Acylated Inulin as a Potential Shale Hydration Inhibitor in Water Based Drilling Fluids for Wellbore Stabilization.

Shale hydration dispersion and swelling are primary causes of wellbore instability in oil and gas reservoir exploration. In this study, inulin, a fructo-oligosaccharide extracted from Jerusalem artichoke roots, was modified by acylation with three acyl chlorides, and the products (C10-, C12-, and C14-inulin) were investigated for their use as novel shale hydration inhibitors. The inhibition properties were evaluated through the shale cuttings hot-rolling dispersion test, the sodium-based bentonite hydration test, and capillary suction. The three acylated inulins exhibited superb hydration-inhibiting performance at low concentrations, compared to the commonly used inhibitors of KCl and poly (ester amine). An inhibition mechanism was proposed based on surface tension measurements, contact angle measurements, Fourier-transform infrared analysis, and scanning electron microscopy. The acylated inulin reduced the water surface tension significantly, thus, retarding the invasion of water into the shale formation. Then, the acylated inulin was adsorbed onto the shale surface by hydrogen bonding to form a compact, sealed, hydrophobic membrane. Furthermore, the acylated inulins are non-toxic and biodegradable, which meet the increasingly stringent environmental regulations in this field. This method might provide a new avenue for developing high-performance and ecofriendly shale hydration inhibitors.

Assessing the performance of MM/PBSA and MM/GBSA methods. 10. Prediction reliability of binding affinities and binding poses for RNA-ligand complexes.

Ribonucleic acid (RNA)-ligand interactions play a pivotal role in a wide spectrum of biological processes, ranging from protein biosynthesis to cellular reproduction. This recognition has prompted the broader acceptance of RNA as a viable candidate for drug targets. Delving into the atomic-scale understanding of RNA-ligand interactions holds paramount importance in unraveling intricate molecular mechanisms and further contributing to RNA-based drug discovery. Computational approaches, particularly molecular docking, offer an efficient way of predicting the interactions between RNA and small molecules. However, the accuracy and reliability of these predictions heavily depend on the performance of scoring functions (SFs). In contrast to the majority of SFs used in RNA-ligand docking, the end-point binding free energy calculation methods, such as molecular mechanics/generalized Born surface area (MM/GBSA) and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA), stand as theoretically more rigorous approaches. Yet, the evaluation of their effectiveness in predicting both binding affinities and binding poses within RNA-ligand systems remains unexplored. This study first reported the performance of MM/PBSA and MM/GBSA with diverse solvation models, interior dielectric constants (εin) and force fields in the context of binding affinity prediction for 29 RNA-ligand complexes. MM/GBSA is based on short (5 ns) molecular dynamics (MD) simulations in an explicit solvent with the YIL force field; the GBGBn2 model with higher interior dielectric constant (εin = 12, 16 or 20) yields the best correlation (Rp = -0.513), which outperforms the best correlation (Rp = -0.317, rDock) offered by various docking programs. Then, the efficacy of MM/GBSA in identifying the near-native binding poses from the decoys was assessed based on 56 RNA-ligand complexes. However, it is evident that MM/GBSA has limitations in accurately predicting binding poses for RNA-ligand systems, particularly compared with notably proficient docking programs like rDock and PLANTS. The best top-1 success rate achieved by MM/GBSA rescoring is 39.3%, which falls below the best results given by docking programs (50%, PLNATS). This study represents the first evaluation of MM/PBSA and MM/GBSA for RNA-ligand systems and is expected to provide valuable insights into their successful application to RNA targets.

Drug resistance revealed by in silico deep mutational scanning and mutation tracker.

As COVID-19 enters its fifth year, it continues to pose a significant global health threat, with the constantly mutating SARS-CoV-2 virus challenging drug effectiveness. A comprehensive understanding of virus-drug interactions is essential for predicting and improving drug effectiveness, especially in combating drug resistance during the pandemic. In response, the Path Laplacian Transformer-based Prospective Analysis Framework (PLFormer-PAF) has been proposed, integrating historical data analysis and predictive modeling strategies. This dual-strategy approach utilizes path topology to transform protein-ligand complexes into topological sequences, enabling the use of advanced large language models for analyzing protein-ligand interactions and enhancing its reliability with factual insights garnered from historical data. It has shown unparalleled performance in predicting binding affinity tasks across various benchmarks, including specific evaluations related to SARS-CoV-2, and assesses the impact of virus mutations on drug efficacy, offering crucial insights into potential drug resistance. The predictions align with observed mutation patterns in SARS-CoV-2, indicating that the widespread use of the Pfizer drug has lead to viral evolution and reduced drug efficacy. PLFormer-PAF's capabilities extend beyond identifying drug-resistant strains, positioning it as a key tool in drug discovery research and the development of new therapeutic strategies against fast-mutating viruses like COVID-19.

