An integrative evaluation of circadian gene TIMELESS as a pan-cancer immunological and predictive biomarker.

Background: The gene TIMELESS, which is involved in the circadian clock and the cell cycle, has recently been linked to various human cancers. Nevertheless, the association between TIMELESS expression and the prognosis of individuals afflicted with pan-cancer remains largely unknown.

Objectives: The present study aims to exhaustively scrutinize the expression patterns, functional attributes, prognostic implications, and immunological contributions of TIMELESS across diverse types of human cancer.

An ensemble framework for farmland quality evaluation based on machine learning and physical models.

Farmland quality (FQ) evaluation is crucial to curb agricultural land's "non-grain" behavior and promote ecological nitrogen trade-off in North China. However, a promising approach to obtain the verified spatial distribution of nitrogen emissions remains to be developed, making it difficult to achieve the precise FQ estimation. Facing this issue, we present a Machine Learning (ML) - Nitrogen Export Verification (NEV) ensemble framework for the precise evaluation of FQ, taking the Beijing-Tianjin-Hebei 200 km traffic zone (zone) as the case.

Pan-cancer analysis identifies RNF43 as a prognostic, therapeutic and immunological biomarker.

Background: RING finger protein 43 (RNF43), an E3 ubiquitin ligase, is a homologous gene mutated in several cancers. However, the pan-cancer panoramic picture of RNF43 and its predictive value for tumor immune phenotypes and immunotherapeutic efficacy are still largely unclear. Our study aims to clarify the functions of RNF43 in predicting the prognosis, immune signature, and immunotherapeutic efficacy in pan-cancer.

Exploring effective ways to increase reliable positive samples for machine learning-based urban waterlogging susceptibility assessments.

Machine learning (ML)-based urban waterlogging susceptibility studies suffer from class imbalance, as fewer positive samples are generally available than potential negative samples. Few studies have considered optimizing the results by improving the quality of training samples. To address this issue, we explored effective approaches to reliably increase the numbers of positive samples for such studies.

Ferroptosis-related gene HIC1 in the prediction of the prognosis and immunotherapeutic efficacy with immunological activity.

Background: Hypermethylated in Cancer 1 (HIC1) was originally confirmed as a tumor suppressor and has been found to be hypermethylated in human cancers. Although growing evidence has supported the critical roles of HIC1 in cancer initiation and development, its roles in tumor immune microenvironment and immunotherapy are still unclear, and no comprehensive pan-cancer analysis of HIC1 has been conducted.

Methods: HIC1 expression in pan-cancer, and differential HIC1 expression between tumor and normal samples were investigated.

ATF3 Modulates the Proliferation, Migration, and Apoptosis of Synovial Fibroblasts after Arthroscopy by Promoting RGS1 Transcription.

Background: Osteoarthritis (OA) is a degenerative joint disease involving both cartilage and synovium. Activating transcription factor 3 (ATF3) and regulator of G protein signaling 1 (RGS1) have been reported to be up-regulated in OA. However, little is known regarding the relationship between these two genes and the mechanism of this relationship in OA development.

Investigation of the clinicopathological and prognostic role of circMTO1 in multiple cancers.

Objective: To observe the prognostic value of circular RNA mitochondrial tRNA translation optimization 1 (circMTO1) in human tumors.

Methods: We searched multiple databases for related reports published before November 01, 2021. The OR/HR and 95% CI were extracted to explore the correlation between circMTO1 expression and clinicopathological features in various cancers.

Pan-cancer analysis of CREB3L1 as biomarker in the prediction of prognosis and immunotherapeutic efficacy.

CAMP response element binding protein 3-like 1 (CREB3L1) has been indicated as a critical biomarker and can modulate multifaced behaviors of tumor cells in diverse cancers. However, a systematic assessment of CREB3L1 in pan-cancer is of absence, and the predictive value of CREB3L1 in cancer prognosis, the tumor immune microenvironment and the efficacy of immunotherapy remains unexplored. CREB3L1 expression in 33 different cancer types was investigated using RNAseq data from The Cancer Genome Atlas (TCGA) database.

Low dose aspirin associated with greater bone mineral density in older adults.

The use of low-dose aspirin in older adults is increasing as is the prevalence of osteoporosis. Aspirin has been shown in numerous studies to affect bone metabolism. However, there is no clear link between low-dose aspirin use and bone mineral density (BMD).

Clinical and radiological outcomes of combined modular prothesis and cortical strut for revision proximal femur in giant cell tumor of bone patients.

