The crosstalk between DNA-damage responses and innate immunity.

DNA damage is typically caused during cell growth by DNA replication stress or exposure to endogenous or external toxins. The accumulation of damaged DNA causes genomic instability, which is the root cause of many serious disorders. Multiple cellular organisms utilize sophisticated signaling pathways against DNA damage, collectively known as DNA damage response (DDR) networks. Innate immune responses are activated following cellular abnormalities, including DNA damage. Interestingly, recent studies have indicated that there is an intimate relationship between the DDR network and innate immune responses. Diverse kinds of cytosolic DNA sensors, such as cGAS and STING, recognize damaged DNA and induce signals related to innate immune responses, which link defective DDR to innate immunity. Moreover, DDR components operate in immune signaling pathways to induce IFNs and/or a cascade of inflammatory cytokines via direct interactions with innate immune modulators. Consistently, defective DDR factors exacerbate the innate immune imbalance, resulting in severe diseases, including autoimmune disorders and tumorigenesis. Here, the latest progress in understanding crosstalk between the DDR network and innate immune responses is reviewed. Notably, the dual function of innate immune modulators in the DDR network may provide novel insights into understanding and developing targeted immunotherapies for DNA damage-related diseases, even carcinomas.

Identification of HRH1 as an alternative receptor for SARS-CoV-2: insights from viral inhibition by repurposable antihistamines.

Numerous coreceptors have been shown to facilitate hACE2-dependent or hACE2-independent infection by SARS-CoV-2. A recent study published in mBio by Yu et al. showed that the histamine receptor H1 (HRH1) functions as an alternative receptor for SARS-CoV-2 via direct binding to viral spike proteins (F. Yu, X. Liu, H. Ou, X. Li, et al., mBio e01088-24, 2024, https://doi.org/10.1128/mbio.01088-24). Furthermore, they present compelling evidence that antihistamine drugs targeting HRH1 potently inhibit SARS-CoV-2 entry. This study highlights the therapeutic potential of repurposable antihistamines against COVID-19.

Increased Tryptophan Catabolism Provides Predictive Value to Chronic Heart Failure Patients with Low-Grade Inflammation.

Kynurenine to tryptophan ratio (KTR), which serves as an indicator for evaluating indoleamine-2,3-dioxygenase activity and inflammation, has been reported to be linked with cardiovascular incidences. However, its correlation with cardiovascular outcomes in patients suffering from heart failure (HF) remains to be explored. The objective of this study was to investigate the prognostic value of KTR in HF. The concentration of tryptophan and kynurenine were quantified by liquid chromatography-tandem mass spectrometry, and the KTR value was calculated in a population of 3150 HF patients. The correlation between plasma KTR levels and the occurrence of adverse cardiovascular events was evaluated for its prognostic value. We also assessed the role of KTR in addition to the classic inflammatory biomarker hypersensitive C-reactive protein (hs-CRP) in different subtypes of HF. We found that increased KTR levels were associated with an elevated risk and severity of the primary endpoints in different subtypes of HF. The simultaneous evaluation of KTR and hs-CRP levels enhanced risk categorization among HF patients. Furthermore, the KTR index presented complementary prognostic value for those HF patients with low-grade inflammation (hs-CRP ≤ 6 mg/L). Our results indicated plasma KTR is an independent risk factor for cardiovascular events. Plasma KTR levels in patients with HF can provide both concurrent and complementary prognostic value to hs-CRP.

A pH ultra-sensitive hydrated iridium oxyhydroxide films electrochemical sensor for label-free detection of Vibrio parahaemolyticus.

Vibrio parahaemolyticus (V. parahaemolyticus) is a major foodborne pathogen, which can cause serious foodborne illnesses like diarrhoea. Rapid on-site detection of foodborne pathogens is an ideal way to respond to foodborne illnesses. Herein, we provide an electrochemical sensor for rapid on-site detection. This sensor utilized a pH-sensitive metal-oxide material for the concurrent isothermal amplification and label-free detection of nucleic acids. Based on a pH-sensitive hydrated iridium oxide oxyhydroxide film (HIROF), the electrode transforms the hydrogen ion compound generated during nucleic acid amplification into potential, so as to achieve a real-time detection. The results can be transmitted to a smartphone via Bluetooth. Moreover, HIROF was applied in nucleic acid device detection, with a super-Nernst sensitivity of 77.6 mV/pH in the pH range of 6.0-8.5, and the sensitivity showed the best results so far. Detection of V. parahaemolyticus by this novel method showed a detection limit of 1.0 × 103 CFU/mL, while the time consumption was only 30 min, outperforming real-time fluorescence loop-mediated isothermal amplification (LAMP). Therefore, the characteristics of compact, portable, and fast make the sensor more widely used in on-site detection.

