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Protein turnover and extracellular proteolysis continuously generate diverse peptide fragments within biological systems, yet the metabolic and pharmacological implications of these peptides remain incompletely understood. Among these transporters, members of the solute carrier family 15 (SLC15), including peptide transporter 1 (PEPT1/SLC15A1) and peptide transporter 2 (PEPT2/SLC15A2), mediate the proton-coupled uptake of dipeptides, tripeptides, and structurally related compounds across cellular membranes. While these transporters have been extensively studied in the context of intestinal peptide absorption and drug delivery, their potential roles in cancer biology remain incompletely understood. Tumor microenvironments are characterized by extensive proteolysis and dynamic metabolic remodeling, processes that can generate diverse peptide fragments derived from extracellular matrix proteins and intracellular protein turnover. These peptides may accumulate locally and potentially serve as substrates for cellular peptide transport systems. Once internalized through peptide transporters, dipeptides are typically hydrolyzed into free amino acids that can support biosynthetic pathways, energy metabolism, and cellular growth. In addition to their potential metabolic roles, certain endogenous dipeptides have also been reported to influence cellular signaling pathways and redox homeostasis. The broad substrate specificity of peptide transporters has also attracted significant interest in pharmacology because numerous clinically used drugs exploit these transport systems for efficient cellular uptake. This property raises the possibility that peptide transporters may be utilized for transporter-mediated drug delivery strategies, including the development of peptide-modified prodrugs or dipeptide-drug conjugates. In this review, we summarize the molecular characteristics and physiological functions of dipeptide transport systems with a particular focus on the SLC15 transporter family. We then discuss emerging evidence linking peptide transporters to tumor metabolism and the tumor microenvironment. Finally, we highlight current progress and future perspectives in exploiting peptide transport systems for transporter-mediated drug delivery and therapeutic targeting in cancer.

High-frequency (HF) skywave propagation exploits ionospheric reflection for beyond-line-of-sight transmission, making time-difference-of-arrival (TDOA)-based geolocation a primary technique for localizing non-cooperative HF emitters. However, reliable TDOA estimation remains challenging due to time-varying ionospheric conditions, wideband multipath dispersion, and low signal-to-noise ratio (SNR). This paper proposes an integrated framework coupling realistic channel synthesis, deep learning-based TDOA estimation, and convex optimization-based localization. Three contributions are made. First, an improved wideband ionospheric channel model is constructed by integrating the International Reference Ionosphere (IRI) with region-specific calibration and a stochastic perturbation module, yielding time-varying multipath responses for physics-consistent waveform generation. Second, a convolutional neural network (CNN)-based TDOA estimator is designed to jointly exploit time-domain complex-baseband in-phase/quadrature (I/Q) waveforms, multi-weight generalized cross-correlation (GCC) feature maps, and channel-state information (CSI) within a unified regression network, achieving robust delay estimation under severe noise and multipath conditions. Third, the geolocation problem is formulated as a bias-regularized constrained least-squares problem with unknown ionospheric excess-delay surrogates, and a semidefinite programming (SDP) relaxation is derived to yield a tractable solution without prescribing a fixed virtual reflection height. Simulations show that the proposed estimator consistently outperforms competing algorithms across a wide SNR range and narrows the gap to the Cramér-Rao lower bound (CRLB) at high SNR. On field-recorded signals, the estimator reduces the mean absolute TDOA deviation by 51% relative to GCC with phase transform (GCC-PHAT), and the end-to-end pipeline achieves a mean geolocation error of 19.67 km across 100 field segments, outperforming all compared baselines.

