Core reference ontology for individualized exercise prescription.

"Exercise is medicine" emphasizes personalized prescriptions for better efficacy. Current guidelines need more support for personalized prescriptions, posing scientific challenges. Facing those challenges, we gathered data from established guidelines, databases, and articles to develop the Exercise Medicine Ontology (EXMO), intending to offer comprehensive support for personalized exercise prescriptions.

Targeted therapy of multiple myeloma by IL21-NKG2D CAR-T cells.

NKG2D chimeric antigen receptor (CAR)-modified T cells (NKG2D CAR-T cells) have been reported to be preclinically efficient in several tumors, but little is known whether NKG2D CAR-T cells co-expressing IL21 (IL21-NKG2D CAR-T cells) display greater antitumor activity in multiple myeloma (MM). In this study, the lentivirus has been produced for expression of the IL21 sequence linked to the extracellular NKG2D sequence with the signal peptide linked through the CD8α hinge-transmembrane domain to the 4-1BB molecule fused with the CD3-ζ chain signaling domain, and the engineered IL21-NKG2D CAR-T cells and NKG2D CAR-T cells were constructed. The CAR expression on CAR-T cells was assessed by flow cytometry, and the killing effects of CAR-T cells on MM were assessed by the cytotoxicity assay and ELISA assay.

Electrochemiluminescence immunoassay system based on PCN-224-Mn and gold-platinum bimetallic nanoflowers for sensitive detection of ochratoxin A.

In this work, a novel Electrochemiluminescence Immunosensor was constructed using PCN-224-Mn and gold-platinum nanoflowers (AuPt NFs) for the ultrasensitive detection of ochratoxin A (OTA). PCN-224 modified with Mn (II) was synthesized as a probe material. The interaction efficiency of PCN-224 with SO was also greatly improved.

PCAO2: an ontology for integration of prostate cancer associated genotypic, phenotypic and lifestyle data.

Disease ontologies facilitate the semantic organization and representation of domain-specific knowledge. In the case of prostate cancer (PCa), large volumes of research results and clinical data have been accumulated and needed to be standardized for sharing and translational researches. A formal representation of PCa-associated knowledge will be essential to the diverse data standardization, data sharing and the future knowledge graph extraction, deep phenotyping and explainable artificial intelligence developing.

CD39 tumor infiltrating T cells from colorectal cancers exhibit dysfunctional phenotype.

Recent studies revealed that CD39 was highly expressed in tumor-specific CD4 tumor infiltrating lymphocytes (TILs). However, the divergent function of CD39 T cells remains to be elucidated in colorectal cancer (CRC). In this study, T cells from CRC patients and tumor-bearing mice were isolated to evaluate the function of CD39 in T cells.

Analysis of China's carbon market price fluctuation and international carbon credit financing mechanism using random forest model.

This study aims to investigate the price changes in the carbon trading market and the development of international carbon credits in-depth. To achieve this goal, operational principles of the international carbon credit financing mechanism are considered, and time series models were employed to forecast carbon trading prices. Specifically, an ARIMA(1,1,1)-GARCH(1,1) model, which combines the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Autoregressive Integrated Moving Average (ARIMA) models, is established.

Inhibition of OTUB2 suppresses colorectal cancer cell growth by regulating β-Catenin signaling.

In the effort to identify deubiquitinating enzymes required for the growth of colorectal cancer (CRC) cells, we found that OTUB2 knockdown markedly inhibited the viability of these cancer cells in culture and in xenografted mice. It was also found that the level of OTUB2 was elevated in primary CRCs, and its high expression was a poor prognostic indicator for the patients. Interestingly, immunoprecipitation and LC-MS/MS analyses suggested that β-Catenin was an OTUB2-interacting protein, and there was a positive correlation between OTUB2 and β-Catenin expression in both CRC tissues and cell lines.

Recent Advances in RNA-Targeted Cancer Therapy.

