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The kidney is vulnerable to ischemia and reperfusion (I/R) injury that can be fatal after major surgery. Currently, there are no effective treatments for I/R-induced kidney injury. Trimethylamine N-oxide (TMAO) is a gut-derived metabolite linked to many diseases, but its role in I/R-induced kidney injury remains unclear. Here, our clinical data reveals an association between preoperative systemic TMAO levels and postoperative kidney injury in patients after post-cardiopulmonary bypass surgery. By genetic deletion of TMAO-producing enzyme flavin-containing monooxygenase 3 (FMO3) and dietary supplementation of choline to modulate TMAO levels, we found that TMAO aggravated acute kidney injury through the triggering of endoplasmic reticulum (ER) stress and worsened subsequent renal fibrosis through TGFβ/Smad signaling activation. Together, our study underscores the negative role of TMAO in I/R-induced kidney injury and highlights the therapeutic potential through the modulation of TMAO levels by targeting FMO3, thereby mitigating acute kidney injury and preventing subsequent renal fibrosis., (© 2024. The Author(s).)

Photothermal therapy (PTT) offers significant potential in cancer treatment due to its short, simple, and less harmful nature. However, obtaining a photothermal agent (PTA) with good photothermal performance and biocompatibility remains a challenge. MXenes, which are PTAs, have shown promising results in cancer treatment. This study presents the preparation of Ti 3 C 2 MXene quantum dots (MXene QDs) using a simple hydrothermal and ultrasonic method and their use as a PTA for cancer treatment. Compared to conventional MXene QDs synthesized using only the hydrothermal method, the ultrasonic process increased the degree of oxidation on the surface of the MXene QDs. This resulted in the presence of more hydrophilic groups such as hydroxyl groups on the MXene QD surfaces, leading to excellent dispersion in the aqueous system and biocompatibility of the prepared MXene QDs without the need for surface modification. The MXene QDs showed great photothermal performance with a photothermal conversion efficiency of 62.5%, resulting in the highest photothermal conversion efficiency among similar materials reported thus far. Both in vitro and in vivo experiments have proved the potent tumor inhibitory effect of the MXene QD-mediated PTT, with minimal harm to mice. Therefore, these MXene QDs hold a significant promise for clinical applications.

Emerging evidence has demonstrated the profound impact of the gut microbiome on cardiovascular diseases through the production of diverse metabolites. Using an animal model of myocardial ischemia-reperfusion (I/R) injury, we found that the prophylactic administration of a well-known probiotic, Bifidobacterium infantis ( B. infantis ), exhibited cardioprotective effects in terms of preserving cardiac contractile function and preventing adverse cardiac remodeling following I/R and that these cardioprotective effects were recapitulated by its metabolite inosine. Transcriptomic analysis further revealed that inosine mitigated I/R-induced cardiac inflammation and cell death. Mechanistic investigations elucidated that inosine suppressed the production of pro-inflammatory cytokines and reduced the numbers of dendritic cells and natural killer cells, achieved through the activation of the adenosine A2A receptor (A2AR) that when inhibited abrogated the cardioprotective effects of inosine. Additionally, in vitro studies using C2C12 myoblasts revealed that inosine attenuated cell death by serving as an alternative carbon source for adenosine triphosphate (ATP) generation through the purine salvage pathway when subjected to oxygen-glucose deprivation/reoxygenation that simulated myocardial I/R injury. Likewise, inosine reversed the I/R-induced decrease in ATP levels in mouse hearts. Taken together, our findings indicate that B. infantis or its metabolite inosine exerts cardioprotective effects against I/R by suppressing cardiac inflammation and attenuating cardiac cell death, suggesting prophylactic therapeutic options for acute ischemic cardiac injury., Competing Interests: The authors declare no conflict of interest., (© 2024 The Author(s). iMeta published by John Wiley & Sons Australia, Ltd on behalf of iMeta Science.)

Adaptive optics (AO) technology is an effective means to compensate for atmospheric turbulence, but the inherent delay error of an AO system will cause the compensation phase of the deformable mirror (DM) to lag behind the actual distortion, which limits the correction performance of the AO technology. Therefore, the feed-forward prediction of atmospheric turbulence has important research value and application significance to offset the inherent time delay and improve the correction bandwidth of the AO system. However, most prediction algorithms are limited to an open-loop system, and the deployment and the application in the actual AO system are rarely reported, so its correction performance improvement has not been verified in practice. We report, to our knowledge, the first successful test of a deep learning-based spatiotemporal prediction model in an actual 3 km laser atmospheric transport AO system and compare it with the traditional closed-loop control methods, demonstrating that the AO system with the prediction model has higher correction performance.

