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Listeria monocytogenes is a widespread foodborne pathogen contaminating foods during their production or processing stages. Fresh meat is susceptible to such contamination if it is not properly preserved. Our study was conducted to reveal the level of contamination and prevalence of Listeria spp. present in livestock and poultry meat from Gansu province. A total of 1,387 samples were collected from five cities in Gansu Province according to standard sampling procedures, of which 174 samples (12.5%) were positive for Listeria species. Among them, 14 isolates of L. monocytogenes (1.0%), 150 isolates of Listeria innocua (10.8%), and ten isolates of Listeria welshimeri (0.7%) were identified by conventional bacteriological and molecular identification methods. All isolates were subjected to serological assays, antimicrobial susceptibility tests, growth curve assays, determination of biofilm-forming capacity, and cluster analysis of the 16S rRNA gene sequences. Four predominant serotypes of L. monocytogenes were identified, including 1/2a (35.7%, 5/14), 1/2b (14.3%, 2/14), 1/2c (42.9%, 6/14), and 4b (7.1%, 1/14). All L. monocytogenes isolates were resistant to tetracycline and cefoxitin. Most L. innocua isolates (63.6%, 14/22) and L. welshimeri (40%, 4/10) were resistant to tetracycline. The high biofilm-forming ability was observed among 1/2c and 1/2a serotype isolates. The cluster analysis of the 16S rRNA gene sequences revealed a close genetic relationship between the three Listeria species. This study fills the gap in the knowledge of livestock and poultry meat that carry Listeria in slaughterhouses and markets in Gansu Province.

Мы определяем некоторые подмногообразия, называемые соответствиями $\theta$-Гекке, в декартовых произведениях колчанных многообразий, неподвижных относительно диаграммных автоморфизмов. Они задают действие образующих алгебр Ли - инвариантов диаграммных автоморфизмов.

Acoustic radiation force (ARF)-based shear wave elastography (SWE) is a clinically available ultrasound imaging mode that noninvasively and quantitatively measures tissue stiffness. Current implementations of ARF-SWE are largely limited to 2-D imaging, which does not provide robust estimation of heterogeneous tissue mechanical properties. Existing 3-D ARF-SWE solutions that are clinically available are based on wobbler probes, which cannot provide true 3-D shear wave motion detection. Although 3-D ARF-SWE based on 2-D matrix arrays have been previously demonstrated, they do not provide a practical solution because of the need for a high channel-count ultrasound system (e.g., 1024-channel) to provide adequate volume rates and the delicate circuitries (e.g., multiplexers) that are vulnerable to the long-duration “push” pulses. To address these issues, here we propose a new 3-D ARF-SWE method based on the 2-D row-column addressing (RCA) array which has a much lower element count (e.g., 256), provides an ultrafast imaging volume rate (e.g., 2000 Hz), and can withstand the push pulses. In this study, we combined the comb-push shear elastography (CUSE) technique with 2-D RCA for enhanced SWE imaging field-of-view.In vitrophantom studies demonstrated that the proposed method had robust 3-D SWE performance in both homogenous and inclusion phantoms. Anin vivostudy on a breast cancer patient showed that the proposed method could reconstruct 3-D elasticity maps of the breast lesion, which was validated using a commercial ultrasound scanner. These results demonstrate strong potential for the proposed method to provide a viable and practical solution for clinical 3-D ARF-SWE.

Ultrasound localization microscopy (ULM) is an emerging super-resolution imaging technique for deep tissue microvascular imaging. However, conventional localization methods are constrained by low microbubble (MB) concentration, as accurate localization requires a strict separation of MB point spread functions (PSFs). Furthermore, deep learning-based localization techniques are often limited in their ability to generalize toin vivoultrasound data due to challenges in accurately modeling highly variable MB PSF distributions and ultrasound imaging conditions. To address these limitations, we propose a novel deep learning-pipeline, LOcalization with Context Awareness (LOCA)-ULM, which employs simulation that incorporates MB context to generate synthetic data that closely resemble real MB signals, and a loss function that considers both MB count and localization loss. Inin silicoexperiments, LOCA-ULM outperformed conventional localization with superior MB detection accuracy (94.0% vs. 74.9%) and a significantly lower MB missing rate (13.2% vs 74.8%).In vivo, LOCA-ULM achieved up to three-fold increase in MB localization efficiency and a × 9.5 faster vessel saturation rate than conventional ULM.

