A Systematic Review on Different Treatment Methods of Bone Metastasis from Cancers

Background and objective Skeletal metastase is one of the most common complications related to advanced cancer. The aim of this study is to analyze the effectiveness and safety of radiotherapy plus intravenous bisphosphonates versus radiotherapy alone for treating bone metastasis. Methods We searched the Cochrane Library, PubMed, EMBASE, CBM, CNKI and VIP, as well as the reference lists of reports and reviews. The quality of included trials was evaluated by the Cochrane Handbook. Data were extracted and evaluated by two reviewers independently. The Cochrane Collaboration’s Rev-Man 5.0 was used for data analysis. Results Twenty-two trials involving 1 585 patients were included. Compared with radiotherapy alone, radiotherapy plus intravenous bisphosphonates was more effective in total effective rate of pain relive (RR=1.21, 95%CI: 1.13-1.30, P < 0.001), average abated time (WMD=16.00, 95%CI: 10.12-21.88, P < 0.001), and quality of life (RR=1.25, 95%CI: 1.08-1.45, P=0.003, with significant differences. Side effects have no significant differences between the two groups except fever (RR=5.61, 95%CI: 3.11-10.13, P < 0.001). Conclusion Current evidence supports more effective of radiotherapy plus intravenous bisphosphonates for bone metastases. The combine treatment is safe and effective

A DNA aptamer for binding and inhibition of DNA methyltransferase 1.

DNA methyltransferases (DNMTs) are enzymes responsible for establishing and maintaining DNA methylation in cells. DNMT inhibition is actively pursued in cancer treatment, dominantly through the formation of irreversible covalent complexes between small molecular compounds and DNMTs that suffers from low efficacy and high cytotoxicity, as well as no selectivity towards different DNMTs. Herein, we discover aptamers against the maintenance DNA methyltransferase, DNMT1, by coupling Asymmetrical Flow Field-Flow Fractionation (AF4) with Systematic Evolution of Ligands by EXponential enrichment (SELEX). One of the identified aptamers, Apt. #9, contains a stem-loop structure, and can displace the hemi-methylated DNA duplex, the native substrate of DNMT1, off the protein on sub-micromolar scale, leading for effective enzymatic inhibition. Apt. #9 shows no inhibition nor binding activity towards two de novo DNMTs, DNMT3A and DNMT3B. Intriguingly, it can enter cancer cells with over-expression of DNMT1, colocalize with DNMT1 inside the nuclei, and inhibit the activity of DNMT1 in cells. This study opens the possibility of exploring the aptameric DNMT inhibitors being a new cancer therapeutic approach, by modulating DNMT activity selectively through reversible interaction. The aptamers could also be valuable tools for study of the functions of DNMTs and the related epigenetic mechanisms

Short-Chain Fatty Acid Propionate Alleviates Akt2 Knockout-Induced Myocardial Contractile Dysfunction

Background and Aims. Dysregulation of Akt has been implicated in diseases such as cancer and diabetes, although little is known about the role of Akt deficiency on cardiomyocyte contractile function. This study was designed to examine the effect of Akt2 knockout-induced cardiomyocyte contractile response and the effect of dietary supplementation of short-chain fatty acid propionate on Akt2 knockout-induced cardiac dysfunction, if any. Methods and Results. Adult male wild-type (WT) and Akt2 knockout mice were treated with propionate (0.3 g/kg, p.o.) or vehicle for 7 days. Oral glucose tolerance test (OGTT) was performed. Cardiomyocyte contractile function and mitochondrial membrane potential were assessed. Expression of insulin-signaling molecules Akt, PTEN, GSK3β, and eNOS receptors for short-chain fatty acids GPR41, and GPR43 as well as protein phosphatase PP2AA, PP2AB, PP2C were evaluated using Western blot analysis. Our results revealed that Akt2 knockout led to overt glucose intolerance, compromised cardiomyocyte contractile function (reduced peak shortening and maximal velocity of shortening/relengthening as well as prolonged relengthening), loss of mitochondrial membrane potential, decreased GPR41 and elevated GPR43 expression, all of which, with the exception of glucose intolerance and elevated GPR43 level, were significantly attenuated by propionate. Neither Akt2 knockout nor propionate affected the expression of protein phosphatases, eNOS, pan, and phosphorylated PTEN and GSK3β. Conclusions. Taken together, these data depicted that Akt2 knockout may elicit cardiomyocyte contractile and mitochondrial defects and a beneficial role of propionate or short-chain fatty acids against Akt2 deficiency-induced cardiac anomalies

GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning

The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Thus, benchmarking and advancing techniques detecting digital manipulation become an urgent issue. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology, which does not involve the most recent technologies like diffusion. The diversity and quality of images generated by diffusion models have been significantly improved and thus a much more challenging face forgery dataset shall be used to evaluate SOTA forgery detection literature. In this paper, we propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection, which contains a large number of forgery faces generated by advanced generators such as the diffusion-based model and more detailed labels about the manipulation approaches and adopted generators. In addition to evaluating SOTA approaches on our benchmark, we design an innovative cross appearance-edge learning (CAEL) detector to capture multi-grained appearance and edge global representations, and detect discriminative and general forgery traces. Moreover, we devise an appearance-edge cross-attention (AECA) module to explore the various integrations across two domains. Extensive experiment results and visualizations show that our detection model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Code and datasets will be available at \url{https://github.com/Jenine-321/GenFac

Simultaneous reconstruction of 3D fluorescence distribution and object surface using structured light illumination and dual-camera detection

