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To evaluate the noise reduction effect of deep learning-based reconstruction algorithms in thin-section chest CT images by analyzing images reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and deep learning image reconstruction (DLIR) algorithms.

The chest CT scan raw data of 47 patients were included in this study. Images of 0.625 mm were reconstructed using six reconstruction methods, including FBP, ASIR hybrid reconstruction (ASIR50%, ASIR70%), and deep learning low, medium and high modes (DL-L, DL-M, and DL-H). After the regions of interest were outlined in the aorta, skeletal muscle and lung tissue of each group of images, the CT values, SD values and signal-to-noise ratio (SNR) of the regions of interest were measured, and two radiologists evaluated the image quality.

CT values, SD values and SNR of the images obtained by the six reconstruction methods showed statistically significant difference (

<0.001). There were statistically significant differences in the image quality scores of the six reconstruction methods (

<0.001). Images reconstruced with DL-H have the lowest noise and the highest overall quality score.

The model based on deep learning can effectively reduce the noise of thin-section chest CT images and improve the image quality. Among the three deep-learning models, DL-H showed the best noise reduction effect.

The model based on deep learning can effectively reduce the noise of thin-section chest CT images and improve the image quality. Among the three deep-learning models, DL-H showed the best noise reduction effect.

The deep learning method was used to automatically segment the tumor area and the cell nucleus based on needle biopsy images of breast cancer patients prior to receiving neoadjuvant chemotherapy (NAC), and then, the features of the cell clusters in the tumor area were identified to predict the level of pathological remission of breast cancer after NAC.

68 breast cancer patients who were to receive NAC at Jiangsu Province Hospital were recruited and the hematoxylin-eosin (HE) stained preoperative biopsy sections of these patients were collected. Unet++ was used to establish a segmentation model and the tumor area and nucleus of the needle biopsy images were automatically segmented accordingly. Then, according to the nuclei in the automatically segmented tumor area, the features of the cells in the tumor were constructed. After that, effective features were selected through the feature selection method and the classifier model was constructed and trained with five-fold cross validation to predict the degrees of the cell clusters which are analyzed and identified in the tumor area can be used to predict the pathological response of the patient to NAC. The method is reliable and replicable. In addition, we found that the textural features of cells in the tumor area was a useful predictor of patient response to NAC, which further confirmed that cell cluster in the tumor area is of great significance to the prediction of treatment outcome.

To explore for the establishment of an experimental technique for profiling transcription factors, namely transcription factor response elements (TFRE), with high throughput and efficiency using human atrial tissue.

Postoperative right atrial tissues from 2 patients, one with preoperative atrial fibrillation and the one with no preoperative atrial fibrillation, were included in the study. The nucleus protein was extracted from the human atrial tissue, and the protein concentration was then measured. A solution with a complex formed through combining magnetic beads with concatenated tandem array of the consensus transcription factor response element DNA sequence (beads-catTFRE) was prepared, and the beads-catTFREs were then used to enrich transcription factors in the nucleoprotein extraction. SDS-PAGE electrophoresis was performed after dissociating beads-catTFRE from nucleoprotein with high temperature and high salt. The gel was then cut and faded before enzymolysis by trypsin in the gels was performed. Aished in this study has high coverage, and the data collected can be used to support further validation studies.

To investigate the potential association between multimorbidity and the handgrip strength of middle-aged and older adults.

The baseline (2011) and second-round follow-up (2015) data of China Health and Retirement Longitudinal Study (CHARLS) were used. Adults≥40 were selected as the subjects of the study. Variables incorporated in the study included handgrip strength, chronic disease prevalence, demographic variables, and health behavior variables. Generalized estimating equations were used to analyze the longitudinal association between handgrip strength and multimorbidity.

A total of 28 368 middle-aged and older adults were included in the baseline and follow-up samples, with an average age of (59.1±9.7) years old, the oldest being 96 while the youngest being 40. Among them, 6 239 were male, accounting for 47.3%. In the second-round follow-up, 9 186 baseline respondents and 5 994 new respondents were covered, reaching a total of 15 180 respondents. Compared with the baseline, a higher proportion of theically significant at baseline, but the follow-up done four years afterwards showed statistical significant correlation between handgrip strength and multimorbidity.

Respondents with lower baseline handgrip strength are associated with increasingly higher risk of multimorbidity over time. Handgrip strength can be used as an effective screening tool for middle-aged and older adults in China to identify those at higher risks of multimorbidity of chronic diseases.

Respondents with lower baseline handgrip strength are associated with increasingly higher risk of multimorbidity over time. Handgrip strength can be used as an effective screening tool for middle-aged and older adults in China to identify those at higher risks of multimorbidity of chronic diseases.

To explore the individual or combined effects of adiponectin, leptin, and soluble leptin receptor (sOB-R) on risks for premenopausal and postmenopausal breast cancer, and to provide evidence for revealing the molecular mechanism between obesity and breast cancer.

469 newly-diagnosed breast cancer cases were sequentially recruited for the study and 469 age-frequency-matched healthy women were enrolled as the controls over the same period of time. Avexitide ic50 The participant baseline information was collected with questionnaires, and plasmic levels of adiponectin, leptin and sOB-R were checked with ELISA. Multivariate unconditional logistic regression was conducted and the analyses were further stratified according to waist-to-hip ratio (WHR) and body mass index (BMI) to explore the effect of the indicators on the risks for premenopausal and postmenopausal breast cancer.

A total of 480 premenopausal and 458 postmenopausal women were included in the study. Among the premenopausal subjects, 249 were breast cancer patients and 231 were controls.