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.

Polystyrene microplastic-induced oxidative stress triggers intestinal barrier dysfunction via the NF-κB/NLRP3/IL-1ß/MCLK pathway.

Emerging evidence has demonstrated the association between microplastics (MPs) with a diameter of <5 mm and the risk of intestinal diseases. However, the molecular mechanisms contributing to MP-induced intestinal barrier dysfunction have not been fully appreciated. In this study, C57BL/6 J mice were exposed to polystyrene microplastics (PS-MPs, 0.2, 1 or 5 µm) at 1 mg/kg body weight daily by oral gavage for 28 days. We found that PS-MPs exposure induced oxidative stress and inflammatory cell infiltration in mice colon, leading to an increased expression of pro-inflammatory cytokine. Moreover, there were an increase in intestinal permeability and decrease in mucus secretion, accompanied by downregulation of tight junction (TJ)-related zonula occluden-1 (ZO-1), occluding (OCLN) and claudin-1 (CLDN-1) in mice colon. Especially, 5 µm PS-MPs (PS5)-induced intestinal epithelial TJ barrier damage was more severe than 0.2 µm PS-MPs (PS0.2) and 1 µm PS-MPs (PS1). In vitro experiments indicated that PS5-induced oxidative stress upregulated the expression of nuclear factor kappa B (NF-κB), nucleotide-binding domain and leucine-rich repeat protein 3 (NLRP3) inflammasome, and myosin light chain kinase (MLCK). Meanwhile, pre-treatment with the antioxidant NAC, NLRP3 inhibitor MCC950 and MLCK inhibitor ML-7 considerably reduced PS5-triggered reactive oxygen species (ROS) production and inflammatory response, inhibited the activation of the NF-κB/NLRP3/MLCK pathway, and upregulated ZO-1, OCLN and CLDN-1 expression in Caco-2 cells. Taken together, our study demonstrated that PS-MPs cause intestinal barrier dysfunction through the ROS-dependent NF-κB/NLRP3/IL-1ß/MLCK pathway.

Assessment of choriocapillaris and choroidal vascular changes in posterior uveitis using swept-source wide-field optical coherence tomography angiography.

PURPOSE: To evaluate choriocapillaris (CC) and choroidal vascular changes in patients with posterior uveitis using swept-source (SS) wide-field optical coherence tomography angiography (OCTA). METHOD: Consecutive patients with posterior uveitis were evaluated using 3×3 mm and 12×12 mm OCTA scan patterns and montage images of 5×12×12 mm or 2×15×9 mm, covering approximately 70°-90° of the retina. The images were quantitatively and qualitatively analysed and compared with healthy controls. RESULTS: Eighty-six eyes of 56 patients with posterior uveitis (mean age 45.2±19.9 years; 58.9% female), and 38 eyes of 19 age-matched healthy controls (57.9% female) were included. The mean perfusion density (PD) in 3×3 mm and 12×12 mm CC scans was significantly lower in eyes with posterior uveitis compared with those of healthy controls. However, no significant difference in the mean PD of choroidal scans was found comparing eyes with posterior uveitis and healthy controls. The mean PD in eyes with active disease was significantly higher compared with the inactive eyes on 12×12 mm choroidal scans (55.61% vs 51.25%, p=0.02), while no difference was found in the CC slabs. CONCLUSION: CC and choroidal assessment using OCTA provides useful information in patients with posterior uveitis. SS-OCTA metrics of the CC and choroidal slabs are promising tools in uveitis patients in the future. TRIAL REGISTRATION NUMBER: NCT02811536.

A tough bio-adhesive inspired by pearl layer and arthropod cuticle structure with desirable water resistance, flame-retardancy, and antibacterial property.

Petroleum-derived formaldehyde resin adhesives are serious hazards to human health and depend on limited resources. Abundant, cheap and renewable biomass materials are expected to replace them. However, the contradictory mechanisms of high mechanical strength and fracture toughness affect the use of bioadhesives. Herein, a biomimetic soybean meal (SM) adhesive inspired by the structure of insect cuticles and shell pearl layer was proposed. Specifically, chitosan (CS@DA) modified 3,4-dihydroxybenzoic acid (DA, rich in catechol moiety) was anchored on molybdenum disulfide nanosheets (MoS2) to construct a biomimetic structure with copper hydroxide and SM substrate (SM-MoS2/CS@DA-Cu). Schiff base, ionic, and hydrogen bonding strengthened the cohesion of the adhesive. The ordered alternating stacking "brick-mortar" structure stimulated the lamellar sliding and crack deflection of MoS2, synergistically reinforcing the toughness. Compared to SM adhesive (0.57 MPa and 0.148 J), the wet shear strength and adhesion work of the SM-MoS2/CS@DA-Cu were 1.68 MPa and 0.867 J, with 194.7 % and 485.8 % increases, respectively. The multiple antimicrobial effects of CS@DA, Schiff base, and Cu2+ increased the applicability period of the adhesive to 40 days. The adhesive also displayed favorable water resistance and flame retardancy. Therefore, this peculiar and efficient biomimetic structural design inspired the development of multi-functional composites.