3-D ultrasound imaging has many advantages over 2-D imaging such as more comprehensive tissue evaluation and less operator dependence. Although many 3-D ultrasound imaging techniques have been developed in the last several decades, a low-cost and accessible solution with high imaging volume rate and imaging quality remains elusive. Recently we proposed a new, high volume rate 3-D ultrasound imaging technique: Fast Acoustic Steering via Tilting Electromechanical Reflectors (FASTER), which uses a water-immersible and fast-tilting acoustic reflector to steer ultrafast plane waves in the elevational direction to achieve high volume rate 3-D ultrasound imaging with conventional 1-D array transducers. However, the initial implementation of FASTER imaging only involves a single fast-tilting acoustic reflector, which is inconvenient to use because the probe cannot be held in the regular upright position. Also, conventional FASTER imaging can only be performed inside a water tank because of the necessity of using water for acoustic conduction. To address these limitations of conventional FASTER, here we developed a novel ultrasound probe clip-on device that encloses a fast-tilting reflector, a redirecting reflector, and an acoustic wave conduction medium. The new FASTER 3-D imaging device can be easily attached to or removed from clinical ultrasound transducers, allowing rapid transformation from 2-D to 3-D ultrasound imaging.In vitroB-mode imaging studies demonstrated that the proposed method provided comparable imaging quality (e.g., spatial resolution and contrast-to-noise ratio) to conventional, mechanical-translation-based 3-D imaging while providing a much faster 3-D volume rate (e.g., 300 Hz vs ∼10 Hz). In addition to B-mode imaging, we also demonstrated 3-D power Doppler imaging and 3-D super-resolution ultrasound localization microscopy with the newly developed FASTER device. Anin vivoimaging study showed that the FASTER device could clearly visualize the 3-D anatomy of the basilic vein of a healthy volunteer, and customized beamforming was implemented to accommodate the speed of sound difference between the acoustic medium and the imaging object (e.g., soft tissue). These results suggest that the newly developed redirecting reflector and the clip-on device could overcome key hurdles for future clinical translation of the FASTER 3-D imaging technology.

Effective household and individual disaster preparedness can minimize physical harm and property damage during catastrophic events. To assess the risk and vulnerability of affected areas, it is crucial for relief agencies to understand the level of public preparedness. Traditionally, government agencies have employed nationwide random telephone surveys to gauge the public’s attitudes and actions towards disaster preparedness. However, these surveys may lack generalizability in certain affected locations due to low response rates or areas not covered by the survey. To address this issue and enhance the comprehensiveness of disaster preparedness assessments, we develop a framework that seamlessly integrates machine learning and simulation. Our approach leverages machine learning algorithms to establish relationships between public attitudes towards disaster preparedness and demographic characteristics. Using Monte Carlo simulation, we generate datasets that incorporate demographic information of the affected location based on government-provided demographic distribution data. The generated dataset is then input into the machine learning model to predict the disaster preparedness attitudes of the affected population. We demonstrate the effectiveness of our framework by applying it to Miami-Dade County, where it accurately predicts the level of disaster preparedness. With this innovative approach, relief agencies can have a clearer and more comprehensive understanding of public disaster preparedness.

In [FMX19], it is proved that the convolution algebra of top Borel-Moore homology on Steinberg variety of type B/C realizes U (slnθ) where slnθ is the fixed point subalgebra of involution on sln. So top Borel-Moore homology of the partial Springer’s fibers gives the representations of U (slnθ). In this paper, we study these representations using the Schur-Weyl duality and Springer theory.