This research presents a practical approach for wavefront reconstruction and correction adaptable to variable targets, with the aim of constructing a high-precision, general extended target adaptive optical system. Firstly, we delve into the detailed design of a crucial component, the distorted grating, simplifying the optical system implementation while circumventing potential issues in traditional phase difference-based collection methods. Subsequently, normalized fine features (NFFs) and structure focus features (SFFs) which both are independent of the imaging target but corresponded precisely to the wavefront aberration are proposed. The two features provide a more accurate and robust characterization of the wavefront aberrations. Then, a Noise-to-Denoised Generative Adversarial Network (N2D-GAN) is employed for denoising real images. And a lightweight network, Attention Mechanism-based Efficient Network (AM-EffNet), is applied to achieve efficient and high-precision mapping between features and wavefronts. A prototype of object-independent adaptive optics system is demonstrated by experimental setup, and the effectiveness of this method in wavefront reconstruction for different imaging targets has been verified. This research holds significant relevance for engineering applications of adaptive optics, providing robust support for addressing challenges within practical systems.

The gut microbiome has emerged as a potential target for the treatment of cardiovascular disease. Ischemia/reperfusion (I/R) after myocardial infarction is a serious complication and whether certain gut bacteria can serve as a treatment option remains unclear. Lactobacillus reuteri (L. reuteri) is a well-studied probiotic that can colonize mammals including humans with known cholesterol-lowering properties and anti-inflammatory effects. Here, the prophylactic cardioprotective effects of L. reuteri or its metabolite γ-aminobutyric acid (GABA) against acute ischemic cardiac injury caused by I/R surgery are demonstrated. The prophylactic gavage of L. reuteri or GABA confers cardioprotection mainly by suppressing cardiac inflammation upon I/R. Mechanistically, GABA gavage results in a decreased number of proinflammatory macrophages in I/R hearts and GABA gavage no longer confers any cardioprotection in I/R hearts upon the clearance of macrophages. In vitro studies with LPS-stimulated bone marrow-derived macrophages (BMDM) further reveal that GABA inhibits the polarization of macrophages toward the proinflammatory M1 phenotype by inhibiting lysosomal leakage and NLRP3 inflammasome activation. Together, this study demonstrates that the prophylactic oral administration of L. reuteri or its metabolite GABA attenuates macrophage-mediated cardiac inflammation and therefore alleviates cardiac dysfunction after I/R, thus providing a new prophylactic strategy to mitigate acute ischemic cardiac injury., (© 2024 The Authors. Advanced Science published by Wiley‐VCH GmbH.)

Multi-line-of-sight wavefront sensing, crucial for next-generation astronomy and laser applications, often increases system complexity by adding sensors. This research introduces, to the best of our knowledge, a novel method for multi-line-of-sight Hartmann-Shack wavefront sensing by using a single sensor, addressing challenges in centroid estimation and classification under atmospheric turbulence. This method contrasts with existing techniques that rely on multiple sensors, thereby reducing system complexity. Innovations include combining edge detection and peak extraction for precise centroid calculation, improved k-means clustering for robust centroid classification, and a centroid filling algorithm for subapertures with light loss. The method's effectiveness was confirmed through simulations for a five-line-of-sight system and experimental setup for two-line and three-line-of-sight systems, demonstrating its potential in real atmospheric aberration correction conditions. Experimental findings indicate that, when implemented in a closed-loop configuration, the method significantly reduces wavefront residuals from 1 λ to 0.1 λ under authentic atmospheric turbulence conditions. Correspondingly, the quality of the far-field spot is enhanced by a factor of 2 to 4. These outcomes collectively highlight the method's robust capability in enhancing optical system performance in environments characterized by genuine atmospheric turbulence.

