PHPB ameliorates memory deficits and reduces oxidative injury in Alzheimer's disease mouse model by activating Nrf2 signaling pathway.

Alzheimer's disease (AD), a progressive neurodegenerative disorder, is the most common cause of dementia in elderly people and substantially affects patient quality of life. Oxidative stress is considered a key factor in the development of AD. Nrf2 plays a vital role in maintaining redox homeostasis and regulating neuroinflammatory responses in AD. Previous studies show that potassium 2-(1-hydroxypentyl)-benzoate (PHPB) exerts neuroprotective effects against cognitive impairment in a variety of dementia animal models such as APP/PS1 transgenic mice. In this study we investigated whether PHPB ameriorated the progression of AD by reducing oxidative stress (OS) damage. Both 5- and 13-month-old APP/PS1 mice were administered PHPB (100 mg·kg-1·d-1, i.g.) for 10 weeks. After the cognition assessment, the mice were euthanized, and the left hemisphere of the brain was harvested for analyses. We showed that 5-month-old APP/PS1 mice already exhibited impaired performance in the step-down test, and knockdown of Nrf2 gene only slightly increased the impairment, while knockdown of Nrf2 gene in 13-month-old APP/PS1 mice resulted in greatly worse performance. PHPB administration significantly ameliorated the cognition impairments and enhanced antioxidative capacity in APP/PS1 mice. In addition, PHPB administration significantly increased the p-AKT/AKT and p-GSK3ß/GSK3ß ratios and the expression levels of Nrf2, HO-1 and NQO-1 in APP/PS1 mice, but these changes were abolished by knockdown of Nrf2 gene. In SK-N-SH APPwt cells and primary mouse neurons, PHPB (10 µM) significantly increased the p-AKT/AKT and p-GSK3ß/GSK3ß ratios and the level of Nrf2, which were blocked by knockdown of Nrf2 gene. In summary, this study demonstrates that PHPB exerts a protective effect via the Akt/GSK3ß/Nrf2 pathway and it might be a promising neuroprotective agent for the treatment of AD.

A CT-based predictive model for stent-induced vessel damage: application to type B aortic dissection.

OBJECTIVES: The distal stent-induced new entry (distal SINE) is a life-threatening device-related complication after thoracic endovascular aortic repair (TEVAR). However, risk factors for distal SINE are not fully determined, and prediction models are lacking. This study aimed to establish a predictive model for distal SINE based on the preoperative dataset. METHODS: Two hundred and six patients with Stanford type B aortic dissection (TBAD) that experienced TEVAR were involved in this study. Among them, thirty patients developed distal SINE. Pre-TEVAR morphological parameters were measured based on the CT-reconstructed configurations. Virtual post-TEVAR morphological and mechanical parameters were computed via the virtual stenting algorithm (VSA). Two predictive models (PM-1 and PM-2) were developed and presented as nomograms to help risk evaluation of distal SINE. The performance of the proposed predictive models was evaluated and internal validation was conducted. RESULTS: Machine-selected variables for PM-1 included key pre-TEVAR parameters, and those for PM-2 included key virtual post-TEVAR parameters. Both models showed good calibration in both development and validation subsamples, while PM-2 outperformed PM-1. The discrimination of PM-2 was better than PM-1 in the development subsample, with an optimism-corrected area under the curve (AUC) of 0.95 and 0.77, respectively. Application of PM-2 in the validation subsample presented good discrimination with an AUC of 0.9727. The decision curve demonstrated that PM-2 was clinically useful. CONCLUSION: This study proposed a predictive model for distal SINE incorporating the CT-based VSA. This predictive model could efficiently predict the risk of distal SINE and thus might contribute to personalized intervention planning. CLINICAL RELEVANCE STATEMENT: This study established a predictive model to evaluate the risk of distal SINE based on the pre-stenting CT dataset and planned device information. With an accurate VSA tool, the predictive model could help to improve the safety of the endovascular repair procedure. KEY POINTS: • Clinically useful prediction models for distal stent-induced new entry are still lacking, and the safety of the stent implantation is hard to guarantee. • Our proposed predictive tool based on a virtual stenting algorithm supports different stenting planning rehearsals and real-time risk evaluation, guiding clinicians to optimize the presurgical plan when necessary. • The established prediction model provides accurate risk evaluation for vessel damage, improving the safety of the intervention procedure.

Bioinformatics combined with clinical data to analyze clinical characteristics and prognosis in patients with HER2 low expression breast cancer.

Background: Human epidermal growth factor receptor 2 (HER2) is a landmark protein in determining the targeted treatment of breast cancer (BC). However, the latest research shows that different intensity of HER2 protein expression levels in BC leads to different clinical characteristics, treatment, and prognosis, especially in HER2 low expression patients. Therefore, this study intends to analyze and compare the clinicopathologic features and prognosis of BC patients with low and zero HER2 expression from The Cancer Genome Atlas (TCGA) database and the data collected by our center. Methods: First, the BC dataset was downloaded from TCGA database, including 345 eligible and with complete clinical information BC patients, to compare the difference between HER2 low expression groups and HER2 zero expression groups and their correlation with estrogen receptor (ER) and progesterone receptor (PR) expression. Then, the clinicopathological data and follow-up of 405 patients with HER2 low expression and HER2 zero expression diagnosed with BC admitted to the Affiliated Hospital of Youjiang Medical University for Nationalities (YJMU) from January 2017 to December 2021 were collected to verify the consistency of the results of the two data sets. Results: Both the clinical samples and the TCGA data showed that the ER and PR rates were higher in the HER2 low expression group compared with the HER2 zero expression group. There were no significant differences in tumor size, lymph node metastasis, distant metastasis, and disease-free survival (DFS). In addition, the data analysis of 405 clinical samples also showed that the HER2 low expression group had a lower 3-year recurrence or metastasis rate compared with the HER2 zero expression group. Conclusions: Compared with HER2 zero expression, HER2 low patients express more ER and PR, and have less short-term recurrence and metastasis, but there is no obvious difference in DFS between the two groups.