Spectroscopic evidence of spin-state excitation in d-electron correlated semiconductor FeSb2.

Iron antimonide (FeSb2) has been investigated for decades due to its puzzling electronic properties. It undergoes the temperature-controlled transition from an insulator to an ill-defined metal, with a cross-over from diamagnetism to paramagnetism. Extensive efforts have been made to uncover the underlying mechanism, but a consensus has yet to be reached. While macroscopic transport and magnetic measurements can be explained by different theoretical proposals, the essential spectroscopic evidence required to distinguish the physical origin is missing. In this paper, through the use of X-ray absorption spectroscopy and atomic multiplet simulations, we have observed the mixed spin states of 3d 6 configuration in FeSb2. Furthermore, we reveal that the enhancement of the conductivity, whether induced by temperature or doping, is characterized by populating the high-spin state from the low-spin state. Our work constitutes vital spectroscopic evidence that the electrical/magnetical transition in FeSb2 is directly associated with the spin-state excitation.

Plant-derived nanoparticles and plant virus nanoparticles: Bioactivity, health management, and delivery potential.

Natural plants have acquired an increasing attention in biomedical research. Recent studies have revealed that plant-derived nanoparticles (PDNPs), which are nano-sized membrane vesicles released by plants, are one of the important material bases for the health promotion of natural plants. A great deal of research in this field has focused on nanoparticles derived from fresh vegetables and fruits. Generally, PDNPs contain lipids, proteins, nucleic acids, and other active small molecules and exhibit unique biological regulatory activity and editability. Specifically, they have emerged as important mediators of intercellular communication, and thus, are potentially suitable for therapeutic purposes. In this review, PDNPs were extensively explored; by evaluating them systematically starting from the origin and isolation, toward their characteristics, including morphological compositions, biological functions, and delivery potentials, as well as distinguishing them from plant-derived exosomes and highlighting the limitations of the current research. Meanwhile, we elucidated the variations in PDNPs infected by pathogenic microorganisms and emphasized on the biological functions and characteristics of plant virus nanoparticles. After clarifying these problems, it is beneficial to further research on PDNPs in the future and develop their clinical application value.

Application value of triglyceride-glucose index and triglyceride-glucose body mass index in evaluating the degree of hepatic steatosis in non-alcoholic fatty liver disease.

BACKGROUND: The elevation of TyG is considered an important factor in promoting the progression of non-alcoholic fatty liver disease (NAFLD), but its impact on the degree of liver steatosis remains unclear. This study aims to explore the relationship between TyG and TyG-related indices, such as triglyceride glucose-body mass index (TyG-BMI), with the degree of liver fat accumulation. METHODS: From January 2021 to March 2022, 1171 participants underwent health check-ups, and all underwent FibroScan transient elastography. The analysis focused on identifying the factors that contribute to the onset of NAFLD and the degree of hepatic steatosis. RESULTS: The predictive value of TyG-BMI (OR = 1.039, 95% CI 1.031-1.046) in triggering NAFLD development was greater than that of TyG alone. The areas under the curve for TyG-BMI and TyG were calculated at 0.808 and 0.720, respectively. TyG-BMI (OR = 1.034, P < 0.001) was identified as a main independent factor affecting hepatic steatosis severity. With each incremental increase in TyG-BMI, the likelihood of experiencing an increase in the extent of hepatic steatosis was 1.034 times higher than that of the preceding unit. CONCLUSIONS: The TyG-BMI showed higher accuracy in predicting NAFLD than did the TyG, and was more closely linked to the severity of hepatic steatosis. Therefore, it can be included as a parameter in health management centers and should be widely used to screen and evaluate patients with NAFLD.

Design and Construction of Enzyme-Based Electrochemical Gas Sensors.

The demand for the ubiquitous detection of gases in complex environments is driving the design of highly specific gas sensors for the development of the Internet of Things, such as indoor air quality testing, human exhaled disease detection, monitoring gas emissions, etc. The interaction between analytes and bioreceptors can described as a "lock-and-key", in which the specific catalysis between enzymes and gas molecules provides a new paradigm for the construction of high-sensitivity and -specificity gas sensors. The electrochemical method has been widely used in gas detection and in the design and construction of enzyme-based electrochemical gas sensors, in which the specificity of an enzyme to a substrate is determined by a specific functional domain or recognition interface, which is the active site of the enzyme that can specifically catalyze the gas reaction, and the electrode-solution interface, where the chemical reaction occurs, respectively. As a result, the engineering design of the enzyme electrode interface is crucial in the process of designing and constructing enzyme-based electrochemical gas sensors. In this review, we summarize the design of enzyme-based electrochemical gas sensors. We particularly focus on the main concepts of enzyme electrodes and the selection and design of materials, as well as the immobilization of enzymes and construction methods. Furthermore, we discuss the fundamental factors that affect electron transfer at the enzyme electrode interface for electrochemical gas sensors and the challenges and opportunities related to the design and construction of these sensors.