Remodeling of T-cell mitochondrial metabolism to treat autoimmune diseases.

T cells are key drivers of the pathogenesis of autoimmune diseases by producing cytokines, stimulating the generation of autoantibodies, and mediating tissue and cell damage. Distinct mitochondrial metabolic pathways govern the direction of T-cell differentiation and function and rely on specific nutrients and metabolic enzymes. Metabolic substrate uptake and mitochondrial metabolism form the foundational elements for T-cell activation, proliferation, differentiation, and effector function, contributing to the dynamic interplay between immunological signals and mitochondrial metabolism in coordinating adaptive immunity. Perturbations in substrate availability and enzyme activity may impair T-cell immunosuppressive function, fostering autoreactive responses and disrupting immune homeostasis, ultimately contributing to autoimmune disease pathogenesis. A growing body of studies has explored how metabolic processes regulate the function of diverse T-cell subsets in autoimmune diseases such as systemic lupus erythematosus (SLE), multiple sclerosis (MS), autoimmune hepatitis (AIH), inflammatory bowel disease (IBD), and psoriasis. This review describes the coordination of T-cell biology by mitochondrial metabolism, including the electron transport chain (ETC), oxidative phosphorylation, amino acid metabolism, fatty acid metabolism, and one­carbon metabolism. This study elucidated the intricate crosstalk between mitochondrial metabolic programs, signal transduction pathways, and transcription factors. This review summarizes potential therapeutic targets for T-cell mitochondrial metabolism and signaling in autoimmune diseases, providing insights for future studies.

Identifying MSMO1, ELOVL6, AACS, and CERS2 related to lipid metabolism as biomarkers of Parkinson's disease.

The mechanisms underlying lipid metabolic disorders in Parkinson's diseases (PD) remain unclear. Weighted Gene Co-Expression Network Analysis (WGCNA) was conducted to identify PD-related modular genes and differentially expressed genes (DEGs). Lipid metabolism-related genes (LMRGs) were extracted from Molecular Signatures Database. Candidate genes were assessed with overlapping modular genes, DEGs, and LMRGs for the purpose of building protein-protein interaction (PPI) networks. Then, biomarkers were generated by machine learning and Backpropagation Neural Network development according to candidate genes. Biomarker-based enrichment and network modulation analyses were executed to investigate related signaling pathways. Following dimensionality reduction clustering and annotation, scRNA-seq was submitted to cellular interactions and trajectory analysis to analyze regulatory mechanisms of critical cells. Finally, qRT-PCR was conducted to confirm the expression of biomarkers in PD patients. Four biomarkers (MSMO1, ELOVL6, AACS, and CERS2) were obtained and highly predictive after analysis mentioned above. Then, OPC, Oli, and Neu cells were the primary expression sites for biomarkers according to scRNA-seq studies. Finally, we confirmed mRNA of MSMO1, ELOVL6 and AACS were downregulated in PD patients comparing with control, while CERS2 was upregulated. In conclusion, MSMO1, ELOVL6, AACS, and CERS2 related to LMRGs could be new biomarkers for diagnosing and treating PD.

Explainable machine learning predicts survival of retroperitoneal liposarcoma: A study based on the SEER database and external validation in China.