Serous cavity effusion is a prevalent pathological condition encountered in clinical settings. Fluid samples obtained from these effusions are vital for diagnostic and therapeutic purposes. Traditionally, cytological examination of smears is a common method for diagnosing serous cavity effusion, renowned for its convenience. However, this technique presents limitations that can compromise its efficiency and diagnostic accuracy. This study aims to overcome these challenges and introduce an improved method for the precise detection of malignant cells in serous cavity effusions. We have developed a transformer-based classification framework, specifically employing the vision transformer (ViT) model, to fulfill this objective. Our research involved collecting smear images and corresponding cytological reports from 161 patients who underwent serous cavity drainage. We meticulously annotated 4836 patches from these images, identifying regions with and without malignant cells, thus creating a unique dataset for smear image classification. The findings of our study reveal that deep learning models, particularly the ViT model, exhibit remarkable accuracy in classifying patches as malignant or non-malignant. The ViT model achieved an impressive area under the receiver operating characteristic curve (AUROC) of 0.99, surpassing the performance of the convolutional neural network (CNN) model, which recorded an AUROC of 0.86. Additionally, we validated our models using an external cohort of 127 patients. The ViT model sustained its high-level screening performance, achieving an AUROC of 0.98 at the patient level, compared to the CNN model's AUROC of 0.84. The visualization of our ViT models confirmed their capability to precisely identify regions containing malignant cells in multiple serous cavity effusion smear images. In summary, our study demonstrates the potential of deep learning models, particularly the ViT model, in automating the screening process for serous cavity effusions. These models offer significant assistance to cytologists in enhancing diagnostic accuracy and efficiency. The ViT model stands out for its advanced self-attention mechanism, making it exceptionally suitable for tasks that necessitate detailed analysis of small, sparsely distributed targets like cellular clusters in serous cavity effusions., (© 2024. The Author(s).)

Spiking Neural Networks (SNNs) have attracted significant attention for their energy-efficient and brain-inspired event-driven properties. Recent advancements, notably Spiking-YOLO, have enabled SNNs to undertake advanced object detection tasks. Nevertheless, these methods often suffer from increased latency and diminished detection accuracy, rendering them less suitable for latency-sensitive mobile platforms. Additionally, the conversion of artificial neural networks (ANNs) to SNNs frequently compromises the integrity of the ANNs' structure, resulting in poor feature representation and heightened conversion errors. To address the issues of high latency and low detection accuracy, we introduce two solutions: timestep compression and spike-time-dependent integrated (STDI) coding. Timestep compression effectively reduces the number of timesteps required in the ANN-to-SNN conversion by condensing information. The STDI coding employs a time-varying threshold to augment information capacity. Furthermore, we have developed an SNN-based spatial pyramid pooling (SPP) structure, optimized to preserve the network's structural efficacy during conversion. Utilizing these approaches, we present the ultralow latency and highly accurate object detection model, SUHD. SUHD exhibits exceptional performance on challenging datasets like PASCAL VOC and MS COCO, achieving a remarkable reduction of approximately 750 times in timesteps and a 30% enhancement in mean average precision (mAP) compared to Spiking-YOLO on MS COCO. To the best of our knowledge, SUHD is currently the deepest spike-based object detection model, achieving ultralow timesteps for lossless conversion.

Multiple instance learning (MIL)-based methods have become the mainstream for processing the megapixel-sized whole slide image (WSI) with pyramid structure in the field of digital pathology. The current MIL-based methods usually crop a large number of patches from WSI at the highest magnification, resulting in a lot of redundancy in the input and feature space. Moreover, the spatial relations between patches can not be sufficiently modeled, which may weaken the model's discriminative ability on fine-grained features. To solve the above limitations, we propose a Multi-scale Graph Transformer (MG-Trans) with information bottleneck for whole slide image classification. MG-Trans is composed of three modules: patch anchoring module (PAM), dynamic structure information learning module (SILM), and multi-scale information bottleneck module (MIBM). Specifically, PAM utilizes the class attention map generated from the multi-head self-attention of vision Transformer to identify and sample the informative patches. SILM explicitly introduces the local tissue structure information into the Transformer block to sufficiently model the spatial relations between patches. MIBM effectively fuses the multi-scale patch features by utilizing the principle of information bottleneck to generate a robust and compact bag-level representation. Besides, we also propose a semantic consistency loss to stabilize the training of the whole model. Extensive studies on three subtyping datasets and seven gene mutation detection datasets demonstrate the superiority of MG-Trans.

By controlling the supersaturation and choosing high-quality seeds, we successfully suppress the prismatic growth of the tetragonal potassium dihydrogen phosphate (KDP) SCFs, realize the rapid growth along the [001] direction, and obtain SCFs less than 10 μm in width with lengths of centimeters. The experimental results show that there exist critical supersaturation points, 22.40% and 41.41% at 25 °C, for initiating the growth of KDP SCF on its pyramidal and prismatic faces, respectively, which are quite different from those of the bulk crystals. We use the mechanism of 2D nucleation on smooth faces to explain the peculiar phenomena, assuming that there is no 2D or 3D defect on the surfaces of the seed fiber crystal. The assumption is supported by AFM observation of the surface micromorphology of the SCFs. Our solution growth technique developed can be used to grow ultrafine SCFs unable to be achieved by existing melt growth techniques.

