Tumor microenvironment-responsive size-changeable and biodegradable HA-CuS/MnO2 nanosheets for MR imaging and synergistic chemodynamic therapy/phototherapy.

Tumor microenvironment (TME)-responsive size-changeable and biodegradable nanoplatforms for multimodal therapy possess huge advantages in anti-tumor therapy. Hence, we developed a hyaluronic acid (HA) modified CuS/MnO2 nanosheets (HCMNs) as a multifunctional nanoplatform for synergistic chemodynamic therapy (CDT)/photothermal therapy (PTT)/photodynamic therapy (PDT). The prepared HCMNs exhibited significant NIR light absorption and photothermal conversion efficiency because of the densely deposited ultra-small sized CuS nanoparticles on the surface of MnO2 nanosheet. They could precisely target the tumor cells and rapidly decomposed into small sized nanostructures in the TME, and then efficiently promote intracellular ROS generation through a series of cascade reactions. Moreover, the local temperature elevation induced by photothermal effect also promote the PDT based on CuS nanoparticles and the Fenton-like reaction of Mn2+, thereby enhancing the therapeutic efficiency. Furthermore, the T1-weighted magnetic resonance (MR) imaging was significantly enhanced by the abundant Mn2+ ions from the decomposition process of HCMNs. In addition, the CDT/PTT/PDT synergistic therapy using a single NIR light source exhibited considerable anti-tumor effect via in vitro cell test. Therefore, the developed HCMNs will provide great potential for MR imaging and multimodal synergistic cancer therapy.

A Predictive Network-Based Immune Checkpoint Blockade Immunotherapeutic Signature Optimizing Patient Selection and Treatment Strategies.

Immune checkpoint blockade (ICB) therapy has brought significant advancements to the field of oncology. However, the diverse responses among patients highlight the need for more accurate predictive tools. In this study, insights are drawn from tumor-immunology pathways, and a novel network-based ICB immunotherapeutic signature, termed ICBnetIS, is constructed. The signature is derived from advanced biological network-based computational strategies involving co-expression networks and molecular interactions networks. The efficacy of ICBnetIS is established through its association with enhanced patient survival and a robust immune response characterized by diverse immune cell infiltration and active anti-tumor immune pathways. The validation process positions ICBnetIS as an effective tool in predicting responses to ICB therapy, analyzing ICB data from a broad collection of over 700 samples from multiple cancer types of more than 15 datasets. It achieves an aggregated prediction AUC of 0.784, which outperforms the other nine renowned immunotherapeutic signatures, indicating the superior predictive capability of ICBnetIS. To sum up, the findings suggest ICBnetIS as a potent tool in predicting ICB therapy responses, offering significant implications for patient selection and treatment optimization in oncology. The study highlights the role of ICBnetIS in advancing personalized treatment strategies, potentially transforming the clinical landscape of ICB therapy.

A method for predicting drugs that can boost the efficacy of immune checkpoint blockade.

Combination therapy is a promising therapeutic strategy to enhance the efficacy of immune checkpoint blockade (ICB); however, predicting drugs for effective combination is challenging. Here we developed a general data-driven method called CM-Drug for screening compounds that can boost ICB treatment efficacy based on core and minor gene sets identified between responsive and nonresponsive samples in ICB therapy. The CM-Drug method was validated using melanoma and lung cancer mouse models, with combined therapeutic efficacy demonstrated in eight of nine predicted compounds. Among these compounds, taltirelin had the strongest synergistic effect. Mechanistic analysis and experimental verification demonstrated that taltirelin can stimulate CD8+ T cells and is mediated by the induction of thyroid-stimulating hormone. This study provides an effective and general method for predicting and evaluating drugs for combination therapy and identifies candidate compounds for future ICB combination therapy.

Age and duration of hypertension are associated with carotid artery tortuosity.