In theory, coherent plane-wave compounding (CPWC) enables ultrafast ultrasound imaging while maintaining a high imaging quality that is comparable to conventional B-mode imaging based on focused beam transmissions. However, in practice, due to the imperfect synthetization of transmit focusing (e.g., heterogeneous speed of sound in tissue, limited range of steering angle, etc.), CPWC suffers from a variety of imaging artifacts resulting from side lobes, grating lobes, and axial lobes. This study focuses on addressing the issues of axial lobes for CPWC, which constitutes an important source of clutter that leads to the degradation of contrast ratio and contrast-to-noise ratio (CR and CNR) of CPWC. We first investigated the source of the axial lobes based on a plane-wave propagation and the delay-andsum (DAS) beamforming. We then proposed a new method that is based on the pixel oriented adaptive apodization (POAA) to eliminate the axial lobes throughout the entire field-of-view (FOV). POAA was first validated in a simulation study, followed by in vitro phantom experiments and an in vivo case study on a carotid artery from a healthy volunteer. In the simulation study, a suppression of axial lobes by 120 dB was observed from wire targets and an improvement of CNR by up to 60% was found in a cyst-mimicking digital phantom. In the phantom experiment, POAA showed an improvement in CNR by around 20% over conventional methods. The effectiveness of axial lobe suppression was finally demonstrated in vivo where POAA showed a substantial suppression of clutters throughout the entire FOV.

Increasing evidence has suggested a link between cerebrovascular disease and the cognitive impairment of patients with Alzheimer’s disease. However, cerebrovascular disease and Alzheimer’s disease share several risk factors making it unclear whether cerebrovascular deficiency and Alzheimer’s disease pathology have additive effects on cognition or if cerebrovascular impairment merely exacerbates existing Alzheimer’s disease-associated cognitive decline. Additionally, early-stage Alzheimer’s disease typically involves hippocampal atrophy, complicating most efforts to elucidate the interplay between cerebral microvascular function and Alzheimer’s disease progression due to the necessity of probing deep-brain structures. The purpose of this study was to demonstrate the use of ultrasound localization microscopy on the 5xFAD mouse model of Alzheimer’s disease (3-month and 6-month-old cohorts) in comparison to age-matched wild-type controls, revealing microvascular scale reconstructions throughout the whole brain depth, to visualize and quantify Alzheimer’s disease-associated vascular impairments. We found that functional decreases in hippocampal and entorhinal flow velocity preceded structural derangements in regional vascular density. In addition to providing global vascular quantifications of deep brain structures with a high local resolution, this technology also permitted hierarchical analysis of individual vessels and, in some cases, potentially allowed for decoupling of arteriole and venous flow contributions. Co-registered histological sectioning confirmed the regionalized hypo-perfusion deficits seen on ultrasound imaging, which were co-localized with amyloid beta plaque deposition.Significance statementThe study of the impact of cerebrovascular disease on Alzheimer’s disease pathology is complicated by the need to image deep-brain structures with high vascular fidelity. We demonstrate that ultrasound localization microscopy, a super-resolution acoustic imaging technique, is capable of imaging cerebrovasculature throughout the entire depth of the brain at a microvascular scale. This technology was applied to the 5xFAD mouse model of Alzheimer’s disease, where it was found that 5xFAD animals have significant impairments in vascular function in the entorhinal cortex and hippocampal region in comparison to age matched controls at the 3-month timepoint. Structural derangements in cerebrovasculature were only observed in the 6-month-old animal cohorts, with a maintained impairment in vascular function.

Super-resolution ultrasound microvessel imaging based on ultrasound localization microscopy (ULM) is an emerging imaging modality that is capable of resolving micron-scaled vessels deep into tissue. In practice, ULM is limited by the need for contrast injection, long data acquisition, and computationally expensive post-processing times. In this study, we present a contrast-free super-resolution Doppler (CS Doppler) technique that uses deep generative networks to achieve super-resolution with short data acquisition. The training dataset is comprised of spatiotemporal ultrafast ultrasound signals acquired from in vivo mouse brains, while the testing dataset includes in vivo mouse brain, chicken embryo chorioallantoic membrane (CAM), and healthy human subjects. The in vivo mouse imaging studies demonstrate that CS Doppler could achieve an approximate 2-fold improvement in spatial resolution when compared with conventional power Doppler. In addition, the microvascular images generated by CS Doppler showed good agreement with the corresponding ULM images as indicated by a structural similarity index of 0.7837 and a peak signal-to-noise ratio of 25.52. Moreover, CS Doppler was able to preserve the temporal profile of the blood flow (e.g., pulsatility) that is similar to conventional power Doppler. Finally, the generalizability of CS Doppler was demonstrated on testing data of different tissues using different imaging settings. The fast inference time of the proposed deep generative network also allows CS Doppler to be implemented for real-time imaging. These features of CS Doppler offer a practical, fast, and robust microvascular imaging solution for many preclinical and clinical applications of Doppler ultrasound.