Study on the Performance and Solidification Mechanism of Multi-Source Solid-Waste-Based Soft Soil Solidification Materials.

In this paper, ground granulated blast furnace slag, steel slag, red mud, waste ceramic powder, and desulfurization gypsum were used as raw materials to develop a kind of multi-source solid-waste-based soft soil solidification material. Three ratios and the strength activity index were used to determine the fractions of different solid wastes. The mineralogical and microstructural characterization was analyzed by X-ray diffraction (XRD), scanning electron microscope (SEM), and thermogravimetric analysis-differential scanning calorimetry (TG&DSC) tests. The results showed that the unconfined compressive strength of the three types of soft soil increases with an increase in the content of the solidifying agent. The failure strain of the stabilized soil decreases from 1.0-1.3% to 0.75-1.0%, and the failure mode gradually changes from plastic failure to brittle failure. The optimum content of the solidifying agent was determined to be 17% (the lime saturation factor (KH), silica modulus (SM), and alumina modulus (IM) of the solidifying agent were set to 0.68, 1.74, and 1.70, respectively), and the unconfined compressive strength (28 d) of the solidified soil (sandy soil, silty clay, and organic clay) was 3.16 MPa, 2.05 MPa, 1.04 MPa, respectively. Both measurements can satisfy the technical requirements for a cement-soil mixing pile, suggesting the possibility of using various types of solid waste as a substitute for cement.

Natural organic-inorganic hybrid structure enabled green biomass adhesive with desirable strength, toughness and mildew resistance.

Plant based proteins are green, sustainable, and renewable materials that show the potential to replace traditional formaldehyde resin. High performance plywood adhesives exhibit high water resistance, strength, toughness, and desirable mildew resistance. Adding petrochemical-based crosslinkers is not economically viable or environmentally benign; this chemical crosslinking strategy makes the imparted high strength and toughness less attractive. Herein, a green approach based on natural organic-inorganic hybrid structure enhancement is proposed. The design of soybean meal-dialdehyde chitosan-amine modified halloysite nanotubes (SM-DACS-HNTs@N) adhesive with desirable strength and toughness enhanced by covalent bonding (Schiff base) crosslinking and toughened by surface-modified nanofillers is demonstrated. Consequently, the prepared adhesive showed a wet shear strength of 1.53 MPa and work of debonding of 389.7 mJ, which increased by 146.8 % and 276.5 %, respectively, due to the cross-linking effect of organic DACS and toughening effect of inorganic HNTs@N. The introduction of DACS and Schiff base generation enhanced the antimicrobial property of the adhesive and increased the mold resistance of the adhesive and plywood. In addition, the adhesive has good economic benefits. This research creates new opportunities for developing biomass composites with desirable performance.

Towards bridging the distribution gap: Instance to Prototype Earth Mover's Distance for distribution alignment.

Despite remarkable success of deep learning, distribution divergence remains a challenge that hinders the performance of many tasks in medical image analysis. Large distribution gap may deteriorate the knowledge transfer across different domains or feature subspaces. To achieve better distribution alignment, we propose a novel module named Instance to Prototype Earth Mover's Distance (I2PEMD), where shared class-specific prototypes are progressively learned to narrow the distribution gap across different domains or feature subspaces, and Earth Mover's Distance (EMD) is calculated to take into consideration the cross-class relationships during embedding alignment. We validate the effectiveness of the proposed I2PEMD on two different tasks: multi-modal medical image segmentation and semi-supervised classification. Specifically, in multi-modal medical image segmentation, I2PEMD is explicitly utilized as a distribution alignment regularization term to supervise the model training process, while in semi-supervised classification, I2PEMD works as an alignment measure to sort and cherry-pick the unlabeled data for more accurate and robust pseudo-labeling. Results from comprehensive experiments demonstrate the efficacy of the present method.

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.

Bioinspired phenol-amine chemistry for developing bioadhesives based on biomineralized cellulose nanocrystals.