Adaptive Optics (AO) technology is an effective means to compensate for wavefront distortion, but its inherent delay error will cause the compensation wavefront on the deformable mirror (DM) to lag behind the changes in the distorted wavefront. Especially when the change in the wavefront is higher than the Shack-Hartmann wavefront sensor (SHWS) sampling frequency, the multi-frame delay will seriously limit its correction performance. In this paper, a highly stable AO prediction network based on deep learning is proposed, which only uses 10 frames of prior wavefront information to obtain high-stability and high-precision open-loop predicted slopes for the next six frames. The simulation results under various distortion intensities show that the prediction accuracy of six frames decreases by no more than 15%, and the experimental results also verify that the open-loop correction accuracy of our proposed method under the sampling frequency of 500 Hz is better than that of the traditional non-predicted method under 1000 Hz.

Electrochemical CO 2 reduction reaction (CO 2 RR), as a promising route to realize negative carbon emissions, is known to be strongly affected by electrolyte cations (i.e., cation effect). In contrast to the widely-studied alkali cations in liquid electrolytes, the effect of organic cations grafted on alkaline polyelectrolytes (APE) remains unexplored, although APE has already become an essential component of CO 2 electrolyzers. Herein, by studying the organic cation effect on CO 2 RR, we find that benzimidazolium cation (Beim + ) significantly outperforms other commonly-used nitrogenous cations (R 4 N + ) in promoting C 2+ (mainly C 2 H 4 ) production over copper electrode. Cyclic voltammetry and in situ spectroscopy studies reveal that the Beim + can synergistically boost the CO 2 to *CO conversion and reduce the proton supply at the electrocatalytic interface, thus facilitating the *CO dimerization toward C 2+ formation. By utilizing the homemade APE ionomer, we further realize efficient C 2 H 4 production at an industrial-scale current density of 331 mA cm -2 from CO 2 /pure water co-electrolysis, thanks to the dual-role of Beim + in synergistic catalysis and ionic conduction. This study provides a new avenue to boost CO 2 RR through the structural design of polyelectrolytes., (© 2023 Wiley-VCH GmbH.)

Pathological primary tumor (pT) stage focuses on the infiltration degree of the primary tumor to surrounding tissues, which relates to the prognosis and treatment choices. The pT staging relies on the field-of-views from multiple magnifications in the gigapixel images, which makes pixel-level annotation difficult. Therefore, this task is usually formulated as a weakly supervised whole slide image (WSI) classification task with the slide-level label. Existing weakly-supervised classification methods mainly follow the multiple instance learning paradigm, which takes the patches from single magnification as the instances and extracts their morphological features independently. However, they cannot progressively represent the contextual information from multiple magnifications, which is critical for pT staging. Therefore, we propose a structure-aware hierarchical graph-based multi-instance learning framework (SGMF) inspired by the diagnostic process of pathologists. Specifically, a novel graph-based instance organization method is proposed, namely structure-aware hierarchical graph (SAHG), to represent the WSI. Based on that, we design a novel hierarchical attention-based graph representation (HAGR) network to capture the critical patterns for pT staging by learning cross-scale spatial features. Finally, the top nodes of SAHG are aggregated by a global attention layer for bag-level representation. Extensive studies on three large-scale multi-center pT staging datasets with two different cancer types demonstrate the effectiveness of SGMF, which outperforms state-of-the-art up to 5.6% in the F1 score.

This Letter introduces the idea of unsupervised learning into object-independent wavefront sensing for the first time, to the best of our knowledge, which can achieve fast phase recovery of arbitrary objects without labels. First, a fine feature extraction method which only depends on the wavefront aberrations is proposed. Then, a lightweight neural network and an optical feature system are combined to form an unsupervised learning model, and the neural network is promoted to be well trained by reversely outputting fine features. Simulation results prove that the proposed method can effectively overcome the aberrations (static or variable) existing in the optical system and achieve wavefront sensing of different objects with high precision and efficiency.

Leukemia classification relies on a detailed cytomorphological examination of Bone Marrow (BM) smear. However, applying existing deep-learning methods to it is facing two significant limitations. Firstly, these methods require large-scale datasets with expert annotations at the cell level for good results and typically suffer from poor generalization. Secondly, they simply treat the BM cytomorphological examination as a multi-class cell classification task, thus failing to exploit the correlation among leukemia subtypes over different hierarchies. Therefore, BM cytomorphological estimation as a time-consuming and repetitive process still needs to be done manually by experienced cytologists. Recently, Multi-Instance Learning (MIL) has achieved much progress in data-efficient medical image processing, which only requires patient-level labels (which can be extracted from the clinical reports). In this paper, we propose a hierarchical MIL framework and equip it with Information Bottleneck (IB) to tackle the above limitations. First, to handle the patient-level label, our hierarchical MIL framework uses attention-based learning to identify cells with high diagnostic values for leukemia classification in different hierarchies. Then, following the information bottleneck principle, we propose a hierarchical IB to constrain and refine the representations of different hierarchies for better accuracy and generalization. By applying our framework to a large-scale childhood acute leukemia dataset with corresponding BM smear images and clinical reports, we show that it can identify diagnostic-related cells without the need for cell-level annotations and outperforms other comparison methods. Furthermore, the evaluation conducted on an independent test cohort demonstrates the high generalizability of our framework.