Metabolic reprogramming is a core hallmark of malignancy, enabling tumor cells to sustain rapid proliferation, evade immune elimination, and develop resistance to therapy. Although a wide range of plant-derived phytochemicals exhibit anticancer activity with comparatively low toxicity, their capacity to disrupt specific metabolic dependencies exploited by tumors has not been comprehensively synthesized. This review brings together current mechanistic evidence showing how major phytochemical classes, including polyphenols, terpenes and terpenoids, glucosinolates, and alkaloids, interfere with pathways central to tumor metabolic fitness, such as aerobic glycolysis, pentose phosphate pathway flux, mitochondrial substrate oxidation, glutamine dependence, and redox homeostasis. It further introduces a pathway-focused framework that links phytochemical mechanisms to quantifiable metabolic outcomes and highlights their potential to remodel the tumor microenvironment by altering nutrient competition, oxidative stress responses, and hypoxia-driven signaling. Key barriers such as poor systemic bioavailability, rapid metabolic degradation, and limited tissue penetration are assessed alongside emerging formulation and delivery strategies designed to enhance therapeutic exposure while preserving low-toxicity profiles. Mapping these mechanistic insights onto clinical development needs allows prioritization of specific phytochemical-metabolic pathway pairs with the strongest potential for translation. This positions plant-derived metabolic disruptors as promising candidates for next-generation, low-toxicity anticancer therapies that strategically exploit defined metabolic vulnerabilities.

Glaucoma is a leading cause of irreversible blindness worldwide, with asymptomatic early stages often delaying diagnosis and treatment. Early and accurate diagnosis requires integrating complementary information from multiple ocular imaging modalities. However, most existing studies rely on single- or dual-modality imaging, such as fundus and optical coherence tomography (OCT), for coarse binary classification, thereby restricting the exploitation of complementary information and hindering both early diagnosis and stage-specific treatment. To address these limitations, we propose glaucoma lesion evaluation and analysis with multimodal imaging (GLEAM), the first publicly available tri-modal glaucoma dataset comprising scanning laser ophthalmoscopy fundus images, circumpapillary OCT images, and visual field pattern deviation maps, annotated with four disease stages, enabling effective exploitation of multimodal complementary information and facilitating accurate diagnosis and treatment across disease stages. To effectively integrate cross-modal information, we propose hierarchical attentive masked modeling (HAMM) for multimodal glaucoma classification. Our framework employs hierarchical attentive encoders and light decoders to focus cross-modal representation learning on the encoder. The attention module, named multimodal-channel graph attention (MCGA), boosts glaucoma classification performance by emulating two key clinical reasoning steps: first, it uses a multi-head modality gating mechanism to replicate ophthalmologists' confidence scoring of fundus, OCT, and VF modalities; then, MCGA leverages a relational graph attention network to cross-examine structural-functional consistencies of weighted modalities. The experiments on GLEAM demonstrate that tri-modal fusion significantly outperforms single-modal and dual-modal configurations. Moreover, our proposed HAMM achieves superior performance compared with state-of-the-art multimodal learning methods. The dataset and code are publicly available via https://github.com/microewing/HAMM.

The nuclear receptor NR5A2 (Liver Receptor Homolog-1, LRH-1) has been well characterized in tissues of endodermal origin for the transcriptional control of development, metabolism, and steroidogenesis. In this minireview, we discuss the so far underappreciated expression and role of LRH-1 in hematopoietic cells. We further highlight how deregulation of LRH-1 may contribute to the pathogenesis of leukemia and immune cell-mediated diseases, and how targeting LRH-1 can be employed in immune cell-targeted therapies. Given that LRH-1 expression and function are highly tissue-specific, we further discuss how these contextual differences may be exploited to achieve therapeutic selectivity, especially focusing on the myeloid and T cell lineage. Although current evidence for LRH-1 functions in these immune cells is yet limited, its established role in the transcriptional regulation of development, differentiation, metabolism, proliferation, and cytokine expression of hematopoietic cells suggests a substantial and largely unexploited potential for therapeutic applications in leukemia and immunopathological diseases.

