Machine vision-based gait scan method for identifying cognitive impairment in older adults.

Objective: Early identification of cognitive impairment in older adults could reduce the burden of age-related disabilities. Gait parameters are associated with and predictive of cognitive decline. Although a variety of sensors and machine learning analysis methods have been used in cognitive studies, a deep optimized machine vision-based method for analyzing gait to identify cognitive decline is needed. Methods: This study used a walking footage dataset of 158 adults named West China Hospital Elderly Gait, which was labelled by performance on the Short Portable Mental Status Questionnaire. We proposed a novel recognition network, Deep Optimized GaitPart (DO-GaitPart), based on silhouette and skeleton gait images. Three improvements were applied: short-term temporal template generator (STTG) in the template generation stage to decrease computational cost and minimize loss of temporal information; depth-wise spatial feature extractor (DSFE) to extract both global and local fine-grained spatial features from gait images; and multi-scale temporal aggregation (MTA), a temporal modeling method based on attention mechanism, to improve the distinguishability of gait patterns. Results: An ablation test showed that each component of DO-GaitPart was essential. DO-GaitPart excels in backpack walking scene on CASIA-B dataset, outperforming comparison methods, which were GaitSet, GaitPart, MT3D, 3D Local, TransGait, CSTL, GLN, GaitGL and SMPLGait on Gait3D dataset. The proposed machine vision gait feature identification method achieved a receiver operating characteristic/area under the curve (ROCAUC) of 0.876 (0.852-0.900) on the cognitive state classification task. Conclusion: The proposed method performed well identifying cognitive decline from the gait video datasets, making it a prospective prototype tool in cognitive assessment.

Hypomethylation of the ENPP3 promoter region contributes to the occurrence and development of ovarian endometriosis via the AKT/mTOR/4EBP1 signaling pathway.

Growing evidence indicates that aberrant methylation is pivotal in the development and progression of endometriosis (EMs). This study explores the relationship between abnormal methylation of the ENPP3 promoter and the pathogenesis of ovarian EMs, focusing on its regulatory effect on ENPP3 expression. We analyzed the methylation levels of ENPP3 in ectopic endometrial tissues from ovarian EMs patients and in normal endometrial tissues from women without EMs. The expression and distribution of ENPP3 were evaluated using RT-qPCR and immunohistochemistry. Transwell assays were conducted to examine the impact of ENPP3 overexpression on the migratory and invasive capabilities of endometrial stromal cells. Our results demonstrated significantly reduced methylation levels at the CpG sites of the ENPP3 promoter region in ectopic endometrial tissues compared to normal endometrial tissues. RT-qPCR findings revealed a marked increase in ENPP3 expression in ovarian EMs tissues relative to endometrial tissues from patients without EMs, and this upregulation was negatively correlated with the methylation levels of the ENPP3 promoter region. Immunohistochemical analyses confirmed elevated ENPP3 expression in the glandular epithelial cells and stroma of ovarian EMs tissues. Furthermore, in vitro experiments showed that overexpressed ENPP3 notably intensified the invasion and migration of endometrial stromal cells. Transcriptome sequencing and functional analyses indicated that the increased ENPP3 expression activated the AKT/mTOR/4EBP1 signaling pathway. In summary, the study suggests that hypomethylation in the ENPP3 promoter region may contribute to the initiation and advancement of ovarian EMs by activating the AKT/mTOR/4EBP1 pathway, supporting the theory that EMs might be an epigenetically regulated disorder.

MVKT-ECG: Efficient single-lead ECG classification for multi-label arrhythmia by multi-view knowledge transferring.

Electrocardiogram (ECG) is a widely used technique for diagnosing cardiovascular disease. The widespread emergence of smart ECG devices has sparked the demand for intelligent single-lead ECG-based diagnostic systems. However, it is challenging to develop a single-lead-based ECG interpretation model for multiple disease diagnosis due to the lack of some key disease information. We aim to improve the diagnostic capabilities of single-lead ECG for multi-label disease classification in a new teacher-student manner, where the teacher trained by multi-lead ECG educates a student who observes only single-lead ECG We present a new disease-aware Contrastive Lead-information Transferring (CLT) to improve the mutual disease information between the single-lead-based ECG interpretation model and multi-lead-based ECG interpretation model. Moreover, We modify the traditional Knowledge Distillation into Multi-label disease Knowledge Distillation (MKD) to make it applicable for multi-label disease diagnosis. The whole knowledge transferring process is inter-lead Multi-View Knowledge Transferring of ECG (MVKT-ECG). By employing the training strategy, we can effectively transfer comprehensive disease knowledge from various views of ECG, such as the 12-lead ECG, to a single-lead-based ECG interpretation model. This enables the model to extract intricate details from single-lead ECG signals and enhances the model's capability of diagnosing and identifying single-lead signals. Extensive experiments on two commonly used public multi-label datasets, ICBEB2018 and PTB-XL demonstrate that our MVKT-ECG yields exceptional diagnostic performance improvements for single-lead ECG. The student outperforms its baseline observably on the PTB-XL dataset (1.3 % on PTB.super, and 1.4 % on PTB.sub), and on ICBEB2018 dataset (3.2 %).

