Widely targeted quantitative lipidomics and prognostic model reveal plasma lipid predictors for nasopharyngeal carcinoma.

BACKGROUND: Dysregulation of lipid metabolism is closely associated with cancer progression. The study aimed to establish a prognostic model to predict distant metastasis-free survival (DMFS) in patients with nasopharyngeal carcinoma (NPC), based on lipidomics. METHODS: The plasma lipid profiles of 179 patients with locoregionally advanced NPC (LANPC) were measured and quantified using widely targeted quantitative lipidomics. Then, patients were randomly split into the training (125 patients, 69.8%) and validation (54 patients, 30.2%) sets. To identify distant metastasis-associated lipids, univariate Cox regression was applied to the training set (P < 0.05). A deep survival method called DeepSurv was employed to develop a proposed model based on significant lipid species (P < 0.01) and clinical biomarkers to predict DMFS. Concordance index and receiver operating curve analyses were performed to assess model effectiveness. The study also explored the potential role of lipid alterations in the prognosis of NPC. RESULTS: Forty lipids were recognized as distant metastasis-associated (P < 0.05) by univariate Cox regression. The concordance indices of the proposed model were 0.764 (95% confidence interval (CI), 0.682-0.846) and 0.760 (95% CI, 0.649-0.871) in the training and validation sets, respectively. High-risk patients had poorer 5-year DMFS compared with low-risk patients (Hazard ratio, 26.18; 95% CI, 3.52-194.80; P < 0.0001). Moreover, the six lipids were significantly correlated with immunity- and inflammation-associated biomarkers and were mainly enriched in metabolic pathways. CONCLUSIONS: Widely targeted quantitative lipidomics reveals plasma lipid predictors for LANPC, the prognostic model based on that demonstrated superior performance in predicting metastasis in LANPC patients.

Image enhancement of color fundus photographs for age-related macular degeneration: the Shanghai Changfeng Study.

AIM: To develop and evaluate a new fundus image optimization software based on red, green, blue channels (RGB) for the evaluation of age-related macular degeneration (AMD) in the Chinese population. METHODS: Fundus images that were diagnosed as AMD from the Shanghai Changfeng Study database were analyzed to develop a standardized optimization procedure. Image brightness, contrast, and color balance were measured. Differences between central lesion area and normal retinal area under different image brightness, contrast, and color balance were observed. The optimal optimization parameters were determined based on the visual system to avoid image distortion. A paired-sample diagnostic test was used to evaluate the enhancement software. Fundus optical coherence tomography (OCT) was used as the gold standard. Diagnostic performances were compared between original images and optimized images using McNemar's test. RESULTS: A fundus image optimization procedure was developed using 86 fundus images of 74 subjects diagnosed with AMD. By observing gray-scale images, choroid can be best displayed in red channel and retina in green channel was found. There was limited information in blue channel. Totally 104 participants were included in the paired sample diagnostic test to assess the performance of the optimization software. After the image enhancement, sensitivity increased from 74% to 88% (P=0.008), specificity decreased slightly from 88% to 84% (P=0.500), and Youden index increased by 0.11. CONCLUSION: The standardized image optimization software increases diagnostic sensitivity and may help ophthalmologists in AMD diagnosis and screening.

A simple and easily implemented risk model to predict 1-year ischemic stroke and systemic embolism in Chinese patients with atrial fibrillation.

