HMSCs exosome-derived miR-199a-5p attenuates sulfur mustard-associated oxidative stress via the CAV1/NRF2 signalling pathway.

Sulfur mustard (SM) is a blister-producing chemical warfare agent which could lead to a cascade of systemic damage, especially severe acute lung injury. Oxidative stress is considered to be vital processes for the SM toxicity mechanism. We previously proved the therapeutic effect of exosomes derived from bone marrow mesenchymal stromal cells in promoting the repair of alveolar epithelial barrier and inhibiting apoptosis. However, the key functional components in exosomes and the underlying mechanisms have not been fully elaborated. This research shed light on the function of the key components of human umbilical cord mesenchymal stem cell-derived exosomes (HMSCs-Ex). We noted that HMSCs-Ex-derived miR-199a-5p played a vital role in reducing pneumonocyte oxidative stress and apoptosis by reducing reactive oxygen species, lipid peroxidation products and increasing the activities of antioxidant enzymes in BEAS-2B cells and mouse models after exposure to SM for 24 h. Furthermore, we demonstrated that the overexpression of miR-199a-5p in HMSCs-Ex treatment induced a further decrease of Caveolin1 and the activation of the mRNA and protein level of NRF2, HO1 and NQO1, compared with HMSCs-Ex administration. In summary, miR-199a-5p was one of the key molecules in HMSCs-Ex that attenuated SM-associated oxidative stress via regulating CAV1/NRF2 signalling pathway.

Ultrasound Probe and Hand-Eye Calibrations for Robot-Assisted Needle Biopsy.

In robot-assisted ultrasound-guided needle biopsy, it is essential to conduct calibration of the ultrasound probe and to perform hand-eye calibration of the robot in order to establish a link between intra-operatively acquired ultrasound images and robot-assisted needle insertion. Based on a high-precision optical tracking system, novel methods for ultrasound probe and robot hand-eye calibration are proposed. Specifically, we first fix optically trackable markers to the ultrasound probe and to the robot, respectively. We then design a five-wire phantom to calibrate the ultrasound probe. Finally, an effective method taking advantage of steady movement of the robot but without an additional calibration frame or the need to solve the AX=XB equation is proposed for hand-eye calibration. After calibrations, our system allows for in situ definition of target lesions and aiming trajectories from intra-operatively acquired ultrasound images in order to align the robot for precise needle biopsy. Comprehensive experiments were conducted to evaluate accuracy of different components of our system as well as the overall system accuracy. Experiment results demonstrated the efficacy of the proposed methods.

A Novel Point Set Registration-Based Hand-Eye Calibration Method for Robot-Assisted Surgery.

Pedicle screw insertion with robot assistance dramatically improves surgical accuracy and safety when compared with manual implantation. In developing such a system, hand-eye calibration is an essential component that aims to determine the transformation between a position tracking and robot-arm systems. In this paper, we propose an effective hand-eye calibration method, namely registration-based hand-eye calibration (RHC), which estimates the calibration transformation via point set registration without the need to solve the AX=XB equation. Our hand-eye calibration method consists of tool-tip pivot calibrations in two-coordinate systems, in addition to paired-point matching, where the point pairs are generated via the steady movement of the robot arm in space. After calibration, our system allows for robot-assisted, image-guided pedicle screw insertion. Comprehensive experiments are conducted to verify the efficacy of the proposed hand-eye calibration method. A mean distance deviation of 0.70 mm and a mean angular deviation of 0.68° are achieved by our system when the proposed hand-eye calibration method is used. Further experiments on drilling trajectories are conducted on plastic vertebrae as well as pig vertebrae. A mean distance deviation of 1.01 mm and a mean angular deviation of 1.11° are observed when the drilled trajectories are compared with the planned trajectories on the pig vertebrae.

Plasminogen activator inhibitor (PAI) trap3, an exocellular peptide inhibitor of PAI-1, attenuates the rearrangement of F-actin and migration of cancer cells.

