A Ceramic-Anode Supported Low Temperature Solid Oxide Fuel Cell

We report the fabrication and evaluation of a ceramic-anode supported button cell LSCM-SDC/SDC/PBSC (thickness 400 μm/20 μm/20 μm). The anode/electrolyte assembly LSCM-SDC/SDC was co-fired at low temperature of 1250°C, where a slight amount of CuO was mixed with LSCM. The CuO (20.3 wt%) were impregnated into the porous substrate to enhance current collecting effect. The cell exhibited power density of 596 mWcm−2 and 381 mWcm−2 at 700°C with wet hydrogen and methane as the fuel respectively, where the silver paste was used as current collectors, the highest performance up to date for the cells with metal oxide anodes at this temperature

Comprehensive comparative analysis of prognostic value of serum systemic inflammation biomarkers for colorectal cancer: Results from a large multicenter collaboration

BackgroundThe incidence of colorectal cancer (CRC) is common and reliable biomarkers are lacking. We aimed to systematically and comprehensively compare the ability of various combinations of serum inflammatory signatures to predict the prognosis of CRC. Moreover, particular attention has been paid to the clinical feasibility of the newly developed inflammatory burden index (IBI) as a prognostic biomarker for CRC.MethodsThe discrimination capacity of the biomarkers was compared using receiver operating characteristic curves and Harrell’s C-index. Kaplan-Meier curves and log-rank tests were used to compare survival differences between the groups. Cox proportional hazard regression analysis was used to determine the independent prognostic factors. Logistic regression analysis was used to assess the relationship between IBI, short-term outcomes, and malnutrition.ResultsIBI had the optimal prediction accuracy among the systemic inflammation biomarkers for predicting the prognosis of CRC. Taking IBI as a reference, none of the remaining systemic inflammation biomarkers showed a gain. Patients with high IBI had significantly worse overall survival than those with low IBI (56.7% vs. 80.2%; log-rank P<0.001). Multivariate Cox regression analysis showed that continuous IBI was an independent risk factor for the prognosis of CRC patients (hazard ratio = 1.165, 95% confidence interval [CI] = 1.043–1.302, P<0.001). High IBI was an independent risk factor for short-term outcomes (odds ratio [OR] = 1.537, 95% CI = 1.258–1.878, P<0.001), malnutrition (OR = 2.996, 95% CI = 1.471–6.103, P=0.003), and recurrence (OR = 1.744, 95% CI = 1.176–2.587, p = 0.006) in CRC patients.ConclusionsIBI, as a reflection of systemic inflammation, is a feasible and promising biomarker for assessing the prognosis of CRC patients

The inflammatory burden index is a superior systemic inflammation biomarker for the prognosis of non‐small cell lung cancer

Abstract Background Systemic inflammation, the most representative tumour–host interaction, plays a crucial role in disease progression and prognosis in patients with non‐small cell lung cancer (NSCLC). Few studies have compared the performance of existing haematological systemic inflammation biomarkers in predicting the prognosis of NSCLC patients. The purpose of this study was to compare the prognostic value of existing systemic inflammation biomarkers and determine the optimal systemic inflammation biomarker in patients with NSCLC through a multicentre prospective study. Methods The predictive accuracy of systemic inflammation biomarkers for prognostic assessment in NSCLC was assessed using C‐statistics. Inter‐group differences in survival were assessed using the log‐rank test and visualized using the Kaplan–Meier method. A restricted cubic spline (RCS) curve was used to explore the association between the biomarkers and survival. Independent prognostic biomarkers for overall survival were determined using multivariable Cox proportional hazards regression analysis. Logistic regression analysis was used to determine independent predictors of 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia. Results The inflammatory burden index (IBI) had the highest C‐statistic for predicting the prognosis of patients with NSCLC, reaching 0.640 (0.617, 0.663). Patients with a high IBI had significantly worse outcomes than those with a low IBI (35.46% vs. 57.22%; log‐rank P < 0.001). The IBI was also able to differentiate the prognosis of patients with NSCLC with the same pathological stage. The RCS curve showed an inverted L‐shaped dose–response relationship between the IBI and survival of patients with NSCLC. Multivariable Cox proportional hazards regression analysis showed that a high IBI was an independent risk factor for death of patients with NSCLC (hazard ratio = 1.229, 95% confidence interval [CI]: 1.131–1.335, P < 0.001). A high IBI was an independent predictor of 90‐day outcomes (odds ratio [OR] = 1.789, 95% CI: 1.489–2.151, P < 0.001), prolonged hospital stays (OR = 1.560, 95% CI: 1.256–1.938, P < 0.001), high hospitalization expenses (OR = 1.476, 95% CI: 1.195–1.822, P < 0.001) and cachexia (OR = 1.741, 95%CI = 1.374–2.207, P < 0.001) in patients with NSCLC. Conclusions The IBI was independently associated with overall survival, 90‐day outcomes, length of hospitalization, hospitalization expenses and cachexia in NSCLC patients. As an optimal systemic inflammation biomarker, the IBI has broad clinical application prospects in predicting the prognosis of patients with NSCLC

