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In a multicenter case-control investigation, we assessed the efficacy of the Electrooculogram Signal Analysis (EOG-SA) method, which integrates attention-related visual evocation, electrooculography, and nonlinear analysis, for distinguishing between intellectual and developmental disabilities (IDD) and typical development (TD) in children. Analyzing 127 participants (63 IDD, 64 TD), we applied nonlinear dynamics for feature extraction. Results indicated EOG-SA's capability to distinguish IDD, with higher template thresholds and Correlation Dimension values correlating with clinical severity. The template threshold proved a robust indicator, with higher values denoting severe IDD. Discriminative metrics showed areas under the curve of 0.91 (template threshold) and 0.85/0.91 (D2), with sensitivities and specificities of 77.6%/95.9% and 93.5%/71.0%, respectively. EOG-SA emerges as a promising tool, offering interpretable neural biomarkers for early and nuanced diagnosis of IDD., (© 2024. The Author(s).)

Pancreatic cancer is an aggressive cancer with silent symptoms and high mortality with less than 11% of the 5-year survival rate. Until now, the significance of genes as clinical biomarkers in the early stages of pancreatic cancer has not been fully understood. Hence, this study aims to reveal the significant genes in the early stages of pancreatic cancer using bioinformatic analysis and in vitro experiments, and to serve as clinical biomarkers for early detection. We used Cancer RNA-Seq Nexus database and identified one tumor suppressor gene (NAGK), and five oncogenes (FXYD3, ACTR1A, B3GNT3, SIGIRR, and EXOC1) that are significant in the early stages of pancreatic cancer. The expression of NAGK, FXYD3, ACTR1A, B3GNT3, SIGIRR, and EXOC1 were determined from the GEPIA, UALCAN, and HPA database. It has been shown that pancreatic cancer tumor dissemination is an event that can occur in early lesions, rather than being solely restricted in the developed primary tumor. Thus, the six hub genes that were differentially expressed between stage I and stage II of primary pancreatic cancer tumors were compared to metastasis-related genes (1938 genes) in the human cancer metastasis database (HCMDB), yielding two overlapped genes (B3GNT3 and FXYD3). To establish the expression correlation between these two specific genes with metastatic characteristics of the early stage of pancreatic cancer and migratory ability in pancreatic cancer cell lines, the expression patterns of B3GNT3 and FXYD3 were examined in four different migratory abilities of pancreatic cancer cell lines, including HPAC, BxPC-3, AsPC-1, and PANC-1, as well as the normal pancreatic duct epithelial cell line HPDE6-C7. The results displayed that the expression of the FXYD3 gene was dramatically increased with the migratory ability enhanced of four pancreatic cancer cell lines. Thus, in the follow-up study, we will demonstrate the functional role of FXYD3 in pancreatic cancer tumorigenesis. This study revealed that the FXYD3 may act as a significant oncogene in the early stage of pancreatic cancer., Competing Interests: None., (AJCR Copyright © 2024.)

Here we explored the potential role of Gαi2 (G protein subunit alpha i2) in endothelial cell function and angiogenesis. Methods: Genetic methodologies such as shRNA, CRISPR/Cas9, dominant negative mutation, and overexpression were utilized to modify Gαi2 expression or regulate its function. Their effects on endothelial cell functions were assessed in vitro . In vivo , the endothelial-specific Gαi2 shRNA adeno-associated virus (AAV) was utilized to silence Gαi2 expression. The impact of this suppression on retinal angiogenesis in control mice and streptozotocin (STZ)-induced diabetic retinopathy (DR) mice was analyzed. Results: Analysis of single-cell RNA sequencing data revealed Gαi2 ( GNAI2 ) was predominantly expressed in retinal endothelial cells and expression was increased in retinal endothelial cells following oxygen-induced retinopathy (OIR) in mice. Moreover, transcriptome analysis linking Gαi2 to angiogenesis-related processes/pathways, supported by increased Gαi2 expression in experimental OIR mouse retinas, highlighted its possible role in angiogenesis. In various endothelial cell types, shRNA-induced silencing and CRISPR/Cas9-mediated knockout (KO) of Gαi2 resulted in substantial reductions in cell proliferation, migration, invasion, and capillary tube formation. Conversely, Gαi2 over-expression in endothelial cells induced pro-angiogenic activities, enhancing cell proliferation, migration, invasion, and capillary tube formation. Furthermore, our investigation revealed a crucial role of Gαi2 in NFAT (nuclear factor of activated T cells) activation, as evidenced by the down-regulation of NFAT-luciferase reporter activity and pro-angiogenesis NFAT-targeted genes ( Egr3 , CXCR7 , and RND1 ) in Gαi2-silenced or -KO HUVECs, which were up-regulated in Gαi2-overexpressing endothelial cells. Expression of a dominant negative Gαi2 mutation (S48C) also down-regulated NFAT-targeted genes, slowing proliferation, migration, invasion, and capillary tube formation in HUVECs. Importantly, in vivo experiments revealed that endothelial Gαi2 knockdown inhibited retinal angiogenesis in mice, with a concomitant down-regulation of NFAT-targeted genes in mouse retinal tissue. In contrast, Gαi2 over-expression in endothelial cells enhanced retinal angiogenesis in mice. Single-cell RNA sequencing data confirmed increased levels of Gαi2 specifically in retinal endothelial cells of mice with streptozotocin (STZ)-induced diabetic retinopathy (DR). Importantly, endothelial Gαi2 silencing ameliorated retinal pathological angiogenesis in DR mice. Conclusion: Our study highlights a critical role for Gαi2 in NFAT activation, endothelial cell activation and angiogenesis, offering valuable insights into potential therapeutic strategies for modulating these processes., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Alzheimer's disease (AD) and vascular dementia (VaD) are the two most common forms of dementia. However, their neuropsychological and pathological features often overlap, making it difficult to distinguish between AD and VaD. In addition to clinical consultation and laboratory examinations, clinical dementia diagnosis in Taiwan will also include Tc-99m-ECD SPECT imaging examination. Through machine learning and deep learning technology, we explored the feasibility of using the above clinical practice data to distinguish AD and VaD. We used the physiological data (33 features) and Tc-99m-ECD SPECT images of 112 AD patients and 85 VaD patients in the Taiwanese Nuclear Medicine Brain Image Database to train the classification model. The results, after filtering by the number of SVM RFE 5-fold features, show that the average accuracy of physiological data in distinguishing AD/VaD is 81.22% and the AUC is 0.836; the average accuracy of training images using the Inception V3 model is 85% and the AUC is 0.95. Finally, Grad-CAM heatmap was used to visualize the areas of concern of the model and compared with the SPM analysis method to further understand the differences. This research method can quickly use machine learning and deep learning models to automatically extract image features based on a small amount of general clinical data to objectively distinguish AD and VaD.