Jie Dong

Aims: Associations between metabolic heterogeneity in women with gestational diabetes mellitus (GDM) and adverse pregnancy outcomes have often been explored without considering interrelated metabolic biomarkers, limiting accurate predictions of outcomes. This study aimed to identify metabolic heterogeneity across distinct classes of biomarkers in GDM women and link this metabolic heterogeneity with adverse pregnancy outcomes through a complementary analytic approach. Methods: We conducted a retrospective cohort study using medical records of GDM women from 2018 to 2023 in Central South China. We identified metabolic heterogeneity of GDM using unsupervised k ‐means clustering based on oral glucose tolerance test (OGTT) 0, 1 and 2 h, uric acid (UA), triglycerides (TG), high‐density lipoprotein (HDL) and low‐density lipoprotein (LDL). We then linked this metabolic heterogeneity to adverse pregnancy outcomes through a complementary analytic approach, including logistic regression for group traits and machine learning for single features. Results: The first approach categorised 2246 GDM women into four distinct metabolic subgroups: reference (36%, relatively normal and average biomarker levels), fasting hyperglycemia (23.3%), hyperuricemia and hypertriglyceridemia (34.9%) and combined metabolic dysregulation (5.8%). The complementary analytic approach yielded consistent results. The hyperuricemia and hypertriglyceridemia subgroup showed higher odds of preterm delivery (OR = 1.86, 95% CI = 1.15–3.01). The combined metabolic dysregulation subgroup had the highest risks of preterm delivery (OR = 3.45, 95% CI = 1.79–6.65), insulin treatment (OR = 14.67, 95% CI = 8.72–24.66) and hypertensive disorders of pregnancy (OR = 2.72, 95% CI = 1.48–5.01). SHAP analysis showed that 57%–71% of the subgroup‐defining biomarkers ranked among the top 15 predictive features. Conclusions: This study confirms the metabolic heterogeneity in GDM women, supporting synergistic effects among metabolic factors and the need for integrated metabolic management. The identified metabolic subgroups may facilitate risk stratification in real‐world settings by integrating biomarkers from multiple metabolic pathways. Women in the hyperuricemia and hypertriglyceridemia subgroup, as well as those in the combined metabolic dysregulation subgroup, urgently require interventions to improve pregnancy outcomes.

Idiopathic pulmonary fibrosis (IPF) is a fatal interstitial lung disease caused by the pathological accumulation of senescent cells. In this fibrotic microenvironment, senescence‐associated β‐galactosidase (SA‐β‐Gal) serves as a key biomarker. However, existing SA‐β‐Gal molecular probes suffer from signal diffusion and rapid clearance due to the extracellular leakage, compromising imaging fidelity. To address this issue, an enzyme‐activated covalent labelling strategy is proposed, and as a design paradigm developing TCFM‐Gal , a imaging tool integrating β‐galactosidic fluorophore with an ortho‐difluoromethyl leaving group. Upon SA‐β‐Gal‐mediated hydrolysis, TCFM‐Gal generates a quinone methide intermediate that covalently anchors to surrounding nucleophiles, confining fluorescence within lysosomes. In senescent cells, TCFM‐Gal achieves extended lysosome‐retained imaging, maintaining over 85% signal retention within 24 h. In the IPF model, TCFM‐Gal enables longitudinal tracking of senescent cells and maintains a high signal intensity after 24 h. Rapid circulatory washout and high interstitial fluid pressure make conventional probes ineffective in the lungs. TCFM‐Gal overcomes this not just by sensing, but by anchoring the signals in situ. Moreover, TCFM‐Gal facilitates staging assessments of fibrosis progression by quantifying the senescence burden across disease phases. This study establishes an enzyme‐activated covalent labeling paradigm for high‐fidelity senescence imaging, advancing the spatiotemporal mapping of senescence in lung diseases.

Chronic diabetic foot ulcers (DFUs) provide a niche for microbial colonization, where complex polymicrobial infections involving Gram-positive bacteria, Gram-negative bacteria, and fungi occur and impair wound healing through biofilm formation.

