SRT data at the single-cell resolution. BayesDeep effectively model count data from SRT studies via a negative binomial regression model. This model incorporates explanatory variables such as cell types and nuclei-shape information for each cell extracted from the paired histology image. A feature selection scheme is integrated to examine the association between the morphological and molecular profiles, thereby improving the model robustness. We applied BayesDeep to two real SRT datasets, successfully demonstrating its capability to reconstruct SRT data at the single-cell resolution. This advancement not only yields new biological insights but also significantly enhances various downstream analyses, such as pseudotime and cell-cell communication.

Quantitative analysis of pulse arrival time and PPG morphological features based cuffless blood pressure estimation: a comparative study between diabetic and non-diabetic groups.

Pulse arrival time (PAT) and PPG morphological features have attracted much interest in cuffless blood pressure (BP) estimation, but their effects are not clearly understood when vascular characteristics are affected by diseases such as diabetes. This work quantitatively analyzes the effect of diabetic disease on the PAT and PPG morphological features-based BP estimation. We selected 112 diabetic patients and 308 non-diabetic subjects from VitalDB, and extracted 16 features including PAT, PPG morphological features, and heart rate. BP estimation performance was statistically compared between groups using linear regression models with several feature sets, and the relative importance of each feature in the optimal feature set was extracted. As a result, the standard deviation of the error and mean absolute error of PAT-based BP estimation were significantly higher in the diabetic group than in the non-diabetic group (p < 0.01). A feature set containing PAT and PPG morphological features achieved the best performance in both groups. However, the relative importance of each feature for BP estimation differed notably between groups. The results indicate that different features are important depending on the vascular characteristics, which could help to construct different models to accommodate specific diseases.

Research progress in the quality evaluation of Salvia miltiorrhiza based on the association of 'morphological features - functional substances - pharmacological action - clinical efficacy'.

Background: Salvia miltiorrhiza (Salvia miltiorrhiza Radix et Rhizoma) is the dried root and rhizome of Salvia miltiorrhiza Bge., a plant of the labiate family. It is a type of traditional Chinese medicine that can promote blood circulation for removing blood stasis. It is often used to treat cardiovascular and cerebrovascular diseases in a clinic.Aim of the study: High-quality Chinese herbal medicines are the premise of the safe and effective use of Traditional Chinese Medicine (TCM) in clinics. We aim to prove the rationality of the traditional identification method, namely, 'the redder the root colour and the thicker the root, the better is the quality', to use the morphological features of Salvia miltiorrhiza as the main index to quickly and directly evaluate its quality. Materials and methods: By referring to relevant ancient books, domestic and foreign literature, and academic papers, we summarised the research progress regarding the morphological features, functional substances, pharmacological action, and clinical efficacy of Salvia miltiorrhiza. Results: The redder the colour, the thicker the root, and the denser the texture, the better is the quality of Salvia miltiorrhiza. In Salvia miltiorrhiza, tanshinone ⅡA and salvianolic acid B are the main functional substances that protect the cardiovascular and cerebrovascular functions. The higher the content of these two functional substances, the better is the clinical efficacy of Salvia miltiorrhiza. Conclusion: The research idea of the correlation between the 'morphological features, functional substances, pharmacological action, and clinical efficacy' can be applied to evaluate the quality of Salvia miltiorrhiza. This research idea and method can also be applied to more Chinese herbal medicines.

Assessment of Lymphovascular Invasion in Breast Cancer Using a Combined MRI Morphological Features, Radiomics, and Deep Learning Approach Based on Dynamic Contrast-Enhanced MRI.

BACKGROUND: Assessment of lymphovascular invasion (LVI) in breast cancer (BC) primarily relies on preoperative needle biopsy. There is an urgent need to develop a non-invasive assessment method. PURPOSE: To develop an effective model to assess the LVI status in patients with BC using magnetic resonance imaging morphological features (MRI-MF), Radiomics, and deep learning (DL) approaches based on dynamic contrast-enhanced MRI (DCE-MRI). STUDY TYPE: Cross-sectional retrospective cohort study. POPULATION: The study included 206 BC patients, with 136 in the training set [97 LVI(-) and 39 LVI(+) cases; median age: 51.5 years] and 70 in the test set [52 LVI(-) and 18 LVI(+) cases; median age: 48 years]. FIELD STRENGTH/SEQUENCE: 1.5 T/T1-weighted images, fat-suppressed T2-weighted images, diffusion-weighted imaging (DWI), and DCE-MRI. ASSESSMENT: The MRI-MF model was developed with conventional MR features using logistic analyses. The Radiomic feature extraction process involved collecting data from categorized DCE-MRI datasets, specifically the first and second post-contrast images (A1 and A2). Next, a DL model was implemented to determine LVI. Finally, we established a joint diagnosis model by combining the MRI-MF, Radiomics, and DL approaches. STATISTICAL TESTS: Diagnostic performance was compared using receiver operating characteristic curve analysis, confusion matrix, and decision curve analysis. RESULTS: Rim sign and peritumoral edema features were used to develop the MRI-MF model, while six Radiomics signature from the A1 and A2 images were used for the Radiomics model. The joint model (MRI-MF + Radiomics + DL models) achieved the highest accuracy (area under the curve [AUC] = 0.857), being significantly superior to the MRI-MF (AUC = 0.724), Radiomics (AUC = 0.736), or DL (AUC = 0.740) model. Furthermore, it also outperformed the pairwise combination models: Radiomics + MRI-MF (AUC = 0.796), DL + MRI-MF (AUC = 0.796), or DL + Radiomics (AUC = 0.826). DATA CONCLUSION: The joint model incorporating MRI-MF, Radiomics, and DL approaches can effectively determine the LVI status in patients with BC before surgery. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

Study of Tribological Properties and Evolution of Morphological Characteristics of Transfer Films in PTFE Composites Synergistically Reinforced with Nano-ZrO2 and PEEK Particles.

The materials tribology community has identified that the transfer film attached to the surface of the counterpart metal during the friction process is not only closely related to the filler modification material but also a key factor affecting the tribological properties of polymer composites; however, there is a lack of feasible methods to quantify the characteristics of the transfer film. In this study, Nano-ZrO2 and polyetheretherketone (PEEK) were filled into a PTFE matrix in order to enhance the wear resistance of polytetrafluoroethylene (PTFE). The tribological properties of the modified PTFE composites were tested using a linear reciprocating friction and wear tester, and the entire friction experiment was designed in seven separate stages. Morphological features were extracted and analyzed from photographs of the transfer film acquired by optical microscopy at each friction stage using an image processing program. The thickness and roughness of the transfer film sections were measured using a non-contact profilometer. Abrasive debris were collected, and their morphological features were observed with an electron microscope. The results showed that the synergistic addition of soft PEEK and hard Nano-ZrO2 particles effectively inhibited interlayer slippage between PTFE molecular chains, dramatically reducing the size and yield of abrasive debris, and facilitated the improvement of the thickness and firmness of the transfer film, which significantly enhanced the wear resistance of the PTFE composites (the lowest volumetric wear rate for Nano-ZrO2/PEEK/PTFE was only 1.76 × 10-4 mm3/Nm). Quantitative analyses of the morphological characteristics of the transfer films revealed that the coverage and roundness of the transfer films gradually increase with the friction stroke, while the aspect ratio and texture entropy subsequently decrease gradually. The coverage, area, mean, third-order moments, and consistency of the transfer film strongly correlated with the volumetric wear rate (correlation coefficient |r| > 0.9).