Infectious diseases remain a major global health challenge, driven by antimicrobial resistance, pathogen persistence, and the limited integration of mechanistically innovative therapeutic approaches. Emerging evidence indicates that epigenetic regulation is fundamental to host-pathogen interactions, influencing transcriptional programmes associated with virulence, immune evasion, stress adaptation, and phenotypic plasticity. In organisms such as bacteria, parasites, and intracellular pathogens, including Mycobacterium tuberculosis and Plasmodium falciparum, chromatin-associated regulators and DNA-modifying enzymes have been identified as dosage-sensitive determinants of infection outcomes. Traditional strategies focus primarily on occupancy-driven enzymatic inhibition. In contrast, targeted protein degradation (TPD) introduces an event-driven pharmacological paradigm in which transient ligand engagement triggers sustained depletion of regulatory proteins. Platforms such as proteolysis-targeting chimeras (PROTACs) and BacPROTACs exemplify the ability to exploit host and pathogen proteolytic systems, thereby expanding the druggable proteome beyond conventional small-molecule targets. This review examines the relationship between epigenetic regulation and pathogen survival, highlights recent advances in degradation technologies, and discusses conceptual and translational challenges in implementing TPD in antimicrobial and antiparasitic drug discovery.

Copper-bearing low-carbon high-strength steels are widely employed in marine engineering. However, the microstructural homogeneity, strength-toughness matching, and low-temperature toughening mechanisms of such steels at high copper contents remain unclear. Existing studies have predominantly focused on the Cu content range of 1-2 wt.%, lacking systematic comparisons regarding microstructural evolution and property regulation throughout the entire rolling-heat treatment process at higher Cu levels. To clarify the influence of Cu content on the microstructural evolution and mechanical properties of CuxNi2.7Mn steels during processing and heat treatment, and to fully exploit the Cu precipitation strengthening effect while suppressing its embrittlement drawback, this study investigates CuxNi2.7Mn steels with Cu contents of 1.35 wt.%, 3.1 wt.%, and 6 wt.%. The specimens were fabricated via vacuum melting and two-stage rolling. Combining in situ observation using a high-temperature laser confocal microscope, optical microscopy, scanning electron microscopy, X-ray diffraction, and mechanical property tests, the effects of different Cu contents on the microstructure, conventional mechanical properties, and low-temperature toughness at -40 °C of the steels in both as-rolled and optimally heat-treated states (solid solution at 900 °C for 1 h + aging at 540 °C for 2 h) were systematically investigated. The results demonstrate that in the as-rolled condition, with increasing Cu content, the Vickers microhardness (HV1) of the steel increases from 183.9 HV1 to 271.9 HV1, the yield strength rises from 556.55 MPa to 852.87 MPa, and the tensile strength increases from 758.53 MPa to 1162.59 MPa. Nevertheless, excessive Cu content induces austenitic grain coarsening, aggregation of Cu-rich precipitates, and stress concentration, resulting in significant deterioration of ductility and toughness. Following optimal heat treatment, the banded structure is completely eliminated, the microstructural homogeneity is substantially improved, and the ductility and toughness are remarkably enhanced compared with the as-rolled state. Meanwhile, the strength continues to increase with rising Cu content, with the 6 wt.% Cu steel achieving a yield strength of 922.51 MPa and a tensile strength of 955.17 MPa. In terms of low-temperature toughness, the 3.1 wt.% Cu steel exhibits the poorest performance (90.8 J), whereas the 6 wt.% Cu steel presents a sharply increased low-temperature impact energy of 152.6 J. This is attributed to the precipitation of particulate phases such as TiC and MnS, which effectively disperse low-temperature stress and hinder crack propagation. Overall, the CuxNi2.7Mn steel with 6 wt.% Cu possesses the highest strength as well as excellent low-temperature toughness after optimal heat treatment, providing theoretical and experimental foundations for the composition design and heat treatment process optimization of high-copper steels for marine applications.

The expanding use of Conilon and Conilon × Robusta hybrids in Brazilian coffee cultivation contrasts with the scarcity of information on the genetic variability underlying their physical yield attributes. This study quantified genetic variability and estimated genetic parameters for fresh fruit mass and volume, fruit-to-bean ratio, and bean and husk proportions in 48 Coffea canephora genotypes, compared the discriminatory power of gravimetric and volumetric metrics in classifying processing efficiency, and identified genotypes combining high bean proportion and genetic divergence for use in breeding programs. A randomized complete block design with three replications was used. The fruit mass required to produce a 60 kg bag of processed coffee (FMM/bag) ranged from 205.29 to 251.46 kg bag-1, representing an 18% difference between the most and least efficient groups identified by the Scott-Knott test. High heritability was found for bean proportion (90.74%) and FMM/bag (84.58%), confirming strong genetic control over fruit-to-bean yield. Mass-based metrics showed greater discriminatory power than volumetric ones, forming four distinct groups versus two. Conilon genotypes tended toward greater yield efficiency. The observed variation indicates exploitable genetic variability for selective gains, with direct implications for crossing strategies and post-harvest processing optimization in C. canephora.

