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Single-cell sequencing (SCS) is an emerging high-throughput technology that can be used to study the genomics, transcriptomics, and epigenetics at a single cell level. SCS is widely used in the diagnosis and treatment of various diseases, including cancer. Over the years, SCS has gradually become an effective clinical tool for the exploration of tumor metastasis mechanisms and the development of treatment strategies. Currently, SCS can be used not only to analyze metastasis-related malignant biological characteristics, such as tumor heterogeneity, drug resistance, and microenvironment, but also to construct metastasis-related cell maps for predicting and monitoring the dynamics of metastasis. SCS is also used to identify therapeutic targets related to metastasis as it provides insights into the distribution of tumor cell subsets and gene expression differences between primary and metastatic tumors. Additionally, SCS techniques in combination with artificial intelligence (AI) are used in liquid biopsy to identify circulating tumor cells (CTCs), thereby providing a novel strategy for treating tumor metastasis. In this review, we summarize the potential applications of SCS in the field of tumor metastasis and discuss the prospects and limitations of SCS to provide a theoretical basis for finding therapeutic targets and mechanisms of metastasis.

Messenger RNA (mRNA) vaccines constitute an emerging therapeutic method with the advantages of high safety and efficiency as well as easy synthesis; thus, they have been widely used in various human diseases, especially in malignant cancers. However, the mRNA vaccine technology has some limitations, such as instability and low transitive efficiency in vivo , which greatly restrict its application. The development of nanotechnology in the biomedical field offers new strategies and prospects for the early diagnosis and treatment of human cancers. Recent studies have demonstrated that Lipid nanoparticle (LNP)-based mRNA vaccines can address the poor preservation and targeted inaccuracy of mRNA vaccines. As an emerging cancer therapy, mRNA vaccines potentially have broad future applications. Unlike other treatments, cancer mRNA vaccines provide specific, safe, and tolerable treatments. Preclinical studies have used personalized vaccines to demonstrate the anti-tumor effect of mRNA vaccines in the treatment of various solid tumors, including colorectal and lung cancer, using these in a new era of therapeutic cancer vaccines. In this review, we have summarized the latest applications and progress of LNP-based mRNA vaccines in cancers, and discussed the prospects and limitations of these fields, thereby providing novel strategies for the targeted therapy of cancers.

A bstract Aspects of black hole thermodynamics and information loss can be derived as a consequence of Virasoro symmetry. To bolster the connection between Virasoro conformal blocks and AdS 3 quantum gravity, we study sl(2) Chern-Simons Wilson line networks and revisit the idea that they compute a variety of CFT 2 observables, including Virasoro OPE blocks, exactly. We verify this in the semiclassical large central charge limit and to low orders in a perturbative 1 /c expansion. Wilson lines connecting the boundary to points in the bulk play a natural role in bulk reconstruction. Because quantum gravity in AdS 3 is rigidly fixed by Virasoro symmetry, we argue that sl(2) Wilson lines provide building blocks for background independent bulk reconstruction. In particular, we show explicitly that they automatically ‘know’ about the uniformizing coordinates appropriate to any background state.

Cancer-associated fibroblasts (CAFs) are the most important component of the stromal cell population in the tumor microenvironment and play an irreplaceable role in oncogenesis and cancer progression. Exosomes, a class of small extracellular vesicles, can transfer biological information (e.g., proteins, nucleic acids, and metabolites as messengers) from secreting cells to target recipient cells, thereby affecting the progression of human diseases, including cancers. Recent studies revealed that CAF-derived exosomes play a crucial part in tumorigenesis, tumor cell proliferation, metastasis, drug resistance, and the immune response. Moreover, aberrant expression of CAF-derived exosomal noncoding RNAs and proteins strongly correlates with clinical pathological characterizations of cancer patients. Gaining deeper insight into the participation of CAF-derived exosomes in tumorigenesis may lead to novel diagnostic biomarkers and therapeutic targets in human cancers.

NADH:ubiquinone oxidoreductase core subunit S8 (NDUFS8) is an essential core subunit and component of the iron-sulfur (FeS) fragment of mitochondrial complex I directly involved in the electron transfer process and energy metabolism. Pathogenic variants of the NDUFS8 are relevant to infantile-onset and severe diseases, including Leigh syndrome, cancer, and diabetes mellitus. With over 1000 nuclear genes potentially causing a mitochondrial disorder, the current diagnostic approach requires targeted molecular analysis, guided by a combination of clinical and biochemical features. Currently, there are only several studies on pathogenic variants of the NDUFS8 in Leigh syndrome, and a lack of literature on its precise mechanism in cancer and diabetes mellitus exists. Therefore, NDUFS8-related diseases should be extensively explored and precisely diagnosed at the molecular level with the application of next-generation sequencing technologies. A more distinct comprehension will be needed to shed light on NDUFS8 and its related diseases for further research. In this review, a comprehensive summary of the current knowledge about NDUFS8 structural function, its pathogenic mutations in Leigh syndrome, as well as its underlying roles in cancer and diabetes mellitus is provided, offering potential pathogenesis, progress, and therapeutic target of different diseases. We also put forward some problems and solutions for the following investigations.

