Notes

Diverse included habitat approach overcomes pandemic-related fisheries keeping track of issues.
Dry mouth sensation cannot be improved completely even though parotids are spared correctly. Our purpose is to develop a nomogram to predict the moderate-to-severe late radiation xerostomia for patients with locoregionally advanced nasopharyngeal carcinoma (LA-NPC) in intensity modulated radiation therapy (IMRT) / volumetric modulated arc radiotherapy (VMAT) era.

A dataset of 311 patients was retrospectively collected between January 2010 and February 2013. The binary logistic regression was to estimate each factor's prognostic value for development of moderate-to-severe patient-reported xerostomia at least 2 years (Xer2y) after completion of radiotherapy. Therefore, we can develop a nomogram according to binary logistic regression coefficients. This novel model was validated by bootstrapping analyses.

Contralateral Parotid mean dose (coMD<24.4Gy), VMAT (yes), and platinum-based concurrent chemoradiotherapy (no) were significantly related to patient-reported xerostomia at least 2 years (Xer2y) (all p < 0.001), and were included in the nomogram. Receiver operating characteristic (ROC) analysis revealed AUC (area under the ROC curve) with the value of 0.811 (0.710-0.912) of the nomogram, which was significantly higher than coMD 0.698 (0.560-0.840) from QUANTEC2010 (p<0.001). Calibration plots illustrated that the predicted Xer2y was close to the actual observation, and decision curve analyses (DCA) indicated valid positive net benefits.

We developed a feasible nomogram to predict patient-rated Xer2y based on comprehensive individual data in patients with LA-NPC in the real world. The proposed model is able to facilitate the development of treatment plan and quality of life improvement.
We developed a feasible nomogram to predict patient-rated Xer2y based on comprehensive individual data in patients with LA-NPC in the real world. The proposed model is able to facilitate the development of treatment plan and quality of life improvement.
The heterogeneous tumor microenvironment (TME) contributes to poor prognosis of hepatocellular carcinoma (HCC). However, determining the modulation of TME during HCC progression remains a challenge.

Herein, the stromal score and immune score of HCC samples from The Cancer Genome Atlas database were calculated using the ESTIMATE algorithm and differentially expressed genes (DEGs) were obtained. Key DEGs were identified based on a protein-protein interaction network and survival analysis. Immunohistochemistry was carried out using primary samples to evaluate key DEGs expression. The
algorithm was applied to evaluate immune components. Gene Set Enrichment Analysis (GSEA) and correlation analysis were carried out to determine the relationship between key DEGs and tumor-infiltrating immune cells (TICs).

The stromal score, immune score and estimate score correlated significantly with 1-year recurrence-free survival of patients with HCC. Interleukin-2 inducible T-cell kinase (ITK) was identified as the most prognostic DEG for patients with HCC. GSEA revealed that genes in the high ITK subgroup were enriched in inflammatory-immunological terms. CIBERSORT analysis identified nine TIC subsets that correlated with ITK expression.

We identified ITK as a novel indicator for early post-surgery tumor recurrence and microenvironment remodeling in HCC, providing a potential therapeutic target to treat HCC.
We identified ITK as a novel indicator for early post-surgery tumor recurrence and microenvironment remodeling in HCC, providing a potential therapeutic target to treat HCC.Mounting evidence has demonstrated the important role of long non-coding RNAs (lncRNAs) in the development and progression of lung cancer. In this study, we combined the methods of bioinformatics analysis and experimental validation, and aim to investigate the clinical significance and underlying mechanism of the novel lncRNA AC079630.4 in lung cancer. Finally, we found that AC079630.4 was significantly down-regulated in lung cancer tissues, including in its subtypes. FPSZM1 Samples with low AC079630.4 expression had a more advanced pathological stage and a worse prognosis than those with high expression. In functional prediction, the KEGG pathway of apoptosis and the TRAIL signaling pathway were enriched in the samples with high AC079630.4 expression. In experimental validation, AC079630.4 over-expression could significantly inhibit the proliferation and clonality, and up-regulated the receptors of TRAIL (TRAIL-R1 and TRAIL-R2) in lung cancer cells. In conclusion, we adopted the methods of bioinformatics analysis and experimental validation, and identified a novel lncRNA of AC079630.4 as a tumor suppressor in lung cancer.
Heroin addiction and withdrawal have been associated with an increased risk for infectious diseases and psychological complications. However, the changes of metabolites in heroin addicts during withdrawal remain largely unknown.

A total of 50 participants including 20 heroin addicts with acute abstinence stage, 15 with protracted abstinence stage and 15 healthy controls, were recruited. We performed metabolic profiling of plasma samples based on ultraperformance liquid chromatography coupled to tandem mass spectrometry to explore the potential biomarkers and mechanisms of heroin withdrawal.

Among the metabolites analyzed, omega-6 polyunsaturated fatty acids (linoleic acid, dihomo-gamma-linolenic acid, arachidonic acid, n-6 docosapentaenoic acid), omega-3 polyunsaturated fatty acids (docosahexaenoic acid, docosapentaenoic acid), aromatic amino acids (phenylalanine, tyrosine, tryptophan), and intermediates of the tricarboxylic acid cycle (oxoglutaric acid, isocitric acid) were significantly reduced during acute heroin withdrawal. Although majority of the metabolite changes could recover after months of withdrawal, the levels of alpha-aminobutyric acid, alloisoleucine, ketoleucine, and oxalic acid do not recover.

In conclusion, the plasma metabolites undergo tremendous changes during heroin withdrawal. Through metabolomic analysis, we have identified links between a framework of metabolic perturbations and withdrawal stages in heroin addicts.
In conclusion, the plasma metabolites undergo tremendous changes during heroin withdrawal. Through metabolomic analysis, we have identified links between a framework of metabolic perturbations and withdrawal stages in heroin addicts.
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