Search

Light has profoundly impacted modern medicine and healthcare, with numerous luminescent agents and imaging techniques currently being used to assess health and treat diseases. As an emerging concept in luminescence, aggregation-induced emission (AIE) has shown great potential in biological applications due to its advantages in terms of brightness, biocompatibility, photostability, and positive correlation with concentration. This review provides a comprehensive summary of AIE luminogens applied in imaging of biological structure and dynamic physiological processes, disease diagnosis and treatment, and detection and monitoring of specific analytes, followed by representative works. Discussions on critical issues and perspectives on future directions are also included. This review aims to stimulate the interest of researchers from different fields, including chemistry, biology, materials science, medicine, etc., thus promoting the development of AIE in the fields of life and health.

Resistiveness to care (RTC) is a common abnormal behavioral symptom in dementia patients during the care process, which seriously negatively impacts the patients and their caregivers. The research on assessment tools for RTC started earlier in foreign countries, and has explored relevant aspects more deeply and comprehensively. Since the Resistiveness to Care Scale for Dementia of the Alzheimer's Type (RTC-DAT) came out in 1999, overseas researchers have made several revisions to its content and methods, and have expanded it to include different scales for assessing RTC in different care settings. In this paper, we present a review of the tools developed overseas to assess RTC in patients with dementia, focusing on the differences in contents and methods of the tools, and the characteristics, strengths and weaknesses of each tool, which may offer insights into China's development of such a tool or introduction of a foreign tool applicable to China. The evaluation method of the assessment tools currently used in foreign research on RTC in patients with dementia is mostly based on the researcher-observed method used in the initial RTC-DAT, which faces many limitations in its practical application. The Refusal of Care Informant Scale (RoCIS) developed in 2022 attempts to use the method of evaluation by consulting caregivers, which is commonly used in other fields, providing a new way for China's introduction or development of a RTC assessment tool applicable to Chinese dementia patients. Considering various types of these tools, we recommend that the tool should be selected according to its applicability assessed comprehensively.

Resistance to care (RTC) is a common abnormal behavioral manifestation of behavioral and psychological symptoms of dementia. Although RTC is strongly associated with agitation or aggressive behavior, it is a specific behavioral response of dementia patients to caregivers, and may result in a negative feedback mechanism as it often directly, negatively impacts caregivers. Currently, there are rare studies on RTC in dementia patients or measuring RTC in these patients using relevant tools in China, which may be because domestic researchers easily confuse RTC with other behavioral concepts. While the concept of RTC was introduced earlier in foreign studies, and has been systematically explored and studied. In this paper, we reviewed available relevant studies, and gave a detailed description of the concept, impact, assessment tools, and interventions regarding RTC, with a view to providing ideas and strategies for the study of RTC in patients with dementia in China.

Adult neurogenesis, arising from quiescent radial-glia-like neural stem cells (RGLs), occurs throughout life in the dentate gyrus. How neural stem cells are maintained throughout development to sustain adult mammalian neurogenesis is not well understood. Here, we show that milk fat globule-epidermal growth factor (EGF) 8 (Mfge8), a known phagocytosis factor, is highly enriched in quiescent RGLs in the dentate gyrus. Mfge8-null mice exhibit decreased adult dentate neurogenesis, and furthermore, adult RGL-specific deletion of Mfge8 leads to RGL overactivation and depletion. Similarly, loss of Mfge8 promotes RGL activation in the early postnatal dentate gyrus, resulting in a decreased number of label-retaining RGLs in adulthood. Mechanistically, loss of Mfge8 elevates mTOR1 signaling in RGLs, inhibition of which by rapamycin returns RGLs to quiescence. Together, our study identifies a neural-stem-cell-enriched niche factor that maintains quiescence and prevents developmental exhaustion of neural stem cells to sustain continuous neurogenesis in the adult mammalian brain.

Esophageal squamous cell carcinoma (ESCC) is characterized by molecular heterogeneity with various immune cell infiltration patterns, which have been associated with therapeutic sensitivity and resistance. In particular, dendritic cells (DCs) are recently discovered to be associated with prognosis and survival in cancer. However, how DCs differ among ESCC patients has not been fully comprehended. Recently, the advance of single-cell RNA sequencing (scRNA-seq) enables us to profile the cell types, states, and lineages in the heterogeneous ESCC tissues. Here, we dissect the ESCC tumor microenvironment at high resolution by integrating 192,078 single cells from 60 patients, including 4379 DCs. We then used Scissor, a method that identifies cell subpopulations from single-cell data that are associated bulk samples with genomic and clinical information, to stratify DCs into Scissorhi and Scissorlow subtypes. We applied the Scissorhi gene signature to stratify ESCC scRNAseq patient, and we found that PD-L1, TIGIT, PVR and IL6 ligand-receptor-mediated cell interactions existed mainly in Scissorhi patients. Finally, based on the Scissor results, we successfully developed a validated prognostic risk model for ESCC and further validated the reliability of the risk prediction model by recruiting 40 ESCC clinical patients. This information highlights the importance of these genes in assessing patient prognosis and may help in the development of targeted or personalized therapies for ESCC. [ABSTRACT FROM AUTHOR]

Semantic segmentation of remote sensing images (RSIs) is pivotal for numerous applications in urban planning, agricultural monitoring, and environmental conservation. However, traditional approaches have primarily emphasized learning within the spatial domain, which frequently leads to less than optimal discrimination of features. Considering the inherent spectral qualities of RSIs, it is essential to bolster these representations by incorporating the spectral context in conjunction with spatial information to improve discriminative capacity. In this paper, we introduce the spectral–spatial context-boosted network (SSCBNet), an innovative network designed to enhance the accuracy semantic segmentation in RSIs. SSCBNet integrates synergetic attention (SYA) layers and cross-fusion modules (CFMs) to harness both spectral and spatial information, addressing the intrinsic complexities of urban and natural landscapes within RSIs. Extensive experiments on the ISPRS Potsdam and LoveDA datasets reveal that SSCBNet surpasses existing state-of-the-art models, achieving remarkable results in F 1 -scores, overall accuracy (OA), and mean intersection over union (mIoU). Ablation studies confirm the significant contribution of SYA layers and CFMs to the model's performance, emphasizing the effectiveness of these components in capturing detailed contextual cues. [ABSTRACT FROM AUTHOR]