As endemic or regionally distinctive taxa in China, Ottelia species exhibit notable ecological adaptability as well as potential nutritional and medicinal value. Nevertheless, a comprehensive characterization and comparative analysis of the chemical constituents across this genus remain lacking, particularly for O. acuminata var. jingxiensis, O. fengshanensis, and O. guanyangensis. This knowledge gap has hindered the systematic exploitation and value-added utilization of Ottelia resources. In the present study, five Ottelia taxa-O. acuminata, O. acuminata var. jingxiensis, O. fengshanensis, O. guanyangensis, and O. alismoides-were investigated. By integrating widely targeted metabolomics, network pharmacology, and in vitro experimental validation, we identified Rivularin, Tenaxin I, Sinensetin, 8-Methoxyapigenin, Chrysoeriol, Hispidulin, Genkwanin, 5,2'-Dihydroxy-7,8-dimethoxyflavone, Kumatakenin, and Pectolinarigenin as key antioxidant constituents in the Genus Ottelia. Network-based analyses further indicated that these compounds predominantly act on PTGS1/2 and AR, and may mediate antioxidant activity primarily through the PI3K-Akt signaling pathway and pathways associated with EGFR tyrosine kinase inhibitor resistance. Collectively, these findings provide a scientific basis for the further development, functional evaluation, and sustainable utilization of the Genus Ottelia.

To overcome the antibiotic resistance problem, exploiting reproducible evolutionary tradeoffs is considered key for designing evolution-proof antibiotic therapies. Yet the predictability of resistance evolution has been viewed as limited, given the often idiosyncratic genetic trajectories observed in laboratory evolution experiments. To address this, we partially mimicked clinical antibiotic pharmacodynamics by imposing strong selection and evolved Escherichia coli under single or sequential antibiotic treatment. Under single-antibiotic selection, endpoint resistance, persistence, and tolerance phenotypes were reproducible, but populations evolving in parallel frequently followed divergent genetic trajectories. Remarkably, sequential antibiotic use redirected these divergent paths toward genotypic and phenotypic convergence, driven by extinction of resistance-conferring mutations when switching to the next effective antibiotic. Single-cell RNA sequencing demonstrated that evolved cultures contain cells occupying distinct metabolic niches and include more cells in states with lower translational activity and higher expression of toxin-antitoxin genes. These findings provide evolutionary insights to inform clinically effective antibiotic treatment strategies that employ sequential treatment.

This work presents a methodology for the generation of continuous fibre trajectories based on principal stress directions in continuous fibre-reinforced additive manufacturing (CFAM). The material system considered consists of continuous carbon fibre (CCF-1.5K) embedded in a CFC-PA thermoplastic matrix. CFAM enables the deposition of fibres along tailored paths, allowing improved alignment with the load direction, compared to traditional composite manufacturing. In this way, the strong anisotropy of composite materials, typically considered a limitation, is exploited as a design opportunity by aligning fibres with the structural load paths. The proposed approach combines finite element analysis with a path generation procedure, including the computation of principal stress directions, the extraction of streamlines of the principal stress field, and a dedicated post-processing stage aimed at obtaining continuous and manufacturable fibre layouts. The effectiveness of the method is assessed through a finite element-based comparison with conventional fibre configurations, showing an increase in global stiffness of approximately 20% with respect to the best-performing unidirectional layout. In addition, the feasibility of the generated trajectories is demonstrated through printing tests performed on a continuous fibre additive manufacturing system. The results confirm that the proposed methodology enables the generation of physically realizable fibre paths while improving structural performance.

