Science Progress最新文献

ObjectiveColorectal cancer (CRC) patients with high microsatellite instability (MSI-H) and mismatch repair deficiency (dMMR) had heterogeneous pathology and distinct prognoses. This study aimed to examine the difference in the gene expression profile of dMMR/MSI-H CRC patients with different disease stages and explore the different molecular mechanisms of disease progression.MethodsA total of 47 patients with dMMR/MSI-H CRC were enrolled and retrospectively studied, including 27 stage II and 20 stage IV patients. Each patient had paired tumor tissue and white blood cell samples, which were analyzed by next-generation sequencing (NGS) of 416 cancer-relevant genes. Pathway enrichment analysis was then performed to analyze the disease stage-specific signaling pathways.ResultsA total of 2878 mutation sites, spanning 378 mutated genes, were detected from the 47 dMMR/MSI-H CRC patients. The mutation frequencies of SMARCA4, EPHA3, MTHFR, RAD50, and PDGFRB were significantly higher in stage II patients than in stage IV patients (p < 0.05), whereas the stage II patients had significantly lower mutation frequencies of TSC2, FGFR1, PTPN13, SMAD3, and STK11 than stage IV patients (p < 0.05). Sixty-three mutated genes were unique to stage II tumors, while 36 mutated genes were exclusively present in stage IV tumors. Pathway analyses demonstrated the PI3K-AKT pathway was shared by both stage II and stage IV tumors, whereas multiple other signaling pathways showed disease stage-specific enrichment.ConclusionThere were profound differences in mutational profile and molecular mechanisms between stage II and stage IV dMMR/MSI-H CRC.

As a key core component of wind turbine generators, the rolling bearings in the gearbox directly affect the overall performance and reliability of the wind turbine generators. Accurate prediction and timely diagnosis can effectively improve the efficiency of the wind turbine generators. This paper takes the rolling bearing operation data as the research object and proposes a bearing fault classification research method based on the combination of variational mode decomposition (VMD) optimization and convolutional neural network-bidirectional gated recurrent unit (CNN-BiGRU)-Attention model. Firstly, to address the sensitivity of intrinsic mode function (IMF) components in the VMD decomposition process, an improved RIEM algorithm is adopted to optimize the hyperparameters of the VMD algorithm. This process aims to adaptively adjust the penalty factor and decomposition layers of the VMD algorithm and find the optimal IMF component to determine the most suitable IMF component in the signal data. Secondly, to fully explore the complex characteristics of fault signals, composite multi-scale slope entropy is used to extract features from the optimized input data. By conducting multidimensional analysis on the local and global characteristics of the signal at different time scales, efficient representation of fault features is achieved. Finally, based on MATLAB, a simulation experiment platform is established. This paper conducts research on the classification of rolling bearing faults through the CNN-BiGRU-Attention model. The results show that the model established in this paper has significant effects and stable performance. The research in this paper provides new technical ideas for fault diagnosis of rolling bearings in wind turbine generator gear.

ObjectiveResistance to platinum-based chemotherapy remains a key obstacle in ovarian cancer treatment. This study aims to investigate the role of Uncoordinated 51-like kinase 2 (ULK2) in chemoresistance of ovarian cancer and elucidate its underlying mechanisms using 3D patient-derived organoids.MethodsSurvival analysis was first performed using the Kaplan‒Meier plotter database. Immunohistochemical profiling delineated differential ULK2 expression patterns between chemoresistant and chemosensitive ovarian cancer tissue samples and organoids. ULK2 overexpression was achieved in cisplatin-resistant ovarian cancer organoids via lentiviral vector transduction. Then, we conducted an in-depth examination of the alterations in phosphorylated proteins induced by ULK2 overexpression using phosphoproteomics technology. To investigate the influence of ULK2 on chemosensitivity in ovarian cancer, Cell Counting Kit-8 (CCK-8) and in vivo experiments were conducted. Glycolysis was quantitatively assessed, and the underlying molecular mechanism was systematically investigated.ResultsULK2 high-expression ovarian cancer exhibited enhanced chemosensitivity and conferred survival advantage. CCK-8 and mouse experiments demonstrated that ULK2 overexpression decreased cisplatin resistance in patient-derived organoids. Gene Ontology (GO) analysis of phosphoproteomics profiling highlighted the predominant role of ULK2 in metabolic processes with experimental validation demonstrating its suppression of glycolysis. Mechanistically, ULK2 attenuated c-Jun expression by phosphorylation of c-Jun at Ser243. Moreover, c-Jun overexpression counteracted the chemosensitivity and glycolytic suppression induced by the ectopic ULK2 expression in ovarian cancer.ConclusionsULK2 overcomes cisplatin resistance in ovarian cancer by downregulating glycolysis, a process mediated by phosphorylation-induced c-Jun degradation. These findings emphasized the role of ULK2 as a tumor suppressor, offering novel insights for chemotherapy in ovarian cancer.

