CD4+CD57+ senescent T cells as promoters of systemic lupus erythematosus pathogenesis and the therapeutic potential of senolytic BCL-2 inhibitor.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease characterized by persistent activation of immune cells and overproduction of autoantibodies. The accumulation of senescent T and B cells has been observed in SLE and other immune-mediated diseases. However, the exact mechanistic pathways contributing to this process in SLE remain incompletely understood. In this study, we found that in SLE patients: (1) the frequency of CD4+CD57+ senescent T cells was significantly elevated and positively correlated with disease activity; (2) the expression levels of B-lymphoma-2 (BCL-2) family and interferon-induced genes (ISGs) were significantly upregulated; and (3) in vitro, the cytokine IL-15 stimulation increased the frequency of senescent CD4+ T cells and upregulated the expression of BCL-2 family and ISGs. Further, treatment with ABT-263 (a senolytic BCL-2 inhibitor) in MRL/lpr mice resulted in decreased: (1) frequency of CD4+CD44hiCD62L-PD-1+CD153+ senescent CD4+ T cells; (2) frequency of CD19+CD11c+T-bet+ age-related B cells; (3) level of serum antinuclear antibody; (4) proteinuria; (5) frequency of Tfh cells; and (6) renal histopathological abnormalities. Collectively, these results indicated a dominant role for CD4+CD57+ senescent CD4+ T cells in the pathogenesis of SLE and senolytic BCL-2 inhibitor ABT-263 may be the potential treatment in ameliorating lupus phenotypes.

Advancing single-cell RNA-seq data analysis through the fusion of multi-layer perceptron and graph neural network.

The advancement of single-cell sequencing technology has smoothed the ability to do biological studies at the cellular level. Nevertheless, single-cell RNA sequencing (scRNA-seq) data presents several obstacles due to the considerable heterogeneity, sparsity and complexity. Although many machine-learning models have been devised to tackle these difficulties, there is still a need to enhance their efficiency and accuracy. Current deep learning methods often fail to fully exploit the intrinsic interconnections within cells, resulting in unsatisfactory results. Given these obstacles, we propose a unique approach for analyzing scRNA-seq data called scMPN. This methodology integrates multi-layer perceptron and graph neural network, including attention network, to execute gene imputation and cell clustering tasks. In order to evaluate the gene imputation performance of scMPN, several metrics like cosine similarity, median L1 distance and root mean square error are used. These metrics are utilized to compare the efficacy of scMPN with other existing approaches. This research utilizes criteria such as adjusted mutual information, normalized mutual information and integrity score to assess the efficacy of cell clustering across different approaches. The superiority of scMPN over current single-cell data processing techniques in cell clustering and gene imputation investigations is shown by the experimental findings obtained from four datasets with gold-standard cell labels. This observation demonstrates the efficacy of our suggested methodology in using deep learning methodologies to enhance the interpretation of scRNA-seq data.

Auxin and carbohydrate control flower bud development in Anthurium andraeanum during early stage of sexual reproduction.

BACKGROUND: Flower buds of Anthurium andraeanum frequently cease to grow and abort during the early flowering stage, resulting in prolonged planting times and increased commercialization costs. Nevertheless, limited knowledge exists of the mechanism of flower development after initiation in A. andraeanum. RESULTS: In this study, the measurement of carbohydrate flow and intensity between leaves and flowers during different growth stages showed that tender leaves are strong sinks and their concomitant flowers are weak ones. This suggested that the tender leaves compete with their concomitant flower buds for carbohydrates during the early growth stages, potentially causing the abortion of the flower buds. The analysis of transcriptomic differentially expressed genes suggested that genes related to sucrose metabolism and auxin response play an important role during flower bud development. Particularly, co-expression network analysis found that AaSPL12 is a hub gene engaged in flower development by collaborating carbohydrate and auxin signals. Yeast Two Hybrid assays revealed that AaSPL12 can interact with AaARP, a protein that serves as an indicator of dormancy. Additionally, the application of exogenous IAA and sucrose can suppress the expression of AaARP, augment the transcriptional abundance of AaSPL12, and consequently expedite flower development in Anthurium andraeanum. CONCLUSIONS: Collectively, our findings indicated that the combination of auxin and sugar signals could potentially suppress the repression of AaARP protein to AaSPL12, thus advancing the development of flower buds in Anthurium andraeanum.

