Urban airborne PM2.5 induces pulmonary fibrosis through triggering glycolysis and subsequent modification of histone lactylation in macrophages.

Airborne fine particulate matter (PM2.5) can cause pulmonary inflammation and even fibrosis, however, the underlying molecular mechanisms of the pathogenesis of PM2.5 exposure have not been fully appreciated. In the present study, we explored the dynamics of glycolysis and modification of histone lactylation in macrophages induced by PM2.5-exposure in both in vivo and in vitro models. Male C57BL/6 J mice were anesthetized and administrated with PM2.5 by intratracheal instillation once every other day for 4 weeks. Mouse RAW264.7 macrophages and alveolar epithelial MLE-12 cells were treated with PM2.5 for 24 h. We found that PM2.5 significantly increased lactate dehydrogenase (LDH) activities and lactate contents, and up-regulated the mRNA expression of key glycolytic enzymes in the lungs and bronchoalveolar lavage fluids of mice. Moreover, PM2.5 increased the levels of histone lactylation in both PM2.5-exposed lungs and RAW264.7 cells. The pro-fibrotic cytokines secreted from PM2.5-treated RAW264.7 cells triggered epithelial-mesenchymal transition (EMT) in MLE-12 cells through activating transforming growth factor-ß (TGF-ß)/Smad2/3 and VEGFA/ERK pathways. In contrast, LDHA inhibitor (GNE-140) pretreatment effectively alleviated PM2.5-induced pulmonary inflammation and fibrosis via inhibiting glycolysis and subsequent modification of histone lactylation in mice. Thus, our findings suggest that PM2.5-induced glycolysis and subsequent modification of histone lactylation play critical role in the PM2.5-associated pulmonary fibrosis.

Desirable Strong and Tough Adhesive Inspired by Dragonfly Wings and Plant Cell Walls.

The production of wood-based panels has a significant demand for mechanically strong and flexible biomass adhesives, serving as alternatives to nonrenewable and toxic formaldehyde-based adhesives. Nonetheless, plywood usually exhibits brittle fracture due to the inherent trade-off between rigidity and toughness, and it is susceptible to damage and deformation defects in production applications. Herein, inspired by the microstructure of dragonfly wings and the cross-linking structure of plant cell walls, a soybean meal (SM) adhesive with great strength and toughness was developed. The strategy was combined with a multiple assembly system based on the tannic acid (TA) stripping/modification of molybdenum disulfide (MoS2@TA) hybrids, phenylboronic acid/quaternary ammonium doubly functionalized chitosan (QCP), and SM. Motivated by the microstructure of dragonfly wings, MoS2@TA was tightly bonded with the SM framework through Schiff base and strong hydrogen bonding to dissipate stress energy through crack deflection, bridging, and immobilization. QCP imitated borate chemistry in plant cell walls to optimize interfacial interactions within the adhesive by borate ester bonds, boron-nitrogen coordination bonds, and electrostatic interactions and dissipate energy through sacrificial bonding. The shear strength and fracture toughness of the SM/QCP/MoS2@TA adhesive were 1.58 MPa and 0.87 J, respectively, which were 409.7% and 866.7% higher than those of the pure SM adhesive. In addition, MoS2@TA and QCP gave the adhesive good mildew resistance, durability, weatherability, and fire resistance. This bioinspired design strategy offers a viable and sustainable approach for creating multifunctional strong and tough biobased materials.

A Self-Supervised Learning Based Framework for Eyelid Malignant Melanoma Diagnosis in Whole Slide Images.

