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Consensus clustering provides an elegant framework to aggregate multiple weak clustering results to learn a consensus one that is more robust and stable than a single result. However, most of the existing methods usually use all data for consensus learning, whereas ignoring the side effects caused by some unreliable or difficult data. To address this issue, in this article, we propose a novel self-paced consensus clustering method with adaptive bipartite graph learning to gradually involve data from more reliable to less reliable ones in consensus learning. At first, we construct an initial bipartite graph from the base results, where the nodes represent the clusters and instances, and the edges indicate that an instance belongs to a cluster. Then, we adaptively learn a structured bipartite graph from this initial one by self-paced learning, i.e., we automatically determine the reliability of each edge with adaptive cluster similarity measuring and involve the edges in bipartite graph learning in order of their reliability. At last, we obtain the final consensus result from the learned structured bipartite graph. We conduct extensive experiments on both toy and benchmark datasets, and the results show the effectiveness and superiority of our method. The codes of this article are released in http://Doctor-Nobody.github.io/codes/code_SCCABG.zip.

The associations of red/processed meat consumption and cancer-related health outcomes have been well discussed. The umbrella review aimed to summarise the associations of red/processed meat consumption and various non-cancer-related outcomes in humans. We systematically searched the systematic reviews and meta-analyses of associations between red/processed meat intake and health outcomes from PubMed, Embase, Web of Science and the Cochrane Library databases. The umbrella review has been registered in PROSPERO (CRD 42021218568). A total of 40 meta-analyses were included. High consumption of red meat, particularly processed meat, was associated with a higher risk of all-cause mortality, CVD and metabolic outcomes. Dose–response analysis revealed that an additional 100 g/d red meat intake was positively associated with a 17 % increased risk of type 2 diabetes mellitus (T2DM), 15 % increased risk of CHD, 14 % of hypertension and 12 % of stroke. The highest dose–response/50 g increase in processed meat consumption at 95 % confident levels was 1·37, 95 % CI (1·22, 1·55) for T2DM, 1·27, 95 % CI (1·09, 1·49) for CHD, 1·17, 95 % CI (1·02, 1·34) for stroke, 1·15, 95 % CI (1·11, 1·19) for all-cause mortality and 1·08, 95 % CI (1·02, 1·14) for heart failure. In addition, red/processed meat intake was associated with several other health-related outcomes. Red and processed meat consumption seems to be more harmful than beneficial to human health in this umbrella review. It is necessary to take the impacts of red/processed meat consumption on non-cancer-related outcomes into consideration when developing new dietary guidelines, which will be of great public health importance. However, more additional randomised controlled trials are warranted to clarify the causality.

The association between serum 25-hydroxy vitamin D (25(OH)D) concentrations and mortality remains unclear among patients with metabolic dysfunction-associated fatty liver disease (MAFLD) or non-alcoholic fatty liver disease (NAFLD).To evaluate the association between serum 25(OH)D concentrations and mortality among individuals with MAFLD/NAFLD.The study included 4651 individuals with fatty liver disease (FLD) (3964 had MAFLD and 3968 had NAFLD) from the Third National Health and Nutrition Examination Survey. Fatty liver disease was identified by ultrasonographic detection of hepatic steatosis. Mortality was ascertained by linkage to the National Death Index up to 31 December 2019. Cox proportional hazards models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) with adjustment of potential confounders.Of 4651 individuals with FLD, 3427 individuals (69.7%) had both MAFLD and NAFLD. During median follow-ups of 25.8 and 26.1 years, we identified 1809 and 1665 deaths among 3964 participants with MAFLD and 3968 participants with NAFLD, respectively. Compared with participants with serum 25(OH)D concentrations ≤30.0 nmol/L, the multivariable-adjusted HR and 95% CI of all-cause mortality were 0.62 (0.43, 0.89) for participants with MAFLD having serum 25(OH)D75.0 nmol/L (P-trend = 0.001), and 0.63 (0.42, 0.95) for participants with NAFLD having serum 25(OH)D75.0 nmol/L (P-trend = 0.002). A nonlinearly inverse association was observed between serum 25(OH)D concentrations and all-cause mortality among participants with MAFLD (Poverall0.001; Pnonlinear = 0.003) or NAFLD (Poverall0.001; Pnonlinear = 0.009), with a threshold effect at around 50.0 nmol/L. The inverse association was stronger among participants with MAFLD aged60 years (P-interaction = 0.001).This study suggested a nonlinearly inverse association between serum 25(OH)D concentrations and all-cause mortality among patients with MAFLD/NAFLD, with a threshold effect at around 50.0 nmol/L of serum 25(OH)D.

