Investigation of high-dose radiotherapy's effect on brain structure aggravated cognitive impairment and deteriorated patient psychological status in brain tumor treatment.

This study aims to investigate the potential impact of high-dose radiotherapy (RT) on brain structure, cognitive impairment, and the psychological status of patients undergoing brain tumor treatment. We recruited and grouped 144 RT-treated patients with brain tumors into the Low dose group (N = 72) and the High dose group (N = 72) according to the RT dose applied. Patient data were collected by using the HADS and QLQ-BN20 system for subsequent analysis and comparison. Our analysis showed no significant correlation between the RT doses and the clinicopathological characteristics. We found that a high dose of RT could aggravate cognitive impairment and deteriorate patient role functioning, indicated by a higher MMSE and worsened role functioning in the High dose group. However, the depression status, social functioning, and global health status were comparable between the High dose group and the Low dose group at Month 0 and Month 1, while being worsened in the High dose group at Month 3, indicating the potential long-term deterioration of depression status in brain tumor patients induced by high-dose RT. By comparing patient data at Month 0, Month 1, Month 3, Month 6, and Month 9 after RT, we found that during RT treatment, RT at a high dose could aggravate cognitive impairment in the short term and lead to worsened patient role functioning, and even deteriorate the overall psychological health status of patients in the long term.

OPUS-Rota5: A highly accurate protein side-chain modeling method with 3D-Unet and RotaFormer.

Accurate protein side-chain modeling is crucial for protein folding and design. This is particularly true for molecular docking as ligands primarily interact with side chains. In this study, we introduce a two-stage side-chain modeling approach called OPUS-Rota5. It leverages a modified 3D-Unet to capture the local environmental features, including ligand information of each residue, and then employs the RotaFormer module to aggregate various types of features. Evaluation on three test sets, including recently released targets from CAMEO and CASP15, shows that OPUS-Rota5 significantly outperforms some other leading side-chain modeling methods. We also employ OPUS-Rota5 to refine the side chains of 25 G protein-coupled receptor targets predicted by AlphaFold2 and achieve a significantly improved success rate in a subsequent "back" docking of their natural ligands. Therefore, OPUS-Rota5 is a useful and effective tool for molecular docking, particularly for targets with relatively accurate predicted backbones but not side chains such as high-homology targets.

OPUS-Fold3: a gradient-based protein all-atom folding and docking framework on TensorFlow.

For refining and designing protein structures, it is essential to have an efficient protein folding and docking framework that generates a protein 3D structure based on given constraints. In this study, we introduce OPUS-Fold3 as a gradient-based, all-atom protein folding and docking framework, which accurately generates 3D protein structures in compliance with specified constraints, such as a potential function as long as it can be expressed as a function of positions of heavy atoms. Our tests show that, for example, OPUS-Fold3 achieves performance comparable to pyRosetta in backbone folding and significantly better in side-chain modeling. Developed using Python and TensorFlow 2.4, OPUS-Fold3 is user-friendly for any source-code level modifications and can be seamlessly combined with other deep learning models, thus facilitating collaboration between the biology and AI communities. The source code of OPUS-Fold3 can be downloaded from http://github.com/OPUS-MaLab/opus_fold3. It is freely available for academic usage.

OPUS-DSD: deep structural disentanglement for cryo-EM single-particle analysis.

Cryo-electron microscopy (cryo-EM) captures snapshots of dynamic macromolecules, collectively illustrating the involved structural landscapes. This provides an exciting opportunity to explore the structural variations of macromolecules under study. However, traditional cryo-EM single-particle analysis often yields static structures. Here we describe OPUS-DSD, an algorithm capable of efficiently reconstructing the structural landscape embedded in cryo-EM data. OPUS-DSD uses a three-dimensional convolutional encoder-decoder architecture trained with cryo-EM images, thereby encoding structural variations into a smooth and easily analyzable low-dimension space. This space can be traversed to reconstruct continuous dynamics or clustered to identify distinct conformations. OPUS-DSD can offer meaningful insights into the structural variations of macromolecules, filling in the gaps left by traditional cryo-EM structural determination, and potentially improves the reconstruction resolution by reliably clustering similar particles within the dataset. These functionalities are especially relevant to the study of highly dynamic biological systems. OPUS-DSD is available at https://github.com/alncat/opusDSD .

