The effect of ulinastatin on hyperglycemia in patients undergoing hepatectomy

AbstractBackgroundTo identify the effect of ulinastatin (UTI) administration on stress-induced hyperglycemia and acute insulin (INS) resistance experienced by patients undergoing partial hepatectomy.MethodsForty-six patients undergoing partial hepatectomy were assigned randomly to the control group (group C) or UTI treatment group (group U). Six cases underwent partial hepatectomy but were not eligible for inclusion. The patients in group U had an intravenous infusion of a total amount of 5000 IU/kg UTI before the induction of anesthesia and at the start of surgery. The patients in group C were given an identical volume of physiological saline in the same manner. Blood samples for the measurement of interleukin-6, cortisol, INS, and glucagon were obtained. Fasting plasma glucose concentration was measured immediately before skin incision (T1), 20 min after the liver lesion was removed (T2), at the end of surgery (T3), as well as on the first (T4) and second mornings after partial hepatectomy (T5). The insulin sensitivity index (ISI) was calculated at these time points.ResultsThe fasting plasma glucose concentration in group U was significantly lower than that in group C at all time points except for T1. In group U, the insulin sensitivity index was higher, and the levels of interleukin-6, cortisol, and INS were lower than that in group C (P < 0.05).ConclusionsThe data suggest that UTI administration improves perioperative hyperglycemia by inhibiting the inflammatory reaction, as well as excessive release of inflammatory factors, and improves INS resistance

Limited driving of elevated CO2 on vegetation greening over global drylands

Drylands are the world's largest biome and dominate the trends and interannual variability of global carbon sinks. Although a 'greening' trend of global drylands has been widely reported, large uncertainties remain in attributing its drivers. It is increasingly emphasized that elevated CO2 has greatly contributed to the vegetation greening over global drylands. Here we quantified the contributions of climate change, elevated CO2, and land use and land cover change (LULCC) on leaf area index (LAI) over drylands, using a process-based land surface model Noah-MP to investigate the drivers of vegetation change. The state-of-the-art model shows better performance in simulating the interannual variability of satellite-observed LAI over global drylands compared with that of the multi-model ensemble mean LAI from the TRENDY results. The area that LAI changes dominated by climate change (44.03%) is three times greater than that by CO2 (13.89%), and climate change also contributes most to the global drylands greening trend (55.07%). LULCC shows regional dominance over 13.35% of the global drylands, which is associated with afforestation, woody plant encroachment, and agricultural intensification. Our results imply that the vegetation greening area driven by elevated CO2 is much limited relative to the overwhelming climatic driving, which should be considered in predictions of trends and interannual variations of global carbon sinks

Progress of MRI in predicting the circumferential resection margin of rectal cancer: A narrative review

Rectal cancer (RC) is the third most frequently diagnosed cancer worldwide, and the status of its circumferential resection margin (CRM) is of paramount significance for treatment strategies and prognosis. CRM involvement is defined as tumor touching or within 1 mm from the outermost part of tumor or outer border of the mesorectal or lymph node deposits to the resection margin. The incidence of involved CRM varied from 5.4 % to 36 %, which may associate with an in consistent definition of CRM, the quality of surgeries, and the different examination modalities. Although T and N status are essential factors in determining whether a patient should receive neoadjuvant therapy before surgery, CRM status is a powerful predictor of local and distant recurrence as well as survival rate. This review explores the significance of CRM, the various assessment methods, and the role of magnetic resonance imaging (MRI) and artificial intelligence-based MRI in predicting CRM status. MRI showed potential advantage in predicting CRM status with a high sensitivity and specificity compared to computed tomography (CT). We also discuss MRI advancements in RC imaging, including conventional MRI with body coil, high-resolution MRI with phased-array coil, and endorectal MRI. Along with a discussion of artificial intelligence-based MRI techniques to predict the CRM status of RCs before and after treatments

