Search

Background: The purpose of this study was to investigate the potential of plasma cfDNA methylation patterns in reflecting tumour methylation changes, focusing on three candidate sites, cg02469161, cg11528914, and cg20131654. These sites were selected for verification, with a particular emphasis on their association with breast cancer. Methods: We conducted a comprehensive analysis of 850k whole-methylation sequencing data to identify potential markers for breast cancer detection. Subsequently, we investigated the methylation status of the genes Ran-binding protein 3 (RANBP3), Lymphocyte cytoplasmic protein 2 (LCP2), and GRB2 related adaptor protein 2 (GRAP2), situated at the specified sites, using cancer and canceradjacent tissues from 17 breast cancer patients. We also examined the methylation patterns in different molecular subtypes and pathological grades of breast cancer. Additionally, we compared the methylation levels of these genes in plasma cfDNA to their performance in tissues. Results: Our analysis revealed that RANBP3, LCP2, and GRAP2 genes exhibited significant methylation differences between cancer and cancer-adjacent tissues. In breast cancer, these genes displayed diagnostic efficiencies of 91.0%, 90.6%, and 92.2%, respectively. Notably, RANBP3 showed a tendency towards lower methylation in HR+ breast cancer, and LCP2 methylation was correlated with tumour malignancy. Importantly, the methylation levels of these three genes in plasma cfDNA closely mirrored their tissue counterparts, with diagnostic efficiencies of 83.3%, 83.9%, and 77.6% for RANBP3, LCP2, and GRAP2, respectively. Conclusions: Our findings propose that the genes RANBP3, LCP2, and GRAP2, located at the identified methylation sites, hold significant potential as molecular markers in blood for the supplementary diagnosis of breast cancer. This study lays the groundwork for a more in-depth investigation into the changes in gene methylation patterns in circulating free DNA (cfDNA) for the early detection not only of breast cancer but also for various other types of cancer

Ultra-high-performance concrete is a cement-based material with ultra-high strength, outstanding toughness, and excellent durability, which enables structures to achieve larger spans and lighter dimensions. In this paper, an improved GA-BP neural network model is constructed based on BP neural network, which is optimized and improved by the GA algorithm. Then, the experimental data were input into the improved GA-BP neural network model by designing experiments with different types and volume doping of blended fiber UHPC, and the ductility of blended fiber UHPC was analyzed in terms of compressive strength and tensile strength. In terms of compressive strength, the compressive strengths of each group of PE fibers with 0.5%, 1.0%, and 1.5% volume doping were from PD/S

Navigation aids play a crucial role in guiding ship navigation and marking safe water areas. Therefore, ensuring the accurate and efficient recognition of a navigation aid’s state is critical for maritime safety. To address the issue of sparse features in navigation aid data, this paper proposes an approach that involves three distinct processes: the extension of rank entropy space, the fusion of multi-domain features, and the extraction of hidden features (EFE). Based on these processes, this paper introduces a new LSTM model termed EFE-LSTM. Specifically, in the feature extension module, we introduce a rank entropy operator for space extension. This method effectively captures uncertainty in data distribution and the interrelationships among features. The feature fusion module introduces new features in the time domain, frequency domain, and time–frequency domain, capturing the dynamic features of signals across multiple dimensions. Finally, in the feature extraction module, we employ the BiLSTM model to capture the hidden abstract features of navigational signals, enabling the model to more effectively differentiate between various navigation aids states. Extensive experimental results on four real-world navigation aid datasets indicate that the proposed model outperforms other benchmark algorithms, achieving the highest accuracy among all state recognition models at 92.32%.

Soft X-ray imaging is a powerful tool to explore the structure of cells, probe material with nanometer resolution, and investigate the energetic phenomena in the universe. Conventional soft X-ray image sensors are by and large Si-based charge coupled devices that suffer from low frame rates, complex fabrication processes, mechanical inflexibility, and required cooling below -60 °C. Here, a soft X-ray photodiode is reported based on low-cost metal halide perovskite with comparable performance to commercial Si-based device. Nanothrough network electrode minimized the optical loss due to the shadowing of insensitive layers, while a multidimensional perovskite heterojunction is generated to reduce the photo-generated carrier loss. This strategy promoted a record quantum efficiency of 8 × 103% without cooling, several orders of magnitude greater than the previously achieved. Flexible and curved soft X-ray imaging arrays are fabricated based on this high-performance device structure, demonstrating stable soft X-ray response and sharp imaging capabilities. This work highlights the low-cost and efficient perovskite photodiode as a strong candidate for the next-generation soft X-ray image sensors.

