Clinical analysis of multiple primary gastrointestinal malignant tumors: A 10-year case review of a single-center.

BACKGROUND: Multiple primary malignant tumors (MPMTs) was first described by Billroth as early as 1889, with the first report published by Warren and Gates in 1932. Since then, numerous cases have been reported. A literature review of 1104269 patients with cancer revealed that the incidence of MPMTs ranged from 0.73 to 11.7%. In recent years, however, there has been a significant upward trend in the incidence of this phenomenon, which may be associated with many different factors, including the advancement of modern diagnostic procedures facilitating the examination and diagnosis of more MPMTs, increased exposure to chemotherapy and radiotherapy that exacerbate the risk of new malignant tumors in patients with cancer, and prolonged survival of patients with cancer allowing sufficient time for the development of new primary cancers. AIM: To analyze the incidence, clinical features, treatment factors, prevalence, and prognosis of patients with MPMTs in the gastrointestinal tract treated in a single center. Additionally, we analyzed the different tumor combinations, time interval between the occurrence of tumors, and staging. METHODS: This retrospective cohort study analyzed 8059 patients with pathologically confirmed gastrointestinal malignant tumors treated at the Gansu Province Hospital in Lanzhou, Gansu, China between June 2011 and June 2020. Of these, 85 patients had MPMTs. The clinical features, treatment factors, prevalence, and prognosis of this latter cohort were analyzed. RESULTS: The incidence of MPMTs in patients with gastrointestinal malignant tumors was 1.05% (85/8059), including 83 double primary malignant tumors and two triple primary malignant tumors of which 57 (67.06%) were synchronous MPMTs (SMPMTs) and 28 (32.94%) were metachronous MPMTs (MMPMTs). The most frequent associations were found between the rectum colon cancers within the SMPMT category and the gastric-colon cancers within the MMPMT category. For the MMPMTs, the median interval was 53 months. The overall 1-, 3- and 5-year survival rates from diagnosis of the first primary cancer were 91.36%, 65.41%, and 45.97%, respectively; those from diagnosis of the second primary cancer were 67.90%, 29.90%, and 17.37%, respectively. CONCLUSION: MPMTs in the gastrointestinal tract have a high incidence and poor prognosis. Thus, it is necessary to perform both gastroscopy and colonoscopy in patients with gastrointestinal tumors. Multidisciplinary comprehensive diagnosis and treatment may improve the diagnosis rate and treatment efficiency of MPMTs.

Hessian Regularized [Formula: see text]-Nonnegative Matrix Factorization and Deep Learning for miRNA-Disease Associations Prediction.

Since the identification of microRNAs (miRNAs), empirical research has demonstrated their crucial involvement in the functioning of organisms. Investigating miRNAs significantly bolsters efforts related to averting, diagnosing, and treating intricate human maladies. Yet, exploring every conceivable miRNA-disease association consumes significant resources and time within conventional wet experiments. On the computational front, forecasting potential miRNA-disease connections serves as a valuable source of preliminary insights for medical investigators. As a result, we have developed a novel matrix factorization model known as Hessian-regularized [Formula: see text] nonnegative matrix factorization in combination with deep learning for predicting associations between miRNAs and diseases, denoted as [Formula: see text]-NMF-DF. In particular, we introduce a novel iterative fusion approach to integrate all similarities. This method effectively diminishes the sparsity of the initial miRNA-disease associations matrix. Additionally, we devise a mixed model framework that utilizes deep learning, matrix decomposition, and singular value decomposition to capture and depict the intricate nonlinear features of miRNA and disease. The prediction performance of the six matrix factorization methods is improved by comparison and analysis, similarity matrix fusion, data preprocessing, and parameter adjustment. The AUC and AUPR obtained by the new matrix factorization model under fivefold cross validation are comparative or better with other matrix factorization models. Finally, we select three diseases including lung tumor, bladder tumor and breast tumor for case analysis, and further extend the matrix factorization model based on deep learning. The results show that the hybrid algorithm combining matrix factorization with deep learning proposed in this paper can predict miRNAs related to different diseases with high accuracy.

Strong Electrochemiluminescence from MOF Accelerator Enriched Quantum Dots for Enhanced Sensing of Trace cTnI.

The development of a sensitive and practical electrochemiluminescence (ECL) bioassay relies on the use of ECL signal tags whose signal intensity is high and stable. In this work, strong ECL emission was achieved from metal organic framework (MOF) accelerator enriched quantum dots (CdTe), which were applied as an efficient ECL signal tag for trace biomarker detection. It is particularly noteworthy that a novel mechanism to drastically enhance the ECL intensity of CdTe is established because isoreticular metal organic framework-3 (IRMOF-3) with 2-amino terephthalic acid (2-NH2-BDC) as the organic ligand not only allows for loading a large amount of CdTe via the encapsulating effect and internal/external decoration but also functions as a novel coreactant accelerator for promoting the conversion of coreactant S2O82- into the sulfate radical anion (SO4•-), further boosting the ECL emission of CdTe. On the basis of the simple sandwich immunoreaction approach, cardiac troponin-I antigen (cTnI), a kind of biomarker related with myocardial infarction, was chosen as a detection model using an IRMOF-3-enriched CdTe labeled antibody as the signal probe. This immunosensor demonstrated desirable assay performance for cTnI with a wide response range from 1.1 fg mL-1 to 11 ng mL-1 and a very low detection limit (0.46 fg mL-1). This suggested that the IRMOF-3-enriched CdTe nanocomposite strategy can integrate the coreactant accelerator and luminophore to significantly enhance the ECL intensity and stability, providing a direction for promising ECL tag preparation with broad applications.