With the rising prevalence of diabetes mellitus around the world, researchers have been searching for a new treatment option that is both more effective and safer than chemotherapy. This study evaluated the antidiabetic activities of aqueous extracts of Eclipta prostrataL. in streptozotocin (STZ)-induced diabetic rats. The rats were randomized into six groups: Normal control, STZ-induced diabetic rats (50 mg/kg), STZ + EPE (100 mg/kg), STZ + EPE (200 mg/kg), STZ+glibenclamide (600 µg/kg) and EPE alone (200 mg/kg). The STZ-induced diabetic rats showed significantly (p

In rotating machinery, the rotor mass unbalance will cause synchronous vibration, in which static unbalance will produce synchronous vibration force, and dynamic unbalance will produce synchronous vibration moment. In order to suppress the vibration moment of the magnetically suspended control moment gyro, a modified dual-channel notch filter (DNF) is proposed in this article. First, the modeling of the rotor system with mass unbalance is performed. Then, the design of modified DNF is carried out. This notch filter uses the equivalent vibration moment calculated by the current and displacement signals as input, and uses the orthogonal characteristics of the moment signals in the X and Y directions to enter the controller at the same time. The stability of the rotor system with a DNF is proven. Finally, the experimental results are given to show that this method can effectively eliminate the synchronous vibration moment in the rotor system.

The boundary conditions of the main girder are complicated for suspension bridges. And they have a significant impact on the static and dynamic response of the structure. This study proposes a model updating method based on influence lines considering boundary conditions. First, rotational and translational spring elements are used to simulate the boundary conditions of the main girder. Then, rotational spring stiffness is estimated by constructing a sensitive objective function based on the influence lines. Translational spring stiffness is estimated by constructing a sensitive objective function based on the temperature-induced displacements at both ends of the girder. To improve calculation efficiency, a Gaussian process model is used instead of the finite element model (FEM). The estimation of boundary conditions is expressed as an optimization whose main goal is to iteratively estimate the boundary conditions to minimize the objective functions. After the boundary conditions are determined, the influence lines-based model updating is carried out by using the adaptive metamodel global optimization method. To validate the proposed method, a long-span suspension bridge is taken as a case study. The results show that the boundary conditions of the main girder have been successfully estimated. The accuracy of the model updating is improved after the boundary conditions are considered. The updated FEM with accurate boundary conditions can be a benchmark model for bridge damage identification or condition assessment.