Monascus spp. are highly valuable microbial resources with extensive applications in both the food and pharmaceutical industries. In the food industry, it

Peptide-based therapeutics are increasingly emerging as a promising alternative to small molecules and protein drugs. However, their clinical development still faces significant challenges, particularly in terms of efficacy and safety, which are largely attributed to their suboptimal absorption, distribution, metabolism, and excretion properties and potential toxicity risks (ADMET). To address these challenges, we developed pepADMET (https://pepadmet.ddai.tech), the first publicly accessible AI-driven platform for the systematic and comprehensive assessment of peptide ADMET properties. The platform integrated 36,643 high-quality data entries and covers 19 key ADMET endpoints. By combining molecular graph representations, enzymatic descriptors, and transfer learning with advanced neural architectures such as graph neural networks and relational graph convolutional networks, the platform effectively captures complex molecular and biological features of peptides, thereby substantially enhancing the predictive performance and robustness of the models. Specifically, we introduced MLR-GAT, a novel multilevel framework specifically designed for peptide toxicity prediction, which can hierarchically identify multiple categories of peptide toxicity rather than focusing solely on hemolytic toxicity. Uniquely, pepADMET for the first time simultaneously supports linear, cyclic, modified, and natural peptides, while also accounting for biological variability across species, organs, and cell lines, thereby enabling more precise and biologically relevant ADMET predictions. As a new comprehensive online resource for multiproperty ADMET evaluation of peptides, pepADMET provides a unified, accurate, and intelligent framework to advance peptide drug design and development.

Intracellular bacterial infections pose a significant challenge to current therapeutic strategies due to the limited penetration of antibiotics through host cell membranes. This study presents a novel computational framework for efficiently screening candidate peptides against these infections. The proposed strategy comprehensively evaluates the essential properties for the clinical application of candidate peptides, including antimicrobial activity, permeation efficiency, and biocompatibility, while also taking into account the speed and reliability of the screening process. A combination of multiple AI-based activity prediction models allows for a thorough assessment of sequences in the cell-penetrating peptides (CPPs) database and quickly identifies candidate peptides with target properties. On this basis, the CPP microscopic dynamics research system was constructed. Exploration of the mechanism of action at the atomic level provides strong support for the discovery of promising candidate peptides. Promising candidates are subsequently validated through in vitro and in vivo experiments. Finally, Crot-1 was rapidly identified from the CPPsite 2.0 database. Crot-1 effectively eradicated intracellular MRSA, demonstrating significantly greater efficacy than vancomycin. Moreover, it exhibited no apparent cytotoxicity to host cells, highlighting its potential for clinical application. This work offers a promising new avenue for developing novel antimicrobial materials to combat intracellular bacterial infections.

As a new modality of ligands, bicyclic peptides hold great promise in the discovery of novel programmed death ligand 1 (PD-L1) targeted radiotracers, which have not yet been reported. In this study, first-in-class bicyclic peptide-based radiotracers [¹⁸F]AlF-BCY509 and [¹⁸F]AlF-BCY10959 were developed and evaluated for PET imaging of tumor PD-L1 expression. The automatic radiosynthesis was achieved with robust radiochemical yields (55.1-90.2%) and high molar activity (42.5-90.8 GBq/μmol). Cell-based assays demonstrated high specificity and affinity of [¹⁸F]AlF-BCY509 and [¹⁸F]AlF-BCY10959 with IC₅₀ values of 9.36 ± 1.35 and 7.12 ± 1.24 nM and K_D values of 11.41 ± 1.04 and 8.09 ± 0.85 nM. In PET imaging, the accumulation of [¹⁸F]AlF-BCY10959 in PD-L1-positive tumors with moderate retention over 120 min was discovered, with the tumor uptake of 14.74 ± 1.67%ID/cc and tumor-to-muscle ratio of 12.41 ± 1.07 at 30 min. The in vivo specificity was strictly verified by PD-L1-knockout and PD-L1-positive tumors with blocking. The biodistribution manifested a rapid distribution and fast clearance from the body, supporting the favorable pharmacokinetics of [¹⁸F]AlF-BCY10959. [¹⁸F]AlF-BCY10959 was excreted through the urinary and hepatobiliary systems, indicating the doomed radiation exposure organs. The effective doses of [¹⁸F]AlF-BCY10959 and [¹⁸F]-FDG were comparable, highlighting its safety for human use. In conclusion, [¹⁸F]AlF-BCY10959 provides an attractive option to detect PD-L1 expression and lays the groundwork to further develop promising bicyclic peptide tracers for clinical use.