Ultrasound localization microscopy can image microvasculature in vivo without sacrificing imaging penetration depth. However, the reliance on super-resolution inference limits the applicability of the technique as subpixel tissue motion can corrupt microvascular reconstruction. Consequently, the majority of previous pre-clinical research on this super-resolution procedure has been restricted to low motion experimental models with ample motion correction and/or data rejection, which precludes the imaging of organ sites with that exhibit a high degree of respiratory and other motion. In this paper, we present a novel anesthesia protocol in rabbits that induces safe controllable periods of apnea to enable the long imaging acquisition times required for ultrasound localization microscopy. We apply this protocol to a VX2 liver tumor model undergoing sorafenib therapy and compare the results to super-resolution images from a conventional high-dose isoflurane anesthesia. We found that the apneic protocol was necessary to correctly identify the poorly vascularized tumor cores, as verified by immunohistochemistry, and to reveal the tumoral microvascular architecture.

Biochar and nitrogen (N) fertilizer application can increase soil carbon sequestration and enhance soil nutrient cycling. However, few studies have systematically explored the effects of the long-term application of biochar and N fertilizer on soil multifunctionality and characterized its driving factors. Based on an 8-year biochar paddy-field experiment in anthropogenic alluvial alkaline soil in northwest China, we measured eleven soil functions associated with soil carbon sequestration and nutrient cycling and four potential factors (soil bacterial and fungal richness, pH, and aggregates) governing soil functions to investigate the effects of three biochar rates (C0, no biochar; C1, 4.5 t ha

Ultrasound localization microscopy (ULM) is an emerging vascular imaging technique that overcomes the resolution-penetration compromise of ultrasound imaging. Accurate and robust microbubble (MB) localization is essential for successful ULM. In this study, we present a deep learning (DL)- based localization technique that uses both Field-II simulation and in vivo chicken embryo chorioallantoic membrane (CAM) data for training. Both radiofrequency (RF) and in-phase quadrature (IQ) data were tested in this study. The simulation experiment shows that the proposed DL-based localization was able to reduce both missing MB localization rate and MB localization error. In general, RF data showed better performance than IQ. For the in vivo CAM study with high MB concentration, DL-based localization was able to reduce the vessel MB saturation time by more than 50% as compared to conventional localization. Additionally, we propose a DL-based framework for real-time visualization of the high-resolution microvasculature. The findings of the paper support the use of DL for more robust and faster MB localization, especially under high MB concentrations. The results indicate that further improvement could be achieved by incorporating temporal information of the MB data.

Ultrafast ultrasound imaging is essential for advanced ultrasound imaging techniques such as ultrasound localization microscopy (ULM) and functional ultrasound (fUS). Current ultrafast ultrasound imaging is challenged by the ultrahigh data bandwidth associated with the radio frequency (RF) signal, and by the latency of the computationally expensive beamforming process. As such, continuous ultrafast data acquisition and beamforming remain elusive with existing software beamformers based on CPUs or GPUs. To address these challenges, the proposed work introduces a novel method of implementing an ultrafast ultrasound beamformer specifically for ultrafast plane wave imaging (PWI) on a field programmable gate array (FPGA) by using high-level synthesis. A parallelized implementation of the beamformer on a single FPGA was proposed by 1) utilizing a delay compression technique to reduce the delay profile size, which enables both run-time pre-calculated delay profile loading from external memory and delay reuse 2) vectorizing channel data fetching which is enabled by delay reuse, and 3) using fixed summing networks to reduce consumption of logic resources. Our proposed method presents two unique advantages over current FPGA beamformers: 1) high scalability that allows fast adaptation to different FPGA resources and beamforming speed demands by using Xilinx High-Level Synthesis as the development tool, and 2) allow a compact form factor design by using a single FPGA to complete the beamforming instead of multiple FPGAs. With the proposed method, a sustainable average beamforming rate of 4.83 G samples/second in terms of input raw RF sample was achieved. The resulting image quality of the proposed beamformer was compared with the software beamformer on the Verasonics Vantage system for both phantom imaging and in vivo imaging of a mouse brain.

