Estimating Brain Age with Global and Local Dependencies

The brain age has been proven to be a phenotype of relevance to cognitive performance and brain disease. Achieving accurate brain age prediction is an essential prerequisite for optimizing the predicted brain-age difference as a biomarker. As a comprehensive biological characteristic, the brain age is hard to be exploited accurately with models using feature engineering and local processing such as local convolution and recurrent operations that process one local neighborhood at a time. Instead, Vision Transformers learn global attentive interaction of patch tokens, introducing less inductive bias and modeling long-range dependencies. In terms of this, we proposed a novel network for learning brain age interpreting with global and local dependencies, where the corresponding representations are captured by Successive Permuted Transformer (SPT) and convolution blocks. The SPT brings computation efficiency and locates the 3D spatial information indirectly via continuously encoding 2D slices from different views. Finally, we collect a large cohort of 22645 subjects with ages ranging from 14 to 97 and our network performed the best among a series of deep learning methods, yielding a mean absolute error (MAE) of 2.855 in validation set, and 2.911 in an independent test set

Mapping the path towards novel treatment strategies: a bibliometric analysis of Hashimoto’s thyroiditis research from 1990 to 2023

BackgroundHashimoto’s thyroiditis (HT), a common form of thyroid autoimmunity, is strongly associated with deteriorating clinical status and impaired quality of life. The escalating global prevalence, coupled with the complexity of disease mechanisms, necessitates a comprehensive, bibliometric analysis to elucidate the trajectory, hotspots, and future trends in HT research.ObjectiveThis study aims to illuminate the development, hotspots, and future directions in HT research through systematic analysis of publications, institutions, authors, journals, references, and keywords. Particular emphasis is placed on novel treatment strategies for HT and its complications, highlighting the potential role of genetic profiling and immunomodulatory therapies.MethodsWe retrieved 8,726 relevant documents from the Web of Science Core Collection database spanning from 1 January 1990 to 7 March 2023. Following the selection of document type, 7,624 articles were included for bibliometric analysis using CiteSpace, VOSviewer, and R software.ResultsThe temporal evolution of HT research is categorized into three distinct phases: exploration (1990-1999), rapid development (1999-2000), and steady growth (2000-present). Notably, the United States, China, Italy, and Japan collectively contributed over half (54.77%) of global publications. Among the top 10 research institutions, four were from Italy (4/10), followed by China (2/10) and the United States (2/10). Recent hotspots, such as the roles of gut microbiota, genetic profiling, and nutritional factors in HT management, the diagnostic dilemmas between HT and Grave’s disease, as well as the challenges in managing HT complicated by papillary thyroid carcinoma and type 1 diabetes mellitus, are discussed.ConclusionAlthough North America and Europe have a considerable academic impact, institutions from emerging countries like China are demonstrating promising potential in HT research. Future studies are anticipated to delve deeper into the differential diagnosis of HT and Grave’s disease, the intricate relationship between gut microbiota and HT pathogenesis, clinical management of HT with papillary thyroid carcinoma or type 1 diabetes, and the beneficial effects of dietary modifications and micronutrients supplementation in HT. Furthermore, the advent of genetic profiling and advanced immunotherapies for managing HT offers promising avenues for future research

NEDD9 Is a Positive Regulator of Epithelial-Mesenchymal Transition and Promotes Invasion in Aggressive Breast Cancer

Epithelial to mesenchymal transition (EMT) plays an important role in many biological processes. The latest studies revealed that aggressive breast cancer, especially the triple-negative breast cancer (TNBC) subtype was frequently associated with apparent EMT, but the mechanisms are still unclear. NEDD9/HEF1/Cas-L is a member of the Cas protein family and was identified as a metastasis marker in multiple cancer types. In this study, we wished to discern the role of NEDD9 in breast cancer progression and to investigate the molecular mechanism by which NEDD9 regulates EMT and promotes invasion in triple-negative breast cancer. We showed that expression of NEDD9 was frequently upregulated in TNBC cell lines, and in aggressive breast tumors, especially in TNBC subtype. Knockdown of endogenous NEDD9 reduced the migration, invasion and proliferation of TNBC cells. Moreover, ectopic overexpression of NEDD9 in mammary epithelial cells led to a string of events including the trigger of EMT, activation of ERK signaling, increase of several EMT-inducing transcription factors and promotion of their interactions with the E-cadherin promoter. Data presented in this report contribute to the understanding of the mechanisms by which NEDD9 promotes EMT, and provide useful clues to the evaluation of the potential of NEDD9 as a responsive molecular target for TNBC chemotherapy