BACKGROUND: Accurate prediction of ischemic stroke is required for deciding anticoagulation use in patients with atrial fibrillation (AF). Even though only 6% to 8% of AF patients die from stroke, about 90% are indicated for anticoagulants according to the current AF management guidelines. Therefore, we aimed to develop an accurate and easy-to-use new risk model for 1-year thromboembolic events (TEs) in Chinese AF patients. METHODS: From the prospective China Atrial Fibrillation Registry cohort study, we identified 6601 AF patients who were not treated with anticoagulation or ablation at baseline. We selected the most important variables by the extreme gradient boosting (XGBoost) algorithm and developed a simplified risk model for predicting 1-year TEs. The novel risk score was internally validated using bootstrapping with 1000 replicates and compared with the CHA2DS2-VA score (excluding female sex from the CHA2DS2-VASc score). RESULTS: Up to the follow-up of 1 year, 163 TEs (ischemic stroke or systemic embolism) occurred. Using the XGBoost algorithm, we selected the three most important variables (congestive heart failure or left ventricular dysfunction, age, and prior stroke, abbreviated as CAS model) to predict 1-year TE risk. We trained a multivariate Cox regression model and assigned point scores proportional to model coefficients. The CAS scheme classified 30.8% (2033/6601) of the patients as low risk for TE (CAS score = 0), with a corresponding 1-year TE risk of 0.81% (95% confidence interval [CI]: 0.41%-1.19%). In our cohort, the C-statistic of CAS model was 0.69 (95% CI: 0.65-0.73), higher than that of CHA2DS2-VA score (0.66, 95% CI: 0.62-0.70, Z = 2.01, P = 0.045). The overall net reclassification improvement from CHA2DS2-VA categories (low = 0/high ≥1) to CAS categories (low = 0/high ≥1) was 12.2% (95% CI: 8.7%-15.7%). CONCLUSION: In Chinese AF patients, a novel and simple CAS risk model better predicted 1-year TEs than the widely-used CHA2DS2-VA risk score and identified a large proportion of patients with low risk of TEs, which could potentially improve anticoagulation decision-making. TRIAL REGISTRATION: www.chictr.org.cn (Unique identifier No. ChiCTR-OCH-13003729).

Improvement of Outcomes in Patients with Lupus Nephritis: Management Evolution in Chinese Patients from 1994 to 2010.

OBJECTIVE: To assess how the longterm outcomes have changed over the past decades in Chinese patients with lupus nephritis (LN). The trends in patient manifestation at presentation, treatment pattern, and therapeutic effects were evaluated. METHODS: A cohort of biopsy-proven patients with LN (n = 1945) from January 1994 to December 2010 was analyzed. Treatment regimens, treatment response, renal relapse, and renal outcome were compared at different time periods (1994-1998, 1999-2004, and 2005-2010). RESULTS: Patients in the later periods had shorter duration of disease, lower serum creatinine value and chronicity at biopsy, and more frequent followup. They were more likely to receive standard-of-care therapies, which included cyclophosphamide, mycophenolate mofetil, and combination therapy. Patients in the later periods had higher probabilities of achieving remission (p < 0.001) and lower probabilities of experiencing renal flare (p = 0.007). The 5-year renal survival rates were 92.6%, 90.6%, and 94.3% in 1994-1998, 1999-2004, and 2005-2010, respectively. The 5-year risk of endstage renal disease (ESRD) did not differ between 1994-1998 and 1999-2004, but was significantly lower in 2005-2010 (HR 0.40, 95% CI 0.19-0.85 vs 1999-2004). In multivariable Cox analysis, standard therapy was independently associated with lower risk of ESRD (adjusted HR 0.72, 95% CI 0.52-0.98, p = 0.04). Variables of renal damage at biopsy (renal function, activity index, and chronicity index) were independently associated with poor outcome. CONCLUSION: The outcomes of Chinese patients with LN have improved from 1994 to 2010. With the increased use of standard therapies, the remission rates have increased and renal relapse has decreased.

A Deep Learning-Based Method for Similar Patient Question Retrieval in Chinese.

The online patient question and answering (Q&A) system, either as a website or a mobile application, attracts an increasing number of users in China. Patients will post their questions and the registered doctors then provide the corresponding answers. A large amount of questions with answers from doctors are accumulated. Instead of awaiting the response from a doctor, the newly posted question could be quickly answered by finding a semantically equivalent question from the Q&A achive. In this study, we investigated a novel deep learning based method to retrieve the similar patient question in Chinese. An unsupervised learning algorithm using deep neural network is performed on the corpus to generate the word embedding. The word embedding was then used as the input to a supervised learning algorithm using a designed deep neural network, i.e. the supervised neural attention model (SNA), to predict the similarity between two questions. The experimental results showed that our SNA method achieved P@1 = 77% and P@5 = 84%, which outperformed all other compared methods.

Estimating the impact of prevention action: a simulation model of cervical cancer progression.

Cervical cancer is one of the highest occurring cancers for women in East Africa. Many studies have shown that disease occurrences and particularly the number of deaths due to the disease can be reduced significantly by screening and vaccination. East Africa and Kenya in particular are undergoing change and taking actions to reduce disease levels. However, up until today disease level in the different districts in Kenya is not known nor what be the prevalence of disease when prevention actions take place. In this paper we propose a novel Bayesian model for estimating disease levels based on available partial reports and demographic information. The result is a simulation engine that provides estimations of the impact of various potential prevention actions.