Association of clinical outcomes and the predictive value of T lymphocyte subsets within colorectal cancer patients

IntroductionTumor immunity is a hot topic in tumor research today, and human immunity is closely related to tumor progression. T lymphocyte is an important component of human immune system, and the changes in their subsets may influence the progression of colorectal cancer (CRC) to some extent. This clinical study systematically describes and analyzes the association of CD4+ and CD8+ T-lymphocyte content and CD4+/CD8+ T-lymphocyte ratio with CRC differentiation, clinical pathological stage, Ki67 expression, T-stage, N-stage, carcinoembryonic antigen (CEA) content, nerve and vascular infiltration, and other clinical features, as well as preoperative and postoperative trends. Furthermore, a predictive model is constructed to evaluate the predictive value of T-lymphocyte subsets for CRC clinical features.MethodsStrict inclusion and exclusion criterion were formulated to screen patients, preoperative and postoperative flow cytometry and postoperative pathology reports from standard laparoscopic surgery were assessed. PASS and SPSS software, R packages were invoked to calculate and analyze.ResultsWe found that a high CD4+ T-lymphocyte content in peripheral blood and a high CD4+/CD8+ ratio were associated with better tumor differentiation, an earlier clinical pathological stage, lower Ki67 expression, shallower tumor infiltration, a smaller number of lymph node metastases, a lower CEA content, and a lower likelihood of nerve or vascular infiltration (P < 0.05). However, a high CD8+ T-lymphocyte content indicated an unpromising clinical profile. After effective surgical treatment, the CD4+ T-lymphocyte content and CD4+/CD8+ ratio increased significantly (P < 0.05), while the CD8+ T-lymphocyte content decreased significantly (P < 0.05). Further, we comprehensively compared the merits of CD4+ T-lymphocyte content, CD8+ T-lymphocyte content, and CD4+/CD8+ ratio in predicting the clinical features of CRC. We then combined the CD4+ and CD8+ T-lymphocyte content to build models and predict major clinical characteristics. We compared these models with the CD4+/CD8+ ratio to explore their advantages and disadvantages in predicting the clinical features of CRC.DiscussionOur results provide a theoretical basis for the future screening of effective markers in reflecting and predicting the progression of CRC. Changes in T lymphocyte subsets affect the progression of CRC to a certain extent, while their changes also reflect variations in the human immune system

Outcome of infants with acute lymphoblastic leukemia treated with the Chinese Children's Cancer Group Acute Lymphoblastic Leukemia 2015 study protocol

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Mechanical Behaviors of Biomaterials Over a Wide Range of Loading Rates

The mechanical behaviors of different kinds of biological tissues, including muscle tissues, cortical bones, cancellous bones and skulls, were studied under various loading conditions to investigate their strain-rate sensitivities and loading-direction dependencies. Specifically, the compressive mechanical behaviors of porcine muscle were studied at quasi-static (\u3c1/s) and intermediate (1/s─102 /s) strain rates. Both the compressive and tensile mechanical behaviors of human muscle were investigated at quasi-static and intermediate strain rates. The effect of strainrate and loading-direction on the compressive mechanical behaviors of human frontal skulls, with its entire sandwich structure intact, were also studied at quasi-static, intermediate and high (102 /s─103 /s) strain rates. The fracture behaviors of porcine cortical bone and cancellous bone were investigated at both quasi-static (0.01mm/s) and dynamic (~6.1 m/s) loading rates, with the entire failure process visualized, in real-time, using the phase contrast imaging technique. Research effort was also focused on studying the dynamic fracture behaviors, in terms of fracture initiation toughness and crack-growth resistance curve (R-curve), of porcine cortical bone in three loading directions: in-plane transverse, out-of-plane transverse and in-plane longitudinal. A hydraulic material testing system (MTS) was used to load all the biological tissues at quasi-static and intermediate loading rates. Experiments at high loading rates were performed on regular or modified Kolsky bars. Tomography of bone specimens was also performed to help understand their microstructures and obtain the basic material properties before mechanical characterizations. Experimental results found that both porcine muscle and human muscle exhibited non-linear and strain-rate dependent mechanical behaviors in the range from quasi-static (10-2 /s─1/s) to intermediate (1/s─102 /s) loading rates. The porcine muscle showed no significant difference in the stress-strain curve between the along-fiber and transverse-to-fiber orientation, while it was found the human muscle was stiffer and stronger along fiber direction in tension than transverse-to fiber direction in compression. The human frontal skulls exhibited a highly loading-direction dependent mechanical behavior: higher ultimate strength, with an increasing ratio of 2, and higher elastic modulus, with an increasing ratio of 3, were found in tangential loading direction when compared with those in the radial direction. A transition from quasi-ductile to brittle compressive mechanical behaviors of human frontal skulls was also observed as loading rate increased from quasi-static to dynamic, as the elastic modulus was increased by factors of 4 and 2.5 in the radial and tangential loading directions, respectively. Experimental results also suggested that the strength in the radial direction was mainly depended on the diploë porosity while the diploë layer ratio played the predominant role in the tangential direction. For the fracture behaviors of bones, straight-through crack paths were observed in both the in-plane longitudinal cortical bone specimens and cancellous bone specimens, while the cracks were highly tortuous in the in-plane transverse cortical bone specimens. Although the extent of toughening mechanisms at dynamic loading rate was comparatively diminished, crack deflections and twists at osteon cement lines were still observed in the transversely oriented cortical bone specimens at not only quasi-static loading rate but also dynamic loading rate

