Peptide lipidation is widely employed to enhance the apparent biological performance of peptide-based systems by improving stability, membrane association, and systemic persistence. However, increased potency is often interpreted uncritically as evidence of improved molecular design. This Perspective highlights that lipidation can reshape peptide behaviour by partially shifting functional control from sequence-encoded molecular recognition toward context-dependent effects in which membrane interactions, carrier binding, supramolecular assembly, and residence time play an increasingly important role. Under such design-dependent conditions, enhanced activity may primarily reflect delivery- and exposure-driven amplification rather than improved intrinsic efficacy. While MIC values and endpoint assays remain valuable benchmarks of activity, they are insufficient on their own to guide rational optimization of lipidated peptides, as they conflate intrinsic activity with lipid-driven distribution and time-dependent effects. Accordingly, this Perspective argues not against lipidation itself, but for reframing lipidation as a deliberately controllable interaction element rather than a generic potency modifier, thereby restoring mechanistic interpretability and design robustness in peptide chemical biology.

Acidified oil wastewater contains high concentrations of phosphorus and organic pollutants, which not only pose environmental risks but also contain recoverable phosphorus resources. However, its complex matrix (high COD and acidic environment) poses great challenges to phosphorus recovery technologies. This study successfully developed a new phosphorus recovery strategy adapted to the wastewater characteristics and optimized key operating parameters using response surface methodology. Key parameters including pH, Fe:P molar ratio, reaction time and residence time were investigated, clarifying their regulatory roles in the complex wastewater system. Under the optimal process conditions (pH = 7.77, Fe:P molar ratio = 2.24, reaction time = 2.23 h, and hydraulic residence time = 4.16 h), the phosphorus recovery rate reached 93.54%, and the vivianite crystallization rate reached 91.39%. Furthermore, a process parameter prediction model (the verification results and the model prediction was less than 2.1%) was established to produce vivianite crystallization with large particle size (d 50 = 113.4 μm) and uniform morphology, realizing synchronous wastewater treatment and phosphorus recovery. This study confirmed the feasibility and controllability of phosphorus recovery through vivianite crystallization in complex acidified oil wastewater matrices, providing a sustainable technical path for addressing the dual issues of phosphorus resource shortage and environmental pollution.

In the present study, high-impact polystyrene (HIPS) nanocomposites filled with nano-silicon In the present study, high-impact polystyrene (HIPS) nanocomposites filled with nano-silicon particles (Si) and multi-walled carbon nanotubes (MWCNTs) were prepared, their structural, dielectric, and mechanical properties were investigated. X-ray diffraction (XRD) analysis confirmed the polycrystalline nature of Si nanoparticles and the preservation of the MWCNTs'graphitic (002) structure. Atomic Force Microscope (AFM) analysis confirmed that the addition of hybrid fillers leads to changes in the surface morphology. It was determined that the root mean square (RMS) surface roughness decreased from approximately 85nm for pure MWCNTs to about 30nm for the HIPS + Si + MWCNT nanocomposite. Dielectric measurements showed that the HIPS+1.5 wt.%Si+1.5 wt.%MWCNT nanocomposite exhibited the highest dielectric response. The dielectric constant increased by approximately 3.5 times at 1kHz compared to neat HIPS. A relaxation maximum observed at 125Hz and was corresponding to an effective relaxation time of τ = 1.27m. At 200kHz, the AC conductivity was calculated as σ ≈ 10-8S/cm, consistent with near-percolation behavior. Mechanical testing revealed that tensile strength increased from 14.06 MPa to 17.49MPa with hybrid filler incorporation, while relative elongation decreased from 82% to 17.88%, indicating the typical stiffness-ductility trade-off. The results demonstrate that hybrid Si/MWCNT fillers induce synergistic effect on dielectric and mechanical performance in HIPS-based nanocomposites.