Limited driving of elevated CO2 on vegetation greening over global drylands

Drylands are the world’s largest biome and dominate the trends and interannual variability of global carbon sinks. Although a ‘greening’ trend of global drylands has been widely reported, large uncertainties remain in attributing its drivers. It is increasingly emphasized that elevated CO _2 has greatly contributed to the vegetation greening over global drylands. Here we quantified the contributions of climate change, elevated CO _2 , and land use and land cover change (LULCC) on leaf area index (LAI) over drylands, using a process-based land surface model Noah-MP to investigate the drivers of vegetation change. The state-of-the-art model shows better performance in simulating the interannual variability of satellite-observed LAI over global drylands compared with that of the multi-model ensemble mean LAI from the TRENDY results. The area that LAI changes dominated by climate change (44.03%) is three times greater than that by CO _2 (13.89%), and climate change also contributes most to the global drylands greening trend (55.07%). LULCC shows regional dominance over 13.35% of the global drylands, which is associated with afforestation, woody plant encroachment, and agricultural intensification. Our results imply that the vegetation greening area driven by elevated CO _2 is much limited relative to the overwhelming climatic driving, which should be considered in predictions of trends and interannual variations of global carbon sinks

Identification of Maize Seed Varieties Using MobileNetV2 with Improved Attention Mechanism CBAM

Seeds are the most fundamental and significant production tool in agriculture. They play a critical role in boosting the output and revenue of agriculture. To achieve rapid identification and protection of maize seeds, 3938 images of 11 different types of maize seeds were collected for the experiment, along with a combination of germ and non-germ surface datasets. The training set, validation set, and test set were randomly divided by a ratio of 7:2:1. The experiment introduced the CBAM (Convolutional Block Attention Module) attention mechanism into MobileNetV2, improving the CBAM by replacing the cascade connection with a parallel connection, thus building an advanced mixed attention module, I_CBAM, and establishing a new model, I_CBAM_MobileNetV2. The proposed I_CBAM_MobileNetV2 achieved an accuracy of 98.21%, which was 4.88% higher than that of MobileNetV2. Compared to Xception, MobileNetV3, DenseNet121, E-AlexNet, and ResNet50, the accuracy was increased by 9.24%, 6.42%, 3.85%, 3.59%, and 2.57%, respectively. Gradient-Weighted Class Activation Mapping (Grad-CAM) network visualization demonstrates that I_CBAM_MobileNetV2 focuses more on distinguishing features in maize seed images, thereby boosting the accuracy of the model. Furthermore, the model is only 25.1 MB, making it suitable for portable deployment on mobile terminals. This study provides effective strategies and experimental methods for identifying maize seed varieties using deep learning technology. This research provides technical assistance for the non-destructive detection and automatic identification of maize seed varieties

Activated hedgehog gene pattern correlates with dismal clinical outcome and tumor microenvironment heterogeneity in hepatocellular carcinoma

Background: Activation of the Hedgehog signaling pathway is linked to the initiation and development of human hepatocellular carcinoma (HCC). However, its impact on clinical outcomes and the HCC microenvironment remains unclear. Methods: We performed comprehensive analyses of Hedgehog pathway genes in a large cohort of HCC patients. Specifically, we utilized univariate Cox regression analysis to identify Hedgehog genes linked to overall survival, and the LASSO algorithm was used to construct a Hedgehog-related gene pattern. We subsequently examined the correlation between the Hedgehog pattern and the HCC microenvironment employing the CIBERSORT and ssGSEA algorithms. Furthermore, Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and the anti-PD-L1 treatment dataset (IMvigor210) are used to evaluate the clinical response of the Hedgehog pattern in predicting immune checkpoint inhibitors. Results: We found that the Hedgehog activation score (HHAS), a prognostic score based on 11 Hedgehog genes, was significantly associated with HCC patient survival. Patients exhibiting high HHAS experienced markedly reduced survival rates compared to those with low HHAS, and HHAS emerged as an independent prognostic factor for HCC. Functional enrichment analysis unveiled the association of the HHAS phenotype with functions related to the immune system, and further investigation demonstrated that HCC patients exhibiting low HHAS displayed elevated levels of anti-tumor immune activation in CD8+ T cells, while high HHAS were linked to immune escape phenotypes and increased infiltration of immune suppressive cells. In addition, in the Immune Checkpoint Inhibitor (ICI) cohort of IMvigor210, patients with higher HHAS had worse ICI treatment outcomes and shortened survival time, indicating that the HHAS is a useful indicator for predicting patient response to immunotherapy. Conclusions: In summary, our study offers valuable insights for advancing research on Hedgehog and its impact on tumor immunity, which provides an opportunity to optimize prognosis and immune therapy for HCC