Perioperative toripalimab and chemotherapy in locally advanced gastric or gastro-esophageal junction cancer: a randomized phase 2 trial.

Perioperative chemotherapy is the standard treatment for locally advanced gastric or gastro-esophageal junction cancer, and the addition of programmed cell death 1 (PD-1) inhibitor is under investigation. In this randomized, open-label, phase 2 study (NEOSUMMIT-01), patients with resectable gastric or gastro-esophageal junction cancer clinically staged as cT3-4aN + M0 were randomized (1:1) to receive either three preoperative and five postoperative 3-week cycles of SOX/XELOX (chemotherapy group, n = 54) or PD-1 inhibitor toripalimab plus SOX/XELOX, followed by toripalimab monotherapy for up to 6 months (toripalimab plus chemotherapy group, n = 54). The primary endpoint was pathological complete response or near-complete response rate (tumor regression grade (TRG) 0/1). The results showed that patients in the toripalimab plus chemotherapy group achieved a higher proportion of TRG 0/1 than those in the chemotherapy group (44.4% (24 of 54, 95% confidence interval (CI): 30.9%-58.6%) versus 20.4% (11 of 54, 95% CI: 10.6%-33.5%)), and the risk difference of TRG 0/1 between toripalimab plus chemotherapy group and chemotherapy group was 22.7% (95% CI: 5.8%-39.6%; P = 0.009), meeting a prespecified endpoint. In addition, a higher pathological complete response rate (ypT0N0) was observed in the toripalimab plus chemotherapy group (22.2% (12 of 54, 95% CI: 12.0%-35.6%) versus 7.4% (4 of 54, 95% CI: 2.1%-17.9%); P = 0.030), and surgical morbidity (11.8% in the toripalimab plus chemotherapy group versus 13.5% in the chemotherapy group) and mortality (1.9% versus 0%), and treatment-related grade 3-4 adverse events (35.2% versus 29.6%) were comparable between the treatment groups. In conclusion, the addition of toripalimab to chemotherapy significantly increased the proportion of patients achieving TRG 0/1 compared to chemotherapy alone and showed a manageable safety profile. ClinicalTrials.gov registration: NCT04250948 .

The Chinese Society of Clinical Oncology (CSCO): Clinical guidelines for the diagnosis and treatment of gastric cancer, 2023.

The 2023 update of the Chinese Society of Clinical Oncology (CSCO) Clinical Guidelines for Gastric Cancer focuses on standardizing cancer diagnosis and treatment in China, reflecting the latest advancements in evidence-based medicine, healthcare resource availability, and precision medicine. These updates address the differences in epidemiological characteristics, clinicopathological features, tumor biology, treatment patterns, and drug selections between Eastern and Western gastric cancer patients. Key revisions include a structured template for imaging diagnosis reports, updated standards for molecular marker testing in pathological diagnosis, and an elevated recommendation for neoadjuvant chemotherapy in stage III gastric cancer. For advanced metastatic gastric cancer, the guidelines introduce new recommendations for immunotherapy, anti-angiogenic therapy and targeted drugs, along with updated management strategies for human epidermal growth factor receptor 2 (HER2)-positive and deficient DNA mismatch repair (dMMR)/microsatellite instability-high (MSI-H) patients. Additionally, the guidelines offer detailed screening recommendations for hereditary gastric cancer and an appendix listing drug treatment regimens for various stages of gastric cancer. The 2023 CSCO Clinical Guidelines for Gastric Cancer updates are based on both Chinese and international clinical research and expert consensus to enhance their applicability and relevance in clinical practice, particularly in the heterogeneous healthcare landscape of China, while maintaining a commitment to scientific rigor, impartiality, and timely revisions.

Toward a Unified Transformer-Based Framework for Scene Graph Generation and Human-Object Interaction Detection.

