A Prognostic Model Based on Nutritional Indexes for Patients With Pan-Cancer: A Real-World Cohort Study.

BACKGROUND: The aim was to identify the nutritional indexes, construct a prognostic model, and develop a nomogram for predicting individual survival probability in pan-cancers. METHODS: Nutritional indicators, clinicopathological characteristics, and previous major treatment details of the patients were collected. The enrolled patients were randomly divided into training and validation cohorts. Least absolute shrinkage and selection operator (Lasso) regression cross-validation was used to determine the variables to include in the cox regression model. The training cohort was used to build the prediction model, and the validation cohort was used to further verify the discrimination, calibration, and clinical effectiveness of the model. RESULTS: A total of 2020 patients were included. The median OS was 56.50 months (95% CI, 50.36-62.65 months). In the training cohort of 1425 patients, through Lasso regression cross-validation, 13 characteristics were included in the model. Cox proportional hazards model was developed and visualized as a nomogram. The C-indexes of the model for predicting 1-, 3-, 5-, and 10-year OS were 0.848, 0.826, 0.814, and 0.799 in the training cohort and 0.851, 0.819, 0.814, and 0.801 in the validation cohort. The model showed great calibration in the two cohorts. Patients with a score of less than 274.29 had a better prognosis (training cohort: HR, 6.932; 95% CI, 5.723-8.397; log-rank p < 0.001; validation cohort: HR, 8.429; 95% CI, 6.180-11.497; log-rank p < 0.001). CONCLUSION: The prognostic model based on the nutritional indexes of pan-cancer can divide patients into different survival risk groups and performed well in the validation cohort.

C-Reactive Protein to Serum Albumin Ratio as a Novel Biomarker to Predict Prognosis in Patients with Chronic Obstructive Pulmonary Disease.

BACKGROUND: This study aimed to evaluate the C-reactive protein to serum albumin ratio (CAR) to predict prognosis in COPD patients with acute exacerbations (AECOPD). METHODS: A retrospective cohort study of AECOPD patients, admitted to a large tertiary hospital between January 2017 and June 2018, was conducted. Univariate and multivariate logistic regression models were built to assess the relationship between variables and different clinical outcomes in one-year follow up. In addition, Kaplan-Meier method was used to estimate the relationship between CAR and the time to first rehospitalization due to acute exacerbation of COPD. RESULTS: A total of 167 AECOPD patients were included in this study, with an overall age of 68.5 ± 9.4 years. Both univariate and multivariate logistic regression analysis demonstrated that CAR at admission was significantly associated with rehospitalization and frequent exacerbations in COPD patients (p < 0.05). Kaplan-Meier curve showed that the rehospitalization event-free rate was significantly higher in the low CAR group than the high CAR group (p < 0.01). CONCLUSIONS: As an easily available parameter, CAR at admission can be an independent predictor for rehospitalization and frequent exacerbations in COPD patients with acute exacerbations.

Corticosteroid Use in the Treatment of COVID-19: A Multicenter Retrospective Study in Hunan, China.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has developed into a worldwide pandemic. This study aimed to retrospectively describe the use of corticosteroids in treating COVID-19. METHODS: For this multicenter retrospective study, medical records from 488 symptomatic COVID-19 patients were reviewed. Patients were divided into severe and nonsevere groups. Baseline characteristics, signs and symptoms, laboratory findings, treatments, and disease outcomes were compared. Specific data for corticosteroid treatment were further analyzed. RESULTS: Four hundred fifty COVID-19 patients were included in this study, including 82 severe patients and 368 nonsevere cases. Out of the 450 patients, 126 (28.0%) received corticosteroid treatment. In the 126 patients treated with corticosteroids, the median daily dose of corticosteroid therapy was 56.6 [interquartile range (IQR): 40.0-78.4] mg and median corticosteroid therapy duration was 5.0 (IQR: 3.0-7.0) days. Among nonsevere cases, patients treated with corticosteroids were significantly older in comparison with patients who did not receive corticosteroid treatment (p<0.01); the proportion of patients receiving antibiotic therapy in the corticosteroid group was significantly higher than that in the noncorticosteroid group (p<0.001); hospitalization length and duration of viral shedding were significantly longer in patients in the corticosteroid group than in the noncorticosteroid group after adjusting for age, sex, and comorbidities (p<0.05). In severe cases, patients treated with corticosteroids were significantly older and comorbidities at admission were significantly more common in patients receiving corticosteroid treatment (p<0.05); the proportion of patients receiving antibiotic therapy in the corticosteroid group was significantly higher than that in the noncorticosteroid group (p<0.001); no significant difference in hospitalization length or viral shedding duration was found between two groups after adjusting for age, sex, and comorbidities (p>0.05). CONCLUSION: Our study indicates that corticosteroids are regarded as one of treatments in the general clinical practice of COVID-19. However, corticosteroid use may be accompanied by increased use of antibiotics, longer hospitalization, and prolonged viral shedding.

The relationship between inflammation and neurocognitive dysfunction in obstructive sleep apnea syndrome.