Fault Diagnosis for Rolling Bearing of Combine Harvester Based on Composite-Scale-Variable Dispersion Entropy and Self-Optimization Variational Mode Decomposition Algorithm.

Because of the influence of harsh and variable working environments, the vibration signals of rolling bearings for combine harvesters usually show obvious characteristics of strong non-stationarity and nonlinearity. Accomplishing accurate fault diagnosis using these signals for rolling bearings is a challenging subject. In this paper, a novel fault diagnosis method based on composite-scale-variable dispersion entropy (CSvDE) and self-optimization variational mode decomposition (SoVMD) is proposed, systematically combining the nonstationary signal analysis approach and machine learning technology. Firstly, an improved SoVMD algorithm is developed to realize adaptive parameter optimization and to further extract multiscale frequency components from original signals. Subsequently, a CSvDE-based feature learning model is established to generate the multiscale fault feature space (MsFFS) of frequency components for the improvement of fault feature learning ability. Finally, the generated MsFFS can serve as the inputs of the Softmax classifier for fault category identification. Extensive experiments on the vibration datasets collected from rolling bearings of combine harvesters are conducted, and the experimental results demonstrate the more superior and robust fault diagnosis performance of the proposed method compared to other existing approaches.

Haplotype-GGGT in long non-coding RNA MALAT1 inhibits brain metastatic lung cancer and lymph nodes of lung cancer via the MALAT1/miR-328/KATNB1.

The up-regulation of katanin P80 has been reported to be correlated with a larger tumor size and lymph node metastasis in non-small-cell lung cancer (NSCLC) patients. And lncRNA MALAT1 was demonstrated to promote the proliferation of chronic myeloid leukemia cells via modulating miR-328. 135 lung cancer patients were divided into 6 groups according to their genotypes of MALAT1. The expression of KATNB1 was negatively correlated with the GGGT genotype of MALAT1. Decreased lymph node size and tumor size of brain metastatic lung were observed in patients with GGGT genotype of MALAT1. The luciferase activities of MALAT1 and KATNB1 were remarkably suppressed by miR-328 in A549 and H460. And the down-regulation of MALAT1 or up-regulation of miR-328 significantly repressed the KATNB1 expression in A549 and H460 cells. MALAT1 expression was reduced in patients carrying haplotype GGGT. A signaling pathway of MALAT1/miR-328/KATNB1 was established to explain the down-regulation of KATNB1 mRNA in patients carrying haplotype GGGT and reduced lymph node size in lung cancer and tumor size in brain metastatic lung cancer.

OPUS-Mut: Studying the Effect of Protein Mutation through Side-Chain Modeling.

Predicting the effect of protein mutation is crucial in many applications such as protein design, protein evolution, and genetic disease analysis. Structurally, mutation is basically the replacement of the side chain of a particular residue. Therefore, accurate side-chain modeling is useful in studying the effect of mutation. Here, we propose a computational method, namely, OPUS-Mut, which significantly outperforms other backbone-dependent side-chain modeling methods including our previous method OPUS-Rota4. We evaluate OPUS-Mut by four case studies on Myoglobin, p53, HIV-1 protease, and T4 lysozyme. The results show that the predicted structures of side chains of different mutants are consistent well with their experimentally determined results. In addition, when the residues with significant structural shifts upon the mutation are considered, it is found that the extent of the predicted structural shift of these affected residues can be correlated reasonably well with the functional changes of the mutant measured by experiments. OPUS-Mut can also help one to identify the harmful and benign mutations and thus may guide the construction of a protein with relatively low sequence homology but with a similar structure.

Advances for pharmacological activities of Polygonum cuspidatum - A review.

CONTEXT: Polygonum cuspidatum Sieb. et Zucc (Polygonaceae), the root of which is included in the Chinese Pharmcopoeia under the name 'Huzhang', has a long history as a medicinal plant and vegetable. Polygonum cuspidatum has been used in traditional Chinese medicine for the treatment of inflammation, hyperlipemia, etc. OBJECTIVE: This article reviews the pharmacological action and the clinical applications of Polygonum cuspidatum and its extracts, whether in vivo or in vitro. We also summarized the main phytochemical constituents and pharmacokinetics of Polygonum cuspidatum and its extracts. METHODS: The data were retrieved from major medical databases, such as CNKI, PubMed, and SinoMed, from 2014 to 2022. Polygonum cuspidatum, pharmacology, toxicity, clinical application, and pharmacokinetics were used as keywords. RESULTS: The rhizomes, leaves, and flowers of Polygonum cuspidatum have different phytochemical constituents. The plant contains flavonoids, anthraquinones, and stilbenes. Polygonum cuspidatum and the extracts have anti-inflammatory, antioxidation, anticancer, heart protection, and other pharmacological effects. It is used in the clinics to treat dizziness, headaches, traumatic injuries, and water and fire burns. CONCLUSIONS: Polygonum cuspidatum has the potential to treat many diseases, such as arthritis, ulcerative colitis, asthma, and cardiac hypertrophy. It has a broad range of medicinal applications, but mainly focused on root medication; its aerial parts should receive more attention. Pharmacokinetics also need to be further investigated.

