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.

METTL3-mediated m6A modification enhances lncRNA H19 stability to promote endothelial cell inflammation and pyroptosis to aggravate atherosclerosis.

This study explored the impact of N6-methyladenosine (m6A) modification on the regulation of long noncoding RNA (lncRNA) and atherosclerosis progression. An atherosclerosis cell model was established by treating human aortic endothelial cells (HAECs) with oxidized low-density lipoprotein. Additionally, an atherosclerotic animal model was developed using ApoE-/- C57BL/6 male mice fed a high-fat diet. Both models were employed to assess the expression changes of proteins associated with m6A modification. First, the effect of m6A modification writer protein methyltransferase-like 3 (METTL3) knockdown on changes in the level of pyroptosis in HAECs was investigated, and bioinformatic analysis confirmed that lncRNA H19 (H19) was the potential target of m6A modification. RNA-binding protein immunoprecipitation assays were subsequently performed to explore the interaction between H19 and the m6A writer protein METTL3, as well as the reader protein recombinant insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2). Finally, the effect of H19 expression on pyroptosis levels in HAECs was evaluated. In the aortas of atherosclerosis mice, overall m6A levels were significantly elevated compared with controls (p < .05), with METTL3 and METTL14 mRNA and protein levels notably increased (p < .05). Similarly, ox-LDL-treated HAECs showed a significant rise in m6A levels, along with increased METTL3 and METTL14 expression (p < .05). METTL3 knockdown in HAECs led to decreased pyroptosis, as evidenced by reduced lactate dehydrogenase release and lower levels of IL-1ß, IL-18, and IL-6 (p < .05). Overexpression of H19 reversed these effects, indicating METTL3's role in promoting atherosclerosis by stabilizing H19 through m6A modification. H19 was the primary target lncRNA molecule of METTL3-mediated m6A modification in the pathogenesis of atherosclerosis. METTL3-mediated m6A modification regulated H19 expression, thereby aggravating atherosclerosis by activating pyroptosis.

DGSIST: Clustering spatial transcriptome data based on deep graph structure Infomax.

Although spatial transcriptomics data provide valuable insights into gene expression profiles and the spatial structure of tissues, most studies rely solely on gene expression information, underutilizing the spatial data. To fully leverage the potential of spatial transcriptomics and graph neural networks, the DGSI (Deep Graph Structure Infomax) model is proposed. This innovative graph data processing model uses graph convolutional neural networks and employs an unsupervised learning approach. It maximizes the mutual information between graph-level and node-level representations, emphasizing flexible sampling and aggregation of nodes and their neighbors. This effectively captures and incorporates local information from nodes into the overall graph structure. Additionally, this paper developed the DGSIST framework, an unsupervised cell clustering method that integrates the DGSI model, SVD dimensionality reduction algorithm, and k-means++ clustering algorithm. This aims to identify cell types accurately. DGSIST fully uses spatial transcriptomics data and outperforms existing methods in accuracy. Demonstrations of DGSIST's capability across various tissue types and technological platforms have shown its effectiveness in accurately identifying spatial domains in multiple tissue sections. Compared to other spatial clustering methods, DGSIST excels in cell clustering and effectively eliminates batch effects without needing batch correction. DGSIST excels in spatial clustering analysis, spatial variation identification, and differential gene expression detection and directly applies to graph analysis tasks, such as node classification, link prediction, or graph clustering. Anticipation lies in the contribution of the DGSIST framework to a deeper understanding of the spatial organizational structures of diseases such as cancer.

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.

The complete mitochondrial genome of Castanopsis carlesii and Castanea henryi reveals the rearrangement and size differences of mitochondrial DNA molecules.

