StatGel: An Innovative hydrogel carrying STAT3-targeted small molecule inhibitor for the treatment of abdominal adhesions.

Adhesions in the abdominal cavity are among the most common complications post abdominal surgery, resulting from excessive fibrous tissue proliferation and collagen synthesis due to various factors. To date, physical barrier materials have been approved for preventing adhesions, though their effectiveness remains unsatisfactory. One of the important causes of abdominal adhesions is the excessive proliferation of fibrotic cells, and our previous research indicated that STAT3 is a promising therapeutic target for anti-fibrosis.

The optimised model of predicting protein-metal ion ligand binding residues.

Metal ions are significant ligands that bind to proteins and play crucial roles in cell metabolism, material transport, and signal transduction. Predicting the protein-metal ion ligand binding residues (PMILBRs) accurately is a challenging task in theoretical calculations. In this study, the authors employed fused amino acids and their derived information as feature parameters to predict PMILBRs using three classical machine learning algorithms, yielding favourable prediction results.

S-DCNN: prediction of ATP binding residues by deep convolutional neural network based on SMOTE.

Background: The realization of many protein functions requires binding with ligands. As a significant protein-binding ligand, ATP plays a crucial role in various biological processes. Currently, the precise prediction of ATP binding residues remains challenging.

Development and External Validation of a Gait Test Based Diagnostic Model for Detecting Mild Cognitive Impairment.

Objective: To address the lack of large-scale screening tools for mild cognitive impairment (MCI), this study aimed to assess the discriminatory ability of several gait tests for MCI and develop a screening tool based on gait test for MCI.

Design: A diagnostic case-control test.

Setting: The general community.

Association of Life's Simple 7 with mild cognitive impairment in community-dwelling older adults in China: a cross-sectional study.

Background: Life's Simple 7 (LS7), a metric composed of seven intervenable cardiovascular risk factors, is initiated by the American Heart Association to improve cardiovascular health. The components of LS7 have been reported as risk factors for dementia. However, few studies investigated the association between LS7 metric and mild cognitive impairment (MCI).

Prediction of metal ion ligand binding residues by adding disorder value and propensity factors based on deep learning algorithm.

Proteins need to interact with different ligands to perform their functions. Among the ligands, the metal ion is a major ligand. At present, the prediction of protein metal ion ligand binding residues is a challenge.

Recognizing protein-metal ion ligands binding residues by random forest algorithm with adding orthogonal properties.

Accurately identifying protein-metal ion ligand binding residues is the key to study protein functions. Because the number of binding residues and non-binding residues is significantly imbalanced, false positives is hard to be eliminated from the binding residues prediction result. Therefore, identification of protein-metal ion ligand binding residues remains challenging.

Recognition of Metal Ion Ligand-Binding Residues by Adding Correlation Features and Propensity Factors.

The realization of many protein functions is inseparable from the interaction with ligands; in particular, the combination of protein and metal ion ligands performs an important biological function. Currently, it is a challenging work to identify the metal ion ligand-binding residues accurately by computational approaches. In this study, we proposed an improved method to predict the binding residues of 10 metal ion ligands (Zn, Cu, Fe, Fe, Co, Mn, Ca, Mg, Na, and K).

Predicting Ca and Mg ligand binding sites by deep neural network algorithm.

Background: Alkaline earth metal ions are important protein binding ligands in human body, and it is of great significance to predict their binding residues.

Results: In this paper, Mg and Ca ligands are taken as the research objects. Based on the characteristic parameters of protein sequences, amino acids, physicochemical characteristics of amino acids and predicted structural information, deep neural network algorithm is used to predict the binding sites of proteins.

Recognition of Ion Ligand Binding Sites Based on Amino Acid Features with the Fusion of Energy, Physicochemical and Structural Features.

Unlabelled: [Background: Rational drug molecular design based on virtual screening requires the ligand binding site to be known. Recently, the recognition of ion ligand binding site has become an important research direction in pharmacology.

Methods: In this work, we selected the binding residues of 4 acid radical ion ligands (NO, CO, SO and PO) and 10 metal ion ligands (Zn, Cu, Fe, Fe, Ca, Mg, Mn, Na, K and Co) as research objects.

Recognizing Ion Ligand-Binding Residues by Random Forest Algorithm Based on Optimized Dihedral Angle.

The prediction of ion ligand-binding residues in protein sequences is a challenging work that contributes to understand the specific functions of proteins in life processes. In this article, we selected binding residues of 14 ion ligands as research objects, including four acid radical ion ligands and 10 metal ion ligands. Based on the amino acid sequence information, we selected the composition and position conservation information of amino acids, the predicted structural information, and physicochemical properties of amino acids as basic feature parameters.

The Identification of Metal Ion Ligand-Binding Residues by Adding the Reclassified Relative Solvent Accessibility.

Many proteins realize their special functions by binding with specific metal ion ligands during a cell's life cycle. The ability to correctly identify metal ion ligand-binding residues is valuable for the human health and the design of molecular drug. Precisely identifying these residues, however, remains challenging work.

Recognizing ion ligand binding sites by SMO algorithm.

Background: In many important life activities, the execution of protein function depends on the interaction between proteins and ligands. As an important protein binding ligand, the identification of the binding site of the ion ligands plays an important role in the study of the protein function.