Objective: Tortuosity of the carotid artery is a common angiographic finding that may impact blood flow and neuronal function. However, information on its prevalence and risk factors remains limited. In this study, we determined to explore the factors affecting carotid artery tortuosity. Methods: The head and neck computed tomography angiography (CTA) imaging and cerebral angiography data performed at the Second Affiliated Hospital of Wenzhou Medical University between January 2019 and September 2021 were collected, and a total of 356 cases were enrolled in the study after screening. Carotid artery tortuosity refers to the angle between the two adjacent segments of the carotid artery, from the opening of the aortic arch on either side to the external orifice of the carotid canal, being less than 150°. A retrospective analysis was performed to compare the general information, laboratory indicators, personal history, and medical history between the two groups. The χ2 test, t-test, and Mann-Whitney U-test were performed to compare the parameters between the two groups. If there were significant differences between the groups, multivariate logistic regression was performed to analyze the factors affecting carotid artery tortuosity. Results: A total of 222 of the 356 cases were determined to have carotid artery tortuosity, accounting for 63.6%. There were statistically significant differences in age, body mass index (BMI), duration of diabetes and hypertension, levels of low-density lipoprotein cholesterol (LDL-C), diastolic blood pressure, history of ischemic and hemorrhagic stroke, and the usage of antihypertensive drugs between the two groups. Multivariate logistic regression analysis of the above factors showed that age (OR = 5.063, 95% CI 2.963-10.26, p < 0.001) and duration of hypertension (OR = 2.356, 95% CI 1.353-8.625, p = 0.021) were associated with a higher incidence of carotid artery tortuosity. Compared to patients who did not consume antihypertensive drugs, the incidence of carotid artery tortuosity was significantly less (OR = 0.094, 95% CI 0.002-0.713, p = 0.019) in those consuming antihypertensive drugs. Conclusion: Carotid artery tortuosity is a relatively common carotid artery disease. The incidence of carotid artery tortuosity may increase with age and the duration of hypertension. The consumption of antihypertensive drugs may reduce the incidence of carotid artery tortuosity.

A novel method for detecting credit card fraud problems.

Credit card fraud is a significant problem that costs billions of dollars annually. Detecting fraudulent transactions is challenging due to the imbalance in class distribution, where the majority of transactions are legitimate. While pre-processing techniques such as oversampling of minority classes are commonly used to address this issue, they often generate unrealistic or overgeneralized samples. This paper proposes a method called autoencoder with probabilistic xgboost based on SMOTE and CGAN(AE-XGB-SMOTE-CGAN) for detecting credit card frauds.AE-XGB-SMOTE-CGAN is a novel method proposed for credit card fraud detection problems. The credit card fraud dataset comes from a real dataset anonymized by a bank and is highly imbalanced, with normal data far greater than fraud data. Autoencoder (AE) is used to extract relevant features from the dataset, enhancing the ability of feature representation learning, and are then fed into xgboost for classification according to the threshold. Additionally, in this study, we propose a novel approach that hybridizes Generative Adversarial Network (GAN) and Synthetic Minority Over-Sampling Technique (SMOTE) to tackle class imbalance problems. Our two-phase oversampling approach involves knowledge transfer and leverages the synergies of SMOTE and GAN. Specifically, GAN transforms the unrealistic or overgeneralized samples generated by SMOTE into realistic data distributions where there is not enough minority class data available for GAN to process effectively on its own. SMOTE is used to address class imbalance issues and CGAN is used to generate new, realistic data to supplement the original dataset. The AE-XGB-SMOTE-CGAN algorithm is also compared to other commonly used machine learning algorithms, such as KNN and Light GBM, and shows an overall improvement of 2% in terms of the ACC index compared to these algorithms. The AE-XGB-SMOTE-CGAN algorithm also outperforms KNN in terms of the MCC index by 30% when the threshold is set to 0.35. This indicates that the AE-XGB-SMOTE-CGAN algorithm has higher accuracy, true positive rate, true negative rate, and Matthew's correlation coefficient, making it a promising method for detecting credit card fraud.

Identification of P21 (CDKN1A) Activated Kinase 4 as a Susceptibility Gene for Familial Non-Medullary Thyroid Carcinoma.