Multiscale topology in interactomic network: from transcriptome to antiaddiction drug repurposing.

The escalating drug addiction crisis in the United States underscores the urgent need for innovative therapeutic strategies. This study embarked on an innovative and rigorous strategy to unearth potential drug repurposing candidates for opioid and cocaine addiction treatment, bridging the gap between transcriptomic data analysis and drug discovery. We initiated our approach by conducting differential gene expression analysis on addiction-related transcriptomic data to identify key genes. We propose a novel topological differentiation to identify key genes from a protein-protein interaction network derived from DEGs. This method utilizes persistent Laplacians to accurately single out pivotal nodes within the network, conducting this analysis in a multiscale manner to ensure high reliability. Through rigorous literature validation, pathway analysis and data-availability scrutiny, we identified three pivotal molecular targets, mTOR, mGluR5 and NMDAR, for drug repurposing from DrugBank. We crafted machine learning models employing two natural language processing (NLP)-based embeddings and a traditional 2D fingerprint, which demonstrated robust predictive ability in gauging binding affinities of DrugBank compounds to selected targets. Furthermore, we elucidated the interactions of promising drugs with the targets and evaluated their drug-likeness. This study delineates a multi-faceted and comprehensive analytical framework, amalgamating bioinformatics, topological data analysis and machine learning, for drug repurposing in addiction treatment, setting the stage for subsequent experimental validation. The versatility of the methods we developed allows for applications across a range of diseases and transcriptomic datasets.

Research on safety risk control of prepared foods from the perspective of supply chain.

Prepared foods bring great convenience to people's lives, but they also entail safety risks in all aspects, from production to sales. The cooperation of the supply chain and the supervision of the government are key to promoting the safety management of prepared foods. This paper considers the government's regulation, focuses on the interaction relationship between the producer and the retailer of prepared foods, and builds an evolutionary game model to analyze the influence of collaborative decision-making between prepared food producers and retailers in preventing and controlling food safety risks under the government's regulatory strategy. The research finds that: (1) Under certain conditions, there are three stable equilibrium strategies within the prepared foods supply chain: bilateral low-safety inputs, unilateral high-safety inputs, and bilateral high-safety inputs. (2) Government regulators can influence the safety input behaviors of prepared food supply chain enterprises by adjusting investigation probabilities and punishment severity. (3) The safety input behaviors of these enterprises are influenced by various factors, including costs, revenues, brand image, reputation, and the consequences associated with contractual violations. This paper represents the first systematic analysis of prepared food safety from a supply chain perspective. It fills a gap in the existing literature in this area, offering guidance and suggestions for prepared food supply chain enterprises, as well as references and recommendations for government regulators.

Integrated Transcriptomics and Metabolomics Analysis Promotes the Understanding of Adventitious Root Formation in Eucommia ulmoides Oliver.

As a primary approach to nutrient propagation for many woody plants, cutting roots is essential for the breeding and production of Eucommia ulmoides Oliver. In this study, hormone level, transcriptomics, and metabolomics analyses were performed on two E. ulmoides varieties with different adventitious root (AR) formation abilities. The higher JA level on the 0th day and the lower JA level on the 18th day promoted superior AR development. Several hub genes executed crucial roles in the crosstalk regulation of JA and other hormones, including F-box protein (EU012075), SAUR-like protein (EU0125382), LOB protein (EU0124232), AP2/ERF transcription factor (EU0128499), and CYP450 protein (EU0127354). Differentially expressed genes (DEGs) and metabolites of AR formation were enriched in phenylpropanoid biosynthesis, flavonoid biosynthesis, and isoflavonoid biosynthesis pathways. The up-regulated expression of PAL, CCR, CAD, DFR, and HIDH genes on the 18th day could contribute to AR formation. The 130 cis-acting lncRNAs had potential regulatory functions on hub genes in the module that significantly correlated with JA and DEGs in three metabolism pathways. These revealed key molecules, and vital pathways provided more comprehensive insight for the AR formation mechanism of E. ulmoides and other plants.

