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.

Novel adversarial semantic structure deep learning for MRI-guided attenuation correction in brain PET/MRI.

OBJECTIVE: Quantitative PET/MR imaging is challenged by the accuracy of synthetic CT (sCT) generation from MR images. Deep learning-based algorithms have recently gained momentum for a number of medical image analysis applications. In this work, a novel sCT generation algorithm based on deep learning adversarial semantic structure (DL-AdvSS) is proposed for MRI-guided attenuation correction in brain PET/MRI. MATERIALS AND METHODS: The proposed DL-AdvSS algorithm exploits the ASS learning framework to constrain the synthetic CT generation process to comply with the extracted structural features from CT images. The proposed technique was evaluated through comparison to an atlas-based sCT generation method (Atlas), previously developed for MRI-only or PET/MRI-guided radiation planning. Moreover, the commercial segmentation-based approach (Segm) implemented on the Philips TF PET/MRI system was included in the evaluation. Clinical brain studies of 40 patients who underwent PET/CT and MR imaging were used for the evaluation of the proposed method under a two-fold cross validation scheme. RESULTS: The accuracy of cortical bone extraction and CT value estimation were investigated for the three different methods. Atlas and DL-AdvSS exhibited similar cortical bone extraction accuracy resulting in a Dice coefficient of 0.78 ± 0.07 and 0.77 ± 0.07, respectively. Likewise, DL-AdvSS and Atlas techniques performed similarly in terms of CT value estimation in the cortical bone region where a mean error (ME) of less than -11 HU was obtained. The Segm approach led to a ME of -1025 HU. Furthermore, the quantitative analysis of corresponding PET images using the three approaches assuming the CT-based attenuation corrected PET (PETCTAC) as reference demonstrated comparative performance of DL-AdvSS and Atlas techniques with a mean standardized uptake value (SUV) bias less than 4% in 63 brain regions. In addition, less that 2% SUV bias was observed in the cortical bone when using Atlas and DL-AdvSS approaches. However, Segm resulted in 14.7 ± 8.9% SUV underestimation in the cortical bone. CONCLUSION: The proposed DL-AdvSS approach demonstrated competitive performance with respect to the state-of-the-art atlas-based technique achieving clinically tolerable errors, thus outperforming the commercial segmentation approach used in the clinic.

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.

Deep Learning-Based Automatic Segmentation of the Proximal Femur from MR Images.

This chapter addresses the problem of segmentation of proximal femur in 3D MR images. We propose a deeply supervised 3D U-net-like fully convolutional network for segmentation of proximal femur in 3D MR images. After training, our network can directly map a whole volumetric data to its volume-wise labels. Inspired by previous work, multi-level deep supervision is designed to alleviate the potential gradient vanishing problem during training. It is also used together with partial transfer learning to boost the training efficiency when only small set of labeled training data are available. The present method was validated on 20 3D MR images of femoroacetabular impingement patients. The experimental results demonstrate the efficacy of the present method.

Microbial synthesis of functional homo-, random, and block polyhydroxyalkanoates by ß-oxidation deleted Pseudomonas entomophila.

Functional polyhydroxyalkanoates (PHAs) allow chemical modifications to widen PHA diversity, promising to increase values of these biodegradable and biocompatible polyesters. Among functional PHAs, unsaturated PHA site chains can be easily grafted to add chemical groups, and to cross-link with other PHA polymer chains. However, it has been very difficult to obtain structurally controllable functional homo-, random, or block PHA. For the first time, a ß-oxidation deleted Pseudomonas entomophila was used to successfully synthesize random copolymers of 3-hydroxydodecanoate (3HDD) and 3-hydroxy-9-decenoate (3H9D). Compositions of the random copolymers P(3HDD-co-3H9D) can be adjusted by ratios of dodecanoic acid (DDA) to 9-decenol (9DEO) fed to the culture of P. entomophila. Homopolymer P3H9D was formed when only 9DEO was added to the culture. Diblock copolymers of P3HDD-b-P3H9D were produced by feeding DDA as the first precursor to form a P3HDD block followed by adding 9DEO as the second precursor to form a second P3H9D block. It was demonstrated that random copolymers P(3HDD-co-3H9D) could be crossed-linked under UV-radiation due to the presence of the unsaturated bonds. Thermal and mechanical characterizations of the above homo-, random, and diblock PHA polymers were conducted. It was found that the diblock polymer P3HDD-b-P3H9D increased at least 2-fold on Young's modulus compared with its random copolymers consisting of similar 3HDD/3H9D ratios. This study demonstrates that PHA functionality could be controlled to meet various requirements.