Genome-Wide Analysis of Histone H3 Lysine9 Modifications in Human Mesenchymal Stem Cell Osteogenic Differentiation

Mesenchymal stem cells (MSCs) possess self-renewal and multi-lineage differentiation potentials. It has been established that epigenetic mechanisms such as histone modifications could be critical for determining the fate of stem cells. In this study, full human genome promoter microarrays and expression microarrays were used to explore the roles of histone modifications (H3K9Ac and H3K9Me2) upon the induction of MSC osteogenic differentiation. Our results revealed that the enrichment of H3K9Ac was decreased globally at the gene promoters, whereas the number of promoters enriched with H3K9Me2 was increased evidently upon osteogenic induction. By a combined analysis of data from both ChIP-on-chip and expression microarrays, a number of differentially expressed genes regulated by H3K9Ac and/or H3K9Me2 were identified, implicating their roles in several biological events, such as cell cycle withdraw and cytoskeleton reconstruction that were essential to differentiation process. In addition, our results showed that the vitamin D receptor played a trans-repression role via alternations of H3K9Ac and H3K9Me2 upon MSC osteogenic differentiation. Data from this study suggested that gene activation and silencing controlled by changes of H3K9Ac and H3K9Me2, respectively, were crucial to MSC osteogenic differentiation

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Connectome-wide network analysis of white matter connectivity in Alzheimer's disease

A multivariate analytical strategy may pinpoint the structural connectivity patterns associated with Alzheimer's disease (AD) pathology in connectome-wide association studies. Diffusion magnetic resonance imaging data from 161 participants including subjects with healthy controls, AD, stable and converting mild cognitive impairment, were selected for group-wise comparisons. A multivariate distance matrix regression (MDMR) analysis was performed to detect abnormality in brain structural network along with disease progression. Based on the seed regions returned by the MDMR analysis, supervised learning was applied to evaluate the disease predictive performance. Nine brain regions, including the left orbital part of superior and middle frontal gyrus, the bilateral supplementary motor area, the bilateral insula, the left hippocampus, the left putamen, and the left thalamus demonstrated extremely significant structural pattern changes along with the progression of AD. The disease classification was more efficient when based on the key connectivity related to these seed regions than when based on whole-brain structural connectivity. MDMR analysis reveals brain network reorganization caused by AD pathology. The key structural connectivity detected in this study exhibits promising distinguishing capability to predict prodromal AD patients. Keywords: Diffusion tensor imaging, Alzheimer's disease, Mild cognitive impairment, Connectome, Multivariate regression, Tractograph

Performance Analysis of Electromyogram Signal Compression Sampling in a Wireless Body Area Network

The rapid growth in demand for portable and intelligent hardware has caused tremendous pressure on signal sampling, transfer, and storage resources. As an emerging signal acquisition technology, compressed sensing (CS) has promising application prospects in low-cost wireless sensor networks. To achieve reduced energy consumption and maintain a longer acquisition duration for high sample rate electromyogram (EMG) signals, this paper comprehensively analyzes the compressed sensing method using EMG. A fair comparison is carried out on the performances of 52 ordinary wavelet sparse bases and five widely applied reconstruction algorithms at different compression levels. The experimental results show that the db2 wavelet basis can sparse EMG signals so that the compressed EMG signals are reconstructed properly, thanks to its low percentage root mean square distortion (PRD) values at most compression ratios. In addition, the basis pursuit (BP) reconstruction algorithm can provide a more efficient reconstruction process and better reconstruction performance by comparison. The experiment records and comparative analysis screen out the suitable sparse bases and reconstruction algorithms for EMG signals, acting as prior experiments for further practical applications and also a benchmark for future academic research