It is still very challenging to obtain colorful and long-afterglow room-temperature phosphorescent (RTP) materials from pure organic polymers. Herein, it is found that chitosan (CS), a natural polymer, not only has its own RTP, but also reacts with different phosphorescent molecules to obtain a multicolor, long-afterglow RTP material. CS can emit RTP with a lifetime of 48 ms. In addition, CS is rich in amino groups, and grafting different phosphorescent molecules onto CS by an amidation reaction can modulate it to emit different colors of phosphorescence and obtain a series of colorful CS derivatives. The obtained polymer films also have ultra-long RTP due to the good film-forming ability. In addition, one of the CS derivatives selected with α-cyclodextrin is used to construct RTP materials with lifetimes of up to seconds. The host-guest interactions are used to suppress nonradiative relaxation and build crystalline domains, thus synergistically enhancing the RTP. Interestingly, the RTP properties of the CS derivative films are extremely sensitive to water and heat stimuli, because water broke the hydrogen bonds between adjacent CS molecules and thus altered the rigid environment in the material. Finally, they can be used as a stimuli-responsive ink and for monitoring environmental humidity., (© 2023 Wiley-VCH GmbH.)

Cancer region detection (CRD) and subtyping are two fundamental tasks in digital pathology image analysis. The development of data-driven models for CRD and subtyping on whole-slide images (WSIs) would mitigate the burden of pathologists and improve their accuracy in diagnosis. However, the existing models are facing two major limitations. Firstly, they typically require large-scale datasets with precise annotations, which contradicts with the original intention of reducing labor effort. Secondly, for the subtyping task, the non-cancerous regions are treated as the same as cancerous regions within a WSI, which confuses a subtyping model in its training process. To tackle the latter limitation, the previous research proposed to perform CRD first for ruling out the non-cancerous region, then train a subtyping model based on the remaining cancerous patches. However, separately training ignores the interaction of these two tasks, also leads to propagating the error of the CRD task to the subtyping task. To address these issues and concurrently improve the performance on both CRD and subtyping tasks, we propose a semi-supervised multi-task learning (MTL) framework for cancer classification. Our framework consists of a backbone feature extractor, two task-specific classifiers, and a weight control mechanism. The backbone feature extractor is shared by two task-specific classifiers, such that the interaction of CRD and subtyping tasks can be captured. The weight control mechanism preserves the sequential relationship of these two tasks and guarantees the error back-propagation from the subtyping task to the CRD task under the MTL framework. We train the overall framework in a semi-supervised setting, where datasets only involve small quantities of annotations produced by our minimal point-based (min-point) annotation strategy. Extensive experiments on four large datasets with different cancer types demonstrate the effectiveness of the proposed framework in both accuracy and generalization., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Motivation: Artificially making clinical decisions for patients with multi-morbidity has long been considered a thorny problem due to the complexity of the disease. Drug recommendations can assist doctors in automatically providing effective and safe drug combinations conducive to treatment and reducing adverse reactions. However, the existing drug recommendation works ignored two critical information. (i) Different types of medical information and their interrelationships in the patient's visit history can be used to construct a comprehensive patient representation. (ii) Patients with similar disease characteristics and their corresponding medication information can be used as a reference for predicting drug combinations., Results: To address these limitations, we propose DAPSNet, which encodes multi-type medical codes into patient representations through code- and visit-level attention mechanisms, while integrating drug information corresponding to similar patient states to improve the performance of drug recommendation. Specifically, our DAPSNet is enlightened by the decision-making process of human doctors. Given a patient, DAPSNet first learns the importance of patient history records between diagnosis, procedure and drug in different visits, then retrieves the drug information corresponding to similar patient disease states for assisting drug combination prediction. Moreover, in the training stage, we introduce a novel information constraint loss function based on the information bottleneck principle to constrain the learned representation and enhance the robustness of DAPSNet. We evaluate the proposed DAPSNet on the public MIMIC-III dataset, our model achieves relative improvements of 1.33%, 1.20% and 2.03% in Jaccard, F1 and PR-AUC scores, respectively, compared to state-of-the-art methods., Availability and Implementation: The source code is available at the github repository: https://github.com/andylun96/DAPSNet., (© The Author(s) 2023. Published by Oxford University Press.)

