Development and validation of a preoperative radiomics-based nomogram to identify patients who can benefit from splenic hilar lymphadenectomy: a pooled analysis of three prospective trials.

BACKGROUND: We aimed to use preoperative computed tomography images to develop a radiomic nomogram to select patients who would benefit from spleen-preserving splenic hilar (No.10) lymphadenectomy (SPSHL). METHODS: A pooled analysis of three distinct prospective studies was performed. The splenic hilar lymph node (SHLN) ratio (sLNR) was established as the quotient of the number of metastatic SHLN to the total number of SHLN. Radiomic features reflecting the phenotypes of the primary tumor (RS1) and SHLN region (RS2) were extracted and used as predictive factors for sLNR. RESULTS: This study included 733 patients: 301 in the D2 group and 432 in the D2+No.10 group. The optimal sLNR cutoff value was set at 0.4, and the D2+No.10 group was divided into three groups: sLNR=0, sLNR≤0.4, and sLNR>0.4. Patients in the D2+No. 10 group were randomly divided into the training (n=302) and validation (n=130) cohorts. The AUCs value of the nomogram, including RS1 and RS2, were 0.952 in the training cohort and 0.888 in the validation cohort. The entire cohort was divided into three groups based on the nomogram scores: low, moderate and high SHLN metastasis burden groups (LMB, MMB and HMB, respectively). A similar 5-year OS rate was found between the D2 and D2+No. 10 groups in the LMB and HMB groups. In the MMB group, the 5-year OS of the D2+No. 10 group (73.4%) was significantly higher than that of the D2 group (37.6%) (P<0.001). CONCLUSIONS: The nomogram showed good predictive ability for distinguishing patients with various SHLN metastasis burdens. It can accurately identify patients who would benefit from SPSHL.

Multiple Riemannian Kernel Hashing for Large-scale Image Set Classification and Retrieval.

Conventional image set methods typically learn from small to medium-sized image set datasets. However, when applied to large-scale image set applications such as classification and retrieval, they face two primary challenges: 1) effectively modeling complex image sets, and 2) efficiently performing tasks. To address the above issues, we propose a novel Multiple Riemannian Kernel Hashing (MRKH) method that leverages the powerful capabilities of Riemannian manifold and Hashing on effective and efficient image set representation. MRKH considers multiple heterogeneous Riemannian manifolds to represent each image set. It introduces a multiple kernel learning framework designed to effectively combine statistics from multiple manifolds, and constructs kernels by selecting a small set of anchor points, enabling efficient scalability for large-scale applications. In addition, MRKH further exploits inter- and intra-modal semantic structure to enhance discrimination. Instead of employing continuous feature to represent each image set, MRKH suggests learning hash code for each image set, thereby achieving efficient computation and storage. We present an iterative algorithm with theoretical convergence guarantee to optimize MRKH, and the computational complexity is linear with the size of dataset. Extensive experiments on five image set benchmark datasets including three large-scale ones demonstrate the proposed method outperforms state-of-the-arts in accuracy and efficiency particularly in large-scale image set classification and retrieval.

Graph Convolutional Multi-Label Hashing for Cross-Modal Retrieval.

Cross-modal hashing encodes different modalities of multimodal data into low-dimensional Hamming space for fast cross-modal retrieval. In multi-label cross-modal retrieval, multimodal data are often annotated with multiple labels, and some labels, e.g.", ocean" and "cloud", often co-occur. However, existing cross-modal hashing methods overlook label dependency that is crucial for improving performance. To fulfill this gap, this article proposes graph convolutional multi-label hashing (GCMLH) for effective multi-label cross-modal retrieval. Specifically, GCMLH first generates word embedding of each label and develops label encoder to learn highly correlated label embedding via graph convolutional network (GCN). In addition, GCMLH develops feature encoder for each modality, and feature fusion module to generate highly semantic feature via GCN. GCMLH uses teacher-student learning scheme to transfer knowledge from the teacher modules, i.e., label encoder and feature fusion module, to the student module, i.e., feature encoder, such that learned hash code can well exploit multi-label dependency and multimodal semantic structure. Extensive empirical results on several benchmarks demonstrate the superiority of the proposed method over existing state-of-the-arts.