Mulberry leaf multi-components exert hypoglycemic effects through regulation of the PI-3K/Akt insulin signaling pathway in type 2 diabetic rats.

ETHNOPHARMACOLOGICAL RELEVANCE: Phytochemicals have unique advantages in the treatment of diabetes due to their multi-target activity and low toxicity. Mulberry leaves, a traditional Chinese herbal medicine, have been used in the prevention and treatment of diabetes for centuries. The main active ingredients in mulberry leaves with regards to the hypoglycemic effect are 1-deoxynojirimycin, flavonoids, and polysaccharides. However, the combined hypoglycemic effects and mechanisms of mulberry leaf multi-components remain unclear. AIM OF THE STUDY: This study explored the anti-diabetic effects of mulberry leaf multi-components (MMC) and the role of the PI-3K/Akt insulin signalling pathway in improving insulin resistance. MATERIALS AND METHODS: The main chemical components of MMC were analyzed using the phenol-sulfuric acid method, aluminum nitrate-sodium nitrite method, and HPLC-ultraviolet/fluorescence detection method. The T2DM rat model was created via feeding a high-fat diet and peritoneal injection of streptozotocin. T2DM rats were divided into four groups: model, model plus metformin, model plus low-dose, and model plus high-dose MMC groups (100 and 200 mg/kg body weight/day, respectively), and plus normal group for a total of five groups. MMC was administered by oral gavage for six weeks. Fasting blood glucose and serum lipid profiles were measured using a glucometer and an automatic biochemistry analyzer, respectively. Serum insulin and adipocytokine levels were analyzed by ELISA. Hepatic glucose metabolizing enzyme activity was evaluated by ELISA and the double antibody sandwich method. Expression of PI-3K/Akt signalling pathway proteins was analyzed by RT-PCR and Western blotting. RESULTS: Extracted 1-deoxynojirimycin, flavonoid, and polysaccharide purity was 70.40%, 52.34%, and 32.60%, respectively. These components were then mixed at a ratio of 1:6:8 to form MMC. MMC significantly reduced serum glucose, insulin, and lipid levels. In diabetic rats, MMC enhanced insulin sensitivity and alleviated inflammatory and oxidative damage by lowing adipocytokine levels and increasing anti-oxidative enzyme activity. Insulin resistance was also mitigated. MMC regulated the activity of key downstream enzymes of hepatic glucose metabolism via activating the expression of PI-3K, Akt, PDX-1, and GLUT4 at the mRNA and protein levels, thereby correcting hepatic glucolipid metabolism disorders and exerting a hypoglycemic effect. CONCLUSION: MMC ameliorated hepatic glucolipid metabolism disorders and improved insulin resistance in T2DM rats by activating the PI-3K/Akt signaling pathway. These results highlight the multi-component, multi-target, and combined effects of MMC, and suggest it may be further developed as a hypoglycemic drug.

A flexible data-free framework for structure-based de novo drug design with reinforcement learning.

Contemporary structure-based molecular generative methods have demonstrated their potential to model the geometric and energetic complementarity between ligands and receptors, thereby facilitating the design of molecules with favorable binding affinity and target specificity. Despite the introduction of deep generative models for molecular generation, the atom-wise generation paradigm that partially contradicts chemical intuition limits the validity and synthetic accessibility of the generated molecules. Additionally, the dependence of deep learning models on large-scale structural data has hindered their adaptability across different targets. To overcome these challenges, we present a novel search-based framework, 3D-MCTS, for structure-based de novo drug design. Distinct from prevailing atom-centric methods, 3D-MCTS employs a fragment-based molecular editing strategy. The fragments decomposed from small-molecule drugs are recombined under predefined retrosynthetic rules, offering improved drug-likeness and synthesizability, overcoming the inherent limitations of atom-based approaches. Leveraging multi-threaded parallel simulations combined with a real-time energy constraint-based pruning strategy, 3D-MCTS achieves remarkable efficiency. At a fixed computational cost, it outperforms other state-of-the-art (SOTA) methods by producing molecules with enhanced binding affinity. Furthermore, its fragment-based approach ensures the generation of more dependable binding conformations, exhibiting a success rate 43.6% higher than that of other SOTAs. This advantage becomes even more pronounced when handling targets that significantly deviate from the training dataset. 3D-MCTS is capable of achieving thirty times more hits with high binding affinity than traditional virtual screening methods, which demonstrates the superior ability of 3D-MCTS to explore chemical space. Moreover, the flexibility of our framework makes it easy to incorporate domain knowledge during the process, thereby enabling the generation of molecules with desirable pharmacophores and enhanced binding affinity. The adaptability of 3D-MCTS is further showcased in metalloprotein applications, highlighting its potential across various drug design scenarios.

