Nanotechnology based gas delivery system: a "green" strategy for cancer diagnosis and treatment.

Gas therapy, a burgeoning clinical treatment modality, has garnered widespread attention to treat a variety of pathologies in recent years. The advent of nanoscale gas drug therapy represents a novel therapeutic strategy, particularly demonstrating immense potential in the realm of oncology. This comprehensive review navigates the landscape of gases endowed with anti-cancer properties, including hydrogen (H2), carbon monoxide (CO), carbon dioxide (CO2), nitric oxide (NO), oxygen (O2), sulfur dioxide (SO2), hydrogen sulfide (H2S), ozone (O3), and heavier gases. The selection of optimal delivery vectors is also scrutinized in this review to ensure the efficacy of gaseous agents. The paper highlights the importance of engineering stimulus-responsive delivery systems that enable precise and targeted gas release, thereby augmenting the therapeutic efficiency of gas therapy. Additionally, the review examines the synergistic potential of integrating gas therapy with conventional treatments such as starvation therapy, ultrasound (US) therapy, chemotherapy, radiotherapy (RT), and photodynamic therapy (PDT). It also discusses the burgeoning role of advanced multimodal and US imaging in enhancing the precision of gas therapy applications. The insights presented are pivotal in the strategic development of nanomedicine platforms designed for the site-specific delivery of therapeutic gases, heralding a new era in cancer therapeutics.

The biological impact of deuterium and therapeutic potential of deuterium-depleted water.

Since its discovery by Harold Urey in 1932, deuterium has attracted increased amounts of attention from the scientific community, with many previous works aimed to uncover its biological effects on living organisms. Existing studies indicate that deuterium, as a relatively rare isotope, is indispensable for maintaining normal cellular function, while its enrichment and depletion can affect living systems at multiple levels, including but not limited to molecules, organelles, cells, organs, and organisms. As an important compound of deuterium, deuterium-depleted water (DDW) possess various special effects, including but not limited to altering cellular metabolism and potentially inhibiting the growth of cancer cells, demonstrating anxiolytic-like behavior, enhancing long-term memory in rats, reducing free radical oxidation, regulating lipid metabolism, harmonizing indices related to diabetes and metabolic syndrome, and alleviating toxic effects caused by cadmium, manganese, and other harmful substances, implying its tremendous potential in anticancer, neuroprotective, antiaging, antioxidant, obesity alleviation, diabetes and metabolic syndrome treatment, anti-inflammatory, and detoxification, thereby drawing extensive attention from researchers. This review comprehensively summarizes the latest progress in deuterium acting on living organisms. We start by providing a snapshot of the distribution of deuterium in nature and the tolerance of various organisms to it. Then, we discussed the impact of deuterium excess and deprivation, in the form of deuterium-enriched water (DEW) and deuterium-depleted water (DDW), on living organisms at different levels. Finally, we focused on the potential of DDW as an adjuvant therapeutic agent for various diseases and disorders.

Prediction of immunotherapy response of bladder cancer with a pyroptosis-related signature indicating tumor immune microenvironment.

Background: Although prognostic models based on pyroptosis-related genes (PRGs) have been constructed in bladder cancer (BLCA), the comprehensive impact of these genes on tumor microenvironment (TME) and immunotherapeutic response has yet to be investigated. Methods: Based on expression profiles of 52 PRGs, we utilized the unsupervised clustering algorithm to identify PRGs subtypes and ssGSEA to quantify immune cells and hallmark pathways. Moreover, we screened feature genes of distinct PRGs subtypes and validated the associations with immune infiltrations in tissue using the multiplex immunofluorescence. Univariate, LASSO, and multivariate Cox regression analyses were employed to construct the scoring scheme. Results: Four PRGs clusters were identified, samples in cluster C1 were infiltrated with more immune cells than those in others, implying a favorable response to immunotherapy. While the cluster C2, which shows an extremely low level of most immune cells, do not respond to immunotherapy. CXCL9/CXCL10 and SPINK1/DHSR2 were identified as feature genes of cluster C1 and C2, and the specimen with high CXCL9/CXCL10 was characterized by more CD8 + T cells, macrophages and less Tregs. Based on differentially expressed genes (DEGs) among PRGs subtypes, a predictive model (termed as PRGs score) including five genes (CACNA1D, PTK2B, APOL6, CDK6, ANXA2) was built. Survival probability of patients with low-PRGs score was significantly higher than those with high-PRGs score. Moreover, patients with low-PRGs score were more likely to benefit from anti-PD1/PD-L1 regimens. Conclusion: PRGs are closely associated with TME and oncogenic pathways. PRGs score is a promising indicator for predicting clinical outcome and immunotherapy response.

