Background Ovarian cancer (OC) ranks fifth as a cause of gynecological cancer-associated death globally. Until now, the molecular mechanisms underlying the tumorigenesis and prognosis of OC have not been fully understood. This study aims to identify hub genes and therapeutic drugs involved in OC. Methods Four gene expression profiles (GSE54388, GSE69428, GSE36668, and GSE40595) were downloaded from the Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) in OC tissues and normal tissues with an adjusted P-value < 0.05 and a |log fold change (FC)| > 1.0 were first identified by GEO2R and FunRich software. Next, Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) analyses were performed for functional enrichment analysis of these DEGs. Then, the hub genes were identified by the cytoHubba plugin and the other bioinformatics approaches including protein-protein interaction (PPI) network analysis, module analysis, survival analysis, and miRNA-hub gene network construction was also performed. Finally, the GEPIA2 and DGIdb databases were utilized to verify the expression levels of hub genes and to select the candidate drugs for OC, respectively. Results A total of 171 DEGs were identified, including 114 upregulated and 57 downregulated DEGs. The results of the GO analysis indicated that the upregulated DEGs were mainly involved in cell division, nucleus, and protein binding, whereas the biological functions showing enrichment in the downregulated DEGs were mainly negative regulation of transcription from RNA polymerase II promoter, protein complex and apicolateral plasma membrane, and glycosaminoglycan binding. As for the KEGG-pathway, the upregulated DEGs were mainly associated with metabolic pathways, biosynthesis of antibiotics, biosynthesis of amino acids, cell cycle, and HTLV-I infection. Additionally, 10 hub genes (KIF4A, CDC20, CCNB2, TOP2A, RRM2, TYMS, KIF11, BIRC5, BUB1B, and FOXM1) were identified and survival analysis of these hub genes showed that OC patients with the high-expression of CCNB2, TYMS, KIF11, KIF4A, BIRC5, BUB1B, FOXM1, and CDC20 were statistically more likely to have poorer progression free survival. Meanwhile, the expression levels of the hub genes based on GEPIA2 were in accordance with those based on GEO. Finally, DGIdb database was used to identify 62 small molecules as the potentially targeted drugs for OC treatment. Conclusions In summary, the data may produce new insights regarding OC pathogenesis and treatment. Hub genes and candidate drugs may improve individualized diagnosis and therapy for OC in future.

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Background Pulmonary mucosa‐associated lymphoid tissue lymphoma (MALToma) is the most frequent subset of primary pulmonary lymphoma. This study aimed to identify radiologic characteristics of pulmonary MALToma based on computed tomography (CT) observations and pathologic features, and further investigate its prognosis. Methods Sixty‐six patients (55.4 ± 10.9 years; 51.5% male) diagnosed as pulmonary MALToma by pathology were retrospectively enrolled. According to distributions and features of lesions shown on CT, patients were divided into three patterns, including single nodular/mass, multiple nodular/mass, and pneumonia‐like consolidative. Results Variety of the location and extent of the lymphomatous infiltration accounted for different characteristics demonstrated at CT. The pneumonia‐like consolidative pattern was the most frequent pattern observed in 42 patients (63.6%), followed by single nodular/mass (21.2%) and multiple nodular/mass (15.2%). CT features included air bronchogram (72.7%), well‐marginated halo sign (53.0%), coarse spiculate with different lengths (72.7%), angiogram sign (77.1% of 35 patients), peribronchovascular thickening (48.5%), irregular cavitation (16.7%) and pulmonary cyst (7.6%). The estimated 5‐year cumulative overall survival rate of pulmonary MALToma was 100.0%. Conclusions Pulmonary MALToma demonstrates several characteristics at CT. Identification of the significant pulmonary abnormalities of this indolent disease entity might be helpful for early diagnosis and optimal treatment.

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With the help of Fe3O4 nagnetic nanoparticles as a solid carrier and an excellent tool for separation, six SNP loci (rs2279115 of BCL2 gene, rs804270 of NEIL2 gene, rs909253 of LTA gene, rs2294008 of PSCA gene, rs3765524 and rs10509670 of PLCE1 gene) were selected to evaluate their relation to gastric cancer risk. Using two kinds of functionalized magnetic nanoparticles and universal tagged arrays, the whole operation procedure including genome DNA extraction and SNP genotyping was performed. All genotypes and allele frequencies were calculated in the cases and controls respectively to analyze their association with gastric cancer risk. Totally 200 pathological samples and 134 normal control subjects were collected. The results demonstrated that four SNP loci (rs2279115, rs804270, rs909253 and rs3765524) showed a potential association with gastric cancer risk, and the other two (rs2294008, rs10509670) possessed no difference/association among cases and controls.

Objectives This work aimed to study the early predictive value of extrapancreatic inflammation on magnetic resonance imaging (EPIM) for acute pancreatitis (AP) severity. Methods The EPIM score, magnetic resonance severity index, Acute Physiology and Chronic Health Evaluation (APACHE II) score, bedside index of severity in AP, and high-sensitivity C-reactive protein levels were evaluated for 337 AP patients. The extrapancreatic inflammation on computed tomography (EPIC) was also assessed for 86 patients undergoing computed tomography. The predictive values of these scores for severe AP and organ failure were evaluated using receiver operating characteristic curve analyses. Results Of the 337 AP patients, 55 (16.3%) had organ failure and 17 (5.0%) had severe AP. The EPIM showed a strong correlation with the EPIC (r = 0.794, P < 0.001) and had a higher correlation with the APACHE II and hospital stay compared with the EPIC. The accuracy of the EPIM in predicting severe AP and organ failure (areas under the curve, 0.844 and 0.817) was consistent with that of the APACHE II and bedside index of severity in AP, and higher than that of the magnetic resonance severity index. Conclusion The EPIM is more helpful in assessing AP severity than the EPIC and can indicate the occurrence of severe AP and organ failure early.

Background: The scoring of Ki-67 is highly relevant for the diagnosis, classification, prognosis, and treatment in breast invasive ductal carcinoma (IDC). Traditional scoring method of Ki-67 staining followed by manual counting, is time-consumption and inter−/intra observer variability, which may limit its clinical value. Although more and more algorithms and individual platforms have been developed for the assessment of Ki-67 stained images to improve its accuracy level, most of them lack of accurate registration of immunohistochemical (IHC) images and their matched hematoxylin-eosin (HE) images, or did not accurately labelled each positive and negative cell with Ki-67 staining based on whole tissue sections (WTS). In view of this, we introduce an accurate image registration method and an automatic identification and counting software of Ki-67 based on WTS by deep learning. Methods: We marked 1017 breast IDC whole slide imaging (WSI), established a research workflow based on the (i) identification of IDC area, (ii) registration of HE and IHC slides from the same anatomical region, and (iii) counting of positive Ki-67 staining. Results: The accuracy, sensitivity, and specificity levels of identifying breast IDC regions were 89.44, 85.05, and 95.23%, respectively, and the contiguous HE and Ki-67 stained slides perfectly registered. We counted and labelled each cell of 10 Ki-67 slides as standard for testing on WTS, the accuracy by automatic calculation of Ki-67 positive rate in attained IDC was 90.2%. In the human-machine competition of Ki-67 scoring, the average time of 1 slide was 2.3 min with 1 GPU by using this software, and the accuracy was 99.4%, which was over 90% of the results provided by participating doctors.