Scene graph generation (SGG) and human-object interaction (HOI) detection are two important visual tasks aiming at localising and recognising relationships between objects, and interactions between humans and objects, respectively. Prevailing works treat these tasks as distinct tasks, leading to the development of task-specific models tailored to individual datasets. However, we posit that the presence of visual relationships can furnish crucial contextual and intricate relational cues that significantly augment the inference of human-object interactions. This motivates us to think if there is a natural intrinsic relationship between the two tasks, where scene graphs can serve as a source for inferring human-object interactions. In light of this, we introduce SG2HOI+, a unified one-step model based on the Transformer architecture. Our approach employs two interactive hierarchical Transformers to seamlessly unify the tasks of SGG and HOI detection. Concretely, we initiate a relation Transformer tasked with generating relation triples from a suite of visual features. Subsequently, we employ another transformer-based decoder to predict human-object interactions based on the generated relation triples. A comprehensive series of experiments conducted across established benchmark datasets including Visual Genome, V-COCO, and HICO-DET demonstrates the compelling performance of our SG2HOI+ model in comparison to prevalent one-stage SGG models. Remarkably, our approach achieves competitive performance when compared to state-of-the-art HOI methods. Additionally, we observe that our SG2HOI+ jointly trained on both SGG and HOI tasks in an end-to-end manner yields substantial improvements for both tasks compared to individualized training paradigms.

Residual circulating tumor DNA after adjuvant chemotherapy effectively predicts recurrence of stage II-III gastric cancer.

BACKGROUND: Circulating tumor DNA (ctDNA) is a promising biomarker for predicting relapse in multiple solid cancers. However, the predictive value of ctDNA for disease recurrence remains indefinite in locoregional gastric cancer (GC). Here, we aimed to evaluate the predictive value of ctDNA in this context. METHODS: From 2016 to 2019, 100 patients with stage II/III resectable GC were recruited in this prospective cohort study (NCT02887612). Primary tumors were collected during surgical resection, and plasma samples were collected perioperatively and within 3 months after adjuvant chemotherapy (ACT). Somatic variants were captured via a targeted sequencing panel of 425 cancer-related genes. The plasma was defined as ctDNA-positive only if one or more variants detected in the plasma were presented in at least 2% of the primary tumors. RESULTS: Compared with ctDNA-negative patients, patients with positive postoperative ctDNA had moderately higher risk of recurrence [hazard ratio (HR) = 2.74, 95% confidence interval (CI) = 1.37-5.48; P = 0.003], while patients with positive post-ACT ctDNA showed remarkably higher risk (HR = 14.99, 95% CI = 3.08-72.96; P < 0.001). Multivariate analyses indicated that both postoperative and post-ACT ctDNA positivity were independent predictors of recurrence-free survival (RFS). Moreover, post-ACT ctDNA achieved better predictive performance (sensitivity, 77.8%; specificity, 90.6%) than both postoperative ctDNA and serial cancer antigen. A comprehensive model incorporating ctDNA for recurrence risk prediction showed a higher C-index (0.78; 95% CI = 0.71-0.84) than the model without ctDNA (0.71; 95% CI = 0.64-0.79; P = 0.009). CONCLUSIONS: Residual ctDNA after ACT effectively predicts high recurrence risk in stage II/III GC, and the combination of tissue-based and circulating tumor features could achieve better risk prediction.

Rational design of genotyping nanodevice for HPV subtype distinction.

There are more than 200 subtypes of human papillomavirus (HPV), and high-risk HPVs are a leading cause of cervical cancer. Identifying the genotypes of HPV is significant for clinical diagnosis and cancer control. Herein, we used programmable and modified DNA as the backbone to construct fluorescent genotyping nanodevice for HPV subtype distinction. In our strategy, the dye-labeled single-stranded recognize-DNA (R-DNA) was hybridized with Black Hole Quencher (BHQ) labeled single-stranded link-DNA (L-DNA) to form three functionalized DNA (RL-DNA). Through the extension of polycytosine (poly-C) in L-DNA, three RL-DNAs can be more firmly adsorbed on graphene oxide to construct reliable genotyping nanodevice. The genotyping nanodevice had low background noise since the dual energy transfer, including Förster resonance energy transfer (FRET) from dye to BHQ and the resonance energy transfer (RET) from dye to graphene oxide. Meanwhile, the programmability of DNA allows the proposed strategy to simultaneously and selectively distinguish several HPV subtypes in solution using DNA labeled with different dyes. To demonstrate clinical potential, we show multiplexed assay of HPV subtypes in cervical scrapes, and it has been successfully applied in HPV-DNA analysis in cervical scrapes samples. The genotyping nanodevice could be developed for simultaneous and multiplex analysis of several oligonucleotides in a homogeneous solution by adjusting the recognition sequence, demonstrating its potential application in the rapid screening of multiple biomarkers.

