Objective To observe the effective outcome of application of Ruyi golden powder and magnesium sulfate in the treatment of phlebitis caused by amiodarrone.Methods 60 patients were randomly divided into the observation group and the control group (30 cases in each group).The external application of Ruyi golden powder which was prepared with 75％ alcohol into a paste was used in the observation group and 50％ magnesium sulphate was used in the control group.Results The total effective rate of two groups of patients was 93.3％ in the observation group and 66.7％ in the control group,the significant difference was found by the comparison of the two groups.Conclusions The external application of Ruyi golden powder with alcohol in the treatment of phlebitis caused by amiodarrone is better than magnesium sulfate.

Objective To investigate the mechanism and clinical characteristics of rapid spontaneous resolution of acute subdural hematoma in children. Methods The clinical data of 9 children with rapid spontaneous resolution of acute subdural hematoma were retrospective analyzed. Results Subdural hematoma of three cases were completely dissolved within 8 h, while those of the other 6 cases were significantly reduced which were completely dissolved in 48-72 h. Conclusions Rapid spontaneous resolution of acute subdural hematoma in children is rare in clinical practice. The redistribution and dilution of hematoma and the anatomical characteristics of the children patient determine the possibility of hematoma dissipation. The conservative treatment can get a good prognosis.

AIM:To determine the relationship between microhistology and cardiac contractility in myocarditis animal model. METHODS:Setting up myocarditis animal model by injecting Coxsackivevirus B 3 (CVB 3) into mice, then observed myocardial morphological changes and measured left ventricular function of mice at the time of first three days and two weeks after injecting CVB 3.RESULTS:Subcellular structure (mitochondria) changed at the first three days after injecting CVB 3. The left ventricular pressure (LVP) and the rate of intraventricular pressure development (d p /d t ) which is the index of reflecting cardiac contractility depressed in this stage (14.2?0.8) kPa and (273.1?10.0)kPa/s, respectively. There were (17.1?0.7)kPa and (359.8?9.3)kPa/s in normal mice, respectively ( P

Objective: To explore the related factors of subependymal hemorrhage (SEH) and cerebral hemodynamic changes. Methods: From October 2012 to October 2017, 200 cases of children with subependymal hemorrhage diagnosed by ultrasound in our department of pediatrics were selected as the observation group , and a total of 150 children who were admitted to the Department of Pediatrics in the same period due to craniocerebral diseases and other serious diseases were selected as control group. The independent risk factors of the children in the observation group were analyzed, and the difference of the maximum systolic blood flow velocity (SV), the diastolic maximum flow velocity (DV), the systolic and diastolic velocity ratio (S/D), the resistance index (RI), and the pulsatile index (PI) were compared between the two groups. Results: Neonatal asphyxia, preterm birth, acidosis, neonatal respiratory distress syndrome (NRDS), patent ductus arteriosus and coagulation dysfunction were independent risk factors for subependymal hemorrhage. The bleeding side SV and DV of the observation group were higher than those of the control group, with statistically significant difference (P<0.001). In the observation group, the bleeding side SV and DV were higher than those of the healthy side, with statistically significant difference (P<0.001). There was no significant difference in bleeding side SV, DV, S/D, RI and PI in 110 cases of single side ependymal hemorrhage (P>0.05). Conclusions Children with ependymal hemorrhage can observe the hemodynamic indexes of anterior cerebral artery (ACA) dynamically by craniocerebral ultrasound, and judge the therapeutic effect by evaluating the systolic and diastolic blood flow velocity, so as to prevent the further aggravation of subependymal hemorrhage

Organoids,recognized as invaluable models in tumor and stem cell research,assume a pivotal role in the meticulous analysis of diverse datasets pertaining to their growth dynamics,drug screening processes and related phenomena.However,the manual scrutiny and conventional statistical methodologies employed in handling organoid data often grapple with challenges such as diminished precision and efficiency,heightened complexity,escalated human resource requirements,and a degree of subjectivity.Acknowledging the remarkable efficacy of artificial intelligence(AI)in the realms of biology and medicine,the incorporation of AI into organoid research stands poised to enhance the objectivity,precision and expediency of analyses.This integration empowers organoids to more effectively fulfill objectives such as disease modeling,drug screening and precision medicine.Notably,significant strides have been made in AI-driven analyses of organoid image data.The amalgamation of deep learning into image analysis facilitates a more meticulous delineation of the microstructural intricacies and nuanced changes within organoids,achieving a level of accuracy akin to that of experts.This not only elevates the precision of organoid morphology and growth recognition,but also contributes to substantial time and cost savings in research endeavors.Furthermore,the infusion of AI technology has yielded breakthroughs in the processing of organoid omics data,resulting in heightened efficiency in data processing and the identification of latent gene expression patterns.This furnishes novel tools for comprehending cellular development and unraveling the intricate mechanisms underlying various diseases.In addition to image data,AI techniques applied to diverse organoid datasets,encompassing electrical signals and spectra,have realized an unbiased classification of organoid types and states,embarking on a comprehensive journey towards characterizing organoids holistically.In the pivotal domain of drug screening for organoids,AI emerges as a stalwart companion,providing robust support for real-time process monitoring and result prediction.Leveraging high-content microscopy images and sophisticated deep learning models,researchers can dynamically monitor organoid responses to drugs,effecting non-invasive detection of drug impacts and amplifying the precision and efficiency of drug screening processes.Despite the significant strides made by AI in organoid research,challenges persist,encompassing hurdles in data acquisition,constraints in sample quality and quantity,and quandaries associated with model interpretability.Overcoming these challenges necessitates dedicated future research efforts aimed at enhancing data consistency,fortifying model interpretability,and exploring methodologies for the seamless fusion of multimodal data.Such endeavors are poised to usher in a more comprehensive and dependable application of AI in organoid research.In summation,the integration of AI technology introduces unparalleled opportunities to organoid research,resulting in noteworthy advancements.Nevertheless,interdisciplinary research and collaborative efforts remain imperative to navigate challenges and propel the more profound integration of AI into organoid research.The future holds promise for AI to assume an even more prominent role in advancing organoid research toward clinical translation and precision medicine.