CBCT measurements were an accurate representation of the clinical thickness of both labial gingiva and bone. In addition, the thickness of the labial gingiva had a moderate association with the underlying bone radiographically.

Background Occult peritoneal metastasis (PM) in advanced gastric cancer (AGC) patients is highly possible to be missed on computed tomography (CT) images. Patients with occult PMs are subject to late detection or even improper surgical treatment. We therefore aimed to develop a radiomic nomogram to preoperatively identify occult PMs in AGC patients. Patients and methods A total of 554 AGC patients from 4 centers were divided into 1 training, 1 internal validation, and 2 external validation cohorts. All patients’ PM status was firstly diagnosed as negative by CT, but later confirmed by laparoscopy (PM-positive n = 122, PM-negative n = 432). Radiomic signatures reflecting phenotypes of the primary tumor (RS1) and peritoneum region (RS2) were built as predictors of PM from 266 quantitative image features. Individualized nomograms of PM status incorporating RS1, RS2, or clinical factors were developed and evaluated regarding prediction ability. Results RS1, RS2, and Lauren type were significant predictors of occult PM (all P < 0.05). A nomogram of these three factors demonstrated better diagnostic accuracy than the model with RS1, RS2, or clinical factors alone (all net reclassification improvement P < 0.05). The area under curve yielded was 0.958 [95% confidence interval (CI) 0.923–0.993], 0.941 (95% CI 0.904–0.977), 0.928 (95% CI 0.886–0.971), and 0.920 (95% CI 0.862–0.978) for the training, internal, and two external validation cohorts, respectively. Stratification analysis showed that this nomogram had potential generalization ability. Conclusion CT phenotypes of both primary tumor and nearby peritoneum are significantly associated with occult PM status. A nomogram of these CT phenotypes and Lauren type has an excellent prediction ability of occult PM, and may have significant clinical implications on early detection of occult PM for AGC.

IntroductionMicroRNAs (miRNAs) are a class of small non-coding RNAs (20 to 24 nucleotides) that post-transcriptionally modulate gene expression. A key oncomir in carcinogenesis is miR-21, which is consistently up-regulated in a wide range of cancers. However, few functional studies are available for miR-21, and few targets have been identified. In this study, we explored the role of miR-21 in human breast cancer cells and tissues, and searched for miR-21 targets.MethodsWe used in vitro and in vivo assays to explore the role of miR-21 in the malignant progression of human breast cancer, using miR-21 knockdown. Using LNA silencing combined to microarray technology and target prediction, we screened for potential targets of miR-21 and validated direct targets by using luciferase reporter assay and Western blot. Two candidate target genes (EIF4A2 and ANKRD46) were selected for analysis of correlation with clinicopathological characteristics and prognosis using immunohistochemistry on cancer tissue microrrays.ResultsAnti-miR-21 inhibited growth and migration of MCF-7 and MDA-MB-231 cells in vitro, and tumor growth in nude mice. Knockdown of miR-21 significantly increased the expression of ANKRD46 at both mRNA and protein levels. Luciferase assays using a reporter carrying a putative target site in the 3' untranslated region of ANKRD46 revealed that miR-21 directly targeted ANKRD46. miR-21 and EIF4A2 protein were inversely expressed in breast cancers (rs = -0.283, P = 0.005, Spearman's correlation analysis).ConclusionsKnockdown of miR-21 in MCF-7 and MDA-MB-231 cells inhibits in vitro and in vivo growth as well as in vitro migration. ANKRD46 is newly identified as a direct target of miR-21 in BC. These results suggest that inhibitory strategies against miR-21 using peptide nucleic acids (PNAs)-antimiR-21 may provide potential therapeutic applications in breast cancer treatment.

BackgroundSubstantial evidence suggests that the presence of inflammatory cells plays a critical role in the development and/or progression of human tumors. Neutrophils are the common inflammatory cells in tumors; however, the infiltration of intratumoral neutrophils in colorectal carcinoma (CRC) and its effect on CRC patients' prognosis are poorly understood.Methodology/Principal FindingsIn this study, the methods of tissue microarray and immunohistochemistry (IHC) were used to investigate the prognostic significance of intratumoral CD66b+ neutrophil in CRC. According to receiver operating characteristic curve analysis, the cutoff score for high intratumoral CD66b+ neutrophil in CRC was defined when the mean counts were more than 60 per TMA spot. In our study, high intratumoral CD66b+ neutrophil was observed in 104/229 (45.4%) of CRCs and in 29/229 (12.7%) of adjacent mucosal tissues. Further correlation analysis showed that high intratumoral neutrophil was positively correlated with pT status, pM status and clinical stage (P<0.05). In univariate survival analysis, a significant association between high intratumoral neutrophil and shortened patients' survival was found (P<0.0001). In different subsets of CRC patients, intratumoral neutrophil was also a prognostic indicator in patients with stage II, stage III, grade 2, grade 3, pT1, pT2, pN0 and pN1 (P<0.05). Importantly, high intratumoral neutrophil was evaluated as an independent prognostic factor in multivariate analysis (P<0.05).Conclusions/SignificanceOur results provide evidence that increased intratumoral neutrophil in CRC may be important in the acquisition of a malignant phenotype, indicating that the presence of intratumoral neutrophil is an independent factor for poor prognosis of patients with CRC.

Background: Preoperative evaluation of the number of lymph node metastasis (LNM) is the basis of individual treatment of locally advanced gastric cancer (LAGC). However, the routinely used preoperative determination method is not accurate enough. Patients and methods: We enrolled 730 LAGC patients from five centers in China and one center in Italy, and divided them into one primary cohort, three external validation cohorts, and one international validation cohort. A deep learning radiomic nomogram (DLRN) was built based on the images from multiphase computed tomography (CT) for preoperatively determining the number of LNM in LAGC. We comprehensively tested the DLRN and compared it with three state-of-the-art methods. Moreover, we investigated the value of the DLRN in survival analysis. Results: The DLRN showed good discrimination of the number of LNM on all cohorts [overall C-indexes (95% confidence interval): 0.821 (0.785e0.858) in the primary cohort, 0.797 (0.771e0.823) in the external validation cohorts, and 0.822 (0.756e0.887) in the international validation cohort]. The nomogram performed significantly better than the routinely used clinical N stages, tumor size, and clinical model (P < 0.05). Besides, DLRN was significantly associated with the overall survival of LAGC patients (n ¼ 271). Conclusion: A deep learning-based radiomic nomogram had good predictive value for LNM in LAGC. In staging-oriented treatment of gastric cancer, this preoperative nomogram could provide baseline information for individual treatment of LAGC.

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The anatomic location and the degree of the lingual concavity presented in this article add more information in implant treatment planning in the mandibular first molar edentulous region.

As a powerful predictor of 5-year DSS among patients with NPC, the newly developed NPC-SVM classifier based on tumor-associated biomarkers will facilitate patient counseling and individualize management of patients with NPC.