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Background The HER2 immunohistochemistry (IHC) test is an essential method for detecting breast cancer (BC) and plays a pivotal role in guiding personalized treatment strategies. However, inconsistencies persist among different pathologists using IHC, especially for HER2-low and HER2-negative. This may lead to discrepant clinical decisions, potentially impacting patient outcomes. Since HER2 exists in both dimeric and monomeric forms in cells, certain binding sites of diagnostic antibodies on HER2 dimers may be partially obscured in detection. Therefore, accurately detecting HER2 dimers in IHC is crucial for improving diagnostic precision. Methods We aligned the structures of HER2 heterodimers and Fabs of pertuzumab and trastuzumab binding to HER2, and found they binding in the same region. To overcome the steric hindrance of HER2 dimers, we employed HER2-binding affibody (Aby) and nanobody (Nby) to construct their fusion protein (Nby-Aby) and human heavy chain ferritin (HFn) based nanoparticles (Nby-HFn, Aby-HFn) for detection. Since the Nby and Aby bind HER2 at two distinct regions that are separate from the HER2 dimerization region, effectively minimizing interference from HER2 dimerization in detection. We assessed the detection performance of Nby-Aby in BC tissues and compared it with conventional HER2 diagnostic antibodies using tissue microarrays (TMAs). Results The Nby-Aby assay had higher detection sensitivity for HER2-positive cells in BC tissues compared to the conventional method. Additionally, significantly higher HER2 scores were observed in most BC tissues on tissue microarrays (TMAs) compared to those diagnosed using the traditional method. These findings suggest that dual-targeting and overcoming steric hindrance in HER2 IHC detection is a promising strategy to enhance diagnostic precision. Conclusions Dual-targeting different regions and overcoming steric hindrance of HER2 in IHC detection through the Nby-Aby fusion protein enhances diagnostic sensitivity, providing a novel strategy for more accurate HER2 IHC assessment in BC diagnosis.

Pulmonary hypertension (PH) is an uncommon yet severe condition characterized by sustained elevation of blood pressure in the pulmonary arteries. The delaying treatment can result in disease progression, right ventricular failure, increased risk of complications, and even death. Early recognition and timely treatment are crucial in halting PH progression, improving cardiac function, and reducing complications. Within this study, we present a highly promising hybrid model, known as bERIME_FKNN, which constitutes a feature selection approach integrating the enhanced rime algorithm (ERIME) and fuzzy K-nearest neighbor (FKNN) technique. The ERIME introduces the triangular game search strategy, which augments the algorithm's capacity for global exploration by judiciously electing distinct search agents across the exploratory domain. This approach fosters both competitive rivalry and collaborative synergy among these agents. Moreover, an random follower search strategy is incorporated to bestow a novel trajectory upon the principal search agent, thereby enriching the spectrum of search directions. Initially, ERIME is meticulously compared to 11 state-of-the-art algorithms using the IEEE CEC2017 benchmark functions across diverse dimensionalities such as 10, 30, 50, and 100, ultimately validating its exceptional optimization capability within the model. Subsequently, employing the color moment and grayscale co-occurrence matrix methodologies, a total of 118 features are extracted from 63 PH patients' and 60 healthy individuals' images, alongside an analysis of 14,514 recordings obtained from these patients utilizing the developed bERIME_FKNN model. The outcomes manifest that the bERIME_FKNN model exhibits a conspicuous prowess in the realm of PH classification, attaining an accuracy and specificity exceeding 99%. This implies that the model serves as a valuable computer-aided tool, delivering an advanced warning system for diagnosis and prognosis evaluation of PH. •The performance of ERIME algorithm is enhanced by Triangular gaming search and Random follower search.•Compared with other high-performance optimizers, ERIME obtains higher quality optimal solutions in IEEE CEC 2017 functions.•The bERIME for pulmonary hypertension is proposed using FKNN.•bERIME_FKNN has technical advantages in the analysis of pulmonary hypertension.•bERIME_FKNN can be used as a tool to assist in the diagnosis of pulmonary hypertension.

