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One of the most prominent features of tumor cells is uncontrolled cell proliferation caused by an abnormal cell cycle, and the abnormal expression of cell cycle-related proteins gives tumor cells their invasive, metastatic, drug-resistance, and anti-apoptotic abilities. Recently, an increasing number of cell cycle-associated proteins have become the candidate biomarkers for early diagnosis of malignant tumors and potential targets for cancer therapies. As an important cell cycle regulatory protein, Cell Division Cycle 25C (CDC25C) participates in regulating G2/M progression and in mediating DNA damage repair. CDC25C is a cyclin of the specific phosphatase family that activates the cyclin B1/CDK1 complex in cells for entering mitosis and regulates G2/M progression and plays an important role in checkpoint protein regulation in case of DNA damage, which can ensure accurate DNA information transmission to the daughter cells. The regulation of CDC25C in the cell cycle is affected by multiple signaling pathways, such as cyclin B1/CDK1, PLK1/Aurora A, ATR/CHK1, ATM/CHK2, CHK2/ERK, Wee1/Myt1, p53/Pin1, and ASK1/JNK-/38. Recently, it has evident that changes in the expression of CDC25C are closely related to tumorigenesis and tumor development and can be used as a potential target for cancer treatment. This review summarizes the role of CDC25C phosphatase in regulating cell cycle. Based on the role of CDC25 family proteins in the development of tumors, it will become a hot target for a new generation of cancer treatments.

Discovery of thermoelectric materials has long been realized by the Edisonian trial and error approach. However, recent progress in theoretical calculations, including the ability to predict structures of unknown phases along with their thermodynamic stability and functional properties, has enabled the so-called inverse design approach. Compared to the traditional materials discovery, the inverse design approach has the potential to substantially reduce the experimental efforts needed to identify promising compounds with target functionalities. By adopting this approach, here we have discovered several unreported half-Heusler compounds. Among them, the p-type TaFeSb-based half-Heusler demonstrates a record high ZT of ~1.52 at 973 K. Additionally, an ultrahigh average ZT of ~0.93 between 300 and 973 K is achieved. Such an extraordinary thermoelectric performance is further verified by the heat-to-electricity conversion efficiency measurement and a high efficiency of ~11.4% is obtained. Our work demonstrates that the TaFeSb-based half-Heuslers are highly promising for thermoelectric power generation. The discovery of thermodynamically stable thermoelectric materials for power generation has relied on empirical methods that were not effective. Here, the authors apply the inverse design approach to identify and experimentally realize TaFeSb-based half Heuslers with high thermoelectric performance.

Background The application of circulating, cell-free, methylated Septin9 ( m SEPT9) DNA in screening and recurrence monitoring is highly promising. CpG island methylator phenotype (CIMP) is associated with microsatellite instability (MSI). The present study was performed to determine the diagnostic accuracy of m SEPT9 for colorectal cancer (CRC) and to evaluate its utility in CRC screening and recurrence monitoring. Methods For screening and diagnosis of CRC, peripheral m SEPT9 detection and fecal occult blood test (FOBT) were performed in 650 subjects, then the level of CEA, CA19–9 and CA724 was quantified in 173 subjects. Clinicopathological parameters and mismatch repair protein were detected among subjects with CRC. For recurrence monitoring of CRC, the sensitivity of m SEPT9 of 70 subjects was compared with tumor markers and contrast enhanced computed tomography (CECT). Results Seventy-three percent of CRC patients were m SEPT9-positive at 94.5% specificity, and 17.1% of patients with intestinal polyps and adenoma were m SEPT9-positive at 94.5% specificity, which were higher than FOBT for the screening of CRC. The sensitivity and specificity of m SEPT9 for diagnosis and recurrence monitoring were higher than that of CEA, CA19–9 and CA724. The combined detection of m SEPT9 and CECT enhanced the sensitivity for recurrence monitoring. Pre-therapeutic levels of m SEPT9 were strongly associated with TNM stage, Dukes stages and mismatch repair deficiency (dMMR). Conclusions m SEPT9 analysis might be popularized as a routine biomarker for CRC screening. The combined detection of m SEPT9 and CECT can play an important role for recurrence monitoring. CIMP was highly associated with the pathological stage of CRC and dMMR.