Benign and malignant diagnosis of nonpalpable breast imaging reporting and data system (BI-RADS) category 0 lesions on digital mammograms (DMs) is very important. We compared the diagnostic performance of non-contrast-enhanced magnetic resonance imaging (MRI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for them. We sought to evaluate BI-RADS category 0 lesions using 3 MRI sequences: short tau inversion recovery (STIR), STIR combined with high b value diffusion-weighted imaging (STIR-DWI), and DCE-MRI.We retrospectively reviewed 114 breast DMs rated as nonpalpable BI-RADS category 0 lesions in 112 patients from January 2014 to June 2019. STIR, high b value DWI, and DCE-MRI were performed for all patients. Two breast radiologists read individual sequences (STIR, DWI, DCE-MRI) and pairs of sequences (STIR-DWI) to detect BI-RADS category 0 lesions in DMs. Receiver operating characteristic (ROC) curve analysis was used to assess diagnostic performance according to a best valuable comparator that combined MRI imaging, clinical, and pathological data.Among of 114 lesions (the median age of patients was 47 years; the median size of the lesion was 19 mm), 32 (48.5%) malignant lesions were missed by STIR, 9 (13.6%) malignant lesions were missed by STIR-DWI, and 3 (4.5%) malignant lesions were missed by DCE-MRI. The principal finding of our study was that STIR-DWI and DCE-MRI showed higher diagnostic accuracy than did STIR (P0.01). STIR-DWI showed higher accuracy [area under the curve (AUC) =0.858; sensitivity =87.8%] for BI-RADS category 0 lesions in DMs than did STIR (AUC =0.754; sensitivity =51.5%), while the performance was comparable to that of DCE-MRI (AUC =0.884; sensitivity =95.5%).Using pairs of sequences (STIR-DWI) is a non-contrast-enhanced MRI technique and had an equal diagnostic performance in distinguishing benign from malignant lesions among nonpalpable BI-RADS category 0 lesions to that of DCE-MRI. As a result, STIR-DWI as having the potential to improve the safety and efficacy in of breast cancer screening, especially in nonpalpable BI-RADS category 0 lesions at in DMs.

In efforts to decarbonise electricity, transport, and heating sectors, policy makers facilitate the integration of renewable energy sources and demand side management in multi-energy systems. With the support of the smart grid, an increasing number of consumers start to produce, store, and consume energy using zero-carbon electricity and heating sources, for example, solar panels, electric vehicles, and air source heat pumps, giving them the new role of multi-energy prosumers. A flexible local energy market structure and intelligent operations of smart grid are crucial factors for accommodating the role of multi-energy prosumers. The blockchain technologies, for example, smart contracts and hypothetical technology, pave the path for the peer-to-peer (P2P) energy markets, which are open and accessible to prosumers with enhanced automation, security, and privacy. The state-of-the-art research and scientific innovations bring these advanced blockchain technologies towards applications into multi-energy systems.\ud\udThis special issue aims to solicit the innovative research on the blockchain empowering peer-to-peer trading in multi-energy systems. The scope of the research includes a single (multiple) energy vector(s) (e.g., electricity, gas, or heat), technologies (e.g., blockchain, smart contracts, or machine learning), theories (e.g., P2P trading mechanisms, pricing schemes, communication protocols, or consensus mechanisms) and applications (e.g., blockchain platforms or prosumer-centric energy scheduling).

Using a retrospective 15-year cohort, stratified by age, this study aimed to analyze the effect of dialysis modality on mortality of ESRD patients in a city of China. Study data were from the medical insurance information system of Kunshan, Jiangsu Province of China, and 1484 patients with ESRD, enrolled from 1 January 2005 to 31 December 2019 were included in this study. The primary outcome event was all-cause mortality, which was calculated in months. Dialysis modalities included hemodialysis (HD) and peritoneal dialysis (PD). Survival analysis and competing-risk regression model were performed in this study.HD costs significantly higher medical expense than the PD treatment regimen. The mean survival time was 121.28 (SE = 3.020) months for HD patients, while that was 94.68 (SE = 3.534) months for the PD. Ten-year survival rates of the young, middle-aged, and elderly were 0.82, 0.56, and 0.26, respectively. For the young (SHR = 0.869, 95% CI: 0.525-1.436) and middle-aged (SHR = 0.715, 95% CI: 0.484-1.057) ESRD patients, different dialysis modalities exhibited no statistical significance on the survival, but for the elderly, HD had a lower risk of mortality than PD (SHR = 0.747, 95% CI: 0.581-0.961).Survival of the young and middle-aged ESRD patients was superior to that of the elderly. Considering both survival time and direct medical costs, we recommend that PD could be a better choice for young and middle-aged ESRD patients, while HD may be suitable for older patients.

