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Building long-lasting antimicrobial and clean surfaces is one of the most effective strategies to inhibit bacterial infection, but obtaining an ideal smart surface with highly efficient, controllable, and regenerative properties still encounters many challenges. Herein, we fabricate an ultrathin brush–hydrogel hybrid coating (PSBMA-P(HEAA- -METAC)) by integrating antifouling polyzwitterionic (PSBMA) brushes and antimicrobial polycationic (P(HEAA- -METAC)) hydrogels. The smart bacterial killing–releasing properties can be achieved independently by the opposite volume and conformation changes between the swelling (shrinking) of P(HEAA- -METAC) hydrogel layer and the shrinking (swelling) of PSBMA brushes. The friction test reveals that both METAC and SBMA components support great lubrication. By tuning the initial organosilane (BrTMOS:KH570) ratios, the prepared PSBMA-P(HEAA- -METAC) coating exhibits different antibacterial abilities from single “capturing–killing” to versatile “capturing–killing–releasing.” Most importantly, 99% of the bacterial-releasing rate can be easily achieved via 0.5 M NaCl treatment. This smart surface not only possesses long-lasting antibacterial performance, only ∼1.09 × 10 cell·cm bacterial residue even after 72 h exposure to bacteria solutions, but also can be regenerated and triggered between water and salt solution multiple times. This work provides a new way to fabricate antibacterial smart hydrogel coatings with bacterial “killing–releasing” functions and shows great potential for biomedical applications.

Natural products are a major source of biological molecules. The 3‐methylfuran scaffold is found in a variety of plant secondary metabolite chemical elicitors that confer host‐plant resistance against insect pests. Herein, the diversity‐oriented synthesis of a natural‐product‐like library is reported, in which the 3‐methylfuran core is fused in an angular attachment to six common natural product scaffolds—coumarin, chalcone, flavone, flavonol, isoflavone and isoquinolinone. The structural diversity of this library is assessed computationally using cheminformatic analysis. Phenotypic high‐throughput screening of β‐glucuronidase activity uncovers several hits. Further in vivo screening confirms that these hits can induce resistance in rice to nymphs of the brown planthopper Nilaparvata lugens. This work validates the combination of diversity‐oriented synthesis and high‐throughput screening of β‐glucuronidase activity as a strategy for discovering new chemical elicitors. Natural‐product‐like libraries: Coumarin, chalcone, flavone, flavonol, isoflavone and isoquinolinone are six naturally occurring compounds that are ubiquitous in diverse plants. To extend molecular diversity and discover chemical elicitors, the diversity‐oriented synthesis is reported of a natural‐product‐like library, in which the 3‐methylfuran core is fused to the six common natural product scaffolds.

Dear editor,Outsourcing of computation has received widespread attention with the development of cloud computing and the proliferation of mobile devices[1].Despite the huge benefits that it provides,it also encounters some security concerns and other challenges.First,the computational

Objectives: This study aimed to investigate differences in brain imaging characteristics among patients with Parkinson’s disease with cognitive impairment (PDCI), Parkinson’s disease without cognitive impairment (PDNCI), and healthy controls (HC), and to develop machine learning–based models for the early diagnosis of PDCI. A total of 48 patients with PDCI, 50 patients with PDNCI, and 47 age- and sex-matched healthy controls were enrolled, all of whom underwent magnetic resonance imaging using a 3.0 T MRI scanner. Arterial spin labeling (ASL) was applied to quantify cerebral blood flow (CBF), and quantitative susceptibility mapping (QSM) was used to assess magnetic susceptibility, while cognitive function was evaluated using standardized neuropsychological scales. Group differences were examined using one-way analysis of variance (ANOVA), and seven machine learning classifiers, including random forest (RF), K-nearest neighbors (KNN), and Extreme Gradient Boosting (XGB), were constructed to discriminate among the PDCI, PDNCI, and HC groups. The ANOVA results revealed significant differences in both CBF and magnetic susceptibility between the HC group and the two PD groups, whereas no significant differences were observed between the PDCI and PDNCI groups. Compared with normative data, patients with PDCI exhibited cognitive impairments exceeding 2 standard deviations in the domains of language, attention, and working memory, as well as impairments exceeding 1 standard deviation in visuospatial function, memory, and executive function. Among the machine learning models, RF, KNN, and XGB achieved perfect classification performance, with all evaluation metrics reaching 1.000, indicating excellent discriminative capability. Feature importance analysis identified increased CBF and magnetic susceptibility in regions such as the left precuneus (Precuneus_L) and left postcentral gyrus (Postcentral_L) as key imaging features distinguishing PDCI, and correlation analyses further demonstrated significant associations between cognitive deficits and alterations in CBF and magnetic susceptibility. These findings suggest that ASL- and QSM-derived imaging features have substantial potential as non-invasive biomarkers for the early diagnosis of PDCI, that patients with PDCI exhibit widespread impairments across multiple cognitive domains—particularly in language, attention, and working memory—and that machine learning models integrating multimodal imaging features provide a reliable and effective approach for early diagnosis and may facilitate personalized treatment strategies in Parkinson’s disease, although future studies with larger sample sizes and independent validation cohorts are warranted to enhance the robustness and generalizability of these models.

