Rheumatoid arthritis (RA) is an autoimmune disease that can induce joint deformities and functional impairment, significantly impacting the overall well-being of individuals. Exosomes, which are cellularly secreted vesicles, possess favorable biological traits such as biocompatibility, stability, and minimal toxicity. Additionally, they contain nucleic acids, lipids, proteins, amino acids, and metabolites, serving as mediators in cellular communication and information exchange. Recent studies have demonstrated the association between exosomes and the pathogenesis of RA. Exosomes derived from mesenchymal stem cells, dendritic cells, and neutrophils exert influence on the biological functions of immune cells and joint cells, however, the precise mechanism remains largely unclarified. This comprehensive review systematically analyzes and summarizes the biological characteristics and functionalities of exosomes derived from diverse cellular sources, thus establishing a scientific foundation for the utilization of exosomes as diagnostic targets and therapeutic modalities in the context of RA.
Background:Developmental hip dysplasia(DDH) is a common pediatric disease.For patients younger than 6 months of age,ultrasound diagnosis is more suitable for screening and assessment of hip development.At present,there is an urgent need for a reproducible and reliable ultrasound screening method for DDH diagnosis. Purpose: To construct and verify an artificial intelligence-assisted deep learning system for ultrasound diagnosis of developmental hip dysplasia in children. Materials and Methods: 2021 standard sections were selected from January 2019 to January 2021. All standard sections were annotated using unified standards through the image transmedia data annotation and audit system.1753 images were randomly selected to train the deep learning system,the remaining 268 were used to test the system. Results: 268 patients were tested. The AUC for diagnosing hip joint maturity was 0.941, (sensitivity 90.5%, specificity 97.8%),while the AUC for Graf classification was 0.685(sensitivity 45.3% specificity 91.7%),compared with clinicians’ measurements. According to the Bland–Altman method, the 95% limits of agreement of α angle was-6.426°~4.811°(Bias=-0.8075,P < 0.001), that of β angle was -5.545°~6.507°(Bias=0.4812,P=0.057). 7 key points measured by AI were statistically different from the clinician values. Conclusions: The artificial intelligence system could quickly and accurately measure the Graf correlation index of standard hip joint ultrasound images.
Ubiquitination of target proteins is mediated via different ubiquitin lysine (K) linkages and determines the protein fates. In particular, K48 ubiquitin linkage targets proteins for degradation, whereas K63 ubiquitin linkage plays a nondegradative role. Parkinson's disease is an age-onset neurodegenerative disorder, which shows selective loss of dopamine neurons in substantia nigra pars compacta (SNC) and ubiquitinated protein aggregates. However, age-related expression of K48 and K63 ubiquitin linkages in SNC dopamine neurons remains elusive. We thus sought to explore the expression of K48 and K63 ubiquitin linkages in dopamine neurons in SNCs of mice at different ages with morphological and biochemical assays. Here our results indicated that in 5-week-old mice, dopamine neurons presented higher levels of K48 and K63 ubiquitin linkages than nondopamine neural cells. Aging promoted the formation of protein aggregates that are positive for both K48 and K63 ubiquitin linkages, together with tyrosine hydroxylase, a dopamine neuron marker. Moreover, 21-month-old mice showed fewer neural cells and tyrosine hydroxylase positive neurons in the SNCs than younger mice. Through biochemical analysis, the 21-month-old mice were shown to express more K48 ubiquitin linkages and less tyrosine hydroxylase and NeuN than the 5-week-old mice. These results suggest the first time that expression of K48 and K63 ubiquitin lysine linkages in midbrain dopamine neurons is age-related and may be involved in the loss of dopamine neurons.
ObjectiveTo construct an artificial intelligence system to measure acetabular index and evaluate its accuracy in clinical application.MethodsA total of 10,219 standard anteroposterior pelvic radiographs were collected retrospectively from April 2014 to December 2018 in our hospital. Of these, 9,219 radiographs were randomly selected to train and verify the system. The remaining 1,000 radiographs were used to compare the system's and the clinicians' measurement results. All plain pelvic films were labeled by an expert committee through PACS system based on a uniform standard to measure acetabular index. Subsequently, eight other clinicians independently measured the acetabular index from 200 randomly selected radiographs from the test radiographs. Bland–Altman test was used for consistency analysis between the system and clinician measurements.ResultsThe test set included 1,000 cases (2,000 hips). Compared with the expert committee measurement, the 95% limits of agreement (95% LOA) of the system was −4.02° to 3.45° (bias = −0.27°, P < 0.05). The acetabular index measured by the system within all age groups, including normal and abnormal groups, also showed good credibility according to the Bland–Altman principle. Comparison of the measurement evaluations by the system and eight clinicians vs. that of, the expert committee, the 95% LOA of the clinician with the smallest measurement error was −2.76° to 2.56° (bias = −0.10°, P = 0.126). The 95% LOA of the system was −0.93° to 2.86° (bias = −0.03°, P = 0.647). The 95% LOA of the clinician with the largest measurement error was −3.41° to 4.25° (bias = 0.42°, P < 0.05). The measurement error of the system was only greater than that of a senior clinician.ConclusionThe newly constructed artificial intelligence system could quickly and accurately measure the acetabular index of standard anteroposterior pelvic radiographs. There is good data consistency between the system in measuring standard anteroposterior pelvic radiographs. The accuracy of the system is closer to that of senior clinicians.
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