Although considerable progress has been achieved regarding the transformers in recent years, the large number of parameters, quadratic computational complexity, and memory cost conditioned on long sequences make the transformers hard to train and implement, especially in edge computing configurations. In this case, a dizzying number of works have sought to make improvements around computational and memory efficiency upon the original transformer architecture. Nevertheless, many of them restrict the context in the attention to seek a trade-off between cost and performance with prior knowledge of orderly stored data. It is imperative to dig deep into an efficient feature extractor for point clouds due to their irregularity and a large number of points. In this paper, we propose a novel skeleton decomposition-based self-attention (SD-SA) which has no sequence length limit and exhibits favorable scalability in long-sequence models. Due to the numerical low-rank nature of self-attention, we approximate it by the skeleton decomposition method while maintaining its effectiveness. At this point, we have shown that the proposed method works for the proposed approach on point cloud classification, segmentation, and detection tasks on the ModelNet40, ShapeNet, and KITTI datasets, respectively. Our approach significantly improves the efficiency of the point cloud transformer and exceeds other efficient transformers on point cloud tasks in terms of the speed at comparable performance.

Transparency Ecosystem for Research and Journals in Medicine (TERM) working group summarized the essential recommendations that should be considered to review and publish a high-quality guideline. These recommendations from editors and reviewers included 10 components of essential requirements: systematic review of existing relevant guidelines, guideline registration, guideline protocol, stakeholders, conflicts of interest, clinical questions, systematic reviews, recommendation consensus, guideline reporting and external review. TERM working group abbreviates them as PAGE (essential requirements for Publishing clinical prActice GuidelinEs), and recommends guideline authors, editors, and peer reviewers to use them for high-quality guidelines.

In recent years, there are several systematic reviews published on animal experiments of Traditional Chinese medicine (TCM). PRISMA (preferred reporting items for systematic reviews and meta-analysis) guidelines provide a guarantee for significantly improving the reporting quality of systematic reviews (SRs) and meta-analysis (MAs) to a certain extent; however, there are still certain defects found in the quality of SRs/MAs of animal experiments of TCM. It has been found that especially, the descriptions of the rationale and animal characteristics of TCM interventions are inadequate. As a result, we have developed a novel reporting guideline for SRs/MAs of animal experimental in the field of TCM (PRISMA-ATCM) to overcome these problems.PRISMA-ATCM reporting guidelines were formed by analyzing both the status and quality of published SRs/MAs of animal experiments and consulting experts in the related fields, and then by Delphi consultation, consensus meeting and revision.Among the 27 items on the PRISMA checklist, Title (1), Structured summary (2), Rationale (3), Objectives (4), Protocol and registration (5), Eligibility criteria (6), Data items (11), Planned methods of analysis (14), Study characteristics (18), Summary of evidence (24), Limitations (25), and Funding (27) have been extensively revised and expanded, to specifically include the details about TCM intervention and animal characteristics. In addition, illustrative examples and explanations have been provided for each item.PRISMA-ATCM could markedly improve the quality SRs/MAs of animal experiments in the field of TCM.

Background: The CyberKnife© system combines real-time image guidance and a dynamic tracking system to implement frameless radiotherapy. This umbrella review is aimed to evaluate the effectiveness and safety of CyberKnife. Methods: A comprehensive search of health technology assessments and systematic reviews was performed among the Embase, PubMed and other grey databases until July 2020. Treatment outcomes were extracted, and the quality of included studies were assessed using AMSTAR-2. Results: Nineteen studies were eligible. CyberKnife not only had a wide range of applications, long overall survival and great local control, but also had a limited toxicity and good cost–effectiveness compared with other radiotherapy equipment. Conclusion: Despite the relatively low quality of the evidence, our findings can still provide a decision reference for policymakers.