BACKGROUND: The protein plasminogen activator inhibitor-1 (PAI-1), an inhibitor specific for urokinase plasminogen activator (uPA) and tissue plasminogen activator (tPA), has been shown to have a key role in cancer metastases. Currently, it is unknown as to whether the exocellular inhibition of PAI-1 can inhibit the migration of cancer cells. METHODS: By fusing the mutated serine protease domain (SPD) of uPA and human serum albumin (HSA), PAItrap3, a protein that traps PAI-1, was synthesized and experiments were conducted to determine if exocellular PAItrap3 attenuates PAI-1-induced cancer cell migration in vitro. RESULTS: PAItrap3 (0.8 µM) significantly inhibited the motility of MCF-7, MDA-MB-231, HeLa and 4T1 cancer cells, by 90%, 50%, 30% and 20%, respectively, without significantly altering their proliferation. The PAI-1-induced rearrangement of F-actin was significantly inhibited by PAItrap3, which produced a decrease in the number of cell protrusions by at least 20%. CONCLUSIONS: In vitro, PAItrap3 inhibited PAI-1-induced cancer cell migration, mainly through inhibiting the rearrangement of F-actin. Overall, these results, provided they can be extrapolated to humans, suggest that the PAItrap3 protein could be used as an exocellular inhibitor to attenuate cancer metastases.

Effect of mechanical error on dual-wedge laser scanning system and error correction.

Compared with the traditional mechanical beam deflector in a beam-scanning system, the dual-wedge scanning system has several advantages, for example, compact structure, fast scanning speed, and low power consumption. High accuracy is the most important factor in dual-wedge scanning, but mechanical errors caused by machining or assembly errors adversely affect this scanning accuracy. Horizontal and angular mechanical errors appear between the incident light and the dual-wedge central optical axes. By building a mathematical model of an ideal dual-wedge scanning trajectory and a trajectory affected by mechanical errors, this paper analyzes the types and degree of influence on the scanning process, as well as the sensitivity of scanned images to different errors. Results show that the angular error has the most significant influence on the scanning image accuracy, in terms of trajectory shape and coverage. To correct the angular error, the two degrees-of-freedom flexible fine-tuning mechanism is customized based on the principle of the cantilever beam type. After finite element analysis and experimental validations, the fine-tuning mechanism can guarantee that the angular error in the dual-wedge central optical axes will be lower than 0.05 deg, thus ensuring scanning trajectory accuracy.

Endothelium-Derived Engineered Extracellular Vesicles Protect the Pulmonary Endothelial Barrier in Acute Lung Injury.

Acute lung injury (ALI) is a severe respiratory disease with a high mortality rate. The integrity of the pulmonary endothelial barrier influences the development and prognosis of ALI. Therefore, it has become an important target for ALI treatment. Extracellular vesicles (EVs) are promising nanotherapeutic agents against ALI. Herein, endothelium-derived engineered extracellular vesicles (eEVs) that deliver microRNA-125b-5p (miRNA-125b) to lung tissues exerting a protective effect on endothelial barrier integrity are reported. eEVs that are modified with lung microvascular endothelial cell-targeting peptides (LET) exhibit a prolonged retention time in lung tissues and targeted lung microvascular endothelial cells in vivo and in vitro. To improve the efficacy of the EVs, miRNA-125b is loaded into EVs. Finally, LET-EVs-miRNA-125b is constructed. The results show that compared to the EVs, miRNA-125b, and EVs-miRNA-125b, LET-EVs-miRNA-125b exhibit the most significant treatment efficacy in ALI. Moreover, LET-EVs-miRNA-125b is found to have an important protective effect on endothelial barrier integrity by inhibiting cell apoptosis, promoting angiogenesis, and protecting intercellular junctions. Sequencing analysis reveals that LET-EVs-miRNA-125b downregulates early growth response-1 (EGR1) levels, which may be a potential mechanism of action. Taken together, these findings suggest that LET-EVs-miRNA-125b can treat ALI by protecting the endothelial barrier integrity.

LatentPCN: latent space-constrained point cloud network for reconstruction of 3D patient-specific bone surface models from calibrated biplanar X-ray images.

