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Radioactive iodine (131I) with a short half-life of ~8.02 days is one of the most commonly used nuclides in nuclear medicine. However, 131I easily poses a significant risk to human health and ecological environment. Therefore, there is an urgent need to develop a secure and efficient strategy to capture and store radioactive iodine. Metal–organic frameworks (MOFs) are a new generation of sorbents with outstanding physical and chemical properties, rendering them attractive candidates for the adsorption and immobilization of iodine. This review focuses on recent research advancements in mechanisms underlying iodine adsorption over MOFs and their derivatives, including van der Waals interactions, complexing interactions, and chemical precipitation. Furthermore, this review concludes by outlining the challenges and opportunities for the safe disposal of radioactive iodine from the perspective of the material design and system evaluation based on our knowledge. Thus, this paper aims to offer necessary information regarding the large-scale production of MOFs for iodine adsorption. [ABSTRACT FROM AUTHOR]

Background: There remains a pressing need to identify biomarkers capable of reliably predicting prognostic outcomes for colorectal cancer (CRC) patients. As several body composition parameters have recently been reported to exhibit varying levels of prognostic significance in particular cancers, the present study was devised to assess the ability of body composition to predict long-term outcomes for CRC patients with different stages of disease., Methods: In total, this retrospective analysis enrolled 327 stage I-III CRC patients whose medical records were accessed for baseline demographic and clinical data. Primary outcomes for these patients included disease-free and overall survival (DFS and OS). The prognostic performance of different musculature, visceral, and subcutaneous fat measurements from preoperative computed tomography (CT) scans was assessed., Results: Over the course of follow-up, 93 of the enrolled patients experienced recurrent disease and 39 died. Through multivariate Cox regression analyses, the visceral/subcutaneous fat area (V/S) ratio was found to be independently associated with patient DFS (HR=1.93, 95% CI: 1.24-3.01, P=0.004), and the skeletal muscle index (SMI) as an independent predictor for OS (HR=0.43, 95% CI: 0.21-0.89, P=0.023). Through subgroup analyses, higher V/S ratios were found to be correlated with reduced DFS among patients with stage T3/4 (P=0.011), lymph node metastasis-positive (P=0.002), and TNM stage III (P=0.002) disease, whereas a higher SMI was associated with better OS in all T stages (P=0.034, P=0.015), lymph node metastasis-positive cases (P=0.020), and in patients with TNM stage III disease (P=0.020)., Conclusion: Both the V/S ratio and SMI offer potential utility as clinical biomarkers associated with long-term CRC patient prognosis. A higher V/S ratio and a lower SMI are closely related to poorer outcomes in patients with more advanced disease., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zhou, Tian, Wu and Liu.)

Objectives: This study aimed to predict severe coronavirus disease 2019 (COVID-19) progression in patients with increased pneumonia lesions in the early days. A simplified nomogram was developed utilizing artificial intelligence (AI)-based quantified computed tomography (CT)., Methods: From 17 December 2019 to 20 February 2020, a total of 246 patients were confirmed COVID-19 infected in Jingzhou Central Hospital, Hubei Province, China. Of these patients, 93 were mildly ill and had follow-up examinations in 7 days, and 61 of them had enlarged lesions on CT scans. We collected the neutrophil-to-lymphocyte ratio (NLR) and three quantitative CT features from two examinations within 7 days. The three quantitative CT features of pneumonia lesions, including ground-glass opacity volume (GV), semi-consolidation volume (SV), and consolidation volume (CV), were automatically calculated using AI. Additionally, the variation volumes of the lesions were also computed. Finally, a nomogram was developed using a multivariable logistic regression model. To simplify the model, we classified all the lesion volumes based on quartiles and curve fitting results., Results: Among the 93 patients, 61 patients showed enlarged lesions on CT within 7 days, of whom 19 (31.1%) developed any severe illness. The multivariable logistic regression model included age, NLR on the second time, an increase in lesion volume, and changes in SV and CV in 7 days. The personalized prediction nomogram demonstrated strong discrimination in the sample, with an area under curve (AUC) and the receiver operating characteristic curve (ROC) of 0.961 and a 95% confidence interval (CI) of 0.917-1.000. Decision curve analysis illustrated that a nomogram based on quantitative AI was clinically useful., Conclusion: The integration of CT quantitative changes, NLR, and age in this model exhibits promising performance in predicting the progression to severe illness in COVID-19 patients with early-stage pneumonia lesions. This comprehensive approach holds the potential to assist clinical decision-making., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Chen, Li, Wu, Liu and Huang.)

Objective: To investigate the quantitative influence of ASIR (adaptive statistical iterative reconstruction) on CT(computed tomography) image histology of patients with primary colorectal cancer., Methods: Sixty three patients with primary colorectal cancer were prospectively selected in the Jingzhou Central Hospital from January 2017 to December 2018; all patients were planned for contrast-enhanced CT examination and 20% ASIR incremental reconstruction. For reasons of interest, single- and multi-slice scans and radio-histological analysis were performed: ASIR effects were calculated by multilevel linear regression method., Results: The total of 56 CT data sets were collected and analyzed. Incremental ASIR levels showed significant statistical changes in most radiohistological features (P<0.05). Single event and multilevel analysis of first-order statistical characteristics showed relatively small changes (median standardization effect B = 0.08). The change level of second-order statistical characteristics in single-level analysis (median B = 0.36) were significantly higher than multilevel analysis (median B = 0.13). The fractal characteristics showed significant statistical changes only in single-level analysis (median B = 0.49)., Conclusions: The incremental level of ASIR can significantly affect the quantification of CT radiohistology of primary colorectal cancer. The second-order statistical and fractal characteristics obtained by single-level analysis can be more variable than those obtained by multi-level analysis.