Flavonoids regulate tumor-associated macrophages - From structure-activity relationship to clinical potential (Review).

In recent years, the strategy for tumor therapy has changed from focusing on the direct killing effect of different types of therapeutic agents on cancer cells to the new mainstream of multi-mode and -pathway combined interventions in the microenvironment of the developing tumor. Flavonoids, with unique tricyclic structures, have diverse and extensive immunomodulatory and anti-cancer activities in the tumor microenvironment (TME). Tumor-associated macrophages (TAMs) are the most abundant immunosuppressive cells in the TME. The regulation of macrophages to fight cancer is a promising immunotherapeutic strategy. This study covers the most comprehensive cognition of flavonoids in regulating TAMs so far. Far more than a simple list of studies, we try to dig out evidence of crosstalk at the molecular level between flavonoids and TAMs from literature, in order to discuss the most relevant chemical structure and its possible relationship with the multimodal pharmacological activity, as well as systematically build a structure-activity relationship between flavonoids and TAMs. Additionally, we point out the advantages of the macro-control of flavonoids in the TME and discuss the potential clinical implications as well as areas for future research of flavonoids in regulating TAMs. These results will provide hopeful directions for the research of antitumor drugs, while providing new ideas for the pharmaceutical industry to develop more effective forms of flavonoids.

Analysis of the expression patterns and clinical relevance of m6A regulators in 33 cancer types.

Background: The role of N6-methyladenine (m6A) RNA methylation in a variety of biological processes is gradually being revealed. Methods: Here, we systematically describe the correlation between the expression pattern of m6A RNA methylation regulatory factors and clinical phenotype, immunity, drug sensitivity, stem cells and prognosis in more than 10,000 samples of 33 types of cancer. Results: The results show that there are significant differences in the expression of 20 m6A RNA methylation regulatory factors in different cancers, and there was a significant correlation with the analysis indicators. Conclusion: In this study, the m6A RNA methylation regulatory factor was found not only to potentially assist in stratifying the prognosis but also to predict or improve the sensitivity of clinical drug therapy.

Biomarker expression analysis in different age groups revealed age was a risk factor for breast cancer.

The relationship between age and breast cancer is ambiguous. Here, we analyzed the differential expression pattern of long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) in different age groups to provide an effective association between age and breast cancer risk at the molecular level. We integrated the microarray information from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data sets. The patients were divided into young ( < 50 years) and old ( ≥ 50 years) age groups and evaluated by differential gene expression, weighted gene correlation network analysis (WGCNA), functional enrichment analyses, and coexpression analysis. To determine their potential clinical significance, univariate Cox regression analysis and survival assessment were conducted. We identified two lncRNAs (AL139280.1 and AP000851.1) and three mRNAs (MT1M, HBB, and TFPI2) as the risk markers, and Gene set enrichment analysis (GSEA) focusing on a single gene revealed that "pyrimidine metabolism," "cell cycle," and "P53 signaling pathway" were coenriched. These data demonstrated that age may be a risk factor for breast carcinogenesis and prognosis and provide an in-depth molecular characterization based on the expression patterns of lncRNAs and mRNAs.

circRNA-associated ceRNA network construction reveals the circRNAs involved in the progression and prognosis of breast cancer.

Recently, increasing evidences show that circular RNAs (circRNAs) are important regulators of various diseases, especially cancer. However, the regulatory role and the potential mechanism of action of circRNAs in breast cancer remain largely unknown. In this study, weighted gene co-expression network analysis was conducted with the differentially expressed miRNAs and mRNAs in breast cancer from The Cancer Genome Atlas database to identify the key modules associated with the carcinogenesis of breast cancer. In the significant turquoise and brown modules, 22 miRNAs and 1877 mRNAs were identified, respectively. Then, We compared and predicted the target genes and performed survival analysis to identify the miRNAs and mRNAs related to the prognosis of breast cancer. A circRNA-related competitive endogenous RNA network was identified by database co-screening, and deleted in liver cancer 1 (DLC1) was identified as a key gene. Finally, to assess how genes in key modules and key genes contribute to the development of breast cancer, relevant pathway information was obtained through DAVID and Gene Set Enrichment Analysis. These data demonstrated that three circRNAs (hsa-circ-0083373, hsa-circ-0083374, and hsa-circ-0083375) that regulate DLC1 expression via hsa-mir-511 and are involved in the pathogenesis and development of breast cancer.

