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Recent years, the immunosuppressive properties of mesenchymal stem cells (MSCs) have been demonstrated in preclinical studies and trials of inflammatory and autoimmune diseases. Emerging evidence indicates that the immunomodulatory effect of MSCs is primarily attributed to the paracrine pathway. As one of the key paracrine effectors, mesenchymal stem cell-derived exosomes (MSC-EXOs) are small vesicles 30-200 nm in diameter that play an important role in cell-to-cell communication by carrying bioactive substances from parental cells. Recent studies support the finding that MSC-EXOs have an obvious inhibitory effect toward different effector cells involved in the innate and adaptive immune response. Moreover, substantial progress has been made in the treatment of autoimmune diseases, including multiple sclerosis (MS), systemic lupus erythematosus (SLE), type-1 diabetes (T1DM), uveitis, rheumatoid arthritis (RA), and inflammatory bowel disease (IBD). MSC-EXOs are capable of reproducing MSC function and overcoming the limitations of traditional cell therapy. Therefore, using MSC-EXOs instead of MSCs to treat autoimmune diseases appears to be a promising cell-free treatment strategy. In this review, we review the current understanding of MSC-EXOs and discuss the regulatory role of MSC-EXOs on immune cells and its potential application in autoimmune diseases.

Breast cancer is one of the most common malignancies. Pathological image processing of breast has become an important means for early diagnosis of breast cancer. Using medical image processing to assist doctors to detect potential breast cancer as early as possible has always been a hot topic in the field of medical image diagnosis. In this paper, a breast cancer recognition method based on image processing is systematically expounded from four aspects: breast cancer detection, image segmentation, image registration, and image fusion. The achievements and application scope of supervised learning, unsupervised learning, deep learning, CNN, and so on in breast cancer examination are expounded. The prospect of unsupervised learning and transfer learning for breast cancer diagnosis is prospected. Finally, the privacy protection of breast cancer patients is put forward.

The classification of gamma-ray bursts (GRBs) has long been an unresolved problem. Early long- and short-burst classification based on duration is not convincing owing to the significant overlap in duration plot, which leads to different views on the classification results. We propose a new classification method based on convolutional neural networks and adopt a sample including 3774 GRBs observed by Fermi-GBM to address the T90 overlap problem. By using count maps that incorporate both temporal and spectral features as inputs, we successfully classify 593 overlapping events into two distinct categories, thereby refuting the existence of an intermediate GRB class. Additionally, we apply the optimal model to extract features from the count maps and visualize the extracted GRB features using the t-SNE algorithm, discovering two distinct clusters corresponding to S-type and L-type GRBs. To further investigate the physical properties of these two types of bursts, we conduct a time-integrated spectral analysis and discover significant differences in their spectral characteristics. The analysis also shows that most GRBs associated with kilonovae belong to the S type, while those associated with supernovae are predominantly L type, with few exceptions. Additionally, the duration characteristics of short bursts with extended emission suggest that they may manifest as either L-type or S-type GRBs. Compared to traditional classification methods (Amati and energy–hardness–duration methods), the new approach demonstrates significant advantages in classification accuracy and robustness without relying on redshift observations. The deep learning classification strategy proposed in this paper provides a more reliable tool for future GRB research.

Biomass-derived recyclable materials that can replace petrochemical-derived plastics are highly sought for a sustainable future. However, incumbent materials often face performance deterioration challenges owing to the aging issues after use in the environment. Here, we present a self-reinforcing, recyclable, unprecedented polyester material derived entirely from biomass lignin and soybeans, mimicking the self-reinforcement mechanism of biological systems. Our material leverages a [2 + 2]-cycloaddition reaction mediated by aromatic π-conjugated vinylidene structures, enhancing performance under ultraviolet light, hygrothermal conditions, and external electric fields. Specifically, the tensile strength, elongation at break, and anti-ultraviolet efficiency can be enhanced to 103 MPa, 560%, and 73%, respectively, far surpassing those of known biomass-derived materials and engineered plastics. Additionally, the material demonstrates outstanding insulativity, barrier properties, flame retardancy, solvent resistance, and recyclability, meeting the demands of sustainable green new energy material. Our strategy for this self-reinforcing biomass recyclable material provides rich possibilities for designing next-generation sustainable materials. Most bio-derived polymers deteriorate during environmental exposure. Here, the authors report a bio-derived polyester containing aromatic p-conjugated vinylidene units, these provided self-reinforcement through [2 + 2] cycloadditions, leading to mechanical enhancements during ageing.

Glucocorticoid-induced TNFR-related protein (GITR) is a member of the TNFR superfamily which is expressed in various cells, including T cells, natural killer cells and some myeloid cells. GITR is activated by its ligand, GITRL, mainly expressed on antigen presenting cells and endothelial cells. It has been acknowledged that the engagement of GITR can modulate both innate and adaptive immune responses. Accumulated evidence suggests GITR/GITRL interaction is involved in the pathogenesis of tumor, inflammation and autoimmune diseases. In this review, we describe the effects of GITR/GITRL activation on effector T cells, regulatory T cells (Tregs) and myeloid cells; summarize its role and the underlying mechanisms in modulating autoimmune diseases.