High-quality perovskite films are essential for achieving high performance of optoelectronic devices; However, solution-processed perovskite films are known to suffer from compositional and structural inhomogeneity due to lack of systematic control over the kinetics during the formation. Here, the microscopic homogeneity of perovskite films is successfully enhanced by modulating the conversion reaction kinetics using a catalyst-like system generated by a foaming agent. The chemical and structural evolution during this catalytic conversion is revealed by a multimodal synchrotron toolkit with spatial resolutions spanning many length scales. Combining these insights with computational investigations, a cyclic conversion pathway model is developed that yields exceptional perovskite homogeneity due to enhanced conversion, having a power conversion efficiency of 24.51% for photovoltaic devices. This work establishes a systematic link between processing of precursor and homogeneity of the perovskite films.

Backgrounds: Dupilumab has demonstrated remarkable efficacy and safety in clinical trials for moderate-to-severe atopic dermatitis (AD). However, long-term real-world evidence, especially in the Chinese population, remains limited. Objective: To investigate the long-term efficacy and safety of dupilumab for moderate-to-severe AD in a real-world clinical setting in China and analyze factors that may influence its long-term treatment outcomes. Methods: This prospective, observational real-world study included moderate-to-severe AD patients from the AD cohort of the dermatology department of Chongqing Hospital of Traditional Chinese Medicine who received dupilumab treatment for≥52 weeks. Efficacy and adverse events were assessed at baseline, weeks 4, 16, 24, and 52. Multivariate logistic regression analysis was used to identify predictive factors for achieving EASI 50 and EASI 75 at week 52. Results: A total of 124 patients were included. At week 52, EASI, SCORAD, IGA, NRS, and DLQI scores were significantly improved compared to baseline. The proportions of patients achieving EASI-50/75 were 50.81%/29.84%, 72.58%/42.74%, 75%/53.23%, and 67.74%/41.94% at weeks 4, 16, 24 and 52, respectively. Female sex, absence of atopic comorbidities, higher baseline EASI, and medication compliance were positive predictive factors for 52-week EASI-50/75. Eosinophil elevation predicted lower EASI-50 attainment. Nineteen adverse events occurred during the 52-week period (incidence rate: 14.52%), mostly mild and manageable. Conclusions: Dupilumab demonstrated significant efficacy and a low incidence of adverse events over 52 weeks in Chinese patients with moderate-to-severe AD, making it an effective and safe long-term treatment option. Predictive factors were identified to guide treatment optimization. [ABSTRACT FROM AUTHOR]

Climate warming has caused widespread glacier retreat across the Tibetan Plateau (TP), with notable impacts observed in both the western Himalayas and southeastern TP. Remarkably, over the past two decades, the rate of glacier mass loss has remained stable or even declined in the western Himalayas, whereas a contrasting trend of acceleration has been evident in the southeastern TP. Among various factors considered, the contrasting winter precipitation pattern across the Himalayas–southeastern TP stands out as an important contributor to the observed differences in glacier mass balances. However, the underlying mechanisms behind this phenomenon remain unclear. Here, we provide evidence of a noticeable shift in the climate regime due to anthropogenic warming, altering atmospheric circulation patterns. This, in turn, has led to a significant change in the dominant winter precipitation pattern, favoring the observed contrasting glacier mass balances trend in winter. Additionally, the Barents–Kara Sea ice has emerged as a plausible driver of the interannual variability of the contrasting precipitation pattern, acting by exerting force on the atmosphere and stimulating the Rossby wave propagation. Consequently, it led to the opposite vertical motion and alterations in the moisture budget between the western Himalayas and southeastern TP. Thus, it is crucial to consider the unprecedented anthropogenic warming and Arctic Sea ice variations as potential drivers shaping both past and future glacier behaviors within this domain. [ABSTRACT FROM AUTHOR]