Olivia K. Ottone, Jorge J. Mundo, Boahen N. Kwakye
et al.
ABSTRACTDespite the high prevalence of age‐dependent intervertebral disc calcification, there is a glaring lack of treatment options for this debilitating pathology. We investigated the efficacy of long‐term oral K3Citrate supplementation in ameliorating disc calcification in LG/J mice, a model of spontaneous age‐associated disc calcification. K3Citrate reduced the incidence of disc calcification without affecting the vertebral bone structure, knee calcification, plasma chemistry, or locomotion in LG/J mice. Notably, a positive effect on grip strength was evident in treated mice. FTIR spectroscopy of the persisting calcified nodules indicated K3Citrate did not alter the mineral composition. Mechanistically, activation of an endochondral differentiation in the cartilaginous endplates and nucleus pulposus (NP) compartment contributed to LG/J disc calcification. Importantly, K3Citrate reduced calcification incidence by Ca2+ chelation throughout the disc while exhibiting a differential effect on NP and endplate cell differentiation. In the NP compartment, K3Citrate reduced the NP cell acquisition of a hypertrophic chondrocytic fate, but the pathologic endochondral program was unimpacted in the endplates. Overall, this study for the first time shows the therapeutic potential of oral K3Citrate as a systemic intervention strategy to ameliorate disc calcification.
Tuhina Neogi, Maureen Dubreuil, Christine Peloquin
et al.
ObjectiveThe objective of this study was to assess the relation of physical therapy (PT) timing, dose, and type with risk of future intra‐articular therapy use in people with knee osteoarthritis (OA) who receive PT.MethodsWe used data from a deidentified claims database (Optum Labs Data Warehouse) from American adults with incident knee OA referred for PT within the first year of their knee OA diagnosis. We categorized people as having previously had intra‐articular therapies or not. We examined the association of timing of PT initiation, number of PT sessions, and type of PT (predominantly active or passive) with intra‐articular therapy use over a period of one year following the first year of diagnosis.ResultsOf the 67,245 individuals with knee OA (age 61.5 ± 11 years, 61% female, 10% Black, 6% Hispanic), 34,804 and 32,441 did and did not have prior intra‐articular therapies, respectively. Among those who had prior intra‐articular therapies, initiating PT at 9 to 12 months post diagnosis was associated with an adjusted risk ratio of 1.44 for future intra‐articular therapy (95% confidence interval 1.35–1.55) compared with those who initiated within a month. For both groups, ≥13 PT sessions was associated with a 10% and 12% lower risk, respectively, compared with 1 to 5 sessions. Active PT was not related to lower risk compared to passive PT interventions.ConclusionInitiating PT earlier and more than 12 PT sessions were significantly associated with lower risk of future intra‐articular therapy use in people with newly diagnosed knee OA.image
Introduction:
Periprosthetic fractures (PPFs) around the knee following total knee arthroplasty (TKA) present a significant challenge in orthopedic management, particularly in patients with rheumatoid arthritis (RA). These fractures are influenced by factors such as bone stock quality, prosthetic status, polyethylene wear, and fracture reducibility. Proper classification and management strategies are essential to optimize outcomes and prevent complications.
Case Report:
A 52-year-old female with RA sustained a right knee PPF following a trivial fall. Clinical and radiographic evaluation classified the fracture based on prosthetic stability and bone quality:
• Type II fracture with a loose prosthesis requiring revision surgery.
She underwent staged revision TKA including:
1.Definitive surgery with revision TKA and bone grafting in figure.
2.Postoperative rehabilitation to restore function figure.
Conclusion:
Management of PPFs in RA patients requires a comprehensive approach, including preoperative planning, prosthetic evaluation, osteoporosis management, and staged surgical intervention when necessary. This case highlights the importance of addressing osteoporosis alongside osteoarthritis in improving TKA outcomes. Further research is needed to refine treatment strategies for PPFs in RA patients.