The improved stratified transformer for organ segmentation of Arabidopsis.

Segmenting plant organs is a crucial step in extracting plant phenotypes. Despite the advancements in point-based neural networks, the field of plant point cloud segmentation suffers from a lack of adequate datasets. In this study, we addressed this issue by generating Arabidopsis models using L-system and proposing the surface-weighted sampling method. This approach enables automated point sampling and annotation, resulting in fully annotated point clouds. To create the Arabidopsis dataset, we employed Voxel Centroid Sampling and Random Sampling as point cloud downsampling methods, effectively reducing the number of points. To enhance the efficiency of semantic segmentation in plant point clouds, we introduced the Plant Stratified Transformer. This network is an improved version of the Stratified Transformer, incorporating the Fast Downsample Layer. Our improved network underwent training and testing on our dataset, and we compared its performance with PointNet++, PAConv, and the original Stratified Transformer network. For semantic segmentation, our improved network achieved mean Precision, Recall, F1-score and IoU of 84.20, 83.03, 83.61 and 73.11%, respectively. It outperformed PointNet++ and PAConv and performed similarly to the original network. Regarding efficiency, the training time and inference time were 714.3 and 597.9 ms, respectively, which were reduced by 320.9 and 271.8 ms, respectively, compared to the original network. The improved network significantly accelerated the speed of feeding point clouds into the network while maintaining segmentation performance. We demonstrated the potential of virtual plants and deep learning methods in rapidly extracting plant phenotypes, contributing to the advancement of plant phenotype research.

Dual Branch Multi-Level Semantic Learning for Few-Shot Segmentation.

Few-shot semantic segmentation aims to segment novel-class objects in a query image with only a few annotated examples in support images. Although progress has been made recently by combining prototype-based metric learning, existing methods still face two main challenges. First, various intra-class objects between the support and query images or semantically similar inter-class objects can seriously harm the segmentation performance due to their poor feature representations. Second, the latent novel classes are treated as the background in most methods, leading to a learning bias, whereby these novel classes are difficult to correctly segment as foreground. To solve these problems, we propose a dual-branch learning method. The class-specific branch encourages representations of objects to be more distinguishable by increasing the inter-class distance while decreasing the intra-class distance. In parallel, the class-agnostic branch focuses on minimizing the foreground class feature distribution and maximizing the features between the foreground and background, thus increasing the generalizability to novel classes in the test stage. Furthermore, to obtain more representative features, pixel-level and prototype-level semantic learning are both involved in the two branches. The method is evaluated on PASCAL- 5i 1 -shot, PASCAL- 5i 5 -shot, COCO- 20i 1 -shot, and COCO- 20i 5 -shot, and extensive experiments show that our approach is effective for few-shot semantic segmentation despite its simplicity.

Characterizing immune variation and diagnostic indicators of preeclampsia by single-cell RNA sequencing and machine learning.

Preeclampsia is a multifactorial and heterogeneous complication of pregnancy. Here, we utilize single-cell RNA sequencing to dissect the involvement of circulating immune cells in preeclampsia. Our findings reveal downregulation of immune response in lymphocyte subsets in preeclampsia, such as reduction in natural killer cells and cytotoxic genes expression, and expansion of regulatory T cells. But the activation of naïve T cell and monocyte subsets, as well as increased MHC-II-mediated pathway in antigen-presenting cells were still observed in preeclampsia. Notably, we identified key monocyte subsets in preeclampsia, with significantly increased expression of angiogenesis pathways and pro-inflammatory S100 family genes in VCAN+ monocytes and IFN+ non-classical monocytes. Furthermore, four cell-type-specific machine-learning models have been developed to identify potential diagnostic indicators of preeclampsia. Collectively, our study demonstrates transcriptomic alternations of circulating immune cells and identifies immune components that could be involved in pathophysiology of preeclampsia.

Dual-Stream Complex-Valued Convolutional Network for Authentic Dehazed Image Quality Assessment.

