The accurate diagnosis of Alzheimer's disease (AD) and its early stage, e.g., mild cognitive impairment (MCI), is essential for timely treatment or possible intervention to slow down AD progression. Recent studies have demonstrated that multiple neuroimaging and biological measures contain complementary information for diagnosis and prognosis. Therefore, information fusion strategies with multi-modal neuroimaging data, such as voxel-based measures extracted from structural MRI (VBM-MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET), have shown their effectiveness for AD diagnosis. However, most existing methods are proposed to simply integrate the multi-modal data, but do not make full use of structure information across the different modalities. In this paper, we propose a novel multi-modal neuroimaging feature selection method with consistent metric constraint (MFCC) for AD analysis. First, the similarity is calculated for each modality (i.e. VBM-MRI or FDG-PET) individually by random forest strategy, which can extract pairwise similarity measures for multiple modalities. Then the group sparsity regularization term and the sample similarity constraint regularization term are used to constrain the objective function to conduct feature selection from multiple modalities. Finally, the multi-kernel support vector machine (MK-SVM) is used to fuse the features selected from different models for final classification. The experimental results on the *

Long non-coding RNAs (lncRNAs) regulate the development of follicles and reproductive diseases, but the mechanisms by which lncRNAs regulate ovarian functions and fertility remain elusive. We profiled the expression of lncRNAs in ovarian tissues of Hu sheep with different prolificacy and identified 21,327 lncRNAs. Many of the lncRNAs were differentially expressed in different groups. We further characterized an lncRNA that was predominantly expressed in the ovaries of the low prolificacy Fec B+ (LPB+) group and mainly present in granulosa cells (GCs), and the expression of this lncRNA decreased during follicular development, which we named follicular development-associated lncRNA ( FDNCR ). Next, we found that FDNCR directly binds miR-543-3p, and decorin ( DCN ) was identified as a target of miR-543-3p. FDNCR overexpression promoted GC apoptosis through increased expression of DCN , which could be attenuated by miR-543-3p. Furthermore, miR-543-3p increased and FDNCR reduced the expression of transforming growth factor-β (TGF-β) pathway-related genes, including TGF-β1 and inhibin beta A ( INHBA ), which were upregulated upon DCN silencing. Our results demonstrated that FDNCR sponges miR-543-3p in GCs and prevents miR-543-3p from binding to the DCN 3′ UTR, resulting in DCN transactivation and TGF-β pathway inhibition and promotion of GC apoptosis in Hu sheep. These findings provide insights into the mechanisms underlying prolificacy in sheep.

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A number of inflammation indicators based on C-reactive protein (CRP) and albumin have been widely used to predict the prognosis in several types of tumors, but their functions in gallbladder cancer (GBC) have rarely been explored. The aim of our study is to evaluate and compare the prognostic values of the C-reactive protein to albumin ratio (CAR), Glasgow prognostic score (GPS), modified Glasgow prognostic score (mGPS) and high-sensitivity modified Glasgow prognostic score (HS-mGPS) in patients with GBC. 144 GBC patients who received curative surgery in our hospital from January 2010 to May 2017 were enrolled in this research. The Kaplan-Meier analysis showed that the median OS of the patients in the high CAR group was significantly shorter than the patients in the low group (p < 0.001), and higher scores of GPS, mGPS and HS-mGPS were also associated with decreased OS, respectively. However, according to the Receiver Operating Characteristic (ROC) curve, the CAR was superior to the other prognostic scores in determining the prognosis for the GBC patients. In the multivariate analysis, CAR was verified as an independent risk factor for poor prognosis, together with tumor differentiation, T stage and postoperative complications. All in all, compared to the other three CRP-albumin-related prognostic predictors, CRA is a better indicator in predicting poor long-term outcomes in GBC patients after radical surgery.

Background Surgery is a potential cure for hepatocellular carcinoma (HCC), but its postoperative recurrence rate is high, its prognosis is poor, and reliable predictive indicators are lacking. This study was conducted to develop a simple, practical, and effective predictive model. Materials and Methods Preoperative clinical and postoperative pathological data on patients with HCC undergoing partial hepatectomies at the Third Affiliated Hospital of Soochow University from January 2010 to December 2015 were retrospectively analyzed, and a nomogram was constructed. The model performance was evaluated using C-indexes, receiver operating characteristic curves, and calibration curves. The results were verified from validation cohort data collected at the same center from January 2016 to January 2017 and compared with the traditional staging systems. Results Three hundred three patients were enrolled in this study: 238 in the training cohort and 65 in the validation cohort. From the univariate and multivariate Cox regression analyses in the training cohort, six independent risk factors, i.e., age, alpha-fetoprotein (AFP), tumor size, satellite nodules, systemic immune inflammation index (SII), and prognostic nutritional index (PNI), were filtered and included in the nomogram. The C-index was 0.701 [95% confidence interval (CI): 0.654–0.748] in the training cohort and 0.705 (95% CI: 0.619–0.791) in the validation cohort. The areas under the curve for the 1- and 3-year recurrence-free survival were 0.706 and 0.716 in the training cohort and 0.686 and 0.743 in the validation cohort, respectively. The calibration curves showed good agreement. Compared with traditional American Joint Committee on Cancer 8th edition (AJCC8th) and Barcelona Clinic Liver Cancer (BCLC) staging systems, our nomogram showed better predictive ability. Conclusion Our nomogram is simple, practical, and reliable. According to our nomogram, predicting the risk of recurrence and stratifying HCC patient management will yield the greatest survival benefit for patients.