Hydrogels constructed by traditional polymer networks usually have poor mechanical properties limiting their applications. Here, we proposed a new strategy for ionic interaction modulation of crystalline micro-nanoparticles (CMNPs) to enhance the mechanical properties of polyacrylamide hydrogels. CMNPs were formed via confinement assembly under an aggregated state based on host-guest interactions between chitosan-grafted polyethylene glycol (CS-PEG) and γ-cyclodextrin (γ-CD). Furthermore, the aggregation behavior of the CMNPs was achieved based on the ionic interaction of CS with citrate (Cit 3- ). These Cit 3- -regulated CMNPs were introduced into PAM hydrogels. The modulus (618.44 kPa, 67.6 times), fracture stress (1054.59 kPa, 25.3 times), and toughness (6.23 MJ m -3 , 41.7 times) of the composite hydrogels were greatly improved without affecting the tensile properties (fracture strain, ~1000 %). Finally, we further designed a strain sensor that could monitor human motion, and we verified its potential application in the field of wearable flexible electronics., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Tissue segmentation is an essential task in computational pathology. However, relevant datasets for such a pixel-level classification task are hard to obtain due to the difficulty of annotation, bringing obstacles for training a deep learning-based segmentation model. Recently, contrastive learning has provided a feasible solution for mitigating the heavy reliance of deep learning models on annotation. Nevertheless, applying contrastive loss to the most abstract image representations, existing contrastive learning frameworks focus on global features, therefore, are less capable of encoding finer-grained features (e.g., pixel-level discrimination) for the tissue segmentation task. Enlightened by domain knowledge, we design three contrastive learning tasks with multi-granularity views (from global to local) for encoding necessary features into representations without accessing annotations. Specifically, we construct: (1) an image-level task to capture the difference between tissue components, i.e., encoding the component discrimination; (2) a superpixel-level task to learn discriminative representations of local regions with different tissue components, i.e., encoding the prototype discrimination; (3) a pixel-level task to encourage similar representations of different tissue components within a local region, i.e., encoding the spatial smoothness. Through our global-to-local pre-training strategy, the learned representations can reasonably capture the domain-specific and fine-grained patterns, making them easily transferable to various tissue segmentation tasks in histopathological images. We conduct extensive experiments on two tissue segmentation datasets, while considering two real-world scenarios with limited or sparse annotations. The experimental results demonstrate that our framework is superior to existing contrastive learning methods and can be easily combined with weakly supervised and semi-supervised segmentation methods.

The study of histopathological phenotypes is vital for cancer research and medicine as it links molecular mechanisms to disease prognosis. It typically involves integration of heterogenous histopathological features in whole-slide images (WSI) to objectively characterize a histopathological phenotype. However, the large-scale implementation of phenotype characterization has been hindered by the fragmentation of histopathological features, resulting from the lack of a standardized format and a controlled vocabulary for structured and unambiguous representation of semantics in WSIs. To fill this gap, we propose the Histopathology Markup Language (HistoML), a representation language along with a controlled vocabulary (Histopathology Ontology) based on Semantic Web technologies. Multiscale features within a WSI, from single-cell features to mesoscopic features, could be represented using HistoML which is a crucial step towards the goal of making WSIs findable, accessible, interoperable and reusable (FAIR). We pilot HistoML in representing WSIs of kidney cancer as well as thyroid carcinoma and exemplify the uses of HistoML representations in semantic queries to demonstrate the potential of HistoML-powered applications for phenotype characterization., (© 2022. The Author(s).)

The electrochemical CO 2 reduction over Cu catalysts has shown great potential in producing a wide range of valuable chemicals, but it is still plagued by a poor controllability on product distribution. Herein, we demonstrate an effective regulation of CO 2 reduction paths through a preanodization treatment of Cu foil electrodes in different electrolytes. The Cu electrode exhibits a superior C 1 and C 2+ product selectivity after being preanodized in NaClO 4 (Cu- NaClO 4 ) and Na 2 HPO 4 electrolyte (Cu- Na 2 HPO 4 ), respectively. Combined with in situ electrochemical Raman, ATR-SEIRAS, and SEM characterizations, the preferential C 1 path is due to the deposition of many Cu nanocrystals with dominant Cu(111) facets on the Cu- NaClO 4 electrode. In contrast, the preferential C 2+ path over the Cu- Na 2 HPO 4 is attributed to formation of a unique Cu nanodendritic morphology, which strengthens the *CO intermediate adsorption and induces an environment of low local H 2 O/CO 2 stoichiometric ratio, thus facilitating C-C coupling for C 2+ production. Our findings may shed light on the rational control of the CO 2 reduction path through engineering of the Cu surface structure.