Generic Interpretable Reaction Condition Predictions with Open Reaction Condition Datasets and Unsupervised Learning of Reaction Center.

Effective synthesis planning powered by deep learning (DL) can significantly accelerate the discovery of new drugs and materials. However, most DL-assisted synthesis planning methods offer either none or very limited capability to recommend suitable reaction conditions (RCs) for their reaction predictions. Currently, the prediction of RCs with a DL framework is hindered by several factors, including: (a) lack of a standardized dataset for benchmarking, (b) lack of a general prediction model with powerful representation, and (c) lack of interpretability. To address these issues, we first created 2 standardized RC datasets covering a broad range of reaction classes and then proposed a powerful and interpretable Transformer-based RC predictor named Parrot. Through careful design of the model architecture, pretraining method, and training strategy, Parrot improved the overall top-3 prediction accuracy on catalysis, solvents, and other reagents by as much as 13.44%, compared to the best previous model on a newly curated dataset. Additionally, the mean absolute error of the predicted temperatures was reduced by about 4 °C. Furthermore, Parrot manifests strong generalization capacity with superior cross-chemical-space prediction accuracy. Attention analysis indicates that Parrot effectively captures crucial chemical information and exhibits a high level of interpretability in the prediction of RCs. The proposed model Parrot exemplifies how modern neural network architecture when appropriately pretrained can be versatile in making reliable, generalizable, and interpretable recommendation for RCs even when the underlying training dataset may still be limited in diversity.

Effect of Low Gravity Solids on Weak Gel Structure and the Performance of Oil-Based Drilling Fluids.

Drilling cuttings from the rock formation generated during the drilling process are generally smashed to fine particles through hydraulic cutting and grinding using a drilling tool, and then are mixed with the drilling fluid during circulation. However, some of these particles are too small and light to be effectively removed from the drilling fluid via solids-control equipment. These small and light solids are referred to as low gravity solids (LGSs). This work aimed to investigate the effect of LGSs on the performance of oil-based drilling fluid (OBDF), such as the rheological properties, high-temperature and high-pressure filtration loss, emulsion stability, and filter cake quality. The results show that when the content of LGSs reached or even exceeded the solid capacity limit of the OBDF, the rheological parameters including the plastic viscosity, gel strength, and thixotropy of OBDF increased significantly. Furthermore, the filtration of OBDF increases, the filter cake becomes thicker, the friction resistance becomes larger, and the stability of emulsion of OBDF also decreases significantly when the concentration of LGSs reached the solid capacity limit of OBDF (6-9 wt% commonly). It was also found that LGSs with a smaller particle size had a more pronounced negative impact on the drilling fluid performance. This work provides guidance for understanding the impact mechanism of LGSs on drilling fluid performance and regulating the performance of OBDF.

Self-Supervised Triplet Contrastive Learning for Classifying Endometrial Histopathological Images.

Early identification of endometrial cancer or precancerous lesions from histopathological images is crucial for precise endometrial medical care, which however is increasing hampered by the relative scarcity of pathologists. Computer-aided diagnosis (CAD) provides an automated alternative for confirming endometrial diseases with either feature-engineered machine learning or end-to-end deep learning (DL). In particular, advanced self-supervised learning alleviates the dependence of supervised learning on large-scale human-annotated data and can be used to pre-train DL models for specific classification tasks. Thereby, we develop a novel self-supervised triplet contrastive learning (SSTCL) model for classifying endometrial histopathological images. Specifically, this model consists of one online branch and two target branches. The second target branch includes a simple yet powerful augmentation module named random mosaic masking (RMM), which functions as an effective regularization by mapping the features of masked images close to those of intact ones. Moreover, we add a bottleneck Transformer (BoT) model into each branch as a self-attention module to learn the global information by considering both content information and relative distances between features at different locations. On public endometrial dataset, our model achieved four-class classification accuracies of 77.31 ± 0.84, 80.87 ± 0.48 and 83.22 ± 0.87% using 20, 50 and 100% labeled images, respectively. When transferred to the in-house dataset, our model obtained a three-class diagnostic accuracy of 96.81% with 95% confidence interval of 95.61-98.02%. On both datasets, our model outperformed state-of-the-art supervised and self-supervised methods. Our model may help pathologists to automatically diagnose endometrial diseases with high accuracy and efficiency using limited human-annotated histopathological images.

