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The formation and maintenance of tissue integrity requires complex, coordinated activities by thousands of genes and their encoded products. Until recently, transcript levels could only be quantified for a few genes in tissues, but advances in DNA sequencing, oligonucleotide synthesis and fluorescence microscopy have enabled the invention of a suite of spatial transcriptomics technologies capable of measuring the expression of many, or all, genes in situ. These technologies have evolved rapidly in sensitivity, multiplexing and throughput. As such, they have enabled the determination of the cell-type architecture of tissues, the querying of cell–cell interactions and the monitoring of molecular interactions between tissue components. The rapidly evolving spatial genomics landscape will enable generalized high-throughput genomic measurements and perturbations to be performed in the context of tissues. These advances will empower hypothesis generation and biological discovery and bridge the worlds of tissue biology and genomics. Spatial transcriptomics workflows, metrics and limitations are reviewed and discussed.

In the past few years, the emergence of pre-training models has brought uni-modal fields such as computer vision (CV) and natural language processing (NLP) to a new era. Substantial works have shown that they are beneficial for downstream uni-modal tasks and avoid training a new model from scratch. So can such pre-trained models be applied to multi-modal tasks? Researchers have explored this problem and made significant progress. This paper surveys recent advances and new frontiers in vision-language pre-training (VLP), including image-text and video-text pre-training. To give readers a better overall grasp of VLP, we first review its recent advances in five aspects: feature extraction, model architecture, pre-training objectives, pre-training datasets, and downstream tasks. Then, we summarize the specific VLP models in detail. Finally, we discuss the new frontiers in VLP. To the best of our knowledge, this is the first survey focused on VLP. We hope that this survey can shed light on future research in the VLP field.

Over the past decade, artificial intelligence (AI) has contributed substantially to the resolution of various medical problems, including cancer. Deep learning (DL), a subfield of AI, is characterized by its ability to perform automated feature extraction and has great power in the assimilation and evaluation of large amounts of complicated data. On the basis of a large quantity of medical data and novel computational technologies, AI, especially DL, has been applied in various aspects of oncology research and has the potential to enhance cancer diagnosis and treatment. These applications range from early cancer detection, diagnosis, classification and grading, molecular characterization of tumors, prediction of patient outcomes and treatment responses, personalized treatment, automatic radiotherapy workflows, novel anti‐cancer drug discovery, and clinical trials. In this review, we introduced the general principle of AI, summarized major areas of its application for cancer diagnosis and treatment, and discussed its future directions and remaining challenges. As the adoption of AI in clinical use is increasing, we anticipate the arrival of AI‐powered cancer care. This review introduced the general principle of AI, summarize major areas of its application for cancer diagnosis, treatment, and precision medicine and discuss its future directions and remaining challenges.

Breast cancer is now the most frequently diagnosed malignancy, and metastasis remains the leading cause of death in breast cancer. However, little is known about the dynamic changes during the evolvement of dissemination. In this study, 65 968 cells from four patients with breast cancer and paired metastatic axillary lymph nodes are profiled using single‐cell RNA sequencing (scRNA‐seq) and spatial transcriptomics. A disseminated cancer cell cluster with high levels of oxidative phosphorylation (OXPHOS), including the upregulation of cytochrome C oxidase subunit 6C and dehydrogenase/reductase 2, is identified. The transition between glycolysis and OXPHOS when dissemination initiates is noticed. Furthermore, this distinct cell cluster is distributed along the tumor's leading edge. The findings here are verified in three different cohorts of breast cancer patients and an external scRNA‐seq dataset, which includes eight patients with breast cancer and paired metastatic axillary lymph nodes. This work describes the dynamic metabolic evolvement of early disseminated breast cancer and reveals a switch between glycolysis and OXPHOS in breast cancer cells as the early event during lymph node metastasis. By single‐cell RNA sequencing and spatial transcriptomics, the early early‐disseminated breast cancer cells are found to travel from the border of primary tumor to axillary lymph nodes. During this metastasis, a switch between glycolysis and oxidative phosphorylation occurs in early disseminated breast cancer cells, indicating an interesting dynamic metabolic evolvement.