Orthopedic surgery, Diseases of the musculoskeletal system
Abstract Background Utilizing liposomal bupivacaine (LB) for postoperative analgesia post-total knee arthroplasty (TKA) is prevalent. However, its effectiveness in pain control, specifically in the parasacral ischial plane block (PIPB) post-TKA, remains unknown. Methods This single-center, double-blinded, randomized controlled trial recruited patients scheduled for unilateral TKA. Forty-five patients were randomly assigned in a 1:1 ratio to receive 133 mg (Group A) or 266 mg (Group B) LB using the block randomization method. The PIPB effectiveness was assessed by evaluating changes in sensory and motor functions. The primary outcome was the cumulative area under the curve (AUC) of the Numerical Rating Scale (NRS) at rest within 72 h postoperatively. All patients were included in the analyses of analgesic efficacy, rehabilitation quality, and adverse events. Results Between January 30, 2024, and May 1, 2024, 45 patients were enrolled and randomly assigned to Group A (n = 22) and Group B (n = 23). A significant between-group difference was observed in the NRS-AUC0-72 h at rest postoperatively (132.3 ± 19.7 vs. 97.3 ± 19.1, p = 0.001), but none was observed in NRS-AUC0-72 h during activity (p = 0.642). Kaplan–Meier survival analysis revealed significant between-group differences in the median onset times of sensory [60 vs. 35(min), p < 0.0001] and motor blocks [85 vs. 50(min), p < 0.0001]. The onset time of sensory block was notably shorter than that of motor block in both groups. No significant variance was observed in the median regression time for the sensory block. A significant between-group difference in the rescue analgesic dosage was observed on the first postoperative day [43.1 vs. 27.2(mg), p = 0.009], with no significant differences in the subsequent two days or the total amount. No significant between-group differences were found in adverse events or rehabilitation quality. Conclusion LB used in the PIPB was effective for analgesia at rest post-TKA, with 266 mg demonstrating superiority. Trial RegistrationThe randomized controlled trial was registered in the Chinese Clinical Trial Registry (https://www.chictr.org.cn/, No: ChiCTR2400079606)
Orthopedic surgery, Diseases of the musculoskeletal system
Valentina Carbonari, Pierangelo Veltri, Pietro Hiram Guzzi
Recent advances in artificial intelligence, particularly large language models LLMs, have shown promising capabilities in transforming rare disease research. This survey paper explores the integration of LLMs in the analysis of rare diseases, highlighting significant strides and pivotal studies that leverage textual data to uncover insights and patterns critical for diagnosis, treatment, and patient care. While current research predominantly employs textual data, the potential for multimodal data integration combining genetic, imaging, and electronic health records stands as a promising frontier. We review foundational papers that demonstrate the application of LLMs in identifying and extracting relevant medical information, simulating intelligent conversational agents for patient interaction, and enabling the formulation of accurate and timely diagnoses. Furthermore, this paper discusses the challenges and ethical considerations inherent in deploying LLMs, including data privacy, model transparency, and the need for robust, inclusive data sets. As part of this exploration, we present a section on experimentation that utilizes multiple LLMs alongside structured questionnaires, specifically designed for diagnostic purposes in the context of different diseases. We conclude with future perspectives on the evolution of LLMs towards truly multimodal platforms, which would integrate diverse data types to provide a more comprehensive understanding of rare diseases, ultimately fostering better outcomes in clinical settings.
As a social being, we have an intimate bond with the environment. A plethora of things in human life, such as lifestyle, health, and food are dependent on the environment and agriculture. It comes under our responsibility to support the environment as well as agriculture. However, traditional farming practices often result in inefficient resource use and environmental challenges. To address these issues, precision agriculture has emerged as a promising approach that leverages advanced technologies to optimise agricultural processes. In this work, a hybrid approach is proposed that combines the three different potential fields of model AI: object detection, large language model (LLM), and Retrieval-Augmented Generation (RAG). In this novel framework, we have tried to combine the vision and language models to work together to identify potential diseases in the tree leaf. This study introduces a novel AI-based precision agriculture system that uses Retrieval Augmented Generation (RAG) to provide context-aware diagnoses and natural language processing (NLP) and YOLOv8 for crop disease detection. The system aims to tackle major issues with large language models (LLMs), especially hallucinations and allows for adaptive treatment plans and real-time disease detection. The system provides an easy-to-use interface to the farmers, which they can use to detect the different diseases related to coffee leaves by just submitting the image of the affected leaf the model will detect the diseases as well as suggest potential remediation methodologies which aim to lower the use of pesticides, preserving livelihoods, and encouraging environmentally friendly methods. With an emphasis on scalability, dependability, and user-friendliness, the project intends to improve RAG-integrated object detection systems for wider agricultural applications in the future.
Victor Gustavo Balera Brito, Austin Bell‐Hensley, Hongjun Zheng
et al.
