While the expression of programmed death ligand-1 (PD-L1) is associated with response to immune therapy, PD-L1-negative patients may still benefit from immune treatment. Programmed death ligand-2 (PD-L2), another crucial immune checkpoint molecule interacting with PD-1, correlates with the efficacy of various tumor immune therapies. This study investigates the expression of PD-L2 in non-small cell lung cancer (NSCLC) patients following anti-PD-1 therapy and its predictive value for clinical survival outcomes. Additionally, we explore the noninvasive, real-time, and dynamic quantitative analysis potential of PD-L2 positron emission tomography (PET) imaging in transplanted tumors. We utilized [⁶⁸Ga]Ga-labeled peptide HN11-1 for PD-L2 PET imaging. The results indicate a higher response rate to anti-PD-1 therapy in patients positive for both PD-L1 and PD-L2, with PD-L2 status independently predicting progression-free survival (PFS) with pembrolizumab treatment. Furthermore, [⁶⁸Ga]Ga-HN11-1 PET imaging demonstrates specificity in assessing PD-L2 status. Overall, we confirm the correlation between high PD-L2 expression and favorable PFS in NSCLC patients post anti-PD-1 therapy and highlight the promising potential of [⁶⁸Ga]Ga-HN11-1 as a specific tracer for PD-L2 in preclinical and initial human trials.

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.

Redox cycling of iron plays a pivotal role in both nutrient acquisition by living organisms and the geochemical cycling of elements in aquatic environments. In nature, iron cycling is mediated by microbial Fe(II)-oxidizers and Fe(III)-reducers or through the interplay of biotic and abiotic iron transformation processes. Here, we unveil a specific iron cycling process driven by one single phototrophic species, Rhodobacter ferrooxidans SW2. It exhibits the capability to reduce Fe(III) during bacterial cultivation. A c-type cytochrome is identified with Fe(III)-reducing activity, implying the linkage of Fe(III) reduction with the electron transport system. R. ferrooxidans SW2 can mediate iron redox transformation, depending on the availability of light and/or organic substrates. Iron cycling driven by anoxygenic photoferrotrophs is proposed to exist worldwide in modern and ancient environments. Our work not only enriches the theoretical basis of iron cycling in nature but also implies multiple roles of anoxygenic photoferrotrophs in iron transformation processes.

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.

The role of the gut microbiome in enhancing the efficacy of anticancer treatments like chemotherapy and radiotherapy is well acknowledged. However, there is limited empirical evidence on its predictive capabilities for neoadjuvant immunochemotherapy (NICT) responses in esophageal squamous cell carcinoma (ESCC). Our study fills this gap by comprehensively analyzing the gut microbiome's influence on NICT outcomes. We analyzed 16S rRNA gene sequences from 136 fecal samples from 68 ESCC patients before and after NICT, along with 19 samples from healthy controls. After NICT, marked microbiome composition changes were noted, including a decrease in ESCC-associated pathogens and an increase in beneficial microbes such as Limosilactobacillus, Lacticaseibacillus, and Staphylococcus. Baseline microbiota profiles effectively differentiated responders from nonresponders, with responders showing higher levels of short-chain fatty acid (SCFA)-producing bacteria such as Faecalibacterium, Eubacterium_eligens_group, Anaerostipes, and Odoribacter, and nonresponders showing increases in Veillonella, Campylobacter, Atopobium, and Trichococcus. We then divided our patient cohort into training and test sets at a 4:1 ratio and utilized the XGBoost-RFE algorithm to identify 7 key microbial biomarkers—Faecalibacterium, Subdoligranulum, Veillonella, Hungatella, Odoribacter, Butyricicoccus, and HT002. A predictive model was developed using LightGBM, which achieved an area under the receiver operating characteristic curve (AUC) of 86.8% [95% confidence interval (CI), 73.8% to 99.4%] in the training set, 76.8% (95% CI, 41.2% to 99.7%) in the validation set, and 76.5% (95% CI, 50.4% to 100%) in the testing set. Our findings underscore the gut microbiome as a novel source of biomarkers for predicting NICT responses in ESCC, highlighting its potential to enhance personalized treatment strategies and advance the integration of microbiome profiling into clinical practice for modulating cancer treatment responses.

Postpartum depression (PPD) represents a important emotional disorder emerging after childbirth, characterized by its complex etiology and challenging management. Despite extensive preclinical and clinical investigations underscoring the role of estrogen fluctuations and estrogen receptor genes in PPD, the precise mechanisms underpinning this condition have remained elusive. In our present study, animal behavioral studies have elucidated a tight link between the aberrant expression of ESR2, miR-10a-5p, and BDNF in the prefrontal cortex of mice exhibiting postpartum depressive-like behavior, shedding light on the potential molecular pathways involved. Integrating bioinformatics, in vivo, and cell transfection methodologies has unraveled the intricate molecular interplay between ESR2, miR-10a-5p, and BDNF. We identified ESR2 as a negative transcription factor that down-regulates miR-10a transcription, while miR-10a-5p serves as a negative regulator that suppresses BDNF expression. This molecular triad contributes to the pathogenesis of PPD by affecting synaptic plasticity, as evidenced by alterations in synapse-related proteins (e.g., SYP, SYN, and PSD95) and glutamate receptor expression. Additionally, primary neuron culture studies have confirmed the critical roles of ESR2 and miR-10a-5p in maintaining neuronal growth and morphology. Therapeutic interventions, including stereotactic and intranasal administration of antagomir or BDNF, have demonstrated significant potential in treating PPD, highlighting the therapeutic implications of targeting the negative transcriptional and regulatory interactions between ESR2, miR-10a-5p, and BDNF. Our findings endorse the hypothesis that estrogen fluctuations and estrogen receptor gene activity are pivotal stressors and risk factors for PPD, affecting central nervous system functionality and precipitating depressive behaviors postpartum.

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.

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.

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.