Background Endothelial-mesenchymal transition (EndoMT) is an emerging adaptive process that modulates lymphatic endothelial function to drive aberrant lymphatic vascularization in the tumour microenvironment (TME); however, the molecular determinants that govern the functional role of EndoMT remain unclear. Here, we show that cancer-associated fibroblast (CAF)-derived PAI-1 promoted the EndoMT of lymphatic endothelial cells (LECs) in cervical squamous cell carcinoma (CSCC). Methods Immunofluorescent staining of α-SMA, LYVE-1 and DAPI were examined in primary tumour samples obtained from 57 CSCC patients. Assessment of cytokines secreted by CAFs and normal fibroblasts (NFs) was performed using human cytokine antibody arrays. The phenotype of EndoMT in lymphatic endothelial cells (LECs), gene expression levels, protein secretion and activity of signaling pathways were measured by real-time RT-PCR, ELISA or western blotting. The function of lymphatic endothelial monolayers was examined by transwell, tube formation assay, transendothelial migration assay in vitro. Lymphatic metastasis was measured using popliteal lymph node metastasis model. Furthermore, association between PAI-1 expression and EndoMT in CSCC was analyzed by immunohistochemistry. The Cancer Genome Atlas (TCGA) databases was used to assess the association of PAI-1 with survival rate in CSCC. Results CAF-derived PAI-1 promoted the EndoMT of LECs in CSCC. LECs undergoing EndoMT could initiate tumour neolymphangiogenesis that facilitated cancer cell intravasation/extravasation, which in turn promoted lymphatic metastasis in CSCC. Mechanistically, PAI-1 activated the AKT/ERK1/2 pathways by directly interacting with low-density lipoprotein receptor-related protein (LRP1), thereby leading to elevated EndoMT activity in LECs. Blockade of PAI-1 or inhibition of LRP1/AKT/ERK1/2 abrogated EndoMT and consequently attenuated CAF-induced tumour neolymphangiogenesis. Furthermore, clinical data revealed that increased PAI-1 levels positively correlated with EndoMT activity and poor prognosis in CSCC patients. Conclusion Our data indicate that CAF-derived PAI-1 acts as an important neolymphangiogenesis-initiating molecular during CSCC progression through modulating the EndoMT of LECs, resulting in promotion of metastasis ability in primary site. PAI-1 could serve as an effective prognostic biomarker and therapeutic target for CSCC metastasis.

Cancer-secreted exosomal miRNAs are emerging mediators of cancer-stromal cross-talk in the tumor environment. Our previous miRNAs array of cervical squamous cell carcinoma (CSCC) clinical specimens identified upregulation of miR-221-3p. Here, we show that miR-221-3p is closely correlated with peritumoral lymphangiogenesis and lymph node (LN) metastasis in CSCC. More importantly, miR-221-3p is characteristically enriched in and transferred by CSCC-secreted exosomes into human lymphatic endothelial cells (HLECs) to promote HLECs migration and tube formation in vitro, and facilitate lymphangiogenesis and LN metastasis in vivo according to both gain-of-function and loss-of-function experiments. Furthermore, we identify vasohibin-1 (VASH1) as a novel direct target of miR-221-3p through bioinformatic target prediction and luciferase reporter assay. Re-expression and knockdown of VASH1 could respectively rescue and simulate the effects induced by exosomal miR-221-3p. Importantly, the miR-221-3p-VASH1 axis activates the ERK/AKT pathway in HLECs independent of VEGF-C. Finally, circulating exosomal miR-221-3p levels also have biological function in promoting HLECs sprouting in vitro and are closely associated with tumor miR-221-3p expression, lymphatic VASH1 expression, lymphangiogenesis, and LN metastasis in CSCC patients. In conclusion, CSCC-secreted exosomal miR-221-3p transfers into HLECs to promote lymphangiogenesis and lymphatic metastasis via downregulation of VASH1 and may represent a novel diagnostic biomarker and therapeutic target for metastatic CSCC patients in early stages.