Enhancing genome-wide populus trait prediction through deep convolutional neural networks.

As a promising model, genome-based plant breeding has greatly promoted the improvement of agronomic traits. Traditional methods typically adopt linear regression models with clear assumptions, neither obtaining the linkage between phenotype and genotype nor providing good ideas for modification. Nonlinear models are well characterized in capturing complex nonadditive effects, filling this gap under traditional methods. Taking populus as the research object, this paper constructs a deep learning method, DCNGP, which can effectively predict the traits including 65 phenotypes. The method was trained on three datasets, and compared with other four classic models-Bayesian ridge regression (BRR), Elastic Net, support vector regression, and dualCNN. The results show that DCNGP has five typical advantages in performance: strong prediction ability on multiple experimental datasets; the incorporation of batch normalization layers and Early-Stopping technology enhancing the generalization capabilities and prediction stability on test data; learning potent features from the data and thus circumventing the tedious steps of manual production; the introduction of a Gaussian Noise layer enhancing predictive capabilities in the case of inherent uncertainties or perturbations; fewer hyperparameters aiding to reduce tuning time across datasets and improve auto-search efficiency. In this way, DCNGP shows powerful predictive ability from genotype to phenotype, which provide an important theoretical reference for building more robust populus breeding programs.

Development and validation of a point-of-care nursing mobile tool to guide the diagnosis of malnutrition in hospitalized adult patients: a multicenter, prospective cohort study.

Malnutrition is a prevalent and severe issue in hospitalized patients with chronic diseases. However, malnutrition screening is often overlooked or inaccurate due to lack of awareness and experience among health care providers. This study aimed to develop and validate a novel digital smartphone-based self-administered tool that uses facial features, especially the ocular area, as indicators of malnutrition in inpatient patients with chronic diseases. Facial photographs and malnutrition screening scales were collected from 619 patients in four different hospitals. A machine learning model based on back propagation neural network was trained, validated, and tested using these data. The model showed a significant correlation (p < 0.05) and a high accuracy (area under the curve 0.834-0.927) in different patient groups. The point-of-care mobile tool can be used to screen malnutrition with good accuracy and accessibility, showing its potential for screening malnutrition in patients with chronic diseases.

Real-time detection of 20 amino acids and discrimination of pathologically relevant peptides with functionalized nanopore.

Precise identification and quantification of amino acids is crucial for many biological applications. Here we report a copper(II)-functionalized Mycobacterium smegmatis porin A (MspA) nanopore with the N91H substitution, which enables direct identification of all 20 proteinogenic amino acids when combined with a machine-learning algorithm. The validation accuracy reaches 99.1%, with 30.9% signal recovery. The feasibility of ultrasensitive quantification of amino acids was also demonstrated at the nanomolar range. Furthermore, the capability of this system for real-time analyses of two representative post-translational modifications (PTMs), one unnatural amino acid and ten synthetic peptides using exopeptidases, including clinically relevant peptides associated with Alzheimer's disease and cancer neoantigens, was demonstrated. Notably, our strategy successfully distinguishes peptides with only one amino acid difference from the hydrolysate and provides the possibility to infer the peptide sequence.

Accumulation of TOX high mobility group box family member 3 promotes the oncogenesis and development of hepatocellular carcinoma through the MAPK signaling pathway.

Microvascular invasion (MVI) has been widely valued in the field of liver surgery because MVI positivity indicates poor prognosis in hepatocellular carcinoma (HCC) patients. However, the potential molecular mechanism underlying the poor prognosis of MVI-positive HCC patients is unclear. Therefore, this study focused on identifying the key genes leading to poor prognosis in patients with a high degree of malignancy of HCC by examining the molecular signaling pathways in MVI-positive HCC patients. Through RNA sequencing, TOX high mobility group box family member 3 (TOX3) was demonstrated to be significantly highly expressed in MVI-positive HCC tissues, which was associated with poor prognosis. The results of in vivo and in vitro showed that TOX3 can promote the oncogenesis and development of HCC by targeting key molecules of the MAPK and EMT signaling pathways. The IP-MS results indicated that proteasome degradation of TOX3 in HCC cells is potentially mediated by a tripartite motif containing 56 (TRIM56, an E3 ligase) in HCC cells. Inhibiting TRIM56 enhances TOX3 protein levels. Overall, our study identified TOX3 as a key gene in the MAPK and EMT signaling pathways in HCC, and its overexpression confers significant proliferation and invasiveness to tumor cells.