OBJECTIVE: We have developed explainable machine learning models to predict the overall survival (OS) of retroperitoneal liposarcoma (RLPS) patients. This approach aims to enhance the explainability and transparency of our modeling results. METHODS: We collected clinicopathological information of RLPS patients from The Surveillance, Epidemiology, and End Results (SEER) database and allocated them into training and validation sets with a 7:3 ratio. Simultaneously, we obtained an external validation cohort from The First Affiliated Hospital of Naval Medical University (Shanghai, China). We performed LASSO regression and multivariate Cox proportional hazards analysis to identify relevant risk factors, which were then combined to develop six machine learning (ML) models: Cox proportional hazards model (Coxph), random survival forest (RSF), ranger, gradient boosting with component-wise linear models (GBM), decision trees, and boosting trees. The predictive performance of these ML models was evaluated using the concordance index (C-index), the integrated cumulative/dynamic area under the curve (AUC), and the integrated Brier score, as well as the Cox-Snell residual plot. We also used time-dependent variable importance, analysis of partial dependence survival plots, and the generation of aggregated survival SHapley Additive exPlanations (SurvSHAP) plots to provide a global explanation of the optimal model. Additionally, SurvSHAP (t) and survival local interpretable model-agnostic explanations (SurvLIME) plots were used to provide a local explanation of the optimal model. RESULTS: The final ML models are consisted of six factors: patient's age, gender, marital status, surgical history, as well as tumor's histopathological classification, histological grade, and SEER stage. Our prognostic model exhibits significant discriminative ability, particularly with the ranger model performing optimally. In the training set, validation set, and external validation set, the AUC for 1, 3, and 5 year OS are all above 0.83, and the integrated Brier scores are consistently below 0.15. The explainability analysis of the ranger model also indicates that histological grade, histopathological classification, and age are the most influential factors in predicting OS. CONCLUSIONS: The ranger ML prognostic model exhibits optimal performance and can be utilized to predict the OS of RLPS patients, offering valuable and crucial references for clinical physicians to make informed decisions in advance.

IL-9 plays a critical role in helminth-induced protection against COVID-19-related cytokine storms.

A recent study published in mBio by Cao et al. demonstrated that the helminth Trichinella sprialis (Ts) alleviates COVID-19-related cytokine storms in an IL-9-dependent way (Z. Cao, J. Wang, X. Liu, Y. Liu, et al., mBio 15:e00905-24, 2024, https://doi.org/10.1128/mbio.00905-24). A cytokine storm is a severe immune response characterized by the overproduction of proinflammatory cytokines, such as TNF-α and IFN-γ, leading to tissue damage and mortality in COVID-19 patients. This study indicated that IL-9 is crucial in protecting against cytokine storm syndromes associated with SARS-CoV-2 infection and proposed that anti-inflammatory molecules from Ts excretory/secretory (TsES) products could be a novel source for treating such illnesses.

NOX1 triggers ferroptosis and ferritinophagy, contributes to Parkinson's disease.

The progressive loss of dopaminergic neurons in the midbrain is the hallmark of Parkinson's disease (PD). A newly emerging form of lytic cell death, ferroptosis, has been implicated in PD. However, it remains unclear in terms of PD-associated ferroptosis underlying causative genes and effective therapeutic approaches. This research explored the underlying mechanism of ferroptosis-related genes in PD. Here, Firstly, we found NOX1 associated with ferroptosis differently in PD patients by bioinformatics analysis. In vitro and in vivo models of PD were constructed to explore the underlying mechanism. qPCR, Western blot analysis, immunohistochemistry, immunofluorescence, Ferro orange, and BODIPY C11 were utilized to analyze the levels of ferroptosis. Transcriptomics sequencing was to investigate the downstream pathway and the analysis of immunoprecipitation to validate the upstream factor. In conclusion, NOX1 upregulation and activation of ferroptosis-related neurodegeneration, therefore, might be useful as a clinical therapeutic agent.

CMR-net: A cross modality reconstruction network for multi-modality remote sensing classification.

In recent years, the classification and identification of surface materials on earth have emerged as fundamental yet challenging research topics in the fields of geoscience and remote sensing (RS). The classification of multi-modality RS data still poses certain challenges, despite the notable advancements achieved by deep learning technology in RS image classification. In this work, a deep learning architecture based on convolutional neural network (CNN) is proposed for the classification of multimodal RS image data. The network structure introduces a cross modality reconstruction (CMR) module in the multi-modality feature fusion stage, called CMR-Net. In other words, CMR-Net is based on CNN network structure. In the feature fusion stage, a plug-and-play module for cross-modal fusion reconstruction is designed to compactly integrate features extracted from multiple modalities of remote sensing data, enabling effective information exchange and feature integration. In addition, to validate the proposed scheme, extensive experiments were conducted on two multi-modality RS datasets, namely the Houston2013 dataset consisting of hyperspectral (HS) and light detection and ranging (LiDAR) data, as well as the Berlin dataset comprising HS and synthetic aperture radar (SAR) data. The results demonstrate the effectiveness and superiority of our proposed CMR-Net compared to several state-of-the-art methods for multi-modality RS data classification.

A LAMP-based hydrogen ion selective electrochemical sensor for highly sensitive detection of Mycoplasma pneumoniae.

