Background: There are no obvious clinical signs and symptoms in the early stages of Alzheimer’s disease (AD), and most patients usually have mild cognitive impairment (MCI) before diagnosis. Therefore, early diagnosis of AD is very critical. This paper mainly discusses the blood biomarkers of AD patients and uses machine learning methods to study the changes of blood transcriptome during the development of AD and to search for potential blood biomarkers for AD.Methods: Individualized blood mRNA expression data of 711 patients were downloaded from the GEO database, including the control group (CON) (238 patients), MCI (189 patients), and AD (284 patients). Firstly, we analyzed the subcellular localization, protein types and enrichment pathways of the differentially expressed mRNAs in each group, and established an artificial intelligence individualized diagnostic model. Furthermore, the XCell tool was used to analyze the blood mRNA expression data and obtain blood cell composition and quantitative data. Ratio characteristics were established for mRNA and XCell data. Feature engineering operations such as collinearity and importance analysis were performed on all features to obtain the best feature solicitation. Finally, four machine learning algorithms, including linear support vector machine (SVM), Adaboost, random forest and artificial neural network, were used to model the optimal feature combinations and evaluate their classification performance in the test set.Results: Through feature engineering screening, the best feature collection was obtained. Moreover, the artificial intelligence individualized diagnosis model established based on this method achieved a classification accuracy of 91.59% in the test set. The area under curve (AUC) of CON, MCI, and AD were 0.9746, 0.9536, and 0.9807, respectively.Conclusion: The results of cell homeostasis analysis suggested that the homeostasis of Natural killer T cell (NKT) might be related to AD, and the homeostasis of Granulocyte macrophage progenitor (GMP) might be one of the reasons for AD.
Recently, female breast cancer (BC) has surpassed lung cancer to occupy the first place of the most commonly diagnosed cancer. The unsatisfactory prognosis of endocrine therapy for breast cancer might be attributed to the discordance in estrogen receptor (ER) status between primary tumors and corresponding metastases, as well as temporal and spatial receptor status heterogeneity at point-in-time between biopsy and treatment. The purpose of this study was to evaluate the prognostic and predictive value of ER status in circulating tumor cells (CTCs) in BC patients. We analyzed ER expression on CTCs isolated using the Pep@MNPs method in 2.0 ml of blood samples from 70 patients with BC and 67 female controls. The predictive and prognostic value of ER expression in CTCs and immunohistochemistry results of biopsies for progression-free survival (PFS) and overall survival (OS) of patients in response to therapies were assessed. The detection rate for CTCs was 95.71% (67/70 patients), with a median of 8 CTCs within 2 ml of peripheral venous blood (PVB). A concordance of 76.56% in ER status between CTCs and corresponding primary tumor and 69.23% between CTCs and corresponding metastases was observed. We also found that patients with ER-positive CTCs (CTC ER+) had longer PFS and OS than those without ER-positive CTCs (CTC ER-). Our findings suggested that ER status in CTCs of BC patients may provide valuable predictive and prognostic insights into endocrine therapies, although further evaluation in larger prospective trials is required.
Mitophagy modulators are proposed as potential therapeutic intervention that enhance neuronal health and brain homeostasis in Alzheimer's disease (AD). Nevertheless, the lack of specific mitophagy inducers, low efficacies, and the severe side effects of nonselective autophagy during AD treatment have hindered their application. In this study, the P@NB nanoscavenger is designed with a reactive‐oxygen‐species‐responsive (ROS‐responsive) poly(l‐lactide‐co‐glycolide) core and a surface modified with the Beclin1 and angiopoietin‐2 peptides. Notably, nicotinamide adenine dinucleotide (NAD+) and Beclin1, which act as mitophagy promoters, are quickly released from P@NB in the presence of high ROS levels in lesions to restore mitochondrial homeostasis and induce microglia polarization toward the M2‐type, thereby enabling it to phagocytose amyloid‐peptide (Aβ). These studies demonstrate that P@NB accelerates Aβ degradation and alleviates excessive inflammatory responses by restoring autophagic flux, which ameliorates cognitive impairment in AD mice. This multitarget strategy induces autophagy/mitophagy through synergy, thereby normalizing mitochondrial dysfunction. Therefore, the developed method provides a promising AD‐therapy strategy.
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