Ribonucleic acid (RNA) plays a pivotal role in gene regulation and protein biosynthesis. Interfering the physiological function of key RNAs to induce cell apoptosis holds great promise for cancer treatment. Many RNA-targeted anti-cancer strategies have emerged continuously.

STAT3-EphA7 axis contributes to the progression of esophageal squamous cell carcinoma.

Background: Our previous study has revealed that EphA7 was upregulated in patient-derived esophageal squamous cell carcinoma (ESCC) xenografts with hyper-activated STAT3, but its mechanism was still unclear.

Materials And Methods: To assess the association between EphA7 and STAT3, western blotting, immunofluorescence, ChIP assay, and qRT-PCR were conducted. Truncated mutation and luciferase assay were performed to examine the promoter activity of EphA7.

Crystal structure and luminescence spectrum of a one-dimensional nickel(II) coordination polymer incorporating 1,4-bis-[(2-methyl-imidazol-1-yl)meth-yl]benzene and adamantane-1,3-di-carboxyl-ate co-ligands.

An Ni coordination polymer, namely, poly[(μ-adamantane-1,3-di-carboxyl-ato-κ , : , )[μ-1,4-bis-(2-methyl-imidazol-1-ylmeth-yl)benzene-κ : ]nickel(II)], [Ni(CHO)(CHN)] or [Ni(adc)(bmib)], (I) [adc = adamantane-1,3-di-carboxyl-ate, CHO and bmib = 1,4-bis-(2-methyl-imidazol-1-ylmeth-yl)benzene, CHN] was synthesized and characterized. It exhibits a one-dimensional extended structure built up from alternating [Ni(bmib)] 26-membered rings and [Ni(adc)] 16-membered rings. The nickel atom lies on a crystallographic twofold axis and both ligands are completed by mirror symmetry.

The design of early warning software systems for financial crises in high-tech businesses using fusion models in the context of sustainable economic growth.

Enterprises are urged to continue implementing the sustainable development strategy in their business operations as "carbon neutrality" and "carbon peak" gradually become the current stage's worldwide targets. High-tech businesses (HTE) need to be better equipped to manage financial risks and avoid financial crises in the face of severe market competition. The most popular machine learning models-logistic regression, XGBoost, and BP neural networks-are chosen as the base models in this study.

The Interplay Between Financial Literacy, Financial Risk Tolerance, and Financial Behaviour: The Moderator Effect of Emotional Intelligence.

Aim: This study aims to examine financial literacy's impact on individual investors' financial behaviour while also investigating the mediating role of financial risk tolerance and the moderator effect of emotional intelligence.

Methods: The study collects time-lagged data from 389 financially independent individual investors from leading educational institutes in Pakistan. Data are analysed using SmartPLS (v 3.

Research on DNA-Binding Protein Identification Method Based on LSTM-CNN Feature Fusion.

Protein is closely related to life activities. As a kind of protein, DNA-binding protein plays an irreplaceable role in life activities. Therefore, it is very important to study DNA-binding protein, which is a subject worthy of study.

Identifying Membrane Protein Types Based on Lifelong Learning With Dynamically Scalable Networks.

Membrane proteins are an essential part of the body's ability to maintain normal life activities. Further research into membrane proteins, which are present in all aspects of life science research, will help to advance the development of cells and drugs. The current methods for predicting proteins are usually based on machine learning, but further improvements in prediction effectiveness and accuracy are needed.

Application of DNA-Binding Protein Prediction Based on Graph Convolutional Network and Contact Map.

DNA contains the genetic information for the synthesis of proteins and RNA, and it is an indispensable substance in living organisms. DNA-binding proteins are an enzyme, which can bind with DNA to produce complex proteins, and play an important role in the functions of a variety of biological molecules. With the continuous development of deep learning, the introduction of deep learning into DNA-binding proteins for prediction is conducive to improving the speed and accuracy of DNA-binding protein recognition.

Research on RNA secondary structure predicting via bidirectional recurrent neural network.

