Nutritional Assessment Tools for Patients with Cancer: A Narrative Review.

Cancer patients are at high risk of malnutrition, which can lead to adverse health outcomes such as prolonged hospitalization, increased complications, and increased mortality. Accurate and timely nutritional assessment plays a critical role in effectively managing malnutrition in these patients. However, while many tools exist to assess malnutrition, there is no universally accepted standard. Although different tools have their own strengths and limitations, there is a lack of narrative reviews on nutritional assessment tools for cancer patients. To address this knowledge gap, we conducted a non-systematic literature search using PubMed, Embase, Web of Science, and the Cochrane Library from their inception until May 2023. A total of 90 studies met our selection criteria and were included in our narrative review. We evaluated the applications, strengths, and limitations of 4 commonly used nutritional assessment tools for cancer patients: the Subjective Global Assessment (SGA), Patient-Generated Subjective Global Assessment (PG-SGA), Mini Nutritional Assessment (MNA), and Global Leadership Initiative on Malnutrition (GLIM). Our findings revealed that malnutrition was associated with adverse health outcomes. Each of these 4 tools has its applications, strengths, and limitations. Our findings provide medical staff with a foundation for choosing the optimal tool to rapidly and accurately assess malnutrition in cancer patients. It is essential for medical staff to be familiar with these common tools to ensure effective nutritional management of cancer patients.

Effects of liraglutide on astrocyte polarization and neuroinflammation in db/db mice: focus on iron overload and oxidative stress.

Neuroinflammation plays a crucial role in the occurrence and development of cognitive impairment in type 2 diabetes mellitus (T2DM), but the specific injury mechanism is not fully understood. Astrocyte polarization has attracted new attention and has been shown to be directly and indirectly involved in neuroinflammation. Liraglutide has been shown to have beneficial effects on neurons and astrocytes. However, the specific protection mechanism still needs to be clarified. In this study, we assessed the levels of neuroinflammation and A1/A2-responsive astrocytes in the hippocampus of db/db mice and examined their relationships with iron overload and oxidative stress. First, in db/db mice, liraglutide alleviated the disturbance of glucose and lipid metabolism, increased the postsynaptic density, regulated the expression of NeuN and BDNF, and partially restored impaired cognitive function. Second, liraglutide upregulated the expression of S100A10 and downregulated the expression of GFAP and C3, and decreased the secretion of IL-1ß, IL-18, and TNF-α, which may confirm that it regulates the proliferation of reactive astrocytes and A1/A2 phenotypes polarize and attenuate neuroinflammation. In addition, liraglutide reduced iron deposition in the hippocampus by reducing the expression of TfR1 and DMT1 and increasing the expression of FPN1; at the same time, liraglutide by up-regulating the levels of SOD, GSH, and SOD2 expression, as well as downregulation of MDA levels and NOX2 and NOX4 expression to reduce oxidative stress and lipid peroxidation. The above may attenuate A1 astrocyte activation. This study preliminarily explored the effect of liraglutide on the activation of different astrocyte phenotypes and neuroinflammation in the hippocampus of a T2DM model and further revealed its intervention effect on cognitive impairment in diabetes. Focusing on the pathological consequences of astrocytes may have important implications for the treatment of diabetic cognitive impairment.

A Pedestrian Detection Network Model Based on Improved YOLOv5.

Advanced object detection methods always face high algorithmic complexity or low accuracy when used in pedestrian target detection for the autonomous driving system. This paper proposes a lightweight pedestrian detection approach called the YOLOv5s-G2 network to address these issues. We apply Ghost and GhostC3 modules in the YOLOv5s-G2 network to minimize computational cost during feature extraction while keeping the network's capability of extracting features intact. The YOLOv5s-G2 network improves feature extraction accuracy by incorporating the Global Attention Mechanism (GAM) module. This application can extract relevant information for pedestrian target identification tasks and suppress irrelevant information, improving the unidentified problem of occluded and small targets by replacing the GIoU loss function used in the bounding box regression with the α-CIoU loss function. The YOLOv5s-G2 network is evaluated on the WiderPerson dataset to ensure its efficacy. Our proposed YOLOv5s-G2 network offers a 1.0% increase in detection accuracy and a 13.2% decrease in Floating Point Operations (FLOPs) compared to the existing YOLOv5s network. As a result, the YOLOv5s-G2 network is preferable for pedestrian identification as it is both more lightweight and more accurate.