Structural basis of the subcortical maternal complex and its implications in reproductive disorders.

The subcortical maternal complex (SCMC) plays a crucial role in early embryonic development. Malfunction of SCMC leads to reproductive diseases in women. However, the molecular function and assembly basis for SCMC remain elusive. Here we reconstituted mouse SCMC and solved the structure at atomic resolution using single-particle cryo-electron microscopy. The core complex of SCMC was formed by MATER, TLE6 and FLOPED, and MATER embraced TLE6 and FLOPED via its NACHT and LRR domains. Two core complexes further dimerize through interactions between two LRR domains of MATERs in vitro. FILIA integrates into SCMC by interacting with the carboxyl-terminal region of FLOPED. Zygotes from mice with Floped C-terminus truncation showed delayed development and resembled the phenotype of zygotes from Filia knockout mice. More importantly, the assembly of mouse SCMC was affected by corresponding clinical variants associated with female reproductive diseases and corresponded with a prediction based on the mouse SCMC structure. Our study paves the way for further investigations on SCMC functions during mammalian preimplantation embryonic development and reveals underlying causes of female reproductive diseases related to SCMC mutations, providing a new strategy for the diagnosis of female reproductive disorders.

Exosome-mediated delivery of miR-519e-5p promotes malignant tumor phenotype and CD8+ T-cell exhaustion in metastatic PTC.

CONTEXT: Distant metastases are the primary cause of therapy failure and mortality in patients with papillary thyroid carcinomas (PTCs). However, the underlying mechanism responsible for the initiation of tumor cell dissemination and metastasis in PTCs has rarely been investigated. OBJECTIVE: The aim of this study was to investigate effects and underlying molecular mechanisms of circulating exosomal microRNAs (miRNAs) in distant metastatic PTCs. METHODS: The most relevant circulating exosomal miRNA to distant metastatic PTCs were verified between distant metastatic PTCs and nondistant metastatic PTCs by miRNA microarray, quantitative real-time polymerase chain reaction (qRT‒PCR) assays and receiver operating characteristic (ROC) curves. The parental and recipient cells of that circulating exosomal miRNA were then explored. In vitro and in vivo experiments were further performed to elucidate the function and potential mechanisms of circulating exosomal miRNAs that contribute to the development of distant metastases. RESULTS: We identified that PTC-derived exosomal miR-519e-5p was significantly upregulated in the circulatory system in distant metastatic PTCs. Further tests demonstrated that PTC cells can acquire a more malignant phenotype via hnRNPA2B1 mediated sorting of tumor suppressor miR-519e-5p into exosomes to activate Wnt signaling pathway via upregulating PLAGL2. Furthermore, miR-519e-5p included in PTC-derived exosomes can be transferred to recipient CD8+ T cells and aid in tumor immune escape in distant organs through inhibiting Notch signaling pathway by downregulating NOTCH2. CONCLUSION: Our findings highlighted the dual role of PTC-derived exosomal miR-519e-5p in distant metastasis, which may improve our understanding of exosome-mediated distant metastatic mechanisms.

Revealing Allosteric Mechanism of Amino Acid Binding Proteins from Open to Closed State.

