The prognostic significance and immune characteristics of bone morphogenetic proteins (BMPs) family: A pan-cancer multi-omics analysis.

Background: Bone morphogenetic proteins (BMPs) are a group of cancer-related proteins vital for development and progression of certain cancer types. Nevertheless, function of BMP family in pan-cancer was not detailedly researched.

Objective: Investigating expression pattern and prognostic value of the BMPs family (BMP1-8A and BMP8B) expression across multiple cancer types.

The prognostic values and immune characteristics of polo-like kinases (PLKs) family: A pan-cancer multi-omics analysis.

Background: In the realm of tumor-targeted therapeutics, Polo-like kinases (PLKs) are a significant group of protein kinases that were found recently as being related to tumors. However, the significance of PLKs in pan-cancer remains systematically studied.

Methods And Materials: We integrated multi-omics data to comprehensively investigate the expression patterns of the PLK family across various cancer types.

Clinical effectiveness of a multitarget urine DNA test for urothelial carcinoma detection: a double-blinded, multicenter, prospective trial.

Urine-based testing is promising for noninvasive diagnosis of urothelial carcinoma (UC) but has suboptimal sensitivity for early-stage tumors. Herein, we developed a multitarget urine tumor DNA test, UI-Seek, for UC detection and evaluated its clinical feasibility. The prediction model was developed in a retrospective cohort (n = 382), integrating assays for FGFR3 and TERT mutations and aberrant ONECUT2 and VIM methylation to generate a UC-score.

The conductivity of Nb2O5 enhanced by the triple effect of fluorine doping, oxygen vacancy, and carbon modification for improving the lithium storage performance.

In view of the inherent pseudocapacitance, rich redox pairs (Nb5+/Nb4+ and Nb4+/Nb3+), and high lithiation potential (1.0-3.0 V vs Li/Li+), Nb2O5 is considered a promising anode material.

Withdrawal notice to: "Pan-cancer analyses of clinical prognosis, immune infiltration, and immunotherapy efficacy for TRPV family using multi-omics data" [Heliyon 9, (2023) 16897].

[This corrects the article DOI: 10.1016/j.heliyon.

Comprehensive characterization of endoplasmic reticulum stress in bladder cancer revealing the association with tumor immune microenvironment and prognosis.

This study constructs a molecular subtype and prognostic model of bladder cancer (BLCA) through endoplasmic reticulum stress (ERS) related genes, thus helping to clinically guide accurate treatment and prognostic assessment. The Bladder Cancer (BLCA) gene expression data was downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We clustered by ERS-related genes which obtained through GeneCards database, results in the establishment of a new molecular typing of bladder cancer.

Enhancing urinary tract infection diagnosis for negative culture patients with metagenomic next-generation sequencing (mNGS).

Background: Metagenomic next-generation sequencing (mNGS) is a promising technology that allows unbiased pathogen detection and is increasingly being used for clinical diagnoses. However, its application in urinary tract infection (UTI) is still scarce.

Methods: The medical records of 33 patients with suspected UTI who were admitted to the Second Hospital of Tianjin Medical University from March 2021 to July 2022 and received urine mNGS were retrospectively analyzed.

Construction and validation of a bladder cancer risk model based on autophagy-related genes.

Autophagy has an important association with tumorigenesis, progression, and prognosis. However, the mechanism of autophagy-regulated genes on the risk prognosis of bladder cancer (BC) patients has not been fully elucidated yet. In this study, we created a prognostic model of BC risk based on autophagy-related genes, which further illustrates the value of genes associated with autophagy in the treatment of BC.

Applying machine learning technologies to explore students' learning features and performance prediction.

To understand students' learning behaviors, this study uses machine learning technologies to analyze the data of interactive learning environments, and then predicts students' learning outcomes. This study adopted a variety of machine learning classification methods, quizzes, and programming system logs, found that students' learning characteristics were correlated with their learning performance when they encountered similar programming practice. In this study, we used random forest (RF), support vector machine (SVM), logistic regression (LR), and neural network (NN) algorithms to predict whether students would submit on time for the course.

A novel model based on immune-related genes for differentiating biliary atresia from other cholestatic diseases.

Purpose: Based on a public gene expression database, this study established the immune-related genetic model that distinguished BA from other cholestasis diseases (DC) for the first time. We explored the molecular mechanism of BA based on the gene model.

Methods: The BA microarray dataset GSE46960, containing BA, other cause of intrahepatic cholestasis than biliary atresia and normal liver gene expression data, was downloaded from the Gene Expression Omnibus (GEO) database.

Dimensionality reduction approach for many-objective epistasis analysis.

In epistasis analysis, single-nucleotide polymorphism-single-nucleotide polymorphism interactions (SSIs) among genes may, alongside other environmental factors, influence the risk of multifactorial diseases. To identify SSI between cases and controls (i.e.

