Cardiac fibrosis has emerged as the primary cause of morbidity, disability, and even mortality in numerous nations. In light of the advancements in precision medicine strategies, substantial attention has been directed toward the development of a practical and precise drug screening platform customized for individual patients. In this study, we introduce a biomimetic cardiac fibrosis-on-a-chip incorporating structural color hydrogels (SCHs) to enable optical high-throughput drug screening. By cocultivating a substantial proportion of cardiac fibroblasts (CFBs) with cardiomyocytes on the SCH, this biomimetic fibrotic microtissue successfully replicates the structural components and biomechanical properties associated with cardiac fibrosis. More importantly, the structural color shift observed in the SCH can be indicative of cardiac contraction and relaxation, making it a valuable tool for evaluating fibrosis progression. By incorporating such fibrotic microtissue into a microfluidic gradient chip, we develop a biomimetic optical cardiac fibrosis-on-a-chip platform that accurately and efficiently screens potential anti-fibrotic drugs. These characteristics suggest that this microphysiological platform possesses the capability to establish a preclinical framework for screening cardiac drugs, and may even contribute to the advancement of precision medicine.

Deep learning relies on learning from extensive data to generate prediction results. This approach may inadvertently capture spurious correlations within the data, leading to models that lack interpretability and robustness. Researchers have developed more profound and stable causal inference methods based on cognitive neuroscience. By replacing the correlation model with a stable and interpretable causal model, it is possible to mitigate the misleading nature of spurious correlations and overcome the limitations of model calculations. In this survey, we provide a comprehensive and structured review of causal inference methods in deep learning. Brain-like inference ideas are discussed from a brain-inspired perspective, and the basic concepts of causal learning are introduced. The article describes the integration of causal inference with traditional deep learning algorithms and illustrates its application to large model tasks as well as specific modalities in deep learning. The current limitations of causal inference and future research directions are discussed. Moreover, the commonly used benchmark datasets and the corresponding download links are summarized.

Background: The nasopharyngeal brush sampling can effectively collect samples from the nasopharynx. The blind brush sampling does not require the guidance of endoscopy, which is favorable for implementation and dissemination in the community. This study explored methylation markers for nasopharyngeal carcinoma (NPC) at both Epstein–Barr virus (EBV) and its host genome levels, aiming to construct a blind brushing diagnostic method. Methods: EBV DNA capture and methylation sequencing and GEO Illumina 450K methylation array data were used respectively for the discovery of EBV and host methylation markers. The diagnostic method was built in training cohort (n = 347) and validated in an independent validation cohort (n = 155). Results: A total of 1 EBV methylation marker (BILF2) and 6 host methylation markers (ITGA4, IMPA2, ITPKB, PI9, AMIGO2, and VAV3) were identified. Both EBV and host methylation markers were almost exclusively detected in NPC samples, with negligible detection in control samples. In validation cohort, the diagnostic method that included only the EBV BILF2 marker showed a sensitivity and specificity of 80.22% and 98.44%, respectively. When combining the EBV-derived marker BILF2 with the host-derived marker IMPA2, the diagnostic method's sensitivity increased to 84.62%, while the specificity remained unchanged (IDI = 4.4%, P = 0.0419). Conclusion: Overall, the blind nasopharyngeal brushing diagnostic method, combining EBV and host methylation markers, showed great potential in NPC detection and could promote its application in nonclinical screening of NPC.

This review comprehensively covers recent developments of cyclodextrin-mediated chemical transformations for green chemistry. These cyclic oligomers of glucose are nontoxic, eco-friendly, and recyclable to accomplish eminent functions in water. Their most important feature is to form inclusion complexes with reactants, intermediates, and/or catalysts. As a result, their cavities serve as sterically restricted and apolar reaction fields to promote the efficiency and selectivity of reactions. Furthermore, unstable reagents and intermediates are protected from undesired side reactions. The scope of their applications has been further widened through covalent or noncovalent modifications. Combinations of them with metal catalysis are especially successful. In terms of these effects, various chemical reactions are achieved with high selectivity and yield so that valuable chemicals are synthesized from multiple components in one-pot reactions. Furthermore, cyclodextrin units are orderly assembled in oligomers and polymers to show their cooperation for advanced properties. Recently, cyclodextrin-based metal–organic frameworks and polyoxometalate–cyclodextrin frameworks have been fabricated and employed for unique applications. Cyclodextrins fulfill many requirements for green chemistry and should make enormous contributions to this growing field.

Dental, oral, and craniofacial diseases can substantially impact the quality of human life, thereby posing a serious public health concern. Although conventional therapies such as surgery have solved these problems largely, the prognosis of patients is not always satisfactory. Cell membrane-coated nanoparticles (CMCNPs) carry nanodrugs with the help of natural cell membranes, therefore utilizing their remarkable ability to interface and interact with their surrounding environment. These nanoparticles have demonstrated substantial advantages in drug targeting, prolonging blood circulation time, penetrating biofilms, and immune escape. With the assistance of CMCNPs, the therapeutic effects of dental, oral, and craniofacial diseases can reach a higher level. CMCNPs have been applied for dental, oral, and craniofacial diseases for various conditions such as head and neck cancer, periodontal disease, and oral biosignal detection. For the therapies of head and neck cancer, CMCNPs have been widely utilized as a tool of chemotherapy, phototherapy, and immunotherapy, while yet to be exploited in imaging technique. In the end, we summarized the challenges and prospectives of CMCNPs for dental, oral, and craniofacial diseases: large-scale production with uniform standards and high quantity, extensive application directions in dental, oral, and craniofacial regions (implant, endodontics), and the promotion of its clinical application.

