Objective To explore the effect of N -acetylcysteine(NAC)in adjunctive treatment of neonatal bronchopneumonia and its influence on serum inflammatory factors.Methods 62 neonatus with bronchopneumonia treated in our hospital from June 2012 to December 2014 were randomly divided into observation group(32 cases)and control group(30 cases)with random number table.The control group was treated with basic therapy,such as relieving a cough and asthma,defervescing,oxygen inhalation,keeping respiratory tract unobstructed,nutritional support,while the observation group was added aerosol inhalation of NAC on the basis of basic therapy.Then the effect of the average time of body temperature returned to normal,the disappearance time of cough,asthma and pulmonary rale,and average hospitalization days of two groups were observed and compared,the levels of serum tumor necrosis factor -α(TNF -α), interleukin -1(IL -1),interleukin -6(IL -6)and interleukin -10(IL -10)of the two groups before and after treatment were detected.Results The total effective rate of the observation group was 90.63%,which was higher than 70% of the control group (χ2 =4.220,P <0.05).The average time of body temperature returned to normal, disappearance time of cough asthma and pulmonary rale,and average hospitalization days of the observation group were shorter than those of the control group (t =-4.211,-3.472,-4.240,-5.413,-7.822,all P <0.05).The levels of serum TNF -α,IL -1,IL -6 and IL -10 of the observation group after treatment were lower than those of the control group (t =-6.421,-3.892,-15.452,-9.773,all P <0.05).Conclusion Using aerosol inhalation of NAC in adjunctive treatment of neonatal bronchopneumonia can significantly improve the clinical effect and shorten the course of disease,meanwhile,it will eases the systemic inflammatory response to damage the organs by regulating the level of inflammatory factors,which is good for the recover of neonatus as soon as possible.

Objective To explore the automated identification and diameter measurement methods for inferior vena cava (IVC) based on clinical ultrasound images of IVC. Methods An automated identification and localization method based on topology and automatic tracking algorithm was proposed. Tracking algorithm was used for identifying and continuously locating to improve the efficiency and accuracy of measurement. Tests were conducted on 18 sets of ultrasound data collected from 18 patients in intensive care unit (ICU),with clinicians' measurements as the gold standard. Results The recognition accuracy of the automated method was 94.44％ (17/18),and the measurement error of IVC diameter was within the range of±1.96s (s was the standard deviation). The automated method could replace the manual method. Conclusion The proposed IVC automated identification and localization algorithm based on topology and automatic tracking algorithm has high recognition success rate and IVC diameter measurement accuracy. It can assist clinicians in identifying and locating IVC,so as to improve the accuracy of IVC measurement.

Objective To propose a classification method for ultrasound images of pedicle screw canals based on radiomics analysis,and to evaluate the integrity of the screw canal.Methods With thoracolumbar spine specimens from 4 fresh cadavers,50 pedicle screw canals were pre-established and ultrasound images of the canals were acquired.A total of 2 000 images(1 000 intact and 1 000 damaged canal samples)were selected.The dataset was randomly divided in a 4∶1 ratio using 5-fold cross-validation to form training and testing sets(consisting of 1 600 and 400 samples,respectively).Firstly,the optimal radius of the region of interest was identified using the Otsu's thresholding method,followed by feature extraction using pyradiomics.Principal component analysis and the least absolute shrinkage and selection operator algorithm were employed for dimensionality reduction and feature selection,respectively.Subsequently,3 machine learning models(support vector machine[SVM],logistic regression,and random forest)and 3 deep learning models(visual geometry group[VGG],ResNet,and Transformer)were used to classify the ultrasound images.The performance of each model was evaluated using accuracy.Results With a region of interest radius of 230 pixels,the SVM model achieved the highest classification accuracy of 96.25%.The accuracy of the VGG model was only 51.29%,while the accuracies of the logistic regression,random forest,ResNet,and Transformer models were 85.50%,80.75%,80.17%,and 75.18%,respectively.Conclusion For ultrasound images of pedicle screw canals,the machine learning model performs better than the deep learning model as a whole,and the SVM model has the best classification performance,which can be used to assist physicians in diagnosis.

OBJECTIVE: To investigate the effect of miR-186 inhibition on the expression of hypoxia-inducible factor-1α (HIF-α) and mitochondrial function in hypoxic vascular endothelial cells. METHODS: Human umbilical vein endothelial cells (HUVECs) cultured in routine or hypoxic conditions for 6 h were examined for the expression of miR-186. A miR-186 inhibitor was transfected in the HUVECs, and the cells were subsequently cultured in hypoxic condition for 6 h to observe the changes in the mitochondrial structure under an electron microscope. The changes in the mRNA and protein expressions of HIF-1α in response to miR-186 interference were tested using real-time fluorescent quantitative PCR and Western blotting. RESULTS: The expression of miR-18 was mildly increased in HUVECs after hypoxic exposure for 6 h (=0.0188). Interference of miR-186 expression obviously promoted the mRNA and protein expressions of HIF-1α in HUVECs. In hypoxic conditions, miR-186 interference significantly reduced mitochondrial damage in HUVECs as observed under electron microscope (=0.0297). CONCLUSIONS Inhibition of miR-186 protects vascular endothelial cells against hypoxic injuries by promoting HIF-α expression to lessen mitochondrial damage, suggesting the possibility of targeted miR-186 interference for treatment of hemorrhagic shock.
Cell Hypoxia ; Human Umbilical Vein Endothelial Cells ; Humans ; Hypoxia ; Hypoxia-Inducible Factor 1, alpha Subunit ; MicroRNAs ; Mitochondria ; Umbilical Veins