Multi-omics study reveals different pathogenesis of the generation of skin lesions in SLE and IDLE patients.

Lupus erythematosus (LE) is a heterogeneous, antibody-mediated autoimmune disease. Isolate discoid LE (IDLE) and systematic LE (SLE) are traditionally regarded as the two ends of the spectrum, ranging from skin-limited damage to life-threatening multi-organ involvement. Both belong to LE, but IDLE and SLE differ in appearance of skin lesions, autoantibody panels, pathological changes, treatments, and immunopathogenesis. Is discoid lupus truly a form of LE or is it a completely separate entity? This question has not been fully elucidated. We compared the clinical data of IDLE and SLE from our center, applied multi-omics technology, such as immune repertoire sequencing, high-resolution HLA alleles sequencing and multi-spectrum pathological system to explore cellular and molecular phenotypes in skin and peripheral blood from LE patients. Based on the data from 136 LE patients from 8 hospitals in China, we observed higher damage scores and fewer LE specific autoantibodies in IDLE than SLE patients, more uCDR3 sharing between PBMCs and skin lesion from SLE than IDLE patients, elevated diversity of V-J recombination in IDLE skin lesion and SLE PBMCs, increased SHM frequency and class switch ratio in IDLE skin lesion, decreased SHM frequency but increased class switch ratio in SLE PBMCs, HLA-DRB1*03:01:01:01, HLA-B*58:01:01:01, HLA-C*03:02:02:01, and HLA-DQB1*02:01:01:01 positively associated with SLE patients, and expanded Tfh-like cells with ectopic germinal center structures in IDLE skin lesions. These findings suggest a significant difference in the immunopathogenesis of skin lesions between SLE and IDLE patients. SLE is a B cell-predominate systemic immune disorder, while IDLE appears limited to the skin. Our findings provide novel insights into the pathogenesis of IDLE and other types of LE, which may direct more accurate diagnosis and novel therapeutic strategies.

Genetic and epigenetic reprogramming in response to internal and external cues by induced transposon mobilization in Moso bamboo.

Long terminal repeat retroelements (LTR-REs) have profound effects on DNA methylation and gene regulation. Despite the vast abundance of LTR-REs in the genome of Moso bamboo (Phyllostachys edulis), an industrial crop in underdeveloped countries, their precise implication of the LTR-RE mobility in stress response and development remains unknown. We investigated the RNA and DNA products of LTR-REs in Moso bamboo under various developmental stages and stressful conditions. Surprisingly, our analyses identified thousands of active LTR-REs, particularly those located near genes involved in stress response and developmental regulation. These genes adjacent to active LTR-REs exhibited an increased expression under stress and are associated with reduced DNA methylation that is likely affected by the induced LTR-REs. Moreover, the analyses of simultaneous mapping of insertions and DNA methylation showed that the LTR-REs effectively alter the epigenetic status of the genomic regions where they inserted, and concomitantly their transcriptional competence which might impact the stress resilience and growth of the host. Our work unveils the unusually strong LTR-RE mobility in Moso bamboo and its close association with (epi)genetic changes, which supports the co-evolution of the parasitic DNAs and host genome in attaining stress tolerance and developmental robustness.

Image-based remote evaluation of PASI scores with psoriasis by the YOLO-v4 algorithm.