Geriatric population is increasing rapidly worldwide, and fragility fracture and complication following orthopaedic surgery in elderly people have now become major challenges for surgeons. Further studies are required to identify potentially modifiable factors associated with surgical site infection (SSI) in geriatric patients. This retrospective, multicenter study was conducted at four level I hospitals in China. During the 31‐month study period, a total of 2341 patients (65 years or older) underwent orthopaedic surgery and complete data were recorded from September 2015 to April 2018. Demographics information, medications and additional comorbidities, surgery‐related variables, and laboratory indexes were extracted and analysed. Receiver‐operating characteristic analysis was performed to detect the optimum threshold of continuous variables. Independent risk factors of SSI were identified by univariate and multivariate analyses. Finally, 63 patients suffered from wound infection within the follow‐up period, indicating a 2.7% incidence rate of SSI. Statistical results showed that open injury (odds ratio [OR], 9.5; 95% confidence interval [CI], 5.4‐16.7), American Society of Anesthesiologists classified III‐IV score (OR, 2.2; 95% CI, 1.3‐3.8), surgical duration of >132 minutes (OR, 2.9; 95% CI, 1.1‐5.0), serum albumin (ALB) of 118 mg/dL (OR, 3.1; 95% CI, 1.1‐5.3) were independent risk factors of postoperative SSI. With the application of sensitive and modifiable variables such as surgical duration and the levels of ALB and GLU, more geriatric patients with sub‐high risk of postoperative SSI could be identified.

ObjectiveTo develop a 3D shear wave elastography (SWE) technique using a 2D row column addressing (RCA) array, with either external vibration or acoustic radiation force (ARF) as the shear wave source.Impact StatementThe proposed method paves the way for clinical translation of 3D-SWE based on the 2D RCA, providing a low-cost and high volume-rate solution that is compatible with existing clinical systems.IntroductionSWE is an established ultrasound imaging modality that provides a direct and quantitative assessment of tissue stiffness, which is significant for a wide range of clinical applications including cancer and liver fibrosis. SWE requires high frame-rate imaging for robust shear wave tracking. Due to the technical challenges associated with high volume-rate imaging in 3D, current SWE techniques are typically confined to 2D. Advancing SWE from 2D to 3D is significant because of the heterogeneous nature of tissue, which demands 3D imaging for accurate and comprehensive evaluation.MethodsA 3D SWE method using a 2D RCA array was developed with a volume-rate up to 2000 Hz. The performance of the proposed method was systematically evaluated on tissue-mimicking elasticity phantoms.Results3D shear wave motion induced by either external vibration or ARF was successfully detected with the proposed method. Robust 3D shear wave speed maps were reconstructed for both homogeneous and heterogeneous phantoms with inclusions.ConclusionThe high volume-rate 3D imaging provided by the 2D RCA array provides a robust and practical solution for 3D SWE with a clear pathway for future clinical translation.

Ultrasound localization microscopy (ULM) demonstrates great potential for visualization of tissue microvasculature at depth with high spatial resolution. The success of ULM heavily depends on the robust localization of isolated microbubbles (MBs), which can be challenging in vivo especially within larger vessels where MBs can overlap and cluster close together. While MB dilution alleviates the issue of MB overlap to a certain extent, it drastically increases the data acquisition time needed for MBs to populate the microvasculature, which is already on the order of several minutes using recommended MB concentrations. Inspired by optical super-resolution imaging based on stimulated emission depletion (STED), here we propose a novel ULM imaging sequence based on microbubble uncoupling via transmit excitation (MUTE). MUTE “silences” MB signals by creating acoustic nulls to facilitate MB separation, which leads to robust localization of MBs especially under high concentrations. The efficiency of localization accomplished via the proposed technique was first evaluated in simulation studies with conventional ULM as a benchmark. Then an in vivo study based on the chorioallantoic membrane (CAM) of chicken embryos showed that MUTE could reduce the data acquisition time by half thanks to the enhanced MB separation and localization. Finally, the performance of MUTE was validated in an in vivo mouse brain study. These results demonstrate the high MB localization efficacy of MUTE-ULM, which contributes to a reduced data acquisition time and improved temporal resolution for ULM.