Amino acid binding proteins (AABPs) undergo significant conformational closure in the periplasmic space of Gram-negative bacteria, tightly binding specific amino acid substrates and then initiating transmembrane transport of nutrients. Nevertheless, the possible closure mechanisms after substrate binding, especially long-range signaling, remain unknown. Taking three typical AABPs-glutamine binding protein (GlnBP), histidine binding protein (HisJ) and lysine/arginine/ornithine binding protein (LAOBP) in Escherichia coli (E. coli)-as research subjects, a series of theoretical studies including sequence alignment, Gaussian network model (GNM), anisotropic network model (ANM), conventional molecular dynamics (cMD) and neural relational inference molecular dynamics (NRI-MD) simulations were carried out. Sequence alignment showed that GlnBP, HisJ and LAOBP have high structural similarity. According to the results of the GNM and ANM, AABPs' Index Finger and Thumb domains exhibit closed motion tendencies that contribute to substrate capture and stable binding. Based on cMD trajectories, the Index Finger domain, especially the I-Loop region, exhibits high molecular flexibility, with residues 11 and 117 both being potentially key residues for receptor-ligand recognition and initiation of receptor allostery. Finally, the signaling pathway of AABPs' conformational closure was revealed by NRI-MD training and trajectory reconstruction. This work not only provides a complete picture of AABPs' recognition mechanism and possible conformational closure, but also aids subsequent structure-based design of small-molecule oncology drugs.

Protocol for identifying immune checkpoint on circulating tumor cells of human pancreatic ductal adenocarcinoma by single-cell RNA sequencing.

Circulating tumor cells (CTCs) are regarded as the "seeds" of tumor metastasis. Identifying immune checkpoints on CTCs is essential for establishing efficient immunotherapies to prevent tumor metastasis. Here, we present a protocol for isolating CTCs and obtaining single-cell suspensions from pancreatic ductal adenocarcinoma liver metastatic patients. We describe steps for biospecimen acquisition, CTC isolation, and tissue dissociation. We then detail procedures for performing single-cell RNA-seq, annotating cell types, and identifying immune checkpoints on CTCs. For complete details on the use and execution of this protocol, please refer to Liu et al. (2023).1.

Artificial intelligence-based multi-omics analysis fuels cancer precision medicine.

With biotechnological advancements, innovative omics technologies are constantly emerging that have enabled researchers to access multi-layer information from the genome, epigenome, transcriptome, proteome, metabolome, and more. A wealth of omics technologies, including bulk and single-cell omics approaches, have empowered to characterize different molecular layers at unprecedented scale and resolution, providing a holistic view of tumor behavior. Multi-omics analysis allows systematic interrogation of various molecular information at each biological layer while posing tricky challenges regarding how to extract valuable insights from the exponentially increasing amount of multi-omics data. Therefore, efficient algorithms are needed to reduce the dimensionality of the data while simultaneously dissecting the mysteries behind the complex biological processes of cancer. Artificial intelligence has demonstrated the ability to analyze complementary multi-modal data streams within the oncology realm. The coincident development of multi-omics technologies and artificial intelligence algorithms has fuelled the development of cancer precision medicine. Here, we present state-of-the-art omics technologies and outline a roadmap of multi-omics integration analysis using an artificial intelligence strategy. The advances made using artificial intelligence-based multi-omics approaches are described, especially concerning early cancer screening, diagnosis, response assessment, and prognosis prediction. Finally, we discuss the challenges faced in multi-omics analysis, along with tentative future trends in this field. With the increasing application of artificial intelligence in multi-omics analysis, we anticipate a shifting paradigm in precision medicine becoming driven by artificial intelligence-based multi-omics technologies.

Immune checkpoint HLA-E:CD94-NKG2A mediates evasion of circulating tumor cells from NK cell surveillance.

Circulating tumor cells (CTCs), shed by primary malignancies, function as "seeds" for distant metastasis. However, it is still largely unknown how CTCs escape immune surveillance. Here, we characterize the transcriptomes of human pancreatic ductal adenocarcinoma CTCs, primary, and metastatic lesions at single-cell scale. Cell-interaction analysis and functional studies in vitro and in vivo reveal that CTCs and natural killer (NK) cells interact via the immune checkpoint molecule pair HLA-E:CD94-NKG2A. Disruption of this interaction by blockade of NKG2A or knockdown of HLA-E expression enhances NK-mediated tumor cell killing in vitro and prevents tumor metastasis in vivo. Mechanistic studies indicate that platelet-derived RGS18 promotes the expression of HLA-E through AKT-GSK3ß-CREB signaling, and overexpression of RGS18 facilitates pancreatic tumor hepatic metastasis. In conclusion, platelet-derived RGS18 protects CTCs from NK-mediated immune surveillance by engaging the immune checkpoint HLA-E:CD94-NKG2A. Interruption of the suppressive signaling prevents tumor metastasis in vivo by immune elimination of CTCs.