Exploring the interaction between two neighbouring monomers has great potential to significantly raise the performance and deepen the mechanistic understanding of heterogeneous catalysis. Herein, we demonstrate that the synergetic interaction between neighbouring Pt monomers on MoS2 greatly enhanced the CO2 hydrogenation catalytic activity and reduced the activation energy relative to isolated monomers. Neighbouring Pt monomers were achieved by increasing the Pt mass loading up to 7.5% while maintaining the atomic dispersion of Pt. Mechanistic studies reveal that neighbouring Pt monomers not only worked in synergy to vary the reaction barrier, but also underwent distinct reaction paths compared with isolated monomers. Isolated Pt monomers favour the conversion of CO2 into methanol without the formation of formic acid, whereas CO2 is hydrogenated stepwise into formic acid and methanol for neighbouring Pt monomers. The discovery of the synergetic interaction between neighbouring monomers may create a new path for manipulating catalytic properties.

Point cloud registration is a multifaceted problem that involves a series of procedures. Many deep learning methods employ complex structured networks to achieve robust registration performance. However, these intricate structures can amplify the challenges of network learning and impede gradient propagation. To address this concern, the soft-hard correspondence (SHC) framework is introduced in the present paper to streamline the registration problem. The framework encompasses two modes: the hard correspondence mode, which transforms the registration problem into a correspondence pair search problem, and the soft correspondence mode, which addresses this new problem. The simplification of the problem provides two advantages. First, it eliminates the need for intermediate operations that lead to error fusion and counteraction, thereby improving gradient propagation. Second, a perfect solution is not necessary to solve the new problem, since accurate registration results can be achieved even in the presence of errors in the found pairs. The experimental results demonstrate that SHC successfully simplifies the registration problem. It achieves performance comparable to complex networks using a simple network and can achieve zero error on datasets with perfect correspondence pairs.

Single-cell ATAC-seq technology advances our understanding of single-cell heterogeneity in gene regulation by enabling exploration of epigenetic landscapes and regulatory elements. However, low sequencing depth per cell leads to data sparsity and high dimensionality, limiting the characterization of gene regulatory elements. Here, we develop scAGDE, a single-cell chromatin accessibility model-based deep graph representation learning method that simultaneously learns representation and clustering through explicit modeling of data generation. Our evaluations demonstrated that scAGDE outperforms existing methods in cell segregation, key marker identification, and visualization across diverse datasets while mitigating dropout events and unveiling hidden chromatin-accessible regions. We find that scAGDE preferentially identifies enhancer-like regions and elucidates complex regulatory landscapes, pinpointing putative enhancers regulating the constitutive expression of CTLA4 and the transcriptional dynamics of CD8A in immune cells. When applied to human brain tissue, scAGDE successfully annotated cis-regulatory element-specified cell types and revealed functional diversity and regulatory mechanisms of glutamatergic neurons. Single-cell ATAC-seq reveals gene regulation at individual cell levels but struggles with data sparsity. Here, authors introduce scAGDE, a deep graph learning framework that improves cell embedding and clustering, outperforming existing methods and uncovering key regulatory mechanisms.

Atherosclerosis (AS), driven by vascular endothelial dysfunction and poses a global health threat. This study compared the therapeutic effects of high-intensity interval training (HIIT) and moderate-intensity continuous training (MICT) on vascular endothelial function in early-stage AS mice, specifically investigating PCSK9 modulation and the TRX/TXNIP/NLRP3/GSDMD-N pathway. ApoE −/− mice (n = 6/group) fed a high-fat diet for 12 weeks were randomized into sedentary (AS-S), HIIT (AS-HIIT), and MICT (AS-MICT) groups, with wild-type mice as control. Training lasted 12 weeks. Outcomes included body weight, lipid profiles (TG, TC, LDL-C, HDL-C), oxidative stress markers (T-SOD, GSH-Px, MDA), vascular function (eNOS expression, ACh-induced vasorelaxation), and TRX/TXNIP/NLRP3/GSDMD-N pathway activity. Both HIIT and MICT reduced body weight (p < 0.05) and improved lipid profile. Exercise groups showed reduced oxidative stress and inflammation pathways (p < 0.05). HIIT and MICT ameliorate early AS by reducing PCSK9 and oxidative/inflammatory pathway levels (p < 0.05), but HIIT demonstrates superior efficacy in improving endothelial function and pathway activation. These findings show HIIT and MICT mitigate endothelial dysfunction in early atherosclerotic mice via PCSK9 inhibition and advocate for HIIT as a prioritized strategy in early AS management.

