Hermansenesbensen5145
circ_0041795 was identified to act as a miR-589-5p sponge. The regulation of circ_0041795 in HG-induced cell injury was achieved by inhibiting miR-589-5p. miR-589-5p targeted YAP1 and relieved HG-induced cell dysfunction via downregulating YAP1. circ_0041795 sponged miR-589-5p to regulate YAP1 level and activated the NF-
B pathway through the miR-589-5p/YAP1 axis.
All these results elucidated that circ_0041795 facilitated the development of DR by inducing miR-589-5p-mediated YAP1 upregulation.
All these results elucidated that circ_0041795 facilitated the development of DR by inducing miR-589-5p-mediated YAP1 upregulation.The purpose of this study was to construct a multidisciplinary collaborative nursing model for pulmonary embolism risk prediction, early warning, and precontrol based on the Smith model and evaluate the application effect and hospitalization satisfaction. 2,037 patients hospitalized in the thoracic surgery department from June 1, 2019, to May 31, 2021, were selected as the research subjects. The control group received routine pulmonary embolism prevention management, while the experimental group received safe, case-based, and programmatic multidisciplinary intervention management based on the Smith policy management model. The data were analyzed statistically. The experimental group's extubation and hospitalization time, D-dimer value, incidence of deep vein thrombosis, and pulmonary embolism on the seventh day after surgery were lower than those in the control group, and the satisfaction of hospitalization in the experimental group was higher than that in the control group. The implementation of the multidisciplinary collaborative nursing model of pulmonary embolism risk prediction, early warning, and precontrol based on the Smith model can promote the preventive effect of pulmonary embolism risk of surgical patients in our department and effectively improve the satisfaction of hospitalization, which is worthy for further promotion.
To compare the effects of different dosing schemes of furosemide on acute heart failure (AHF).
Literature that compared the efficacy of continuous and intermittent administration of furosemide in AHF patients was retrieved from PubMed, Embase, the Cochrane Library, and ISI Web of Science from inception to May 2022. The primary endpoints included overall weight loss, 24-hour urine volume, length of hospital stay, 24-hour brain natriuretic peptide (BNP) level change, and all-cause mortality. The RevmMan5.4 software was used to analyze the extracted data.
A total of 10 studies with 775 patients, including 338 receiving continuous furosemide administration and 387 receiving intermittent furosemide administration, were included. selleck chemical The analysis results showed significant differences in weight loss (MD = 1.08, 95% CI (0.75~1.40),
< 0.00001) and 24-hour urine volume (MD =335.23, 95% CI (140.98~529.47),
= 0.0007) between the 2 groups. There was no significant difference in terms of length of hospital stay (MD = -0.71, 95% CI (-2.74~1.31),
= 0.49) and all-cause mortality (RR = 1.59, 95% CI (0.92~2.75),
= 0.10).
Compared with intermittent administration, continuous infusion of furosemide had a significant effect on the 24-hour urine volume and total weight loss in patients with AHF.
Compared with intermittent administration, continuous infusion of furosemide had a significant effect on the 24-hour urine volume and total weight loss in patients with AHF.Traditional image segmentation methods often encounter problems of low segmentation accuracy and being time-consuming when processing complex tooth Computed Tomography (CT) images. This paper proposes an improved segmentation method for tooth CT images. Firstly, the U-Net network is used to construct a tooth image segmentation model. A large number of feature maps in downsampling are supplemented to downsampling to reduce information loss. At the same time, the problem of inaccurate image segmentation and positioning is solved. Then, the attention module is introduced into the U-Net network to increase the weight of important information and improve the accuracy of network segmentation. Among them, subregion average pooling is used instead of global average pooling to obtain spatial features. Finally, the U-Net network combined with the improved attention module is used to realize the segmentation of tooth CT images. And based on the image collection provided by West China Hospital for experimental demonstration, compared with other algorithms, our method has better segmentation performance and efficiency. The contours of the teeth obtained are clearer, which is helpful to assist the doctor in the diagnosis.Rib fractures are common injuries caused by chest trauma, which may cause serious consequences. It is essential to diagnose rib fractures accurately. Low-dose thoracic computed tomography (CT) is commonly used for rib fracture diagnosis, and convolutional neural network- (CNN-) based methods have assisted doctors in rib fracture diagnosis in recent years. However, due to the lack of rib fracture data and the irregular, various shape of rib fractures, it is difficult for CNN-based methods to extract rib fracture features. As a result, they cannot achieve satisfying results in terms of accuracy and sensitivity in detecting rib fractures. Inspired by the attention mechanism, we proposed the CFSG U-Net for rib fracture detection. The CSFG U-Net uses the U-Net architecture and is enhanced by a dual-attention module, including a channel-wise fusion attention module (CFAM) and a spatial-wise group attention module (SGAM). CFAM uses the channel attention mechanism to reweight the feature map along the channel dimension and refine the U-Net's skip connections. SGAM uses the group technique to generate spatial attention to adjust feature maps in the spatial dimension, which allows the spatial attention module to capture more fine-grained semantic information. To evaluate the effectiveness of our proposed methods, we established a rib fracture dataset in our research. The experimental results on our dataset show that the maximum sensitivity of our proposed method is 89.58%, and the average FROC score is 81.28%, which outperforms the existing rib fracture detection methods and attention modules.