Neuroblastoma is characterized by noticeable resistance to chemotherapy, largely driven by the ability of tumour cells to reorganize stress-adaptive signalling networks rather than relying on single oncogenic drivers. We conducted a study to investigate the pharmacological mode of action of doxorubicin in modifying adaptive signalling pathways in SH-SY5Y neuroblastoma cells, and whether the capacity of plant metabolites can exploit emergent biochemical vulnerabilities. Transcriptomic profiling through RNA sequencing conducted 48 h post-doxorubicin exposure unveiled the organized disruption of pathways linked with amyloidogenic processes, oncogenic signalling pathways, oxidative stress, and DNA repair. The protein-protein interactions, coupled with Kyoto Encyclopedia of Genes and Genomes pathway evaluations, revealed five network-central-hubs: BRAF, GSK3β, PARP1, BACE1, and MAOB. Structural docking integrated with 200 ns molecular dynamics simulations illustrated binding stability across multiple targets driven by three metabolites, Lactol binding to BRAF (-54.13 kcal/mol) and MAOB (-39.08 kcal/mol), Amino(1H-indol-2-yl)acetic acid to BACE1 (-41.07 kcal/mol) and GSK3β (-47.38 kcal/mol), and Quercetin-3-(6″-malonyl-glucoside) binding to PARP1 (-46.03 kcal/mol). In vitro Cell Counting Kit-8 proliferation assays validated the significant anti-neuroblastoma efficacy, with the lowest IC50 (0.2397 µM) being exhibited by Amino(1H-indol-2-yl)acetic acid, followed by Lactol (1.226 µM) and Quercetin-3-(6″-malonyl-glucoside) (1.301 µM), which mirrored the cytotoxic action of doxorubicin (1.306 µM). These results suggest that plant-derived metabolites may interact with stress-adaptive signalling pathways connected with neuroblastoma. However, direct experimental validation of target engagement and pathway modulation will be required to confirm these predicted interactions.

An important goal of environmental health research is to assess the health risks posed by mixtures of multiple environmental exposures. In these mixtures analyses, flexible models such as Bayesian kernel machine regression and multiple index models are appealing because they allow for arbitrary non-linear exposure-outcome relationships. However, this flexibility comes at the cost of low power, particularly when exposures are highly correlated and the health effects are weak, as is typical in environmental health studies. We propose a multivariate index modeling strategy that borrows strength across exposures and outcomes by exploiting similar mixture component weights and exposure-response relationships. In the special case of distributed lag models, in which exposures are measured repeatedly over time, we jointly encourage co-clustering of lag profiles and exposure-response curves to more efficiently identify critical windows of vulnerability and characterize important exposure effects. We then extend the proposed approach to the multiple index model setting where the true index structure-the number of indices and their composition-is unknown, and introduce variable importance measures to quantify component contributions to mixture effects. Using time series data from the National Morbidity, Mortality and Air Pollution Study, we demonstrate the proposed methods by jointly modeling three mortality outcomes and two cumulative air pollution measurements with a maximum lag of 14 days.

The rapid advancement of generative artificial intelligence technologies has introduced new security challenges, raising significant concerns. As deepfake technology becomes more sophisticated, it might be exploited by malicious actors to generate highly realistic fake images, thereby compromising the authenticity and reliability of the original content. Driven by this concern, this article introduces a universal deepfake detection model, multiscale spatial frequency-aware transformer and saturation analysis (MSFTSA), based on a multiscale, spatial-frequency-aware Transformer and saturation analysis. Unlike existing methods, MSFTSA reexamines fundamental differences between real and fake images across the frequency, spatial, and saturation domains. For this purpose, an efficient multiscale frequency-domain decoupling module has been designed to capture image features from different frequency bands, assisting in identifying inherent fake characteristics across multiple frequency scales. In addition, a spatial scattering module (SSM) is introduced to model global relationships between multiscale frequency features, achieving full-frequency interactive learning in the spatial domain. Furthermore, image saturation is used as a critical indicator to distinguish between real and fake images. Extensive experiments across multiple deepfake image datasets generated by generative adversarial networks (GANs) and diffusion models (DMs) demonstrate MSFTSA's performance, significantly outperforming existing state-of-the-art methods and showcasing exceptional generalization capability and robustness.