Transverse Loading on Single High-Performance Fibers by Round-Head Indenters and the Fibers’ Failure Visualization

High-performance fibers are well-known for their high stiffness and strength under axial tension. However, in their many applications as critical components of textiles and composites, transverse loads widely exist in their normal service life. In this study, we modified a micro material testing system to transverse load single fibers using round-head indenters. By integrating the loading platform with the Scanning Electron Microscopy (SEM) operating at a low-vacuum mode, we visualized the failure processes of fibers without conductive coatings. Post-fracture analysis was conducted to provide complementary information about the fibers’ failure. The energy dissipation was compared with the axial tensile experiments. Three inorganic and two organic fibers were investigated, namely carbon nanotube, ceramic, glass, aramid, and ultrahigh molecule weight polyethylene fibers. Different failure characteristics were reported. It is revealed that the organic fibers had higher energy dissipation than the inorganic fibers under the transverse loading by the round-head indenters. The fiber’s energy dissipation under transverse loading was no more than 17.9% of that subjected to axial tension. Such a reduced energy dissipation is believed to be due to the stress concentration under the indenter. It is suggested that the fiber’s material constituent, structural characteristics, and stress concentration under the indenter should be considered in the fiber model for textiles and composites

Transverse Loading on Single High-Performance Fibers by Round-Head Indenters and the Fibers’ Failure Visualization

High-performance fibers are well-known for their high stiffness and strength under axial tension. However, in their many applications as critical components of textiles and composites, transverse loads widely exist in their normal service life. In this study, we modified a micro material testing system to transverse load single fibers using round-head indenters. By integrating the loading platform with the Scanning Electron Microscopy (SEM) operating at a low-vacuum mode, we visualized the failure processes of fibers without conductive coatings. Post-fracture analysis was conducted to provide complementary information about the fibers’ failure. The energy dissipation was compared with the axial tensile experiments. Three inorganic and two organic fibers were investigated, namely carbon nanotube, ceramic, glass, aramid, and ultrahigh molecule weight polyethylene fibers. Different failure characteristics were reported. It is revealed that the organic fibers had higher energy dissipation than the inorganic fibers under the transverse loading by the round-head indenters. The fiber’s energy dissipation under transverse loading was no more than 17.9% of that subjected to axial tension. Such a reduced energy dissipation is believed to be due to the stress concentration under the indenter. It is suggested that the fiber’s material constituent, structural characteristics, and stress concentration under the indenter should be considered in the fiber model for textiles and composites

Estimating and mapping the dynamics of soil salinity under different crop types using Sentinel-2 satellite imagery