As a chronic relapsing disease, psoriasis is characterized by widespread skin lesions. The Psoriasis Area and Severity Index (PASI) is the most frequently utilized tool for evaluating the severity of psoriasis in clinical practice. Nevertheless, long-term monitoring and precise evaluation pose difficulties for dermatologists and patients, which is time-consuming, subjective and prone to evaluation bias. To develop a deep learning system with high accuracy and speed to assist PASI evaluation, we collected 2657 high-quality images from 1486 psoriasis patients, and images were segmented and annotated. Then, we utilized the YOLO-v4 algorithm to establish the model via four modules, we also conducted a human-computer comparison through quadratic weighted Kappa (QWK) coefficients and intra-class correlation coefficients (ICC). The YOLO-v4 algorithm was selected for model training and optimization compared with the YOLOv3, RetinaNet, EfficientDet and Faster_rcnn. The model evaluation results of mean average precision (mAP) for various lesion features were as follows: erythema, mAP = 0.903; scale, mAP = 0.908; and induration, mAP = 0.882. In addition, the results of human-computer comparison also showed a median consistency for the skin lesion severity and an excellent consistency for the area and PASI score. Finally, an intelligent PASI app was established for remote disease assessment and course management, with a pleasurable agreement with dermatologists. Taken together, we proposed an intelligent PASI app based on the image YOLO-v4 algorithm that can assist dermatologists in long-term and objective PASI scoring, shedding light on similar clinical assessments that can be assisted by computers in a time-saving and objective manner.

Safety and adherence of bictegravir/emtricitabine/tenofovir alafenamide for HIV post-exposure prophylaxis among adults in Guiyang China: a prospective cohort study.

BACKGROUND: The effectiveness of post-exposure prophylaxis (PEP) depends on participants adherence, making it crucial to assess and compare regimen options to enhance human immunodeficiency virus (HIV) prophylaxis strategies. However, no prospective study in China has shown that the completion rate and adherence of single-tablet regimens in HIV PEP are higher than those of multi-tablet preparations. Therefore, this study aimed to assess the completion rate and adherence of two HIV PEP regimens. METHODS: In this single-center, prospective, open-label cohort study, we included 179 participants from May 2022 to March 2023 and analyzed the differences in the 28-day medication completion rate, adherence, safety, tolerance, and effectiveness of bictegravir/emtricitabine/tenofovir alafenamide (BIC/FTC/TAF) and tenofovir disoproxil fumarate, emtricitabine, and dolutegravir (TDF/FTC + DTG). RESULTS: The PEP completion rate and adherence were higher in the BIC/FTC/TAF group than in the TDF/FTC + DTG group (completion rate: 97.8% vs. 82.6%, P = 0.009; adherence: 99.6 ± 2.82% vs. 90.2 ± 25.29%, P = 0.003). The incidence of adverse reactions in the BIC/FTC/TAF and TDF/FTC + DTG groups was 15.2% and 10.3% (P = 0.33), respectively. In the TDF/FTC + DTG group, one participant stopped PEP owing to adverse reactions (1.1%). No other participants stopped PEP due to adverse events. CONCLUSIONS: BIC/FTC/TAF and TDF/FTC + DTG have good safety and tolerance as PEP regimens. BIC/FTC/TAF has a higher completion rate and increased adherence, thus, is recommended as a PEP regimen. These findings emphasize the importance of regimen choice in optimizing PEP outcomes. TRIAL REGISTRATION: The study was registered in the Chinese Clinical Trial Registry (registration number: ChiCTR2200059994(2022-05-14), https://www.chictr.org.cn/bin/project/edit?pid=167391 ).

Human-multimodal deep learning collaboration in 'precise' diagnosis of lupus erythematosus subtypes and similar skin diseases.