Ultrasound localization microscopy is a super-resolution imaging technique that exploits the unique characteristics of contrast microbubbles to side-step the fundamental trade-off between imaging resolution and penetration depth. However, the conventional reconstruction technique is confined to low microbubble concentrations to avoid localization and tracking errors. Several research groups have introduced sparsity- and deep learning-based approaches to overcome this constraint to extract useful vascular structural information from overlapping microbubble signals, but these solutions have not been demonstrated to produce blood flow velocity maps of the microcirculation. Here, we introduce Deep-SMV, a localization free super-resolution microbubble velocimetry technique, based on a long short-term memory neural network, that provides high imaging speed and robustness to high microbubble concentrations, and directly outputs blood velocity measurements at a super-resolution. Deep-SMV is trained efficiently using microbubble flow simulation on real in vivo vascular data and demonstrates real-time velocity map reconstruction suitable for functional vascular imaging and pulsatility mapping at super-resolution. The technique is successfully applied to a wide variety of imaging scenarios, include flow channel phantoms, chicken embryo chorioallantoic membranes, and mouse kidney, tumor, and brain imaging.

Deep learning (DL)-based super-resolution ultrasound microvascular imaging (SR-UMI) has gained interest in recent years owing to the success of DL in medical imaging applications. Mainstream DL techniques require an abundance of labeled training data. Since high-quality in vivo training data are challenging to obtain for medical imaging, most of the current studies have relied on simulations to generate training data of microbubbles (MBs) flowing in vasculature. This study investigates the effect of different MB point spread function (PSF) generation methods on the performance of DL models for SR-UMI. We used Gaussian shaped PSF (Gaussian-PSF), numerical simulation software (Field II-PSF), and experimentally collected PSFs (Exp-PSF) to generate DL training sets. The training sets were used to train DL models that performed both MB localization- and non-localization-based SR-UMI. The quality of the models trained with different datasets were evaluated based on their in vivo performance (mouse brain, chicken embryo), with optical imaging as ground truth for some applications. We discovered that Exp-PSF achieved the best performance overall, due to its high resemblance to real experimental data. Finally, we present a pipeline for obtaining Exp-PSF and integrating them with ultrasound MB simulation, with a publicly available Exp-PSF dataset provided via Github.

Phase aberration (PA) significantly deteriorates imaging quality for transcranial imaging because of large speed of sound discrepancy and complex geometry of the skull. Recently, ultrasound localization microscopy (ULM) has demonstrated promising transcranial imaging results of deep brain microvasculature through intact skull. Robust PA correction is essential for high-fidelity transcranial ULM imaging because microbubble localization is highly susceptible to PA. Most existing PA correction methods rely on “guide stars,” which may not be practical for ULM because the microbubble concentration may be too high to serve as guide stars. Furthermore, most conventional PA correction techniques assume globally or locally homogeneous delay law, thus resulting in suboptimal correction performance. To address these issues, we propose a deep learning-based PA correction approach that directly operates on the pre-beamformed, raw channel data. By taking advantage of the structured microbubble signals and using conditional generative adversarial networks to utilize specific aberrated channel data, our proposed method achieved pixel-wise PA correction. In silico studies using micro-CT scanned mouse skulls demonstrate that the lateral beam width at −20 dB is improved from 0.82 ±0.33 mm to 0.48 ± 0.12 mm at 20 MHz applying proposed method, and the mean localization error is reduced from 67.99 ± 21.50 μm to 21.45 ± 14.84 μm.