Effectively evaluating the perceptual quality of dehazed images remains an under-explored research issue. In this paper, we propose a no-reference complex-valued convolutional neural network (CV-CNN) model to conduct automatic dehazed image quality evaluation. Specifically, a novel CV-CNN is employed that exploits the advantages of complex-valued representations, achieving better generalization capability on perceptual feature learning than real-valued ones. To learn more discriminative features to analyze the perceptual quality of dehazed images, we design a dual-stream CV-CNN architecture. The dual-stream model comprises a distortion-sensitive stream that operates on the dehazed RGB image, and a haze-aware stream on a novel dark channel difference image. The distortion-sensitive stream accounts for perceptual distortion artifacts, while the haze-aware stream addresses the possible presence of residual haze. Experimental results on three publicly available dehazed image quality assessment (DQA) databases demonstrate the effectiveness and generalization of our proposed CV-CNN DQA model as compared to state-of-the-art no-reference image quality assessment algorithms.

Controlled Synthesis of Lead-Free Double Perovskite Colloidal Nanocrystals for Nonvolatile Resistive Memory Devices.

Although lead-free double perovskites such as Cs2AgBiBr6 have been widely explored, they still remain a daunting challenge for the controlled synthesis of lead-free double perovskite nanocrystals with highly tunable morphology and band structure. Here, we report the controlled synthesis of lead-free double perovskite colloidal nanocrystals including Cs2AgBiBr6 and Cs2AgInxBi1-xBr6 via a facile wet-chemical synthesis method for the fabrication of high-performance nonvolatile resistive memory devices. Cs2AgBiBr6 colloidal nanocrystals with well-defined cuboidal, hexagonal, and triangular morphologies are synthesized through a facile wet-chemical approach by tuning the reaction temperature from 150 to 190 °C. Further incorporating indium into Cs2AgBiBr6 to synthesize alloyed Cs2AgInxBi1-xBr6 nanocrystals not only can induce the indirect-to-direct bandgap transition with enhanced photoluminescence but also can improve its structural stability. After optimizing the active layers and device structure, the fabricated Ag/polymethylene acrylate@Cs2AgIn0.25Bi0.75Br6/ITO resistive memory device exhibits a low power consumption (the operating voltage is ∼0.17 V), excellent cycling stability (>10 000 cycles), and good synaptic property. Our study would enable the facile wet-chemical synthesis of lead-free double perovskite colloidal nanocrystals in a highly controllable manner for the development of high-performance resistive memory devices.

A scoring model for predicting early recurrence of gastric cancer with normal preoperative tumor markers: A multicenter study.

INTRODUCTION: Prognostic factors for postoperative early recurrence (ER) of gastric cancer (GC) in patients with normal or abnormal preoperative tumor markers (pre-TMs) remain unclear. MATERIALS AND METHODS: 2875 consecutive patients with GC who underwent radical gastrectomy (RG) between January 2010 and December 2016 were enrolled and randomly divided into training and internal validation groups. ER was defined as recurrence within two years of gastrectomy. Normal pre-TMs were defined as CEA≤5 ng/mL and CA199 ≤ 37 U/mL. Least absolute shrinkage selection operator (LASSO) Cox regression analysis was used to screen ER predictors. The scoring model was validated using 546 patients from another hospital. RESULTS: A total of 3421 patients were included. Multivariate Cox analysis showed that pre-TMs was an independent prognostic factor for ER. Survival after ER was equally poor in the normal and abnormal pre-TMs groups (P = 0.160). Based on LASSO Cox regression, the ER of patients with abnormal pre-TMs was only associated with the pT and pN stages; however, in patients with normal pre-TMs, it was also associated with tumor size, perineural invasion, and prognostic nutritional index. Scoring model constructed for patients with normal pre-TMs had better predictive performance than TNM staging (concordance-index:0.826 vs. 0.807, P < 0.001) and good reproducibility in both validation sets. Moreover, through risk stratification, the scoring model could not only identify the risk of ER but also distinguish ER patterns and adjuvant chemotherapy benefit subgroups. CONCLUSION: pre-TMs is an independent prognostic factor for ER in GC after RG. The established scoring model demonstrates excellent predictive performance and clinical utility.