Background: Familial non-medullary thyroid carcinoma (FNMTC) is a genetically predisposed disease with unclear genetic mechanisms. This makes research on susceptibility genes important for the diagnosis and treatment options. Methods: This study included a five-member family affected by papillary thyroid carcinoma. The candidate genes were identified through whole-exome sequencing and Sanger sequencing in family members, other FNMTC patients, and sporadic non-medullary thyroid carcinoma patients. The pathogenicity of the mutation was predicted using in silico tools. Cell phenotype experiments in vitro and models of lung distant metastasis in vivo were conducted to confirm the characteristics of the mutation. Transcriptome sequencing and mechanistic validation were employed to compare the disparities between PAK4 wild-type (WT) and PAK4 mutant (MUT) cell lines. Results: This mutation alters the protein structure, potentially increasing instability by affecting hydrophobicity, intra-molecular hydrogen bonding, and phosphorylation sites. It specifically promotes phosphorylated PAK4 nuclear translocation and expression in thyroid tissue and cell lines. Compared with the WT cells line, PAK4 I417T demonstrates enhanced proliferation, invasiveness, accelerated cell division, and inhibition of cell apoptosis in vitro. In addition, it exhibits a significant propensity for metastasis in vivo. It activates tumor necrosis factor signaling through increased phosphorylation of PAK4, JNK, NFκB, and c-Jun, unlike the WT that activates it via the PAK4-NFκ-MMP9 axis. In addition, PAK4 MUT protein interacts with matrix metalloproteinase (MMP)3 and regulates MMP3 promoter activity, which is not observed in the WT. Conclusions: Our study identified PAK4: c.T1250C: p.I417T as a potential susceptibility gene for FNMTC. The study concludes that the mutant form of PAK4 exhibits oncogenic function, suggesting its potential as a novel diagnostic molecular marker for FNMTC.

AnnoSM: An Automated Annotation Tool for Determining the Substituent Modes on the Parent Skeleton Based on a Characteristic MS/MS Fragment Ion Library.

Mass spectrometry (MS) is a powerful technology for the structural elucidation of known or unknown small molecules. However, the accuracy of MS-based structure annotation is still limited due to the presence of numerous isomers in complex matrices. There are still challenges in automatically interpreting the fine structure of molecules, such as the types and positions of substituents (substituent modes, SMs) in the structure. In this study, we employed flavones, flavonols, and isoflavones as examples to develop an automated annotation method for identifying the SMs on the parent molecular skeleton based on a characteristic MS/MS fragment ion library. Importantly, user-friendly software AnnoSM was built for the convenience of researchers with limited computational backgrounds. It achieved 76.87% top-1 accuracy on the 148 authentic standards. Among them, 22 sets of flavonoid isomers were successfully differentiated. Moreover, the developed method was successfully applied to complex matrices. One such example is the extract of Ginkgo biloba L. (EGB), in which 331 possible flavonoids with SM candidates were annotated. Among them, 23 flavonoids were verified by authentic standards. The correct SMs of 13 flavonoids were ranked first on the candidate list. In the future, this software can also be extrapolated to other classes of compounds.

Spatiotemporal and direct capturing global substrates of lysine-modifying enzymes in living cells.

Protein-modifying enzymes regulate the dynamics of myriad post-translational modification (PTM) substrates. Precise characterization of enzyme-substrate associations is essential for the molecular basis of cellular function and phenotype. Methods for direct capturing global substrates of protein-modifying enzymes in living cells are with many challenges, and yet largely unexplored. Here, we report a strategy to directly capture substrates of lysine-modifying enzymes via PTM-acceptor residue crosslinking in living cells, enabling global profiling of substrates of PTM-enzymes and validation of PTM-sites in a straightforward manner. By integrating enzymatic PTM-mechanisms, and genetically encoding residue-selective photo-crosslinker into PTM-enzymes, our strategy expands the substrate profiles of both bacterial and mammalian lysine acylation enzymes, including bacterial lysine acylases PatZ, YiaC, LplA, TmcA, and YjaB, as well as mammalian acyltransferases GCN5 and Tip60, leading to discovery of distinct yet functionally important substrates and acylation sites. The concept of direct capturing substrates of PTM-enzymes via residue crosslinking may extend to the other types of amino acid residues beyond lysine, which has the potential to facilitate the investigation of diverse types of PTMs and substrate-enzyme interactive proteomics.

Optimization of FFPE preparation and identification of gene attributes associated with RNA degradation.