Denture base materials made using three-dimensional printing (3D printing) have expanded in availability, contributing to the meteoric rise of this innovative dental technique. Despite 3D-printed denture resins having good biocompatibility and esthetics, achieving superior mechanics in these materials is still difficult. This study aimed to find out how adding calcium carbonate (CaCO₃) nanoparticles to 3D-printed denture base resin affected its surface hardness, impact strength, and flexural strength. The ninety 3D printed samples were allocated into three groups following the amount of CaCO₃ NPs added to the resin: one control group had no CaCO₃, and two modified groups-one had 1.5 wt.%, and the other group had 2 wt.%. Surface hardness, impact, and flexural strength were the three test specifications used to further divide each group into three subgroups. Analysis was also conducted utilizing energy dispersive X-ray spectroscopy (EDX) and field emission scanning electron microscopy (FESEM). All data were statistically analyzed. The results verified that adding CaCO₃ NPs to the 3D-printed denture resin significantly boosted its impact strength, flexural, and surface hardness (P value < 0.05). These findings indicate potential for developing a novel denture base constructed from 3D-printed nanocomposites with enhanced material properties.

The cut flower business has been growing rapidly worldwide, with a positive and significant impact on the economies of many countries. Maintaining quality and extending the vase life of cut flowers are crucial aspects of the floral industry. Synthetic preservatives (silver nitrate, silver thiosulfate, nano-silver, hydroxy quinoline, thiabendazole, and aluminum compounds) have been commercially used in the vase to maintain the quality and longevity of cut flowers for a long time. However, these preservatives may persist in the environment, causing severe health hazards and environmental pollution, and are also expensive. Therefore, cut flower industries seek low-cost, eco-friendly, and safer alternatives. In this context, natural preservatives (NPs), including plant extracts (PEs) and essential oils (EOs), offer a promising and sustainable alternative to synthetic preservatives in the vase. This review highlights the potential NPs and their role in enhancing the quality and vase life of cut flowers. We discussed how these preservatives exert their beneficial effects, such as inhibiting microbial growth, reducing ethylene production, and enhancing water uptake, and also explored the potential issues associated with them. We conducted a structured literature review and summarized the most commonly used EOs and PEs, their optimal dosages, efficacy, and combinations, and concluded with future directions to enhance the vase life of cut flowers sustainably.

Wellbore instability in deep hard-brittle shale formations, primarily induced by hydration-driven strength degradation upon interaction with water-based fluids, poses a critical challenge to hydrocarbon extraction. Conventional triaxial testing for assessing shale hydration behavior is often constrained by substantial sample requirements, extended duration, and high operational costs. In response, this study develops an efficient alternative approach centered on the indentation hardness method. While standard indentation tests are typically limited to hardness and plasticity coefficients, this work establishes theoretical models-based on contact mechanics, elasticity theory, and the Mohr-Coulomb criterion-to derive elastic modulus, Poisson's ratio, and uniaxial compressive strength from indentation data. Experimental analysis of homogenized Longmaxi shale revealed a dense, low-porosity microstructure dominated by non-expansive clay minerals and quartz. Freshwater immersion tests displayed a three-stage absorption trend-rapid, slow, and stable-reaching near-saturation after 72 hours. Pronounced mechanical degradation was observed within the initial 300 hours of immersion, characterized by marked reductions in compressive strength, elastic modulus, and indentation hardness, alongside a stepwise increase in Poisson's ratio; this degradation trend decelerated thereafter. Validation experiments confirmed that single-point indentation hardness measurements provide mechanical equivalence to uniaxial compression responses. As a result, indentation testing on shale chips following fluid immersion offers an efficient and reliable means of evaluating time-dependent fluid-rock interactions. The proposed methodology minimizes core material requirements, enhances operational efficiency, and mitigates the influence of heterogeneity, thereby offering considerable practical value for shale hydration assessment and wellbore stability forecasting.

ObjectiveQuantify recruitment of Native Hawaiian and Pacific Islander (NHPI) participants from 22 Alzheimer's disease (AD) clinical trials over 5 years and utilize choropleth maps as a visual tool to identify where in the Hawaiian community recruited participants are located in order to better inform future recruitment efforts and improve equity and population diversity for future AD clinical trials.MethodsA retrospective chart review was conducted at a dual-site origin clinical trial center in Hawai'i. Inclusion criteria were a diagnosis of mild cognitive impairment and participation in one or more AD clinical trials conducted between 2020 and 2024. Demographic information of clinical trial participants was collected via chart review and included self-identified race/ethnicity, age, residence, and number of clinical trials the patient has participated in. Clinical trial participants were categorized by ZIP codes established by the US Census Bureau. Differences across race/ethnicity groups were assessed using either Pearson's Chi-squared test or Fisher's exact test.ResultsA total of 244 patients participated across the state of Hawai'i in 22 AD clinical trials between 2020 and 2024. Of this total, 169 (69%) patients provided their race/ethnicity, and 75 (31%) did not provide their race/ethnicity. White patients had the highest percentage of participation (44%), followed by Asian patients (34%) and NHPI patients (15%). The population distribution visualized in this study's choropleth maps suggests that NHPI were under-recruited from the west side of O'ahu.ConclusionsOur retrospective study applied choropleth maps to visualize the recruitment data and patterns of AD clinical trials. By utilizing choropleth maps to analyze recruitment areas, the NHPI community and other underrepresented populations may benefit from targeted, culturally informed recruitment strategies.

