Early diagnostic value of ECT whole-body bone imaging combined with PINP and ß-CTX for bone metastasis of lung cancer.

OBJECTIVE: This research was aimed at investigating the early diagnostic value of emission computed tomograph (ECT) whole-body bone imaging combined with PINP and ß-CTX for bone metastasis of lung cancer. METHODS: Case data of 86 lung cancer patients were categorized into lung cancer with bone metastasis (LCWBM, 46 cases) and lung cancer without bone metastasis (LCWOBM, 40 cases) groups according to the presence or absence of bone metastasis. Patients' general information were collected. ECT whole-body bone imaging was used to detect bone metastases and the grading of the extent of disease (EOD) in both groups, and electrochemiluminescence was utilized to detect the serum levels of PINP and ß-CTX. Spearman correlation analysis was employed to evaluate the correlation between EOD grading and PINP and ß-CTX levels. Logistic univariate and multivariate regression was implemented to analyze the risk factors of bone metastasis of lung cancer. Receiver operating characteristic (ROC) curve was applied to analyze the diagnostic efficacy of the single test of ECT whole-body bone imaging, PINP, or ß-CTX and the combination of the three tests. RESULTS: The differences in pathological type, clinical stage and EOD grading, the number of positive ECT cases, and the expression levels of PINP and ß-CTX between the LCWBM and LCWOBM groups were statistically significant. In LCWBM patients with different EOD grading, the trends of the expression of PINP and ß-CTX were grade 3 > grade 2 > grade 1 and grade 0. Further correlation analyses revealed that EOD grading showed a significant positive correlation with the PINP and ß-CTX expression levels. Univariate logistic regression analysis demonstrated that adenocarcinoma, TNM stage IV, ECT positivity, and high expression of PINP and ß-CTX were associated with bone metastasis of lung cancer, and multivariate logistic regression analysis indicated that ECT positivity, high expression of PINP and ß-CTX were independent risk factors for bone metastasis of lung cancer. The area under the curve (AUC) of ECT, PINP, and ß-CTX alone for the diagnosis of bone metastasis of lung cancer were 0.872, 0.888, and 0.874, respectively, and the AUC for the combined diagnosis of the three was 0.963, which was greater than that of any one of the individual indices, with a sensitivity of 86.96% and a specificity of 97.50% at a Youden index of 0.845. CONCLUSION: ECT whole-body bone imaging combined with PINP and ß-CTX has high diagnostic value for bone metastasis of lung cancer.

One-Pot Synthesis of Melamine Formaldehyde Resin-Derived N-Doped Porous Carbon for CO2 Capture Application.

The design and synthesis of porous carbons for CO2 adsorption have attracted tremendous interest owing to the ever-soaring concerns regarding climate change and global warming. Herein, for the first time, nitrogen-rich porous carbon was prepared with chemical activation (KOH) of commercial melamine formaldehyde resin (MF) in a single step. It has been shown that the porosity parameters of the as-prepared carbons were successfully tuned by controlling the activating temperature and adjusting the amount of KOH. Thus, as-prepared N-rich porous carbon shows a large surface area of 1658 m2/g and a high N content of 16.07 wt%. Benefiting from the unique physical and textural features, the optimal sample depicted a CO2 uptake of up to 4.95 and 3.30 mmol/g at 0 and 25 °C under 1 bar of pressure. More importantly, as-prepared adsorbents show great CO2 selectivity over N2 and outstanding recyclability, which was prominently important for CO2 capture from the flue gases in practical application. An in-depth analysis illustrated that the synergetic effect of textural properties and surface nitrogen decoration mainly determined the CO2 capture performance. However, the textural properties of carbons play a more important role than surface functionalities in deciding CO2 uptake. In view of cost-effective synthesis, outstanding textural activity, and the high adsorption capacity together with good selectivity, this advanced approach becomes valid and convenient in fabricating a unique highly efficient N-rich carbon adsorbent for CO2 uptake and separation from flue gases.

One-Pot Synthesis of N-Rich Porous Carbon for Efficient CO2 Adsorption Performance.

N-enriched porous carbons have played an important part in CO2 adsorption application thanks to their abundant porosity, high stability and tailorable surface properties while still suffering from a non-efficient and high-cost synthesis method. Herein, a series of N-doped porous carbons were prepared by a facile one-pot KOH activating strategy from commercial urea formaldehyde resin (UF). The textural properties and nitrogen content of the N-doped carbons were carefully controlled by the activating temperature and KOH/UF mass ratios. As-prepared N-doped carbons show 3D block-shaped morphology, the BET surface area of up to 980 m2/g together with a pore volume of 0.52 cm3/g and N content of 23.51 wt%. The optimal adsorbent (UFK-600-0.2) presents a high CO2 uptake capacity of 4.03 mmol/g at 0 °C and 1 bar. Moreover, as-prepared N-doped carbon adsorbents show moderate isosteric heat of adsorption (43-53 kJ/mol), acceptable ideal adsorption solution theory (IAST) selectivity of 35 and outstanding recycling performance. It has been pointed out that while the CO2 uptake was mostly dependent on the textural feature, the N content of carbon also plays a critical role to define the CO2 adsorption performance. The present study delivers favorable N-doped carbon for CO2 uptake and provides a promising strategy for the design and synthesis of the carbon adsorbents.

Application of Enhanced T1WI of MRI Radiomics in Glioma Grading.

Objective: To explore the application value of the radiomics method based on enhanced T1WI in glioma grading. Materials and Methods: A retrospective analysis was performed using data of 114 patients with glioma, which was confirmed using surgery and pathological tests, at our hospital between January 2017 and November 2020. The patients were randomly divided into the training and test groups in a ratio of 7 : 3. The Analysis Kit (AK) software was used for radiomic analysis, and a total of 461 tumor texture features were extracted. Spearman correlation analysis and the least absolute shrinkage and selection (LASSO) algorithm were employed to perform feature dimensionality reduction on the training group. A radiomics model was then constructed for glioma grading, and the validation group was used for verification. Results: The area under the ROC curve (AUC) of the proposed model was calculated to identify its performance in the training group, which was 0.95 (95% CI = 0.905-0.994), accuracy was 84.8%, sensitivity was 100%, and specificity was 77.8%. The AUC of the validation group was 0.952 (95% CI = 0.871-1.000), accuracy was 93.9%, sensitivity was 90.0%, and specificity was 95.6%. Conclusions: The radiomics model based on enhanced T1WI improved the accuracy of glioma grading and better assisted clinical decision-making.