Prognostic Significance of PD-L1 Expression in Gastric Cancer Patients with Peritoneal Metastasis.

BACKGROUND: Recently, many studies have explored the relationship between the expression of programmed death ligand 1 (PD-L1) and prognosis in gastric cancer, but there is still controversy. Additionally, few studies have specifically investigated the expression of PD-L1 in patients with peritoneal metastasis. METHODS: Immunohistochemistry was used to analyze the expression of PD-L1 in gastric cancer patients with peritoneal metastasis. The combined positive score (CPS) was calculated to evaluate the expression of PD-L1, and the clinicopathological data were analyzed to explore prognostic significance. RESULTS: In total, 147 gastric cancer patients with peritoneal metastasis were enrolled. The negative PD-L1 expression was defined as a CPS < 1, and high PD-L1 expression was defined as a CPS ≥ 10. PD-L1 expression with CPS ≥ 1 and CPS-negative was detected in 67 (45.58%) and 80 (54.42%) patients, respectively. High PD-L1 expression at PD-L1 CPS ≥ 10 was detected in 21(14.29%) patients. The median overall survival (OS) was 18.53 months in the CPS < 10 group and 27.00 months in the CPS ≥ 10 group; the OS difference between the two groups was significant (p = 0.015). Multivariate analysis demonstrated that a poor Eastern Cooperative Oncology Group performance score (ECOG PS) (p = 0.002) and severe peritoneal metastasis (p = 0.033) were significantly associated with poor survival, while palliative chemotherapy (p = 0.002) and high PD-L1 expression (p = 0.008) were independent and significantly favorable prognostic factors. CONCLUSIONS: Our study demonstrated that PD-L1 expression was widely presented in gastric cancer patients with peritoneal metastasis, while a CPS no less than 10 predicted better prognosis.

Simultaneous Response of Mitochondrial MicroRNAs to Identify Cell Apoptosis with Multiple Responsive Intelligent DNA Biocomputing Nanodevices.

Challenges remained in precisely real-time monitoring of apoptotic molecular events at the subcellular level. Herein, we developed a new type of intelligent DNA biocomputing nanodevices (iDBNs) that responded to mitochondrial microRNA-21 (miR-21) and microRNA-10b (miR-10b) simultaneously which were produced during cell apoptosis. By hybridizing two hairpins (H1 and H2) onto DNA nanospheres (DNSs) that had been previously modified with mitochondria-targeted triphenylphosphine (TPP) motifs, iDBNs were assembled in which two localized catalytic hairpins self-assembly (CHA) reactions occurred upon the co-stimulation of mitochondrial miR-21 and miR-10b to perform AND logic operations, outputting fluorescence resonance energy transfer (FRET) signals for sensitive intracellular imaging during cell apoptosis. Owing to the spatial confinement effects of DNSs, it was discovered that iDBNs had a high efficiency and speed of logic operations by high local concentrations of H1 and H2, making the simultaneous real-time responses of mitochondrial miR-21 and miR-10b reliable and sensitive during cell apoptosis. These results demonstrated that iDBNs were simultaneously responsive to multiple biomarkers, which greatly improved the detection accuracy to identify the cell apoptosis, demonstrating that iDBNs are highly effective and reliable for the diagnosis of major disease and screening of anticancer drugs.

Visual identification of 1O2-induced crystal structure transformation of single Zr-MOF by dark-field microscopy.

