Predictors of Long-Time Survivors in Nonmetastatic Colorectal Signet Ring Cell Carcinoma: A Large Population-Based Study.

Background: Colorectal signet ring cell carcinoma (SRCC) is a rare and distinct subtype of colorectal cancer (CRC), with extremely poor prognosis and aggressive tumor biological behavior. In this study, we aimed to analyze the clinicopathological characteristics and to identify the independent predictors of long-time survivors (LTSs) of nonmetastatic colorectal SRCC. Methods: Patients diagnosed with nonmetastatic colorectal SRCC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. We compared and analyzed the clinicopathological characteristics between LTSs (patients survived over 5 years) and non-LTSs (patients survived of or less than 5 years). Afterwards, multivariate logistic regression analysis was used to identify independent predictors of LTSs, which were further used to construct a nomogram model to predict the probability of being LTSs. Results: We enrolled 2050 patients with nonmetastatic colorectal SRCC, consisting of 1441 non-LTSs and 609 LTSs. Multivariate logistic regression analysis revealed that race, marital status, tumor infiltration, lymph node involvement, and primary tumor treatment were independent predictors of LTSs. In addition, these five parameters were incorporated into a nomogram model to predict the probability of being LTSs. In terms of the model performance, the calibration curve revealed good agreement between observed and predicted probability of LTSs, and receiving operator characteristic curve showed acceptable discriminative capacity in the training and validation cohorts. Conclusion: Collectively, we analyzed and profiled the clinicopathological characteristics of LTSs in patients with nonmetastatic colorectal SRCC. Race, marital status, T stage, N stage, and primary tumor treatment were independent predictors of LTSs.

RWSF-BLP: a novel lncRNA-disease association prediction model using random walk-based multi-similarity fusion and bidirectional label propagation.

An increasing number of studies and experiments have demonstrated that long noncoding RNAs (lncRNAs) have a massive impact on various biological processes. Predicting potential associations between lncRNAs and diseases not only can improve our understanding of the molecular mechanisms of human diseases but also can facilitate the identification of biomarkers for disease diagnosis, treatment, and prevention. However, identifying such associations through experiments is costly and demanding, thereby prompting researchers to develop computational methods to complement these experiments. In this paper, we constructed a novel model called RWSF-BLP (a novel lncRNA-disease association prediction model using Random Walk-based multi-Similarity Fusion and Bidirectional Label Propagation), which applies an efficient random walk-based multi-similarity fusion (RWSF) method to fuse different similarity matrices and utilizes bidirectional label propagation to predict potential lncRNA-disease associations. Leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-fold-CV) were implemented in the evaluation RWSF-BLP performance. Results showed that, RWSF-BLP has reliable AUCs of 0.9086 and 0.9115 ± 0.0044 under the framework of LOOCV and 5-fold-CV and outperformed other four canonical methods. Case studies on lung cancer and leukemia demonstrated that potential lncRNA-disease associations can be predicted through our method. Therefore, our method can accurately infer potential lncRNA-disease associations and may be a good choice in future biomedical research.

A systematic review and meta-analysis on the efficacy of Compound Kushen Injection in 3 kinds of digestive tract tumor.