Contrastive Transformer Hashing for Compact Video Representation.

Video hashing learns compact representation by mapping video into low-dimensional Hamming space and has achieved promising performance in large-scale video retrieval. It is challenging to effectively exploit temporal and spatial structure in an unsupervised setting. To fulfill this gap, this paper proposes Contrastive Transformer Hashing (CTH) for effective video retrieval. Specifically, CTH develops a bidirectional transformer autoencoder, based on which visual reconstruction loss is proposed. CTH is more powerful to capture bidirectional correlations among frames than conventional unidirectional models. In addition, CTH devises multi-modality contrastive loss to reveal intrinsic structure among videos. CTH constructs inter-modality and intra-modality triplet sets and proposes multi-modality contrastive loss to exploit inter-modality and intra-modality similarities simultaneously. We perform video retrieval tasks on four benchmark datasets, i.e., UCF101, HMDB51, SVW30, FCVID using the learned compact hash representation, and extensive empirical results demonstrate the proposed CTH outperforms several state-of-the-art video hashing methods.

UCHL1-PKM2 axis dysregulation is associated with promoted proliferation and invasiveness of urothelial bladder cancer cells.

BACKGROUND: Bladder cancer is one of the most common type of cancers globally, and the majority of cases belong to urothelial bladder carcinoma (UBC) type. Current researches have demonstrated that multiple genomic abnormalities are related to the sensitivity of cisplatin-based chemotherapy in bladder cancer patients. Previous findings have indicated a controversial role of Ubiquitin Carboxy-Terminal Hydrolase L1 (UCHL1) in malignancy, so we aimed to further explore the role of UCHL1 in UBC. METHODS: UBC cell lines and The Cancer Genome Atlas (TCGA) in-silico datasets were utilized to investigate UCHL1 expression pattern and functional as well as prognostic impacts in UBC cancer cell line models and patients. UCHL1 overexpression and silencing vectors and subsequent immunoprecipitation/ubiquitination experiments in combination of cellular functional assays were conducted to explore UCHL1-PKM2 interaction axis and its significance in UBC malignancy. RESULTS: UCHL1 was significantly up-regulated in UBC cancer cells and UCHL1 high-expression was associated with higher pathology/clinical grade and significantly inferior overall prognosis of UBC patients. UCHL1 interacted with PKM2 and enhanced PKM2 protein level through inhibition of PKM2 protein degradation via ubiquitination process. UCHL1-PKM2 interaction significantly promoted UBC cellular proliferation, metastasis and invasion activities. CONCLUSION: UCHL1-PKM2 interaction played an interesting role in UBC tumor cell proliferation, migration and metastasis. Our study suggests PKM2-targeted treatment might have a potential value in metastatic malignancy therapy development in the future.

Multi-Biometric Unified Network for Cloth-Changing Person Re-Identification.

Person re-identification (re-ID) aims to match the same person across different cameras. However, most existing re-ID methods assume that people wear the same clothes in different views, which limit their performance in identifying target pedestrians who change clothes. Cloth-changing re-ID is a quite challenging problem as clothes occupying a large number of pixels in an image becomes invalid or even misleads information. To tackle this problem, we propose a novel Multi-biometric Unified Network (MBUNet) for learning the robustness of cloth-changing re-ID model by exploiting clothing-independent cues. Specifically, we first introduce a multi-biological feature branch to extract a variety of biological features, such as the head, neck, and shoulders to resist cloth-changing. Then, a differential feature attention module (DFAM) is embedded in this branch, which can extract discriminative fine-grained biological features. Besides, we design a differential recombination on max pooling (DRMP) strategy and simultaneously apply a direction-adaptive graph convolutional layer to mine more robust global and pose features. Finally, we propose a Lightweight Domain Adaptation Module (LDAM) that combines the attention mechanism before and after the waveblock to capture and enhance transferable features across scenarios. To further improve the performance of the model, we also integrate mAP optimization into the objective function of our model for joint training to solve the discrete optimization problem of mAP. Extensive experiments on five cloth-changing re-ID datasets demonstrate the advantages of our proposed MBUNet. The code is available at https://github.com/liyeabc/MBUNet.

