Objective: To investigate the expressions of serum miRNA-126 (miR-126) and miRNA-30c (miR-30c) in patients with pancreatic cancer, and to analyze the relationship with the occurrence of pancreatic cancer as well as the diagnostic value. Methods: A total of 110 patients with pancreatic cancer diagnosed at the 928th Hospital of the Joint Service Support Force of PLA from January 2014 to December 2018 were selected, and 110 healthy people were also selected as the control group. The expression levels of serum miR-126 and miR-30c of 110 patients and the healthy controls were detected by using real-time quantitative polymerase chain reaction (qRT-PCR), and their relationship with clinicopathological features of pancreatic cancer was analyzed. Results: The levels of serum miR-126 and miR-30c in pancreatic cancer group were lower than those in the healthy control group (0.43±0.12 vs. 1.02±0.27, t = 19.394, P < 0.01; 0.52±0.17 vs. 1.04±0.31, t = 15.426, P < 0.01). There was a positive correlation between levels of serum miR-126 and miR-30c in patients with pancreatic cancer (r = 0.399, P < 0.01). Expression levels of serum miR-126 and miR-30c were not correlated with age, gender and body mass index (BMI) of pancreatic cancer patients (all P > 0.05), but related with tumor size, differentiation degree, TNM stage and lymph node metastasis (all P < 0.05). High expression levels of serum miR-126 and miR-30c were protective factors for pancreatic cancer (all P < 0.05), while smoking, drinking and gallstones were risk factors for pancreatic cancer (all P < 0.05). The area under the curve of combined detection of serum miR-126 and miR-30c for diagnosis of pancreatic cancer was 0.906, the sensitivity was 90.80%, and the specificity was 82.20%. Conclusions The expression levels of serum miR-126 and miR-30c in patients with pancreatic cancer are low. Both of them may be involved in the occurrence and development of pancreatic cancer, and may be used as early diagnostic markers of pancreatic cancer.

Objective To discuss the temperature difference of infant incubator treatment for the baby to provide important guarantee, to ensure its safe operationMethods Using a completely independent of the infant incubator's temperature monitoring and alarm system of infant incubator temperature and monitoring and alarm system, 20 sets of real-time monitoring and automatic logging data, different brand infant incubator temperature changes compared with infant incubator's own body temperature, and analysis.Results20 sets of different brand infant incubator, 10(50%) in the devices panel display data with a monitoring device differences in measured data, clinical pose a safety hazard. Conclusions For clinical use of infant incubator temperature real-time monitor, by the monitoring system for additional auxiliary monitoring and alarm, thereby improve infant incubator clinical application security, reduce the incidence of related medical accidents, improve the quality of medical treatment.

Objective:To realize the optimization and upgradation of the detection method of you only look once(YOLO)algorithm model based on the improved YOLO network on the ultrasound image for breast tumor.Methods:A total of 659 images of breast tumor of the Kaggle database were selected as the initially dataset,and the image annotation tool Labelimg was used to conduct pre-labeling for the detection targets in the images.According to a ratio as 7:3,629 images of the 659 images were divided into the train set and validation set,and the other 30 images were used as the test set.The convolutional block attention module(CBAM)and bidirectional feature pyramid network(BiFPN)were introduced into the original YOLO algorithm to underwent structural improvement,which was named as YOLOv5-BiFPN-CBAM.Both the train set and validation set were placed in original YOLO algorithm model and YOLOv5-BiFPN-CBAM model to conduct train,which included 200 rounds of iterative training.The obtained optimal weight files were used in the final test of test set.Results:After 200 rounds of iterative train for two kinds of models,the test results of validation set indicated that the mean values of average precision of two kinds of models were respectively 72.1%and 80.5%for all ultrasound images of breast tumor.The result,that the optimal weight file of improved model was tested by test set,indicated the test ability of improved model was significantly enhanced than that of original model for small target in image.Conclusion:Compared with the original YOLO algorithm model,the improved YOLO algorithm model has higher recognition capability for image,which also enhances precision and sensitivity in identifying small targets of ultrasound images of breast tumor.This model is helpful to improve the diagnostic efficiency in clinical practice for breast tumor.

Objective To investigate the correlations between the FBN1 gene mutation types and the clinical phenotype . Methods 87 probands with Marfan or Marfan-like syndromes and their family members were enrolled in this study ( total 300 cases).The clinical manifestations of each patients involving the ocular, cardiovascular system, skeletal system and other im-plicated systems were collected and evaluated .According to the clinical manifestations , these patients were divided into two groups, namely aortic dissection group and aortic root aneurysm group.Blood samples were taken from patients and DNA se-quencing was performed on each patient by the genetic aortic disease gene Panel .The detected single nucleotide variants ( SNVs)/indel were interpreted according to the ACMG guidelines, and the pathogenic variation was confirmed through Sanger sequencing.The aortic wall tissue was obtained from MFS patients who underwent surgery .The correlations between genotypes and clinical phenotypes were further explored by comparing the aortic wall tissue histological specimens of each genotype pa-tient.Results A total of 92 FBN1 mutations(31％) were detected in 300 people with Marfan syndromes or Marfan-like syn-dromes, 18 of which were undiscovered mutations.There were 49 missense mutations(53.26％), 13 splicing mutations (14.13％), 17 frameshift mutations(18.48％), and 13 nonsense mutations(14.13％).In this cohort, 24 cases had aortic dissection and 25 cases were aortic root aneurysm.Statistical analysis revealed that patients with aortic dissection mostly ap-peared in frameshift mutations(29.17％ vs.4.00％, P =0.017).However, patients with aortic root aneurysm mostly ap-peared in missense mutations(72.00％ vs.37.50％, P =0.015), and accompanied with ectopia lentis(41.67％ vs. 8.33％, P=0.008).Pathological specimens staining found that elastic fibers in the aortic wall of patients with frameshift mu-tations are sparser, and the smooth muscle cells are more deficient and more disorganized than patients with missense muta-tions.Conclusion FBN1 gene frameshift mutations result a lack of elastic fibers and disorganized smooth muscle cells in aor-tic wall and are presented more in patients with aortic dissection than aortic root aneurysm .