ABSTRACT MicroRNAs are small non‐coding RNAs that regulate cellular pathways by targeting multiple mRNAs, playing critical roles in skeletal development and homeostasis. Our previous miRNA profiling studies identified higher levels of miR‐127‐3p in the hypertrophic zone of developing human growth plates while another group found this miRNA to be more highly expressed in murine hindlimb cartilage compared to calvarial bone. Other published work revealed elevated circulating miR‐127‐3p levels in osteoporotic patients and in long bones of ovariectomized mice. Collectively, these findings suggest a role for miR‐127‐3p in regulating bone formation. To fill a knowledge gap, we designed a study to determine the function of miR‐127‐3p in regulating osteogenic differentiation of human bone marrow‐derived mesenchymal stromal cells (hBMSCs). While inhibition of miR‐127‐3p had no effect, mimic overexpression robustly inhibited in vitro osteogenesis. Bulk RNA‐sequencing showed a number of cellular pathways affected, including suppression of proliferation‐related pathways, which was confirmed by decreased BrdU incorporation. To assess the translational potential of the bone suppressing function of this miRNA, we utilized a pre‐clinical mouse model of trauma‐induced heterotopic ossification involving Achilles tendon transection. Local delivery of miR‐127‐3p mimics via peptide‐based nanoparticle technology significantly reduced ectopic bone formation at the proximal site of the transected tendon. These studies demonstrate that approaches to overexpress miR‐127‐3p and induce bone suppressing activity may be of therapeutic value as a means to treat many forms of heterotopic ossification.
Abstract Background Although early hemivertebra (HV) resection and short fusion (within 4 segments) have been successful in treating congenital HV, there is limited research comparing the outcomes of the shortest-segment fusion (2 segments) versus 3 or 4 segments, particularly in young children. To evaluate the efficacy of posterior hemivertebrectomy combined with two or more segments fusion in children under the age of 10 years with a solitary simple lower thoracic or lumbar HV (T8-L5). Methods This retrospective study included patients under the age of 10 with lower thoracic or lumbar solitary simple HV who underwent hemivertebra resection (HVR) and transpedicular short fusion and were divided into HV ± 1 group (2 segment fusion) and HV ± 2 group (3 or 4-segment fusion). The study recorded preoperative, postoperative (1 week), and the latest follow-up radiographic parameters and complications. The results of the coronal and sagittal planes were analyzed, and the main curve, segmental scoliosis curve, compensatory scoliosis curve, segmental kyphosis curve, and trunk shift were compared. Results The study included 35 patients (15 in the HV ± 1 group and 20 in the HV ± 2 group) with a mean age of 5.26 ± 2.31 years and a mean follow-up of 22.54 months (12–68). The mean preoperative Cobb angle was 32.66° ± 7.339° (HV ± 1) and 29.31°±6.642° (HV ± 2). The final Cobb angle was 10.99°± 7.837° (HV ± 1) and 8.22° ± 4.295° (HV ± 2). The main curve corrected by 72% (HV ± 1), 75% (HV ± 2) postoperatively and 67% (HV ± 1), 72% (HV ± 2) at the final follow-up (P > 0.05). There were no significant differences in the correction of the segmental scoliosis curve, compensatory scoliosis curve, segmental kyphosis curve, and trunk shift between the HV ± 1 and HV ± 2 groups (P > 0.05). The unplanned reoperation rate for HV in the thoracolumbar region (T11-L2) is significantly higher (P = 0.038). Conclusion In the context of solitary simple lower thoracic or lumbar HV (T8-L5), HV ± 1 segment fusion suffices and yields comparable correction outcomes in the midterm period when compared to HV ± 2. The reoperation rate exhibited a statistically significant increase in the thoracolumbar region.
Anthony J. Goff, Lester E. Jones, Chien Joo Lim
et al.