Eyelid malignant melanoma (MM) is a rare disease with high mortality. Accurate diagnosis of such disease is important but challenging. In clinical practice, the diagnosis of MM is currently performed manually by pathologists, which is subjective and biased. Since the heavy manual annotation workload, most pathological whole slide image (WSI) datasets are only partially labeled (without region annotations), which cannot be directly used in supervised deep learning. For these reasons, it is of great practical significance to design a laborsaving and high data utilization diagnosis method. In this paper, a self-supervised learning (SSL) based framework for automatically detecting eyelid MM is proposed. The framework consists of a self-supervised model for detecting MM areas at the patch-level and a second model for classifying lesion types at the slide level. A squeeze-excitation (SE) attention structure and a feature-projection (FP) structure are integrated to boost learning on details of pathological images and improve model performance. In addition, this framework also provides visual heatmaps with high quality and reliability to highlight the likely areas of the lesion to assist the evaluation and diagnosis of the eyelid MM. Extensive experimental results on different datasets show that our proposed method outperforms other state-of-the-art SSL and fully supervised methods at both patch and slide levels when only a subset of WSIs are annotated. It should be noted that our method is even comparable to supervised methods when all WSIs are fully annotated. To the best of our knowledge, our work is the first SSL method for automatic diagnosis of MM at the eyelid and has a great potential impact on reducing the workload of human annotations in clinical practice.

Automated quantification of cartilage quality for hip treatment decision support.

PURPOSE: Preservation surgery can halt the progress of joint degradation, preserving the life of the hip; however, outcome depends on the existing cartilage quality. Biochemical analysis of the hip cartilage utilizing MRI sequences such as delayed gadolinium-enhanced MRI of cartilage (dGEMRIC), in addition to morphological analysis, can be used to detect early signs of cartilage degradation. However, a complete, accurate 3D analysis of the cartilage regions and layers is currently not possible due to a lack of diagnostic tools. METHODS: A system for the efficient automatic parametrization of the 3D hip cartilage was developed. 2D U-nets were trained on manually annotated dual-flip angle (DFA) dGEMRIC for femoral head localization and cartilage segmentation. A fully automated cartilage sectioning pipeline for analysis of central and peripheral regions, femoral-acetabular layers, and a variable number of section slices, was developed along with functionality for the automatic calculation of dGEMRIC index, thickness, surface area, and volume. RESULTS: The trained networks locate the femoral head and segment the cartilage with a Dice similarity coefficient of 88 ± 3 and 83 ± 4% on DFA and magnetization-prepared 2 rapid gradient-echo (MP2RAGE) dGEMRIC, respectively. A completely automatic cartilage analysis was performed in 18s, and no significant difference for average dGEMRIC index, volume, surface area, and thickness calculated on manual and automatic segmentation was observed. CONCLUSION: An application for the 3D analysis of hip cartilage was developed for the automated detection of subtle morphological and biochemical signs of cartilage degradation in prognostic studies and clinical diagnosis. The segmentation network achieved a 4-time increase in processing speed without loss of segmentation accuracy on both normal and deformed anatomy, enabling accurate parametrization. Retraining of the networks with the promising MP2RAGE protocol would enable analysis without the need for B1 inhomogeneity correction in the future.

Three-Dimensional Magnetic Resonance Imaging Bone Models of the Hip Joint Using Deep Learning: Dynamic Simulation of Hip Impingement for Diagnosis of Intra- and Extra-articular Hip Impingement.