The tumor immune microenvironment of nasopharyngeal carcinoma after gemcitabine plus cisplatin treatment.

Gemcitabine plus cisplatin (GP) chemotherapy is the standard of care for nasopharyngeal carcinoma (NPC). However, the mechanisms underpinning its clinical activity are unclear. Here, using single-cell RNA sequencing and T cell and B cell receptor sequencing of matched, treatment-naive and post-GP chemotherapy NPC samples (n = 15 pairs), we show that GP chemotherapy activated an innate-like B cell (ILB)-dominant antitumor immune response. DNA fragments induced by chemotherapy activated the STING type-I-interferon-dependent pathway to increase major histocompatibility complex class I expression in cancer cells, and simultaneously induced ILB via Toll-like receptor 9 signaling. ILB further expanded follicular helper and helper type 1 T cells via the ICOSL-ICOS axis and subsequently enhanced cytotoxic T cells in tertiary lymphoid organ-like structures after chemotherapy that were deficient for germinal centers. ILB frequency was positively associated with overall and disease-free survival in a phase 3 trial of patients with NPC receiving GP chemotherapy ( NCT01872962 , n = 139). It also served as a predictor for favorable outcomes in patients with NPC treated with GP and immunotherapy combined treatment (n = 380). Collectively, our study provides a high-resolution map of the tumor immune microenvironment after GP chemotherapy and uncovers a role for B cell-centered antitumor immunity. We also identify and validate ILB as a potential biomarker for GP-based treatment in NPC, which could improve patient management.

One-Click-Based Perception for Interactive Image Segmentation.

Existing deep learning-based interactive image segmentation methods have significantly reduced the user's interaction burden with simple click interactions. However, they still require excessive numbers of clicks to continuously correct the segmentation for satisfactory results. This article explores how to harvest accurate segmentation of interested targets while minimizing the user interaction cost. To achieve the above goal, we propose a one-click-based interactive segmentation approach in this work. For this particularly challenging problem in the interactive segmentation task, we build a top-down framework dividing the original problem into a one-click-based coarse localization followed by a fine segmentation. A two-stage interactive object localization network is first designed, which aims to completely enclose the target of interest based on the supervision of object integrity (OI). Click centrality (CC) is also utilized to overcome the overlapping problem between objects. This coarse localization helps to reduce the search space and increase the focus of the click at a higher resolution. A principled multilayer segmentation network is then designed by a progressive layer-by-layer structure, which aims to accurately perceive the target with extremely limited prior guidance. A diffusion module is also designed to enhance the information flow between layers. Besides, the proposed model can be naturally extended to multiobject segmentation task. Our method achieves the state-of-the-art performance under one-click interaction on several benchmarks.

New Strategy for the Design of Anti-Corrosion Coatings in Bipolar Plates Based on Hybrid Organic-Inorganic Layers.

As a star material in conducting polymers, a polypyrrole coating was assembled onto the surface of 316 stainless steel by an electrochemical method. In the next step, the composite layer consisting of carbon nitride nanosheets (CNNS) and polymethyl methacrylate (PMMA) was sprayed. The corrosion manner of composite coatings in a simulated proton-exchange membrane fuel cell (PEMFC) environment was evaluated. The results show that the final coating generated at a voltage of 1.0 has demonstrated the optimized corrosion resistance. The polypyrrole layer improves the corrosion resistance of the stainless steel substrate, and the CNNS/PMMA coating further strengthens the physical barrier effect of the coating in corrosive solutions.

Immune response and homeostasis mechanism following administration of BBIBP-CorV SARS-CoV-2 inactivated vaccine.