Turbulent entrainment‐mixing processes profoundly influence the relationship between radar reflectivity factor and liquid water content (Z‐LWC) of cloud droplets. However, quantification of the entrainment‐mixing mechanisms based on the Z‐LWC relationship is still lacking. To address this gap, 12,218 entrainment‐mixing cases are simulated using the Explicit Mixing Parcel Model. We examine the variations of the parameters in the power‐law relationship Z = aLWCb, and the relationship between parameter b and homogeneous mixing degree (ψ), a measure quantifying entrainment‐mixing processes. The results indicate that parameter b distributes within the range of 1–2, with a positive correlation between parameter b and ψ. The b‐ψ relationship is fitted, which connects the Z‐LWC relationship for various entrainment‐mixing types. The results suggest the potential for employing a remote sensing approach to investigate the entrainment‐mixing mechanisms of non‐precipitating small cumulus/stratocumulus clouds, thereby overcoming the limitations of traditional observational studies that rely solely on aircraft observations. Plain Language Summary Clouds play a pivotal role in the Earth's weather and climate. Among the cloud‐related processes, the entrainment‐mixing process profoundly influences the relationship between radar reflectivity factor and liquid water content of cloud droplets. However, quantification of the entrainment‐mixing mechanisms based on the aforementioned relationship remains incomplete. The high‐resolution results quantify a robust connection between entrainment‐mixing processes and the relationship, indicating the potential for a remote sensing approach to study the entrainment‐mixing mechanisms of non‐precipitating small cumulus/stratocumulus clouds. This approach could overcome the limitations of traditional observational studies that rely solely on aircraft observations. Key Points The influence of the entrainment‐mixing process on the parameter b in the Z = aLWCb and the physical mechanisms are examined A robust relationship between the parameter b and the homogeneous mixing degree is established The result suggests a potential remote sensing approach for studying entrainment‐mixing mechanisms

The TET family of dioxygenases promote DNA demethylation by oxidizing 5-methylcytosine to 5-hydroxymethylcytosine (5hmC). Hypothalamic agouti-related peptide-expressing (AGRP-expressing) neurons play an essential role in driving feeding, while also modulating nonfeeding behaviors. Besides AGRP, these neurons produce neuropeptide Y (NPY) and the neurotransmitter GABA, which act in concert to stimulate food intake and decrease energy expenditure. Notably, AGRP, NPY, and GABA can also elicit anxiolytic effects. Here, we report that in adult mouse AGRP neurons, CRISPR-mediated genetic ablation of Tet3, not previously known to be involved in central control of appetite and metabolism, induced hyperphagia, obesity, and diabetes, in addition to a reduction of stress-like behaviors. TET3 deficiency activated AGRP neurons, simultaneously upregulated the expression of Agrp, Npy, and the vesicular GABA transporter Slc32a1, and impeded leptin signaling. In particular, we uncovered a dynamic association of TET3 with the Agrp promoter in response to leptin signaling, which induced 5hmC modification that was associated with a chromatin-modifying complex leading to transcription inhibition, and this regulation occurred in both the mouse models and human cells. Our results unmasked TET3 as a critical central regulator of appetite and energy metabolism and revealed its unexpected dual role in the control of feeding and other complex behaviors through AGRP neurons.

Background The neuroimmune interaction mechanisms of neurodegenerative diseases have received increasing attention. Parkinson's disease (PD) is the second most common neurodegenerative disease, with potential immunoregulatory abnormalities. However, the causal roles of specific immune cell proteins remain unclear. Methods We obtained PD and immune cell protein data from an open and free genome‐wide association study (GWAS) for subsequent analysis. Two‐sample MR analyses with inverse‐variance weighted (IVW), MR‐Egger regression, weighted median, and weighted mode methods were used to evaluate the causal effects. Sensitivity analyses incorporated Cochran's Q test for SNP heterogeneity (prioritizing IVW estimates when present) alongside MR‐Egger intercept and leave‐one‐out evaluations to address horizontal pleiotropy. Results The IVW analysis revealed that the genetically predicted level of three immune cell proteins per standard‐deviation increase was positively associated with PD, including CD38 (OR = 1.13, 95%CI: 1.05–1.22, P = 0.001), FcγRIIIB (OR = 1.06, 95%CI: 1.01–1.11, P = 0.019), and CUL4B (OR = 1.11, 95% CI: 1.00–1.20, P = 0.012). The IVW analysis also revealed that the genetically predicted level of ADAMTSs per standard‐deviation increase was inversely associated with PD (OR = 0.89, 95% CI: 0.81–0.98, P = 0.013). Conclusions We demonstrate that CD38, FcγRIIIB, and CUL4B are risk factors for PD, whereas ADAMTSs is a protective factor. Mendelian randomization (MR) analysis was applied to elucidate the causal relationship between immune cell proteins and PD. Our study reveals a possible causal effect of immune cell proteins on the risk of PD and provides new ideas for the prevention and management of PD through immune cell proteins.