Inspired by the phenol-amine chemistry and biomineralization of insect cuticles, we developed a green and facile strategy for preparing a bio-adhesive with excellent adhesion properties, mildew resistance, and antibacterial activity. This biomimetic strategy incorporates functional catechol-modified ε-polylysine and vanillin via grafting and Schiff base reactions. The biomineralized cellulose nanocrystals were prepared using a cellulose nanocrystal bio-template by regulating the in-situ biomineralization of inorganic nanoparticles, thereby building an optimized organic-inorganic mineralization framework in the polymer. The bonding strength of composite adhesive was significantly improved by multiple cross-linking networks through sacrificial hydrogen bonds, electrostatic interactions, and dynamic covalent bonds. The adhesion strength of the composite adhesive reached 1.13 MPa, which was 151% higher than the pristine adhesive. As a result of the synergistic effect of the catechol component, cationic ε-polylysine, and aldehyde group, the bio-adhesive also exhibited favorable anti-mildew and anti-bacterial properties.

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.

Vanadium(IV)-Chlorodipicolinate Protects against Hepatic Steatosis by Ameliorating Lipid Peroxidation, Endoplasmic Reticulum Stress, and Inflammation.

Non-alcoholic fatty liver disease (NAFLD) is increasingly prevalent and represents a growing challenge in terms of prevention and treatment. The aim of this study is to investigate the protective effects and the underlying mechanisms of vanadium(IV)-chlorodipicolinate ([VIVO(dipic-Cl)(H2O)2, VOdipic-Cl]) in a mouse model of NAFLD induced by a high-fat diet (HFD). VOdipic-Cl (10 mg/kg/day body weight) treatment for 4 weeks significantly controlled body weight gain, and effectively reduced the increase in serum and hepatic triglyceride (TG) and total cholesterol (TC) levels, mitigated pathological injury, decreased malondialdehyde (MDA) level, and inhibited endoplasmic reticulum (ER) stress and inflammatory response in the livers of C57BL/6 obese mice. Moreover, RNA-sequencing analysis revealed distinct transcriptional profiles with differentially expressed genes (DEGs) in livers. We found that VOdipic-Cl effectively down-regulated genes related to lipid synthesis and up-regulated genes related to fatty acid transport and lipolysis, and down-regulated the expression of genes related to ER stress and immune response in the livers of obese mice. In conclusion, VOdipic-Cl effectively prevented hepatic steatosis by controlling body weight, mitigating oxidative stress, and regulating the expression of genes related to lipid metabolism, ER stress and immune response, which provides new insights into the molecular mechanism of the protective effect of VOdipic-Cl against hepatic steatosis.

Comparison of Indocyanine Green Angiography and Swept-Source Wide-Field Optical Coherence Tomography Angiography in Posterior Uveitis.

Purpose: To compare indocyanine green angiography (ICGA) and swept-source wide-field optical coherence tomography angiography (SS-OCTA) for the assessment of patients with posterior uveitis. Method: SS-OCTA montage images of 5 x 12 x 12 mm or 2 x 15 x 9 mm, covering ~70-90 degree of the retina of consecutive patients with posterior uveitis were acquired. The choriocapillaries and choroidal slabs were compared to findings on ICGA. Results: Sixty-eight eyes of 41 patients were included (mean age 47.2 ± 20.4 years; 58.5% female). In 23 (34%) lesions were visible on OCTA, but not discernable on ICGA. In turn, out of the 45 eyes with clearly discernable lesions on ICGA, 22 (49%) and 21 (47%) eyes showed no corresponding areas of flow deficit on OCTA in the CC and choroidal slab, respectively. Lesion size strongly correlated among ICGA and OCTA choriocapillaries- (CC) (r = 0.99, p ≤ 0.0001) and choroidal slabs (r = 0.99, p ≤ 0.0001), respectively. The mean lesion size on the late frames of ICGA (8.45 ± 5.47 mm2) was larger compared to the lesion size on OCTA CC scan (7.98 ± 5.47 mm2, p ≤ 0.0001) and choroidal scan (7.69 ± 5.10 mm2, p = 0.002), respectively. The lesion size on OCTA CC scan was significantly larger than on the OCTA choroidal scan (p ≤ 0.0001). Conclusion: SS-wide field OCTA may be a promising tool to assess posterior uveitis patients and may replace ICGA to a certain extent in the future.

AGMB-Transformer: Anatomy-Guided Multi-Branch Transformer Network for Automated Evaluation of Root Canal Therapy.

Accurate evaluation of the treatment result on X-ray images is a significant and challenging step in root canal therapy since the incorrect interpretation of the therapy results will hamper timely follow-up which is crucial to the patients' treatment outcome. Nowadays, the evaluation is performed in a manual manner, which is time-consuming, subjective, and error-prone. In this article, we aim to automate this process by leveraging the advances in computer vision and artificial intelligence, to provide an objective and accurate method for root canal therapy result assessment. A novel anatomy-guided multi-branch Transformer (AGMB-Transformer) network is proposed, which first extracts a set of anatomy features and then uses them to guide a multi-branch Transformer network for evaluation. Specifically, we design a polynomial curve fitting segmentation strategy with the help of landmark detection to extract the anatomy features. Moreover, a branch fusion module and a multi-branch structure including our progressive Transformer and Group Multi-Head Self-Attention (GMHSA) are designed to focus on both global and local features for an accurate diagnosis. To facilitate the research, we have collected a large-scale root canal therapy evaluation dataset with 245 root canal therapy X-ray images, and the experiment results show that our AGMB-Transformer can improve the diagnosis accuracy from 57.96% to 90.20% compared with the baseline network. The proposed AGMB-Transformer can achieve a highly accurate evaluation of root canal therapy. To our best knowledge, our work is the first to perform automatic root canal therapy evaluation and has important clinical value to reduce the workload of endodontists.