A concise and rapid detection method for Mycoplasma pneumoniae is urgently required due to its severe impact on human health. To meet such a need, this study proposed and constructed an innovative point-of-care testing (POCT) platform that consists of a hydrogen ion-selective loop-mediated isothermal amplification (H+-LAMP) sensor and an electrochemical detection device. The H+-LAMP sensor successfully integrated the working and reference electrodes and converted the H+ generated during the LAMP process into an electrochemical signal. High sensitivity and stability for pathogen detection were also achieved by treating the working electrode with an electrodeposited polyaniline solid contact layer and by using an ion-selective membrane. As a result, the sensor shows a sensitivity of 68.26 mV per pH, a response time of less than 2 s, and a potential drift of less than 5 mV within one hour, which well meets the urgent need. The results also demonstrated that the detection limit for Mycoplasma pneumoniae was lowered to 1 copy per µL, the nucleic acid extraction and detection process could be completed in 30 minutes, and the impact of interfering ions on the sensor was negligible. Validation with 20 clinical samples yielded satisfactory results. More importantly, the storage lifespan of such an electrochemical sensor is over seven days, which is a great advantage for on-site pathogen detection. Therefore, the hydrogen ion-selective sensor constructed in this investigation is particularly suitable as a core component for instant pathogen detection platforms.

Comparative analysis of stripe rust resistance in seedling stage and Yr gene incidence in spring and winter wheat from Xinjiang, China.

Background: Stripe rust, caused by the fungus Puccinia striiformis f.sp. tritici (Pst), poses a significant threat to global wheat production. Objectives: This study aims to analyze the distribution of stripe rust resistance genes, characterize resistance phenotypes at the seedling stage of 137 spring and 149 winter wheat varieties in Xinjiang, China, and discern differences in resistance between spring and winter wheat varieties. Design: We used various Pst races (CYR23, CYR29, CYR31, CYR32, CYR33, CYR34) to characterize seedling resistance of spring and winter wheat varieties and to correlate resistance to the presence of wheat resistance genes (Yr5, Yr9, Yr10, Yr15, Yr17, Yr18, Yr26, Yr41, Yr80, Yr81) using molecular markers. Results: Among spring wheat varieties, 62, 60, 42, 26, 51, and 24 varieties exhibited resistance to CYR23, CYR29, CYR31, CYR32, CYR33, and CYR34, respectively, with four varieties resistant to all varieties. Among winter wheat varieties, 66, 32, 69, 26, 83, 40 varieties demonstrated resistance to CYR23, CYR29, CYR31, CYR32, CYR33, and CYR34, respectively, with four varieties resistant to all varieties. Molecular testing revealed that, in spring wheat, 2, 17, 21, 61, 10, 0, 10, 79, and 32 varieties carried Yr9, Yr10, Yr15, Yr17, Yr18, Yr26, Yr41, Yr80, and Yr81 genes, respectively. In winter wheat, 40, 20, 7, 143, 15, 1, 6, 38, and 54 varieties carried Yr9, Yr10, Yr15, Yr17, Yr18, Yr26, Yr41, Yr80, and Yr81 genes, respectively. Notably, winter wheat exhibited a significantly higher resistance frequency than spring wheat, particularly in the incidence of Yr9, Yr10, Yr17, Yr18, and multi-gene combinations. Conclusion: In summary, this study provides information on seedling stage resistance to stripe rust 286 Xinjiang wheat varieties, elucidates the distribution of resistance genes in this population, and offers a mechanistic basis for breeding durable resistance in wheat. varieties from Xinjiang.

Survival nomogram and risk classification system for patients with adrenocortical carcinoma: a study based on the SEER database and external validation in China.