Understanding the structural transformation of photocatalysts under illumination provides detailed insights to solve the poisoning of photocatalysts. In this study, the 1O2-induced transformation of crystalline structure in nanosized Zr-porphyrin metal organic frames (ZrTCPP MOFs) is confirmed by a dark-field microscopy (DFM) system. Under continuous illumination, the energy of excited ZrTCPP transfers to oxygen in the surroundings and generates 1O2, which causes the nanosized crystal ZrTCPP to change into the amorphous state The crystalline structure of ZrTCPP corresponds to the multiple resonance peaks of the scattering spectra, and the amorphous structure corresponds to the single scattering peak. Therefore, the crystalline change is characterized by characteristic peaks changes in the scattering spectra under DFM, showing that a real-time and in situ DFM imaging method is a good platform for monitoring the crystal structure transformation at the single particle level.

CRISPR-Cas12a Coupled with DNA Nanosheet-Amplified Fluorescence Anisotropy for Sensitive Detection of Biomolecules.

DNA nanosheets (DNSs) have been utilized effectively as a fluorescence anisotropy (FA) amplifier for biosensing. But, their sensitivity needs to be further improved. Herein, CRISPR-Cas12a with strong trans-cleavage activity was utilized to enhance the FA amplification ability of DNSs for the sensitive detection of miRNA-155 (miR-155) as a proof-of-principle target. In this method, the hybrid of the recognition probe of miR-155 (T1) and a blocker sequence (T2) was immobilized on the surface of magnetic beads (MBs). In the presence of miR-155, T2 was released by a strand displacement reaction, which activated the trans-cleavage activity of CRISPR-Cas12a. The single-stranded DNA (ssDNA) probe modified with a carboxytetramethylrhodamine (TAMRA) fluorophore was cleaved in large quantities and could not bind to the handle chain on DNSs, inducing a low FA value. In contrast, in the absence of miR-155, T2 could not be released and the trans-cleavage activity of CRISPR-Cas12a could not be activated. The TAMRA-modified ssDNA probe remained intact and was complementary to the handle chain on the DNSs, and a high FA value was obtained. Thus, miR-155 was detected through the obviously decreased FA value with a low limit of detection (LOD) of 40 pM. Impressively, the sensitivity of this method was greatly improved about 322 times by CRISPR-Cas12a, confirming the amazing signal amplification ability of CRISPR-Cas12a. At the same time, the SARS-CoV-2 nucleocapsid protein was detected by the strategy successfully, indicating that this method was general. Moreover, this method has been applied in the analysis of miR-155 in human serum and the lysates of cells, which provides a new avenue for the sensitive determination of biomarkers in biochemical research and disease diagnosis.

Electrostatic assemblies of molecularly imprinted polymers on the surface of electrospun nanofiber membranes for the point-of-care detection of thiodiglycol, a sulfur mustard poisoning metabolic marker.

In this study, molecularly imprinted polymers (MIPs) were assembled on the surface of ethylene imine polymer (PEI)/poly(vinyl alcohol) (PVA) electrospun nanofiber membranes for the point-of-care testing (POCT) of thiodiglycol (TDG), a sulfur mustard poisoning metabolic marker, using concentrated gold nanoparticles (AuNPs) as the signal reporting units. The MIPs/PEI/PVA nanofiber membranes could capture TDG specifically through the recognition interaction between MIPs and TDG. Then, AuNPs were adsorbed onto the MIPs/PEI/PVA nanofiber membranes through the Au-S interaction between TDG and AuNPs to produce a visible red color. In order to improve the sensitivity, the silver-enhanced solutions were used to deepen the color of the nanofiber membranes and the software Image J was used to read the gray value as the signal response for subsequent analysis. There was a good linear relationship between the color change of the MIPs/PEI/PVA nanofiber membranes and the TDG concentration from 0.1 ng mL-1 to 1.0 µg mL-1, and the limit of detection was 38 pg mL-1. This method was applied for the selective detection of TDG in urine, showing great potential for the clinical diagnosis of mustard gas poisoning.

[Efficacy and safety of rituximab in children and adolescents with mature B-cell non-Hodgkin's lymphoma: a Meta analysis]. / 利妥昔单抗治疗儿童及青少年成熟Bç»†èƒžéžéœå¥‡é‡‘æ·‹å·´ç˜¤ç–—æ•ˆåŠå®‰å ¨æ€§çš„Meta分析.

