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Background and Aims: To investigate the longitudinal trend of health-related quality of life (HRQOL) from the start to the end of concurrent chemoradiotherapy and survival in patients with advanced nasopharyngeal carcinoma (NPC).Methods: A total of 145 patients with stage II–IVb NPC, who were a subsample of a randomized phase III clinical trial, were recruited in this study. HRQOL was measured weekly for a total of 6 weeks during concurrent chemoradiotherapy by the Chinese version of the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire core 30. Longitudinal trends of HRQOL domains over time were analyzed using mixed models. Survival rates were estimated using Kaplan-Meier method.Results: During a median follow-up of 45 months, the 3-year progression-free survival rate, overall survival rate, and distant metastasis-free survival rate were highly at 86.8% (95% CI: 80.1%, 91.4%), 95.1% (95% CI: 90.1%, 97.6%), and 91.0% (95% CI: 84.9%, 94.6%), respectively. The average weekly declines of five functioning domains were 1.83–3.52 points during the treatment period, with role functioning having the largest decline rate (−2.52 points per week, 95% CI: −4.50, −2.55; p < 0.001). Loss of appetite is the most affected symptom, with severe appetite loss ranging from 35.9 to 61.1%. The average increases of symptoms were 0.63–5.16 points per week during treatment period (all p-values for time

Lung adenocarcinoma (LUAD) is one of the most prevalent forms of lung cancer. Competitive endogenous RNA (ceRNA) plays an important role in the pathogenesis of lung cancer. Phosphatase and tensin homolog (PTEN) is one of the most frequently deleted tumour suppressor genes in LUAD. The present study aimed to identify a novel PTEN-associated-ceRNA regulatory network and identify potential prognostic markers associated with LUAD. Transcriptome sequencing profiles of 533 patients with LUAD were obtained from TCGA database, and differentially expressed genes (DEGs) were screened in LUAD samples with PTEN high- (PTENhigh) and low- (PTENlow) expression. Eventually, an important PTEN-related marker was identified, namely, the LINC00460/miR-150-3p axis. Furthermore, the predicted target genes (EME1/HNRNPAB/PLAUR/SEMA3A) were closely related to overall survival and prognosis. The LINC00460/miR-150-3p axis was identified as a clinical prognostic factor through Cox regression analysis. Methylation analyses suggested that abnormal regulation of the predicted target genes might be caused by hypomethylation. Furthermore, immune infiltration analysis showed that the LINC00460/miR-150-3p axis could alter the levels of immune infiltration in the tumour immune microenvironment, and promote the clinical progression of LUAD. To specifically induce PTEN deletion in the lungs, we constructed an STP mouse model (SFTPC-rtTA/tetO-cre/Ptenflox/+). Quantitative PCR (qPCR) and immunohistochemical (IHC) analysis were used to detect predicted target genes. Therefore, we revealed that the PTEN-related LINC00460/miR-150-3p axis based on ceRNA mechanism plays an important role in the development of LUAD and provides a new direction and theoretical basis for its targeted therapy.

This study aimed to investigate the effects of quercetin (QUE) on fat deposition and the underlying mechanism. Fish were fed four test diets: normal fat diet (NFD), high-fat diet (HFD), and HFD supplemented with 0.5 or 1.0 g/kg quercetin (QUE0.5 or QUE1.0). The results showed that HFD feeding resulted in poor growth and feed utilization while QUE treatment reversed this. The fat contents of serum and liver were increased by HFD and QUE supplementation significantly decreased fat content. Furthermore, gene expressions and ultrastructure observation showed that mitochondrial biogenesis and mitophagy were inhibited and endoplasmic reticulum stress (ERS) in the HFD group. QUE can activate the biogenesis and autophagy of mitochondria and suppress ERS, which is related to its fat-lowering effect.

BackgroundThe role of next generation sequencing (NGS) for identifying high risk mutations in thyroid nodules following fine needle aspiration (FNA) biopsy continues to grow. However, ultrasound diagnosis even using the American College of Radiology’s Thyroid Imaging Reporting and Data System (TI-RADS) has limited ability to stratify genetic risk. The purpose of this study was to incorporate an artificial intelligence (AI) algorithm of thyroid ultrasound with object detection within the TI-RADS scoring system to improve prediction of genetic risk in these nodules.MethodsTwo hundred fifty-two nodules from 249 patients that underwent ultrasound imaging and ultrasound-guided FNA with NGS with or without resection were retrospectively selected for this study. A machine learning program (Google AutoML) was employed for both automated nodule identification and risk stratification. Two hundred one nodules were used for model training and 51 reserved for testing. Three blinded radiologists scored the images of the test set nodules using TI-RADS and assigned each nodule as high or low risk based on the presence of highly suspicious imaging features on TI-RADS (very hypoechoic, taller-than-wide, extra-thyroidal extension, punctate echogenic foci). Subsequently, the TI-RADS classification was modified to incorporate AI for T4 nodules while treating T1-3 as low risk and T5 as high risk. All diagnostic predictions were compared to the presence of a high-risk mutation and pathology when available.ResultsThe AI algorithm correctly located all nodules in the test dataset (100% object detection). The model predicted the malignancy risk with a sensitivity of 73.9%, specificity of 70.8%, positive predictive value (PPV) of 70.8%, negative predictive value (NPV) of 73.9% and accuracy of 72.4% during the testing. The radiologists performed with a sensitivity of 52.1 ± 4.4%, specificity of 65.2 ± 6.4%, PPV of 59.1 ± 3.5%, NPV of 58.7 ± 1.8%, and accuracy of 58.8 ± 2.5% when using TI-RADS and sensitivity of 53.6 ± 17.6% (p=0.87), specificity of 83.3 ± 7.2% (p=0.06), PPV of 75.7 ± 8.5% (p=0.13), NPV of 66.0 ± 8.8% (p=0.31), and accuracy of 68.7 ± 7.4% (p=0.21) when using AI-modified TI-RADS.ConclusionsIncorporation of AI into TI-RADS improved radiologist performance and showed better malignancy risk prediction than AI alone when classifying thyroid nodules. Employing AI in existing thyroid nodule classification systems may help more accurately identifying high-risk nodules.