The BBIBP-CorV severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) inactivated vaccine has been authorized for emergency use and widely distributed. We used single-cell transcriptome sequencing to characterize the dynamics of immune responses to the BBIBP-CorV inactivated vaccine. In addition to the expected induction of humoral immunity, we found that the inactivated vaccine induced multiple, comprehensive immune responses, including significantly increased proportions of CD16+ monocytes and activation of monocyte antigen presentation pathways; T cell activation pathway upregulation in CD8+ T cells, along with increased activation of CD4+ T cells; significant enhancement of cell-cell communications between innate and adaptive immunity; and the induction of regulatory CD4+ T cells and co-inhibitory interactions to maintain immune homeostasis after vaccination. Additionally, comparative analysis revealed higher neutralizing antibody levels, distinct expansion of naive T cells, a shared increased proportion of regulatory CD4+ T cells, and upregulated expression of functional genes in booster dose recipients with a longer interval after the second vaccination. Our research will support a comprehensive understanding of the systemic immune responses elicited by the BBIBP-CorV inactivated vaccine, which will facilitate the formulation of better vaccination strategies and the design of new vaccines.

Rapid determination of chemical components and antioxidant activity of the fruit of Crataegus pinnatifida Bunge by NIRS and chemometrics.

OBJECTIVE: To establish a method for quality evaluation of the fruit of Crataegus pinnatifida Bunge, also known as Shanzha, by near-infrared spectroscopy combined with chemometrics. METHOD: Seventy-two batches of Shanzha samples were collected, and the content of total components (flavonoids, phenols and organic acids), monomer components (chlorogenic acid, hyperoside and isoquercitrin), as well as the antioxidant activity of 60% ethanol extract were determined by usual methods. Then, all measured values were correlated with the near infrared spectra of Shanzha, and the partial least squares regression models were established. As to improve the model performance, various methods for spectra pretreatment and wavelength selection were investigated. RESULTS: After optimization, the models obtained the coefficients of determination in both calibration and prediction >0.9, and the residual prediction deviations >3, indicating that the models had good prediction abilities. CONCLUSION: The present method can serve as an alternative to the methods for comprehensive and rapid quality evaluation of Shanzha.

Transcriptional expression of CXCL10 and STAT1 in lupus nephritis and the intervention effect of triptolide.

OBJECTIVE: This study screened out the key genes associated with the occurrence and development of lupus nephritis (LN) using bioinformatics methods, and then explored the expression of key genes in LN and the inhibitory effect of triptolide. METHODS: The GEO2R online tool in the GEO database was used to perform differential analysis of gene expression in LN tissues and normal kidney tissues. The GO function and KEGG pathway enrichment analysis of differentially expressed genes (DEGs), STRING, and Cytoscape software were used to build a protein-protein interaction network (PPI) to screen out the Hub gene. Mouse glomerular mesangial cells (MMC) were randomly divided into a control group, an interferon-γ (IFN-γ) stimulation group, and a triptolide intervention group. The relative expression of CXCL10 mRNA in each group was detected by real-time fluorescent quantitative PCR (RT-PCR). CXCL10 secretion was detected by enzyme-linked immunosorbent assay (ELISA), and Western blot was used to detect the expression of the JAK/STAT1 signaling pathway-related proteins STAT1 and p-STAT1 in each group. RESULTS: Bioinformatics showed that there were 22 DEGs expression differences in the GEO database. The GO enrichment analysis showed that biological process (BP) such as the type I interferon signaling pathway, innate immune response, IFN-γ-mediated signaling pathway, virus defense response, and immune response were significantly regulated by DEGs. Through the combination of String database analysis and cytoscape software, it was found that STAT1 and CXCL10 are closely related to LN. Experimental results showed that IFN-γ induces the expression of CXCL10 mRNA and protein by activating the JAK/STAT1 signaling pathway, while triptolide inhibits the expression of CXCL10 mRNA and protein by inhibiting the JAK/STAT1 signaling pathway. CONCLUSION: STAT1 and CXCL10 are the key genes in the occurrence and development of LN. IFN-γ induces the expression of CXCL10 by activating the JAK/STAT1 signaling pathway, while triptolide inhibits the expression of CXCL10 by blocking the JAK/STAT1 signaling pathway. Inhibition of the JAK/STAT1 signaling pathway and CXCL10 expression is expected to become a potential target for the treatment of LN. Key Points • Bioinformatics showed that there were 22 DEGs expression differences in the GEO database. • Through the combination of String database analysis and Cytoscape software, it was found that STAT1 and CXCL10 are closely related to LN. • Experimental results showed that IFN-γ induces the expression of CXCL10 mRNA and protein by activating the JAK/STAT1 signaling pathway, while triptolide inhibits the expression of CXCL10 mRNA and protein by inhibiting the JAK/STAT1 signaling pathway.