Fluorescence molecular tomography (FMT) serves as a noninvasive modality for visualizing volumetric fluorescence distribution within biological tissues, thereby proving to be an invaluable imaging tool for preclinical animal studies. The conventional FMT relies upon a point-by-point raster scan strategy, enhancing the dataset for subsequent reconstruction but concurrently elongating the data acquisition process. The resultant diminished temporal resolution has persistently posed a bottleneck, constraining its utility in dynamic imaging studies. We introduce a novel system capable of simultaneous FMT and surface extraction, which is attributed to the implementation of a rapid line scanning approach and dual-camera detection. The system performance was characterized through phantom experiments, while the influence of scanning line density on reconstruction outcomes has been systematically investigated via both simulation and experiments. In a proof-of-concept study, our approach successfully captures a moving fluorescence bolus in three dimensions with an elevated frame rate of approximately 2.5 seconds per frame, employing an optimized scan interval of 5 mm. The notable enhancement in the spatio-temporal resolution of FMT holds the potential to broaden its applications in dynamic imaging tasks, such as surgical navigation.</p

Prediction of early mucosal healing of Crohn’s disease after treatment with biologics- a novel nomogram based on radiomics and clinical risk factors

BackgroundPredicting endoscopic remission is crucial for optimizing clinical treatment strategies and switching biologics in Crohn’s disease (CD). Mucosal healing (MH) is a key therapeutic target. This study aimed to develop a clinically applicable prediction model for early MH in CD patients receiving biological therapy.MethodsThis study retrospectively analyzed 120 CD patients diagnosed between 2018 and 2023, randomly divided into a training cohort and an internal validation cohort 1. Additionally, 34 prospectively enrolled CD patients diagnosed between 2024 and 2025 formed an internal validation cohort 2. Clinical indicators and conventional imaging features were evaluated to establish a clinical model. Radiomics features were extracted from computed tomography enterography (CTE) images, with regions of interest (ROIs) manually delineated to align with ulcerated intestinal segments identified through colonoscopy. A radiomics model was constructed, and a radiomics score (Rad-score) was derived. A clinical-radiomics nomogram was then developed by integrating Rad-score with clinical risk factors. Model performance was assessed using discrimination, calibration, decision curve analysis (DCA), and clinical impact curves.ResultsThe clinical-radiomics nomogram demonstrated strong predictive performance, with AUC values of 0.948 (95% CI: 0.902–0.995) in the training cohort, 0.925 (95% CI: 0.805–1.0) in the internal validation cohort 1, and 0.940 (95% CI: 0.802–0.993) in the internal validation cohort 2. The nomogram outperformed standalone clinical and radiomics models, with DCA confirming its clinical utility.ConclusionThe developed nomogram effectively predicts early MH in CD patients undergoing biological therapy, providing a practical tool for clinicians to optimize treatment strategies and improve outcomes

Power Optimization of Wave Energy Converter (WEC) Arrray Based on Sea Conditions of a Wind Farm

[Introduction] In order to respond to the national initiative of intensive sea use, develop clean energy, and contribute to carbon neutralization, a preliminary analysis was conducted on the multi-energy integration mode of offshore wind power and wave energy devices, and the WEC was optimized to achieve higher power output. [Method] Based on potential flow theory, the floating fan platform - WEC array was simulated to analyze the influence of the dimension and the inherent period of the WEC on the output power of the WEC. [Result] The simulation results show that under the same inherent period, the flatter the WEC is, the greater the total power of the WEC array is, and the economic difference of the WEC is small. For sea conditions, the economic difference of WEC array under different inherent periods is great, so it should be considered comprehensively. [Conclusion] In the known sea conditions, the inherent period and the dimenson of WECs can be optimized to achieve higher power output and increase energy output per unit sea area

Scene Consistency Representation Learning for Video Scene Segmentation

A long-term video, such as a movie or TV show, is composed of various scenes, each of which represents a series of shots sharing the same semantic story. Spotting the correct scene boundary from the long-term video is a challenging task, since a model must understand the storyline of the video to figure out where a scene starts and ends. To this end, we propose an effective Self-Supervised Learning (SSL) framework to learn better shot representations from unlabeled long-term videos. More specifically, we present an SSL scheme to achieve scene consistency, while exploring considerable data augmentation and shuffling methods to boost the model generalizability. Instead of explicitly learning the scene boundary features as in the previous methods, we introduce a vanilla temporal model with less inductive bias to verify the quality of the shot features. Our method achieves the state-of-the-art performance on the task of Video Scene Segmentation. Additionally, we suggest a more fair and reasonable benchmark to evaluate the performance of Video Scene Segmentation methods. The code is made available.Comment: Accepted to CVPR 202

High-performance two-dimensional Schottky diodes utilizing chemical vapour deposition-grown graphene-MoS2 heterojunctions

Heterostructures based on two-dimensional (2D) materials have attracted enormous interest as they display unique functionalities and have potential to be applied in next-generation electronics. In this report, we fabricated three types of heterostructures based on chemical vapor deposition-grown graphene and MoS2. A significant rectification was observed in the Au-MoS2-Gr heterojunction, with a rectification ratio over 2 × 104. The rectifying behavior is reproducible among nearly all 44 devices and is attributed to an asymmetrical Schottky barrier at Au-MoS2 and MoS2-graphene contacts. This rectification can be tuned by external gating and laser illumination, which have different impact on the rectifying ratio. This modulation of the Schottky barrier is evidenced by output characteristics of two symmetrical heterostructures: Au-MoS2-Au and Gr-MoS2-Gr field-effect transistors. The effective heights of MoS2-graphene and MoS2-Au Schottky barriers and their response to back-gate voltage and laser irradiation were extracted from output characteristics of Au-MoS2-Au and Gr-MoS2-Gr field-effect transistors. The tuned Schottky barriers could be explained by the Fermi level change of graphene and MoS2. These results contributed to our understanding of 2D heterostructures and have potential applications in novel electronics and optoelectronics