BACKGROUND: Castanopsis carlesii is a dominant tree species in subtropical evergreen broad-leaved forests and holds significant ecological value. It serves as an excellent timber tree species and raw material for cultivating edible fungi. Henry Chinquapin (Castanea henryi) wood is known for its hardness and resistance to water and moisture, making it an exceptional timber species. Additionally, its fruit has a sweet and fruity taste, making it a valuable food source. However, the mitogenomes of these species have not been previously reported. To gain a better understanding of them, this study successfully assembled high-quality mitogenomes of C. carlesii and Ca. henryi for the first time. RESULTS: Our research reveals that the mitochondrial DNA (mtDNA) of C. carlesii exhibits a unique multi-branched conformation, while Ca. henryi primarily exists in the form of two independent molecules that can be further divided into three independent molecules through one pair of long repetitive sequences. The size of the mitogenomes of C. carlesii and Ca. henryi are 592,702 bp and 379,929 bp respectively, which are currently the largest and smallest Fagaceae mitogenomes recorded thus far. The primary factor influencing mitogenome size is dispersed repeats. Comparison with published mitogenomes from closely related species highlights differences in size, gene loss patterns, codon usage preferences, repetitive sequences, as well as mitochondrial plastid DNA segments (MTPTs). CONCLUSIONS: Our study enhances the understanding of mitogenome structure and evolution in Fagaceae, laying a crucial foundation for future research on cell respiration, disease resistance, and other traits in this family.

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.

Magnetic superhydrophobic cellulose nanofibril based aerogel with rope-ladder like structure incorporating both superelasticity and excellent oil absorption.

Achieving an equilibrium between exceptional oil absorption and remarkable elasticity has emerged as a formidable challenge for magnetic porous materials designed for oil absorption. Here, we propose an original, magnetic and superhydrophobic cellulose nanofibril (CNF) based aerogel system with a rope-ladder like skeleton by to greatly improve the issue. Within this system, CNF as the skeleton was combined with multiwalled carbon nanotubes (MWCNT)@Fe3O4 as the magnetic and enhanced component, both methyltrimethoxysilane (MTMS) and acetonitrile-extracted lignin (AEL) as the soft-hard associating constituents. The resultant CNF based aerogel shows a rope-ladder like pore structure to contribute to high elasticity and excellent oil absorption (28.34-61.09 g/g for various oils and organic solvents) under the synergistic effect of Fe3O4@MWCNT, AEL and MTMS, as well as good specific surface area (27.97 m2/g), low density (26.4 mg/cm3). Notably, despite the introduced considerable proportion (0.5 times of mass-CNF) of Fe3O4@MWCNT, the aerogel retained an impressive compression-decompression rate (88%) and the oil absorption efficiency of above 87% for various oils due to the soft-hard associating structure supported by both MTMS and AEL. This study provides a prospective strategy to balance between high elasticity and excellent oil absorption of CNF based aerogel doping inorganic particles.

In-situ remediation of cadmium contamination in paddy fields: from rhizosphere soil to rice kernel.

Cadmium (Cd) has become an important heavy metal pollutant because of its strong migration and high toxicity. The industrial production process aggravated the Cd pollution in rice fields. Human exposure to Cd through rice can cause kidney damage, emphysema, and various cardiovascular and metabolic diseases, posing a grave threat to health. As modern technology develops, the Cd accumulation model in rice and in-situ remediation of Cd pollution in cornfields have been extensively studied and applied, so it is necessary to sort out and summarize them systematically. Therefore, this paper reviewed the primary in-situ methods for addressing heavy metal contamination in rice paddies, including chemical remediation (inorganic-organic fertilizer remediation, nanomaterials, and composite remediation), biological remediation (phytoremediation and microbial remediation), and crop management remediation technologies. The factors that affect Cd transformation in soil and Cd migration in crops, the advantages and disadvantages of remediation techniques, remediation mechanisms, and the long-term stability of remediation were discussed. The shortcomings and future research directions of in situ remediation strategies for heavily polluted paddy fields and genetic improvement strategies for low-cadmium rice varieties were critically proposed. To sum up, this review aims to enhance understanding and serve as a reference for the appropriate selection and advancement of remediation technologies for rice fields contaminated with heavy metals.