This work aims to explore an accurate and effective method for recognizing dance movement features, providing precise personalized guidance for sports dance teaching. First, a human skeletal graph is constructed. A graph convolutional network (GCN) is employed to extract features from the nodes (joints) and edges (bone connections) in the graph structure, capturing both spatial relationships and temporal dynamics between joints. The GCN generates effective motion representations by aggregating the features of each node and its neighboring nodes. A dance movement recognition model combining GCN and a Siamese neural network (SNN) is proposed. The GCN module is responsible for extracting spatial features from the skeletal graph, while the SNN module evaluates the similarity between different skeletal sequences by comparing their features. The SNN employs a twin network structure, where two identical and parameter-sharing feature extraction networks process two input samples and calculate their distance or similarity in a high-dimensional feature space. The model is trained and validated on the COCO dataset. The results show that the proposed GCN-SNN model achieves an accuracy of 96.72% and an F1 score of 86.55%, significantly outperforming other comparison models. This work not only provides an efficient and intelligent personalized guidance method for sports dance teaching but also opens new avenues for the application of artificial intelligence in the education sector.

M-health apps have developed rapidly and are widely accepted, but users’ continued intention to use m-health apps has not been fully explored. This study was designed to obtain a better understanding of users’ continued intention to use m-health apps. We developed a theoretical model by incorporating the protection motivation theory and network externalities and conducted an empirical study of a 368-respondent sample. The results showed that: (1) perceived vulnerability has a direct impact on users’ self-efficacy and response efficacy; (2) self-efficacy and response efficacy have a direct impact on users’ attitudes and continued intention; (3) network externalities affect users’ attitudes and continued intention, among which direct network externalities have an indirect impact on users’ continued intention through attitude; and (4) the impacts of self-efficacy, response efficacy, and indirect network externalities on continued intention are partially meditated by attitudes.

The desire for foods with lower glycemic indices has led to the exploration of functional ingredients and novel food processing techniques. The glycemic index (GI) is a well-recognized tool to assess the capacity of foods to raise blood glucose levels. Among cereal crops, oats have shown the greatest promise for mitigating glycemic response. This review evaluated decades of research on the effects of oat components on the GI level of oat-based foods with specific emphasis on oat starch, β-glucans, proteins, and phenolics. The effects of commonly used processing techniques in oats on GI level, including heating, cooling, and germination were also discussed. In addition, the GI of oat-based foods in various physical formats such as whole grain, flakes, and flour was systematically summarized. The aim of this review was to synthesize knowledge of the field and to provide a deeper understanding of how the chemical composition and processing of oats affect GI, thereby further benefiting the development of low-GI oat foods.

Cyclophilins (CyPs) are involved in basic cellular functions and a wide variety of pathophysiological processes. Many CyPs have been identified as the aetiological agent and influence on the immune system. In the present study, the physicochemical and immunologic characteristics of three proteins of CyPs family (CyPA, CyPB and CyPE) were analyzed. The results indicated that CyPE showed a closer evolutionary relationship with allergenic CyPA. The structure and antigenicity of CyPE was significantly similar with CyPA. B-cell epitopes of CyPE and CyPA were predicted via multiple immunoinformatics tools. Three consensus B-cell epitopes of CyPE and CyPAs were finally determined. To verify results of in silico analysis, three proteins of CyPs family (CyPA, CyPE and CyPB) were cloned and expressed from Dermatophagoides pteronyssinus . ELISA results indicated that the positive reaction rates of the three proteins to patient serum are CyPA (21.4%), CyPE (7.1%), and CyPB (0%), illustrating that the IgE activity was exhibited in CypA and CypE excluding CyPB. Structure and immunoinformatics analysis demonstrated that the RNA-binding motif of CyPE could reduce the immunogenicity of PPIase domain of CyPE. The reason that CyPB has no IgE activity might be the structure mutation of CyPB on B-cell epitopes.