Abstract Background Physical activity is a guideline-recommended first-line intervention for people with knee osteoarthritis. Physical activity levels, and its potential correlates, is underexplored in Asian populations with knee osteoarthritis. Methods Participants enrolled in a longitudinal study in Singapore self-reported physical activity (UCLA activity score), function (Knee Osteoarthritis Outcome Score [KOOS-12]), kinesiophobia (Brief fear of movement [BFOM]), self-efficacy (ASES-8), and quality of life (EQ-5D-5 L). One-Way ANOVA was used to test the difference in outcomes between UCLA categories, while ordinal logistic regression was used to identify the associated factors to physical activity level. Results Seventy-three percent of all enrolled participants (n = 311/425) reported either inactivity or low physical activity (median 4, IQR 3–5). Significant, weak, positive correlations were observed be-tween UCLA activity score and either KOOS-12 (Spearman’s rho: 0.1961; p < 0.001), ASES-8 (0.1983; p = 0.004), or EQ-5D-5 L (0.2078; p < 0.001). A significant, weak, negative correlation was observed between physical activity and BFOM (-0.2183; p < 0.001). Significant differences in function between groups (moderate vs. inactive or low physical activity) were not clinically important. Participants with obesity, from the eldest age category (i.e. ≥75), or who identified as Malay or female, were less physically active than those with a healthy BMI, below the age of 54, or who identified as Chinese or male, respectively. Conclusion Healthcare professionals in Asia should be aware of the large proportion of people with knee osteoarthritis who are either inactive or have low physical activity levels. Screening for, and offering interventions to promote, physical activity and its correlates should be prioritised.
In recent years, ML algorithms have been shown to be useful for predicting diseases based on health data and posed a potential application area for these algorithms such as modeling of diseases. The majority of these applications employ supervised rather than unsupervised ML algorithms. In addition, each year, the amount of data in medical science grows rapidly. Moreover, these data include clinical and Patient-Related Factors (PRF), such as height, weight, age, other physical characteristics, blood sugar, lipids, insulin, etc., all of which will change continually over time. Analysis of historical data can help identify disease risk factors and their interactions, which is useful for disease diagnosis and prediction. This wealth of valuable information in these data will help doctors diagnose accurately and people can become more aware of the risk factors and key indicators to act proactively. The purpose of this study is to use six supervised ML approaches to fill this gap by conducting a comprehensive experiment to investigate the correlation between PRF and Diabetes, Stroke, Heart Disease (HD), and Kidney Disease (KD). Moreover, it will investigate the link between Diabetes, Stroke, and KD and PRF with HD. Further, the research aims to compare and evaluate various ML algorithms for classifying diseases based on the PRF. Additionally, it aims to compare and evaluate ML algorithms for classifying HD based on PRF as well as Diabetes, Stroke, Asthma, Skin Cancer, and KD as attributes. Lastly, HD predictions will be provided through a Web-based application on the most accurate classifier, which allows the users to input their values and predict the output.
The development of an intelligent agricultural decision-supporting system for crop selection and disease forecasting in Bangladesh is the main objective of this work. The economy of the nation depends heavily on agriculture. However, choosing crops with better production rates and efficiently controlling crop disease are obstacles that farmers have to face. These issues are addressed in this research by utilizing machine learning methods and real-world datasets. The recommended approach uses a variety of datasets on the production of crops, soil conditions, agro-meteorological regions, crop disease, and meteorological factors. These datasets offer insightful information on disease trends, soil nutrition demand of crops, and agricultural production history. By incorporating this knowledge, the model first recommends the list of primarily selected crops based on the soil nutrition of a particular user location. Then the predictions of meteorological variables like temperature, rainfall, and humidity are made using SARIMAX models. These weather predictions are then used to forecast the possibilities of diseases for the primary crops list by utilizing the support vector classifier. Finally, the developed model makes use of the decision tree regression model to forecast crop yield and provides a final crop list along with associated possible disease forecast. Utilizing the outcome of the model, farmers may choose the best productive crops as well as prevent crop diseases and reduce output losses by taking preventive actions. Consequently, planning and decision-making processes are supported and farmers can predict possible crop yields. Overall, by offering a detailed decision support system for crop selection and disease prediction, this work can play a vital role in advancing agricultural practices in Bangladesh.
The recent focus on microbes in human medicine highlights their potential role in the genetic framework of diseases. To decode the complex interactions among genes, microbes, and diseases, computational predictions of gene-microbe-disease (GMD) associations are crucial. Existing methods primarily address gene-disease and microbe-disease associations, but the more intricate triple-wise GMD associations remain less explored. In this paper, we propose a Heterogeneous Causal Metapath Graph Neural Network (HCMGNN) to predict GMD associations. HCMGNN constructs a heterogeneous graph linking genes, microbes, and diseases through their pairwise associations, and utilizes six predefined causal metapaths to extract directed causal subgraphs, which facilitate the multi-view analysis of causal relations among three entity types. Within each subgraph, we employ a causal semantic sharing message passing network for node representation learning, coupled with an attentive fusion method to integrate these representations for predicting GMD associations. Our extensive experiments show that HCMGNN effectively predicts GMD associations and addresses association sparsity issue by enhancing the graph's semantics and structure.