Solvent-Type Passivation Strategy Controls Solid-State Self-Quenching-Resistant Behavior in Sulfur Dots.

Treatment of sulfur dots with polyethylene glycol (PEG) has been an efficient way to achieve a high luminescence quantum yield, and such a PEG-related quantum dot (QD)-synthesis strategy has been well documented. However, the polymeric insulating capping layer acting as the "thick shell" will significantly slow down the electron-transfer efficiency and severely hamper its practical application in an optoelectric field. Especially, the employment of synthetic polymers with long alkyl chains or large molecular weights may lead to structural complexity or even unexpected changes of physical characteristics for QDs. Therefore, in sulfur dot preparation, it is a breakthrough to use short-chain molecular species to replace PEG for better control and reproducibility. In this article, a solvent-type passivation (STP) strategy has been reported, and no PEG or any other capping agent is required. The main role of the solvent, ethanol, is to directly react with NaOH, and the generated sodium ethoxide passivates the surface defects. The afforded STP-enhanced emission sulfur dots (STPEE-SDs) possess not only the self-quenching-resistant feature in the solid state but also the extension of fluorescence band toward the wavelength as long as 645 nm. The realization of sulfur dot emission in the deep-red region with a decent yield (8.7%) has never been reported. Moreover, a super large Stokes shift (300 nm, λex = 345 nm, λem = 645 nm) and a much longer decay lifetime (109 µs) have been found, and such values can facilitate to suppress the negative influence from background signals. Density functional theory demonstrates that the surface passivation via sodium ethoxide is dynamically favorable, and the spectroscopic insights into emission behavior could be derived from the passivation effect of the sulfur vacancy as well as the charge-transfer process dominated by the highly electronegative ethoxide layer.

Novel organic selenium source hydroxy-selenomethionine counteracts the blood-milk barrier disruption and inflammatory response of mice under heat stress.

Heat stress (HS) in summer has caused huge economic losses to animal husbandry production recently. When mammary gland is exposed to high temperatures, it will cause blood-milk barrier damage. Hydroxy-selenomethionine (HMSeBA) is a new selenium source with better guarantee of animals' production performance under stress, but whether it has protective effect on heat stress-induced blood-milk damage is still unclear. We established mammary epithelial cells and mice heat stress injury models to fill this research gap, and hope to provide theoretical basis for using HMSeBA to alleviate heat stress damage mammary gland. The results showed that (1) Heat stress significantly decreases in vitro transepithelial electrical resistance (TEER) and cell viability (P < 0.01), and significantly decreases clinical score, histological score, and total alveoli area of mice mammary gland tissue (P < 0.01). (2) HMSeBA significantly increases TEER and fluorescein sodium leakage of HS-induced monolayer BMECs (P < 0.01), significantly improves the milk production and total area of alveoli (P < 0.01), and reduces clinical score, histological score, mRNA expression of heat stress-related proteins, and inflammatory cytokines release of heat-stressed mice (P < 0.01). (3) HMSeBA significantly improves tight junction structure damage, and significantly up-regulated the expression of tight junction proteins (ZO-1, claudin 1, and occludin) as well as signal molecules PI3K, AKT, and mTOR (P < 0.01) in heat-stressed mammary tissue. (4) HMSeBA significantly increases glutathione peroxidase (GSH-Px), total antioxidant capacity (T-AOC), and superoxide dismutase release (SOD) (P < 0.01) and significantly reduce malondialdehyde (MDA) expression (P < 0.01) in heat-stressed mammary tissue. In conclusion, this study implemented heat-stressed cell and mice model and showed that HMSeBA significantly regulate antioxidant capacity, inhibited inflammation, and regulate tight junction proteins expression in blood-milk barrier via PI3K/AKT/mTOR signaling pathway, so as to alleviate mammary gland damage and ensure its structure and function integrity.