Soil salinization is one of the main factors contributing to land degradation, affecting ecological equilibrium, environmental health, and the sustainable development of agriculture. Due to the spatial and temporal heterogeneity of soil properties and environmental conditions in a large-scale region, the monitoring accuracy of soil salinization can be challenging. This study investigated whether the classification of diverse crop types on a time series can improve the prediction accuracy of regional soil salinity levels. Specifically, we evaluated the changes in soil salt content (SSC) under diverse vegetation cover over time in the Hetao Irrigation District (HID) using multi-phase Sentinel-2 imagery and ground-truth data collected from June to September 2021 and 2022. Focused on sunflower and maize fields, this study analyzed the impact of classifying these two crop types and examining four distinct time series on the accuracy of SSC estimation. Five indices were selected as characteristic parameters from a pool of 17 vegetation indices (VIs) and 13 soil salinity indices (SIs) derived from satellite images. Moreover, three machine learning algorithms were used to establish SSC estimation models. The findings underscored the efficacy of classifying crop types and considering different time series in enhancing the response sensitivity of spectral indices to SSC and improving modeling accuracy. Among the spectral indices, VIs made more contributions to the SSC estimation model than SIs, achieving the highest coefficient of determination (R2) of 0.71. The artificial neural networks algorithm outperformed the other two machine learning algorithms in terms of accuracy and stability, yielding an optimal R2 of 0.72 and a Root Mean Square Error (RMSE) of 0.15%. This study proposed a modeling and mapping approach that considers crop types and various time series, offering valuable insights for accurately assessing soil salinization, guiding strategies for its prevention and remediation

A Kitchen Standard Dress Detection Method Based on the YOLOv5s Embedded Model

In order to quickly and accurately detect whether a chef is wearing a hat and mask, a kitchen standard dress detection method based on the YOLOv5s embedded model is proposed. Firstly, a complete kitchen scene dataset was constructed, and the introduction of images for the wearing of masks and hats allows for the low reliability problem caused by a single detection object to be effectively avoided. Secondly, the embedded detection system based on Jetson Xavier NX was introduced into kitchen standard dress detection for the first time, which accurately realizes real-time detection and early warning of non-standard dress. Among them, the combination of YOLOv5 and DeepStream SDK effectively improved the accuracy and effectiveness of standard dress detection in the complex kitchen background. Multiple sets of experiments show that the detection system based on YOLOv5s has the highest average accuracy of 0.857 and the fastest speed of 31.42 FPS. Therefore, the proposed detection method provided strong technical support for kitchen hygiene and food safety

Multi-Year Crop Type Mapping Using Sentinel-2 Imagery and Deep Semantic Segmentation Algorithm in the Hetao Irrigation District in China

Accurately obtaining the multi-year spatial distribution information of crops combined with the corresponding agricultural production data is of great significance to the optimal management of agricultural production in the future. However, there are still some problems, such as low generality of crop type mapping models and susceptibility to cloud pollution in large-area crop mapping. Here, the models were constructed by using multi-phase images at the key periods to improve model generality. Multi-phase images in key periods masked each other to obtain large-area cloud-free images, which were combined with the general models to map large areas. The key periods were determined by calculating the global separation index (GSI) of the main crops (wheat, maize, sunflower, and squash) in different growth stages in the Hetao Irrigation District (HID) in China. The multi-phase images in the key period were used to make the data set and were then combined with a variety of deep learning algorithms (U-Net, U-Net++, Deeplabv3+, and SegFormer) to construct general models. The selection of the key periods, the acquisition of regional cloud-free images, and the construction of the general crop mapping models were all based on 2021 data. Relevant models and methods were respectively applied to crop mapping of the HID from 2017 to 2020 to study the generality of mapping methods. The results show that the images obtained by combining multi-phase images in the key period effectively avoided the influence of clouds and aerosols in large areas. Compared with the other three algorithms, U-Net had better mapping results. The F1-score, mean intersection-over-union, and overall accuracy were 78.13%, 75.39% and 96.28%, respectively. The crop mapping model was applied to images in 2020, and its average overall accuracy was more than 88.28%. When we applied the model to map crops (county food crops, cash crops, and cultivated land area) from 2017 to 2019, the regression analysis between the mapping areas obtained by the model and the ground measurements was made. The R2 was 0.856, and the RMSE was 17,221 ha, which reached the application accuracy, indicating that the mapping method has certain universality for mapping in different years