Super-resolution microvascular imaging (SRMI) based on ultrasound localization microscopy (ULM) breaks the diffraction limit of conventional ultrasound by exploiting intravascular microbubbles. However, microbubble injection makes ULM not entirely noninvasive and less practical. On the other hand, power Doppler (PD) is contrast-free and widely available in clinic, but PD’s spatial resolution falls short of revealing tissue microvasculature. In this study, we propose a deep learning-based, contrast-free SRMI method to improve the spatial resolution of PD. We designed a modified U-Net structure with 4 layers in depth with 64 to 512 channels. Ultrafast ultrasound data (1000 Hz frame rate) acquired from 16 mice were made into the 4080 data blocks used for training. Reconstructed ULM images using 32 s acquisition served as the labelled ground truth. Contrast-free ultrasound data using 0.4 s acquisition were used as the network input. The results have shown that the proposed DL-based method can significantly improve the spatial resolution of contrast-free power Doppler without creating fake vessels. The full width half-maximum (FWHM) of the cross-sectional profile of a microvessel was improved from 66 to 24 μm. Robust generalizability of the proposed DL method was also demonstrated on various tissues and organs that were absent in training.

Heart valve disease is associated with high morbidity and mortality worldwide resulting in hundreds of thousands of heart valve replacements each year. Tissue engineered heart valves (TEHVs) have the potential to overcome the major limitations of traditional replacement valves; however, leaflet retraction has led to the failure of TEHVs in preclinical studies. As native unmodified hyaluronic acid (HA) is known to promote healthy tissue development in native heart valves, we hypothesize that adding unmodified HA to fibrin-based scaffolds common to tissue engineering will reduce retraction by increasing cell-scaffold interactions and density of the scaffolds. Using a custom high-throughput culture system, we found that incorporating HA into millimeter-scale fibrin-based cell-populated scaffolds increases initial fiber diameter and cell-scaffold interactions, causing a cascade of mechanical, morphological, and cellular responses. These changes lead to higher levels of scaffold compaction and stiffness, increased cell alignment, and less bundling of fibrin fibers by the cells during culture. These effects significantly reduce scaffold retraction and total contractile force each by around 25%. These findings increase our understanding of how HA alters tissue remodeling and could inform the design of the next generation of tissue engineered heart valves to help reduce retraction. Statement of significance Tissue engineered heart valves (TEHVs) have the potential to overcome the major limitations of traditional replacement valves; however, leaflet retraction induced by excessive myofibroblast activation has led to failure in preclinical studies. Developing valves are rich in hyaluronic acid (HA), which helps maintain a physiological environment for tissue remodeling without retraction. We hypothesized that adding unmodified HA to TEHVs would reduce retraction by increasing cell-scaffold interactions and density of the scaffolds. Using a high-throughput tissue culture platform, we demonstrate that HA incorporation into a fibrin-based scaffold can significantly reduce tissue retraction and total contractile force by increasing fiber bundling and altering cell–mediated matrix remodeling, therefore increasing gel density and stiffness. These finding increase our knowledge of native HA's effects within the extracellular matrix, and provide a new tool for TEHV design.

Resveratrol (Res) has been shown to exhibit anti-cancer properties in gastric cancer. However, its clinical application is limited by its poor pharmacokinetics, stability, and low solubility. Hence, this study aimed to explore and verify a better delivery system for gastric cancer therapy. Using transmission electron microscopy, Fourier transform infrared (FTIR) spectroscopy, and ultraviolet (UV) spectrometry, we observed the shape and encapsulation of resveratrol-modified mesoporous silica nanoparticles (MSN-Res) that were synthesized by chemical methods. To explore the anti-cancer effects of these MSN-Res in vivo and in vitro, we established AGS and HGC-27 tumor-bearing mouse models. Meanwhile, the proliferation of gastric cancer cells in vitro and in vivo was assessed by Cell Counting Kit-8, EdU, and Ki-67 immunohistochemical staining methods, while cellular apoptosis, and invasion and migration were detected by TdT-mediated dUTP nick end labeling (TUNEL) and Transwell assays, respectively. FTIR and UV results showed that we successfully synthesized and loaded drugs. Safety evaluation experiments showed that neither MSN-SH nor MSN-Res had toxic effects on the normal tissues of animals. Moreover, in vitro experiments revealed that MSN-Res significantly inhibited the proliferation, invasion, and migration of gastric cancer cells. Furthermore, TUNEL assay showed that MSN-Res promoted apoptosis in gastric cancer. These results were confirmed by the nude mouse tumorigenesis experiment. In conclusion, we demonstrated that MSN-Res showed better inhibitory effect on the development of gastric cancer than Res alone, indicating that MSN-Res could be a promising drug delivery system for gastric cancer treatment.