The efficient separation of tungsten and molybdenum represents a pivotal challenge for the effective, high-value-added utilization and recycling of these strategic metal resources. Developing clean and recyclable separation processes has become a major focus of research, reflecting a growing emphasis on sustainability. This study proposes a method for the efficient and deep separation of molybdenum and tungsten from tungstate-based mixed oxides by leveraging their differential coordination properties with hydrogen peroxide. The composites prepared by mechanical mixing were characterized using techniques such as ICP, SEM, XRD, and Raman spectroscopy. The results demonstrated a significant difference in the dissolution behavior of MoO3 and WO3 in hydrogen peroxide, indicating a substantial coordination disparity between MoO3 and WO3 toward H2O2, which can be effectively exploited for Mo/W separation. Additionally, hydrothermal synthesis was employed to simulate the separation under more realistic conditions. In this study, hydrogen peroxide was selected as an effective reagent for separation, and the influence of multiple variables was systematically evaluated. The results demonstrated that under optimal conditions-specifically at a molar ratio nMo/nW = 40, a temperature of 65 °C, nH2O2/nM = 1.25 and a reaction time of 1.5 h-a maximum separation factor of 124 between tungsten and molybdenum was achieved. This process exhibits significant potential for industrial application due to its low consumption of H2O2, large separation factor, and cost-effectiveness.

Renal cell carcinoma (RCC) is acknowledged as a heterogeneous malignancy underlined by complex genetic, metabolic, and immune dysregulation. In particular, molecular studies have revealed distinct oncogenic mechanisms that have been exploited and studied as therapeutic intervention targets. These include hypoxia-driven signaling, chromosomal translocations, and gene fusion events that affect tumor progression. This review provides a comprehensive overview of these targets and rethinks RCC management. Therapeutic concepts include the targeting of genomic fusion biology with emerging cell-based immunotherapies or targeted molecular inhibition, and orthomolecular therapeutic strategies are presented. Two clinical and pathological features are highlighted-namely, the TFE3 fusion proteins in translocation RCC and the growing role of hypoxia-inducible factor-2α (HIF-2α) inhibitors in clear-cell RCC. We also present recent data on novel immunotherapeutic approaches, including autologous hematopoietic stem and progenitor cell-based interferon-α gene therapy, as well as chimeric antigen receptor T-cell therapy. These therapies are discussed in light of their mechanistic rationale, translational potential, and existing clinical challenges due to unwanted side effects. At last, orthomolecular and natural product-based therapies are reviewed for their potential as adjunctive therapies that might be used for oxidative stress management, the targeting of tumor metabolism and immune effects, and to increase standard treatment tolerance. This review points to a multidimensional framework that might support further research and studies in precision-guided RCC management, as integrative approaches may enhance therapeutic efficacy, reduce toxicity, and support the development of personalized interventions for advanced or treatment-resistant RCC.

The white-backed planthopper (Sogatella furcifera) serves as the vector for the southern rice black-streaked dwarf virus (SRBSDV), with varying transmission efficiencies across populations. This research identifies Wolbachia, a common insect symbiont, as a key facilitator in breaching the salivary gland barrier for SRBSDV, revealing a mechanism by which the virus exploits an insect symbiont for transmission. Through field and laboratory investigations, it was observed that high-transmission (HT) planthopper populations contained high levels of Wolbachia, while low-transmission (LT) populations had minimal titers. Experiments involving thoracic injections confirmed Wolbachia's specific role in infiltrating salivary glands, as SRBSDV was unable to colonize glands in Wolbachia-depleted insects despite being present systemically. Ultrastructural analysis showed Wolbachia enveloping SRBSDV particles within gland cells, further supported by molecular assays indicating a direct interaction between Wolbachia surface protein (WSP) and viral P8 capsid protein. Disruption of this interaction using anti-WSP antibodies reduced salivary gland viral load and transmission rates, underscoring its functional importance. These results contrast with Wolbachia's antiviral effects in mosquitoes, highlighting a context-dependent "hitchhiking" strategy for viral dissemination. The WSP-P8 interaction presents a specific target for inhibiting SRBSDV transmission without resorting to pesticides, proposing a symbiont-informed approach as a sustainable strategy against rice viral diseases.