Magnetic resonance imaging, MRI, relying on (19)F nuclei has attracted much attention, because the isotopes exhibit a high gyromagnetic ratio (comparable to that of protons) and have 100% natural abundance. Furthermore, due to the very low traces of intrinsic fluorine in biological tissues, fluorine labeling allows easy visualization in vivo using (19)F-based MRI. However, one of the drawbacks of the available fluorine tracers is their very limited solubility in water. Here, we detail the design and preparation of a set of water-compatible fluorine-rich polymers as contrast agents that can enhance the effectiveness of (19)F-based MRI. The agents are synthesized using the nucleophilic addition reaction between poly(isobutylene-alt-maleic anhydride) copolymer and a mixture of amine-appended fluorine groups and polyethylene glycol (PEG) blocks. This allows control over the polymer architecture and stoichiometry, resulting in good affinity to water solutions. We further investigate the effects of introducing additional segmental mobility to the fluorine moieties in the polymer, by inserting a PEG linker between the moieties and the polymer backbone. We find that controlling the polymer stoichiometry and introducing additional segmental mobility enhance the NMR signals and narrow the peak profile. In particular, we assess the impact of the PEG linker on T(2)* and T(1) relaxation times, using a series of gradient-recalled echo images with varying echo times, TE, or recovery time, TR, respectively. We find that for equivalent concentrations, the PEG linker greatly increases T(2)*, while maintaining high T(1) values, as compared to polymers without this linker. Phantom images collected from these compounds show bright signals over a background with high intensities.

The Li4Ti5O12/Co3O4 composites were prepared by hydrothermal reaction method with different Co3O4 mass content (3%, 7%, 11%, and 15%). The Li4Ti5O12 nanoparticles were set in-situ on the Co3O4 sheet. Co ion was doped into the Li4Ti5O12 lattice. The first cycle specific capacity firstly increased and then decreased with Co3O4 content increasing, which the discharge capacity reached the peaking value that the first capacity was 1111 mAh/g and the specific discharge capacity retained 240 mAh/g after 200 cycles. After 200 cycles of charge and discharge, the retention of the capacity was 96.4% at 0.1 A/g, and the retention of the capacity was 98.4% at 0.5 A/g.

Li4Ti5O12/SnO2 composite with different SnO2 contents were prepared by hydrothermal method. SnO2 nanosheets were in situ formed on the surface of Li4Ti5O12 nanoparticles. At the same time, Sn ions were doped into the Li4Ti5O12 lattice, which effectively improved the conductivity of Li4Ti5O12. When the content of SnO2 was 8 %, the electrochemical performance of Li4Ti5O12/SnO2 composite was the best. The first discharge specific capacity was 480.54 mAh/g. The capacity remained at 276.8 mAh/g after 200 cycles at 0.1 A/g, and the capacity retention was as high as 87.4% (compared with the 10th cycle).

This paper exploits potential games to model the integrated demand response / economic dispatch problem and proposes a synchronous log-linear learning (LLL) architecture for generators and EV aggregators to simultaneously make real-time decisions. Furthermore, to address the reality that each EV aggregator needs to manage dynamic portfolios of EVs in a geographically spread area and participate on behalf of most recent demands of its managed EVs, the concept of Age of Information (AoI) is explicitly modeled and incorporated. It is proven that the proposed framework has guaranteed convergence to a Nash equilibrium that is also a global optimizer, using the stochastic stability theory, perturbed Markov process, and revision trees. Numerical test results on a 15-generator, 15-aggregator benchmark network validate the proposed framework and show that the explicit consideration of AoI improves the dynamic characteristics of the proposed synchronous LLL compared to conventional learning schemes in potential games.

