Amyloid-β (Aβ) accumulation is one of the cardinal pathological hallmarks of Alzheimer's disease and plays an important role in its pathogenesis. Although the neurotoxic effects of Aβ has been extensively studied, recent studies have revealed that it may also have protective effects. Here, we review novel findings that have shifted our understanding of the role of Aβ in the pathogenesis of Alzheimer's disease. An in-depth and comprehensive understanding of Aβ will provide us with a broader perspective on the treatment of Alzheimer's disease.

Objective: Human papillomavirus (HPV) infection, especially with high-risk HPV (HR-HPV) genotypes, is closely associated with cervical cancer. This study aimed to observe the epidemiological characteristics of HPV infection among healthy women in Beijing, China. Materials and Methods: Cervical specimens were collected from 29,436 healthy women, who underwent health check-ups in Peking Union Medical College Hospital between 2016 and 2019. A commercial kit was used for the detection of 15 HR-HPV and two low-risk HPV (LR-HPV) genotypes. Results: A total of 3586 (12.18%) participants tested positive for HPV, 3467 of which were infected with HR-HPVs. The most prevalent genotypes were HPV52, 58, 16, 51, and 56. Moreover, while infection with a single genotype (9.84%) was more prevalent, HPV16+52 was the most common combination in those infected with multiple HPVs. Furthermore, the highest infection rate among age groups was in women aged < 25 years (20.92%). No significant difference in the prevalence was observed from 2016 to 2019. However, HPV incidence in Beijing was significantly different than that in all other areas in China, except for Zhengzhou (p < 0.05). Conclusion: Our findings could serve as potential reference for better understanding of the epidemiological characteristics of HPV infection in Beijing.

Neuroblastoma breakpoint family, member 1 (NBPF1), appears to be a double-edged sword with regard to its role in carcinogenesis. On the one hand, the tumor-suppressing functions of NBPF1 have been definitively observed in neuroblastoma, prostate cancer, cutaneous squamous cell carcinoma, and cervical cancer. On the other hand, there is evidence that NBPF1 regulates the colony formation, invasion, and maintenance of liver cancer cells and hence functions as an oncogene. The roles of NBPF1 are strictly dependent on the biological context and type of organization. However, a systematic pan-cancer analysis has thus far not been undertaken, and the significance of NBPF1 in the occurrence and progression of many malignancies is uncertain. In this paper, bioinformatics techniques were employed to analyze NBPF1 expression across different cancers and investigate the relationship between NBPF1 and clinical features, prognosis, genetic alteration, and tumor immune microenvironment, respectively. Our results show that NBPF1 is variably expressed in distinct tumor tissues and is also closely linked to clinical outcomes. In particular, compared to other tumor types, there was a strong negative correlation between NBPF1 expression and various components of the tumor microenvironment in adrenocortical carcinoma (ACC). We thus developed an NBPF1-derived immune risk model based on NBPF1-related immune genes; ACC patients with a high-risk score tended to have a poorer prognosis, accompanied by immune hyporesponsiveness. NBPF1 can be used as a prognostic biomarker for multiple cancers. Moreover, anti-NBPF1 immunotherapy may be suitable for treating ACC patients.

Backgrounds: Exploring the molecular features that drive breast cancer invasion and migration remains an important biological and clinical challenge. In recent years, the use of interpretable machine learning models has enhanced our understanding of the underlying mechanisms of disease progression.Methods: In this study, we present a novel gene–pathway–compound‐related sparse deep neural network (GPC‐Net) for investigating breast cancer invasion and migration. The GPC‐Net is an interpretable neural network model that utilizes molecular data to predict cancer status. It visually represents genes, pathways, and associated compounds involved in these pathways.Results: Compared with other modeling methods, GPC‐Net demonstrates superior performance. Our research identifies key genes, such as ADCY8, associated with invasive breast cancer and verifies their expression in breast cancer cells. In addition, we conducted a preliminary exploration of several pathways.Conclusion: GPC‐Net is among the pioneering deep neural networks that incorporate pathways and compounds, aiming to balance interpretability and performance. It is expected to offer a more convenient approach for future biomedical research.