To study the changes in the intestinal flora and its relationship with nutritional status for patients with cancer pain.
A prospective research method was adopted. One hundred twenty cancer patients with cancer pain were selected as the research objects, who were treated in our hospital from June 2019 to June 2020, and 120 cancer patients without cancer pain were selected as the control group, who were treated in the same period. The differences of the intestinal flora and nutritional status of patients with different severity between the observation group and the control group were compared to analyze the changes of intestinal flora in patients with cancer pain and its correlation with nutritional status.
Hemoglobin (HB) (
= 17.141,
≤ 0.001), albumin (ALB) (
= 27.654,
≤ 0.001), prealbumin (PAB) (
= 96.192,
≤ 0.001), and total protein (TP) (
= 18.781,
≤ 0.001) in the observation group were significantly lower than those in the control group. There were statistically significant diffeed as an important basis for improving the treatment of cancer pain.
There was a significant correlation between the changes in intestinal flora and nutritional status for patients with cancer pain, which could be used as an important basis for improving the treatment of cancer pain.
Regarding the imperfect mechanism of occurrence and development of prostate adenocarcinoma (PRAD), this study investigated mRNA-modified FUS/NRF2 signalling to inhibit ferroptosis and promote prostate adenocarcinoma growth.
Bioinformatics analysis was used to obtain the expression of FUS and its mRNA modification in PRAD. The expression of FUS in prostate cells (CRPC) and the level of m
A methylation modification, ferroptosis (P53 and GPX4), apoptosis (Caspase3), ferroptosis (P53 and GPX4), and apoptosis (Caspase3) in CRPC after ferroptosis inducer Erastin, ferroptosis inhibitor, and FUS knockdown were detected. Autophagy (LC3B), oxidative stress (GSH and ROS), and expression of NRF2/HO-1 pathway are indicators.
FUS was highly expressed in PRAD and phenomenally reduced the survival rate of patients. After knocking down FUS, the level of m
A methylation was significantly reduced, and the expressions of ferroptosis markers P53 and GPX4 were phenomenally reduced, while the levels of apoptosis and autophagy markers Caspase3 and LC3B remained unchanged. Upregulated and NRF2/HO-1 pathway indicators were upregulated. It shows that m
A methylation modification is reduced when FUS is the low expression, inhibits the expression of P53 and GPX4, downregulates GSH, upregulates ROS, activates the NRF2/HO-1 pathway, and promotes ferroptosis to inhibit the occurrence of RPAD.
The increase of m
A methylation modification can increase the expression of FUS, thereby promoting the expression of P53 and GPX4, upregulating GSH, downregulating ROS, inhibiting the NRF2/HO-1 pathway, inhibiting ferroptosis, and promoting the growth of PRAD.
The increase of m6A methylation modification can increase the expression of FUS, thereby promoting the expression of P53 and GPX4, upregulating GSH, downregulating ROS, inhibiting the NRF2/HO-1 pathway, inhibiting ferroptosis, and promoting the growth of PRAD.To address the problem of low precision in feature segmentation of biological images with large noise, a microfeature segmentation algorithm for biological images using improved density peak clustering was proposed. First, the center pixel and edge information of a biological image were obtained to remove some redundant information. The three-dimensional space of the image is constructed, and the coordinate system is used to describe every superpixel of the biological image. Second, the image symmetry and reversibility are used to obtain the stopping position of pixels, other adjacent points are used to obtain the current color and shape information, and more vectors are used to express the density to complete the image pretreatment. Finally, the improved density peak clustering method is used to cluster the image, and the pixels completed by clustering and the remaining pixels are evenly distributed into the space to segment the image so as to complete the microfeature segmentation of the biological image based on the improved density peak clustering method. The results show that the proposed algorithm improves the segmentation efficiency, segmentation integrity rate, and segmentation accuracy. The time consumed by the proposed biological image microfeature segmentation algorithm is always less than 2 minutes, and the segmentation integrity rate can reach more than 90%. Furthermore, the proposed algorithm can reduce the missing condition and the noise of the segmented image and improve the image feature segmentation effect.
Based on computerized tomography (CT) radiomics and clinical data, a model was established to predict the prognosis of patients with gastrointestinal pancreatic neuroendocrine neoplasms (GP-NENs).
In the data collection, the clinical imaging and survival follow-up data of 225 GP-NENs patients admitted to Xiangyang No.1 People's Hospital and Jiangsu Province Hospital of Chinese Medicine from August 2015 to February 2021 were collected. According to the follow-up results, they were divided into the nonrecurrent group (
= 108) and the recurrent group (
= 117), based on which a training set and a test set were established at a ratio of 7/3. In the training set, a variety of models were established with significant clinical and imaging data (
< 0.05) to predict the prognosis of GP-NENs patients, and then these models were verified in the test set.
Our newly developed combined prediction model had high predictive efficacy. Univariate analysis showed that Radscore 1/2/3, age, Ki-67 index, tumor pathological type, tumor primary site, and TNM stage were risk factors for the prognosis of GP-NENs patients (all
< 0.