Individualized threshold of the involuntary weight loss in prognostic assessment of cancer

Abstract Background Involuntary weight loss (WL) is a common symptom in cancer patients and is associated with poor outcomes. However, there is no standardized definition of WL, and it is unclear what magnitude of weight loss should be considered significant for prognostic purposes. This study aimed to determine an individualized threshold for WL that can be used for prognostic assessment in cancer patients. Methods Univariate and multivariate analyses of overall survival (OS) were performed using Cox proportional hazard models. The Kaplan–Meier method was performed to estimate the survival distribution of different WL levels. Logistic regression analysis was used to determine the relationship between WL and 90‐day outcomes. Restricted cubic splines with three knots were used to examine the effects of WL on survival under different body mass index (BMI) conditions. Results Among the 8806 enrolled patients with cancer, median survival time declined as WL increased, from 25.1 to 20.1, 17.8 and 16.4 months at <2%, 2–5%, 5–10% and ≥10% WL, respectively (P < 0.001). Multivariate adjusted Cox regression analysis showed that the risk of adverse prognosis increased by 18.1% based on the SD of WL (5.45 U) (HR: 1.181, 95% CI: 1.144–1.219, P < 0.001). Similarly, categorical WL was independently associated with OS in patients with cancer. With the worsening of WL, the risk of a poor prognosis in patients increases stepwise. Compared with <2% WL, all‐cause mortalities were 15.1%, 37% and 64.2% higher in 2–5%, 5–10%, and ≥10% WL, respectively. WL can effectively stratify the prognosis of both overall and site‐specific cancers. The clinical prognostic thresholds for WL based on different BMI levels were 4.21% (underweight), 5.03% (normal), 6.33% (overweight), and 7.60% (obese). Multivariate logistic regression analysis showed that WL was independently associated with 90‐day outcomes in patients with cancer. Compared with patients with <2% WL, those with ≥10% WL had more than twice the risk of 90‐day outcomes (OR: 3.277, 95% CI: 2.287–4.694, P < 0.001). Systemic inflammation was a cause of WL deterioration. WL mediates 6.3–10.3% of the overall association between systemic inflammation and poor prognoses in patients with cancer. Conclusions An individualized threshold for WL based on baseline BMI can be used for prognostic assessment in cancer patients. WL and BMI should be evaluated simultaneously in treatment decision‐making, nutritional intervention, and prognosis discussions of patients with cancer

Associations of low hand grip strength with 1 year mortality of cancer cachexia: a multicentre observational study