Brendan Antiochos, Daniela Trejo-Zambrano, Paride Fenaroli
et al.
Background: Nucleic acid binding proteins are frequently targeted as autoantigens in systemic lupus erythematosus (SLE) and other interferon (IFN)-linked rheumatic diseases. The AIM-like receptors (ALRs) are IFN-inducible innate sensors that form supramolecular assemblies along double-stranded (ds)DNA of various origins. Here, we investigate the ALR absent in melanoma 2 (AIM2) as a novel autoantigen in SLE, with similar properties to the established ALR autoantigen interferon-inducible protein 16 (IFI16). We examined neutrophil extracellular traps (NETs) as DNA scaffolds on which these antigens might interact in a pro-immune context. Methods: AIM2 autoantibodies were measured by immunoprecipitation in SLE and control subjects. Neutrophil extracellular traps were induced in control neutrophils and combined with purified ALR proteins in immunofluorescence and DNase protection assays. SLE renal tissues were examined for ALR-containing NETs by confocal microscopy. Results: AIM2 autoantibodies were detected in 41/131 (31.3%) SLE patients and 2/49 (4.1%) controls. Our SLE cohort revealed a frequent co-occurrence of anti-AIM2, anti-IFI16, and anti-DNA antibodies, and higher clinical measures of disease activity in patients positive for antibodies against these ALRs. We found that both ALRs bind NETs in vitro and in SLE renal tissues. We demonstrate that ALR binding causes NETs to resist degradation by DNase I, suggesting a mechanism whereby extracellular ALR-NET interactions may promote sustained IFN signaling. Conclusions: Our work suggests that extracellular ALRs bind NETs, leading to DNase resistant nucleoprotein fibers that are targeted as autoantigens in SLE. Funding: These studies were funded by NIH R01 DE12354 (AR), P30 AR070254, R01 GM 129342 (JS), K23AR075898 (CM), K08AR077100 (BA), the Jerome L. Greene Foundation and the Rheumatology Research Foundation. Dr. Antiochos and Dr. Mecoli are Jerome L. Greene Scholars. The Hopkins Lupus Cohort is supported by NIH grant R01 AR069572. Confocal imaging performed at the Johns Hopkins Microscopy Facility was supported by NIH Grant S10 OD016374.
Chaoran Liu, Pui Yan Wong, Simon Kwoon Ho Chow
et al.
Background: Cognitive impairment is a major challenge for elderlies, as it can progress in a rapid manner and effective treatments are limited. Sarcopenic elderlies have a higher risk of dementia. This scoping review aims to reveal whether muscle is a mediator of cognitive function from pre-clinical evidence. Methods: PubMed, Embase, and Web of Science were searched to Feb 2nd, 2022, using the keywords (muscle) AND (cognition OR dementia OR Alzheimer) AND (mouse OR rat OR animal). The PRISMA guideline was used in this study. Results: A total of 17 pre-clinical studies were selected from 7638 studies. 4 studies reported that muscle atrophy and injury harmed memory, functional factors, and neurons in the brain for rodents with or without Alzheimer's disease (AD). 3 studies observed exercise induced muscle to secrete factors, including lactate, fibronectin type III domain-containing protein 5 (FNDC5), and cathepsin B, which plays essential roles in the elevation of cognitive functions and brain-derived neurotrophic factor (BDNF) levels. Muscle-targeted treatments including electrical stimulation and intramuscular injections had effective remote effects on the hippocampus. 6 studies showed that muscle-specific overexpression of scFv59 and Neprilysin, or myostatin knockdown alleviated AD symptoms. 1 study showed that muscle insulin resistance also led to deficient hippocampal neurogenesis in MKR mice. Conclusions: The skeletal muscle is involved in the mediation of cognitive function. The evidence was established by the response in the brain (altered number of neurons, functional factors, and other AD pathological characteristics) with muscle atrophy or injury, muscle secretory factors, and muscle-targeted treatments. The translational potential of this paper: This study summarizes the current evidence in how muscle affects cognition in molecular levels, which supports muscle-specific treatments as potential clinical strategies to prevent cognitive dysfunction.