To address the limitations of flux regulation in traditional permanent magnet synchronous generators and the low power density of electrically excited generators, an interior double-radial asymmetric permanent magnet (PM) and salient-pole electromagnetic hybrid excitation generator are introduced in this study. Equations for the no-load induced electromotive force, the voltage adjustment range, and the total harmonic distortion (THD) are derived theoretically through the analysis of generator parameter relationships. The optimization parameters include the offset angles of the double-layer asymmetric PMs and the structural parameters of the salient-pole rotor. A multi-objective optimization model is established with the no-load induced electromotive force amplitude, the voltage adjustment range, and the THD as the objectives. Samples are generated by Latin Hypercube Sampling, followed by sensitivity analysis of the optimization parameters. The optimization parameters are then screened using Pareto front analysis and a defined parameter matching coefficient. The optimal magnet pole parameters are determined. As a result of optimization, the no-load induced electromotive force amplitude increases by 18.7%, the voltage adjustment range expands by 17.6%, and the THD decreases by 38.2%. Finally, a prototype is fabricated and tested, and the results confirm both the accuracy of the theoretical analysis and the effectiveness of the optimization method. The output characteristics of the designed generator are thereby significantly improved.

The increasing demand for sustainable energy production necessitates the development of innovative technologies for converting municipal waste into valuable energy offering a viable alternative to fossil fuels. This study presents a flexible, portable, and expandable waste-to-energy concept that integrates gasification and pyrolysis processes production of combustible gases and liquid fuels. Particular emphasis is placed on the use of transparent and interpretable modelling approaches to support system optimization and future scalability. The proposed methodology is demonstrated on two experimental systems currently operated at CEET Explorer, VSB - Technical University of Ostrava, Czech Republic: (i) A primary gasification facility equipped with a plasma torch, reactor, hydrogen separator and tank, fuel cells, and renewable grid connections; and (ii) a secondary pyrolysis unit designed to maximize pyrolysis oil production. Both systems are modelled and simulated using in-house software developed in Python, employing stoichiometric balances, symbolic regression, and polynomial regression to represent chemical reactions and energy flows. The findings demonstrate that transparent models - such as stoichiometric modelling combined with interpretable machine learning - can accurately reproduce the operational behaviour of waste-to-energy processes. Gasification is optimized for hydrogen generation and electricity production via fuel cells, whereas pyrolysis favours liquid fuel yield with syngas as a by-product. Molar mass relations are applied to ensure consistent conversion between mass and volume across gasification, pyrolysis, and combustion pathways, maintaining the conservation of mass. Overall, the integration of stoichiometric balance models with symbolic and polynomial regression provides a reliable and interpretable framework for simulating real waste-to-energy systems. The current results, based on bio-wood waste from the Czech Republic, validate the proposed methodology, which is made openly available to promote transparency, reproducibility, and further advancement of sustainable waste-to-energy technologies.

BackgroundThe lack of a systematic selection framework for the selection of linear feed mechanisms in precision machine tools results in a mismatch between the performance of the mechanism and the specific application requirements in terms of accuracy, stiffness and load capacity, which restricts the optimization design of high-performance machining systems.ObjectivesWe are committed to establishing a systematic classification system to categorize existing technologies and define their quantified performance boundaries, in order to guide the optimal choices of institutions and future innovation directions.MethodsThis review establishes a structured classification system, dividing mechanisms into four clear categories: typical linear drive mechanisms, linear linkage mechanisms, high-precision feed mechanisms and novel linear mechanisms. We compared and analyzed their working principles based on key parameters such as positioning accuracy, structural stiffness and load capacity; quantified their performance boundaries; and provided their applications. At the end of each section, a table is listed to summarize the content for easy reference.DiscussionsThe analysis reveals that a typical linear feed mechanism, as the basic unit of machine tool linear motion, is widely used but has low accuracy. A linear linkage mechanism may not have high accuracy, but it can help machine tools complete specific structures. A high-precision linear feed mechanism has high precision, usually reaching the micrometer level, and is applied in scenarios with high precision requirements. The new linear feed mechanism represents the direction of technological development and guides the optimization design of machine tools.ResultsThe performance-oriented classification framework developed in this study effectively resolves the selection challenge for precision linear feed mechanisms in machine tools. Its theoretical contribution lies in proposing a systematic performance spectrum, while its practical significance is to provide engineers with a clear decision-making tool for mechanism selection and to illuminate directed pathways for future innovation in precision motion systems.