Abstract Backgrounds Hand grip strength (HGS) is one of diagnose criteria factors of sarcopenia and is associated with the survival of patients with cancer. However, few studies have addressed the association of HGS and 1 year mortality of patients with cancer cachexia. Methods This cohort study included 8466 patients with malignant solid tumour from 40 clinical centres throughout China. Cachexia was diagnosed using the 2011 International cancer cachexia consensus. The hazard ratio (HR) of all cancer cachexia mortality was calculated using Cox proportional hazard regression models. Kaplan–Meier curves were generated to evaluate the association between HGS and the 1 year mortality of patients with cancer cachexia. The interaction analysis was used to explore the combined effect of low HGS and other factors on the overall survival of patients with cancer cachexia. Results Among all participants, 1434 (16.9%) patients with cancer were diagnosed with cachexia according to the 2011 International cancer cachexia consensus with a mean (SD) age of 57.75 (12.97) years, among which there were 871 (60.7%) male patients. The HGS optimal cut‐off points of male and female patients were 19.87 and 14.3 kg, respectively. Patients with cancer cachexia had lower HGS than those patients without cachexia (P < 0.05). In the multivariable Cox analysis, low HGS was an independent risk factor of cachexia [HR: 1.491, 95% confidence interval (CI): 1.257–1.769] after adjusting other factors. In addition, all of cancer cachexia patients with lower HGS had unfavourable 1 year survival (P < 0.001). In a subset analysis, low HGS was an independent prognosis factor of male patients with cancer cachexia (HR: 1.623, 95% CI: 1.308–2.014, P < 0.001), but not in female patients (HR: 1.947, 95% CI: 0.956–3.963, P = 0.0662), and low HGS was associated with poor 1 year survival of digestive system, respiratory system, and other cancer cachexia patients (all P < 0.05). Low HGS has combined effects with high neutrophil‐to‐lymphocyte ratio or low albumin on unfavourable overall survival of patients with cancer cachexia. Conclusions Low HGS was associated with poor 1 year survival of patients with cancer cachexia

Predicted lean body mass trajectories, and cancer risk and cancer‐specific and all‐cause mortality: A prospective cohort study

Abstract Background Although many studies have investigated the association between body composition, cancer risk and mortality, predicting these risks through a single body composition measurement undoubtedly increases the limitations of the study. Few studies have explored the association between the trajectory of changes in body composition and the risk of cancer and death. We aimed to explore the association of predicted lean mass trajectories with cancer risk, cancer‐specific mortality and all‐cause mortality. Methods The participants in this study were all from the Kailuan cohort, a prospective, periodic, resurvey cohort study initiated in 2006. Latent mixture modelling was used to identify predicted lean mass trajectories for 2006–2010. The hazard ratios (HRs) and 95% confidence intervals (95% CIs) of the Cox proportional hazard models were used to describe the association between predicted lean mass trajectories and cancer risk and cancer‐specific and all‐cause mortality during follow‐up (2010–2021). Results A total of 44 374 participants (average age, 53.01 ± 11.41 years, 78.99% men and 21.01% women) were enrolled in this study. Five distinct trajectories were identified: low‐stable (n = 12 060), low‐increasing (n = 8027), moderately stable‐decreasing (n = 4725), moderately stable‐increasing (n = 8053) and high‐stable (n = 11 509). During the 11‐year follow‐up period, 2183 cancer events were recorded. After adjusting for age, predicted fat mass in 2010, sex, BMI, sedentary, physical activity, smoke, alcohol use, salt consumption, high‐fat diet, high‐sensitivity C‐reactive protein, serum creatinine, family history of tumour, hypertension, diabetes mellitus, compared with the low‐stable group, participants in the low‐increasing group (HR = 0.851, 95% CI, 0.748–0.969), moderately stable‐increasing group (HR = 0.803, 95% CI, 0.697–0.925) and high‐stable group (HR = 0.770, 95% CI, 0.659–0.901) had a lower cancer risk, but not in the moderately stable‐decreasing group (HR = 0.864, 95% CI, 0.735–1.015). Compared with the low‐stable group, the risk of cancer‐specific mortality was reduced by 25.4% (8.8–38.9%), 36.5% (20.3–49.4%) and 35.4% (17.9–49.2%), and the risk of all‐cause mortality was reduced by 24.2% (16.9–30.8%), 37.0% (30.0–43.2%) and 47.4% (41.0–53.1%) in the low‐increasing, moderately stable‐increasing group and high‐stable groups, respectively. Conclusions Predicted lean mass trajectories may be closely associated with cancer risk and cancer‐specific and all‐cause mortality. Regular monitoring of body composition is necessary

Cholesterol-modified prognostic nutritional index (CPNI) as an effective tool for assessing the nutrition status and predicting survival in patients with breast cancer