In the absence of directing auxiliaries, the catalytic addition of carbogenic groups to unactivated alkenes with control of regioselectivity remains an ongoing challenge in organic chemistry. Here we describe a directing-group-free, nickel-catalysed strategy that couples a broad array of unactivated and activated olefins with aryl-substituted triflates and organometallic nucleophiles to afford diarylation adducts in either regioisomeric form, in up to 93% yield and >98% site selectivity. By switching the reagents involved, the present strategy may be extended to other classes of dicarbofunctionalization reactions. Mechanistic and computational investigations offer insights into the origin of the observed regiochemical outcome and the utility of the method is highlighted through the concise syntheses of biologically active molecules. The catalyst control principles reported are expected to advance efforts towards the development of general site-selective alkene functionalizations, removing the requirement for neighbouring activating groups. Without directing auxiliaries, the addition of carbogenic groups to unactivated alkenes is typically inefficient and suffers from poor regioselectivity. Now, a directing-group-free, nickel catalyst-controlled strategy has been developed, enabling the site-selective dicarbofunctionalization of a broad array of activated and unactivated alkenes.

Antibody‐drug conjugates (ADCs) are a rapidly developing therapeutic approach in cancer treatment that has shown remarkable activity in breast cancer. Currently, there are two ADCs approved for the treatment of human epidermal growth factor receptor 2‐positive breast cancer, one for triple‐negative breast cancer, and multiple investigational ADCs in clinical trials. However, drug resistance has been noticed in clinical use, especially in trastuzumab emtansine. Here, the mechanisms of ADC resistance are summarized into four categories: antibody‐mediated resistance, impaired drug trafficking, disrupted lysosomal function, and payload‐related resistance. To overcome or prevent resistance to ADCs, innovative development strategies and combination therapy options are being investigated. Analyzing predictive biomarkers for optimal therapy selection may also help to prevent drug resistance.

Cognitive decline remains an unaddressed problem for the elderly. We show that myelination is highly active in young mice and greatly inhibited in aged mice, coinciding with spatial memory deficits. Inhibiting myelination by deletion of Olig2 in oligodendrocyte precursor cells impairs spatial memory in young mice, while enhancing myelination by deleting the muscarinic acetylcholine receptor 1 in oligodendrocyte precursor cells, or promoting oligodendroglial differentiation and myelination via clemastine treatment, rescues spatial memory decline during aging.Wang et al. show that myelination is greatly inhibited in aged brains. Enhancing myelination by ablation of M1R in OPCs or clemastine treatment promotes oligodendroglial differentiation and consequently rescues spatial memory decline during aging.

The key to high-quality development in the textile and apparel industry lies in enhancing technological innovation and optimizing the efficiency of technological innovation. Based on data from 60 A-share listed companies in the textile and apparel sector in China from 2013 to 2022, this study employs a three-stage DEA model and the Malmquist index model to measure changes in technological innovation efficiency from static and dynamic perspectives. Additionally, it uses a Tobit model to analyze the impact and mechanisms of management and financial factors on technological innovation efficiency. The results indicate that: (1) Compared to the manufacturing industry and its sub-sectors, the overall technological innovation efficiency of listed textile and apparel companies was relatively low and showed a declining trend between 2013 and 2022; (2) Over the decade, the average total factor productivity of these listed companies increased by 1.7%, exhibiting a \"W\" shaped fluctuation, with technological progress, pure technical efficiency, and scale efficiency all showing weak improvement; (3) Management and financial factors significantly influence technological innovation efficiency. Specifically, employee quality, profitability, and operational capability are positively correlated with technological innovation efficiency and have long-term effectiveness, while firm age, management costs, equity concentration, development ability, and debt repayment capacity are negatively correlated with technological innovation efficiency; (4) Different types of enterprises show differences in the significance of management factors, while whether the same person holds both managerial positions significantly affects financial factors.