The senescence of cardiovascular endothelial cells (ECs) is a major risk factor in the development of aging-related cardiovascular diseases. However, the molecular dynamics in cardiovascular EC aging are poorly understood. Here, we characterized the transcriptomic landscape of cardiovascular ECs during aging and observed that ribosome biogenesis, inflammation, apoptosis and angiogenesis-related genes and pathways changed with age. We also highlighted the importance of collagen genes in the crosstalk between ECs and other cell types in cardiovascular aging. Moreover, transcriptional regulatory network analysis revealed Jun as a candidate transcription factor involved in murine cardiovascular senescence and we validated the upregulation of Jun in aged cardiovascular ECs both in vitro and in vivo . Altogether, our study reveals the transcriptomic reprogramming in the aging murine cardiovascular ECs, which deepens the understanding of the molecular mechanisms of cardiovascular aging and provides new insights into potential therapeutic targets against age-related cardiovascular diseases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Gou, Chu, Xiao, Liu, Zhang, Gao and Song.)

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases in the elderly. The aim of this study was to explore the effects of AD on cardiac function and autonomic nervous function, and the feasibility of electrocardiogram (ECG) in monitoring the development of AD. APP/PS1 double transgenic mice were used in the Morris water maze (MWM) experiment to evaluate the changes of cognitive ability of AD mice, then the non-invasive ECG acquisition system was used and the changes of ECG intervals and heart rate variability (HRV) were analyzed. AD mice already had cognitive dysfunction at the age of 5 months, reaching the level of mild dementia, and the degree of dementia increased with the course of disease. There were no significant changes in ECG intervals in the AD group at each month. The mean square of successive RR interval differences, percentage of intervals >6 ms different from preceding interval, and normalized high frequency power component in the AD group were decreased and low-to-high frequency power ratio and normalized low frequency power component were increased. Combined with the results of the MWM, it was shown that the regulation mechanism of sympathetic and parasympathetic nerves in mice was already imbalanced in early stage AD, which was manifested as the increase of excessive activity of sympathetic nerves and the inhibition of parasympathetic activities. Therefore, ECG-based analysis of HRV may become a means of daily monitoring of AD and provide an auxiliary basis for clinical diagnosis.

Here we develop a simple and effective nose/tongue sensor array based on Co-N-C single-atom nanozymes-3,3',5,5'-tetramethylbenzidine (TMB)-H 2 O 2 for colorimetric discrimination of antioxidants, which makes use of the color reaction of TMB oxidation by H 2 O 2 in two different pH (3.8 and 4.6) environments under the catalysis of Co-N-C nanoenzyme with peroxidase-like activity. Different antioxidants have varying reducing ability to the oxidation products of TMB (oxTMB), thus resulting in differential absorbance and color changes. Linear discriminant analysis (LDA) results indicate that the sensor array successfully identified 7 antioxidants, i.e., glutathione (GSH), ascorbic acid (AA), cysteine (Cys), tannin (TA), Catechin (C), dopamine (DA), and uric acid (UA) in both buffer and even serum samples. Additionally, the performance of the sensor array was validated with antioxidant mixtures, individual antioxidants with different concentrations, and target antioxidants and interfering substances. In general, the versatile sensor array based on Co-N-C single-atom nanozymes provides an excellent strategy for identifying a variety of antioxidants, which exhibits a broad application prospect in medical diagnosis., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Aberrations degrade the accuracy of quantitative, imaging-based measurements, like particle image velocimetry (PIV). Adaptive optical elements can in principle correct the wavefront distortions, but are limited by their technical specifications. Here we propose an actuator-free correction based on a multiple-input deep convolutional neural network which uses an additional input from a wavefront sensor to correct time-varying distortions. It is applied for imaging flow velocimetry to conduct measurements through a fluctuating air-water phase boundary. Dataset for neural network is generated by an experimental setup with a deformable mirror. Correction performance of trained model is estimated in terms of image quality, which is improved significantly, and flow measurement results, where the errors induced by the distortion from fluctuating phase boundary can be corrected by 82 %. The technique has the potential to replace classical closed-loop adaptive optical systems where the performance of the actuators is not sufficient.

