BackgroundPrevious methods for Kupffer cells (KCs) isolation require sophisticated skills and tedious procedures. Few studies have attempted to explore the self-renewal capacity of KCs in vitro. Therefore, the aim of this study was to establish a simple method for rat KCs isolation and further investigate the mitotic potential of KCs in vitro.MethodsKCs were obtained by performing one-step perfusion, enzymatic tissue treatment, differential centrifugation and selective adherence. The proliferation ability of cultured KCs was determined by MTT assay and Propidium Iodide FACS analysis. Phagocytic assay and ED-1, ED-2 immunofluorescence were used to identify cell phenotype. After stimulation with LPS, the expression of surface antigens (MHCII, CD40, CD80, and CD86) and the production of cytokines (NF-κB, TNF-α, IL-6 and IL-10) were measured for cell function identification.ResultsKCs were isolated with certain numbers and reasonable purities. The KCs were able to survive until at least passage 5 (P5), and at P3 showed equally strong phagocytic activity as primary KCs (P0). After stimulation with LPS, the change in the expression of surface antigens and the production of cytokines for P3 cells was similar to that for P0 cells.ConclusionsOur study provides a simple and efficient method for KCs isolation, and reveals that self-renewing KCs have the same phagocytic activity and functions as primary KCs.

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AimsTo evaluate the diagnostic value of three-dimensional rotational angiography (3D-RA) of intracranial micro-aneurysms (diameter ≤ 3 mm) and provide guidance on the value of endovascular treatment. Materials and methods 43 patients with intracranial micro-aneurysms were analyzed retrospectively, all patients had undergone angiography with both conventional 2D-DSA(Two-Dimensional Digital Subtraction Angiography) and rotational angiography with three-dimensional reconstruction; the frequency of detection of aneurysms, depiction of aneurysm neck, radiation dose, and the dosage of contrast agent were recorded respectively. Results 55 pieces of aneurysms were detected out from the 43 cases with intracranial micro-aneurysms by 3D-RA. But only 39 cases were detected out using 2D-DSA from the 55 samples, there were significant differences with regards to detection rate (P < 0.05). There were significant differences in radiation dose and dosage of contrast agent (P < 0.05) between the two methods of using 3D-RA can improve the detection rate of micro-aneurysms, which bestows obvious advantages on displaying the shape of aneurysms, the aneurysm neck at the best angle, and the relationship with the parent artery, at the same time, the amount of contrast agent and radiation dose are reduced in 3D-RA compared to 2D-DSA. Keywords Three-dimensional rotational angiography, Intracranial micro-aneurysm, Three dimensional reconstruction AimsIn order to improve the medical imaging, some immune computation theories and immune algorithms were reviewed and compared. Materials and methodsThe immune computation theories include the self and nonself theory, danger theory, artificial immune network etc. The immune algorithms include self/nonself detection algorithm, normal model construction algorithm, clonal selection algorithm, negative selection algorithm, danger model algorithm and hybrid immune algorithm etc. We improved the clonal selection algorithm to attain the optimal threshold for better segmentation of the medical images than the traditional approach. Results The X-ray medical image of the tuberculosis was processed with the improved clonal selection algorithm and noise filtering, and the output medical image of our approach is better for diagnosis than that of traditional image processing methods. ConclusionsThe immune algorithm can be improved to establish a better medical imaging, and this kind of medical application system is inspired from the human immune system. AcknowledgementsSupported by the project grants from National Natural Science Foundation of China (Grand No. 61673007, 61271114, 11572084, 11472061 and 61203325) Aims Traditional medical image classification methods focus on feature representation and classifier design. However, they seldom concerns data selection used for model training, which plays key role for model tuning and parameter optimization. This paper proposes a novel medical image classification method according to guided bagging. Materials and methods First, unsupervised learning is implemented...

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