Abstract Background Malnutrition is associated with poor overall survival (OS) in breast cancer patients; however, the most predictive nutritional indicators for the prognosis of patients with breast cancer are not well-established. This study aimed to compare the predictive effects of common nutritional indicators on OS and to refine existing nutritional indicators, thereby identifying a more effective nutritional evaluation indicator for predicting the prognosis in breast cancer patients. Methods This prospective study analyzed data from 776 breast cancer patients enrolled in the “Investigation on Nutritional Status and its Clinical Outcome of Common Cancers” (INSCOC) project, which was conducted in 40 hospitals in China. We used the time-dependent receiver operating characteristic curve (ROC), Kaplan–Meier survival curve, and Cox regression analysis to evaluate the predictive effects of several nutritional assessments. These assessments included the patient-generated subjective nutrition assessment (PGSGA), the global leadership initiative on malnutrition (GLIM), the controlling nutritional status (CONUT), the nutritional risk index (NRI), and the prognostic nutritional index (PNI). Utilizing machine learning, these nutritional indicators were screened through single-factor analysis, and relatively important variables were selected to modify the PNI. The modified PNI, termed the cholesterol-modified prognostic nutritional index (CPNI), was evaluated for its predictive effect on the prognosis of patients. Results Among the nutritional assessments (including PGSGA, GLIM, CONUT, NRI, and PNI), PNI showed the highest predictive ability for patient prognosis (time-dependent ROC = 0.58). CPNI, which evolved from PNI, emerged as the superior nutritional index for OS in breast cancer patients, with the time-dependent ROC of 0.65. It also acted as an independent risk factor for mortality (p < 0.05). Moreover, the risk of malnutrition and mortality was observed to increase gradually among both premenopausal and postmenopausal age women, as well as among women categorized as non-overweight, overweight, and obese. Conclusions The CPNI proves to be an effective nutritional assessment tool for predicting the prognosis of patients with breast cancer

The advanced lung cancer inflammation index is the optimal inflammatory biomarker of overall survival in patients with lung cancer

Abstract Backgrounds Malnutrition and systemic inflammatory responses are associated with poor overall survival (OS) in lung cancer patients, but it remains unclear which biomarkers are better for predicting their prognosis. This study tried to determine the best one among the existing common nutrition/inflammation‐based indicators of OS for patients with lung cancer. Materials and methods There were 16 nutrition or systemic inflammation‐based indicators included in this study. The cut‐off points for the indicators were calculated using maximally selected rank statistics. The OS was evaluated using the Kaplan–Meier estimator, and univariate and multivariate Cox proportional hazard models were used to determine the relationship between the indicators and OS. A time‐dependent receiver operating characteristic curves (time‐ROC) and C‐index were calculated to assess the predictive ability of the different indicators. Results There were 1772 patients with lung cancer included in this study. In univariate analysis, all 16 indicators were significantly associated with OS of the patients (all P < 0.001). Except for platelet‐to‐lymphocyte ratio, all other indicators were independent predictors of OS in multivariate analysis (all P < 0.05). Low advanced lung cancer inflammation index (ALI) was associated with higher mortality risk of lung cancer [hazard ratio, 1.30; 95% confidence interval (CI), 1.13–1.49]. The results of the time‐AUC and C‐index analyses indicated that the ALI (C‐index: 0.611) had the best predictive ability on the OS in patients with lung cancer. In different sub‐groups, the ALI was the best indicator for predicting the OS of lung cancer patients regardless of sex (C‐index, 0.609 for men and 0.613 for women) or smoking status (C‐index, 0.629 for non‐smoker and 0.601 for smoker) and in patients aged <65 years (C‐index, 0.613). However, the modified Glasgow prognostic score was superior to the other indicators in non‐small cell lung cancer patients (C‐index, 0.639) or patients aged ≥65 years (C‐index, 0.610), and the glucose‐to‐lymphocyte ratio performed better prognostic ability in patients with small cell lung cancer (C‐index, 0.601). Conclusions The prognostic ability of the ALI is superior to the other inflammation/nutrition‐based indicators for all patients with lung cancer