Formalin-fixed paraffin-embedded (FFPE) tissues are widely available specimens for clinical studies. However, RNA degradation in FFPE tissues often restricts their utility. In this study, we determined optimal FFPE preparation conditions, including tissue ischemia at 4°C (<48 h) or 25°C for a short time (0.5 h), 48-h fixation at 25°C and sampling from FFPE scrolls instead of sections. Notably, we observed an increase in intronic reads and a significant change in gene rank based on expression level in the FFPE as opposed to fresh-frozen (FF) samples. Additionally, we found that more reads were mapped to genes associated with chemical stimulus in FFPE samples. Furthermore, we demonstrated that more degraded genes in FFPE samples were enriched in genes with short transcripts and high free energy. Besides, we found 40 housekeeping genes exhibited stable expression in FF and FFPE samples across various tissues. Moreover, our study showed that FFPE samples yielded comparable results to FF samples in dimensionality reduction and pathway analyses between case and control samples. Our study established the optimal conditions for FFPE preparation and identified gene attributes associated with degradation, which would provide useful clues for the utility of FFPE tissues in clinical practice and research.

Chemical Composition of Commercial Cannabis.

Cannabis is widely used for medicinal and recreational purposes. As a result, there is increased interest in its chemical components and their physiological effects. However, current information on cannabis chemistry is often outdated or scattered across many books and journals. To address this issue, we used modern metabolomics techniques and modern bioinformatics techniques to compile a comprehensive list of >6000 chemical constituents in commercial cannabis. The metabolomics methods included a combination of high- and low-resolution liquid chromatography-mass spectrometry (MS), gas chromatography-MS, and inductively coupled plasma-MS. The bioinformatics methods included computer-aided text mining and computational genome-scale metabolic inference. This information, along with detailed compound descriptions, physicochemical data, known physiological effects, protein targets, and referential compound spectra, has been made available through a publicly accessible database called the Cannabis Compound Database (https://cannabisdatabase.ca). Such a centralized, open-access resource should prove to be quite useful for the cannabis community.

N-acetylcysteine alleviated tris(2-chloroisopropyl) phosphate-induced sperm motility decline and functional dysfunction in mice through reversing oxidative stress and DNA damage.

The decline in male fertility caused by environmental pollutants has attracted worldwide attention nowadays. Tris(2-chloroisopropyl) phosphate (TCPP) is a chlorine-containing organophosphorus flame retardant applied in many consumer products and has multiple side effects on health. However, whether TCPP impairs spermatogenesis remains unclear. In this study, we found that TCPP reduced the sperm motility and blastocyst formation, inhibited proliferation and induced apoptosis in mice testes and spermatocyte cell line GC-2. Moreover, TCPP induced imbalance of oxidant and anti-oxidant, DNA damage and mitochondrial dysfunction, thus induced abnormal spermatogenesis. In this process, p53 signaling pathway was activated and N-acetylcysteine treatment partially alleviated the side effects of TCPP, including decrease of sperm motility, activation of p53 signaling pathway and DNA damage. Finally, our study verified that TCPP elevated reactive oxygen species (ROS), decreased mitochondrial membrane potential and induced apoptosis in human semen samples. Overall, ROS mediated TCPP-induced germ cell proliferation inhibition and apoptosis, which finally led to the decline of sperm motility.

ncRNADrug: a database for validated and predicted ncRNAs associated with drug resistance and targeted by drugs.

Drug resistance is a major barrier in cancer treatment and anticancer drug development. Growing evidence indicates that non-coding RNAs (ncRNAs), especially microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), play pivotal roles in cancer progression, therapy, and drug resistance. Furthermore, ncRNAs have been proven to be promising novel therapeutic targets for cancer treatment. Reversing dysregulated ncRNAs by drugs holds significant potential as an effective therapeutic strategy for overcoming drug resistance. Therefore, we developed ncRNADrug, an integrated and comprehensive resource that records manually curated and computationally predicted ncRNAs associated with drug resistance, ncRNAs targeted by drugs, as well as potential drug combinations for the treatment of resistant cancer. Currently, ncRNADrug collects 29 551 experimentally validated entries involving 9195 ncRNAs (2248 miRNAs, 4145 lncRNAs and 2802 circRNAs) associated with the drug resistance of 266 drugs, and 32 969 entries involving 10 480 ncRNAs (4338 miRNAs, 6087 lncRNAs and 55 circRNAs) targeted by 965 drugs. In addition, ncRNADrug also contains associations between ncRNAs and drugs predicted from ncRNA expression profiles by differential expression analysis. Altogether, ncRNADrug surpasses the existing related databases in both data volume and functionality. It will be a useful resource for drug development and cancer treatment. ncRNADrug is available at http://www.jianglab.cn/ncRNADrug.

