The prognostic value of age-adjusted Charlson comorbidity index in laparoscopic resection for hilar cholangiocarcinoma.

The prognostic role of the Age-Adjusted Charlson Comorbidity Index (ACCI) in hilar cholangiocarcinoma patients undergoing laparoscopic resection is unclear. To evaluate ACCI's effect on overall survival (OS) and recurrence-free survival (RFS), we gathered data from 136 patients who underwent laparoscopic resection for hilar cholangiocarcinoma at Zhengzhou University People's Hospital between 1 June 2018 and 1 June 2022. ACCI scores were categorized into high ACCI (ACCI > 4.0) and low ACCI (ACCI ≤ 4.0) groups. We examined ACCI's association with OS and RFS using Cox regression analyses and developed an ACCI-based nomogram for survival prediction. Our analysis revealed that higher ACCI scores (ACCI > 4.0) (HR = 2.14, 95%CI: 1.37-3.34) were identified as an independent risk factor significantly affecting both OS and RFS in postoperative patients with hilar cholangiocarcinoma (p < 0.05). TNM stage III-IV (HR = 7.42, 95%CI: 3.11-17.68), not undergoing R0 resection (HR = 1.58, 95%CI: 1.01-2.46), hemorrhage quantity > 350 mL (HR = 1.92, 95%CI: 1.24-2.97), and not receiving chemotherapy (HR = 1.89, 95%CI: 1.21-2.95) were also independent risk factors for OS. The ACCI-based nomogram accurately predicted the 1-, 2-, and 3-year OS rates, with Area Under the Curve (AUC) values of 0.818, 0.844, and 0.924, respectively. Calibration curves confirmed the nomogram's accuracy, and decision curve analysis highlighted its superior predictive performance. These findings suggest that a higher ACCI is associated with a worse prognosis in patients undergoing laparoscopic resection for hilar cholangiocarcinoma. The ACCI-based nomogram could aid clinicians in making accurate predictions about patient survival and facilitate individualized treatment planning.

Exploring Soybean Flower and Pod Variation Patterns During Reproductive Period Based on Fusion Deep Learning.

The soybean flower and the pod drop are important factors in soybean yield, and the use of computer vision techniques to obtain the phenotypes of flowers and pods in bulk, as well as in a quick and accurate manner, is a key aspect of the study of the soybean flower and pod drop rate (PDR). This paper compared a variety of deep learning algorithms for identifying and counting soybean flowers and pods, and found that the Faster R-CNN model had the best performance. Furthermore, the Faster R-CNN model was further improved and optimized based on the characteristics of soybean flowers and pods. The accuracy of the final model for identifying flowers and pods was increased to 94.36 and 91%, respectively. Afterward, a fusion model for soybean flower and pod recognition and counting was proposed based on the Faster R-CNN model, where the coefficient of determinationR2 between counts of soybean flowers and pods by the fusion model and manual counts reached 0.965 and 0.98, respectively. The above results show that the fusion model is a robust recognition and counting algorithm that can reduce labor intensity and improve efficiency. Its application will greatly facilitate the study of the variable patterns of soybean flowers and pods during the reproductive period. Finally, based on the fusion model, we explored the variable patterns of soybean flowers and pods during the reproductive period, the spatial distribution patterns of soybean flowers and pods, and soybean flower and pod drop patterns.

Analysing the phenotype development of soybean plants using low-cost 3D reconstruction.

With the development of digital agriculture, 3D reconstruction technology has been widely used to analyse crop phenotypes. To date, most research on 3D reconstruction of field crops has been limited to analysis of population characteristics. Therefore, in this study, we propose a method based on low-cost 3D reconstruction technology to analyse the phenotype development during the whole growth period. Based on the phenotypic parameters extracted from the 3D reconstruction model, we identified the "phenotypic fingerprint" of the relevant phenotypes throughout the whole growth period of soybean plants and completed analysis of the plant growth patterns using a logistic growth model. The phenotypic fingerprint showed that, before the R3 period, the growth of the five varieties was similar. After the R5 period, the differences among the five cultivars gradually increased. This result indicates that the phenotypic fingerprint can accurately reveal the patterns of phenotypic changes. The logistic growth model of soybean plants revealed the time points of maximum growth rate of the five soybean varieties, and this information can provide a basis for developing guidelines for water and fertiliser application to crops. These findings will provide effective guidance for breeding and field management of soybean and other crops.

In situ study on interactions between hydroxyl groups in kaolinite and re-adsorption water.

The interactions between O-H groups in kaolinite and re-adsorption water is an important aspect that should be considered in the hydraulic fracturing method for the production of shale gas, because the external water adsorbed by kaolinite in shale would significantly affect the desorption of methane. In this study, the interactions were investigated via changing the amount of O-H groups and re-adsorption water in kaolinite by heating treatment and water re-adsorption. To overcome the overlap of IR vibration bands of the O-H functional groups in H2O and those in parent kaolinite, kaolinite samples with D2O re-adsorption were prepared by drying the H2O from raw kaolinite and soaking the dried kaolinite in D2O. The interactions between O-H groups in kaolinite and D2O molecules were investigated by in situ DRIFT and TG-MS. The results demonstrated that the vibration at 3670 ± 4 cm-1 in the DRIFT spectra could be due to the outer O-H groups of the octahedral sheet on the upper surface of the kaolinite microcrystal structure, rather than a type of inner-surface O-H group. All types of O-H groups, including the inner O-H groups in kaolinite, could be transformed into O-D groups after D2O re-adsorption at room temperature. The inner-surface O-H groups in kaolinite are the most preferred sites for D2O re-adsorption; thus, they would be the key factor for studying the effect of re-adsorption water on methane desorption. When the temperature increased from 100 °C to 300 °C, two layers of kaolinite slipped away from each other, resulting in the transformation of inner-surface O-H groups into outer O-H groups. Thus, the temperature range of 100 to 300 °C was suggested for the heat treatment of kaolinite to decrease the content of inner-surface O-H groups; thereby, the amount of re-adsorption water was reduced. However, to thoroughly remove the re-adsorption water, a temperature higher than 650 °C should be used.