Background: Adrenocortical carcinoma (ACC) is an extremely rare and highly invasive malignant tumor. However, there is currently no reliable method to predict the prognosis of ACC. Our objective is to construct a nomogram and a risk classification system to predict the 1-year, 3-year, and 5-year overall survival (OS) of ACC. Methods: We retrieved clinicopathological data of patients diagnosed with ACC in The Surveillance, Epidemiology, and End Results (SEER) database and divided them into training and validation cohorts with a 7:3 ratio. Simultaneously, we collected an external validation cohort from The First Affiliated Hospital of Naval Medical University (Shanghai, China). Univariate and multivariate Cox analyses were performed to identify relevant risk factors, which were then combined to develop a correlation nomogram. The predictive performance of the nomogram was evaluated using the concordance index (C-index), receiver-operating characteristic curve (ROC), and calibration curves. Decision curve analysis (DCA) was applied to assess the clinical utility of the nomogram. In addition, Kaplan-Meier survival curves were generated to demonstrate the variation in OS between groups. Results: The final nomogram consisted of five factors: age, T, N, M, and history of chemotherapy. Our prognostic model demonstrated significant discriminative ability, with C-index and the area under the receiver operating characteristic (AUC) values exceeding 0.70. Additionally, DCA validated the clinical utility of the nomogram. In the entire cohort, the median OS for patients in the low- and high-risk groups was 70 and 10 months, respectively. Conclusions: A nomogram and a corresponding risk classification system were developed in order to predict the OS of patients diagnosed with ACC. These tools have the potential to provide valuable support for patient counseling and assist in the decision-making process related to treatment options.

Drug Analysis of Voriconazole Combined with Granulocyte Colony Stimulating Factor.

BACKGROUND: The aim of the study was to improve the clinical cognition of leukemia-like reaction caused by voriconazole and granulocyte colony-stimulating factor and to avoid misdiagnosis or delayed diagnosis. METHODS: A case of drug analysis of Voriconazole combined with granulocyte colony stimulating factor was retrospectively analyzed and related literature was reviewed. RESULTS: Blood routine of the patient on July 29: WBC 13.48 x 109/L, neutrophil 85.3%, lymphocyte 13.4%, hemoglobin 111 g/L, platelet 285 x 109/L. Vancomycin was given to prevent intracranial infection. Lumbar puncture was performed on July 30, cerebrospinal fluid was sent for routine and biochemical examination, leukocytes were 0.15 x 109/L, monocytes 45%, polynuclear cells 55%, protein 1.172 g/L, Acinetobacter baumannii and Candida clorbicus were detected in sputum culture, vancomycin and meropenem static sites were given to prevent intracranial secondary infection. Fungi were detected in urine culture, and voriconazole was given to prevent fungal infection. Blood routine: White blood cell 0.61 x 109/L, neutrophil 23%, lymphocyte 73.8%, red blood cell 2.65 x 1012/L, hemoglobin 77 g/L, platelet 17 x 109/L, bone marrow was extracted after medication. Bone marrow images show poor myelodysplasia, with granulocytes dominated by protoearly cells. Subsequent flow cytometry, chromosomal karyotype, and fusion gene analysis were performed to exclude the possibility of leukemia. Flow cytometry showed that the proportion of myeloid primordial cells was not high, the granulocytes were mainly at the early and young stage, no abnormal phenotype was observed in erythrocytes, monocytes and NK cells, no obvious mature B lymphocytes were observed, and the ratio of CD4+/CD8+ was decreased. Karyotype results showed that there was no mitotic phase. The results of fusion gene analysis showed that the fusion gene was negative or lower than the detection sensitivity. Voliconazole was stopped first, and granulocyte colony stimulating factor was stopped 3 days later. Two weeks later, blood and bone marrow images basically recovered, white blood cell 7.88 x 109/L, neutrophil 46.3%, lymphocyte 48.2%, hemoglobin 126 g/L, platelet 142 x 109/L, bone marrow hyperplasia active. The proportion of three series is roughly normal. CONCLUSIONS: The reason for the occurrence of leukemia-like reaction in this patient was considered to be related to voriconazole and granulocyte colony stimulating factor, cessation of voriconazole and granulocyte colony stimulating factor, and recovery of blood and bone marrow images. In the clinical use of voriconazole and granulocyte colony stimulating factor, close attention should be paid to the drug interaction and individualized medication should be carried out to ensure the safety of medication.

When DNA-damage responses meet innate and adaptive immunity.