Prevalence of Intimate Partner Violence and Associated Factors Among People With HIV: A Large-Sample Cross-Sectional Study in China.

To assess the prevalence and exacerbating factors of intimate partner violence in people with human immunodeficiency virus (PWH) in China, we conducted a cross-sectional study, involving 2792 PWH in 4 provinces in China from 1 September 2020 to 1 June 2021. The categories of intimate partner violence (IPV) included physical violence, sexual violence, emotional abuse, and controlling behavior. The severity of a violent act was divided into mild, moderate, and severe. Among PWH, the prevalence of IPV was 15.4% (95% confidence interval, 14.1%-16.8%). The severity of physical violence was mainly moderate, and the severity of sexual violence, emotional abuse, and controlling behavior was mainly mild. The prevalence of IPV in men was higher than that in women. Results from the multivariable logistic regression showed that age, ethnic, registered residence, education, and duration of HIV antiretroviral therapy were factors related to IPV in PWH (P < .05).

Consecutive injections of low-dose interleukin-2 improve symptoms and disease control in patients with chronic spontaneous urticaria.

PURPOSE: To describe the effectiveness and tolerability of low-dose interleukin (IL)-2 in treating patients with chronic spontaneous urticaria (CSU) refractory to H1-antihistamines. METHODS: This retrospective study included CSU patients who received treatment with at least one cycle of IL-2, injected intramuscularly at a dose of 1.0 million international units daily for 7 consecutive days, after failing treatment with H1-antihistamines. Patients were followed up for ≥12 weeks. RESULTS: Of the 15 patients, 7 (46.7%) and 11 (73.3%) achieved complete response at Week 2 and Week 12, respectively. The mean change of urticaria control test (UCT) and weekly urticaria activity score (UAS7) from baseline was 6.6 (95% CI, 4.2 to 8.9) and - 16.9 (95% CI, -24.0 to -9.8), respectively, at Week 12. Local injection-site reactions were the most common adverse events. No serious adverse events were reported. CONCLUSION: Low-dose IL-2 treatment improves symptoms and disease control for CSU patients refractory to H1-antihistamines.

Sirtuin 1 overexpression contributes to the expansion of follicular helper T cells in Systemic lupus erythematosus and serves as an easy-to-intervene therapeutic target.

OBJECTIVE: SIRT1, an NAD+-dependent deacetylase, is up-regulated in CD4+ T cells from SLE patients and MRL/lpr lupus-like mice. This study aimed to explore the role of SIRT1 in Tfh cell expansion and its potential value as a therapeutic target for SLE. METHODS: Frequencies of CD4+CXCR5+PD-1+ Tfh cells in peripheral blood from SLE patients and their expression of SIRT1 and BCL-6 were determined with flow cytometry. Naïve CD4+ T cells were transfected with SIRT1-expressing lentivirus and small interfering RNA (siRNA) targeting SIRT1, respectively, and then cultured in a Tfh-polarizing condition to study the impact of SIRT1 on Tfh cell differentiation. This impact was also evaluated in both CD4+ T cells and naïve CD4+ T cells by treatment with SIRT1 inhibitors (EX527 and nicotinamide) in vitro. MRL/lpr mice and pristane-induced lupus mice were treated with continuous daily intake of nicotinamide, and their lupus phenotypes including skin rash, arthritis, proteinuria and serum anti-dsDNA autoantibodies were compared with controls. RESULTS: Expression of SIRT1 was elevated in Tfh cells from SLE patients and positively correlated with Tfh cell frequencies. SIRT1 expression gradually increased during Tfh cell differentiation. Overexpression of SIRT1 by lentiviral vectors significantly promoted Tfh cell differentiation/proliferation. Reciprocally, suppressing expression of SIRT1 by siRNA and inhibiting SIRT1 activity by EX-527 or nicotinamide hindered Tfh cell expansion. Continuous daily intake of nicotinamide alleviated lupus-like phenotypes and decreased serum CXCL13 in the two mouse models. CONCLUSION: SIRT1 overexpression contributes to the expansion of Tfh cells in SLE and may serve as a potential target for treatment.

