Objective To upgrade ordinary gastrointestinal machine to a digital one which can be connected to PACS.Methods By adding extra camera lens,frame grabber and processing software,ordinary gastrointestinal machine was transformed.Results The updated machine could work in digital way with fully DICOM support.Conclusion By analyzing the X-ray imaging chain,a digitalization method of gastrointestinal machine is presented,which can server as a guideline to digital transformation of non-digital X-ray machines.

Objective To explore the value of combined detection of parathyroid hormone(PTH),β2-microglobulin(β2-MG)and high sensitivity C-reactive protein(hs-CRP)in differentiating diagnosis of acute and chronic renal failure. Meth?ods Patients diagnosed with renal failure was divided into acute renal failure patients group (n=64) and chronic renal fail?ure group (n=74) . Other patients with non-renal failure kidney disease (NRF group, n=80) and healthy adult (control group, n=80) were also selected. Levels of PTH,β2-MG, hs-CRP , PTH+β2-MG, PTH+hs-CRP,β2-MG+hs-CRP and PTH+β2-MG+ hs-CRP were compared between these four groups to choose the optimal combination for differential diagnosis. Re?sults Hs-CRP in ARF group was significantly higher than that in control group and CRF group. Levels of PTH andβ2-MG were significantly lower in ARF group than that in CRF groups but not in control group (P < 0.01). hs-CRP andβ2-MG in the ARF group were moderately higher (68.7%) and severely higher (81.2%) while PTH was mild higher (25%) in ARF group than those in control group, On the other hand, hs-CRP andβ2-MG in the ARF group were moderately higher (56.8%) and severely higher (98.6%) while PTH was mild higher (39.2%) in ARF group than those in control group. Combina?tion of hs-CRP withβ2-MG could increase total case rate (TCR) and Youden index (YI) of ARF;while combination of PTH andβ2-MG could improve the TCR and YI of CRF. Conclusion Combined detection of PTH,β2-MG and hs-CRP is use?ful in differential diagnosis of ARF and CRF.

Fiber photometry is a sensitive and easy way to detect changes in fluorescent signals. The combination of fiber photometry with various fluorescent biomarkers has substantially advanced neuroscience research over the last decade. Despite the wide use of fiber photometry in biomedical fields, the lack of a detailed and comprehensive protocol has limited progress and sometimes complicated the interpretation of data. Here, we describe detailed procedures of fiber photometry for the long-term monitoring of neuronal activity in freely-behaving animals, including surgery, apparatus setup, data collection, and analysis.
Animals ; Brain ; metabolism ; Calcium Signaling ; Female ; Male ; Mice ; Neurons ; metabolism ; Neurosurgical Procedures ; Optical Fibers ; Optical Imaging ; instrumentation ; methods ; Photometry ; instrumentation ; methods

Objective: To investigate the distribution and resistance of pathogens isolated from blood cultures in patients with hematological malignancies after chemotherapy in Union Hospital of Fujian Medical University so as to understand the real situation of blood stream infection (BSI) and provide the basis for rational use of antibiotics in clinic. Methods: The data of 657 strains isolated from blood culture specimens of patients with hematological malignancies from January 2013 to December 2016 were collected analyzed. Results: A total of 657 cases of blood culture positive bacterial strains were included in the study, involving 410 cases (62.4%) with single Gram-negative bacteria (G^- bacteria) , 163 cases (24.8%) with single Gram-positive bacteria (G⁺ bacteria) , 50 cases (7.6%) with single fungi. The most common 5 isolates in blood culture were Klebsiella pneumoniae (17.5%) , Escherichia coli (17.2%) , Coagulase negative staphylococci (CNS) (14.9%) , Pseudomonas aeruginosa (14.2%) and Staphylococcus aureus (3.5%) . The extended-spectrum beta-lactamase (ESBL) production rates of Klebsiella pneumoniae and Escherichia coli were 25.2% and 55.8%, respectively. ESBL producing strains were almost more resistant than non-ESBL producing strains. The resistance rates of Enterobacteriaceae to carbapenems, piperacillin/tazobactam and tigecycline were lower than 14.0%. The resistance rates of Pseudomonas aeruginosa to a variety of drugs were lower than 12.0%. Tigecycline-resistant Acinetobacter baumannii bacteria were not detected, and the resistance rates of Acinetobacter baumannii to cefixime and cefotaxime were 7.1%. Methicillin-resistant strains in CNS (MRCNS) and in Staphylococcus aureus (MRSA) accounted for 84.7% and 43.5%, respectively. Vancomycin, linezolid and tigecycline-resistant G⁺ bacteria were not detected. Conclusion The pathogens isolated from blood culture were widely distributed. Most of them were G^- bacteria, and the resistance to antibiotics was quite common. Furhermore, vancomycin, linezolid and tigecycline can be chosen empirically to treat patiens who ar suspected to have G⁺ bacterial BSI.