Immobilized nanoscale zero-valent iron for synergistic enhanced removal of pentachlorobenzene with Pseudomonas sp. JS100.

Highly chlorinated benzenes usually have a low efficient degradation in environment. Here we proposed a synergistic removal strategy of pentachlorobenzene (PeCB) using Pseudomonas sp. JS100 coupled with immobilized nanoscale zero-valent iron (NZVI). The structural and textural features of the synergistic system were characterized by X-ray powder diffraction, field emission scanning electron microscopy, and a specific surface area and pore size analysis. Nanoscale zero-valent iron particles were dispersed and attached to the biofilter, which increased the specific surface area to 34.5 m2 g-1. The batch experiment revealed that the removal efficiency of PeCB reached 80.2% in the synergistic system within 48 h. The degradation followed pseudo-first-order reaction kinetics, and the reaction rate constant was measured to be 0.0336 h-1. In the degradation mechanism, PeCB was degraded by NZVI to lower chlorobenzenes, which were utilized by Pseudomonas sp. JS100 as nutrients, thereby achieving rapid removal of PeCB.

Studying protein-protein interaction through side-chain modeling method OPUS-Mut.

Protein side chains are vitally important to many biological processes such as protein-protein interaction. In this study, we evaluate the performance of our previous released side-chain modeling method OPUS-Mut, together with some other methods, on three oligomer datasets, CASP14 (11), CAMEO-Homo (65) and CAMEO-Hetero (21). The results show that OPUS-Mut outperforms other methods measured by all residues or by the interfacial residues. We also demonstrate our method on evaluating protein-protein docking pose on a dataset Oligomer-Dock (75) created using the top 10 predictions from ZDOCK 3.0.2. Our scoring function correctly identifies the native pose as the top-1 in 45 out of 75 targets. Different from traditional scoring functions, our method is based on the overall side-chain packing favorableness in accordance with the local packing environment. It emphasizes the significance of side chains and provides a new and effective scoring term for studying protein-protein interaction.

Adaptive Stochastic Resonance-Based Processing of Weak Magnetic Slippage Signals of Bearings.

Slip is one of the most common forms of failure in aviation bearings, and it can pose a great threat to the stable operation of aviation bearings. Bearing cage speed monitoring methods based on weak magnetic detection can achieve nondestructive measurements. However, the method suffers from solid signal background noise due to the high sensitivity of the sensor. Therefore, in this paper, an adaptive stochastic resonance algorithm was proposed in response to the characteristics of the weak magnetic detection signal and the problem of solid noise. In addition, by adaptively adjusting the coefficients of the stochastic resonance system-by an improved moth flame optimization algorithm-the drawback in which the stochastic resonance method required artificially set parameters for extracting the feature frequencies of the weak magnetic signals was solved. In this process, we used parameters, such as general refined composite multi-scale sample entropy, as the adaptation function of the optimization algorithm. In the end, simulation and experimental outcomes verified the efficacy of the approach put forward.

High On-Treatment Platelet Reactivity as Predictor of Long-term Clinical Outcomes in Stroke Patients with Antiplatelet Agents.

