Advancing Open Science

Metabolic syndrome, driven by obesity, poses a significant global health burden. P. incarnata leaf polyphenol extract (PP), a crude 70% ethanol extract, exhibits potential bioactive properties, yet its efficacy and underlying mechanisms against metabolic disorders remain unclear. This study investigated effects of this extract (PP) on high-fat diet (HFD)-induced metabolic syndrome in mice and explored possible involvement of the gut–liver–adipose axis. Thirty mice were divided into five groups: ND (normal diet), HFD, PC-HFD (HFD + orlistat), PP1 (HFD + 75 mg/kg/day PP), and PP2 (HFD + 150 mg/kg/day PP). After 8 weeks, high-dose PP (PP2) significantly attenuated body weight gain, and reduced food intake, hyperlipidemia, and systemic inflammation. In addition, PP intervention altered the gut microbiota composition and was associated with enrichment of beneficial genera and elevated fecal short-chain fatty acids (SCFAs). Hepatic transcriptomics showed that PP2 extensively reversed HFD-induced transcriptional suppression, while fecal metabolomics indicated restoration of key metabolite profiles. Furthermore, PP2 markedly upregulated thermogenic gene expression in adipose tissue. Collectively, these findings suggest that PP, particularly at the high dose, ameliorated HFD-induced obesity and related metabolic disturbances; these benefits were accompanied by decreased caloric intake, changes in gut microbiota composition, hepatic transcriptomic profiles, and adipose thermogenic gene expression.

Glioblastoma (GBM) is the most aggressive primary brain tumor with limited therapeutic options and extremely poor prognosis. Aberrant activation of the receptor tyrosine kinase MET drives tumor progression, therapeutic resistance, and reduced survival, particularly in the mesenchymal GBM subtype. Given its crucial role in GBM recurrence and progression, we investigated the mechanisms of resistance to MET inhibition using patient-derived glioma-initiating cells (GICs) and orthotopic xenograft mouse models. GICs were treated with the MET inhibitor crizotinib to elucidate the mechanism of adaptive resistance. Prolonged MET inhibition induced a senescent-like phenotype in GICs, associated with downregulation of BNIP3, a mitochondrial protein regulating mitophagy. We showed that BNIP3 downregulation led to activation of mTOR signaling, promoting cellular survival and adaptive resistance. Combining crizotinib with the mTOR inhibitor everolimus effectively suppressed mTOR activity, reduced cell viability, and induced mitochondrial alterations, apoptosis, and necroptosis. In orthotopic GBM xenograft models, combined MET and mTOR inhibition significantly prolonged survival compared with single-agent treatments. Notably, sequential treatment—crizotinib followed by everolimus—further enhanced therapeutic efficacy. These effects were achieved without significant weight loss, supporting tolerability of the treatment regimen. Our findings identify the BNIP3-mTOR axis as a critical mediator of resistance to MET inhibition and demonstrate that combined inhibition of MET and mTOR exhibits significant synergy against GBM.

Background/Objectives: The volume computed tomography (CT) dose index (CTDIvol) is a scanner output, not an organ dose, and cannot express how tube-current modulation varies along a patient. An organ-specific weighted CTDIvol addressing this has been reported before, in single-institution cohorts and often from inputs routine archives do not retain. New here is not the quantity but what an open, multi-vendor operationalisation reveals: whether its inputs survive archive curation and what the fallback costs when they do not. Methods: Forty abdominal CT series, ten per manufacturer, were drawn from the Cancer Imaging Archive and twelve organs segmented with TotalSegmentator at inference. Of 480 requested organ–series combinations, 455 were produced. A rule-based acquisition-constancy criterion admitted 39 series. Results: Modulation weights spanned 0.59 to 1.69, so the index departs from the whole-scan CTDIvol by up to 70% within one acquisition. A recorded CTDIvol survived in 29 of 40 archived headers and was reconstructable in 5 and unavailable in 6, availability differing markedly between manufacturers. Forcing that reconstruction on series that did retain a value agreed to within 12% on three scanner models and diverged by 58% and 84% on two others. Estimated organ mass was broadly consistent with International Commission on Radiological Protection (ICRP) Publication 89 for liver and kidneys. Conclusions: This index is not an absorbed dose; the implementation is open.

Elastic metamaterials have been widely applied in elastic wave manipulation. Nevertheless, their performance is inherently restricted by two intrinsic coupling mechanisms: the modulus and density follow a power-law scaling relationship under static conditions, and the wave velocity is positively correlated with the modulus under dynamic conditions. To overcome these limitations, this study proposes a type of elastic metamaterial based on 3D-printed polymers, namely mass-loaded metamaterial (MLM). The structure is constructed by embedding discrete mass blocks into a lattice frame. The proposed design realizes the dual decoupling of modulus from density and wave velocity. The elastic modulus of the structure can be maximally increased by 307% while keeping the wave velocity constant; additionally, a 35% reduction in wave velocity can be achieved without deteriorating the elastic modulus. This distinctive decoupling effect possesses great application value in the field of underwater sound absorption. The structure can simultaneously exhibit high modulus and low wave velocity characteristics, which improves sound absorption performance and guarantees mechanical stability under high hydrostatic pressure.

