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Background The prognostic value of traditional clinical indicators for locally recurrent nasopharyngeal carcinoma is limited because of their inability to reflect intratumor heterogeneity. We aimed to develop a radiomic signature to reveal tumor immune heterogeneity and predict survival in locally recurrent nasopharyngeal carcinoma. Methods This multicenter, retrospective study included 921 patients with locally recurrent nasopharyngeal carcinoma. A machine learning signature and nomogram based on pretreatment magnetic resonance imaging features were developed for predicting overall survival in a training cohort and validated in 2 independent cohorts. A clinical nomogram and an integrated nomogram were constructed for comparison. Nomogram performance was evaluated by concordance index and receiver operating characteristic curve analysis. Accordingly, patients were classified into risk groups. The biological characteristics and immune infiltration of the signature were explored by RNA-sequencing analysis. Results The machine learning signature and nomogram demonstrated comparable prognostic ability to a clinical nomogram, achieving concordance indexes of 0.729, 0.718, and 0.731 in the training, internal, and external validation cohorts, respectively. Integration of the signature and clinical variables statistically improved the predictive performance. The proposed signature effectively distinguished patients between risk groups with statistically distinct overall survival rates. Subgroup analysis indicated the recommendation of local salvage treatments for low-risk patients. Exploratory RNA-sequencing analysis revealed differences in interferon response and lymphocyte infiltration between risk groups. Conclusions A magnetic resonance imaging–based radiomic signature predicted overall survival more accurately. The proposed signature associated with tumor immune heterogeneity may serve as a valuable tool to facilitate prognostic stratification and guide individualized management for locally recurrent nasopharyngeal carcinoma patients. [ABSTRACT FROM AUTHOR]

Background: The prognostic value of traditional clinical indicators for locally recurrent nasopharyngeal carcinoma is limited because of their inability to reflect intratumor heterogeneity. We aimed to develop a radiomic signature to reveal tumor immune heterogeneity and predict survival in locally recurrent nasopharyngeal carcinoma., Methods: This multicenter, retrospective study included 921 patients with locally recurrent nasopharyngeal carcinoma. A machine learning signature and nomogram based on pretreatment magnetic resonance imaging features were developed for predicting overall survival in a training cohort and validated in 2 independent cohorts. A clinical nomogram and an integrated nomogram were constructed for comparison. Nomogram performance was evaluated by concordance index and receiver operating characteristic curve analysis. Accordingly, patients were classified into risk groups. The biological characteristics and immune infiltration of the signature were explored by RNA-sequencing analysis., Results: The machine learning signature and nomogram demonstrated comparable prognostic ability to a clinical nomogram, achieving concordance indexes of 0.729, 0.718, and 0.731 in the training, internal, and external validation cohorts, respectively. Integration of the signature and clinical variables statistically improved the predictive performance. The proposed signature effectively distinguished patients between risk groups with statistically distinct overall survival rates. Subgroup analysis indicated the recommendation of local salvage treatments for low-risk patients. Exploratory RNA-sequencing analysis revealed differences in interferon response and lymphocyte infiltration between risk groups., Conclusions: A magnetic resonance imaging-based radiomic signature predicted overall survival more accurately. The proposed signature associated with tumor immune heterogeneity may serve as a valuable tool to facilitate prognostic stratification and guide individualized management for locally recurrent nasopharyngeal carcinoma patients., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Microplastics （MPs） are ubiquitous in the marine environment and have become an emerging pollutant that is attracting great attention. To reveal the pollution characteristics of MPs in surface seawater of coastal waters in Guangdong Province, nine bays （estuaries） were selected from Jiangmen to Shantou. The distribution and compositional characteristics of MPs were investigated through field sampling, oxidation digestion, and visual and compositional identification, and their potential sources were analyzed. The ecological risks were assessed by combining the pollution load index and the polymer risk index. The results showed that MPs were detected in all 30 surface seawater samples from the coastal waters of Guangdong Province, with an abundance range of 70-920 n·m -3 and an average abundance of （295.3 ±175.3） n·m -3 . The highest MPs abundance was found in the Pearl River estuary, and the lowest abundance was found in Shenquan bay. The distribution patterns were mainly influenced by human activities and ocean currents. The dominant polymer types included polypropylene （31.2%）, phenol resin （16.0%）, polyethylene terephthalate （15.3%）, and polyethylene （10.9%）. The main shape, color, and size categories of MPs were fiber （57.5%）, transparent （72.0%）, and 0.5-1 mm （32.8%）, respectively. The possible sources of MPs mainly included aquaculture, fishing, navigation, tourism, municipal sewage discharge, and ocean current transportation. The model assessment results showed that the pollution load risk of MPs was relatively low, but the polymer risk was at a medium-high level. This study provides a data basis for the action plan of plastic pollution control in Guangdong Province and supports the prevention and control of marine MPs pollution.

It is recognized in recent years that activation of delta-opioid receptor (DOR) elicits neuroprotection against hypoxia and ischemia. However, the underlying mechanisms are not well understood yet. Mitochondrial dysfunction plays a key role in hypoxic neuronal injury, but the effect of DOR activation on neurons with a mitochondrial respiratory chain deficiency is poorly elucidated. In this study we tested the effects of DOR activation and inhibition on cultured cortical neurons after inhibiting mitochondrial respiratory chain with sodium azide (NaN(3)) in days 8 cultures. Neuronal injury was assessed by lactate dehydrogenase release. Changes in DOR proteins were investigated using an antibody against the N-terminus of the DOR, which recognizes the 60, 48, and 32 kDa proteins. Our main findings are that 1) delta- but not mu-opioid receptor activation reduces NaN(3)-induced neuronal damage, and this neuroprotective effect is abolished by DOR antagonist (naltrindole, NTI); 2) prolonged DOR inhibition with NTI further increases NaN(3)-induced neuronal damage; 3) NaN(3) treatment down-regulates DOR protein levels in neurons, and the 60 and 32 kDa proteins are particularly sensitive; 4) DADLE, besides activating DOR directly, also reverses the decrease of neuronal DOR protein levels induced by NaN(3), which may contribute greatly to its neuroprotective effect; 5) NTI reverses NaN(3)-induced down-regulation of DOR proteins as well, the effect of NTI amplifying NaN(3)-induced neuronal damage therefore is probably due to its inhibition on DOR activity only. In conclusion, these data suggest that DOR activation plays an important role in neuroprotection against mitochondrial respiratory chain injury.