CPI-Pred: A deep learning framework for predicting functional parameters of compound-protein interactions.

Recent advancements in deep learning have enabled functional annotation of genome sequences, facilitating the discovery of new enzymes and metabolites. However, accurately predicting compound-protein interactions (CPI) from sequences remains challenging due to the complexity of these interactions and the sparsity and heterogeneity of available data, which constrain the generalization of patterns across their solution space. In this work, we introduce CPI-Pred, a versatile deep learning model designed to predict compound-protein interaction function.

Tryptophan metabolism-related gene CYP1B1 serves as a shared biomarker for both Parkinson's disease and insomnia.

Parkinson's disease (PD) and insomnia are prevalent neurological disorders, with emerging evidence implicating tryptophan (TRP) metabolism in their pathogenesis. However, the precise mechanisms by which TRP metabolism contributes to these conditions remain insufficiently elucidated. This study explores shared tryptophan metabolism-related genes (TMRGs) and molecular mechanisms underlying PD and insomnia, aiming to provide insights into their shared pathogenesis.

PRCP is a promising drug target for intracranial aneurysm rupture supported via multi-omics analysis.

Background: Cerebral aneurysms are life-threatening cerebrovascular disorders. Currently, there are no effective treatments for preventing disease progression. Mendelian randomisation (MR) is widely used to repurify licensed drugs and identify new therapeutic targets.

Novel inflammatory and insulin resistance indices provide a clue in cerebral amyloid angiopathy.

This study investigated the correlation of newly identified inflammatory and insulin resistance indices with cerebral amyloid angiopathy (CAA), and explored their potential to differentiate CAA from hypertensive arteriopathy (HA). We retrospectively analyzed 514 consecutive patients with cerebral small vessel disease (CSVD)-related haemorrhage, comparing the differences in novel inflammatory and insulin resistance indices between patients with CAA and HA. Univariate regression, LASSO and multivariate regression were used to screen variables and construct a classification diagnosis nomogram.

Causal Effects of Blood Metabolites and Obstructive Sleep Apnea: A Mendelian Randomization Study.

Background: Obstructive sleep apnea (OSA) is one of the most common forms of sleep-disordered breathing. Studies have shown that certain changes in metabolism play an important role in the pathophysiology of OSA. However, the causal relationship between these metabolites and OSA remains unclear.

Rhodium-Catalyzed Intramolecular Nitrogen Atom Insertion into Arene Rings.

In this study, we describe the direct insertion of an intramolecular nitrogen atom into an aromatic C-C bond. In this transformation, carbamoyl azides are activated by a Rh catalyst and subsequently directly inserted into the C-C bond of an arene ring to access fused azepine products. This transformation is challenging, owing to the existence of a competitive C-H amination pathway.

Correlation between neutrophil gelatinase phase lipocalin and cerebral small vessel disease.

Background: Cerebral small vessel disease (CSVD) is common in the elderly population. Neutrophil gelatinase-associated lipocalin (NGAL) is closely related to cardiovascular and cerebrovascular diseases. NGAL causes pathological changes, such as damage to the vascular endothelium, by causing inflammation, which results in other related diseases.

Smoking, Coffee Consumption, Alcohol Intake, and Obstructive Sleep Apnea: A Mendelian Randomization Study.

Background: Previous studies revealed that obstructive sleep apnea (OSA) and smoking, alcohol consumption, and coffee intake are closely related. This study aimed to evaluate the causal effect between these factors and OSA.

Methods: The published genome-wide association study data (GWAS) provided genetic tools.

Association of Blood Lipid Profile Components with White Matter Hyperintensity Burden in Cerebral Small Vessel Disease.

Background: Cerebral small vessel disease (CSVD) refers to a common cerebrovascular disease and white matter hyperintensities (WMHs) constitute a typical feature of CSVD. However, there has not been a large number of studies investigating the relationship between lipid profile components and WMHs.

Methods: Altogether, 1019 patients with CSVD were enrolled at the First Affiliated Hospital of Zhengzhou University between April 2016 to December 2021.

Mechanism and Practice of Multifracturing Using Dynamic Loads in a Low-Permeability Coal Reservoir.

The geological conditions of coal reservoirs in China are complex, and the reservoir permeability is generally lower. Multifracturing is an effective method of improving reservoir permeability and coalbed methane (CBM) production. In this study, two types of dynamic loads, CO blasting and a pulse fracturing gun (PF-GUN), were used to conduct multifracturing engineering tests in nine surface CBM wells in the Lu'an mining area in the central and eastern parts of the Qinshui Basin.

