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As the most powerful antigen-presenting cell type, dendritic cells (DCs) can induce potent antigen-specific immune responses in vivo, hence becoming optimal cell population for vaccination purposes. DCs can be derived ex vivo in quantity and manipulated extensively to be endowed with adequate immune-stimulating capacity. After pulsing with cancer antigens in various ways, the matured DCs are administrated back into the patient. DCs home to lymphoid organs to present antigens to and activate specific lymphocytes that react to a given cancer. Ex vivo pulsed DC vaccines have been vigorously investigated for decades, registering encouraging results in relevant immunotherapeutic clinical trials, while facing some solid challenges. With more details in DC biology understood, new theory proposed, and novel technology introduced (featuring recently emerged mRNA vaccine technology), it is becoming increasingly likely that ex vivo pulsed DC vaccine will fulfill its potential in cancer immunotherapy.

Carbon nanotube (CNT), particularly single-walled CNT, possesses exceptional properties, and can be utilized in many high-end applications including high-performance electronics. However, the atomic arrangement of a CNT determines its band structure, making the atomic-precision fabrication one of most important topics for the development of this material. In this perspective, the author gives a personal summary on the history, current status of the atomic-precision fabrication of CNT and outlines the remaining challenges as well as the possible paths that may lead the production of atomically precise CNTs from ‘fabrication’ to ‘manufacturing’.

A retrospective analysis of routine surveillance data involving children hospitalized in central Wuhan, China, for acute lower respiratory infection in early January 2020 revealed six cases of Covid-19. The authors report clinical characteristics of the children and laboratory data.

The pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has resulted in an unprecedented setback for global economy and health. SARS-CoV-2 has an exceptionally high level of transmissibility and extremely broad tissue tropism. However, the underlying molecular mechanism responsible for sustaining this degree of virulence remains largely unexplored. In this article, we review the current knowledge and crucial information about how SARS-CoV-2 attaches on the surface of host cells through a variety of receptors, such as ACE2, neuropilin-1, AXL, and antibody–FcγR complexes. We further explain how its spike (S) protein undergoes conformational transition from prefusion to postfusion with the help of proteases like furin, TMPRSS2, and cathepsins. We then review the ongoing experimental studies and clinical trials of antibodies, peptides, or small-molecule compounds with anti-SARS-CoV-2 activity, and discuss how these antiviral therapies targeting host–pathogen interaction could potentially suppress viral attachment, reduce the exposure of fusion peptide to curtail membrane fusion and block the formation of six-helix bundle (6-HB) fusion core. Finally, the specter of rapidly emerging SARS-CoV-2 variants deserves a serious review of broad-spectrum drugs or vaccines for long-term prevention and control of COVID-19 in the future.

Considering the data collection and labeling cost in real-world applications, training a model with limited examples is an essential problem in machine learning, visual recognition, etc. Directly training a model on such few-shot learning (FSL) tasks falls into the over-fitting dilemma, which would turn to an effective task-level inductive bias as a key supervision. By treating the few-shot task as an entirety, extracting task-level pattern, and learning a task-agnostic model initialization, the model-agnostic meta-learning (MAML) framework enables the applications of various models on the FSL tasks. Given a training set with a few examples, MAML optimizes a model via fixed gradient descent steps from an initial point chosen beforehand. Although this general framework possesses empirically satisfactory results, its initialization neglects the task-specific characteristics and aggravates the computational burden as well. In this manuscript, we propose our AdaptiVely InitiAlized Task OptimizeR (Aviator) approach for few-shot learning, which incorporates task context into the determination of the model initialization. This task-specific initialization facilitates the model optimization process so that it obtains high-quality model solutions efficiently. To this end, we decouple the model and apply a set transformation over the training set to determine the initial top-layer classifier. Re-parameterization of the first-order gradient descent approximation promotes the gradient back-propagation. Experiments on synthetic and benchmark data sets validate that our Aviator approach achieves the state-of-the-art performance, and visualization results demonstrate the task-adaptive features of our proposed Aviator method.

Structural variations (SVs) exert important functional impacts on biological phenotypic diversity. Here we show a ring synthetic yeast chromosome V (ring_synV) can be used to continuously generate complex genomic variations and improve the production of prodeoxyviolacein (PDV) by applying Synthetic Chromosome Recombination and Modification by LoxP-mediated Evolution (SCRaMbLE) in haploid yeast cells. The SCRaMbLE of ring_synV generates aneuploid yeast strains with increased PDV productivity, and we identify aneuploid chromosome I, III, VI, XII, XIII, and ring_synV. The neochromosome of SCRaMbLEd ring_synV generated more unbalanced forms of variations, including duplication, insertions, and balanced forms of translocations and inversions than its linear form. Furthermore, of the 29 novel SVs detected, 11 prompted the PDV biosynthesis; and the deletion of uncharacterized gene YER182W is related to the improvement of the PDV. Overall, the SCRaMbLEing ring_synV embraces the evolution of the genome by modifying the chromosome number, structure, and organization, identifying targets for phenotypic comprehension. Genome structural variation can play an important functional role in phenotypic diversity. Here the authors use the SCRaMbLE system on a ring synthetic chromosome V to generate complex rearrangements distinct from a rearranged linear chromosome.