Establishment of a prediction model for histological chorioamnionitis and its association with outcomes of premature infants.

Aim: This study aims to construct a prediction model for histological chorioamnionitis (HCA) and analyze the associations between the predicted risk of HCA and adverse outcomes in preterm infants. Methods: In total, 673 subjects were included in this cohort study and divided into HCA group (n = 195) and non-HCA group (n = 478). A stepwise method was used to screen the predictors for HCA, binary logistic regression was used to construct the prediction model, and the associations between the predicted risk of HCA and adverse outcomes were analyzed. Results: HCA occurred in 195 patients, accounting for 29.0%. The sensitivity of the prediction model was 0.821 [95% confidence interval (CI): 0.767-0.874)], the specificity was 0.684 (95% CI: 0.642-0.726), the positive predictive value was 0.514 (0.459-0.570), the negative predictive value was 0.903 (95% CI: 0.873-0.934), the area under the curve was 0.821 (95% CI: 0.786-0.855), and the accuracy was 0.724 (95% CI: 0.690-0.757). The predicted risk of HCA was associated with a higher risk of bronchopulmonary dysplasia (BPD) [odds ratio (OR) = 3.48, 95% CI: 1.10-10.95)], sepsis (OR = 6.66, 95% CI: 2.17-20.43), and neonatal infections (OR = 9.85, 95% CI: 3.59-26.98), but not necrotizing enterocolitis (OR = 0.67, 95% CI: 0.24-1.88), retinopathy of prematurity (OR = 1.59, 95% CI: 0.37-6.85), and brain damage (OR = 1.77, 95% CI: 0.82-3.83). After adjusting for confounders including gestational week at birth and birth weight, the risk of neonatal infections (OR = 5.03, 95% CI: 2.69-9.41) was increased in preterm infants' exposure to HCA. Conclusion: The model showed good predictive performance for identifying pregnant women with a higher risk of HCA. In addition, HCA was associated with the risk of BPD, sepsis, and infections in neonates.

How Good Are Current Docking Programs at Nucleic Acid-Ligand Docking? A Comprehensive Evaluation.

Nucleic acid (NA)-ligand interactions are of paramount importance in a variety of biological processes, including cellular reproduction and protein biosynthesis, and therefore, NAs have been broadly recognized as potential drug targets. Understanding NA-ligand interactions at the atomic scale is essential for investigating the molecular mechanism and further assisting in NA-targeted drug discovery. Molecular docking is one of the predominant computational approaches for predicting the interactions between NAs and small molecules. Despite the availability of versatile docking programs, their performance profiles for NA-ligand complexes have not been thoroughly characterized. In this study, we first compiled the largest structure-based NA-ligand binding data set to date, containing 800 noncovalent NA-ligand complexes with clearly identified ligands. Based on this extensive data set, eight frequently used docking programs, including six protein-ligand docking programs (LeDock, Surflex-Dock, UCSF Dock6, AutoDock, AutoDock Vina, and PLANTS) and two specific NA-ligand docking programs (rDock and RLDOCK), were systematically evaluated in terms of binding pose and binding affinity predictions. The results demonstrated that some protein-ligand docking programs, specifically PLANTS and LeDock, produced more promising or comparable results compared with the specialized NA-ligand docking programs. Among the programs evaluated, PLANTS, rDock, and LeDock showed the highest performance in binding pose prediction, and their top-1 and best root-mean-square deviation (rmsd) success rates were as follows: PLANTS (35.93 and 76.05%), rDock (27.25 and 72.16%), and LeDock (27.40 and 64.37%). Compared with the moderate level of binding pose prediction, few programs were successful in binding affinity prediction, and the best correlation (Rp = -0.461) was observed with PLANTS. Finally, further comparison with the latest NA-ligand docking program (NLDock) on four well-established data sets revealed that PLANTS and LeDock outperformed NLDock in terms of binding pose prediction on all data sets, demonstrating their significant potential for NA-ligand docking. To the best of our knowledge, this study is the most comprehensive evaluation of popular molecular docking programs for NA-ligand systems.

Will emission trading promote enterprise diversification? Evidence from China.