In cancer therapy, inhibiting tubulin polymerization is a key approach for modifying microtubule dynamics required for cell survival and proliferation. Microtubule destabilizing agents (MDAs), also known as tubulin polymerization inhibitors, prevent tubulin heterodimers from forming microtubules, resulting in catastrophic cellular collapse. A novel series of thiazole-based compounds 8a-o was developed to inhibit tubulin polymerization and assess for its antiproliferative efficacy against the NCI 60 cell line. The structures of the newly synthesized compounds were confirmed using 1H NMR, 13C NMR, and elemental microanalyses. All 15 compounds (8a-o) were assessed for antiproliferative action at a single dosage (10 μM) and analyzed against the comprehensive 60-cell panel at five concentrations (0.01, 0.1, 1, 10, and 100 μM). The results from the one-dose and five-dose studies demonstrate that 8b, 8c, 8d, 8m, and 8o are the most prominent antiproliferative agents, exhibiting the most favourable low-micromolar GI50 values across various cell lines, frequently advancing to low-micromolar TGI, and, in numerous sensitive cell lines, achieving LC50 values within the single-digit micromolar range. Compounds 8b, 8d and 8m showed significant anti-tubulin activity, with IC50 values ranging from 3.86 to 7.19 μM, compared to the reference CA-4 (IC50 = 2.40 μM). In the MCF-7 breast cancer cell line, compound 8m drove a significant accumulation of cells in the G2/M phase, increasing from 13.74% to 45.35%. G2/M arrest is frequently associated with DNA damage or the inhibition of microtubule dynamics, which aligns with Western blot results demonstrating a decrease in tubulin (50 kDa) expression following treatment with 8m. Apoptotic and necrotic experiments indicate that 8m stimulates a defined programmed cell death pathway rather than inducing non-specific toxic necrosis. Molecular docking corroborated their binding at the colchicine site, while in silico ADMET profiling indicated a promising drug-like profile for compound 8m.

Bacterial infections, especially those involving drug-resistant pathogens and biofilms, pose a severe global health threat. Conventional antibiotic therapies are limited by poor penetration, low specificity, and bacterial resistance mechanisms. Magnetic nanoparticles (MNPs) offer a promising alternative by combining magnetically guided targeting, magnetothermal/photothermal effects, multifunctional drug delivery, and imaging capabilities. Their antibacterial efficacy depends critically on the anatomical and pathological features of the infection site. For skin and superficial infections, near-infrared (NIR) light, particularly in the second biological window (NIR-II), enables synergistic photothermal/photodynamic/chemodynamic therapies. For deep soft tissue and bone infections, alternating magnetic fields (AMF) provide deep-penetrating magnetothermal activation or targeted enrichment, often combined with image-guided intervention. For cavity organ and implant-related infections, surface functionalization, local drug delivery, and endoscopic energy application allow precise interfacial intervention. This review systematically discusses MNP-based strategies tailored to different infection sites, integrating advances in material design, synergistic mechanisms, and preclinical progress. It also addresses challenges in multifunctional integration, biosafety, and clinical translation, and outlines future directions toward intelligent, theranostic, and synergistic antibacterial platforms.

This study successfully constructed shikonin-Co nanoparticles (Co-Shik NPs) and systematically evaluated their healing effects on diabetic wounds. The nanoparticles, prepared via a self-assembly method, exhibited uniform particle size and stable structure. In vitro experiments demonstrated that the material showed no cytotoxicity at a concentration of 16mg/L, significantly scavenged reactive oxygen species, and reduced the H2O2-induced apoptosis rate from 25.1% to 10.31%. Animal experiments revealed that the nanoparticle-treated group achieved a wound healing rate of 95% by day 12, which was significantly superior to the control group. Co-Shik NPs effectively modulated inflammatory factors (reducing IL-1β and TNF-α, elevating IL-10), alleviated oxidative stress, and promoted collagen deposition and epidermal regeneration. This study provides a novel material for diabetic wound treatment, deepens the understanding of the biological activities of natural product-metal complexes, and holds significant theoretical value and application prospects.

Tumor immunity arises from the coordinated action of innate and adaptive immune systems but is hindered by immune escape within the immunosuppressive tumor microenvironment (TME), for which ion channels and ion pumps have proved to be important. These proteins regulate a wide range of cellular processes to influence cancer progression and immune cell functions, of which ion channels maintain intracellular ion concentrations, cytosolic pH, and cell volume and functions and are essential for cancer development and immune regulation. Ion pumps correlate closely with ion channels and show an important effect on tumor immunity. Of these, H+-ATPases, especially vacuolar H+-ATPase (V-ATPase), play critical roles in cancer progression, metastasis, and immune evasion, while Na+/K+-ATPase (NKA) interacts with ion channels and H+-ATPase and hence contributes to antitumor immune responses. Thus, several cardiac glycoside inhibitors have been reported to exert potent antitumor and immunomodulatory activities. In the present perspective article, the interconnections among NKA, ion channels, H+-ATPases, and immune responses are addressed, with the potential activities of cardiac glycosides on tumor immunity discussed.

