Introduction. Alzheimer’s disease (AD), the most common neurodegenerative disorder, is characterized by the accumulation of amyloid-β (Aβ) peptide and hyperphosphorylated tau protein. Accumulating evidence has revealed that the slow progressive deterioration of AD is associated with oxidative stress and chronic inflammation in the brain. Nuclear factor erythroid 2- (NF-E2-) related factor 2 (Nrf2), which acts through the Nrf2/ARE pathway, is a key regulator of the antioxidant and anti-inflammatory response. Although recent data show a link between Nrf2 and AD-related cognitive decline, the mechanism is still unknown. Thus, we explored how Nrf2 protects brain cells against the oxidative stress and inflammation of AD in a mouse model of AD (APP/PS1 transgenic (AT) mice) with genetic removal of Nrf2. Methods. The spatial learning and memory abilities of 12-month-old transgenic mice were evaluated using a Morris water maze test. Hippocampal levels of Nrf2, Aβ, and p-tauS404 and of astrocytes and microglia were determined by immunostaining. Inflammatory cytokines were determined by ELISA and quantitative real-time polymerase chain reaction (qRT-PCR). Oxidative stress was measured by 8-hydroxydeoxyguanosine immunohistochemistry, and the antioxidant response was determined by qRT-PCR. Results. The spatial learning and memory abilities of AT mice were impaired after Nrf2 deletion. Aβ and p-tauS404 accumulation was increased in the hippocampus of AT/Nrf2-KO mice. Astroglial and microglial activation was exacerbated, followed by upregulation of the proinflammatory cytokines IL-1β, IL-6, and TNF-α. Conclusion. Our present results show that Nrf2 deficiency aggravates AD-like pathology in AT mice. This phenotype was associated with increased levels of oxidative and proinflammatory markers, which suggests that the Nrf2 pathway may be a promising therapeutic target for AD.

Ferroptosis and iron-related redox imbalance aggravate traumatic brain injury (TBI) outcomes. NRF2 is the predominant transcription factor regulating oxidative stress and neuroinflammation in TBI, but its role in iron-induced post-TBI damage is unclear. We investigated ferroptotic neuronal damage in the injured cortex and observed neurological deficits post-TBI. These were ameliorated by the iron chelator deferoxamine (DFO) in wild-type mice. In Nrf2-knockout (Nrf2^−/−) mice, more sever ferroptosis and neurological deficits were detected. Dimethyl fumarate (DMF)-mediated NRF2 activation alleviated neural dysfunction in TBI mice, partly due to TBI-induced ferroptosis mitigation. Additionally, FTH-FTL and FSP1 protein levels, associated with iron metabolism and the ferroptotic redox balance, were highly NRF2-dependent post-TBI. Thus, NRF2 is neuroprotective against TBI-induced ferroptosis through both the xCT-GPX4- and FTH-FTL-determined free iron level and the FSP1-regulated redox status. This yields insights into the neuroprotective role of NRF2 in TBI-induced neuronal damage and its potential use in TBI treatment.

Biomarkers such as B-type natriuretic peptide (BNP), N-terminal pro-BNP (NT-proBNP), cardiac troponin (cTn), and CK-MB contribute significantly to the diagnosis of cardiovascular disease (CVD). Recent studies have demonstrated that suppression of tumorigenicity 2 (ST2) is associated with CVD, but a meta-analysis of ST2 levels in different CVDs has yet to be conducted. Therefore, the present study aimed to investigate soluble ST2 (sST2) levels in patients with ischemic heart disease (IHD), myocardial infarction (MI), and heart failure (HF). A total of 1,425 studies were searched across four databases, of which 16 studies were included in the meta-analysis. The Newcastle–Ottawa Quality Assessment Scale (NOS) values of all 16 studies were ≥7. The meta-analysis results indicated that the sST2 level was not correlated with IHD (standard mean difference [SMD] = 0.58, 95% confidence interval [95% CI] = 0.00 to 1.16, p = 0.05) or MI (weighted mean difference [WMD] = 0.17, 95% CI = −0.22 to 0.55, p = 0.40) but was significantly associated with HF (WMD = 0.21, 95% CI = 0.04 to 0.38, p = 0.02; I² = 99%, p < 0.00001). sST2 levels did not differ significantly between patients with IHD or MI and healthy individuals; however, we believe that ST2 could be used as an auxiliary diagnostic biomarker of HF.

The functions of the brain and heart, which are the two main supporting organs of human life, are closely linked. Numerous studies have expounded the mechanisms of the brain‐heart axis and its related clinical applications. However, the effect of heart disease on brain function, defined as the heart‐brain axis, is less studied even though cognitive dysfunction after heart disease is one of its most frequently reported manifestations. Hypoperfusion caused by heart failure appears to be an important risk factor for cognitive decline. Blood perfusion, the immune response, and oxidative stress are the possible main mechanisms of cognitive dysfunction, indicating that the blood‐brain barrier, glial cells, and amyloid‐β may play active roles in these mechanisms. Clinicians should pay more attention to the cognitive function of patients with heart disease, especially those with heart failure. In addition, further research elucidating the associated mechanisms would help discover new therapeutic targets to intervene in the process of cognitive dysfunction after heart disease. This review discusses cognitive dysfunction in relation to heart disease and its potential mechanisms.

Forensic microbiology has been widely used in the diagnosis of causes and manner of death, identification of individuals, detection of crime locations, and estimation of postmortem interval. However, the traditional method, microbial culture, has low efficiency, high consumption, and a low degree of quantitative analysis. With the development of high-throughput sequencing technology, advanced bioinformatics, and fast-evolving artificial intelligence, numerous machine learning models, such as RF, SVM, ANN, DNN, regression, PLS, ANOSIM, and ANOVA, have been established with the advancement of the microbiome and metagenomic studies. Recently, deep learning models, including the convolutional neural network (CNN) model and CNN-derived models, improve the accuracy of forensic prognosis using object detection techniques in microorganism image analysis. This review summarizes the application and development of forensic microbiology, as well as the research progress of machine learning (ML) and deep learning (DL) based on microbial genome sequencing and microbial images, and provided a future outlook on forensic microbiology.