This study examines the impact of the Chinese regional emission trading system (ETS) pilots on enterprise transformation from the perspective of diversification. We use data on Chinese A-share listed companies from 2004 to 2021, and adopt the staggered difference-in-differences (DID) and difference-in-difference-in-differences (DDD) models. The empirical results show that first, the ETS significantly increases the product quantity and revenue diversification of regulated firms. Second, the ETS promotes enterprise diversification through three channels: emission cost, emission risk, and market efficiency. Third, the ETS has a greater impact on the diversification of state-owned enterprises, firms with high business concentration, and firms with low innovation investment. Fourth, the ETS-driven diversification has not been successful as it has increased firms' costs and reduced their profitability. We recommend introducing industrial policies to guide the transformation of enterprises, encourage them to improve their innovation capabilities, and choose appropriate transformation strategies.

Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking.

Graph neural networks (GNNs) have been widely used in molecular property prediction, but explaining their black-box predictions is still a challenge. Most existing explanation methods for GNNs in chemistry focus on attributing model predictions to individual nodes, edges or fragments that are not necessarily derived from a chemically meaningful segmentation of molecules. To address this challenge, we propose a method named substructure mask explanation (SME). SME is based on well-established molecular segmentation methods and provides an interpretation that aligns with the understanding of chemists. We apply SME to elucidate how GNNs learn to predict aqueous solubility, genotoxicity, cardiotoxicity and blood-brain barrier permeation for small molecules. SME provides interpretation that is consistent with the understanding of chemists, alerts them to unreliable performance, and guides them in structural optimization for target properties. Hence, we believe that SME empowers chemists to confidently mine structure-activity relationship (SAR) from reliable GNNs through a transparent inspection on how GNNs pick up useful signals when learning from data.

The chromosome-level genome of Eucommia ulmoides provides insights into sex differentiation and α-linolenic acid biosynthesis.

Eucommia ulmoides Oliver is a typical dioecious plant endemic to China that has great medicinal and economic value. Here, we report a high-quality chromosome-level female genome of E. ulmoides obtained by PacBio and Hi-C technologies. The size of the female genome assembly was 1.01 Gb with 17 pseudochromosomes and 31,665 protein coding genes. In addition, Hi-C technology was used to reassemble the male genome released in 2018. The reassembled male genome was 1.24 Gb with the superscaffold N50 (48.30 Mb), which was increased 25.69 times, and the number of predicted genes increased by 11,266. Genome evolution analysis indicated that E. ulmoides has undergone two whole-genome duplication events before the divergence of female and male, including core eudicot γ whole-genome triplication event (γ-WGT) and a recent whole genome duplication (WGD) at approximately 27.3 million years ago (Mya). Based on transcriptome analysis, EuAP3 and EuAG may be the key genes involved in regulating the sex differentiation of E. ulmoides. Pathway analysis showed that the high expression of ω-3 fatty acid desaturase coding gene EU0103017 was an important reason for the high α-linolenic acid content in E. ulmoides. The genome of female and male E. ulmoides presented here is a valuable resource for the molecular biological study of sex differentiation of E. ulmoides and also will provide assistance for the breeding of superior varieties.

The FomYjeF Protein Influences the Sporulation and Virulence of Fusarium oxysporum f. sp. momordicae.

Fusarium oxysporum causes vascular wilt in more than 100 plant species, resulting in massive economic losses. A deep understanding of the mechanisms of pathogenicity and symptom induction by this fungus is necessary to control crop wilt. The YjeF protein has been proven to function in cellular metabolism damage-repair in Escherichia coli and to play an important role in Edc3 (enhancer of the mRNA decapping 3) function in Candida albicans, but no studies have been reported on related functions in plant pathogenic fungi. In this work, we report how the FomYjeF gene in F. oxysporum f. sp. momordicae contributes to conidia production and virulence. The deletion of the FomYjeF gene displayed a highly improved capacity for macroconidia production, and it was shown to be involved in carbendazim's associated stress pathway. Meanwhile, this gene caused a significant increase in virulence in bitter gourd plants with a higher disease severity index and enhanced the accumulation of glutathione peroxidase and the ability to degrade hydrogen peroxide in F. oxysporum. These findings reveal that FomYjeF affects virulence by influencing the amount of spore formation and the ROS (reactive oxygen species) pathway of F. oxysporum f. sp. momordicae. Taken together, our study shows that the FomYjeF gene affects sporulation, mycelial growth, pathogenicity, and ROS accumulation in F. oxysporum. The results of this study provide a novel insight into the function of FomYjeF participation in the pathogenicity of F. oxysporum f. sp. momordicae.