Background: Femoral bone deficiency is a challenging problem in revision proximal femoral replacement. The purpose of this study is to evaluate the clinical and radiological outcomes of revision proximal femoral replacement as a salvage treatment for severe bone loss after oncologic proximal replacement surgery in patient with benign giant cell tumor of bone.

Methods: 16 patients (6 men and 10 women) were included in this retrospective study, with a mean age of 46.

Integrative analysis of expression, prognostic significance and immune infiltration of RFC family genes in human sarcoma.

Objective: To reveal the expression and prognostic value of replication factor C family genes (RFCs) in patients with sarcoma.

Results: The results showed that the mRNA expression levels of RFC2, RFC3, RFC4, and RFC5 were increased in sarcoma tissues. In addition, Cancer Cell Line Encyclopedia (CCLE) dataset analysis indicated that RFC1, RFC2, RFC3, RFC4, and RFC5 were elevated expressed in sarcoma cell lines.

Construction of a Novel Signature and Prediction of the Immune Landscape in Soft Tissue Sarcomas Based on N6-Methylandenosine-Related LncRNAs.

N6-methylandenosine-related long non-coding RNAs (m6A-related lncRNAs) are critically involved in cancer development. However, the roles and clinical significance of m6A-related lncRNAs in soft tissue sarcomas (STS) are inconclusive, thereby warranting further investigations. Transcriptome profiling data were extracted from The Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx).

An Optimized Weighted Naïve Bayes Method for Flood Risk Assessment.

Floods occur frequently and cause considerable damage to local environments. Effectively assessing the flood risk contributes to reducing loss caused by such disasters. In this study, the weighted naïve Bayes (WNB) method was selected to evaluate flood risk, and the entropy weight method was employed to compute the weights.

Functions and mechanisms of circular RNAs in regulating stem cell differentiation.

Stem cells are a class of undifferentiated cells with great self-renewal and differentiation capabilities that can differentiate into mature cells in specific tissue types. Stem cell differentiation plays critical roles in body homoeostasis, injury repair and tissue generation. The important functions of stem cell differentiation have resulted in numerous studies focusing on the complex molecular mechanisms and various signalling pathways controlling stem cell differentiation.

Flood susceptibility mapping by integrating frequency ratio and index of entropy with multilayer perceptron and classification and regression tree.

Episodes of frequent flooding continue to increase, often causing serious damage and tools to identify areas affected by such disasters have become indispensable in today's society. Using the latest techniques can make very accurate flood predictions. In this study, we introduce four effective methods to evaluate the flood susceptibility of Poyang County, in China, by integrating two independent models of frequency ratio and index of entropy with multilayer perceptron and classification and regression tree models.

Prognostic and clinicopathological value of circPVT1 in human cancers: A meta-analysis.

Background: Circular RNA PVT1 (circPVT1) is significantly upregulated in various human cancers and is related to poor clinical outcome of cancer patients. However, the prognostic and clinicopathological value of circPVT1 in diverse human cancers remains controversial and inconclusive.

Aim: The objective of our study is to evaluate the prognostic and clinicopathological role of circPVT1 for cancer patients.

A novel optimized repeatedly random undersampling for selecting negative samples: A case study in an SVM-based forest fire susceptibility assessment.

The negative sample selection method is a key issue in studies of using machine learning approaches to spatially assess natural hazards. Recently, a Repeatedly Random Undersampling (RRU) was proposed to address the randomness problem faced in Single Random Sampling. However, the RRU cannot guarantee that the generated classifier has the best classification performance during the repeatedly random sampling process.

Total knee arthroplasty in a patient with ipsilateral hip ankylosis: A case report.

Rationale: There is a large number of people that have knee degeneration in China. Total knee arthroplasty is one of the most effective methods of treatment in the later stages of the disease. However, there are challenges when performing total knee arthroplasty on patients with ipsilateral hip akylosis.

A spatial assessment of urban waterlogging risk based on a Weighted Naïve Bayes classifier.

Urban waterlogging occurs frequently and often causes considerable damage that seriously affects the natural environment, human life, and the social economy. The spatial evaluation of urban waterlogging risk represents an essential analytic step that can be used to prevent urban waterlogging and minimize related losses. The Weighted Naïve Bayes (WNB) classifier is a powerful method for knowledge discovery and probability inference under conditions of uncertainty; a WNB classifier can be applied to estimate the likelihood of hazards.