Analysis of Factors Influencing Prognosis and Assessment of 60 Cases of Decompensated Cirrhotic Patients with Portal Hypertension.

Objective: To investigate the risk factors for the development of portal hypertension in patients with decompensated cirrhosis and analyze their prognosis. Methods: Patients with decompensated cirrhosis who were admitted to our hospital and Qu fu People's Hospital from June 2022 to June 2023 were included in this study. Among them, there were 45 male and 15 female patients, with a median age of 56 (range: 35-77) years. A comparative analysis was performed between Group A (hepatic venous pressure gradient, HVPG <16 mmHg) and Group B (HVPG ≥16 mmHg) patients, along with various clinical outcomes. Multivariate analysis was conducted to explore the risk factors influencing the occurrence of portal hypertension and adverse prognosis in patients with cirrhosis. Results: In Group A patients with portal hypertension, we observed lower levels of aspartate aminotransferase, laminin, serum hyaluronic acid, type III procollagen N-terminal peptide, total bile acids, and cholylglycine acid compared to Group B. On the other hand, levels of alanine aminotransferase, white blood cells, and serum albumin were higher in Group A than in Group B. These differences between the groups were statistically significant (P < 0.05). Multivariate analysis of the aforementioned risk factors indicated that low white blood cell count, high cholylglycine acid levels, and high serum hyaluronic acid levels were identified as independent risk factors for the occurrence of difficult-to-control complications in decompensated portal hypertension among patients with liver cirrhosis (P < 0.05). Conclusion: Liver cirrhosis patients with portal hypertension and multiple risk factors like low white blood cell count and high liver transaminase levels should be cautious regarding the progression of portal hypertension when combined with splenomegaly, liver fibrosis, and bile stasis, as it often indicates a poor prognosis.

[Development and Evaluation of Prognostic Nomogram Model for Adult Ventricle Glioma Patients].

Objective: To explore the prognostic factors of adult ventricle glioma (AVG) and to construct and evaluate a survival-related prognostic nomogram model, which could provide further reference for the clinical management of AVG patients. Methods: The patients covered in the study were selected from the Surveillance Epidemiology and End Results (SEER) database (1973-2016). They all had definite histological diagnosis of AVG. They were assigned randomly to the training cohort and the validation cohort by random number table at a 2/1 ratio. Survival analysis was performed by Kaplan-Meier analysis. Cox regression analysis was employed to determine the independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS). Then, integrating the basic characteristics of patients, the survival-related nomogram predictive model for OS and CSS in the training cohort was constructed, respectively. After that, internal cross validation and external validation of the model were carried out with the training cohort and the validation cohort in succession. The authenticity and reliability of the nomogram model were evaluated by calculating the concordance index (C-index). Calibration plots were constructed to assess the agreement between the predicted values and the observed values in the training cohort and the validation cohort. Results: A total of 369 AVG patients, including 218 males and 151 females, were included. The median age of the patients was 53. According to the WHO classification of gliomas, 66 (17.9%) patients had grade Ⅱ gliomas, 73 (19.8%) had grade Ⅲ gliomas, and 230 (62.3%) had grade Ⅳ gliomas. Regarding the extent of resection (EOR), 59 (16.0%) had gross total resection (GTR) and 145 (39.3%) had subtotal resection (STR) or partial resection (PR). Of all the patients, 167 (45.3%) received postoperative radiotherapy and 143 (38.8%) received postoperative chemotherapy. Patients were randomized into the training cohort ( n=246) and the validation cohort ( n=123), and there was no significant difference ( P>0.05) in the basic clinical characteristics between the training cohort and the validation cohort. In the training cohort, Cox regression analysis revealed that the independent prognostic factors for OS and CSS included age≥65, grades Ⅲ and Ⅳ according to the WHO classification of gliomas, and not receiving radiotherapy. Furthermore, 5 variables, including age, gender, WHO grades, surgery, and radiotherapy, were used to construct the nomogram model for predicting 6-month, 1-year, and 2-year OS and CSS. The results of internal cross validation in the training cohort showed that the C-indexes of OS and CSS were 0.758 and 0.765, respectively. The external validation results of the validation cohort showed that the C-indexes of OS and CSS were 0.733 and 0.719, respectively. Calibration plots for 6-month, 1-year, and 2-year OS in the training cohort showed relatively good agreement, while in the validation cohort the agreement was relatively low. The 6-month, 1-year, and 2-year CSS calibration plots had results similar to the calibration plots of OS. Conclusion: This nomogram predictive model of OS and CSS showed moderately reliable predictive performance, providing helpful reference information for clinicians to make quick and simple assessment of the survival probability of AVG patients.

[Design and development of the trace Cu2+ optical chemistry sensor].

The present paper proposes an optical chemistry sensor used for real-time detection of trace Cu2+, including the optical perception module and the signal processing module. The optical perception module gives the output of the laser light and excitation of fluorescence. The signal processing module completes optoelectronic conversion and amplification of the weak fluorescence signal as well as data processing and display. A self-developed optical acquisition, conversion and processing system was used to complete rapid detection of trace Cu2+, miniaturizing and reducing the cost of the testing instruments. The experimental results show the proposed sensor responds linearly in a concentration range of 30-1 000 nmol L(-1), with the linear equation y = 0.109 77x + 11.872 32, the linearity 0.994 82, the standard deviation 3.994 24, the detection limit 30 nmol x L(-1), and the response time of the sensor 40 seconds. The experiment determines the interference of other ions with the Cu2+ test results. It shows that this sensor meets the demand of field detection for trace Cu2+ site testing.