PURPOSE: Accurate three-dimensional (3D) models play crucial roles in computer assisted planning and interventions. MR or CT images are frequently used to derive 3D models but have the disadvantages that they are expensive or involving ionizing radiation (e.g., CT acquisition). An alternative method based on calibrated 2D biplanar X-ray images is highly desired. METHODS: A point cloud network, referred as LatentPCN, is developed for reconstruction of 3D surface models from calibrated biplanar X-ray images. LatentPCN consists of three components: an encoder, a predictor, and a decoder. During training, a latent space is learned to represent shape features. After training, LatentPCN maps sparse silhouettes generated from 2D images to a latent representation, which is taken as the input to the decoder to derive a 3D bone surface model. Additionally, LatentPCN allows for estimation of a patient-specific reconstruction uncertainty. RESULTS: We designed and conducted comprehensive experiments on datasets of 25 simulated cases and 10 cadaveric cases to evaluate the performance of LatentLCN. On these two datasets, the mean reconstruction errors achieved by LatentLCN were 0.83 mm and 0.92 mm, respectively. A correlation between large reconstruction errors and high uncertainty in the reconstruction results was observed. CONCLUSION: LatentPCN can reconstruct patient-specific 3D surface models from calibrated 2D biplanar X-ray images with high accuracy and uncertainty estimation. The sub-millimeter reconstruction accuracy on cadaveric cases demonstrates its potential for surgical navigation applications.

Discussion of the Segregation and Low Hardness of Large-Diameter M3 High-Speed Steel Produced by Spray Forming.

As an advanced near-net-shape processing method in which directly preformed, semi-finished products are created from liquid metals, spray forming has become popular in the development and application of new materials and is supporting industrialization. However, as investigated in this work, the problems of segregation and low hardness exist in the actual industrialization process, particularly for large-diameter M3 high-speed steel. It was here found that the annual ring segregation morphologies were mostly distributed from the edge to 1/2R, with a large number of stripes primarily enriched in C, Mo, and Cr elements, and the degree of segregation was mild. The ring segregation was located at the 1/2R position, where the main elemental enrichments were C, W, Mo, Cr, and V, and the segregation degree was severe. The formation of segregation during deposition is described based on an equilibrium solidification model. A slow cooling rate and heat dissipation from the surface to the inside were judged to be the main factors causing segregation and changes in the carbide morphology. In terms of hardness, with the increase in the quenching temperature to 1230 °C, the tempering hardness increased significantly. The analysis shows that a faster cooling rate in the atomization stage caused the solidified droplets to exhibit rapid solidification characteristics, and there was a higher proportion of MC carbide in the deposited billet. MC carbides cannot be fully dissolved using the conventional heat treatment process, which decreases the C, Cr, Mo, and V contents in the solution and, thus, reduces the secondary hardening capability. The findings show that, when the spray forming process is used to prepare large-diameter materials, it should not be considered a rapid solidification technology simply because of its atomization stage. Moreover, more attention should be paid to the influence of microstructure transformation during atomization and deposition.

Recent advances in biomimetic nanodelivery systems: New brain-targeting strategies.

In recent years, brain diseases have seriously threatened human health due to their high morbidity and mortality. Achieving efficient drug delivery to provide satisfactory therapeutic outcomes is currently the greatest challenge in treating brain diseases. The main challenges are the structural peculiarities of the brain and the inability to transport drugs across the blood-brain barrier. Biomimetic nanodelivery systems (BNDSs) applied to the brain have been extensively developed in the preclinical phase to surmount these challenges. Considering the inherent properties of BNDSs, the substantially enhanced ability of BNDS to carry therapeutic agents and their higher selectivity toward lesions offer new opportunities for developing safe and effective therapies. This review summarizes brain-targeting nanotherapies, particularly advanced therapies with biomimetic nano-assistance. Prospects for developing BNDSs and the challenges of their clinical translation are discussed. Understanding and implementing biomimetic nanotherapies may facilitate the development of new targeted strategies for brain disorders.

Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring.

Approaches to reliably predict the developmental potential of embryos and select suitable embryos for blastocyst culture are needed. The development of time-lapse monitoring (TLM) and artificial intelligence (AI) may help solve this problem. Here, we report deep learning models that can accurately predict blastocyst formation and usable blastocysts using TLM videos of the embryo's first three days. The DenseNet201 network, focal loss, long short-term memory (LSTM) network and gradient boosting classifier were mainly employed, and video preparation algorithms, spatial stream and temporal stream models were developed into ensemble prediction models called STEM and STEM+. STEM exhibited 78.2% accuracy and 0.82 AUC in predicting blastocyst formation, and STEM+ achieved 71.9% accuracy and 0.79 AUC in predicting usable blastocysts. We believe the models are beneficial for blastocyst formation prediction and embryo selection in clinical practice, and our modeling methods will provide valuable information for analyzing medical videos with continuous appearance variation.