No abstract

Lung cancer screening based on low-dose CT (LDCT) has now been widely applied because of its effectiveness and ease of performance. Radiologists who evaluate a large LDCT screening images face enormous challenges, including mechanical repetition and boring work, the easy omission of small nodules, lack of consistent criteria, etc. It requires an efficient method for helping radiologists improve nodule detection accuracy with efficiency and cost-effectiveness. Many novel deep neural network-based systems have demonstrated the potential for use in the proposed technique to detect lung nodules. However, the effectiveness of clinical practice has not been fully recognized or proven. Therefore, the aim of this study to develop and assess a deep learning (DL) algorithm in identifying pulmonary nodules (PNs) on LDCT and investigate the prevalence of the PNs in China. Radiologists and algorithm performance were assessed using the FROC score, ROC-AUC, and average time consumption. Agreement between the reference standard and the DL algorithm in detecting positive nodules was assessed per-study by Bland-Altman analysis. The Lung Nodule Analysis (LUNA) public database was used as the external test. The prevalence of NCPNs was investigated as well as other detailed information regarding the number of pulmonary nodules, their location, and characteristics, as interpreted by two radiologists. Lung cancer screening has now been widely applied because of its effectiveness and ease of performance. More than 10 million chest CT scans were performed in the United States alone in 2012, highlighting the potential for this clinical scenario 1. Radiologists who evaluate a large number of low-dose CT (LDCT) screening images face enormous challenges, including mechanical repetition and boring work, the easy omission of small nodules, lack of consistent criteria, etc. 2-5. Artificial intelligence (AI) is showing rapid advantages and exciting achievements in the fields of imaging diagnosis and/or evaluation 6-12. AI detection of lung nodules has long been expected to be an effective assistant in daily clinical practice, especially for LDCT lung nodule screening. Thus far, many novel deep neural networkbased systems have demonstrated the potential for use in the proposed technique for helping radiologists improve nodule detection accuracy with efficiency and cost-effectiveness 13-18. However, the majority of the proposed systems were trained on CT scans from the Lung Image Database Consortium/Image Database Resource Initiative (LIDC-IDRI), the LUNA16 (Lung Nodule Analysis 2016) database and the ANODE09 (Automatic Nodule Detection 2009) database 19-24. The effectiveness in clinical practice has not been fully recognized or proven. From the previous study, the prevalence of pulmonary nodules (PNs) varies greatly in different populations, ranging from 13 to 58% 25-32. Different demographic features, selection criteria of participants, and the referral pattern of the study centre may explain such differences 27. In China, the incidence of non-c...

Using the combination of a cDNA library prepared from membrane glucocorticoid (mGR)-enriched S-49 cells and a mouse leukocyte genomic library, we have cloned a 7.3 kb full-length glucocorticoid receptor 1A cDNA. Primer extension, 5'RACE, and long distance PCR identified the transcription start site as being located at 1026 bp from the ATG codon. The first 1,013 nucleotides (nts) of the full length sequence constitute 5' UTR sequence (exon 1), the next 2349 bp, the coding region, and the last 3,907 bp, the 3'UTR. The entire 5'UTR sequence is unique to transcript 1A. The 3'UTR sequence is approximately 88.5 % conserved with the rat 3'UTR. Western blot analysis compared the molecular weight of in vitro translation products from the cloned 1A cDNA with partially purified cellular mGR. Both preparations contained the novel 150 KD and the 94 KD classical GR peptides, suggesting that transcript 1A encodes both receptor forms. Transfection of mGR-less and glucocorticoid lysis-resistant AtT-20 and HL-60 cells with full-length GR 1A cDNA imparted both mGR expression and glucocorticoid lysis-sensitivity to these cells.

DHT down-regulates NF-kappaB mediated IL-6 expression by human TCC lines in response to BCG. This effect depends on a functional androgen receptor signaling pathway and it can be blocked by the inhibition of androgen/androgen receptor binding.