Discovery of Novel, Potent, Selective, and Orally Available DGKα Inhibitors for the Treatment of Tumors.

DGKα, also named diacylglycerol kinase alpha, plays an important role in signal transduction, phosphorylating the membrane lipid diacylglycerol (DAG), to phosphatidic acid (PA). Increasing evidence indicates that DGKα-mediated T-cell dysfunction plays a significant role in the development of resistance of the PD-1 blockade. In this article, we report the discovery of compound as a novel, potent, orally available DGKα inhibitor with excellent kinase selectivity, a favorable ADME profile, and robust in vivo antitumor activity in combination with anti-PD-1 therapy.

Cytochalasins from the marine-derived fungus Acremonium implicatum DFFSCS001 and their anti-inflammatory activities.

Nine undescribed cytochalasins, acrechalasins A‒I, together with three known analogs 21-O-deacetyl-L-696,474 and diaporthichalasins B and A were isolated from the marine-derived fungus Acremonium implicatum DFFSCS001. Their structures were elucidated by spectroscopic methods, and the absolute configurations of acrechalasins C and D and diaporthichalasin B were determined by single-crystal X-ray diffractions. Among them, acrechalasins A and B had a rare open-chain cytochalasin skeleton with a monocyclic butyrolactam group.

A multi-scale neighbor topology guided transformer and Kolmogorov-Arnold network enhanced feature learning model for disease-related circRNA prediction.

As circular non-coding RNA (circRNA) is closely associated with various human diseases, identifying disease-related circRNAs can provide a deeper understanding of the mechanisms underlying disease pathogenesis. Advanced circRNA-disease association prediction methods mainly focus on graph learning techniques such as graph convolutional networks and graph attention networks. However, these methods do not fully encode the multi-scale neighbor topologies of each node, and the dependencies among the pairwise attributes.

DeSC: Learning Deep Semantic Descriptor for NeRF Registration.

NeRF registration has gained increasing attention recently. While existing research demonstrates considerable potential for this task, most methods primarily focus on either global geometric or rendering photometric information during feature learning, overlooking the rich cross-modal information inherent in the NeRF embedding feature space. In this paper, we propose DeSC, a novel NeRF registration approach that leverages the rich cross-modal features from NeRF to learn robust semantic descriptors.

Methionine Restriction Attenuates Scar Formation in Fibroblasts Derived from Patients with Post-Burn Hypertrophic Scar.

Methionine restriction (MetR) is a common adjuvant treatment for cancer. However, studies of MetR have paid little attention to its potential implications for fibrosis. Hypertrophic scarring (HTS) is an abnormal fibrotic response after burn trauma that results from the excessive activation of fibroblasts.

Rational Design and Identification of ISM7594 as a Tissue-Agnostic FGFR2/3 Inhibitor.

The selective inhibition of fibroblast growth factor receptors (FGFR) presents a significant challenge due to the high degree of sequence and the close structural similarity of the subtypes. Herein, we designed selective dual FGFR2/3 inhibitors based on the in-depth understanding of protein-ligand interaction contributions. We efficiently identified ISM7594 () with distinctive flexible hinge binder and a unique central core that potently inhibited FGFR2/3 and selectively spared FGFR1/4.

New Bis-sesquiterpenoids From the Lindera glauca With Anti-inflammatory Activity by Inhibiting the Lipopolysaccharide-induced Expression of Inducible Nitric Oxide Synthase.

Three new sesquiterpenoid dimers linderasesquiterpenoids A-C (1-3) and three known compounds 4-6 were co-isolated from the Lindera glauca. Their structures were elucidated based on spectroscopic methods and electronic circular dichroism calculations. Among them, linderasesquiterpenoid A (1) featuring the rare [4, 9] ring system was the first example of sesquiterpenoid dimers, and also the sesquiterpenoid dimers components were first found in this plant.

Eutymeroterpenoids A-G with cytotoxic and anti-inflammatory activity from the fungus Eutypella scoparia GZU-4-19Y.

Eutymeroterpenoids A-G (1-7), seven new eremophilane-type sesquiterpenoids, together with one known compound cryptosphaerolide (8), were isolated from marine-derived Eutypella scoparia GZU-4-19Y. Their structures were established by analysis of their NMR data and electronic circular dichroism calculations. All of them featured the unique 11‑carbon alkyl chain were rarely found in natural sesquiterpenoids.