Ultra-thin asphalt overlay has become the mainstream measure of road preventive maintenance due to its good economic benefits and road performance. However, hot mix asphalt concrete technology is widely used at present, which is not the most ideal way to promote energy saving and emission reduction in the field of road maintenance. At the same time, the ultra-thin friction course based on cold mix technology, such as slurry seal layer, micro-surface, and other technologies, are still far behind the hot mix friction course in terms of crack resistance. In this research, by establishing an integrated design of materials and structures, a cold paving technology called “high-toughness cold-mixed ultra-thin pavement (HCUP)” is proposed. The high-viscosity emulsified bitumen prepared by using high-viscosity and high-elasticity modified bitumen is used as the binder and sticky layer of HCUP. The thickness of HCUP is 0.8–2.0 cm, the typical thickness is 1.2 cm, and the nominal maximum size of the coarse aggregate is 8 mm. Indoor tests show that HCUP-8 has water stability, anti-skid performance, high temperature performance, peeling resistance, and crack resistance that are not weaker than traditional hot-mixed ultra-thin wear layers such as AC-10, Novachip, and GT-8. At the same time, the test road paving further proved that HCUP-8 has excellent road performance with a view to providing new ideas for low-carbon and environmentally friendly road materials.

Cardiovascular diseases (CVDs) pose a significant global health threat due to their complex pathogenesis and high incidence, imposing a substantial burden on global healthcare systems. Integrins, a group of heterodimers consisting of α and β subunits that are located on the cell membrane, have emerged as key players in mediating the occurrence and progression of CVDs by regulating the physiological activities of endothelial cells, vascular smooth muscle cells, platelets, fibroblasts, cardiomyocytes, and various immune cells. The crucial role of integrins in the progression of CVDs has valuable implications for targeted therapies. In this context, the development and application of various integrin antibodies and antagonists have been explored for antiplatelet therapy and anti-inflammatory-mediated tissue damage. Additionally, the rise of nanomedicine has enhanced the specificity and bioavailability of precision therapy targeting integrins. Nevertheless, the complexity of the pathogenesis of CVDs presents tremendous challenges for monoclonal targeted treatment. This paper reviews the mechanisms of integrins in the development of atherosclerosis, cardiac fibrosis, hypertension, and arrhythmias, which may pave the way for future innovations in the diagnosis and treatment of CVDs.

This study employs both experimental and numerical simulation methods to systematically investigate the influence of sudden expansion diameter ratios on methane–air premixed flame propagation, explosion overpressure, and the evolution of turbulent structures. The results show that with the increase in the diameter ratio, the flame propagation velocity and explosion overpressure present a nonlinear trend of first increasing, then decreasing, and then increasing. Specifically, when the diameter ratio is 1.5, an optimal balance between turbulence enhancement and energy dissipation is achieved, and the overpressure attenuation rate is 47.61%. However, when the diameter ratio increases to 2.0, the turbulence intensity significantly escalates, the peak flame propagation speed increases by 81%, the peak explosion overpressure increases by 69%, and the overpressure attenuation efficiency decreases, which brings greater safety challenges. Moreover, when the diameter ratio is between 1.5 and 2.0, the turbulence intensity of the premixed gas explosion flow field is significantly increased, and the stable “tulip flame” propagation velocity range is extended from 16~35 m/s to 16~42 m/s. When the diameter ratio is 2.0, a distinctive four-vortex structure is formed, with strong turbulent mixing and fast energy dissipation. The vortex structure evolves with the diameter ratio, transitioning from a symmetric and stable double-vortex form to a complex multi-vortex system. The research results provide theoretical support for the prevention of explosions.

Forkhead box (FOX) genes are a family of transcription factors that participate in many biological activities, from early embryogenesis to the formation of organs, and from regulation of glucose metabolism to regulation of longevity. Given the extensive influence in the multicellular process, FOX family proteins are responsible for the progression of many types of cancers, especially lung cancer, breast cancer, prostate cancer, and other cancers. Breast cancer is the most common cancer among women, and 2.3 million women were diagnosed in 2020. So, various drugs targeting the FOX signaling pathway have been developed to inhibit breast cancer progression. While the role of the FOX family gene in cancer development has not received enough attention, discovering more potential drugs targeting the FOX signaling pathway is urgently demanded. Here, we review the main members in the FOX gene family and summarize their signaling pathway, including the regulation of the FOX genes and their effects on breast cancer progression. We hope this review will emphasize the understanding of the role of the FOX gene in breast cancer and inspire the discovery of effective anti-breast cancer medicines targeting the FOX gene in the future.