BACKGROUND: Dynamic 3-dimensional (3D) simulation of hip impingement enables better understanding of complex hip deformities in young adult patients with femoroacetabular impingement (FAI). Deep learning algorithms may improve magnetic resonance imaging (MRI) segmentation. PURPOSE: (1) To evaluate the accuracy of 3D models created using convolutional neural networks (CNNs) for fully automatic MRI bone segmentation of the hip joint, (2) to correlate hip range of motion (ROM) between manual and automatic segmentation, and (3) to compare location of hip impingement in 3D models created using automatic bone segmentation in patients with FAI. STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 3. METHODS: The authors retrospectively reviewed 31 hip MRI scans from 26 symptomatic patients (mean age, 27 years) with hip pain due to FAI. All patients had matched computed tomography (CT) and MRI scans of the pelvis and the knee. CT- and MRI-based osseous 3D models of the hip joint of the same patients were compared (MRI: T1 volumetric interpolated breath-hold examination high-resolution sequence; 0.8 mm3 isovoxel). CNNs were used to develop fully automatic bone segmentation of the hip joint, and the 3D models created using this method were compared with manual segmentation of CT- and MRI-based 3D models. Impingement-free ROM and location of hip impingement were calculated using previously validated collision detection software. RESULTS: The difference between the CT- and MRI-based 3D models was <1 mm, and the difference between fully automatic and manual segmentation of MRI-based 3D models was <1 mm. The correlation of automatic and manual MRI-based 3D models was excellent and significant for impingement-free ROM (r = 0.995; P < .001), flexion (r = 0.953; P < .001), and internal rotation at 90° of flexion (r = 0.982; P < .001). The correlation for impingement-free flexion between automatic MRI-based 3D models and CT-based 3D models was 0.953 (P < .001). The location of impingement was not significantly different between manual and automatic segmentation of MRI-based 3D models, and the location of extra-articular hip impingement was not different between CT- and MRI-based 3D models. CONCLUSION: CNN can potentially be used in clinical practice to provide rapid and accurate 3D MRI hip joint models for young patients. The created models can be used for simulation of impingement during diagnosis of intra- and extra-articular hip impingement to enable radiation-free and patient-specific surgical planning for hip arthroscopy and open hip preservation surgery.

MRI-based 3D models of the hip joint enables radiation-free computer-assisted planning of periacetabular osteotomy for treatment of hip dysplasia using deep learning for automatic segmentation.

INTRODUCTION: Both Hip Dysplasia(DDH) and Femoro-acetabular-Impingement(FAI) are complex three-dimensional hip pathologies causing hip pain and osteoarthritis in young patients. 3D-MRI-based models were used for radiation-free computer-assisted surgical planning. Automatic segmentation of MRI-based 3D-models are preferred because manual segmentation is time-consuming.To investigate(1) the difference and(2) the correlation for femoral head coverage(FHC) between automatic MR-based and manual CT-based 3D-models and (3) feasibility of preoperative planning in symptomatic patients with hip diseases. METHODS: We performed an IRB-approved comparative, retrospective study of 31 hips(26 symptomatic patients with hip dysplasia or FAI). 3D MRI sequences and CT scans of the hip were acquired. Preoperative MRI included axial-oblique T1 VIBE sequence(0.8 mm3 isovoxel) of the hip joint. Manual segmentation of MRI and CT scans were performed. Automatic segmentation of MRI-based 3D-models was performed using deep learning. RESULTS: (1)The difference between automatic and manual segmentation of MRI-based 3D hip joint models was below 1 mm(proximal femur 0.2 ±â€¯0.1 mm and acetabulum 0.3 ±â€¯0.5 mm). Dice coefficients of the proximal femur and the acetabulum were 98 % and 97 %, respectively. (2)The correlation for total FHC was excellent and significant(r = 0.975, p < 0.001) between automatic MRI-based and manual CT-based 3D-models. Correlation for total FHC (r = 0.979, p < 0.001) between automatic and manual MR-based 3D models was excellent.(3)Preoperative planning and simulation of periacetabular osteotomy was feasible in all patients(100 %) with hip dysplasia or acetabular retroversion. CONCLUSIONS: Automatic segmentation of MRI-based 3D-models using deep learning is as accurate as CT-based 3D-models for patients with hip diseases of childbearing age. This allows radiation-free and patient-specific preoperative simulation and surgical planning of periacetabular osteotomy for patients with DDH.

Holistic decomposition convolution for effective semantic segmentation of medical volume images.

Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in many different 2D medical image analysis tasks. In clinical practice, however, a large part of the medical imaging data available is in 3D, e.g, magnetic resonance imaging (MRI) data, computed tomography (CT) data and data generated by many other modalities. This has motivated the development of 3D CNNs for volumetric image segmentation in order to benefit from more spatial context. Due to GPU memory restrictions caused by moving to fully 3D, state-of-the-art methods depend on subvolume/patch processing and the size of the input patch is usually small, limiting the incorporation of larger context information for a better performance. In this paper, we propose a novel Holistic Decomposition Convolution (HDC), which learns a number of separate kernels within the same layer and can be regarded as an inverse operation to the previously introduced Dense Upsampling Convolution (DUC), for an effective and efficient semantic segmentation of medical volume images. HDC consists of a periodic down-shuffling operation followed by a conventional 3D convolution. HDC has the advantage of significantly reducing the size of the data for sub-sequential processing while using all the information available in the input irrespective of the down-shuffling factors. We apply HDC directly to the input data, whose output will be used as the input to sub-sequential CNNs. In order to achieve volumetric dense prediction at final output, we need to recover full resolution, which is done by using DUC. We show that both HDC and DUC are network agnostic and can be combined with different CNNs for an improved performance in both training and testing phases. Results obtained from comprehensive experiments conducted on both MRI and CT data of different anatomical regions demonstrate the efficacy of the present approach.

Automatic MRI-based Three-dimensional Models of Hip Cartilage Provide Improved Morphologic and Biochemical Analysis.

BACKGROUND: The time-consuming and user-dependent postprocessing of biochemical cartilage MRI has limited the use of delayed gadolinium-enhanced MRI of cartilage (dGEMRIC). An automated analysis of biochemical three-dimensional (3-D) images could deliver a more time-efficient and objective evaluation of cartilage composition, and provide comprehensive information about cartilage thickness, surface area, and volume compared with manual two-dimensional (2-D) analysis. QUESTIONS/PURPOSES: (1) How does the 3-D analysis of cartilage thickness and dGEMRIC index using both a manual and a new automated method compare with the manual 2-D analysis (gold standard)? (2) How does the manual 3-D analysis of regional patterns of dGEMRIC index, cartilage thickness, surface area and volume compare with a new automatic method? (3) What is the interobserver reliability and intraobserver reproducibility of software-assisted manual 3-D and automated 3-D analysis of dGEMRIC indices, thickness, surface, and volume for two readers on two time points? METHODS: In this IRB-approved, retrospective, diagnostic study, we identified the first 25 symptomatic hips (23 patients) who underwent a contrast-enhanced MRI at 3T including a 3-D dGEMRIC sequence for intraarticular pathology assessment due to structural hip deformities. Of the 23 patients, 10 (43%) were male, 16 (64%) hips had a cam deformity and 16 (64%) hips had either a pincer deformity or acetabular dysplasia. The development of an automated deep-learning-based approach for 3-D segmentation of hip cartilage models was based on two steps: First, one reader (FS) provided a manual 3-D segmentation of hip cartilage, which served as training data for the neural network and was used as input data for the manual 3-D analysis. Next, we developed the deep convolutional neural network to obtain an automated 3-D cartilage segmentation that we used as input data for the automated 3-D analysis. For actual analysis of the manually and automatically generated 3-D cartilage models, a dedicated software was developed. Manual 2-D analysis of dGEMRIC indices and cartilage thickness was performed at each "full-hour" position on radial images and served as the gold standard for comparison with the corresponding measurements of the manual and the automated 3-D analysis. We measured dGEMRIC index, cartilage thickness, surface area, and volume for each of the four joint quadrants and compared the manual and the automated 3-D analyses using mean differences. Agreement between the techniques was assessed using intraclass correlation coefficients (ICC). The overlap between 3-D cartilage volumes was assessed using dice coefficients and means of all distances between surface points of the models were calculated as average surface distance. The interobserver reliability and intraobserver reproducibility of the software-assisted manual 3-D and the automated 3-D analysis of dGEMRIC indices, thickness, surface and volume was assessed for two readers on two different time points using ICCs. RESULTS: Comparable mean overall difference and almost-perfect agreement in dGEMRIC indices was found between the manual 3-D analysis (8 ± 44 ms, p = 0.005; ICC = 0.980), the automated 3-D analysis (7 ± 43 ms, p = 0.015; ICC = 0.982), and the manual 2-D analysis.Agreement for measuring overall cartilage thickness was almost perfect for both 3-D methods (ICC = 0.855 and 0.881) versus the manual 2-D analysis. A mean difference of -0.2 ± 0.5 mm (p < 0.001) was observed for overall cartilage thickness between the automated 3-D analysis and the manual 2-D analysis; no such difference was observed between the manual 3-D and the manual 2-D analysis.Regional patterns were comparable for both 3-D methods. The highest dGEMRIC indices were found posterosuperiorly (manual: 602 ± 158 ms; p = 0.013, automated: 602 ± 158 ms; p = 0.012). The thickest cartilage was found anteroinferiorly (manual: 5.3 ± 0.8 mm, p < 0.001; automated: 4.3 ± 0.6 mm; p < 0.001). The smallest surface area was found anteroinferiorly (manual: 134 ± 60 mm; p < 0.001, automated: 155 ± 60 mm; p < 0.001). The largest volume was found anterosuperiorly (manual: 2343 ± 492 mm; p < 0.001, automated: 2294 ± 467 mm; p < 0.001). Mean average surface distance was 0.26 ± 0.13 mm and mean Dice coefficient was 86% ± 3%. Intraobserver reproducibility and interobserver reliability was near perfect for overall analysis of dGEMRIC indices, thickness, surface area, and volume (ICC range, 0.962-1). CONCLUSIONS: The presented deep learning approach for a fully automatic segmentation of hip cartilage enables an accurate, reliable and reproducible analysis of dGEMRIC indices, thickness, surface area, and volume. This time-efficient and objective analysis of biochemical cartilage composition and morphology yields the potential to improve patient selection in femoroacetabular impingement (FAI) surgery and to aid surgeons with planning of acetabuloplasty and periacetabular osteotomies in pincer FAI and hip dysplasia. In addition, this validation paves way to the large-scale use of this method for prospective trials which longitudinally monitor the effect of reconstructive hip surgery and the natural course of osteoarthritis. LEVEL OF EVIDENCE: Level III, diagnostic study.

