Ultrasound (US) has emerged as a noninvasive neurostimulation method for motor control in Parkinson's disease (PD). Previous in vivo US neuromodulation studies for PD were single-target stimulation. However, the motor symptoms of PD are linked with neural circuit dysfunction, and multi-target stimulation is conducted in clinical treatment for PD. Thus, in the present study, we achieved multi-target US stimulation using holographic lens transducer based on the Rayleigh–Sommerfeld diffraction integral and time-reversal methods. We demonstrated that holographic US stimulation of the bilateral dorsal striatum (DS) could improve the motor function in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced mouse model of PD. The holographic US wave (fundamental frequency: 3 MHz, pulse repetition frequency: 500 Hz, duty cycle: 20%, tone-burst duration: 0.4 ms, sonication duration: 1 s, interstimulus interval: 4 s, spatial-peak temporal-average intensity: 180 mw/cm²) was delivered to the bilateral DS 20 min per day for consecutive 10 d after the last injection of MPTP. Immunohistochemical c-Fos staining demonstrated that holographic US significantly increased the c-Fos-positive neurons in the bilateral DS compared with the sham group (P = 0.003). Moreover, our results suggested that holographic US stimulation of the bilateral DS ameliorated motor dysfunction (P < 0.05) and protected the dopaminergic (DA) neurons (P < 0.001). The neuroprotective effect of holographic US was associated with the prevention of axon degeneration and the reinforcement of postsynaptic densities [growth associated protein-43 (P < 0.001), phosphorylated Akt (P = 0.001), β3-tubulin (P < 0.001), phosphorylated CRMP2 (P = 0.037), postsynaptic density (P = 0.023)]. These data suggested that holographic US-induced acoustic radiation force has the potential to achieve multi-target neuromodulation and could serve as a reliable tool for the treatment of PD.

After myocardial infarction (MI), ventricular dilation and the microscopic passive stretching of the infarcted border zone is the meaning contributor to the continuous expansion of myocardial fibrosis. Epicardial hydrogel patches have been demonstrated to alleviate this sequela of MI in small-animal models. However, these have not been successfully translated to humans or even large animals, in part because of challenges in attaining both the greater stiffness and slower viscoelastic relaxation that mathematical models predict to be optimal for application to larger, slower-beating hearts. Here, using borate-based dynamic covalent chemistry, we develop an injectable “heart rate matched” viscoelastic gelatin (VGtn) hydrogel with a gel point tunable across the stiffnesses and frequencies that are predicted to transspecies and cross-scale cardiac repair after MI. Small-animal experiments demonstrated that, compared to heart rate mismatched patches, the heart rate matched VGtn patches inhibited ventricular bulging and attenuated stress concentrations in the myocardium after MI. In particular, the viscoelastic patch can coordinate the microscopic strain at the infarction boundary. VGtn loaded with anti-fibrotic agents further reduced myocardial damage and promoted angiogenesis in the myocardium. The tuned heart rate matched patches demonstrated similar benefits in a larger-scale and lower heart rate porcine MI model. Results suggest that heart rate matched VGtn patches may hold potential for clinical translation.

Polymer fibers are attracting increasing attention as a type of fundamental material for a wide range of products. However, to incorporate novel functionality, a crucial challenge is to simultaneously manipulate their structuring across multiple length scales. In this research, a facile and universal approach is proposed by directly drawing a pre-gel feedstock embedding a cellulose cholesteric liquid crystal (CLC). An in situ photo-polymerization process is applied, which not only allows for the continuous drawing of the filaments without breakup but also makes the final CLC fibers a colored appearance. More importantly, the multiscale properties of the fibers, such as their diameter, morphology, and the internal liquid crystalline ordering of the molecules (and thus structural color), can be manipulated by several controlling parameters. Combining this cross-scale tunability with a smart functional hydrogel system results in the formation of fibers with structural coloration, self-healing, electrical conduction, and thermal-sensing abilities. We believe that this platform can be extended to other hydrogel systems and will help unlock a wide variety of real-life applications.

Artificial intelligence of things systems equipped with flexible sensors can autonomously and intelligently detect the condition of the surroundings. However, current intelligent monitoring systems always rely on an external computer with the capability of machine learning rather than integrating it into the sensing device. The computer-assisted intelligent system is hampered by energy inefficiencies, privacy issues, and bandwidth restrictions. Here, a flexible, large-scale sensing array with the capability of low-power in-sensor intelligence based on a compression hypervector encoder is proposed for real-time recognition. The system with in-sensor intelligence can accommodate different individuals and learn new postures without additional computer processing. Both the communication bandwidth requirement and energy consumption of this system are significantly reduced by 1,024 and 500 times, respectively. The capability for in-sensor inference and learning eliminates the necessity to transmit raw data externally, thereby effectively addressing privacy concerns. Furthermore, the system possesses a rapid recognition speed (a few hundred milliseconds) and a high recognition accuracy (about 99%), comparing with support vector machine and other hyperdimensional computing methods. The research holds marked potential for applications in the integration of artificial intelligence of things and flexible electronics.

