Somatomotor-Visual Resting State Functional Connectivity Increases After Two Years in the UK Biobank Longitudinal Cohort

Functional magnetic resonance imaging (fMRI) and functional connectivity (FC) have been used to follow aging in both children and older adults. Robust changes have been observed in children, where high connectivity among all brain regions changes to a more modular structure with maturation. In older adults, prior work has identified changes in connectivity associated with the default mode network (DMN); other work has used brain age to predict pre-clinical Alzheimer's disease. In this work, we find an increasing connectivity between the Somatomotor (SMT) and Visual (VIS) Networks using the Power264 atlas in a longitudinal cohort of the UK Biobank (UKB). This cohort consists of 2,722 subjects, with scans being taken an average of two years apart. The average connectivity increase between SMT-VIS is 6.8% compared to the younger scan baseline (from $\rho=0.39$ to $\rho=0.42$), and occurs in male, female, older subject ($>65$ years old), and younger subject ($<55$ years old) groups. Among all inter-network connections, this average SMT-VIS connectivity is the best predictor of relative scan age, accurately predicting which scan is older 57% of the time. Using the full FC and a training set of 2,000 subjects, one is able to predict which scan is older 82.5% of the time when using the difference of FC between the two scans as input to a classifier. This previously under-reported relationship may shed light on normal changes in aging brain FC, identifies a potential confound for longitudinal studies, and proposes a new area for investigation, specifically the SMT-VIS connectivity.Comment: 12 pages, 10 figures, 3 table

A Review of Integrative Imputation for Multi-Omics Datasets

Multi-omics studies, which explore the interactions between multiple types of biological factors, have significant advantages over single-omics analysis for their ability to provide a more holistic view of biological processes, uncover the causal and functional mechanisms for complex diseases, and facilitate new discoveries in precision medicine. However, omics datasets often contain missing values, and in multi-omics study designs it is common for individuals to be represented for some omics layers but not all. Since most statistical analyses cannot be applied directly to the incomplete datasets, imputation is typically performed to infer the missing values. Integrative imputation techniques which make use of the correlations and shared information among multi-omics datasets are expected to outperform approaches that rely on single-omics information alone, resulting in more accurate results for the subsequent downstream analyses. In this review, we provide an overview of the currently available imputation methods for handling missing values in bioinformatics data with an emphasis on multi-omics imputation. In addition, we also provide a perspective on how deep learning methods might be developed for the integrative imputation of multi-omics datasets

Monitoring the progression of metastatic breast cancer on nanoporous silica chips

Breast cancer accounted for 15 per cent of total cancer deaths in female patients in 2010. Although significant progress has been made in treating early-stage breast cancer patients, there is still no effective therapy targeting late-stage metastatic breast cancers except for the conventional chemotherapy interventions. Until effective therapy for later-stage cancers emerges, the identification of biomarkers for the early detection of tumour metastasis continues to hold the key to successful management of breast cancer therapy. Our study concentrated on the low molecular weight (LMW) region of the serum protein and the information it contains for identifying biomarkers that could reflect the ongoing physiological state of all tissues. Owing to technical difficulties in harvesting LMW species, studying these proteins/peptides has been challenging until now. In our study, we have recently developed nanoporous chip-based technologies to separate small proteins/peptides from the large proteins in serum. We used nanoporous silica chips, with a highly periodic nanostructure and uniform pore size distribution, to isolate LMW proteins and peptides from the serum of nude mice with MDA-MB-231 human breast cancer lung metastasis. By matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and biostatistical analysis, we were able to identify protein signatures unique to different stages of cancer development. The approach and results reported in this study possess a significant potential for the discovery of proteomic biomarkers that may significantly enhance personalized medicine targeted at metastatic breast cancer

The prognosis value of CONUT and SIS score for recurrent or metastatic esophageal squamous cell carcinoma patients treated with second-line immunotherapy

ObjectiveTo investigate the predictive value of Controlling Nutritional Status (CONUT) score and systemic inflammation (SIS) score in the prognosis, short-term efficacy, and immune-related side effects of patient with recurrent or metastatic esophageal squamous cell carcinoma (R/M ESCC) receiving immunotherapy as second line therapy combined with or without radiotherapy.MethodsForty-eight patients with R/M ESCC who received second-line therapy with Camrelizumab were retrospectively studied. They were divided into the high and low score groups according to the CONUT and SIS score. Univariate and multivariate analyses were used to analyze factors that might affect patient prognosis and the effects of different CONUT score and SIS on the short-term efficacy and immune-related toxic and side effects of patients.ResultsThe 1- and 2-year overall survival (OS) and progression-free survival (PFS) rates were 42.9% and 22.5%, and 29.0% and 5.8%, respectively. The CONUT score ranged from 0 to 6 (3.31 ± 1.43), whereas the SIS score ranged from 0 to 2 (1.19 ± 0.73). Multivariate analysis showed that treatment related toxicity, number of cycles of Camrelizumab used, short-term effect and SIS score were independent prognostic factors for OS (P=0.044, 0.021, 0.021, 0.030, respectively), whereas SIS and CONUT scores were independent prognostic factors for PFS (P=0.005, 0.047, respectively). Patients with low CONUT/SIS score had a low incidence rate of immune-related adverse reactions (X2 = 9.735, 5.693; P=0.002, 0.017) and better short-term efficacy (X2 = 4.427, 7.438; P=0.035, 0.006).ConclusionR/M ESCC patients with low CONUT/SIS score have better prognosis, higher objective response rate, lower incidence of immune-related toxic and side effects after receiving immunotherapy as second-line therapy. CONUT scores and SIS scores may be reliable prognostic indicators for patient receiving immunotherapy as second-line therapy for R/M ESCC