Environments such as light condition influence the spread of infectious diseases by affecting insect vector behavior. However, whether and how light affects the host defense which further affects insect preference and performance, remains unclear, nor has been demonstrated how pathogens co-adapt light condition to facilitate vector transmission. We previously showed that begomoviral βC1 inhibits MYC2-mediated jasmonate signaling to establish plant-dependent mutualism with its insect vector. Here we show red-light as an environmental catalyzer to promote mutualism of whitefly-begomovirus by stabilizing βC1, which interacts with PHYTOCHROME-INTERACTING FACTORS (PIFs) transcription factors. PIFs positively control plant defenses against whitefly by directly binding to the promoter of terpene synthase genes and promoting their transcription. Moreover, PIFs interact with MYC2 to integrate light and jasmonate signaling and regulate the transcription of terpene synthase genes. However, begomovirus encoded βC1 inhibits PIFs’ and MYC2’ transcriptional activity via disturbing their dimerization, thereby impairing plant defenses against whitefly-transmitted begomoviruses. Our results thus describe how a viral pathogen hijacks host external and internal signaling to enhance the mutualistic relationship with its insect vector.

Background Gastric cancer (GC) is one of the most common solid malignant tumors worldwide with a high-recurrence-rate. Identifying the molecular signatures and specific biomarkers of GC might provide novel clues for GC prognosis and targeted therapy. Methods Gene expression profiles were obtained from the ArrayExpress and Gene Expression Omnibus database. Differentially expressed genes (DEGs) were picked out by R software. The hub genes were screened by cytohubba plugin. Their prognostic values were assessed by Kaplan–Meier survival analyses and the gene expression profiling interactive analysis (GEPIA). Finally, qRT-PCR in GC tissue samples was established to validate these DEGs. Results Total of 295 DEGs were identified between GC and their corresponding normal adjacent tissue samples in E-MTAB-1440, GSE79973, GSE19826, GSE13911, GSE27342, GSE33335 and GSE56807 datasets, including 117 up-regulated and 178 down-regulated genes. Among them, 7 vital upregulated genes (HMMR, SPP1, FN1, CCNB1, CXCL8, MAD2L1 and CCNA2) were selected. Most of them had a significantly worse prognosis except SPP1. Using qRT-PCR, we validated that their transcriptions in our GC tumor tissue were upregulated except SPP1 and FN1, which correlated with tumor relapse and predicts poorer prognosis in GC patients. Conclusions We have identified 5 upregulated DEGs (HMMR, CCNB1, CXCL8, MAD2L1, and CCNA2) in GC patients with poor prognosis using integrated bioinformatical methods, which could be potential biomarkers and therapeutic targets for GC treatment.

Background Head and neck squamous cell carcinoma (HNSCC) is a highly aggressive malignancy with a poor prognosis. Identifying reliable prognostic biomarkers and therapeutic targets is crucial for improving patient outcomes. This study aimed to systematically identify proliferation-essential genes (PEGs) associated with HNSCC prognosis using CRISPR-Cas9 screening data. Methods CRISPR-Cas9 screening data from the DepMap database were used to identify PEGs in HNSCC cells. A prognostic PEGs signature was constructed using univariate Cox regression, least absolute shrinkage and selection operator (LASSO) Cox regression, and multivariate Cox regression analyses. The predictive accuracy of the signature was validated in internal and external datasets. Weighted gene co-expression network analysis (WGCNA), gene set enrichment analysis (GSEA), and immune infiltration analysis were used to investigate the underlying mechanism between high and low-risk patients. Random forest analysis and functional experiments were conducted to investigate the role of key proliferation essential genes in HNSCC progression. Results A total of 1511 PEGs were identified. A seven-gene prognostic PEGs signature (MRPL33, NAT10, PSMC1, PSMD11, RPN2, TAF7, and ZNF335) was developed and validated, demonstrating robust prognostic performance in stratifying HNSCC patients by survival risk. WGCNA and GSEA analyses revealed a marked downregulation of immune-related pathways in high-risk patients. Immune infiltration analysis validated those high-risk patients had reduced immune scores, stromal scores, and ESTIMATE scores, as well as decreased infiltration of multiple immune cell types. Among the identified genes, PSMC1 was highlighted as a pivotal regulator of HNSCC proliferation and migration, as confirmed by functional experiments. Conclusions This study identifies a novel PEGs signature that effectively predicts HNSCC prognosis and stratifies patients by survival risk. PSMC1 was identified as a key gene promoting malignant progression, offering potential as a therapeutic target for HNSCC.

Physalis Alkekengi L. var. franchetii (Mast.) Makino (PAF), which is used in both food and medicine, has a long history of about 1800 years of application in China. There are many active constituents in the calyx of PAF. Physalins and physalins with a single oxygen bridge are the unique components of the PAF calyx. Physalins with multiple biological activities, including anticancer activity, antimicrobial activity, anti-inflammatory activity, etc., have been found. As such, physalins deserve to be studied further. In this study, we aimed to extract, separate, and identify the effective components of physalins from the calyx of PAF and investigate ability to inhibit the proliferation of tumor cell lines. Three new physalins, physalin VIII (1), 3α-hydroxy-2,3,25,27-tetrahydro-4,7-didehydro-7-deoxyneophysalin A (2), and physalin IX (3), along with three known compounds, physalin L (4), physalin D (5), and alkekengilin A (6) were isolated from PAF calyxes. Physalin D was superior to the positive control drug cisplatin in inhibiting the proliferation of five tumor cell lines. The physalin compounds exhibited potential antitumor activity, being deemed worthy of further research in the fields of antitumor drug development and the application in health foods.