Thermo-responsive hydrogels can dynamically modulate incident light, providing a broad prospect for development of smart windows, which are of pivotal importance for energy conservation in buildings. However, these hydrogels normally exhibit slow response speed and tend to contract over extended phase transition, compromising structural integrity of smart windows. In this study, a solid–liquid switchable thermochromic hydrogel, denoted as SL-PNIPAm, was synthesized by cross-linking PNIPAm with AMEO through dynamic imine bonds. Due to its distinctive solid–liquid transformation characteristics, SL-PNIPAm demonstrates rapid response time (within 5 s) and retains structural integrity without undergoing shrinkage during heating/cooling and freezing/thawing cycles. SL-PNIPAm can also be encapsulated within 2 glass panels to prepare smart windows, which showed extraordinary luminous transmittance (T_lum = 96.8%) and solar modulation ability (ΔT_solar = 89.7%) and effectively reduced the indoor temperature (22 °C) in a simulated indoor experiment. Energy consumption simulation investigations are performed in diverse cities. The results reveal that SLW is capable of achieving a remarkable 54% reduction of HVAC energy consumption, leading to substantial decrease in CO₂ emissions by up to 40 kg m⁻² annually. This work develops a new hydrogel system with outstanding durability for smart windows and will promote the development and renovation of thermochromic smart windows.

Lung ischemia–reperfusion injury (IRI) stands as the primary culprit behind primary graft dysfunction (PGD) after lung transplantation, yet viable therapeutic options are lacking. In the present study, we used a murine hilar clamp (1 h) and reperfusion (3 h) model to study IRI. The left lung tissues were harvested for metabolomics, transcriptomics, and single-cell RNA sequencing. Metabolomics of plasma from human lung transplantation recipients was also performed. Lung histology, pulmonary function, pulmonary edema, and survival analysis were measured in mice. Integrative analysis of metabolomics and transcriptomics revealed a marked up-regulation of arachidonate 12-lipoxygenase (ALOX12) and its metabolite 12-hydroxyeicosatetraenoic acid (12-HETE), which played a pivotal role in promoting ferroptosis and neutrophil extracellular trap (NET) formation during lung IRI. Additionally, single-cell RNA sequencing revealed that ferroptosis predominantly occurred in pulmonary endothelial cells. Importantly, Alox12-knockout (KO) mice exhibited a notable decrease in ferroptosis, NET formation, and tissue injury. To investigate the interplay between endothelial ferroptosis and NET formation, a hypoxia/reoxygenation (HR) cell model using 2 human endothelial cell lines was established. By incubating conditioned medium from HR cell model with neutrophils, we found that the liberation of high mobility group box 1 (HMGB1) from endothelial cells undergoing ferroptosis facilitated the formation of NETs by activating the TLR4/MYD88 pathway. Last, the administration of ML355, a targeted inhibitor of Alox12, mitigated lung IRI in both murine hilar clamp/reperfusion and rat left lung transplant models. Collectively, our study indicates ALOX12 as a promising therapeutic strategy for lung IRI.

Hydrologic reconstructions from North America are largely unknown for the Middle Miocene. Examination of fungal palynomorph assemblages coupled with traditional plant-based palynology permits delineation of local, as opposed to regional, climate signals and provides a baseline for study of ancient fungas. Here, the Fungi in a Warmer World project presents paleoecology and paleoclimatology of 351 fungal morphotypes from 3 sites in the United States: the Clarkia Konservat-Lagerstätte site (Idaho), the Alum Bluff site (Florida), and the Bouie River site (Mississippi). Of these, 83 fungi are identified as extant taxa and 41 are newly reported from the Miocene. Combining new plant-based paleoclimatic reconstructions with funga-based paleoclimate reconstructions, we demonstrate cooling and hydrologic changes from the Miocene climate optimum to the Serravallian. In the southeastern United States, this is comparable to that reconstructed with pollen and paleobotany alone. In the northwestern United States, cooling is greater than indicated by other reconstructions and hydrology shifts seasonally, from no dry season to a dry summer season. Our results demonstrate the utility of fossil fungi as paleoecologic and paleoclimatic proxies and that warmer than modern geological time intervals do not match the “wet gets wetter, dry gets drier” paradigm. Instead, both plants and fungi show an invigorated hydrological cycle across mid-latitude North America.

Game theory problems are widely applied in many research areas such as computer science and finance, with the key issue being how to quickly make decisions. Here, we present a novel quantum algorithm for game theory problems based on a continuous quantum walk. Our algorithm exhibits quantum advantage compared to classical game algorithms. Furthermore, we exploit the analogy between the wave function of the Schrödinger equation and the voltage in Kirchhoff's law to effectively translate the design of quantum game trees into classical circuit networks. We have theoretically simulated the quantum game trees and experimentally validated the quantum functionality speedup on classical circuit networks. Due to the robust scalability and stability inherent in classical circuit networks, quantum game trees implemented within this framework hold promise for addressing more intricate application scenarios.

Melting and solidification of lunar regolith are pivotal for comprehending the evolutionary dynamics of lunar volcanism, geology, and impact history. Additionally, insights gained from these processes can contribute to the advancement of in situ resource utilization technologies, for instance additive manufacturing and resource extraction systems. Herein, we conduct the direct observation of the melting and rapid solidification of lunar particles returned by the Chang'E 5 mission. The melting temperature and melting sequence were obtained. Bubble generation, growth, and release were clearly observed, with a maximum bubble diameter of 5 µm, which is supposed to be according to the release of volatiles that embedded in the particles. During the solidification process, evident crystallization occurred with incremental crystal growth rate approximately of 27 nm/s. Scanning electron microscopy and energy-dispersive x-ray spectroscopy results verified that the Fe-rich mineral crystalizes first. These results would improve the understanding of the evolution of lunar volcanism, geology, and impact history.