The purpose was to explore the value of high on-treatment platelet reactivity (HTPR) in predicting long-term clinical outcomes for stroke patients. The platelet reactivity was assayed after being treated with either 75 mg clopidogrel or 100 mg aspirin daily with VerifyNow System in stroke patients. HTPR for clopidogrel was defined as PRU ≥ 208, and that for aspirin was defined as ARU ≥ 550. CYP2C19 genotyping was performed using the Sequenom MassARRAY iPLEX platform. The primary endpoint was a composite of recurrent ischemic stroke, transient ischemic attack, myocardial infarction, or ischemic vascular death. The safety endpoint was bleeding. In the clopidogrel group, among 345 patients recruited, 174 of them were categorized as HTPR. A total of 270 patients were followed up for 54 months. There was a significant association between HTPR and the primary endpoint (HRadj 2.13 [95% CI, 1.43-3.15], p < 0.001). Among the 314 participants genotyped for CYP2C19, 187 (59.6%) were classified as CYP2C19 loss-of-function allele carriers. Patients with at least 1 loss-of-function allele were more likely to present with HTPR (ORadj 2.61 [95%CI, 1.43-4.77], p = 0.008), and had a higher risk of the primary endpoint (HRadj 2.05 [95% CI, 1.30, 3.25], p = 0.002). In the aspirin group, among 140 patients recruited, 28 of them were categorized as HTPR. A total of 121 patients were followed up for 30 months. Similarly, there was a significant association between HTPR and the primary endpoint (HRadj 3.28 [95% CI, 1.52-7.71], p = 0.002). HTPR is an independent risk factor for ischemic events during long-term follow-up in stroke patients. Platelet function testing is helpful to evaluate the effect of antiplatelet therapy for stroke patients.

Experimental study of mulching effects on water restoration of deep, desiccated soil in a loess hilly region.

Drought climate and human activity have led to serious soil desiccation in the loess hilly region. Mulching measures can effectively increase rainfall infiltration and reduce evaporation, thus improving soil moisture status. The objective of this study was to explore the characteristics of water restoration of deep desiccated soil under different mulching measures and determine a promising mulching measure. We built large-scale simulation desiccated soil columns in the field (soil columns with an 0.8-m diameter and 10-m depth). Six kinds of treatments were tested: film mulching, gravel mulching, branch mulching, planting of jujube tree (Ziziphus jujuba Mill.) and robinia (Robinia pseudoacacia L.), and bare land as the control. The results showed that different mulching measures can increase soil water storage and alleviate the soil drying phenomenon to different degrees. Compared with other treatments, film mulching is more effective, with larger increment of soil water storage, deeper restoration of soil dry layer, and simple to operate in loess hilly region. So, we recommend film mulching, which is a feasible method to improve the soil desiccation status in hilly loess areas.

OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors.

Accurate protein side-chain modeling is crucial for protein folding and protein design. In the past decades, many successful methods have been proposed to address this issue. However, most of them depend on the discrete samples from the rotamer library, which may have limitations on their accuracies and usages. In this study, we report an open-source toolkit for protein side-chain modeling, named OPUS-Rota4. It consists of three modules: OPUS-RotaNN2, which predicts protein side-chain dihedral angles; OPUS-RotaCM, which measures the distance and orientation information between the side chain of different residue pairs and OPUS-Fold2, which applies the constraints derived from the first two modules to guide side-chain modeling. OPUS-Rota4 adopts the dihedral angles predicted by OPUS-RotaNN2 as its initial states, and uses OPUS-Fold2 to refine the side-chain conformation with the side-chain contact map constraints derived from OPUS-RotaCM. Therefore, we convert the side-chain modeling problem into a side-chain contact map prediction problem. OPUS-Fold2 is written in Python and TensorFlow2.4, which is user-friendly to include other differentiable energy terms. OPUS-Rota4 also provides a platform in which the side-chain conformation can be dynamically adjusted under the influence of other processes. We apply OPUS-Rota4 on 15 FM predictions submitted by AlphaFold2 on CASP14, the results show that the side chains modeled by OPUS-Rota4 are closer to their native counterparts than those predicted by AlphaFold2 (e.g. the residue-wise RMSD for all residues and core residues are 0.588 and 0.472 for AlphaFold2, and 0.535 and 0.407 for OPUS-Rota4).

Controlled Assembly of Conjugated Ladder Molecules with Different Bridging Structures toward Optoelectronic Application.