Concurrent pembrolizumab with AVD for untreated classic Hodgkin lymphoma.

Concurrent administration of pembrolizumab with chemotherapy in untreated classic Hodgkin lymphoma (CHL) has not been studied previously. To investigate this combination, we conducted a single-arm study of concurrent pembrolizumab with AVD (doxorubicin, vinblastine, and dacarbazine; APVD) for untreated CHL. We enrolled 30 patients and met the primary safety end point with no observed significant treatment delays in the first 2 cycles. Twelve patients experienced grade 3 or 4 nonhematologic adverse events (AEs), most commonly febrile neutropenia and infection/sepsis. Grade 3 or 4 immune-related AEs, including alanine aminotransferase elevation and aspartate aminotransferase elevation were observed in 3 patients. One patient experienced an episode of grade 2 colitis and arthritis. Six patients missed at least 1 dose of pembrolizumab because of AEs, primarily grade 2 or higher transaminitis. Among 29 response-evaluable patients, the best overall response rate was 100% and the complete response rate was 90%. With a median follow-up of 2.1 years, the 2-year progression-free survival (PFS) and overall survival were 97% and 100%, respectively. To date, no patient who has withheld or discontinued pembrolizumab because of toxicity has progressed. Clearance of circulating tumor DNA (ctDNA) was associated with superior PFS when measured after cycle 2 and at the end of treatment (EOT). None of the 4 patients with persistent uptake by fluorodeoxyglucose positron emission tomography (PET) at EOT yet negative ctDNA have relapsed to date. Concurrent APVD shows promising safety and efficacy but may yield spurious PET findings in some patients. This trial was registered at www.clinicaltrials.gov as #NCT03331341.

MetalProGNet: a structure-based deep graph model for metalloprotein-ligand interaction predictions.

Metalloproteins play indispensable roles in various biological processes ranging from reaction catalysis to free radical scavenging, and they are also pertinent to numerous pathologies including cancer, HIV infection, neurodegeneration, and inflammation. Discovery of high-affinity ligands for metalloproteins powers the treatment of these pathologies. Extensive efforts have been made to develop in silico approaches, such as molecular docking and machine learning (ML)-based models, for fast identification of ligands binding to heterogeneous proteins, but few of them have exclusively concentrated on metalloproteins. In this study, we first compiled the largest metalloprotein-ligand complex dataset containing 3079 high-quality structures, and systematically evaluated the scoring and docking powers of three competitive docking tools (i.e., PLANTS, AutoDock Vina and Glide SP) for metalloproteins. Then, a structure-based deep graph model called MetalProGNet was developed to predict metalloprotein-ligand interactions. In the model, the coordination interactions between metal ions and protein atoms and the interactions between metal ions and ligand atoms were explicitly modelled through graph convolution. The binding features were then predicted by the informative molecular binding vector learned from a noncovalent atom-atom interaction network. The evaluation on the internal metalloprotein test set, the independent ChEMBL dataset towards 22 different metalloproteins and the virtual screening dataset indicated that MetalProGNet outperformed various baselines. Finally, a noncovalent atom-atom interaction masking technique was employed to interpret MetalProGNet, and the learned knowledge accords with our understanding of physics.

Molecular characterization of a novel victorivirus isolated from the phytopathogenic fungus Phaeobotryon rhois.

Phaeobotryon rhois is an important pathogenic fungus that causes dieback and canker disease of woody hosts. A novel mycovirus, tentatively named "Phaeobotryon rhois victorivirus 1" (PrVV1), was identified in P. rhois strain SX8-4. The PrVV1 has a double-stranded RNA (dsRNA) genome that is 5,224 base pairs long and contains two open reading frames (ORF1 and ORF2), which overlap at a AUGA sequence. ORF1 encodes a polypeptide of 786 amino acids (aa) that contains the conserved coat protein (CP) domain of victoriviruses, while ORF2, encodes a large polypeptide of 826 aa that contains the conserved RNA-dependent RNA polymerase (RdRp) domain of victoriviruses. Our analysis of genomic structure, homology, and phylogeny indicated that PrVV1 is a novel member of the genus Victorivirus in the family Totiviridae. This is the first report of the complete genome sequence of a victorivirus that infects P. rhois.