Obstructive sleep apnea syndrome (OSAS), a state of sleep disorder, is characterized by repetitive apnea, chronic hypoxia, oxygen desaturation, and hypercapnia. Previous studies have revealed that intermittent hypoxia (IH) conditions in OSAS patients elicited neuron injury (especially in the hippocampus and cortex), leading to cognitive dysfunction, a significant and extraordinary complication of OSAS patients. The repeated courses of airway collapse and obstruction in OSAS patients resulted in apnea and arousal during sleep, leading to IH and excessive daytime sleepiness (EDS) and subsequently contributing to the development of inflammation. IH-mediated inflammation could further trigger various types of cognitive dysfunction. Many researchers have found that, besides continuous positive airway pressure (CPAP) treatment and surgery, anti-inflammatory substances might alleviate IH-induced neurocognitive dysfunction. Clarifying the role of inflammation in IH-mediated cognitive impairment is crucial for potentially valuable therapies and future research in the related domain. The objective of this article was to critically review the relationship between inflammation and cognitive deficits in OSAS.

1H NMR based pharmacometabolomics analysis of metabolic phenotype on predicting metabolism characteristics of losartan in healthy volunteers.

Inter-individual variability in drug metabolism and disposition is common in both preclinical and clinical researches. Losartan and its active metabolite EXP3174 present a high degree of inter-individual differences in blood concentrations that affect drug efficacy and side effect. Pharmacometabolomics has been increasingly applied on predicting the drug responses by analyzing the differences in metabolic profile. A pre-dose metabolic phenotype was investigated to interpret inter-individual variations in the metabolism characteristics of losartan. 1H Nuclear Magnetic Resonance (NMR) spectroscopy-based metabolic profiles were performed on 36 healthy Chinese male volunteers by measuring their pre-dose plasma samples. After oral administration of losartan, the concentrations of losartan and its bioactive metabolite EXP3174 were monitored by liquid chromatography-mass spectrometry (LC-MS). Orthogonal partial least-squares (O-PLS) model was conducted to select potential biomarkers that substantially contributed to the inter-individual variations in the metabolism features via analyzing the ratio of pharmacokinetics (PK) parameters of its metabolite to parent drug. Potential metabolites such as glycine, phosphorylcholine, choline, creatine, creatinine, lactate, citrate, α-glucose, and lipids showed strong correlations with metabolism features of losartan. In addition, the pathway analysis revealed that baseline lipid metabolism, the glycine, serine and threonine pathway, and glycolysis or gluconeogenesis metabolism pathway were significantly associated with the ratio of PK parameters of EXP3174 to losartan. Step-wise multiple linear regression (MLR) was constructed to investigate the potential roles of the selected biomarkers in predicting individualized metabolism characteristics of losartan. These results showed that the pre-dose individual metabolic traits may be a useful approach for characterizing individual differences in losartan metabolism characteristics and therefore for expediting personalized dose-setting in further clinical studies.

Prediction of platinum-based chemotherapy efficacy in lung cancer based on LC-MS metabolomics approach.

Lung cancer is the common cause of cancer-related death worldwide. Platinum-based chemotherapy is the cornerstone of treatment for lung cancer. Platinum sensitivity is a major possibility for effective cancer treatment. In this study, several potential biomarkers were identified for evaluating and predicting the response to platinum-based chemotherapy. LC-MS-based metabolomics was performed on plasma samples from 43 lung cancer patients with different chemotherapy efficacy. By combing multivariate statistical analysis, pathway analysis with correlation analysis, 8 potential biomarkers were significantly associated with platinum chemotherapy response. Moreover, a prediction model with these biomarkers involved in citric acid cycle, glutamate metabolism and amino acid metabolism, showed 100% sensitivity and 100% specificity for predicting chemotherapy response in a validation set. Interestingly, 2-hydroxyglutaric acid (2-HG) as an oncometabolite accumulated in lung cancer was remarkably elevated in the partial response (PR) patients. Collectively, our findings implicated that metabolomics can serve as a potential tool to select lung cancer patients that are more likely to benefit from the platinum-based treatment.

A Long Noncoding RNA ZEB1-AS1 Promotes Tumorigenesis and Predicts Poor Prognosis in Glioma.

Emerging studies show that long noncoding RNAs (lncRNAs) have important roles in carcinogenesis. lncRNA ZEB1 antisense 1 (ZEB1-AS1) is a novel lncRNA, whose clinical significance, biological function, and underlying mechanism remains unclear in glioma. Here, we found that ZEB1-AS1 was highly expressed in glioma tissues, being closely related to clinical stage of glioma. Moreover, patients with high ZEB1-AS1 levels had poor prognoses, with the evidence provided by multivariate Cox regression analysis indicating that ZEB1-AS1 expression could serve as an independent prognostic factor in glioma patients. Functionally, silencing of ZEB1-AS1 could significantly inhibit cell proliferation, migration, and invasion, as well as promote apoptosis. Knockdown of ZEB1-AS1 significantly induced the G0/G1 phase arrest and correspondingly decreased the percentage of S phase cells. Further analysis indicated that ZEB1-AS1 could regulate the cell cycle by inhibiting the expression of G1/S transition key regulators, such as Cyclin D1 and CDK2. Furthermore, ZEB1-AS1 functioned as an important regulator of migration and invasion via activating epithelial to mesenchymal transition (EMT) through up-regulating the expression of ZEB1, MMP2, MMP9, N-cadherin, and Integrin-ß1 as well as decreasing E-cadherin levels in the metastatic progression of glioma. Additionally, forced down-regulation of ZEB1-AS1 could dramatically promote apoptosis by increasing the expression level of Bax and reducing Bcl-2 expression in glioma. Taken together, our data suggest that ZEB1-AS1 may serve as a new prognostic biomarker and therapeutic target of glioma.