Structural design of organic π-conjugated small molecules allows the energy band structure and electronic properties of the molecules to be tuned as needed, which provides a feasible strategy for enhancing the performance of optoelectronic devices. The introduction of bridging structures is a common structural modification method to adjust the rigidity and coplanarity of the molecular backbone, thus affecting the molecular packing. However, patterning of organic single-crystalline microstructures based on conjugated ladder molecules with different bridging structures still remains challenging for large-area integration of optoelectronic devices. In this paper, a controlled dewetting process is applied to obtain organic single-crystalline arrays with precise positioning and a regular morphology based on two isomers with silicon-oxygen bridging and their two carbon-oxygen-bridged analogues. Molecules with different bridging structures show disparate packing models due to the difference of dihedral angles and ring tensions. A microwire-array ultraviolet photodetector based on the oxygen-silicon-bridging ladder molecule exhibits a high light on/off ratio of 24 and a responsivity of 0.63 mA W-1 owing to the effective π-π stacking governed by the molecular planarity. This work not only provides a universal method for the integration of organic optoelectronic devices but also explains the effect of bridging structure engineering on molecular assembly and optoelectronic performance.

OPUS-X: an open-source toolkit for protein torsion angles, secondary structure, solvent accessibility, contact map predictions and 3D folding.

MOTIVATION: The development of an open-source platform to predict protein 1D features and 3D structure is an important task. In this paper, we report an open-source toolkit for protein 3D structure modeling, named OPUS-X. It contains three modules: OPUS-TASS2, which predicts protein torsion angles, secondary structure and solvent accessibility; OPUS-Contact, which measures the distance and orientation information between different residue pairs; and OPUS-Fold2, which uses the constraints derived from the first two modules to guide folding. RESULTS: OPUS-TASS2 is an upgraded version of our previous method OPUS-TASS. OPUS-TASS2 integrates protein global structure information and significantly outperforms OPUS-TASS. OPUS-Contact combines multiple raw co-evolutionary features with protein 1D features predicted by OPUS-TASS2, and delivers better results than the open-source state-of-the-art method trRosetta. OPUS-Fold2 is a complementary version of our previous method OPUS-Fold. OPUS-Fold2 is a gradient-based protein folding framework based on the differentiable energy terms in opposed to OPUS-Fold that is a sampling-based method used to deal with the non-differentiable terms. OPUS-Fold2 exhibits comparable performance to the Rosetta folding protocol in trRosetta when using identical inputs. OPUS-Fold2 is written in Python and TensorFlow2.4, which is user-friendly to any source-code-level modification. AVAILABILITYAND IMPLEMENTATION: The code and pre-trained models of OPUS-X can be downloaded from https://github.com/OPUS-MaLab/opus_x. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Synergistic Effect of Ginkgo biloba Extract 50 and Aspirin Against Platelet Aggregation.

PURPOSE: We aimed to investigate potential synergistic antiplatelet effects of Ginkgo biloba extract (GBE50) in combination with aspirin using in vitro models. METHODS: Arachidonic acid (AA), platelet activating factor (PAF), adenosine 5'-diphosphate (ADP) and collagen were used as inducers. The antiplatelet effects of GBE50, aspirin and 1:1 combination of GBE50 and aspirin were detected by microplate method using rabbit platelets. Synergy finder 2.0 was used to analyze the synergistic antiplatelet effect. The compounds in GBE50 were identified by UPLC-Q/TOF-MS analysis and the candidate compounds were screened by TCMSP database. The targets of candidate compounds and aspirin were obtained in TCMSP, CCGs, Swiss target prediction database and drugbank. Targets involving platelet aggregation were obtained from GenCLiP database. Compound-target network was constructed and GO and KEGG enrichment analyses were performed to identify the critical biological processes and signaling pathways. The levels of thromboxane B2 (TXB2), cyclic adenosine monophosphate (cAMP) and PAF receptor (PAFR) were detected by ELISA to determine the effects of GBE50, aspirin and their combination on these pathways. RESULTS: GBE50 combined with aspirin inhibited platelet aggregation more effectively. The combination displayed synergistic antiplatelet effects in AA-induced platelet aggregation, and additive antiplatelet effects occurred in PAF, ADP and collagen induced platelet aggregation. Seven compounds were identified as candidate compounds in GBE50. Enrichment analyses revealed that GBE50 could interfere with platelet aggregation via cAMP pathway, AA metabolism and calcium signaling pathway, and aspirin could regulate platelet aggregation through AA metabolism and platelet activation. ELISA experiments showed that GBE50 combined with aspirin could increase cAMP levels in resting platelets, and decreased the levels of TXB2 and PAFR. CONCLUSION: Our study indicated that GBE50 combined with aspirin could enhance the antiplatelet effects. They exerted both synergistic and additive effects in restraining platelet aggregation. The study highlighted the potential application of GBE50 as a supplementary therapy to treat thrombosis-related diseases.