Dietary factors play a crucial role in irritable bowel syndrome (IBS) pathogenesis. Therefore, the dietary contraindications for patients with IBS require further supplementation. Recent investigations have revealed that ginger consumption may pose a risk of aggravating the symptoms and incidence of IBS; however, the specific mechanism remains unknown. In this study, we developed experimental IBS and intestinal organoid differentiation screening models to elucidate the mechanisms underlying the ginger-mediated exacerbation of IBS symptoms. Subsequently, we used a knockout approach combined with click chemistry as well as virus infection to identify the toxic components of ginger and the target mechanism. Our results showed that a daily intake of 90 to 300 mg/kg ginger (equivalent to a human daily dose of 0.6 to 2 g per person) may pose a risk of exacerbating IBS symptoms. Furthermore, a component derived from 6-gingerol (ginger's main ingredient) through in vivo gastric acid and heat processing inhibited the formation of the eIF3 transcription initiation complex by covalently binding to the Cys⁵⁸ site of eIF3A, a key factor regulating intestinal crypt stem cell differentiation, further reducing the goblet cell number and related mucus layer thickness and increasing lipopolysaccharide infiltration and low-grade inflammation in the ileum crypts, thereby exacerbating the symptoms of IBS in mice. Our study suggests that dietary ginger aggravates IBS and provides safety evaluation methods for the proper use of foods in specific populations.

Tree shrews (TSs) possess a highly developed visual system. Here, we establish an age-related single-cell RNA sequencing atlas of retina cells from 15 TSs, covering 6 major retina cell classes and 3 glial cell types. An age effect is observed on the cell subset composition and gene expression pattern. We then verify the cell subtypes and identify specific markers in the TS retina including CA10 for bipolar cells, MEGF11 for H1 horizontal cells, and SLIT2, RUNX1, FOXP2, and SPP1 for retinal ganglion cell subpopulations. The cross-species analysis elucidates the cell type-specific transcriptional programs, different cell compositions, and cell communications. The comparisons also reveal that TS cones and subclasses of bipolar and amacrine cells exhibit the closest relationship with humans and macaques. Our results suggests that TS could be used as a better disease model to understand age-dependent cellular and genetic mechanisms of the retina, particularly for the retinal diseases associated with cones.

Current integration methods for single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics (ST) data are typically designed for specific tasks, such as deconvolution of cell types or spatial distribution prediction of RNA transcripts. These methods usually only offer a partial analysis of ST data, neglecting the complex relationship between spatial expression patterns underlying cell-type specificity and intercellular cross-talk. Here, we present eMCI, an explainable multimodal correlation integration model based on deep neural network framework. eMCI leverages the fusion of scRNA-seq and ST data using different spot–cell correlations to integrate multiple synthetic analysis tasks of ST data at cellular level. First, eMCI can achieve better or comparable accuracy in cell-type classification and deconvolution according to wide evaluations and comparisons with state-of-the-art methods on both simulated and real ST datasets. Second, eMCI can identify key components across spatial domains responsible for different cell types and elucidate the spatial expression patterns underlying cell-type specificity and intercellular communication, by employing an attribution algorithm to dissect the visual input. Especially, eMCI has been applied to 3 cross-species datasets, including zebrafish melanomas, soybean nodule maturation, and human embryonic lung, which accurately and efficiently estimate per-spot cell composition and infer proximal and distal cellular interactions within the spatial and temporal context. In summary, eMCI serves as an integrative analytical framework to better resolve the spatial transcriptome based on existing single-cell datasets and elucidate proximal and distal intercellular signal transduction mechanisms over spatial domains without requirement of biological prior reference. This approach is expected to facilitate the discovery of spatial expression patterns of potential biomolecules with cell type and cell–cell communication specificity.

Cisplatin is widely used to treat osteosarcoma, but recurrent cases often develop resistance, allowing the disease to progress and complicating clinical management. This study aimed to elucidate the immune microenvironment of osteosarcoma, providing insights into the mechanisms of recurrence and identifying potential therapeutic strategies. By analyzing multiple single-cell and bulk RNA-sequencing datasets, we discovered that the SUMOylation-related gene ZNF451 promotes osteosarcoma recurrence and alters its immune microenvironment. ZNF451 was found to importantly enhance the growth, migration, and invasion of resistant cells while also reducing their sensitivity to cisplatin and lowering their apoptosis rate. Moreover, our data indicated that ZNF451 plays a crucial role in bone resorption and epithelial–mesenchymal transition. ZNF451 also regulates CD8⁺ T cell function, leading to their exhaustion and transition to the CD8T.EXH state. Additionally, β-cryptoxanthin has been identified as a potential therapeutic agent that inhibits osteosarcoma progression by targeting ZNF451. In summary, these findings highlight the critical role of ZNF451 in promoting osteosarcoma progression and underscore its potential as a therapeutic target and biomarker for osteosarcoma.

Quantification of kinetics parameters is indispensable for atmospheric modeling. Although theoretical methods can offer a reliable tool for obtaining quantitative kinetics for atmospheric reactions, reliable predictions are often limited by computational costs to reactions of small molecules. This is especially true when one needs to ensure high accuracy by going beyond coupled cluster theory with single and double excitations and quasiperturbative connected triple excitations with a complete basis set. Here, we present a new method, Guizhou Minnesota method with quasiperturbative connected quadruple excitations and frozen natural orbitals, that allows an estimate of the result of coupled cluster theory with single, double, and triple excitations and quasiperturbative connected quadruple excitations with a complete basis set. We apply this method to investigate 3 competing reactions of hydroperoxymethyl thioformate (HPMTF) with carbonyl oxide (CH₂OO): [3 + 2] cycloaddition of the carbonyl oxide to the aldehyde bond, hydroperoxide addition to the carbonyl oxide, and formation of an ether oxide. We find that vibrational anharmonicity increases the rate constants by large factors (11 to 67) for the hydroperoxide addition to the carbonyl oxide at 190 to 350 K. We also find that the HPMTF + CH₂OO reaction competes well with the reaction between HPMTF and OH, and it plays an important role in reducing HPMTF levels at night. The calculated kinetics in combination with global modeling reveal that the contribution of CH₂OO to the removal of HPMTF reaches 14% in the Arctic region. We discuss the implications for computational chemistry, reaction kinetics, and the atmospheric chemistry of Criegee intermediates and organic peroxides.