Background/Objectives: Ischemic stroke (IS) induces substantial phenotypic remodeling within the neurovascular unit, and pericytes have been implicated in the cellular responses to ischemic injury. However, the molecular characteristics and regulatory mechanisms underlying pericyte phenotypic remodeling after ischemic stroke remain incompletely understood. Methods: In this study, we used PDGFR-β-CreERT2; ZsGreen lineage-tracing mice and a middle cerebral artery occlusion (MCAO) model to characterize the temporal changes in PDGFR-β-lineage cells following ischemic injury. In vitro, human brain vascular pericytes (HBVPs) exposed to oxygen–glucose deprivation/reperfusion (OGD/R) and subsequently maintained in neurobasal medium to examine ischemia-related phenotypic changes. The involvement of KLF2-associated signaling was further investigated using KLF2 knockdown approaches. Results: We observed increased expression of Nestin in PDGFR-β-lineage cells during the early post-ischemic period, followed by the emergence of subsets co-expressing GFAP or DCX during the subacute stage. These findings indicate the acquisition of neuroglial- and neuronal-associated molecular features, rather than providing definitive evidence of lineage conversion or functional differentiation. The expression of these markers declined at later stages, whereas PDGFR-β-lineage cells were also associated with vascular remodeling during the recovery phase. In vitro, OGD/R-treated HBVPs exhibited increased expression of DCX, MAP2, and NeuN. KLF2 knockdown attenuated these molecular changes. Consistently, OGD/R was associated with increased KLF2, phosphorylated ERK1/2, and MAP2 expression, whereas KLF2 knockdown reduced ERK1/2 phosphorylation and MAP2 expression. These findings suggest that KLF2 is associated with ERK1/2 activation and MAP2 expression during ischemia-related phenotypic remodeling of PDGFR-β-lineage cells. Conclusions: Overall, our results identify transient neuroglial- and neuronal-associated molecular changes in pericytes after ischemic injury and provide evidence for an association between KLF2 and ERK1/2–MAP2 signaling in this process, while further studies are required to determine whether these changes represent stable lineage conversion or functional neuronal differentiation.

Hydrogels, with their high water content, tissue-like softness, and excellent biocompatibility, are prime candidates for dynamic load-bearing interfaces such as cartilage replacement and implant coatings. However, the toughening structures introduced to enhance damage resistance often compromise surface lubrication: highly dissipative networks, while suppressing bulk crack propagation, frequently increase interfacial friction and accelerate wear. This toughness–lubrication trade-off constitutes a central bottleneck limiting the long-term service of hydrogels under dynamic contact conditions. This review examines the friction and wear behavior of various hydrogel systems and, from the dual perspectives of bulk mechanical reinforcement and surface lubrication regulation, summarizes the core design mechanisms of toughening and hydration lubrication strategies, respectively. Based on this analysis, this review proposes a functional decoupling design principle: hierarchical structures—ranging from homogeneous to heterogeneous—in which the bulk dissipates mechanical load while the surface maintains hydration lubrication, thereby reconciling mechanical toughness with lubrication. Finally, this review surveys cutting-edge applications of such materials in tissue engineering, device coatings, drug delivery, electronic energy-harvesting and storage devices, and soft actuators, providing a reference for the development of hydrogels that integrate excellent mechanical properties with lubrication functionality.

The effect of Ti content on the microstructure and mechanical properties of 1Cr10Co6MoVNbN steel was systematically investigated by comparing a low-Ti steel (LTS, 0.0066 wt.%) with a high-Ti steel (HTS, 0.02 wt.%). Increasing the Ti content was found to markedly alter the precipitate distribution and, consequently, the creep and high-temperature tensile behavior. In HTS, the higher Ti level promoted the preferential formation of (Ti,Nb)N inclusions during solidification; EDS analyses indicated that the matrix of HTS was substantially depleted of Nb, N, Mo, and V relative to LTS, consistent with this preferential nitride formation. In the tempered state, HTS exhibited larger NbC particles that were frequently attached to (Ti,Nb)N inclusions, whereas LTS contained finer, more uniformly dispersed NbC and abundant needle-like Cr₂N precipitates within martensitic laths. These microstructural differences correlated with a substantially higher 100 h creep strain in HTS compared with LTS at 550 °C and 325 MPa, and with a 550 °C tensile strength in HTS that fell below the aerospace standard requirement. Post-creep examination further revealed void formation around (Ti,Nb)N inclusions in HTS, suggesting that these inclusions act as stress concentrators that reduce the effective load-bearing area during creep. The results indicate that strict control of Ti content is essential in Nb–N-strengthened martensitic steels to avoid excessive (Ti,Nb)N formation and the associated degradation of high-temperature mechanical performance.

Owing to their geographical dispersion and high deployment costs, distributed photovoltaic (DPV) stations often lack access to high-precision meteorological data, which degrades power forecasting accuracy and threatens grid operational stability. Accordingly, developing a low-cost ultra-short-term power forecasting model is critical for power system dispatching. This study proposes a site-level ultra-short-term power forecasting method incorporating satellite cloud imagery (SCI) for DPV systems. First, three core forecasting challenges are analyzed: cross-modal data correlation establishment, spatial alignment between heterogeneous data structures, and spatiotemporal correlation extraction among dispersed stations. Second, a forecasting model taking satellite imagery and multi-station power outputs as inputs is constructed. It aligns multimodal data via a data embedding layer, and integrates multi-source features through cuboid self-attention modules and an encoder–decoder architecture. Third, a Multi-Scale Correlation Mechanism (MSCM) is proposed to capture spatiotemporal associations across geographically dispersed stations. Experimental results based on a real-world dataset from Hebei Province show strong performance. The full model with satellite cloud imagery achieves a Root Mean Square Error (RMSE) of 0.112 and a Mean Absolute Error (MAE) of 0.059, outperforming baseline models including the backpropagation (BP) neural network, long short-term memory (LSTM) network, and Transformer model.