Chest X-rays (CXR) often reveal rare diseases, demanding precise diagnosis. However, current computer-aided diagnosis (CAD) methods focus on common diseases, leading to inadequate detection of rare conditions due to the absence of comprehensive datasets. To overcome this, we present a novel benchmark for long-tailed multi-label classification in CXRs, encapsulating both common and rare thoracic diseases. Our approach includes developing the "LTML-MIMIC-CXR" dataset, an augmentation of MIMIC-CXR with 26 additional rare diseases. We propose a baseline method for this classification challenge, integrating adaptive negative regularization to address negative logits' over-suppression in tail classes, and a large loss reconsideration strategy for correcting noisy labels from automated annotations. Our evaluation on LTML-MIMIC-CXR demonstrates significant advancements in rare disease detection. This work establishes a foundation for robust CAD methods, achieving a balance in identifying a spectrum of thoracic diseases in CXRs. Access to our code and dataset is provided at:https://github.com/laihaoran/LTML-MIMIC-CXR.
Energy storage (ES) and virtual energy storage (VES) are key components to realizing power system decarbonization. Although ES and VES have been proven to deliver various types of grid services, little work has so far provided a systematical framework for quantifying their adequacy contribution and credible capacity value while incorporating human and market behavior. Therefore, this manuscript proposed a novel evaluation framework to evaluate the capacity credit (CC) of ES and VES. To address the system capacity inadequacy and market behavior of storage, a two-stage coordinated dispatch is proposed to achieve the trade-off between day-ahead self-energy management of resources and efficient adjustment to real-time failures. And we further modeled the human behavior with storage operations and incorporate two types of decision-independent uncertainties (DIUs) (operate state and self-consumption) and one type of decision-dependent uncertainty (DDUs) (available capacity) into the proposed dispatch. Furthermore, novel reliability and CC indices (e.g., equivalent physical storage capacity (EPSC)) are introduced to evaluate the practical and theoretical adequacy contribution of ES and VES, as well as the ability to displace generation and physical storage while maintaining equivalent system adequacy. Exhaustive case studies based on the IEEE RTS-79 system and real-world data verify the significant consequence (10%-70% overestimated CC) of overlooking DIUs and DDUs in the previous works, while the proposed method outperforms other and can generate a credible and realistic result. Finally, we investigate key factors affecting the adequacy contribution of ES and VES, and reasonable suggestions are provided for better flexibility utilization of ES and VES in decarbonized power system.
Abstract Background Systemic lupus erythematosus (SLE) is characterized by loss of immune tolerance and imbalance of immune cell subsets. Natural killer (NK) cells contribute to regulate both the innate and adaptive immune response. In this study, we aimed to detect alterations of peripheral NK cells and explore intrinsic mechanisms involving in NK cell abnormality in SLE. Methods Blood samples from healthy controls (HCs) and patients with SLE and rheumatoid arthritis (RA) were collected. The NK count, NK subsets (CD56bright, CD56dimCD57−, and CD56dimCD57+), phenotypes, and apoptosis were evaluated with flow cytometer. Mitochondrial reactive oxygen species (mtROS) and total ROS levels were detected with MitoSOX Red and DCFH-DA staining respectively. Published data (GSE63829 and GSE23695) from Gene Expression Omnibus (GEO) was analyzed by Gene Set Enrichment Analysis (GSEA). Results Total peripheral NK count was down-regulated in untreated SLE patients in comparison to that in untreated RA patients and HCs. SLE patients exhibited a selective reduction in peripheral CD56dimCD57+ NK cell proportion, which was negatively associated with disease activity and positively correlated with levels of complement(C)3 and C4. Compared with HCs, peripheral CD56dimCD57+ NK cells from SLE patients exhibited altered phenotypes, increased endogenous apoptosis and higher levels of mtROS and ROS. In addition, when treated with hydrogen peroxide (H2O2), peripheral CD56dimCD57+ NK cell subset was more prone to undergo apoptosis than CD56dimCD57− NK cells. Furthermore, this NK cell subset from SLE patients exhibited impaired cytotoxicity in response to activated CD4+ T cells in vitro. Conclusion Our study demonstrated a selective loss of mature CD56dimCD57+ NK cell subset in SLE patients, which may caused by preferential apoptosis of this subset under increased oxidative stress in SLE. The attenuated in vitro cytotoxicity of CD56dimCD57+ NK cells may contribute to the impaired ability of eliminating pathogenic CD4+ T cells in SLE.