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.

Corrigendum: Comparison of indocyanine green angiography and swept-source wide-field optical coherence tomography angiography in posterior uveitis.

[This corrects the article DOI: 10.3389/fmed.2022.853315.].

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.

Grhl2 deficiency impairs otic development and hearing ability in a zebrafish model of the progressive dominant hearing loss DFNA28.

Congenital and progressive hearing impairment is a common distressing disease. The progressive dominant hearing loss DFNA28 in human is associated with a frameshift mutation of Grainyhead-like 2 (GRHL2) but its etiology and mechanism remain unknown. Here we report a zebrafish grhl2b(T086) mutant line in which grhl2b expression is interrupted by an insertion of a Tol2 transposon element. The mutants exhibit enlarged otocysts, smaller or eliminated otoliths, malformed semicircular canals, insensitiveness to sound stimulation and imbalanced swimming motion. Since grainyhead-like family members can regulate epithelial adhesion, we examined the expression of some genes encoding junction proteins in mutants. We show that the expression of claudin b (cldnb) and epcam is abolished or dramatically reduced and apical junctional complexes are abnormal in otic epithelial cells of mutant embryos. Co-injection of cldnb and epcam mRNA could largely rescue the mutant phenotype. Injection of human wild-type GRHL2 mRNA but not the mutant GRHL2 mRNA derived from DFNA28 patients into grhl2b(T086) mutant embryos could rescue the inner-ear defects. Furthermore, we demonstrate that Grhl2b directly binds to the enhancers and promotes the expression of cldnb and epcam. Thus, this work reveals an evolutionarily conserved function of Grhl2 in otic development and provides a fish model for further studying mechanisms of Grhl2-related hearing loss.

Production of medium-chain-length 3-hydroxyalkanoic acids by ß-oxidation and phaC operon deleted Pseudomonas entomophila harboring thioesterase gene.

3-Hydroxyalkanoic acids (3HA) are precious precursors for synthesis of value added chemicals. According to their carbon chain lengths, 3HA can be divided into two groups: short-chain-length (SCL) 3HA consisting of 3-5 carbon atoms and medium-chain-length (MCL) 3HA containing 6-14 carbon atoms. To produce MCL 3HA, a metabolic engineered pathway expressing tesB gene, a thioesterase encoding gene that has been reported to catalyze acyl-CoA to free fatty acids, was constructed in Pseudomonas entomophila L48. When tesB of Escherichia coli encoding thioesterase II was introduced into polyhydroxyalkanoate (PHA) synthase and ß-oxidation pathway deleted mutant of P. entomophila LAC31 derived from wild type P. entomophila L48, 6.65g/l 3-hydroxytetradecanoic acid (3HTD) and 4.6g/l 3-hydroxydodecanoic acid (3HDD) were obtained, respectively, when tetradecanoic acid or dodecanoic acid as related carbon sources was added in shake flask cultures. Moreover, 1.8g/l of 3-hydroxydecanoic (3HD) acid was also produced by P. entomophila LAC31 harboring PTE1 gene cloned from Saccharomyces cerevisiae using corresponding fatty acid decanoic acid. Interestingly, shake flask studies indicated that PTE1 harboring strain showed advantages over tesB expressing one for 3HDD and 3HD production, while tesB favored 3HTD production by P. entomophila LAC31. For the first time our study revealed that fine chemicals 3HTD, 3HDD or 3HD could be efficiently produced by metabolic engineered ß-oxidation in Pseudomonas spp grown on related fatty acids.

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.

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.

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.

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.

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.

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.

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.

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.