Results: In this study, four acid radical ion ligands (NO,CO,SO,PO) and ten metal ion ligands (Zn,Cu,Fe,Fe,Ca,Mg,Mn,Na,K,Co) are selected as the research object, and the Sequential minimal optimization (SMO) algorithm based on sequence information was proposed, better prediction results were obtained by 5-fold cross validation.

Prediction of acid radical ion binding residues by K-nearest neighbors classifier.

Background: Proteins perform their functions by interacting with acid radical ions. Recently, it was a challenging work to precisely predict the binding residues of acid radical ion ligands in the research field of molecular drug design.

Results: In this study, we proposed an improved method to predict the acid radical ion binding residues by using K-nearest Neighbors classifier.

Recognizing five molecular ligand-binding sites with similar chemical structure.

Accurate identification of ligand-binding sites and discovering the protein-ligand interaction mechanism are important for understanding proteins' functions and designing new drugs. Meanwhile, accurate computational prediction and mechanism research are two grand challenges in proteomics. In this article, ligand-binding residues of five ligands (ATP, ADP, GTP, GDP, and NAD) are predicted as a group, due to their similar chemical structures and close biological function relations.

Electrohydrodynamic Drying of Chinese Wolfberry in a Multiple Needle-to-Plate Electrode System.

In order to systematically and comprehensively investigate electrohydrodynamic (EHD) drying characteristics and mechanisms in a multiple needle-to-plate electrode system, drying experiments of Chinese wolfberry were conducted by blocking ionic wind and changing needle spacing in a multiple needle-to-plate electrode system. Drying characteristics, quality parameters, and the microstructure of Chinese wolfberry fruits were measured. Results show that ionic wind plays a very important role during the drying process.

Using feature optimization-based support vector machine method to recognize the β-hairpin motifs in enzymes.

β-Hairpins in enzyme, a kind of special protein with catalytic functions, contain many binding sites which are essential for the functions of enzyme. With the increasing number of observed enzyme protein sequences, it is of especial importance to use bioinformatics techniques to quickly and accurately identify the β-hairpin in enzyme protein for further advanced annotation of structure and function of enzyme. In this work, the proposed method was trained and tested on a non-redundant enzyme β-hairpin database containing 2818 β-hairpins and 1098 non-β-hairpins.

Identification of metal ion binding sites based on amino acid sequences.

The identification of metal ion binding sites is important for protein function annotation and the design of new drug molecules. This study presents an effective method of analyzing and identifying the binding residues of metal ions based solely on sequence information. Ten metal ions were extracted from the BioLip database: Zn2+, Cu2+, Fe2+, Fe3+, Ca2+, Mg2+, Mn2+, Na+, K+ and Co2+.

Protein ligand-specific binding residue predictions by an ensemble classifier.

Background: Prediction of ligand binding sites is important to elucidate protein functions and is helpful for drug design. Although much progress has been made, many challenges still need to be addressed. Prediction methods need to be carefully developed to account for chemical and structural differences between ligands.

Recognizing metal and acid radical ion-binding sites by integrating ab initio modeling with template-based transferals.

Motivation: More than half of proteins require binding of metal and acid radical ions for their structure and function. Identification of the ion-binding locations is important for understanding the biological functions of proteins. Due to the small size and high versatility of the metal and acid radical ions, however, computational prediction of their binding sites remains difficult.

The recognition of multi-class protein folds by adding average chemical shifts of secondary structure elements.

The recognition of protein folds is an important step in the prediction of protein structure and function. Recently, an increasing number of researchers have sought to improve the methods for protein fold recognition. Following the construction of a dataset consisting of 27 protein fold classes by Ding and Dubchak in 2001, prediction algorithms, parameters and the construction of new datasets have improved for the prediction of protein folds.

Prediction of complex super-secondary structure βαβ motifs based on combined features.

Prediction of a complex super-secondary structure is a key step in the study of tertiary structures of proteins. The strand-loop-helix-loop-strand (βαβ) motif is an important complex super-secondary structure in proteins. Many functional sites and active sites often occur in polypeptides of βαβ motifs.

Recognition of 27-class protein folds by adding the interaction of segments and motif information.

The recognition of protein folds is an important step for the prediction of protein structure and function. After the recognition of 27-class protein folds in 2001 by Ding and Dubchak, prediction algorithms, prediction parameters, and new datasets for the prediction of protein folds have been improved. However, the influences of interactions from predicted secondary structure segments and motif information on protein folding have not been considered.

Predicting enzyme subclasses by using random forest with multicharacteristic parameters.

In order to predict enzyme subclasses, this paper builds a new enzyme database in term of previous ideas and methods. Based on protein sequence, by selecting increment of diversity value, low-frequency of power spectral density, matrix scoring values and motif frequency as characteristic parameters to describe the sequence information, a Random Forest algorithm for predicting enzyme subclass is proposed. Using the Jack-knife test, the overall success rate identifying the 18 subclasses of oxidoreductases, the 8 subclasses of transferases, the 5 subclasses of hydrolases, the 6 subclasses of lyases, the 6 subclasses of isomerases, and the 6 subclasses of ligases are 90.