Oral ENPP1 inhibitor designed using generative AI as next generation STING modulator for solid tumors.

Despite the STING-type-I interferon pathway playing a key role in effective anti-tumor immunity, the therapeutic benefit of direct STING agonists appears limited. In this study, we use several artificial intelligence techniques and patient-based multi-omics data to show that Ectonucleotide Pyrophosphatase/Phosphodiesterase 1 (ENPP1), which hydrolyzes STING-activating cyclic GMP-AMP (cGAMP), is a safer and more effective STING-modulating target than direct STING agonism in multiple solid tumors. We then leverage our generative chemistry artificial intelligence-based drug design platform to facilitate the design of ISM5939, an orally bioavailable ENPP1-selective inhibitor capable of stabilizing extracellular cGAMP and activating bystander antigen-presenting cells without inducing either toxic inflammatory cytokine release or tumor-infiltrating T-cell death.

Multi-Knowledge Graph and Multi-View Entity Feature Learning for Predicting Drug-Related Side Effects.

Computational prediction of potential drug side effects plays a crucial role in reducing health risks for clinical patients and accelerating drug development. Recent methods have constructed heterogeneous graphs that represent drugs and their side effects, utilizing graph learning strategies such as graph convolutional networks to predict associations between them. However, existing approaches fail to fully exploit the diverse topologies and semantics present in multiple knowledge graphs.

Blockchain-Empowered H-CPS Architecture for Smart Agriculture.

This study integrates blockchain technology into smart agriculture to enhance its productivity and sustainability. By combining blockchain with remote sensing, artificial intelligence (AI), and the Internet of Things (IoT), a Human-Cyber-Physical System (H-CPS) architecture tailored for agricultural applications is proposed. It supports real-time crop management, data-driven decision-making, and transparent trading of agricultural products.

KNDM: A Knowledge Graph Transformer and Node Category Sensitive Contrastive Learning Model for Drug and Microbe Association Prediction.

It has been proven that the microbiome in human bodies can promote or inhibit the treatment effects of the drugs by affecting their toxicities and activities. Therefore, identifying drug-related microbes helps in understanding how drugs exert their functions under the influence of these microbes. Most recent methods for drug-related microbe prediction are developed based on graph learning.

Audio Deepfake Detection: What Has Been Achieved and What Lies Ahead.

Advancements in audio synthesis and manipulation technologies have reshaped applications such as personalised virtual assistants, voice cloning for creative content, and language learning tools. However, the misuse of these technologies to create audio deepfakes has raised serious concerns about security, privacy, and trust. Studies reveal that human judgement of deepfake audio is not always reliable, highlighting the urgent need for robust detection technologies to mitigate these risks.

CCA: Contrastive cluster assignment for supervised and semi-supervised medical image segmentation.

Transformers have shown great potential in vision tasks such as semantic segmentation. However, most of the existing transformer-based segmentation models neglect the cross-attention between pixel features and class features which impedes the application of transformers. Inspired by the concept of object queries in k-means Mask Transformer, we develop cluster learning and contrastive cluster assignment (CCA) for medical image segmentation in this paper.

Bioaspermeroterpenes A-D: Four New Meroterpenoids From the Biotransformation Extract by the Fungus Penicillium herquei GZU-31-6.

Bioaspermeroterpenes A-D, four new 3,5-dimethylorsellinic acid-derived meroterpenoids were co-isolated from the biotransformation extract of aspermeroterpene F by the fungus Penicillium herquei GZU-31-6. Their structures were elucidated based on spectroscopic methods. In the bioassays, all the isolates were evaluated for their inhibitory effect on transforming growth factor-β1-induced activation of hepatic stellate cells.

Design, Synthesis, and Biological Evaluation of Novel Orally Available Covalent CDK12/13 Dual Inhibitors for the Treatment of Tumors.

Cyclin-dependent kinases 12 and 13 (CDK12/13) safeguard genomic integrity by preferentially regulating gene expression in the DNA damage response (DDR). The CDK12/13-mediated upregulation of DDR genes and pathways significantly contributes to both tumorigenesis and the development of resistance to antitumor therapies. Thus, the functional inhibition of CDK12/13 offers an attractive strategy to combat carcinogenesis, particularly for refractory and treatment-resistant cancers.