This study introduces a sustainable and efficient method for synthesizing quinazolinones, a class of heterocyclic compounds with significant pharmaceutical applications, via a multicomponent reaction (MCR) strategy. The process employs a novel magnetically recoverable palladium catalyst, enabling the coupling of aryl or heteroaryl iodides with a carbonyl source and 2-aminobenzamide in an eco-friendly PEG/water solvent system, facilitated by potassium carbonate as a base. The magnetic Pd catalyst exhibits robust catalytic activity, achieving high product yields (82%-98%) across diverse substrates, including electron-rich and electron-deficient aryl/heteroaryl iodides, underscoring its broad applicability. The catalyst is synthesized and characterized through various techniques, including FT-IR, BET, TGA, EDX, VSM, SEM, TEM, and XRD, which affirm its uniformity and stability. Key advantages of this protocol include exceptional atom economy, elimination of toxic solvents, and mild reaction conditions. The catalyst's magnetic properties allow effortless recovery via external magnetization, retaining >89% activity over five consecutive cycles, enhancing cost-effectiveness and sustainability. The methodology advances sustainable synthetic practices and holds promise for scalable applications in medicinal and industrial chemistry. This work highlights the transformative potential of magnetic nanocatalysts in developing eco-conscious routes to biologically relevant heterocycles.

The growing demand for green hydrogen requires efficient, cost-effective electrocatalysts for the oxygen evolution reaction (OER), a process currently hindered by sluggish kinetics. This study explores the optimisation of the spinel oxide NiFe2O4 through the partial Fe substitution with Cr and Mn, synthesised via a subcritical hydrothermal method, as an alternative to the standard Pt-group metals (PGM)-based electrocatalysts for the OER in alkaline environment. The work aims to establish a direct correlation between the chemical nature of the dopant, the resulting physicochemical properties, and the electrocatalytic performance. Detailed structural and surface characterisation, including XRD, TEM, and XPS, revealed distinct behaviours for the two dopants. Cr incorporation successfully produced phase-pure spinel nanoparticles with significantly reduced crystallite sizes and very high specific surface area (up to 226m2/g). In contrast, high Mn substitution led to the formation of secondary phases (Ni(OH)2) and nanoscale inhomogeneity, which persisted even after calcination, suggesting an incomplete inclusion of the three different metals in the same spinel lattice. Electrochemical investigations demonstrated that the nature of the dopant strongly influences OER activity. While Mn-doped samples showed higher apparent activity than pristine NiFe2O4, this improvement was attributed solely to an increased number of active sites (surface area) rather than improved intrinsic kinetics. Conversely, the Cr-substituted sample NiFeCrO4 exhibited superior performance, surprisingly matching the OER performances of the benchmark IrOX. This outstanding activity was ascribed to a synergistic effect: the material combines a high specific surface area with enhanced intrinsic kinetics, driven by an optimal composition rich in Cr3+ which is hypothesised to modulate the overall occupation to a favourable value for promoting the OER.

Accurately identifying virulence-associated proteins secreted by Gram-negative pathogens is essential for elucidating bacterial pathogenic mechanisms and developing novel antimicrobial interventions. However, traditional experimental approaches for effector identification are time-consuming and labor-intensive. Recent advances in machine learning (ML), ranging from handcrafted features to context-aware embeddings derived from protein language models, have significantly improved secreted effector prediction. Here, we provide a systematic overview of ML-based methods for secreted effector prediction, surveying available database resources, negative dataset construction strategies, feature representation approaches, and model architectures spanning classical machine learning to deep learning. We discuss fundamental challenges, including data scarcity and class imbalance, evaluation bias, and model interpretability. Finally, we outline future directions encompassing multimodal data integration, meta-learning to address data limitations, and uncertainty quantification to enhance predictive robustness.