OBJECTIVES: To study the efficacy and safety of rituximab combined with chemotherapy in the treatment of children and adolescents with mature B-cell non-Hodgkin's lymphoma (B-NHL) through a Meta analysis. METHODS: The databases including PubMed, Embase, the Cochrane Library, ClinicalTrials.gov, Web of Science, China National Knowledge Infrastructure, Wanfang Data, and Weipu were searched to obtain 10 articles on rituximab in the treatment of mature B-NHL in children and adolescents published up to June 2022, with 886 children in total. With 3-year event-free survival (EFS) rate, 3-year overall survival (OS) rate, complete remission rate, mortality rate, and incidence rate of adverse reactions as outcome measures, RevMan 5.4 software was used for Meta analysis, subgroup analysis, sensitivity analysis, and publication bias analysis. RESULTS: The rituximab+chemotherapy group showed significant increases in the 3-year EFS rate (HR=0.38, 95%CI: 0.25-0.59, P<0.001), 3-year OS rate (HR=0.29, 95%CI: 0.14-0.61, P=0.001), and complete remission rate (OR=3.72, 95%CI: 1.89-7.33, P<0.001) as well as a significant reduction in the mortality rate (OR=0.31, 95%CI: 0.17-0.57, P<0.001), as compared with the chemotherapy group without rituximab. There was no significant difference in the incidence rate of adverse reactions between the two groups (OR=1.28, 95%CI: 0.85-1.92, P=0.24). CONCLUSIONS: The addition of rituximab to the treatment regimen for children and adolescents with mature B-cell non-Hodgkin's lymphoma can bring significant survival benefits without increasing the incidence of adverse reactions.

Label-Guided Generative Adversarial Network for Realistic Image Synthesis.

Generating photo-realistic images from labels (e.g., semantic labels or sketch labels) is much more challenging than the general image-to-image translation task, mainly due to the large differences between extremely sparse labels and detail rich images. We propose a general framework Lab2Pix to tackle this issue from two aspects: 1) how to extract useful information from the input; and 2) how to efficiently bridge the gap between the labels and images. Specifically, we propose a Double-Guided Normalization (DG-Norm) to use the input label for semantically guiding activations in normalization layers, and use global features with large receptive fields for differentiating the activations within the same semantic region. To efficiently generate the images, we further propose Label Guided Spatial Co-Attention (LSCA) to encourage the learning of incremental visual information using limited model parameters while storing the well-synthesized part in lower-level features. Accordingly, Hierarchical Perceptual Discriminators with Foreground Enhancement Masks are proposed to toughly work against the generator thus encouraging realistic image generation and a sharp enhancement loss is further introduced for high-quality sharp image generation. We instantiate our Lab2Pix for the task of label-to-image in both unpaired (Lab2Pix-V1) and paired settings (Lab2Pix-V2). Extensive experiments conducted on various datasets demonstrate that our method significantly outperforms state-of-the-art methods quantitatively and qualitatively in both settings.

State-Aware Compositional Learning Toward Unbiased Training for Scene Graph Generation.

How to avoid biased predictions is an important and active research question in scene graph generation (SGG). Current state-of-the-art methods employ debiasing techniques such as resampling and causality analysis. However, the role of intrinsic cues in the features causing biased training has remained under-explored. In this paper, for the first time, we make the surprising observation that object identity information, in the form of object label embeddings (e.g. GLOVE), is principally responsible for biased predictions. We empirically observe that, even without any visual features, a number of recent SGG models can produce comparable or even better results solely from object label embeddings. Motivated by this insight, we propose to leverage a conditional variational auto-encoder to decouple the entangled visual features into two meaningful components: the object's intrinsic identity features and the extrinsic, relation-dependent state feature. We further develop two compositional learning strategies on the relation and object levels to mitigate the data scarcity issue of rare relations. On the two benchmark datasets Visual Genome and GQA, we conduct extensive experiments on the three scenarios, i.e., conventional, few-shot and zero-shot SGG. Results consistently demonstrate that our proposed Decomposition and Composition (DeC) method effectively alleviates the biases in the relation prediction. Moreover, DeC is model-free, and it significantly improves the performance of recent SGG models, establishing new state-of-the-art performance.

