Accurate visualization and 3-dimensional (3D) morphological profiling of small model organisms can provide quantitative phenotypes benefiting genetic analysis and modeling of human diseases in tractable organisms. However, in the highly studied nematode Caenorhabditis elegans, accurate morphological phenotyping remains challenging because of notable decrease in image resolution of distant signal under high magnification and complexity in the 3D reconstruction of microscale samples with irregular shapes. Here, we develop a robust robotic system that enables the contactless, stable, and uniform rotation of C. elegans for multi-view fluorescent imaging and 3D morphological phenotyping via the precise reconstruction of 3D models. Contactless animal rotation accommodates a variety of body shapes and sizes found at different developmental stages and in mutant strains. Through controlled rotation, high-resolution fluorescent imaging of C. elegans structures is obtained by overcoming the limitations inherent in both widefield and confocal microscopy. Combining our robotic system with machine learning, we create, for the first time, precise 3D reconstructions of C. elegans at the embryonic and adult stages, enabling 3D morphological phenotyping of mutant strains in an accurate and comprehensive fashion. Intriguingly, our morphological phenotyping discovered a genetic interaction between 2 RNA binding proteins (UNC-75/CELF and MBL-1/MBNL), which are highly conserved between C. elegans and humans and implicated in neurological and muscular disorders. Our system can thus generate quantitative morphological readouts facilitating the investigation of genetic variations and disease mechanisms. More broadly, our method will also be amenable for 3D phenotypic analysis of other biological samples, like zebrafish and Drosophila larvae.

Transmissive metasurfaces are essentially conducive to stealth, absorbers, and communications. However, most of the current schemes only allow microwave to transmit and generally adopt multilayer structures or thick dielectric substrates to improve the electromagnetic performance, restricting optical transmission and conformal application. In addition, most metasurfaces still require metal wires and external power suppliers for programmability. Here, we propose and design an intelligent transmissive microwave metasurface with optical sensing and transparency, which provides both microwave and optical channels without redundant optical devices and power suppliers, and the 2 transmission channels are associated with each other. The metasurface is realized by validly integrating photosensitive materials into microwave meta-structures. As a demonstration, we fabricate an ultrathin optically transparent transmissive metasurface based on polyethylene terephthalate substrate and photoresistors, whose thickness is only 0.125 mm. We further construct cross-wavelength transmission links based on the metasurface sample and experimentally validate that the microwave transmissions vary with light intensities under full-polarization and large-angle incidences, and this metasurface possesses high optical transparency. The intelligent transmissive microwave metasurface with optical sensing and transparency has potential applications in optical–microwave hybrid transmission devices and stealth technology.

While a hippocampal–cortical dialogue is generally thought to mediate memory consolidation, which is crucial for engram function, how it works remains largely unknown. Here, we examined the interplay of neural signals from the retrosplenial cortex (RSC), a neocortical region, and from the hippocampus in memory consolidation by simultaneously recording sharp-wave ripples (SWRs) of dorsal hippocampal CA1 and neural signals of RSC in free-moving mice during the delayed spatial alternation task (DSAT) and subsequent sleep. Hippocampal–RSC coordination during SWRs was identified in nonrapid eye movement (NREM) sleep, reflecting neural reactivation of decision-making in the task, as shown by a peak reactivation strength within SWRs. Using modified generalized linear models (GLMs), we traced information flow through the RSC–CA1–RSC circuit around SWRs during sleep following DSAT. Our findings show that after spatial training, RSC excitatory neurons typically increase CA1 activity prior to hippocampal SWRs, potentially initiating hippocampal memory replay, while inhibitory neurons are activated by hippocampal outputs in post-SWRs. We further identified certain excitatory neurons in the RSC that encoded spatial information related to the DSAT. These neurons, classified as splitters and location-related cells, showed varied responses to hippocampal SWRs. Overall, our study highlights the complex dynamics between the RSC and hippocampal CA1 region during SWRs in NREM sleep, underscoring their critical interplay in spatial memory consolidation.