BACKGROUND: Lupus erythematosus (LE) is a spectrum of autoimmune diseases. Due to the complexity of cutaneous LE (CLE), clinical skin image-based artificial intelligence is still experiencing difficulties in distinguishing subtypes of LE. OBJECTIVES: We aim to develop a multimodal deep learning system (MMDLS) for human-AI collaboration in diagnosis of LE subtypes. METHODS: This is a multi-centre study based on 25 institutions across China to assist in diagnosis of LE subtypes, other eight similar skin diseases and healthy subjects. In total, 446 cases with 800 clinical skin images, 3786 multicolor-immunohistochemistry (multi-IHC) images and clinical data were collected, and EfficientNet-B3 and ResNet-18 were utilized in this study. RESULTS: In the multi-classification task, the overall performance of MMDLS on 13 skin conditions is much higher than single or dual modals (Sen = 0.8288, Spe = 0.9852, Pre = 0.8518, AUC = 0.9844). Further, the MMDLS-based diagnostic-support help improves the accuracy of dermatologists from 66.88% ± 6.94% to 81.25% ± 4.23% (p = 0.0004). CONCLUSIONS: These results highlight the benefit of human-MMDLS collaborated framework in telemedicine by assisting dermatologists and rheumatologists in the differential diagnosis of LE subtypes and similar skin diseases.

Improved Intrusion Detection Based on Hybrid Deep Learning Models and Federated Learning.

The security of the Industrial Internet of Things (IIoT) is of vital importance, and the Network Intrusion Detection System (NIDS) plays an indispensable role in this. Although there is an increasing number of studies on the use of deep learning technology to achieve network intrusion detection, the limited local data of the device may lead to poor model performance because deep learning requires large-scale datasets for training. Some solutions propose to centralize the local datasets of devices for deep learning training, but this may involve user privacy issues. To address these challenges, this study proposes a novel federated learning (FL)-based approach aimed at improving the accuracy of network intrusion detection while ensuring data privacy protection. This research combines convolutional neural networks with attention mechanisms to develop a new deep learning intrusion detection model specifically designed for the IIoT. Additionally, variational autoencoders are incorporated to enhance data privacy protection. Furthermore, an FL framework enables multiple IIoT clients to jointly train a shared intrusion detection model without sharing their raw data. This strategy significantly improves the model's detection capability while effectively addressing data privacy and security issues. To validate the effectiveness of the proposed method, a series of experiments were conducted on a real-world Internet of Things (IoT) network intrusion dataset. The experimental results demonstrate that our model and FL approach significantly improve key performance metrics such as detection accuracy, precision, and false-positive rate (FPR) compared to traditional local training methods and existing models.

Ethereum Phishing Scam Detection Based on Data Augmentation Method and Hybrid Graph Neural Network Model.

The rapid advancement of blockchain technology has fueled the prosperity of the cryptocurrency market. Unfortunately, it has also facilitated certain criminal activities, particularly the increasing issue of phishing scams on blockchain platforms such as Ethereum. Consequently, developing an efficient phishing detection system is critical for ensuring the security and reliability of cryptocurrency transactions. However, existing methods have shortcomings in dealing with sample imbalance and effective feature extraction. To address these issues, this study proposes an Ethereum phishing scam detection method based on DA-HGNN (Data Augmentation Method and Hybrid Graph Neural Network Model), validated by real Ethereum datasets to prove its effectiveness. Initially, basic node features consisting of 11 attributes were designed. This study applied a sliding window sampling method based on node transactions for data augmentation. Since phishing nodes often initiate numerous transactions, the augmented samples tended to balance. Subsequently, the Temporal Features Extraction Module employed Conv1D (One-Dimensional Convolutional neural network) and GRU-MHA (GRU-Multi-Head Attention) models to uncover intrinsic relationships between features from the time sequences and to mine adequate local features, culminating in the extraction of temporal features. The GAE (Graph Autoencoder) concept was then leveraged, with SAGEConv (Graph SAGE Convolution) as the encoder. In the SAGEConv reconstruction module, by reconstructing the relationships between transaction graph nodes, the structural features of the nodes were learned, obtaining reconstructed node embedding representations. Ultimately, phishing fraud nodes were further identified by integrating temporal features, basic features, and embedding representations. A real Ethereum dataset was collected for evaluation, and the DA-HGNN model achieved an AUC-ROC (Area Under the Receiver Operating Characteristic Curve) of 0.994, a Recall of 0.995, and an F1-score of 0.994, outperforming existing methods and baseline models.