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The effect of laser irradiation and tensile stress on the microstructure and soft magnetic properties of the FeSiBNbCu nanocrystalline alloy prepared using a continuous laser has been investigated. The experimental results indicate that a decreased laser scanning speed provides more thermal energy to induce nanocrystals and encourage grain growth. When the scanning speed is excessively high, the crystallization process will cease due to a lack of energy to drive diffusion phase transitions. Nevertheless, the introduction of tensile stress could significantly promote crystallization in FeSiBNbCu alloy samples irradiated at these high laser scanning speeds. This phenomenon can be attributed to the augmentation of compressive thermal stress at the interface between the laser-treated track and the untreated region. This heightened compressive stress promotes the diffusivity of atoms, and, as a result, the transformation from amorphous to crystalline states can be enhanced. As the applied tensile stress increases, both grain size and crystalline volume fraction exhibit a proportional augmentation. Consequently, these changes manifest in the soft magnetic properties. The crystalline volume fraction can reach 62%, and the coercivity is 2.9 A/m at the optimized scanning speed; these values correspond to 54% and 3.3 A/m under specific tensile stress loading. [ABSTRACT FROM AUTHOR]

The impact of laser annealing on the nanocrystalline Fe–Si–B–Cu–Nb soft magnetic alloy has been investigated. The experimental results showed that the microstructure of nanocrystals produced by laser annealing is comparable to that of furnace annealing. However, the annealing time plays a crucial role in determining the grain size and crystalline fraction evolution, thereby affecting both the coercivity and saturation magnetization. Nonetheless, the volume crystalline fraction of the laser annealed samples is lower than that of the furnace annealed ones due to the ultrahigh heating and cooling rate, which hinders the growth of nanocrystals and crystallization region. In addition, laser annealed samples demonstrate no distinct crystallization at high scanning speeds, even with noticeable changes in soft magnetic properties. Furthermore, the correlation between coercivity and grain size is examined, revealing a progressive decline in coercivity with a decrease in grain size. It is also found that the rule Hc ∝ D6 is also valid in laser annealing for relatively larger grains, whereas the rule becomes Hc ∝ D3 for smaller grains. [ABSTRACT FROM AUTHOR]

The NK cell is an important component of the tumor microenvironment of pancreatic ductal adenocarcinoma (PDAC), also plays a significant role in PDAC development. This study aimed to explore the relationship between NK cell marker genes and prognosis, immune response of PDAC patients. By scRNA-seq data, we found the proportion of NK cells were significantly downregulated in PDAC and 373 NK cell marker genes were screened out. By TCGA database, we enrolled 7 NK cell marker genes to construct the signature for predicting prognosis in PDAC patients. Cox analysis identified the signature as an independent factor for pancreatic cancer. Subsequently, the predictive power of signature was validated by 6 GEO datasets and had an excellent evaluation. Our analysis of relationship between the signature and patients' immune status revealed that the signature has a strong correlation with immunocyte infiltration, inflammatory reaction, immune checkpoint inhibitors (ICIs) response. The NK cell marker genes are closely related to the prognosis and immune capacity of PDAC patients, and they have potential value as a therapeutic target., (© 2024. The Author(s).)

Background: Axillary lymph nodes (ALN) status serves as a crucial prognostic indicator in breast cancer (BC). The aim of this study was to construct a radiogenomic multimodal model, based on machine learning and whole-transcriptome sequencing (WTS), to accurately evaluate the risk of ALN metastasis (ALNM), drug therapeutic response and avoid unnecessary axillary surgery in BC patients., Methods: In this study, conducted a retrospective analysis of 1078 BC patients from The Cancer Genome Atlas (TCGA), The Cancer Imaging Archive (TCIA), and Foshan cohort. These patients were divided into the TCIA cohort ( N =103), TCIA validation cohort ( N =51), Duke cohort ( N =138), Foshan cohort ( N =106), and TCGA cohort ( N =680). Radiological features were extracted from BC radiological images and differentially expressed gene expression was calibrated using technology. A support vector machine model was employed to screen radiological and genetic features, and a multimodal model was established based on radiogenomic and clinical pathological features to predict ALNM. The accuracy of the model predictions was assessed using the area under the curve (AUC) and the clinical benefit was measured using decision curve analysis. Risk stratification analysis of BC patients was performed by gene set enrichment analysis, differential comparison of immune checkpoint gene expression, and drug sensitivity testing., Results: For the prediction of ALNM, rad-score was able to significantly differentiate between ALN- and ALN+ patients in both the Duke and Foshan cohorts ( P <0.05). Similarly, the gene-score was able to significantly differentiate between ALN- and ALN+ patients in the TCGA cohort ( P <0.05). The radiogenomic multimodal nomogram demonstrated satisfactory performance in the TCIA cohort (AUC 0.82, 95% CI: 0.74-0.91) and the TCIA validation cohort (AUC 0.77, 95% CI: 0.63-0.91). In the risk sub-stratification analysis, there were significant differences in gene pathway enrichment between high and low-risk groups ( P <0.05). Additionally, different risk groups may exhibit varying treatment responses ( P <0.05)., Conclusion: Overall, the radiogenomic multimodal model employs multimodal data, including radiological images, genetic, and clinicopathological typing. The radiogenomic multimodal nomogram can precisely predict ALNM and drug therapeutic response in BC patients., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)