BACKGROUND: Chemotherapy has become the main means to prolong the life of patients with advanced digestive tract cancer; however, it is associated with serious toxicity and side effects. Compound Kushen Injection (CKI) is a pure Chinese herbal preparation, which can assist chemotherapy, inhibit tumor cell proliferation, and reduce adverse reactions of chemotherapy. In this study, we systematically evaluated reports of CKI as an adjuvant to chemotherapeutic treatment of digestive tract cancer in recent years and provided evidence for clinical diagnosis and treatment. METHODS: The databases of PubMed, Chinese Biomedical Literature (CBM), China National Knowledge Infrastructure (CNKI) and Web Of Science were searched for clinical randomized controlled trials (RCTs) related to adjuvant chemotherapy with CKI in the treatment of advanced gastrointestinal tumors published from January 2000 to September 2021. After screening the qualified literatures, RevMan 5.4 software was used to evaluate the bias of the included literatures and perform meta-analysis. RESULTS: A total of 12 articles were included in the selection, incorporating 1080 study participants in all; meta-analysis results showed that application of the CKI in the process of chemotherapy for digestive tract tumors could improve the efficacy [odds ratio (OR) =3.11; 95% confidence interval (CI): 2.26 to 4.47, Z=7.00, P<0.00001], increase the patients' median survival time (months) (OR =3.00; 95% CI: 1.47 to 4.52, Z=3.84, P=0.0001), increase the level of CD3+ [mean difference (MD) =4.11; 95% CI: 3.24 to 4.98], CD4+ level (MD =8.24; 95% CI: 3.72 to 12.76), reduce the CD8+ level (MD =-5.42; 95% CI: -8.09 to -2.76), reduce the tumor markers carcinoembryonic antigen (CEA; MD =-14.26; 95% CI: -14.81 to -13.71), CA199 (MD =-138.87; 95% CI: -143.21 to -132.52), and reduce the adverse reactions of chemotherapy: leukopenia (OR =0.28; 95% CI: 0.19 to 0.43), thrombocytopenia (OR =0.38; 95% CI: 0.24 to 061), decreased hemoglobin (OR =0.55; 95% CI: 0.31 to 0.98), and nausea and vomiting symptoms (OR =0.35; 95% CI: 0.24 to 0.53). DISCUSSION: Adjuvant chemotherapy with CKI in the treatment of digestive tract tumors can effectively improve the symptoms of patients, improve immunity, reduce the level of serum tumor markers, improve efficacy, and reduce toxic and side effects.

HAUBRW: Hybrid algorithm and unbalanced bi-random walk for predicting lncRNA-disease associations.

An increasing number of research shows that long non-coding RNA plays a key role in many important biological processes. However, the number of disease-related lncRNAs found by researchers remains relatively small, and experimental identification is time consuming and labor intensive. In this study, we propose a novel method, namely HAUBRW, to predict undiscovered lncRNA-disease associations. First, the hybrid algorithm, which combines the heat spread algorithm and the probability diffusion algorithm, redistributes the resources. Second, unbalanced bi-random walk, is used to infer undiscovered lncRNA disease associations. Seven advanced models, i.e. BRWLDA, DSCMF, RWRlncD, IDLDA, KATZ, Ping's, and Yang's were compared with our method, and simulation results show that the AUC of our method is more perfect than the other models. In addition, case studies have shown that HAUBRW can effectively predict candidate lncRNAs for breast, osteosarcoma and cervical cancer. Therefore, our approach may be a good choice in future biomedical research.

[Quality evaluation of Astragali Radix through chemical pattern recognition of fingerprint by HPLC-dAD-ELSD].

OBJECTIVE: To develop an HPLC-DAD-ELSD method for detecting the fingerprint of Astragali Radix and evaluate the quality through similarity calculation and chemical pattern recognition. METHOD: Separation was performed at 25 degreeC on an Agilent Zorbax ODS C18 column(4.6 mm x250 mm,5 microm). Gradient elution was performed with the mobile phases of acetonitrile and water containing 0. 2% formic acid. The flow rate was 0. 8 mL min-1 , and sample size was 10 microL. The UV detection wavelength was set at 280 nm. The drift tube temperature for ELSD was set at 110 degreeC , and the nebulizing gas flow rate was 3.0 L min-1. The similarity calculation and chemical pattern recognition were used for fingerprint analysis. RESULT: The HPLC-DAD-ELSD method for chromatographic fingerprint of Astragali Radix showed better results of stability, precision and repeatability. The reference chromatographic fingerprint of Astragali Radix was established on the eighteen Astragali Radix samples from different sources. The results of similarity calculation were higher than 0. 83, which was in accordance with the result of chemical pattern recognition analysis. CONCLUSION: Fingerprint and chemical pattern recognition analysis could effectively distinguish Astragali Radix from different source, which could be applied to the quality control of Astragali Radix.

[Rapid determination of puerarin in Xiaoke pill powder by near-infrared spectroscopy].

OBJECTIVE: To determine the content of puerarin in Xiaoke pill powder rapidly by near-infrared spectroscopy. METHOD: Near-infrared diffuse reflectance spectroscopy technology was used to collect NIR spectra of Xiaoke pills powder. With HPLC analysis values as reference, the fast determination method of puerarin was established with partial least squares (PLS). RESULT: The R2, RMSECV and RPD of the calibration model for puerarin were 0.980 1, 0.131 and 7.09. The average relative deviation of the predication set was 3.2%. CONCLUSION: The method is accurate, fast and simple, which could be generalized to the on-line quality control of Xiaoke pill powder production.