Objective:To propose a machine learning-based markerless beam′s eye view (BEV) tumor tracking algorithm that can be applied to low-quality megavolt (MV) images with multileaf collimator (MLC)-induced occlusion and non-rigid deformation.Methods:This study processed the registration of MV images using the window template matching method and end-to-end unsupervised network Voxelmorph and verified the accuracy of the tumor tracking algorithm using dynamic chest models. Phantom QA plans were executed after the treatment offset was manually set on the accelerator, and 682 electronic portal imaging device (EPID) images obtained during the treatment were collected as fixed images. Moreover, the digitally reconstructed radiography (DRR) images corresponding to the portal angles in the planning system were collected as floating images for the study of target volume tracking. In addition, 533 pairs of EPID and DRR images of 21 lung tumor patients treated with radiotherapy were collected to conduct the study of tumor tracking and provide quantitative result of changes in tumor locations during the treatment. Image similarity was used for third-party validation of the algorithm.Results:The algorithm could process images with different degrees (10%-80%) of data missing and performed well in non-rigid registration of images with data missing. As shown by the phantom verification, 86.8% and 80% of the tracking errors were less than 3 mm and less than 2 mm, respectively, and the normalized mutual information (NMI) varied from 1.18 ± 0.02 to 1.20 ± 0.02 after registration ( t = -6.78, P = 0.001). The tumor motion of the clinical cases was dominated by translation, with an average displacement of 3.78 mm and a maximum displacement of 7.46 mm. The registration result of the cases showed the presence of non-rigid deformations, and the corresponding NMI varied from 1.21 ± 0.03 before registration to 1.22 ± 0.03 after registration ( t = -2.91, P = 0.001). Conclusions:The tumor tracking algorithm proposed in this study has reliable tracking accuracy and high robustness and can be used for non-invasive and real-time tumor tracking requiring no additional equipment and radiation dose.

Objective:To evaluate the feasibility of predicting lung cancer target position by online optical surface motion monitoring.Methods:CT images obtained in different ways of stereotactic body radiotherapy (SBRT) plans from 16 lung cancer cases were selected for experimental simulation. The planned CT and the original target position were taken as the reference, and the 10 phases of CT in four dimension CT and each cone beam (CBCT) were taken as the floating objects, on which the floating target location was delineated. The binocular visual surface imaging method was used to obtain point cloud data of reference and floating image body surface, while the point cloud feature information was extracted for comparison. Based on the random forest algorithm, the feature information difference and the corresponding target area position difference were fitted, and an online prediction model of the target area position was constructed.Results:The model had a high prediction success rate for the target position. The variance explainded and root mean squared error ( RMSE) of left-right, superior-inferior, anterior-posterior directions were 99.76%, 99.25%, 99.58%, and 0.0447 mm, 0.0837 mm, 0.0616 mm, respectively. Conclusion:The online monitoring of lung SBRT target position proposed in this study is feasible, which can provide reference for online monitoring and verification of target position and dose evaluation in clinical radiotherapy.

Objective:To propose a markerless beam's eye view (BEV) tumor tracking algorithm, which can be applied to megavolt (MV) images with poor image quality, multi-leaf collimator (MLC) occlusion and non-rigid deformation.Methods:Window template matching, image structure transformation and demons non-rigid registration method were used to solve the registration problem in MV images. The quality assurance (QA) plan was generated in the phantom and executed after manually setting the treatment offset on the accelerator, and 682 electronic portal imaging device (EPID) images in the treatment process were collected as fixed images. Meanwhile, the digitally reconstructured radiograph (DRR) images corresponding to the field angle in the planning system were collected as floating images to verify the accuracy of the algorithm. In addition, a total of 533 images were collected from 21 cases of lung tumor treatment data for tumor tracking study, providing quantitative results of tumor location changes during treatment. Image similarity was used for third-party verification of tracking results.Results:The algorithm could cope with different degrees (10%-80%) of image missing. In the phantom verification, 86.8% of the tracking errors were less than 3 mm, and 80% were less than 2 mm. Normalized mutual information (NMI) varied from 1.182±0.026 to 1.202±0.027 ( P<0.005) before and after registration and the change of Hausdorff distance (HD) was from 57.767±6.474 to 56.664±6.733 ( P<0.005). The case results were predominantly translational (-6.0 mm to 6.2 mm), but non-rigid deformation still existed. NMI varied from 1.216±0.031 to 1.225±0.031 ( P<0.005) before and after registration and the change of HD was from 46.384±7.698 to 45.691±8.089 ( P<0.005). Conclusions:The proposed algorithm can cope with different degrees of image missing and performs well in non-rigid registration with data missing images which can be applied in different radiotherapy technologies. It provides a reference idea for processing MV images with multi-modality, partial data and poor image quality.

Licking behavior is important for water intake. The deep mesencephalic nucleus (DpMe) has been implicated in instinctive behaviors. However, whether the DpMe is involved in licking behavior and the precise neural circuit behind this behavior remains unknown. Here, we found that the activity of the DpMe decreased during water intake. Inhibition of vesicular glutamate transporter 2-positive (VGLUT2⁺) neurons in the DpMe resulted in increased water intake. Somatostatin-expressing (SST⁺), but not protein kinase C-δ-expressing (PKC-δ⁺), GABAergic neurons in the central amygdala (CeA) preferentially innervated DpMe VGLUT2⁺ neurons. The SST⁺ neurons in the CeA projecting to the DpMe were activated at the onset of licking behavior. Activation of these CeA SST⁺ GABAergic neurons, but not PKC-δ⁺ GABAergic neurons, projecting to the DpMe was sufficient to induce licking behavior and promote water intake. These findings redefine the roles of the DpMe and reveal a novel CeA^SST-DpMe^VGLUT2 circuit that regulates licking behavior and promotes water intake.