A Practical Risk Classification of Early Recurrence in Hepatocellular Carcinoma Patients with Microvascular invasion after Hepatectomy: A Decision Tree Analysis.

PURPOSE: This study was designed to establish risk classifications for early recurrence in hepatocellular carcinoma (HCC) patients with microvascular invasion (MVI) after hepatectomy. METHODS: The data of 563 HCC patients with MVI after hepatectomy from two hospitals were retrospectively reviewed. Kaplan-Meier curves and Cox proportional hazards regression models were used to analyse early recurrence. The risk classification for early recurrence was established by using classification and regression tree (CART) analysis and validated by using two independent validation cohorts from two hospitals. RESULTS: Multivariate analysis revealed that four indices, namely, infection of chronic viral hepatitis, MVI classification, tumour size, and serum alpha-fetoprotein (AFP), were independent prognostic factors for early recurrence in HCC patients with MVI. By CART analysis, MVI classification and serum AFP became the nodes of a decision tree and 3-stratification classifications that satisfactorily determined the risk of early recurrence were established. The area under the time-dependent receiver operating characteristic curve (AUC) values of the classification for early recurrence at 0.5, 1.0, and 2.0 years were 0.75, 0.73, and 0.71, respectively, which were all significantly higher than three common classic HCC stages (BCLC stage, Chinese stage, and TNM stage). The calibration curves showed good agreement between predictions by classification for early recurrence and actual survival outcomes. These prediction results also were confirmed in the independent internal and external validation cohorts. CONCLUSIONS: The 3 stratification classifications enabled satisfactory risk evaluation of early recurrence in HCC patients with MVI after hepatectomy.

Unipolar and bipolar electrograms to predict successful ablation site of premature ventricular contractions originating from the free wall of the tricuspid annulus.

INTRODUCTION: This study aimed to identify the characteristics of unipolar and bipolar electrogram (UniEGM and BiEGM) in guiding successful ablation of premature ventricular contractions (PVCs) originating from the free wall of the ventricular aspect of the tricuspid annulus (TA). We hypothesized that the negative concordance pattern (NCP) on the onset of UniEGM and BiEGM, together with the least value of the difference between the earliest BiEGM and UniEGM dV/dTmax, might improve the accuracy of conventional mapping. METHODS AND RESULTS: Thirty consecutive patients who underwent successful catheter ablation from February 2018 to July 2021 were retrospectively analyzed. The BiEGM and UniEGM for successful ablation sites were compared with those for non-successful ablation sites. Among the 30 patients, 30 successful and 26 nonsuccessful ablation sites were compared. The earliest activation time of the BiEGM (BiEGMoneset-QRS) was 25 ± 6 ms for the successful ablation sites and 21 ± 6 ms for the nonsuccessful ablation sites (p = .47). The value of the difference in the earliest BiEGM and UniEGM dV/dTmax differed between successful and nonsuccessful ablation sites (6.4 ± 3.6 ms vs. 10.4 ± 6.8 ms). NCP was observed at 90.0% and 42.3% of the successful and nonsuccessful ablation sites, respectively. Alignment of NCP and BiEGMonset-UniEGM ≤6 ms was applied as the mapping criterion for successful PVC suppression (73.1% sensitivity and 87.7% specificity). The area under the receiver-operating characteristic curve for this cutoff was 0.85. CONCLUSION: Mapping based on an NCP at the onset of the BiEGM and UniEGM and the least difference value of the earliest BiEGM and UniEGM dV/dTmax had an excellent predictive value for successful ablation. These strategies may reduce the number of radiofrequency catheter ablation (RFCA) applications for free-wall tricuspid annular PVCs.