Background: RNA secondary structure prediction is an important research content in the field of biological information. Predicting RNA secondary structure with pseudoknots has been proved to be an NP-hard problem. Traditional machine learning methods can not effectively apply protein sequence information with different sequence lengths to the prediction process due to the constraint of the self model when predicting the RNA secondary structure.

ANCA: A Web Server for Amino Acid Networks Construction and Analysis.

Amino acid network (AAN) models empower us to gain insights into protein structures and functions by describing a protein 3D structure as a graph, where nodes represent residues and edges as amino acid interactions. Here, we present the ANCA, an interactive Web server for Amino Acids Network Construction and Analysis based on a single structure or a set of structures from the Protein Data Bank. The main purpose of ANCA is to provide a portal for three types of an environment-dependent residue contact energy (ERCE)-based network model, including amino acid contact energy network (AACEN), node-weighted amino acid contact energy network (NACEN), and edge-weighted amino acid contact energy network (EACEN).

Mesoporous silica nanoparticles loaded with capsaicin and their oxidation resistance in meat preservation.

In this work, capsaicin (CAP) was loaded into mesoporous silica nanoparticles (MSNs) with grain diameters of 50 nm, 100 nm and 400 nm by free diffusion to obtain the support materials Cap@MSN-50, Cap@MSN-100 and Cap@MSN-400, respectively. The loading capacity was separately calculated as 854.77, 713.

PCLiON: An Ontology for Data Standardization and Sharing of Prostate Cancer Associated Lifestyles.

Background: Researches on Lifestyle medicine (LM) have emerged in recent years to garner wide attention. Prostate cancer (PCa) could be prevented and treated by positive lifestyles, but the association between lifestyles and PCa is always personalized.

Objectives: In order to solve the heterogeneity and diversity of different data types related to PCa, establish a standardized lifestyle ontology, promote the exchange and sharing of disease lifestyle knowledge, and support text mining and knowledge discovery.

Use Chou's 5-Step Rule to Predict DNA-Binding Proteins with Evolutionary Information.

The knowledge of DNA-binding proteins would help to understand the functions of proteins better in cellular biological processes. Research on the prediction of DNA-binding proteins can promote the research of drug proteins and computer acidified drugs. In recent years, methods based on machine learning are usually used to predict proteins.

PCaLiStDB: a lifestyle database for precision prevention of prostate cancer.

The interaction between genes, lifestyles and environmental factors makes the genesis and progress of prostate cancer (PCa) very heterogeneous. Positive lifestyle is important to the prevention and controlling of PCa. To investigate the relationship between PCa and lifestyle at systems level, we established a PCa related lifestyle database (PCaLiStDB) and collected the PCa-related lifestyles including foods, nutrients, life habits and social and environmental factors as well as associated genes and physiological and biochemical indexes together with the disease phenotypes and drugs.

Network vulnerability-based and knowledge-guided identification of microRNA biomarkers indicating platinum resistance in high-grade serous ovarian cancer.

Background: High-grade serous ovarian cancer (HGSC), the most common ovarian carcinoma type, is associated with the highest mortality rate among all gynecological malignancies. As chemoresistance has been demonstrated as the major challenge in improving the prognosis of HGSC patients, we here aimed to identify microRNA (miRNA) biomarkers for predicting platinum resistance and further explore their functions in HGSC.

Results: We developed and applied our network vulnerability-based and knowledge-guided bioinformatics model first time for the study of drug-resistance in cancer.

Biomarker microRNAs for prostate cancer metastasis: screened with a network vulnerability analysis model.

Background: Prostate cancer (PCa) is a fatal malignant tumor among males in the world and the metastasis is a leading cause for PCa death. Biomarkers are therefore urgently needed to detect PCa metastatic signature at the early time. MicroRNAs are small non-coding RNAs with the potential to be biomarkers for disease prediction.

XML, Ontologies, and Their Clinical Applications.

The development of information technology has resulted in its penetration into every area of clinical research. Various clinical systems have been developed, which produce increasing volumes of clinical data. However, saving, exchanging, querying, and exploiting these data are challenging issues.