Effect of interface layer on the enhancement of thermal conductivity of SiC-Water nanofluids: Molecular dynamics simulation.

To investigate the impact of interfacial layer effects on the thermal conductivity of nanofluids and the microscopic mechanisms of enhanced thermal conductivity, this study employed non-equilibrium molecular dynamics to compute the thermal conductivity, number density, radial distribution function, and mean square displacement distribution of SiC nanofluids. The impact of nanoparticle volume fraction and particle size parameters on the thermal conductivity of nanofluids and the structure of interfacial adsorption layers was discussed. The simulation calculation results show that the coefficient of thermal conductivity of nanofluid is positively related to the volume fraction of nanoparticles, increasing from 0.6529 W/(m·K) to 0.8159 W/(m·K), and the enhancement of thermal conductivity by the volume fraction can be up to 33.97 %. The thermal conductivity is inversely correlated with the change in particle size, and the maximum improvement in thermal conductivity by particle size can reach up to 12.05 %. The simulated results of the thermal conductivity of nanofluid are almost consistent with the predicted results of the Yu&Choi model, and the error is controlled within 5 %. Simultaneously, the thickness of the interfacial adsorption layer decreases with an increase in particle size. This reduction arises due to larger particles having a smaller specific surface area, resulting in fewer particle surfaces covered by the interface layer. Moreover, the impact of particle size on the arrangement and affinity of molecules within the interface layer contributes to this decrease. Overall, interface layer effects exhibit a dual impact on the thermal conduction of nanofluids. The structured formation and high-density distribution of the adsorption layer contribute to enhanced heat transfer, while thermal resistance between nanoparticle surfaces and the fluid restricts heat transmission.

Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT.

As a disease with high morbidity and high mortality, lung cancer has seriously harmed people's health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability.

MKK3 K329 Mutation Attenuates Diabetes-Associated Cognitive Dysfunction by Blocking the MKK3-RAGE Interaction and Inhibiting Neuroinflammation.

The receptor for advanced glycation end products (RAGE) contributes to diabetes-associated cognitive dysfunction (DACD) through the interaction of its C-terminal AAs 2-5 with mitogen-activated protein kinase kinase 3 (MKK3). However, the associated MKK3 binding site is unknown. Here, db/db mice were used as a model for type 2 diabetes. GST pull-down assays and AutoDock Vina simulations were conducted to identify the key RAGE binding site in MKK3. This binding site was mutated to investigate its effects on DACD and to elucidate the underlying mechanisms. The interaction of MKK3 and RAGE, the levels of inflammatory factors, and the activation of microglia and astrocytes were tested. Synaptic morphology and plasticity in hippocampal neurons were assessed via electrophysiological recordings and Golgi staining. Behavioral tests were used to assess cognitive function. In this study, MKK3 bound directly to RAGE via its lysine 329 (K329), leading to the activation of the nuclear factor-κB (NF-κB) signaling pathway, which in turn triggered neuroinflammation and synaptic dysfunction, and ultimately contributed to DACD. MKK3 mutation at K329 reversed synaptic dysfunction and cognitive deficits by downregulating the NF-κB signaling pathway and inhibiting neuroinflammation. These results confirm that neuroinflammation and synaptic dysfunction in the hippocampus rely on the direct binding of MKK3 and RAGE. We conclude that MKK3 K329 binding to C-terminal RAGE (ct-RAGE) is a key mechanism by which neuroinflammation and synaptic dysfunction are induced in the hippocampus. This study presents a novel mechanism for DACD and proposes a novel therapeutic avenue for neuroprotection in DACD.