Ag nanowires (Ag NWs) with different diameters were successfully synthesized by polyol reduction method. The Ag NWs’ size was controlled by the chloride concentration in the reaction system (0.12 g/mL–1/1.5/2 mL for samples 1, 2 and 3, respectively). The Ag NW samples were fully characterized by SEM and PXRD analyses. The biological activity of Ag NWs 1–3 for the treatment of traumatic subarachnoid hemorrhage complicated disease was evaluated, and the mechanism was discussed. First, the inhibitory activity of Ag NWs 1–3 on the levels of S100 and VEGF in serum was examined via ELISA detection. Real-time RT-PCR assay was subsequently used to test the activation for the NF-κB signaling pathway in brain tissue after treatment with the compound.

Clustering ensemble integrates multiple base clustering results to obtain a consensus result and thus improves the stability and robustness of the single clustering method. Since it is natural to use a hypergraph to represent the multiple base clustering results, where instances are represented by nodes and base clusters are represented by hyperedges, some hypergraph based clustering ensemble methods are proposed. Conventional hypergraph based methods obtain the final consensus result by partitioning a pre-defined static hypergraph. However, since base clusters may be imperfect due to the unreliability of base clustering methods, the pre-defined hypergraph constructed from the base clusters is also unreliable. Therefore, directly obtaining the final clustering result by partitioning the unreliable hypergraph is inappropriate. To tackle this problem, in this paper, we propose a clustering ensemble method via structured hypergraph learning, i.e., instead of being constructed directly, the hypergraph is dynamically learned from base results, which will be more reliable. Moreover, when dynamically learning the hypergraph, we enforce it to have a clear clustering structure, which will be more appropriate for clustering tasks, and thus we do not need to perform any uncertain postprocessing, such as hypergraph partitioning. Extensive experiments show that, our method not only performs better than the conventional hypergraph based ensemble methods, but also outperforms the state-of-the-art clustering ensemble methods.

Magnetically suspended control moment gyro (MSCMG) is used in high-performance satellite platforms such as ultraquiet and ultrastable satellite. However, due to the unbalanced mass and sensor runout, the harmonic vibrations of synchronous and multifrequency will be generated, which will affect satellite performance. Aiming at the problem of harmonic vibration suppression of MSCMG, this article proposes a control algorithm based on multisynchronous rotating frame transformation. The electromagnetic force of the active magnetic bearing in MSCMG is directly used as the input signal of the control algorithm to achieve zero magnetic force control. This algorithm uses the orthogonal characteristics of the output signals of the X -direction and Y -direction displacement sensors. The vibration force in both directions can be suppressed by one controller simultaneously, saving the computing resources and having a faster response speed. The stability of the proposed method and the robustness to frequency fluctuations are analyzed. By changing the phase compensation angle in different frequency ranges, the stability of the system in the whole frequency band is ensured. Finally, experimental results are given to verify that the proposed method can achieve high precision and fast response suppression of harmonic vibration.

Cancer metastasis causes most cancer-related deaths, and modeling cancer invasion holds potential in drug discovery and companion diagnostics. Although 2D cocultures have been developed to study cancer invasion, it is challenging to recreate the 3D cancer invasion of an individual cancer patient. Here, we report an acoustic bioprinting technology that can precisely construct tumor microtissues for modeling cancer invasion in 3D. By using acoustic droplet technology, we can precisely encapsulate cancer associated fibroblasts (CAFs) derived from a colorectal cancer patient into gel droplets and print them into a 3D CAF microtissue. After depositing a tumor organoid derived from the same patient, our 3D bio-printed microtissue can be used to model cancer cell migration and invasion from the tumor organoid to the 3D CAF microtissue. We further used 3D bio-printed microtissues to investigate cancer invasion dynamics as well as their treatment response using time-lapse imaging. Thus, our acoustic 3D bioprinting technology can be widely used for establishing various microtissues for modeling cancer invasion and other diseases, highlighting its potential in personalized treatment.

The T1–T2 dual-modal image fusion based on PEGylated ultrafine superparamagnetic iron oxide nanocrystal (SPIO@PEG) contrast-enhanced MRI has great potential for the evaluation and diagnosis of liver fibrosis.

