SPDEF drives pancreatic adenocarcinoma progression via transcriptional upregulation of S100A16 and activation of the PI3K/AKT signaling pathway.

Pancreatic adenocarcinoma (PAAD) is a notably aggressive malignancy with limited treatment options and an unfavorable prognosis for patients. We aimed to investigate molecular mechanisms by which Sam's pointed domain-containing ETS transcription factor (SPDEF) exerts effects on PAAD progression. We analyzed differentially expressed genes (DEGs) and their integration with ETS family members using the The Cancer Genome Atlas (TCGA) database, hence identifying SPDEF as a core gene in PAAD. Kaplan-Meier survival analysis confirmed SPDEF's prognostic potential. In vitro experiments validated the association with cell proliferation and apoptosis, affecting pancreatic cancer cell dynamics. We detected increased SPDEF expression in PAAD tumor samples. Our in vitro studies revealed that SPDEF regulates mRNA and protein expression levels, and significantly affects cell proliferation. Moreover, SPDEF was associated with reduced apoptosis and enhanced cell migration and invasion. In-depth analysis of SPDEF-targeted genes revealed four crucial genes for advanced prognostic model, among which S100A16 was significantly correlated with SPDEF. Mechanistic analysis showed that SPDEF enhances the transcription of S100A16, which in turn enhances PAAD cell migration, proliferation, and invasion by activating the PI3K/AKT signaling pathway. Our study revealed the critical role of SPDEF in promoting PAAD by upregulating S100A16 transcription and stimulating the PI3K/AKT signaling pathway. This knowledge deepened our understanding of pancreatic cancer's molecular progression and unveiled potential therapeutic strategies targeting SPDEF-driven pathways.

Red Cell Distribution Width is Related to Mild Cognitive Impairment: A Cross-Sectional Study of Community Residents.

BACKGROUND AND OBJECTIVE: Previous literature has reported that red cell distribution width (RDW) correlated with Alzheimer's disease (AD), but the correlation with mild cognitive impairment (MCI) was not clear. This study aimed to investigate MCI in the residents aged ≥65 living in the suburban of Shanghai, China. MATERIALS AND METHODS: A total of 550 participants were recruited as MCI (MCI group, 226) and normal (NC group, 284) groups and received blood examination voluntarily. Blood routine indexes were tested by blood tests using Sysmex XT-4000i (Japan). The Chi-square test, t-test, and linear regression analysis were used to find the statistical difference and correlation of data, respectively. RESULTS: Each cognition domain of MCI was found to be impaired, the weight of which, however, was different in integral damage. Most MCI people had impairment of attention among cognitive domains (235, 88.3%). According to the results of the binary logistic regression, the highest weight among impaired cognitive domains was for attention in MCI, and the Wald value of attention was higher than those of others (Wald = 51.83). Additionally, RDW had the greatest negative correlation with attention score (P < 0.05). CONCLUSIONS: Increased RDW may be considered as a biomarker of MCI.

Metabolites of intestinal fora can be used as diagnostic and progressive markers for mild cognitive impairment.