A Multicomponent Nucleic Acid Enzyme-Cleavable Quantum Dot Nanobeacon for Highly Sensitive Diagnosis of Tuberculosis with the Naked Eye.

Clinical tuberculosis (TB) screening and diagnosis are crucial for controlling the spread of this life-threatening infectious disease. In this work, a novel, rapid, and simple colorimetric detection platform for TB was developed based on a quantum dot-based nanobeacon (QD-NB) and multicomponent nucleic acid enzyme (MNAzyme). In the presence of target DNA (IS1081 gene fragment), the recombinase polymerase amplification (RPA) was performed and the amplicons were chemically DNA-denatured and then subjected to MNAzyme reaction.

Enzyme Activity Prediction of Sequence Variants on Novel Substrates using Improved Substrate Encodings and Convolutional Pooling.

Protein engineering is currently being revolutionized by deep learning applications, especially through natural language processing (NLP) techniques. It has been shown that state-of-the-art self-supervised language models trained on entire protein databases capture hidden contextual and structural information in amino acid sequences and are capable of improving sequence-to-function predictions. Yet, recent studies have reported that current compound-protein modeling approaches perform poorly on learning interactions between enzymes and substrates of interest within one protein family.

Ultra-Broadband, Omnidirectional, High-Efficiency Metamaterial Absorber for Capturing Solar Energy.

In this study, we investigated an absorber based on a center-aligned tandem nanopillar array for ultra-broadband solar energy harvesting theoretically. A high-efficiency, omnidirectional absorber was obtained by introducing the center-aligned tandem nanopillar array embedded in an AlO dielectric layer. The multi-coupling modes at different wavelengths were interpreted.

Rapid syntheses of N-fused heterocycles via acyl-transfer in heteroaryl ketones.

The wide-ranging potencies of bioactive N-fused heterocycles inspire the development of synthetic transformations that simplify preparation of their complex, diverse structural motifs. Heteroaryl ketones are ubiquitous, readily available, and inexpensive molecular scaffolds, and are thus synthetically appealing as precursors in preparing N-fused heterocycles via intramolecular acyl-transfer. To best of our knowledge, acyl-transfer of unstrained heteroaryl ketones remains to be demonstrated.

Coulometric back titration based on all-solid-state electrodes for phenylephrine hydrochloride determination.

Existing coulometric titration generally used liquid reference electrodes, with limitations of frequent maintenance and liquid leakage risk and requiring vertical working position. Herein, we proposed an all-solid coulometric titration based on the glassy carbon electrode (GCE) and preliminarily utilized it in phenylephrine hydrochloride (PHE) analysis with a universal back titration program. Subsequently, the long-term stability of the GCE in bromine and iodine solution was evaluated comprehensively by electrochemical characterization and titration measurement.

Ultra-Broadband, Polarization-Irrelevant Near-Perfect Absorber Based on Composite Structure.

This paper proposes a near-perfect absorption device based on a cross-shaped titanium nanostructure and a multilayered structure. The multilayered bottom structure consists of alternately SiO and Ti. The whole device is put on a TiN substrate.

Determination of tuberculosis-related volatile organic biomarker methyl nicotinate in vapor using fluorescent assay based on quantum dots and cobalt-containing porphyrin nanosheets.

Methyl nicotinate (MN) is a representative and typical volatile organic marker of Mycobacterium tuberculosis, and the specific detection of MN in human breath facilitates non-invasive, rapid, and accurate epidemic screening of tuberculosis infection. Herein, we constructed a fluorescent assay consisted of CdTe quantum dots (QD) and cobalt-metalized tetrakis(4-carboxyphenyl) porphyrin (CoTCPP) nanosheets to determine methyl nicotinate (MN) in vapor samples. Red-emission QD (λ=370 nm, λ=658 nm) acts as signal switches whose fluorescence signals can be effectively quenched by CoTCPP nanosheets but restored in the presence of MN.

FOXO4 Inhibits the Migration and Metastasis of Colorectal Cancer by Regulating the /β-Catenin Axis.

Adenomatous polyposis coli 2 () is a colorectal cancer (CRC) tumor-suppressor gene. The progression of several kinds of cancer is closely associated with Forkhead box O4 (FOXO4). However, the function of FOXO4 in CRC is unclear.

Machine learning enabled self-calibration single fiber endoscopic imaging.

Single fiber scanners (SFSs), with the advantages of compact size, versatility, large field of view, and high resolution, have been applied in many areas. However, image distortions persistently impair the imaging quality of the SFS, although many efforts have been made to address the problem. In this Letter, we propose a simple and complete solution by combining the piezoelectric (PZT) self-induction sensor and machine learning algorithms.