High levels of tumor necrosis factor receptor type II (TNFR2) are preferentially expressed by immunosuppressive CD4⁺Foxp3⁺ regulatory T cells (T_regs), especially those present in the tumor microenvironment, as initially reported by us. There is compelling evidence that targeting TNFR2 markedly enhances antitumor immune responses. Furthermore, a broad spectrum of human cancers also expresses TNFR2, while its expression by normal tissue is very limited. We thus hypothesized that TNFR2 may be harnessed for tumor-targeted delivery of chemotherapeutic agents. In this study, we performed a proof-of-concept study by constructing a TNFR2-targeted PEGylated poly(dl-lactic-co-glycolic acid) (PLGA-PEG) nanodrug delivery system [designated as TNFR2-PLGA-ADR (Adriamycin)]. The results of in vitro study showed that this TNFR2-targeted delivery system had the properties in cellular binding and cytotoxicity toward mouse colon cancer cells. Further, upon intravenous injection, TNFR2-PLGA-ADR could efficiently accumulate in MC38 and CT26 mouse colon tumor tissues and preferentially bind with tumor-infiltrating T_regs. Compared with ADR and ISO-PLGA-ADR, the in vivo antitumor effect of TNFR2-PLGA-ADR was markedly enhanced, which was associated with a decrease of TNFR2⁺ T_regs and an increase of IFNγ⁺CD8⁺ cytotoxic T lymphocytes in the tumor tissue. Therefore, our results clearly show that targeting TNFR2 is a promising strategy for designing tumor-specific chemoimmunotherapeutic agent delivery system.

Rheumatoid arthritis (RA) is an autoimmune disease with features of synovial inflammation, cartilage erosion, bone destruction, and pain and is currently lacking a satisfactory treatment strategy. Dihydroartemisinin (DHA), the active metabolite of artemisinin, has exhibited outstanding suppressive effects on RA without obvious side effects. However, the underlying mechanisms remain unclear, which limits its further clinical application. The purpose of this study is to reveal the pharmacodynamic mechanism of DHA against RA by means of a combination of single-cell RNA sequencing (RNA-seq), proteomics, as well as transcriptomics both in vivo and in vitro. In our results, DHA effectively reduced the degree of redness, swelling, and pain in RA rats and dramatically changed the synovial tissue microenvironment under the pathological state. Within this microenvironment, fibroblasts, macrophages, B cells, and endothelial cells were the major affected cell types, primarily through DHA targeting the extracellular matrix (ECM) structural constituent signaling pathway. In addition, we confirmed that DHA regulated the ECM by modulating matrix metalloproteinase 2 (MMP2) and MMP3 in the synovial tissue of RA rats. Moreover, DHA induced apoptosis in MH7A cells, further validating the bioinformatics data. In conclusion, DHA effectively reduced the inflammatory response and improved the immune microenvironment in synovial tissue by inhibiting MMP2 and MMP3. Our findings provide a basis for the application of DHA in the treatment of RA.

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.

Rational regulation of reactive oxygen species (ROS) plays a vital importance in maintaining homeostasis of living biological systems. For ROS-related pathologies, chemotherapy technology derived from metal nanomaterials currently occupies a pivotal position. However, they suffer from inherent issues such as complicated synthesis, batch-to-batch variability, high cost, and potential biological toxicity caused by metal elements. Here, we reported for the first time that dual-action 3,5-dihydroxy-1-ketonaphthalene-structured small-molecule enzyme imitator (DHKNase) exhibited 2-edged ROS regulation, catering to the execution of physiology-beneficial ROS destiny among diverse pathologies in living systems. Based on this, DHKNase is validated to enable remarkable therapeutic effects in 2 classic disease models, including the pathogen-infected wound-healing model and the dextran sulfate sodium (DSS)-caused inflammatory bowel disease (IBD). This work provides a guiding landmark for developing novel natural small-molecule enzyme imitator and significantly expands their application potential in the biomedical field.

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.

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.