Subgraph Topology and Dynamic Graph Topology Enhanced Graph Learning and Pairwise Feature Context Relationship Integration for Predicting Disease-Related miRNAs.

As an increasing number of microRNAs (miRNAs) have become biomarkers of various human diseases, prediction of the candidate disease-related miRNAs is helpful for facilitating the early diagnosis of diseases. Most of the recent prediction models concentrated on learning of the features from the heterogeneous graph composed of miRNAs and diseases. However, they failed to fully exploit the subgraph structures consisting of multiple miRNA and disease nodes, and they also did not completely integrate the context relationships among the pairwise features.

Slit1 Promotes Hypertrophic Scar Formation Through the TGF-β Signaling Pathway.

Slit1 is a secreted protein that is closely related to cell movement and adhesion. Few studies related to fibrosis exist, and the preponderance of current research is confined to the proliferation and differentiation of neural systems. Hypertrophic scars (HTSs) are delineated by an overproduction of the extracellular matrix (ECM) by activated fibroblasts, leading to anomalous fibrosis, which is a severe sequela of burns.

Neighborhood Topology-Aware Knowledge Graph Learning and Microbial Preference Inferring for Drug-Microbe Association Prediction.

The human microbiota may influence the effectiveness of drug therapy by activating or inactivating the pharmacological properties of drugs. Computational methods have demonstrated their ability to screen reliable microbe-drug associations and uncover the mechanism by which drugs exert their functions. However, the previous prediction methods failed to completely exploit the neighborhood topologies of the microbe and drug entities and the diverse correlations between the microbe-drug entity pair and the other entities.

Interactive multi-hypergraph inferring and channel-enhanced and attribute-enhanced learning for drug-related side effect prediction.

Identifying the potential side effects for the interested drugs can help reduce harm to patients caused by drugs in clinical use and decrease the risk of drug development failure. Multiple functionally similar drugs often have multiple similar side effects, resulting in the closed relationships among these nodes. However, most of previous methods did not completely encode the features from the biological perspective to mine the complex associations between the drugs and side effects.

Facile constructed meroterpenoids with novel hexadecahydroacephenanthrylene carbon skeleton using the biotransformation and chemical synthesis method.

Bioaspermeroterpenoid A (1), the first meroterpenoid with an unprecedented hexadecahydroacephenanthrylene carbon skeleton, together with two analogues bioaspermeroterpenoids B and C (2 and 3) were co-isolated from the biotransformation extract of aspermeroterpene C by the fungus Penicillium herquei GZU-31-6. On the other hand, bioaspermeroterpenoid Aa (1a) featuring the same hexadecahydroacephenanthrylene carbon skeleton was synthesized from the precursor aspermeroterpene C by the nucleophilic addition reaction in the presence of CHONa. Furthermore, bioaspermeroterpenoids A and C showed good inhibitory activities against lipopolysaccharide (LPS)-induced nitric oxide (NO) production in RAW 264.

Temperature switchable linkers suitable for triggered drug release in cancer thermo-chemotherapy.

In drug delivery systems, a stimuli-responsive linker that attaches a targeting carrier and a cytotoxic payload can be dissociated to release the payload on the target over the action of a stimuli, thereby it would harden the selectivity, specificity and potency of the cytotoxic agent against targeted tissues whilst sparing the drug-induced toxicity on normal cells. Oligonucleotide duplexes can unwind and be separated into single-stranded random coils under a defined temperature, and this property makes the oligonucleotide an appealing thermo-responsive linker. In this work, we studied the melting temperatures of different DNA linkers with various lengths and mismatches inserted in the double helix with either different numbers or positions.

Glasesterterpenoids A-C: three sesterterpenoids with 7-cyclohexyldecahydronaphthalene carbon skeleton isolated from the root of Lindera glauca.

Three novel sesterterpenoids glasesterterpenoids A-C (1-3), featuring an unprecedented 7-cyclohexyldecahydronaphthalene carbon skeleton, were isolated from the root of Lindera glauca (L. glauca). Their structures were elucidated by quantum chemical calculations and spectroscopic methods.