Purpose: The aim of the work was to analyze the metabolites of the intestinal microbiota from the patients with mild cognitive impairment (MCI) and progressive MCI due to Alzheimer's disease (AD). Method: Two cohorts were established. The first one included 87 subjects with 30 healthy controls (NC), 22 patients with MCI due to AD, and 35 patients with AD. The second cohort included 87 patients with MCI due to AD, who were followed up for 2 years and finally were divided into progressive MCI due to AD group (P-G) and unprogressive MCI due to AD group (U-G) according their cognitive levels. Fecal samples were collected to all patients at the baseline time point. Differential metabolites were subjected to pathway analysis by MetaboAnalyst. Results: In the first cohort, we found 21 different metabolites among the three groups (AD, MCI, and NC). In the second cohort, we identified 19 differential metabolites between the P-G and U-G groups. By machine learning analysis, we found that seven characteristic metabolites [Erythrodiol, alpha-Curcumene, Synephrine, o-Hydroxylaminobenzoate, 3-Amino-4-hydroxybenzoic acid, 2-Deoxystreptamine, and 9(S] were of characteristic significance for the diagnosis of MCI due to AD, and six metabolites (Indolelactate, Indole-3-acetaldehyde, L-Proline, Perillyl, Mesaconate, and Sphingosine) were the characteristic metabolites of early warning for the progression of MCI due to AD. D-Glucuronic acid was negatively correlated with Apolipoprotein E4 (APOE4). Perillyl alcohol was negatively correlated with all of the five biomarkers [P-tau181, Neurofilament light chain (NF-light), Aß1-42, Aß1-40, and glial fibrillary acidic protein (GFAP)], but Indoleacetaldehyde was positively correlated with three biomarkers (P-tau181, Aß1-42, and GFAP). Three characteristic metabolites (3-Amino-4-hydroxybenzoate, 2-Deoxystreptamine, and p-Synephrine) were positively correlated with Aß1-42. 2-Deoxystreptamine, 9(S)-HPOT, and Indoleacetaldehyde were positively correlated with GFAP. L-Proline and Indoleacetaldehyde were positively correlated with NF-light. Conclusion: Specific metabolites of intestinal fora can be used as diagnostic and progressive markers for MCI.

The mitochondrial quality control system: a new target for exercise therapeutic intervention in the treatment of brain insulin resistance-induced neurodegeneration in obesity.

Obesity is a major global health concern because of its strong association with metabolic and neurodegenerative diseases such as diabetes, dementia, and Alzheimer's disease. Unfortunately, brain insulin resistance in obesity is likely to lead to neuroplasticity deficits. Since the evidence shows that insulin resistance in brain regions abundant in insulin receptors significantly alters mitochondrial efficiency and function, strategies targeting the mitochondrial quality control system may be of therapeutic and practical value in obesity-induced cognitive decline. Exercise is considered as a powerful stimulant of mitochondria that improves insulin sensitivity and enhances neuroplasticity. It has great potential as a non-pharmacological intervention against the onset and progression of obesity associated neurodegeneration. Here, we integrate the current knowledge of the mechanisms of neurodegenration in obesity and focus on brain insulin resistance to explain the relationship between the impairment of neuronal plasticity and mitochondrial dysfunction. This knowledge was synthesised to explore the exercise paradigm as a feasible intervention for obese neurodegenration in terms of improving brain insulin signals and regulating the mitochondrial quality control system.

Predicting the Rapid Progression of Mild Cognitive Impairment by Intestinal Flora and Blood Indicators through Machine Learning Method.

INTRODUCTION: The aim of the work was to establish a prediction model of mild cognitive impairment (MCI) progression based on intestinal flora by machine learning method. METHOD: A total of 1,013 patients were recruited, in which 87 patients with MCI finished a two-year follow-up. To establish a prediction model, 61 patients were randomly divided into a training set and 26 patients were divided into a testing set. A total of 121 features including demographic characteristics, hematological indicators, and intestinal flora abundance were analyzed. RESULTS: Of the 87 patients who finished a two-year follow-up, 44 presented rapid progression. Model 1 was established based on 121 features with the accuracy 85%, sensitivity 85%, and specificity 83%. Model 2 was based on the first fifteen features of model 1 (triglyceride, uric acid, alanine transaminase, F-Clostridiaceae, G-Megamonas, S-Megamonas, G-Shigella, G-Shigella, S-Shigella, average hemoglobin concentration, G-Alistipes, S-Collinsella, median cell count, average hemoglobin volume, low-density lipoprotein), with the accuracy 97%, sensitivity 92%, and specificity 100%. Model 3 was based on the first ten features of model 1, with the accuracy 97%, sensitivity 86%, and specificity 100%. Other models based on the demographic characteristics, hematological indicators, or intestinal flora abundance features presented lower sensitivity and specificity. CONCLUSION: The 15 features (including intestinal flora abundance) could establish an effective model for predicting rapid MCI progression.