To evaluate the accuracy and safety of C1–C2 pedicle screw placement using O-arm navigation, individualized 3D-printed guides, and C-arm fluoroscopy. Clinical data of 47 patients who underwent C1–C2 spinal fixation surgery at our institution between January 2015 and December 2020 were retrospectively analyzed. The cohort included 28 males and 19 females, aged 15–59 years (mean age: 46.23 ± 9.97 years). Patients were categorized into three groups based on the screw placement technique: navigation group (11 cases; O-arm S8 navigation system), guide group (15 cases; individualized 3D-printed guides), and fluoroscopy group (21 cases; C-arm fluoroscopy guided by anatomical landmarks). Outcome measures included surgical time, screw placement time, intraoperative blood loss, single-pass screw placement success rate, screw placement accuracy, and complication rate. Surgical Metrics: The Navigation group demonstrated a mean surgical time of 120.72 ± 11.14 min, screw placement time of 20.00 ± 1.09 min, and blood loss of 225.81 ± 25.58 ml. The Guide group reported significantly shorter surgical time (97.46 ± 9.03 min, P  < 0.001), shorter screw placement time (15.80 ± 1.93 min, P  < 0.001), and reduced blood loss (162.66 ± 18.52 ml, P  < 0.001). The Fluoroscopy group showed longer surgical time (121.04 ± 12.81 min) and higher blood loss (239.04 ± 24.54 ml) compared to the other groups. Screw Placement Success and Accuracy: A total of 188 screws were placed (44 in the Navigation group, 60; guide group, and 84; Fluoroscopy group). The single-pass success rates were 100% (44/44) in the navigation group, 93.3% (56/60) in the guide group, and 80.9% (68/84) in the fluoroscopy group ( P  = 0.002). Screw placement accuracy was 100% (44/44) in the navigation group, 98.3% (59/60) in the guide group, and 85.7% (72/84) in the fluoroscopy group ( P  = 0.039). Notably, three screws in the fluoroscopy group breached the vertebral artery foramen; however, no cerebrovascular ischemic events were observed. Complications: Two patients in the fluoroscopy group developed postoperative occipitocervical pain owing to intraoperative irritation of the C2 nerve root. Symptoms resolved after corticosteroid and diuretic therapy. No occipitocervical pain or other complications were reported in the Navigation or Guide group. All the incisions healed without infection or delayed recovery. O-arm S8 navigation system and individualized guide plate assisted atlantoaxial screw placement can achieve high and stable accuracy, which is better than the traditional freehand screw placement technique under fluoroscopy; O-arm navigation technology has an advantage in the one-time success rate of atlantoaxial screw placement, which is higher than that of the guide plate group and the fluoroscopy group; Individualized guide plate combined with lateral fluoroscopy can accurately place atlantoaxial screws, save operation time and reduce bleeding.

Neural crest stem cells arising from caudal hindbrain (often called cardiac and posterior vagal neural crest) migrate long distances to form cell types as diverse as heart muscle and enteric ganglia, abnormalities of which lead to common congenital birth defects. Here, we explore whether individual caudal hindbrain neural crest precursors are multipotent or predetermined toward these particular fates and destinations. To this end, we perform lineage tracing of chick neural crest cells at single-cell resolution using two complementary approaches: retrovirally mediated multiplex clonal analysis and single-cell photoconversion. Both methods show that the majority of these neural crest precursors are multipotent with many clones producing mesenchymal as well as neuronal derivatives. Time-lapse imaging demonstrates that sister cells can migrate in distinct directions, suggesting stochasticity in choice of migration path. Perturbation experiments further identify guidance cues acting on cells in the pharyngeal junction that can influence this choice; loss of CXCR4 signaling results in failure to migrate to the heart but no influence on migration toward the foregut, whereas loss of RET signaling does the opposite. Taken together, the results suggest that environmental influences rather than intrinsic information govern cell fate choice of multipotent caudal hindbrain neural crest cells. Neural crest stem cells formed from the caudal hindbrain migrate long distances to the heart and gut, but how cell fate is determined is unclear. Here, the authors use multiplex clonal analysis and single-cell photoconversion lineage tracing to show environmental not intrinsic factors affect the cell fate of multipotent caudal hindbrain cells in the chick.

With the rapid advancement of artificial intelligence, the demand for personalized and adaptive learning has driven the development of intelligent educational systems. This article proposes a novel adaptive learning-driven architecture that combines multimodal behavioral modeling and personalized educational resource recommendation. Specifically, we introduce a multimodal fusion (MMF) algorithm to extract and integrate heterogeneous learning behavior data—including text, images, and interaction logs— via stacked denoising autoencoders and Restricted Boltzmann Machines. We further design an adaptive learning (AL) module that constructs a student-resource interaction graph and dynamically recommends learning materials using a graph-enhanced contrastive learning strategy and a dual-MLP-based enhancement mechanism. Extensive experiments on the Students’ Academic Performance Dataset demonstrate that our method significantly reduces prediction error (mean absolute error (MAE) = 0.01, mean squared error (MSE) = 0.0053) and achieves high precision (95.3%) and recall (96.7%). Ablation studies and benchmark comparisons validate the effectiveness and generalization ability of both MMF and AL. The system exhibits strong scalability, real-time responsiveness, and high user satisfaction, offering a robust technical foundation for next-generation AI-powered educational platforms.