When cells proliferate, stress on DNA replication or exposure to endogenous or external insults frequently results in DNA damage. DNA-Damage Response (DDR) networks are complex signaling pathways used by multicellular organisms to prevent DNA damage. Depending on the type of broken DNA, the various pathways, Base-Excision Repair (BER), Nucleotide Excision Repair (NER), Mismatch Repair (MMR), Homologous Recombination (HR), Non-Homologous End-Joining (NHEJ), Interstrand Crosslink (ICL) repair, and other direct repair pathways, can be activated separately or in combination to repair DNA damage. To preserve homeostasis, innate and adaptive immune responses are effective defenses against endogenous mutation or invasion by external pathogens. It is interesting to note that new research keeps showing how closely DDR components and the immune system are related. DDR and immunological response are linked by immune effectors such as the cyclic GMP-AMP synthase (cGAS)-Stimulator of Interferon Genes (STING) pathway. These effectors act as sensors of DNA damage-caused immune response. Furthermore, DDR components themselves function in immune responses to trigger the generation of inflammatory cytokines in a cascade or even trigger programmed cell death. Defective DDR components are known to disrupt genomic stability and compromise immunological responses, aggravating immune imbalance and leading to serious diseases such as cancer and autoimmune disorders. This study examines the most recent developments in the interaction between DDR elements and immunological responses. The DDR network's immune modulators' dual roles may offer new perspectives on treating infectious disorders linked to DNA damage, including cancer, and on the development of target immunotherapy.

Adverse drug reaction signal detection methods in spontaneous reporting system: A systematic review.

BACKGROUND: A series of signal detection methods have been developed to detect adverse drug reaction (ADR) signals in spontaneous reporting system. However, different signal detection methods yield quite different signal detection results, and we do not know which method has the best detection performance. How to choose the most suitable signal detection method is an urgent problem to be solved. In this study, we systematically reviewed the characteristics and application scopes of current signal detection methods, with the goal of providing references for the optimization selection of signal detection methods in spontaneous reporting system. METHODS: We searched six databases from inception to January 2023. The search strategy targeted literatures regarding signal detection methods in spontaneous reporting system. We used thematic analysis approach to summarize the advantages, disadvantages, and application scope of each signal detection method. RESULTS: A total of 93 literatures were included, including 27 reviews and 66 methodological studies. Moreover, 31 signal detection methods were identified in these literatures. Each signal detection method has its inherent advantages and disadvantages, resulting in different application scopes of these methods. CONCLUSION: Our systematic review finds that there are variabilities in the advantages, disadvantages, and application scopes of different signal detection methods. This finding indicates that the most suitable signal detection method varies across different drug safety scenarios. Moreover, when selecting signal detection method in a particular drug safety scenario, the following factors need to be considered: purpose of research, database size, drug characteristics, adverse event characteristics, and characteristics of the relations between drugs and adverse events.

Gut microbiota-dependent phenylacetylglutamine in cardiovascular disease: current knowledge and new insights.

Phenylacetylglutamine (PAGln) is an amino acid derivate that comes from the amino acid phenylalanine. There are increasing studies showing that the level of PAGln is associated with the risk of different cardiovascular diseases. In this review, we discussed the metabolic pathway of PAGln production and the quantitative measurement methods of PAGln. We summarized the epidemiological evidence to show the role of PAGln in diagnostic and prognostic value in several cardiovascular diseases, such as heart failure, coronary heart disease/atherosclerosis, and cardiac arrhythmia. The underlying mechanism of PAGln is now considered to be related to the thrombotic potential of platelets via adrenergic receptors. Besides, other possible mechanisms such as inflammatory response and oxidative stress could also be induced by PAGln. Moreover, since PAGln is produced across different organs including the intestine, liver, and kidney, the cross-talk among multiple organs focused on the function of this uremic toxic metabolite. Finally, the prognostic value of PAGln compared to the classical biomarker was discussed and we also highlighted important gaps in knowledge and areas requiring future investigation of PAGln in cardiovascular diseases.

Oxygen Vacancies-Induced Antifouling Photoelectrochemical Aptasensor for Highly Sensitive and Selective Determination of α-Fetoprotein.

Accurate measurement of cancer markers in urine is a convenient method for tumor monitoring. However, the concentration of cancer markers in urine is so low that it is difficult to achieve their measurement. Photoelectrochemical (PEC) sensors are a promising technology to realize the detection of trace cancer markers due to their high sensitivity. Currently, the interference of nonspecific biomolecules in urine is the main reason affecting the high sensitivity and selectivity of PEC sensors in detecting cancer markers. In this work, a strategy of oxygen vacancy (OV) modulation is proposed to construct a fouling-resistant PEC aptamer sensing platform for the detection of α-fetoprotein (AFP), a liver cancer marker. The introduction of OVs induces the formation of intermediate localized states in the photoelectric material, which not only facilitates the separation of photogenerated carriers but also leads to the redshift of the light absorption edge. More importantly, OVs with positive electrical properties can be employed to modify the antifouling layer (C-PEG) with negatively charged groups through an electrostatic interaction. The synergistic effect of OVs, antifouling layer, and aptamer resulted in a TiO2/OVs/C-PEG-based PEC sensor achieves a wide linear range from 1 pg/mL to 100 ng/mL and a low detection limit of 0.3 pg/mL for AFP. In addition, the sensor successfully realized the determination of AFP in urine samples and accurately differentiated between normal people and liver cancer patients in the early and advanced stages. This project is of great significance in advancing the application of photoelectrochemical bioanalytical technology to achieve the detection of cancer markers in urine by investigating the construction of an OVs-regulated fouling-resistant sensing interface.