Objectives: We aimed to evaluate the effect of Shenfu injection in a rat model of ischemic heart failure and explore its mechanism. Methods: A rat model of ischemic heart failure after myocardial infarction was established by ligating the left anterior descending coronary artery. Forty-eight hours after surgery, the rats were intraperitoneally administered Shenfu injection for 7 weeks. Then, left ventricular fractional shortening and left ventricular ejection fraction were measured using transthoracic echocardiography, whereas heart rate and left ventricular end-diastolic pressure were measured using a MD3000 biosignal acquisition and processing system. The hearts and lungs of the rats were excised and weighed to measure the heart and lung weight indexes. In addition, cardiac histopathological changes were observed via hematoxylin-eosin and Masson's trichrome staining, and serum cardiac troponin content was detected using a cardiac troponin ELISA kit. Furthermore, matrix-assisted laser desorption/ionization-mass spectrometry imaging was used to detect the levels and distribution of small molecules in the hearts of rats with ischemic heart failure. Results: We found that Shenfu injection can significantly increase left ventricular fractional shortening and left ventricular ejection fraction in rats with ischemic heart failure and significantly reduce the left ventricular end-diastolic pressure, heart and lung weight indexes, and cardiac troponin content; improve cardiac tissue morphology; and reduce infarct size. In addition, the matrix-assisted laser desorption/ionization-mass spectrometry imaging results demonstrated that 22:6 phospholipids were predominately distributed in the non-infarct zone, whereas 20:4 phospholipids tended to concentrate in the infarct zone. Shenfu injection significantly reduced taurine, glutathione, and phospholipids levels in the hearts of rats with ischemic heart failure and primarily changed the distribution of these molecules in the non-infarct zone. Conclusion: Shenfu injection induced obvious myocardial protective effects in rats with ischemic heart failure by stimulating antioxidation and changing the phospholipid levels and distribution., (Copyright © 2019 Wu, Dai, Liu, Lin, Gao, Tian, Zhao, Sun and Pu.)

Ethnopharmacological Relevance: Shenfu injection is a popular Chinese herbal formula that has been widely used in the treatment of shock in China., Aim of the Study: To investigate the effect of Shenfu injection on lipopolysaccharide (LPS)-induced septic shock in rabbits., Materials and Methods: We established a septic shock model in rabbits by administering an intravenous injection of 0.6 mg/kg LPS to anesthetized rabbit, and 15 min after LPS challenge, the rabbits were intravenously administered the Shenfu injection. In these in vivo experiments, the jugular vein of the rabbits was cannulated for LPS and drug administration, and the right common carotid artery was cannulated to record the mean arterial pressure (MAP) over a 6-h period. In addition, various serum biochemical parameters, including lactate dehydrogenase (LDH), aspartate aminotransferase (AST), glutamate transaminase (ALT), creatinine (Cre), and urea nitrogen (Urea), were measured at 0, 3, and 6 h. Serum LPS levels at 6 h were determined by the test kit. And histological changes in the heart, liver and kidney tissues were observed by HE staining. Furthermore, some related small molecules in the heart tissues were detected by MALDI-TOF-MSI., Results: We found that Shenfu injection can increase the MAP, decrease the serum LPS, LDH and AST levels, and improve the tissue morphology of the heart, liver and kidney in rabbits with LPS-induced septic shock. In addition, Shenfu injection can increase the contents of ATP and taurine while reducing the content of AMP in the heart tissue during septic shock., Conclusions: These results indicate that Shenfu injection exerts a protective effect on LPS-induced septic shock in rabbits., (Copyright © 2019 Elsevier B.V. All rights reserved.)

The novel magnetically retrievable Bi 2 WO 6 /MCNOs (MCNOs, magnetic carbon nano-onions) composite was synthesized via a simple hydrothermal method. The characteristics of the as-prepared materials were explored by XRD, Raman, SEM, TEM, XPS, UV-vis DRS, N 2 adsorption-desorption techniques and FT-IR techniques, and the paramagnetic nature of the as-prepared composite was determined by hysteresis loops measurements. In this work, rhodamine B (RhB), methyl orange (MO), methylene blue (MB), tetracycline (TC) and p-nitrophenol (PNP) were chosen as the typical organic pollutants to evaluate the photodegradation efficiency of the as-prepared composite. The results indicated that Bi 2 WO 6 /MCNOs have a superior performance on photocatalytic activity than pure Bi 2 WO 6 . Furthermore, a possible mechanism was proposed based on the physic-chemical and photocatalytic properties. This work will provide new methodology for designing next generation photocatalyst for environmental purification., (Copyright © 2018 Elsevier Inc. All rights reserved.)