The application of poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) scaffolds for tendon repair in the rat model.

Tendon injuries and defects present a substantial burden to global healthcare economies. There are no synthetic/biosynthesised implants available which can restore full function or match the mechanical properties of native tendon. Therefore, poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (PHBHHx) was investigated for its utility as a scaffold in a rat Achilles tendon repair model. Porous PHBHHx tubes and fibres were prepared with particle leaching and extrusion methods, respectively. Collagen gels reinforced by polymer fibres were inserted into the lumen of scaffold tubes to create the operational scaffold unit. Mechanical testing demonstrated that PHBHHx scaffolds had comparable mechanical properties to rat tendon, with maximal loads of 23.73 ± 1.08 N, compared to 17.35 ± 1.76 N in undamaged rat Achilles tendon. Sprague-Dawley (SD) rats were split into four experimental groups: control, PHBHHx scaffold only, PHBHHx scaffold and collagen, PHBHHx scaffold, collagen and tenocyte compositions for implantation to repair an induced Achilles tendon defect. No secondary immune response to PHBHHx was observed over a 40 days period of implantation. Movement was restored in PHBHHx scaffold-collagen-tenocyte recipient rats at an earlier time point than in other experimental groups, with complete load-bearing and function returning 20 days post-surgery as determined by the Achilles Functional Index. In vitro testing of tendon constructs after 40 days demonstrated reductions in PHBHHx molecular weight and polydispersity index accompanied by an increase in mean chain length indicating degradation of smaller polymer chain subunits. Similarly a reduction in PHBHHx tube ultimate tensile strength and elastic modulus was observed. Histological analysis provided evidence of tissue remodelling and cell alignment. In summary, PHBHHx scaffolds have been successfully applied in an in vivo tendon repair model raising promise for future utility in tissue engineering applications.