GRB 240619A, detected by Fermi-Gamma-ray Burst Monitor with a duration of ∼36.1 s at redshift z = 0.3965, a short, hard main emission (ME), a quiescent interval, and a soft extended emission (EE). We perform a detailed analysis of the temporal and spectral properties of both phases using Bayesian inference and machine learning techniques. Our main results show: (1) the spectral lags are 0.27 ± 0.03 s (ME) and 0.13 ± 0.19 s (EE), with the ME phase following the lag–luminosity relation; (2) The minimum variability timescales are ∼0.04 s (ME) and ∼0.48 s (EE), consistent with short gamma-ray bursts (SGRB) and long gamma-ray bursts (LGRB) characteristics, respectively, suggesting that the radiation in the two phases may originate from different physical mechanisms. (3) the effective amplitude feff ≈ 3.18 suggests an intrinsic origin consistent with SGRBs; (4) the spectral hardness, peak energy, Amati/Yonetoku relations, and energy–hardness–duration classification indicate that the ME phase is more similar to SGRBs, while the EE phase resembles LGRBs; (5) the local event rate density ( 4.66−3.86+11.25×10−3 Gpc−3 yr−1) further highlights its peculiarity. Spectral analysis indicates that its nonthermal emission can be well explained by the fast-cooling synchrotron radiation model. Curvature effect analysis suggests jet acceleration in the EE phase, with the jet becoming Poynting-flux dominated. The combined evidence suggests that GRB 240619A is an SGRB with EE, likely originating from a compact binary merger, and exhibiting physical properties similar to those of GRB 211211A.

This study presents detailed time-integrated and time-resolved spectral analysis of the Fermi Gamma-ray Burst Monitor observations of the bright GRB 231129C. The results reveal its distinct spectral characteristics, featuring a hard low-energy spectral index (α) and soft high-energy spectral index (β), similar to GRB 090902B, suggesting a possible dominance of thermal emission. Further analysis indicates that 92% of the spectral indices exceed the synchrotron “line of death,” with the hardest index at α ∼ +0.44. Simultaneously, 53% of the spectra can be well fitted by the nondissipative photosphere model, supporting a potential origin from a nondissipative photosphere. Additionally, we observe strong correlations between the spectral index α and peak energy E p with flux. For the α−F relationship, we employ F = F 0 e (3.00±0.10)α to describe it, whereas the E p−F relationship requires a smoothly bending power-law function. Based on the framework proposed by Hascoët et al. and Gao & Zhang, the jet characteristics of this burst were studied, revealing that both methods support the suitability of a pure fireball model for this GRB at small initial jet radii.

Maize tassels play a critical role in plant growth and yield. Extensive RGB images obtained using unmanned aerial vehicle (UAV) and the prevalence of deep learning provide a chance to improve the accuracy of detecting maize tassels. We used images from UAV, a mobile phone, and the Maize Tassel Counting dataset (MTC) to test the performance of faster region-based convolutional neural network (Faster R-CNN) with residual neural network (ResNet) and a visual geometry group neural network (VGGNet). The results showed that the ResNet, as the feature extraction network, was better than the VGGNet for detecting maize tassels from UAV images with 600 × 600 resolution. The prediction accuracy ranged from 87.94% to 94.99%. However, the prediction accuracy was less than 87.27% from the UAV images with 5280 × 2970 resolution. We modified the anchor size to [852, 1282, 2562] in the region proposal network according to the width and height of pixel distribution to improve detection accuracy up to 89.96%. The accuracy reached up to 95.95% for mobile phone images. Then, we compared our trained model with TasselNet without training their datasets. The average difference of tassel number was 1.4 between the calculations with 40 images for the two methods. In the future, we could further improve the performance of the models by enlarging datasets and calculating other tassel traits such as the length, width, diameter, perimeter, and the branch number of the maize tassels.

Background and aims Odontarrhena chalcidica is a well-known nickel hyperaccumulator which also take up zinc efficiently, but little is known regarding its zinc uptake mechanisms. Therefore, this study compared the uptake kinetics of zinc and nickel. Methods Plants were cultivated under various metals (nickel, zinc, cobalt and iron) concentrations of the hydroponic solutions. The plants underwent assessment for a range of parameters, including metal concentrations in the plant, expression level of selected metal transporters, and absorption kinetics models for nickel and zinc in the roots. Results The plants accumulated high concentrations of zinc in roots (5000 mg kg −1 ). Zinc uptake was not influenced by nickel, but nickel uptake was severely suppressed by zinc (drop by 87.4%), while significantly stimulated by zinc deficiency (increase by 30.4%). The root uptake of zinc and nickel fit Michaelis-Menten kinetics with K m for zinc and nickel of 8.99 and 18.3 µM, respectively, while the V max of nickel was approximately 1.8 times greater than that of zinc. The presence of zinc greatly decreased both the V max and K m of nickel. RT-Q-PCR results showed that zinc exposure only stimulated the gene expression levels of zinc transporter (e.g., ZIP10 ), whilst nickel exposure stimulates gene expression levels of both zinc and iron transporters. Conclusions O. chalcidica preferentially takes up zinc over nickel, probably via relatively high-affinity transporters. Whilst nickel uptake is via a broader range of transporters, and the zinc transporters appear to be part of the nickel transport systems.

With the ability to induce T cell activation and elicit humoral responses, B cells are generally considered as effectors of the immune system. However, the emergence of regulatory B cells (Bregs) has given new insight into the role of B cells in immune responses. Bregs exhibit immunosuppressive functions via diverse mechanisms, including the secretion of anti-inflammatory cytokines and direct cell contact. The balance between Bregs and effector B cells is important for the immune tolerance. In this review, we focus on recent advances in the characteristics of Bregs and their functional roles in autoimmunity.