In many real-world unsupervised learning applications, given data with balanced distribution, that is, there are an approximately equal number of instances in each class, we often need to construct a model to reveal such balance. However, in many data, especially the high-dimensional ones, the data in the original feature space often do not present such balance due to the redundant and noisy features. To tackle this problem, we apply an unsupervised spectral feature selection method to select some informative features, which can better reveal the balanced structure of data. Although spectral feature selection is one of the most popular unsupervised feature selection methods and has been widely studied, none of the existing spectral feature selection methods consider the balance property of data. To address this issue, in this article, we propose a novel balanced spectral feature selection (BSFS) method, which not only selects the discriminative features but also picks those to reveal the balanced structure of data. To the best of our knowledge, this is the first spectral feature selection method considering balance structure of data. By introducing a balanced regularization term, we integrate the balanced spectral clustering and feature selection into a unified framework seamlessly. At last, the experiments on benchmark datasets show that the proposed one outperforms the conventional feature selection methods in both clustering performance and balance, which demonstrates the effectiveness and efficiency of the proposed method.

The G2-like (GLK) transcription factors are plant-specific transcription factors (TFs) that perform extensive and significant roles in regulating chloroplast development. Here, genome-wide identification, classification, conserved motifs, cis-elements, chromosomal locations, MCScanX, evolution and expression patterns of the PtGLK genes in the woody model plant Populus trichocarpa were analyzed in detail. In total, 55 putative PtGLKs (PtGLK1-PtGLK55) were identified and divided into 11 distinct subfamilies according to the gene structure, motif composition and phylogenetic analysis. Synteny analysis showed that 22 orthologous pairs and highly conserved microscopy between regions of GLK genes across P. thrchocarpa and Arabidopsis were identified. Furthermore, analysis of the duplication events and divergence times provided insight into the evolutionary patterns of GLK genes. The previously published transcriptome data indicated that PtGLK genes presented distinct expression patterns in various tissues and different stages. Additionally, several PtGLKs were significantly upregulated under the responses of cold stress, osmotic stress, and MeJA and GA treatments, implying that they might take part in abiotic stress and phytohormone responses. Overall, our results provide comprehensive information on the PtGLK gene family and elucidate the potential functional characterization of PtGLK genes in P. trichocarpa.

Fibroblast activation protein (FAP) is higher expressed on cancer-associated fibroblasts (CAFs) in most malignant epithelial neoplasms, which is lower expressed in normal tissues. As a promising small molecular probe, FAP inhibitor (FAPI) shows the specific binding to FAP. This study aimed to explore a novel molecular probe [99mTc]Tc-HYNIC-FAPI targeting CAFs. The in vitro characteristics of the probe were also evaluated. The FAPI targeting FAP was designed, synthesized and conjugated with the chelator 6-hydrazinylnicotinic acid (HYNIC) for radiolabeling with 99mTc. The radiolabeling yield, radiochemical purity and stability were evaluated by Instant thin-layer chromatography (ITLC) and High performance liquid chromatography (HPLC). Lipophilicity was performed by the distribution coefficient test. The binding and migration ability of the probe was assessed using the FAP transfected tumor cell line. The radiolabeling yield of [99mTc]Tc-HYNIC-FAPI was (97.29 ± 0.46) %. The radiochemical purity was more than 90% and kept stable until 6 h. The radioligand was shown as lower lipophilicity, of which logD7.4 value was − 2.38 $$\pm$$ ± 0.13. In vitro experiments, the results indicated that the probe showed binding properties, and inhibited the migration of tumor cells. The novel [99mTc]Tc-HYNIC-FAPI probe was successfully radiosynthesized and exhibited good radiochemical purity, stability and in vitro binding ability to tumor cells. The [99mTc]Tc-HYNIC-FAPI will be a promising SPECT/CT imaging probe.