Multi-Omics Analysis Reveals the Role of Changes in Platelet Activation Pathways in the Survival Outcome of Patients with Esophageal Squamous Cell Carcinoma.

Objective: The objective of this study was to integrate metabolomics and transcriptomics data to identify key diagnostic and prognostic markers for esophageal squamous cell carcinoma (ESCC). Plasma samples were collected from 85 ESCC patients at different stages and 50 healthy volunteers for non-targeted metabolomic analysis. Methods: Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was employed for non-targeted metabolomic analysis. Subsequently, we integrated the metabolomic data with transcriptomic data from the Gene Expression Omnibus (GEO) and prognosis data from The Cancer Genome Atlas Program (TCGA) to perform pathway analysis. Our focus was on pathways that involve both metabolites and upstream genes, as they often exhibit higher accuracy. Results: Through the integration of metabolomics and transcriptomics, we identified significant alterations in the platelet activation pathway in ESCC. This pathway involves the participation of both metabolites and genes, making it a more accurate reflection of pathological changes associated with the disease. Notably, metabolite arachidonic acid (AA) and chemokine receptor type 2(CXCR2) were significantly downregulated in ESCC, while genes collagen type I alpha 1(COL1A1), collagen type I alpha 2(COL1A2), collagen type III alpha 1(COL3A1), type 3 inositol 1,4,5-trisphosphate receptor (ITPR3), and insulin-like growth factor II mRNA binding protein 3(IGF2BP3) were significantly upregulated, indicating the presence of tumor-induced platelet activation in ESCC. Further analysis of prognosis data revealed that high expression of COL1A1, IGF2BP3, and ITPR3 was associated with a favorable prognosis for ESCC, while high CXCR2 expression was linked to an adverse prognosis. In addition, we combined COL1A1, ITPR3, IGF2BP3, CXCR2, and AA to form a diagnostic biomarker panel. The receiver operating characteristic curve (ROC) demonstrated excellent diagnostic capability (AUC=0.987). Conclusion: Our study underscores the significant role of platelet activation pathways and related genes in the diagnosis and prognosis of ESCC patients. These findings offer promising insights for improving the clinical management of ESCC.

A prognostic prediction model for ovarian cancer using a cross-modal view correlation discovery network.

Ovarian cancer is a tumor with different clinicopathological and molecular features, and the vast majority of patients have local or extensive spread at the time of diagnosis. Early diagnosis and prognostic prediction of patients can contribute to the understanding of the underlying pathogenesis of ovarian cancer and the improvement of therapeutic outcomes. The occurrence of ovarian cancer is influenced by multiple complex mechanisms, including the genome, transcriptome and proteome. Different types of omics analysis help predict the survival rate of ovarian cancer patients. Multi-omics data of ovarian cancer exhibit high-dimensional heterogeneity, and existing methods for integrating multi-omics data have not taken into account the variability and inter-correlation between different omics data. In this paper, we propose a deep learning model, MDCADON, which utilizes multi-omics data and cross-modal view correlation discovery network. We introduce random forest into LASSO regression for feature selection on mRNA expression, DNA methylation, miRNA expression and copy number variation (CNV), aiming to select important features highly correlated with ovarian cancer prognosis. A multi-modal deep neural network is used to comprehensively learn feature representations of each omics data and clinical data, and cross-modal view correlation discovery network is employed to construct the multi-omics discovery tensor, exploring the inter-relationships between different omics data. The experimental results demonstrate that MDCADON is superior to the existing methods in predicting ovarian cancer prognosis, which enables survival analysis for patients and facilitates the determination of follow-up treatment plans. Finally, we perform Gene Ontology (GO) term analysis and biological pathway analysis on the genes identified by MDCADON, revealing the underlying mechanisms of ovarian cancer and providing certain support for guiding ovarian cancer treatments.