Intracranial mesenchymal tumor with multiple extracranial metastases: A case report and literature review.

This study presents a rare case of an older woman with an intracranial mesenchymal tumor in the right frontal and parietal lobes. Despite prompt surgical intervention, her condition rapidly deteriorated because of tumor dissemination, leading to her demise. We highlight the tumor's marked invasiveness and heterogeneity, coupled with a propensity for distant systemic metastasis, which negatively impacted the patient's prognosis. This particular clinical behavior had not been previously reported, making this a novel observation. Thus, through a comprehensive review of relevant literature, we aim to provide valuable insights for further understanding, diagnosing, and treating such tumors.

ICBcomb: a comprehensive expression database for immune checkpoint blockade combination therapy.

The success of immune checkpoint blockade (ICB) promotes the immunotherapy to be a new pillar in cancer treatment. However, the low response rate of the ICB therapy limits its application. To increase the response rate and enhance efficacy, the ICB combination therapy has emerged and its clinical trials are increasing. Nevertheless, the gene expression profile and its pattern of ICB combination were not comprehensively studied, which limits the understanding of the ICB combination therapy and the identification of new drugs. Here, we constructed ICBcomb (http://bioinfo.life.hust.edu.cn/ICBcomb/), a comprehensive database, by analyzing the human and mouse expression data of the ICB combination therapy and comparing them between groups treated with ICB, other drugs or their combinations. ICBcomb contains 1399 samples across 29 cancer types involving 52 drugs. It provides a user-friendly web interface for demonstrating the results of the available comparisons in the ICB combination therapy datasets with five functional modules: [1, 2] the 'Dataset/Disease' modules for browsing the expression, enrichment and comparison results in each dataset or disease; [3] the 'Gene' module for inputting a gene symbol and displaying its expression and comparison results across datasets/diseases; [4] the 'Gene Set' module for GSVA/GSEA enrichment analysis on the built-in gene sets and the user-input gene sets in different comparisons; [5] the 'Immune Cell' module for immune cell infiltration comparison between different groups by immune cell abundance analysis. The ICBcomb database provides the first resource for gene expression profile and comparison in ICB combination therapy, which may provide clues for discovering the mechanism of effective combination strategies and new combinatory drugs.

Effectiveness of potato late blight (Phytophthora infestans) forecast by meteorological estimation in mountainous terrain based on CARAH rules.

Potato late blight (PLB) caused by Phytophthora infestans (Mont.) de Bary, its incidence and development are highly dependent on meteorological conditions. To solve the problem of PLB in mountainous terrain under the condition of limited meteorological monitoring capability, the air temperature and humidity was estimated based on the basic meteorological datasets, the forecast effect of the onset period and infection cycle of PLB based on CARAH rules was evaluated. The average MAE, RMSE and CI of the estimated air temperature and observations were 1.17 °C, 1.52 °C and 0.95, respectively. The average MAE, RMSE and CI of the estimated relative humidity and observations were 8.0 %, 10.7 % and 0.53, respectively. The curve of the infection cycle of PLB at different locations were estimated from the basic meteorological datasets based on the CARAH rules, and the false alarm and missing ratios were 8.8 % and 4.6 % respectively. It may be delayed by 1 or 2 fungal generations compared to the observations, and then the protective fungicide should be adjusted to a systemic fungicide. The false alarm of the infection cycle of PLB may increase in dry air conditions, and the missing report may occur in humid condition.

Efficient estimation for large-scale linkage disequilibrium patterns of the human genome.