Semisupervised Network Embedding With Differentiable Deep Quantization.

Learning accurate low-dimensional embeddings for a network is a crucial task as it facilitates many downstream network analytics tasks. For large networks, the trained embeddings often require a significant amount of space to store, making storage and processing a challenge. Building on our previous work on semisupervised network embedding, we develop d-SNEQ, a differentiable DNN-based quantization method for network embedding. d-SNEQ incorporates a rank loss to equip the learned quantization codes with rich high-order information and is able to substantially compress the size of trained embeddings, thus reducing storage footprint and accelerating retrieval speed. We also propose a new evaluation metric, path prediction, to fairly and more directly evaluate the model performance on the preservation of high-order information. Our evaluation on four real-world networks of diverse characteristics shows that d-SNEQ outperforms a number of state-of-the-art embedding methods in link prediction, path prediction, node classification, and node recommendation while being far more space- and time-efficient.

Dark-Field Imaging Monitoring of Adenosine Triphosphate in Live Cells by Au NBPs@ZIF-8 Nanoprobes.

Monitoring the fluctuation of adenosine triphosphate (ATP) level in living cells could promote the understanding of metabolic pathways and cell biology. Here, we proposed a highly sensitive, selective, and biocompatible nanoprobe with core-shell structure, namely Au NBPs@ZIF-8 composed by gold nanobipyramids (Au NBPs) and zeolitic imidazolate framework-8 (ZIF-8), for monitoring intracellular ATP level fluctuation in living cells. Because the coordination between ATP and Zn2+ (the metal node of ZIF-8) was much stronger than that between 2-methylimidazole and Zn2+, which caused the decomposition of the ZIF-8 shell and the exposure of Au NBPs in the presence of ATP, it led to the change of the localized surface plasmon resonance scattering properties of nanoprobes under dark-field microscopy. Tricolor (RGB) analysis showed that R/G value had a good linear relationship with the ATP concentrations in the range of 10 µM to 4 mM (R2 = 0.999) with a detection limit of 5.28 µM. This ATP sensing platform also exhibited excellent selectivity in complex intracellular interfering substances. Besides, we realized intracellular ATP real-time imaging in HeLa cells and observed the ATP level fluctuation under dark-field microscopy. The method mentioned here could be further applied for delivery of therapeutics for biomedical applications.

Toward Graph Self-Supervised Learning With Contrastive Adjusted Zooming.

Graph representation learning (GRL) is critical for graph-structured data analysis. However, most of the existing graph neural networks (GNNs) heavily rely on labeling information, which is normally expensive to obtain in the real world. Although some existing works aim to effectively learn graph representations in an unsupervised manner, they suffer from certain limitations, such as the heavy reliance on monotone contrastiveness and limited scalability. To overcome the aforementioned problems, in light of the recent advancements in graph contrastive learning, we introduce a novel self-supervised GRL algorithm via graph contrastive adjusted zooming, namely, G-Zoom, to learn node representations by leveraging the proposed adjusted zooming scheme. Specifically, this mechanism enables G-Zoom to explore and extract self-supervision signals from a graph from multiple scales: micro (i.e., node level), meso (i.e., neighborhood level), and macro (i.e., subgraph level). First, we generate two augmented views of the input graph via two different graph augmentations. Then, we establish three different contrastiveness on the above three scales progressively, from node, neighboring, to subgraph level, where we maximize the agreement between graph representations across scales. While we can extract valuable clues from a given graph on the micro and macro perspectives, the neighboring-level contrastiveness offers G-Zoom the capability of a customizable option based on our adjusted zooming scheme to manually choose an optimal viewpoint that lies between the micro and macro perspectives to better understand the graph data. In addition, to make our model scalable to large graphs, we use a parallel graph diffusion approach to decouple model training from the graph size. We have conducted extensive experiments on real-world datasets, and the results demonstrate that our proposed model outperforms the state-of-the-art methods consistently.