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.

Posterior Extra-articular Ischiofemoral Impingement Can Be Caused by the Lesser and Greater Trochanter in Patients With Increased Femoral Version: Dynamic 3D CT-Based Hip Impingement Simulation of a Modified FABER Test.

BACKGROUND: Posterior extra-articular hip impingement has been described for valgus hips with increased femoral version (FV). These patients can present clinically with lack of external rotation (ER) and extension and with a positive posterior impingement test. But we do not know the effect of the combination of deformities, and the impingement location in early flexion is unknown. PURPOSE: To evaluate patient-specific 3-dimensional computed tomography (3D CT) scans of hips with increased FV and control hips for differences in range of motion, location and prevalence of osseous posterior intra- and extra-articular hip impingement. STUDY DESIGN: Case series; Level of evidence, 4. METHODS: Osseous 3D models based on segmentation of 3D CT scans were analyzed for 52 hips (38 symptomatic patients) with positive posterior impingement test and increased FV (>35°). There were 26 hips with an increased McKibbin instability index >70 (unstable hips). Patients were mainly female (96%), with an age range of 18 to 45 years. Of them, 21 hips had isolated increased FV (>35°); 22 hips had increased FV and increased acetabular version (AV; >25°); and 9 valgus hips (caput-collum-diaphyseal angle >139°) had increased FV and increased AV. The control group consisted of 20 hips with normal FV, normal AV, and no valgus (caput-collum-diaphyseal angle <139°). Validated 3D CT-based collision detection software for impingement simulation was used to calculate impingement-free range of motion and location of hip impingement. Surgical treatment was performed after the 3D CT-based impingement simulation in 27 hips (52%). RESULTS: Hips with increased FV had significantly (P < .001) decreased extension and ER at 90° of flexion as compared with the control group. Posterior impingement was extra-articular (92%) in hips with increased FV. Valgus hips with increased FV and AV had combined intra- and extra-articular impingement. Posterior hip impingement occurred between the ischium and the lesser trochanter at 20° of extension and 20° of ER. Impingement was located between the ischium and the greater trochanter or intertrochanteric area at 20° of flexion and 40° of ER, with a modification of the flexion-abduction-ER (FABER) test. CONCLUSION: Posterior extra-articular ischiofemoral hip impingement can be caused by the lesser and greater trochanter or the intertrochanteric region. We recommend performing the modified FABER test during clinical examination in addition to the posterior impingement test for female patients with high FV. In addition, 3D CT can help for surgical planning, such as femoral derotation osteotomy and/or hip arthroscopy or resection of the lesser trochanter.

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.

Surface modification of black phosphorus-based nanomaterials in biomedical applications: Strategies and recent advances.

Black phosphorus-based nanomaterials (BPNMs), an emerging member of two-dimensional (2D) nanomaterials, possess excellent physicochemical properties and hold great potential for application in advanced nanomedicines. However, the bare BPNMs easily decrease their biomedical activities due to their degradability and in vivo interactions with biological macromolecules such as plasma proteins, largely restricting their biomedical application. A variety of surface modifications, via chemical, physical or biological approaches, have been developed for BPNMs to avoid these limitations and achieve stable, long-lasting and safe therapeutic effects, thus enlighten the development of the multifunctional BPNMs for more practical application in the field of biomedicine. The present review summarizes the recent advances in the surface modification of BPNMs and the resultant expansion of their biomedical applications. Focus is put on the strategy and method of modification while the effects incurred on the behavior and potential toxicity of BPNMs are also included. The future and challenge of the surface modification of the therapeutic BPNMs are finally discussed.

Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge.

Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be challenging due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, an initial set of training data is generally needed for constructing priors or for training. Furthermore, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provided 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results showed that the performance of CT WHS was generally better than that of MRI WHS. The segmentation of the substructures for different categories of patients could present different levels of challenge due to the difference in imaging and variations of heart shapes. The deep learning (DL)-based methods demonstrated great potential, though several of them reported poor results in the blinded evaluation. Their performance could vary greatly across different network structures and training strategies. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated good performance, though the accuracy and computational efficiency could be limited. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mmwhs/).

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.