In this study, we proposed an efficient algorithm (X-LD) for estimating linkage disequilibrium (LD) patterns for a genomic grid, which can be of inter-chromosomal scale or of small segments. Compared with conventional methods, the proposed method was significantly faster, dropped from O(nm2) to O(n2m)-n the sample size and m the number of SNPs, and consequently we were permitted to explore in depth unknown or reveal long-anticipated LD features of the human genome. Having applied the algorithm for 1000 Genome Project (1KG), we found (1) the extended LD, driven by population structure, universally existed, and the strength of inter-chromosomal LD was about 10% of their respective intra-chromosomal LD in relatively homogeneous cohorts, such as FIN, and to nearly 56% in admixed cohort, such as ASW. (2) After splitting each chromosome into upmost of more than a half million grids, we elucidated the LD of the HLA region was nearly 42 folders higher than chromosome 6 in CEU and 11.58 in ASW; on chromosome 11, we observed that the LD of its centromere was nearly 94.05 folders higher than chromosome 11 in YRI and 42.73 in ASW. (3) We uncovered the long-anticipated inversely proportional linear relationship between the length of a chromosome and the strength of chromosomal LD, and their Pearson's correlation was on average over 0.80 for 26 1KG cohorts. However, this linear norm was so far perturbed by chromosome 11 given its more completely sequenced centromere region. Uniquely chromosome 8 of ASW was found most deviated from the linear norm than any other autosomes. The proposed algorithm has been realized in C++ (called X-LD) and is available at https://github.com/gc5k/gear2, and can be applied to explore LD features in any sequenced populations.

Unraveling the synergistic effects of Cu-Ag tandem catalysts during electrochemical CO2 reduction using nanofocused X-ray probes.

Controlling the selectivity of the electrocatalytic reduction of carbon dioxide into value-added chemicals continues to be a major challenge. Bulk and surface lattice strain in nanostructured electrocatalysts affect catalytic activity and selectivity. Here, we unravel the complex dynamics of synergistic lattice strain and stability effects of Cu-Ag tandem catalysts through a previously unexplored combination of in situ nanofocused X-ray absorption spectroscopy and Bragg coherent diffraction imaging. Three-dimensional strain maps reveal the lattice dynamics inside individual nanoparticles as a function of applied potential and product yields. Dynamic relations between strain, redox state, catalytic activity and selectivity are derived. Moderate Ag contents effectively reduce the competing evolution of H2 and, concomitantly, lead to an enhanced corrosion stability. Findings from this study evidence the power of advanced nanofocused spectroscopy techniques to provide new insights into the chemistry and structure of nanostructured catalysts.

A structural similarity networking assisted collision cross-section prediction interval filtering strategy for multi-compound identification of complex matrix by ion-mobility mass spectrometry.

Ion mobility coupled with mass spectrometry (IM-MS), an emerging technology for analysis of complex matrix, has been facing challenges due to the complexities of chemical structures and original data, as well as low-efficiency and error-proneness of manual operations. In this study, we developed a structural similarity networking assisted collision cross-section prediction interval filtering (SSN-CCSPIF) strategy. We first carried out a structural similarity networking (SSN) based on Tanimoto similarities among Morgan fingerprints to classify the authentic compounds potentially existing in complex matrix. By performing automatic regressive prediction statistics on mass-to-charge ratios (m/z) and collision cross-sections (CCS) with a self-built Python software, we explored the IM-MS feature trendlines, established filtering intervals and filtered potential compounds for each SSN classification. Chemical structures of all filtered compounds were further characterized by interpreting their multidimensional IM-MS data. To evaluate the applicability of SSN-CCSPIF, we selected Ginkgo biloba extract and dripping pills. The SSN-CCSPIF subtracted more background interferences (43.24%∼43.92%) than other similar strategies with conventional ClassyFire criteria (10.71%∼12.13%) or without compound classification (35.73%∼36.63%). Totally, 229 compounds, including eight potential new compounds, were characterized. Among them, seven isomeric pairs were discriminated with the integration of IM-separation. Using SSN-CCSPIF, we can achieve high-efficient analysis of complex IM-MS data and comprehensive chemical profiling of complex matrix to reveal their material basis.