Identification of a dysregulated CircRNA-associated gene signature for predicting prognosis, immune landscape, and drug candidates in bladder cancer.

Increasing evidences have demonstrated that circular RNA (circRNAs) plays a an essential regulatory role in initiation, progression and immunotherapy resistance of various cancers. However, circRNAs have rarely been studied in bladder cancer (BCa). The purpose of this research is to explore new circRNAs and their potential mechanisms in BCa.

Multiobjective optimization-driven primer design mechanism: towards user-specified parameters of PCR primer.

Primers are critical for polymerase chain reaction (PCR) and influence PCR experimental outcomes. Designing numerous combinations of forward and reverse primers involves various primer constraints, posing a computational challenge. Most PCR primer design methods limit parameters because the available algorithms use general fitness functions.

Fuzzy-Based Multiobjective Multifactor Dimensionality Reduction for Epistasis Analysis.

Epistasis detection is vital for understanding disease susceptibility in genetics. Multiobjective multifactor dimensionality reduction (MOMDR) was previously proposed to detect epistasis. MOMDR was performed using binary classification to distinguish the high-risk (H) and low-risk (L) groups to reduce multifactor dimensionality.

MOAI: a multi-outcome interaction identification approach reveals an interaction between vaspin and carcinoembryonic antigen on colorectal cancer prognosis.

Identifying and characterizing the interaction between risk factors for multiple outcomes (multi-outcome interaction) has been one of the greatest challenges faced by complex multifactorial diseases. However, the existing approaches have several limitations in identifying the multi-outcome interaction. To address this issue, we proposed a multi-outcome interaction identification approach called MOAI.

Effect of Baseline Characteristics and Tumor Burden on Vaspin Expression and Progressive Disease in Operable Colorectal Cancer.

Colorectal cancer is a highly heterogeneous malignancy in the Asian population, and it is considered an important prognostic factor for baseline characteristics, tumor burden, and tumor markers. This study investigated the effect of baseline characteristics and tumor burden on tumor marker expression and progressive disease in colorectal cancer by using partial least squares variance-based path modeling (PLS-PM). PLS-PM can be used to evaluate the complex relationship between prognostic variables and progressive disease status with a small sample of measurements and structural models.

An improved fuzzy set-based multifactor dimensionality reduction for detecting epistasis.

Objective: Epistasis identification is critical for determining susceptibility to human genetic diseases. The rapid development of technology has enabled scalability to make multifactor dimensionality reduction (MDR) measurements an effective calculation tool that achieves superior detection. However, the classification of high-risk (H) or low-risk (L) groups in multidrug resistance operations calls for extensive research.

Top-down strategy synthesis of fluorinated graphdiyne for lithium ion battery.

As a novel carbon allotrope, graphdiyne exhibits excellent electrochemical properties such as high specific capacities, outstanding rate performances, and long cycle lives. These properties are attributed to its sp- and sp-hybridized bonding and a natural large pore structure. Doping with light elements is a facile way to improve the electrochemical performance of graphdiyne.

Effective hybrid approach for protein structure prediction in a two-dimensional Hydrophobic-Polar model.

Hydrophobic-polar (HP) models are widely used to predict protein folding and hydrophobic interactions. Numerous optimization algorithms have been proposed to predict protein folding using the two-dimensional (2D) HP model. However, to obtain an optimal protein structure from the 2D HP model remains challenging.

Fluorinated graphdiyne as a significantly enhanced fluorescence material.

The chemical modification of graphdiyne (GDY) using light elements is a possible route to regulate its unique structure and optoelectronic properties. In this paper it is shown that directly heating a mixture of xenon difluoride and GDY produces partially fluorinated GDY with covalent C-F bonding and localized sp-carbon hybridization because of the breaking of the acetylenic bond. It is seen that the fluorescence of GDY is significantly enhanced because of the fluorine doping.

Class Balanced Multifactor Dimensionality Reduction to Detect Gene-Gene Interactions.

Detecting gene-gene interactions in single-nucleotide polymorphism data is vital for understanding disease susceptibility. However, existing approaches may be limited by the sample size in case-control studies. Herein, we propose a balance approach for the multifactor dimensionality reduction (BMDR) method to increase the accuracy of estimates of the prediction error rate in small samples.

Catfish Taguchi-Based Binary Differential Evolution Algorithm for Analyzing Single Nucleotide Polymorphism Interactions in Chronic Dialysis.

Single-nucleotide polymorphism (SNP)-SNP interactions are crucial for understanding the association between disease-related multifactorials for disease analysis. Existing statistical methods for determining such interactions are limited by the considerable computation required for evaluating all potential associations between disease-related multifactorials. Identifying SNP-SNP interactions is thus a major challenge in genetic association studies.