A nomogram for predicting the risk of cancer-related cognitive impairment in breast cancer patients based on a scientific symptom model.

Cancer-related cognitive impairment is a significant clinical challenge observed in patients with breast cancer, manifesting during or after treatment. This impairment leads to deteriorations in memory, processing speed, attention, and executive functioning, which profoundly impact patients' occupational performance, daily living activities, and overall quality of life. Grounded in the Symptom Science Model 2.0, this study investigates the contributing factors to Cancer-related cognitive impairment in breast cancer patients and develops a predictive nomogram for this demographic. Employing both univariate and multivariate logistic regression analyses, this investigation delineates the predictive factors influencing outcomes in breast cancer patients. A nomogram was constructed leveraging these identified predictive factors, accompanied by internal validation through bootstrap resampling methodology (1000 bootstrap samples). The efficacy of the predictive model was assessed by employing the Hosmer-Lemeshow goodness-of-fit test and calibration curves. The prevalence of cognitive impairment in breast cancer patients was identified to be 45.83%.Multivariate logistic regression analysis identified the independent predictors of Cancer-related cognitive impairment in breast cancer patients as place of residence, educational level, chemotherapy, benefit finding, post-traumatic growth, anxiety, fear of cancer progression, and fasting blood glucose levels. these factors were integrated into the nomogram. The Hosmer-Lemeshow goodness-of-fit test demonstrated that the prediction model was appropriately calibrated (χ2 = 11.520, P = 0.174). Furthermore, the model exhibited an area under the curve of 0.955 (95% CI 0.939 to 0.971) and a sensitivity of 0.906, evidencing its robust discriminative capacity and accuracy. Utilizing the Symptom Science Model 2.0 as a framework, this study comprehensively examines the multifaceted factors influencing Cancer-related cognitive impairment in breast cancer patients, spanning five critical domains: complex symptoms, phenotypic characterization, biobehavioral factors, social determinants of health, and patient-centered experiences. A predictive nomogram model was established, demonstrating satisfactory predictive accuracy and capability. This model is capable of identifying breast cancer patients with cognitive impairments with high precision. The findings furnish empirical evidence in support of the early detection, diagnosis, and intervention strategies for high-risk breast cancer patients afflicted with Cancer-related cognitive impairment.

Hydrochemistry, Sources and Management of Fracturing Flowback Fluid in Tight Sandstone Gasfield in Sulige Gasfield (China).

Hydraulic fracturing technologies have been frequently utilized in the oil and gas industry as exploration and development efforts have progressed, resulting in a significant increase in the extraction of natural gas and petroleum from low-permeability reservoirs. However, hydraulic fracturing requires a large amount of freshwater, and the process results in the production of large volumes of flowback water along with natural gas. In this study, three tight sandstone gas wells were fractured in the Sulige gasfield (China), and a total of 103 flowback fluid samples were collected. The hydrochemical characteristics, water quality and sources of hydrochemical components in the flowback fluid were discussed. The results show that the flowback fluid is characterized by high salinity (Total dissolved solids (TDS) up to 38,268 mg/L, Cl- up to 24,000 mg/L), high concentrations of metal ions (e.g., Fe, Sr2+, Ba2+) and high chemical oxygen demand (COD). The flowback fluid is a complex mixture of fracturing fluid and formation water, and its composition is impacted by water-rock interactions that occur during hydraulic fracturing. The major contaminants include COD, Fe, Ba2+, Cl-, Mn and pH, which constitute a high risk of environmental pollution. Meanwhile, chemical elements such as K, Ba and Sr are unusually enriched in the flowback fluid, which has an excellent potential for recycle of chemical elements. The Sulige gasfield's flowback fluid recovery methods and treatment scenarios were discussed, taking into consideration the pollution and resource characteristics of the flowback fluid. Options for dealing with the flowback fluid include deep well reinjection, reuse for making up fracturing fluid, recycling of chemical elements and diverse reuse of flowback water. This research offers guidance for managing the fracturing flowback fluid in unconventional oil and gas fields.

Identifying strontium sources of flowback fluid and groundwater pollution using 87Sr/86Sr and geochemical model in Sulige gasfield, China.

Hydraulic fracturing technology has made unconventional oil and gas development economically viable; however, it can lead to potential environmental issues such as groundwater pollution. Strontium isotope (87Sr/86Sr) is considered as a sensitive tracer to indicate potential groundwater contamination. In this study, strontium (Sr) and 87Sr/86Sr sources of hydraulic fracturing flowback fluid are identified with 87 flowback fluid samples and 5 borehole core samples. High Sr concentrations and 87Sr/86Sr values were found in fracturing flowback fluid. The hydrogeochemistry evidence shows high Sr and 87Sr/86Sr in fracturing flowback fluid mainly comes from formation water with high ion concentrations, while Sr and 87Sr/86Sr of formation water develop in diagenesis and long term water-rock interaction (e.g., feldspar dissolution and clay mineral transformations) under the high temperature and pressure. A complete evaluation system was executed to assess the sensitivity of 87Sr/86Sr indicating potential pollution on groundwater. The mixing curves which 87Sr/86Sr combined with Sr and Cl were also established by mixing models to indicate groundwater pollution. The modeling results show mineral dissolution/precipitation and cation exchange have little impact on 87Sr/86Sr in the mixing process between fracturing flowback fluid and groundwater, which 87Sr/86Sr can identify contamination when only 0.89% of fracturing flowback fluid mixes with groundwater. Finally, the potential contamination pathways are discussed. It is highly unlikely fracturing flowback fluid contaminates groundwater and soil through upward migration, whereas leakage is a more prevalent pollution pathway.