We consider the canonical quantization of an ordinary fluid. The resulting long-distance effective field theory is derivatively coupled, and therefore strongly coupled in the UV. The system however exhibits a number of peculiarities, associated with the vortex degrees of freedom. On the one hand, these have formally a vanishing strong-coupling energy scale, thus suggesting that the effective theory’s regime of validity is vanishingly narrow. On the other hand, we prove an analog of Coleman’s theorem, whereby the semiclassical vacuum has no quantum counterpart, thus suggesting that the vortex premature strong-coupling phenomenon stems from a bad identification of the ground state and of the perturbative degrees of freedom. Finally, vortices break the usual connection between short distances and high energies, thus potentially impairing the unitarity of the effective theory.

As a novel inflammatory marker, Systemic Immune-Inflammation Index (SII) has recently been recognized as a prognostic indicator for a variety of diseases including malignant cancers, coronary artery disease, hyperlipidemia, and hepatic steatosis. Carotenoids are a group of abundant lipid-soluble phytochemicals, and studies have suggested that they have antioxidant, antiapoptotic, and anti-inflammatory properties. However, a systematic analysis of the association between serum carotenoids and SII is still lacking. The purpose of this investigation was to explore the association between serum carotenoid concentration and SII. The cross-sectional investigation included general people (age ≥ 20) with complete information on SII and five different serum carotenoids (Trans-lycopene, β-carotene, α-carotene, lutein/zeaxanthin, and β-cryptoxanthin). Multivariate linear regression analyses were used to evaluate the association between serum carotenoids and SII among general people. The potential non-linear relationship was determined using threshold effect analysis and fitted smoothing curves. Subgroup analysis was performed to explore the potential stratified factors. 15903 participants were enrolled in our investigation. Based on multivariate linear regressions, the highest quartiles of serum carotenoids were found significantly associated with SII compared with the lowest quartiles. The results showed the negative association between SII and the concentration of five different serum carotenoids. According to the non-linear analysis, we found that there are non-linear relationships between β-carotene and trans-lycopene and SII in general people with an inflection point of 6.90 (log2-transformed, ug/dL) and 4.01 (log2-transformed, ug/dL), respectively. The results from subgroup analysis provide several potential moderating effects, such as race, current drinker, and age. This study revealed the relationship between the concentration of several serum carotenoids and SII across the general American population. Further prospective and longitude investigations are needed.

A bstract One can obtain exact information about Virasoro conformal blocks by analytically continuing the correlators of degenerate operators. We argued in recent work that this technique can be used to explicitly resolve information loss problems in AdS 3 /CFT 2 . In this paper we use the technique to perform calculations in the small 1/ c ∝ G N expansion: (1) we prove the all-orders resummation of logarithmic factors ∝ 1 c log z in the Lorentzian regime, demonstrating that 1 /c corrections directly shift Lyapunov exponents associated with chaos, as claimed in prior work, (2) we perform another all-orders resummation in the limit of large c with fixed cz , interpolating between the early onset of chaos and late time behavior, (3) we explicitly compute the Virasoro vacuum block to order 1 /c 2 and 1 /c 3 with external dimensions fixed, corresponding to 2 and 3 loop calculations in AdS 3 , and (4) we derive the heavy-light vacuum blocks in theories with N = 1 , 2 superconformal symmetry.

In the textile industry, surface defects can greatly damage the value of fabric, but the coexistence of subtle defects and elongated defects poses a significant challenge to the localization of them. Existing convolutional neural networks-deep learning methods, especially the YOLO series, can present promising fabric defect detection. However, their performance is limited in simultaneously learning local and global features, leading to inaccurate localization results. To address this issue, this paper proposes a Fabric Defect Detection Network (FDDNet) based on Spatial Depth-Transforming Convolution (SDTC) and Multiscale Dilated Self-attention Fusion Module (MDSFM). Firstly, to enhance the local feature characterization capability of the backbone network, our FDDNet proposes spatial depth-transforming convolutions to preserve more fine-grained information. Subsequently, to effectively integrate global and local information and enhance global-local modeling capability, the multiscale dilated self-attention fusion module is introduced by combining self-attention mechanisms and dilated convolutions, thus enabling the model to percept scale changes and achieving multi-scale defect localization. Experiment results conducted on the publicly available Tianchi fabric dataset and a self-made denim dataset show that, the proposed FDDNet can achieve the AP50 of 54% and 56.8% respectively, which outperforms mainstream state-of-the-art methods.

Micropeptides are small peptide chains translated from non-coding RNAs ( ncRNAs ), typically ranging from < 100 to ~ 200 amino acids in length, and usually encoded by short or small open reading frames ( sORFs/smORFs ). Recent advances in high-throughput sequencing and multi-omics technologies have enabled genome-wide identification of sORFs across diverse species. Notably, a subset of these sORFs encodes functional micropeptides that play critical roles in biological processes such as inflammation, metabolism, and tumorigenesis. Emerging evidence suggests that tumor-associated micropeptides regulate key hallmarks of cancer, including proliferation, metastasis, metabolic reprogramming, angiogenesis, ion homeostasis, and immune evasion. In this review, we define the definition of micropeptides and discuss cutting-edge methodologies for their discovery, such as ribosome profiling (Ribo-seq), mass spectrometry (MS), and sORF -centric bioinformatics pipelines. Furthermore, we systematically summarize the functional mechanisms of micropeptides in tumor initiation, progression, therapeutic response, and drug resistance. This synthesis aims to provide novel perspectives on cancer biology and highlights micropeptides promising candidates for use as diagnostic biomarkers and targeted therapies.