Plasmonic Hot-Electron-Painted Au@Pt Nanoparticles as Efficient Electrocatalysts for Detection of H2O2.

Plasmon-driven catalysis of metal nanostructures has garnered wide interest. Here, a photogenerated plasmonic hot-electron painting strategy was reported to form Au@Pt composite nanoparticles (Au@Pt NPs) with high catalytic reactivity without using reducing agents. Au nanoparticles, including Au nanospheres (Au NSs), Au nanorods (Au NRs), and Au nanobipyramids (Au NBPs), generated hot electrons under localized surface plasmon resonance (LSPR) excitation, which made the platinum precursor reduced as a consequence that Pt(0) atoms were painted on the surface of Au NPs to form an asymmetric Pt shell outside the plasmonic Au core. Compared with bare Au NPs, Au@Pt NPs exhibited significantly enhanced electrocatalytic activity toward reduction of H2O2 due to the bimetallic synergistic effect and great dispersion of Au@Pt NP-modified indium tin oxide (Au@Pt NPs/ITO). It exhibited a linear detection of H2O2 in a wide concentration range from 0.5 to 1000 µM with a low detection limit of 0.11 µM (S/N = 3). Therefore, the plasmonic hot-electron-painted Au@Pt NPs represent a novel and simple method for the design of advanced noble asymmetric metal nanomaterials.

In Situ Imaging of Ion Motion in a Single Nanoparticle: Structural Transformations in Selenium Nanoparticles.

Intraparticle ion motions are critical to the structure and properties of nanomaterials, but rarely disclosed. Herein, in situ visualization of ion motions in a single nanoparticle is presented by dark-field microscopy imaging, which shows HgCl2 -induced structural transformation of amorphous selenium nanoparticles (SeNPs) with the main composition of Se8 . Owing to the high binding affinity with selenium and coulomb interactions, Hg2+ ions can permeate into the interior of SeNPs, making the amorphous Se8 turn to polycrystalline Hg3 Se2 Cl2 . As a proof of concept, SeNPs then serve as a highly effective scavenger for selective removal of Hg2+ ions from solution. This new finding offers visual proof for the photophysical process involving intraparticle ion motion, demonstrating that tracking the ion motions is a novel strategy to comprehend the formation mechanism with the purpose of developing new nanostructures like nanoalloys and nano metal compounds.

Genomic Analysis Uncovers the Prognostic and Immunogenetic Feature of Pyroptosis in Gastric Carcinoma: Indication for Immunotherapy.

Crosstalk between pyroptosis and tumor immune microenvironment (TIME) in cancer has yet to be elucidated. Herein, we aimed to explore the role of pyroptosis and its association with TIME in gastric cancer. Unsupervised clustering was performed to identify the pyroptosis-related clusters. Pyroptosis risk score was constructed using LASSO Cox regression. Clinicopathological and genetic data of pyroptosis clusters and pyroptosis risk scores were explored. Reproducibility of pyroptosis risk score in predicting response to immunotherapy and screening potential antitumor drugs was also investigated. Three pyroptosis clusters with distinct prognosis, immune cell fractions and signatures, were constructed. A low-pyroptosis risk score was characterized by increased activated T-cell subtype and M1 macrophage, decreased M2 macrophage, higher MSI status, and TMB. Meanwhile, low-score significantly correlated with PD-L1 expression, antigen presentation markers, and IFN-γ signature. The 5-year AUCs of PRS were 0.67, 0.62, 0.65, 0.67, and 0.67 in the TCGA, three external public and one real-world validation (SYSUCC) cohorts. Multivariable analyses further validated the prognostic performance of the pyroptosis risk scoring system, with HRs of 2.43, 1.83, 1.78, 2.35, and 2.67 (all p < 0.05) in the five cohorts. GSEA indicated significant enrichment of DNA damage repair pathways in the low-score group. Finally, the pyroptosis risk scoring system was demonstrated to be useful in predicting response to immunotherapy, and in screening potential antitumor drugs. Our study highlights the crucial role of interaction between pyroptosis and TIME in gastric cancer. The pyroptosis risk scoring system can be used independently to predict the survival of individuals and their response to immunotherapy.