Lifestyle habits and gastric cancer in an East Asian population: a Mendelian randomization study.

Background: Epidemiological evidence suggests an association between lifestyle habits (smoking, alcohol consumption, tea, coffee intake, etc.) and gastric cancer (GC). However, the causal relationship remains uncertain. Therefore, the purpose of this study was to ascertain whether there is a causal connection between them. Methods: Two-sample Mendelian randomization (MR) analysis was performed using the publicly available Genome Wide Association Study summary datasets using six methods: inverse variance weighting (IVW), weighted median, MR using a Robust Adjusted Profile Score (MR.Raps), MR using a Robust Adjusted Profile Score (MR-PRESSO), Radial regression of MR, and Causal Analysis Using Summary Effect Estimates (CAUSE). A sensitivity analysis was conducted to assess the robustness of the results. Results: In an East Asian population, we found that increased tea intake reduced the risk of GC [odds ratio (OR)= 0.90, 95% confidence interval (CI)= 0.82-0.99, P = 0.037] while there was a positive association between smoking and GC (OR = 1.58, 95% CI = 1.04-2.39, P = 0.032). No causal relationship between alcohol and coffee intake and GC. Sensitivity analyses demonstrated the robustness of these causal associations. Conclusions: Our study suggests that tea intake may reduce the risk of GC, for which smoking is a potential risk factor. Nevertheless, a larger and more diverse sample size is needed for further validation.

Modulations of local synchrony over time lead to resting-state functional connectivity in a parsimonious large-scale brain model.

Biophysical models of large-scale brain activity are a fundamental tool for understanding the mechanisms underlying the patterns observed with neuroimaging. These models combine a macroscopic description of the within- and between-ensemble dynamics of neurons within a single architecture. A challenge for these models is accounting for modulations of within-ensemble synchrony over time. Such modulations in local synchrony are fundamental for modeling behavioral tasks and resting-state activity. Another challenge comes from the difficulty in parametrizing large scale brain models which hinders researching principles related with between-ensembles differences. Here we derive a parsimonious large scale brain model that can describe fluctuations of local synchrony. Crucially, we do not reduce within-ensemble dynamics to macroscopic variables first, instead we consider within and between-ensemble interactions similarly while preserving their physiological differences. The dynamics of within-ensemble synchrony can be tuned with a parameter which manipulates local connectivity strength. We simulated resting-state static and time-resolved functional connectivity of alpha band envelopes in models with identical and dissimilar local connectivities. We show that functional connectivity emerges when there are high fluctuations of local and global synchrony simultaneously (i.e. metastable dynamics). We also show that for most ensembles, leaning towards local asynchrony or synchrony correlates with the functional connectivity with other ensembles, with the exception of some regions belonging to the default-mode network.

Hypermethylation of the GRHL2 promoter region is associated with ovarian endometriosis.

Abnormal gene expression caused by epigenetic changes, including DNA methylation, is associated with the development and progression of endometriosis. Grainyhead-like 2 gene (GRHL2), a suppressor of epithelial-mesenchymal transition, has been suggested to be associated with the occurrence, progression and poor survival of a variety of cancers. Although endometriosis is a benign disease, it has the biological behaviour of migration and invasion as malignant tumor. This study aims to determine whether the abnormal expression of the GRHL2 caused by aberrant methylation of its promoter is associated with the pathogenesis of ovarian endometriosis. Our results demonstrated that GRHL2 promoter region was significantly hypermethylated in the ectopic endometrium of patients with ovarian endometriosis compared with the normal endometrium of control patients. In contrast, the levels of GRHL2 mRNA and protein were significantly lower in the ectopic endometrium than in the control endometrium. Correlation analysis showed the methylation levels of GRHL2 were significantly negatively correlated with the mRNA expression of GRHL2. Moreover, the in vitro results suggested that the knockdown of GRHL2 could significantly increase the invasion and migration ability of EECs and may promote ZEB1 and vimentin expression while decreasing the expression of E-cadherin in EECs. Taken together, these results suggest that the low expression of GRHL2 caused by hypermethylation of the GRHL2 promoter is associated with ovarian endometriosis. The knockdown of GRHL2 may be involved in the occurrence of endometriosis by increasing EEC migration and invasion. This study provides more evidence for the hypothesis that endometriosis may be an epigenetic regulatory disorder.