Hepatocellular carcinoma (HCC) is the third-leading cause of cancer deaths globally. Although considerable progress has been made in the treatment, clinical outcomes of HCC patients are still poor. Therefore, it is necessary to find novel prognostic factors upon which prevention and treatment strategies can be formulated. Ficolin-3 (FCN3) protein is a member of the human ficolin family. It activates complement through pathways associated with mannose-binding lectin-associated serine proteases. Herein, we identified that FCN3 was downregulated in HCC tissues and decreased FCN3 expression was closely related to poor prognosis. Overexpression of FCN3 induced apoptosis and inhibited cell proliferation via the p53 signaling pathway. Mechanistically, FCN3 modulated the nuclear translocation of eukaryotic initiation factor 6 (EIF6) by binding ribosome maturation factor (SBDS), which induced ribosomal stress and activation of the p53 pathway. In addition, Y-Box Binding Protein 1 (YBX1) involved in the transcription and translation level regulation of FCN3 to SBDS. Besides, a negative feedback loop in the downstream of FCN3 involving p53, YBX1 and SBDS was identified.

Background Food-borne carbon dots (CDs) are widely generated during food processing and are inevitably ingested by humans causing toxicity. However, the toxic effects of food-borne CDs on the blood glucose metabolism are unknown. Results In this study, we brewed beer via a representative strategy and extracted the melting-barley CDs (MBCDs) to explore the toxic effects on blood glucose in mice. We found the accumulation of fluorescent labeled MBCDs in various organs and oral administration of MBCDs can cause visceral toxicity, manifested as liver damage. Mice were orally administered MBCDs (5 and 25 mg/kg) for 16 weeks, and increased levels of fasting blood glucose were observed in both MBCDs-treated groups. Transcriptomic analyses revealed that MBCDs activate oxidative stress, inflammatory responses, the MAPK cascade, and PI3K/Akt signaling in mice livers. Mechanistically, MBCDs exposure-induced reactive oxygen species (ROS) overproduction activates the nuclear factor-κB (NF-κB) signaling pathway and MAPK cascade, thereby promoting phosphorylated insulin receptor substrate (IRS)-1 at Ser307 and inducing insulin resistance (IR). Meanwhile, the IR promoted gluconeogenesis, which enhanced MBCDs-induced hyperglycemia of mice. Importantly, inhibition of the ROS significantly attenuated the MBCDs-induced inflammatory response and MAPK cascade, thereby alleviating IR and hyperglycemia in mice. Conclusion In summary, this study revealed that MBCDs promote ROS overproduction and thus induced IR, resulting in imbalance of glucose homeostasis in mice. More importantly, this study was further assessed to reveal an imperative emphasis on the reevaluation of dietary and environmental CDs exposure, and has important implications for T2DM prevention research. Graphical Abstract

End-stage kidney disease (ESKD) is associated with cognitive impairment and affects different aspects of cortical morphometry, but where these changes converge remains unclear. Fractal dimension (FD) is used to represent cortical complexity (CC), which describes the structural complexity of the cerebral cortex by integrating different cortical morphological measures. This study aimed to investigate changes in CC in patients with ESKD prior to initiation of dialysis and to evaluate the relationship between changes in CC, cognitive performance, and uremic toxins. Forty-nine patients with ESKD naive to dialysis and 31 healthy controls (HCs) were assessed using structural magnetic resonance imaging (MRI) and cognitive tests, including evaluations of global cognitive function, memory, and executive function. Clinical laboratory blood tests were performed on all patients with ESKD, including measurement of nine uremic toxin-related indices. Cortical complexity was measured using MRI data to determine regional FD values. We estimated the association between cognitive performance, uremic toxin levels, and CC changes. Compared to HCs, patients with ESKD showed significantly lower CC in the left precuneus (p = 0.006), left middle temporal cortex (p = 0.010), and left isthmus cingulate cortex (p = 0.018). Furthermore, lower CC in the left precuneus was associated with impaired long-term delayed memory (Pearson r = 0.425, p = 0.012) in patients with ESKD. Our study suggests that regional decreases in CC are an additional characteristic of patients with ESKD naive to dialysis, related to impaired long-term memory performance. These findings may help further understand the underlying neurobiological mechanisms between brain structural changes and cognitive impairment in ESKD patients.