Development of a risk scoring system for evaluating the prognosis of patients with Her2-positive breast cancer.

BACKGROUND: As one of the many breast cancer subtypes, human epidermal growth factor receptor 2 (Her2)-positive breast cancer has higher invasiveness and poor prognosis, although the advent of anti-Her2 drugs has brought good news to patients. However, the emergence of drug resistance still limits its clinical efficacy, so there is an urgent need to explore new targets and develop a risk scoring system to improve treatments and evaluate patient prognosis. METHODS: Differentially expressed mRNAs associated with Her2-positive breast cancer were screened from a TCGA cohort. The prognostic risk scoring system was constructed according to univariate and Lasso Cox regression model analyses and combined with clinical factors (such as age and TNM) for univariate and multivariate analyses to verify the specificity and sensitivity of the risk scoring system. Finally, based on correlation and CNV mutation analyses, we explored the research value of the mRNAs involved in the system as key genes of the model. RESULTS: In this study, six mRNAs were screened and identified to construct a prognostic risk scoring system, including four up-regulated mRNA (RDH16, SPC25, SPC24, and SCUBE3) and two down-regulated mRNA (DGAT2 and CCDC69). The risk scoring system can divide Her2-positive breast cancer samples into high-risk and low-risk groups to evaluate patient prognosis. In addition, whether through the time-dependent receiver operating characteristics curve or compared with clinical factors, the risk scoring system showed high predictive sensitivity and specificity. Moreover, some CNV mutations in mRNA increase patient risk by influencing expression levels. CONCLUSION: The risk scoring system constructed in this study is helpful to improve the screening of high-risk patients with Her2-positive breast cancer and is beneficial for implementing early diagnosis and personalized treatment. It is suggested that these mRNAs may play an important role in the progression of Her2-positive breast cancer.

Prognostic value of aberrantly expressed methylation gene profiles in lung squamous cell carcinoma: A study based on The Cancer Genome Atlas.

Currently, research on genome-scale epigenetic modifications for studying the pathogenesis of lung cancer is lacking. Aberrant DNA methylation, as the most common and important modification in epigenetics, is an important means of regulating genomic function and can be used as a biomarker for the diagnosis and prognosis of lung squamous cell carcinoma (LUSC). In this paper, methylation information and gene expression data from patients with LUSC were extracted from the TCGA database. Univariate and multivariate COX analyses were used to screen abnormally methylated genes related to the prognosis of LUSC. The relationship between key DNA methylation sites and the transcriptional expression of LUSC-related genes was explored. A prognostic risk model constructed by four abnormally methylated genes (VAX1, CH25H, AdCyAP1, and Irx1) was used to predict the prognosis of LUSC patients. Also, the methylation levels of the key gene IRX1 are significantly correlated with the prognosis and correlated with the methylation of the site cg09232937 and cg10530883. This study is based on high-throughput data mining and provides an effective bioinformatics basis for further understanding the pathogenesis and prognosis of LUSC, which has important theoretical significance for follow-up studies on LUSC.

Four lncRNAs associated with breast cancer prognosis identified by coexpression network analysis.

Previous studies on long noncoding RNA (lncRNA) have made breakthroughs in the treatment of several tumors, and these findings have brought attention to the lncRNA signature of breast cancer. Increased understanding of genomic architecture and achievement of innovative therapeutic strategies has prompted creation of a novel oncological model for the treatment of solid cancers. In this study, we systematically analyzed the transcriptome of breast cancer tissues to gain more in-depth knowledge of tumor biology. Gene coexpression relationships were studied in 206 samples from The Cancer Genome Atlas database, and nine coexpression modules were identified. After screening and analysis, we identified four important prognosis-related lncRNAs (HOTAIR, SNHG16, HCP5, and TINCR), and constructed a prognostic model, one (HCP5) of which has not previously been identified in the context of breast cancer. Importantly, an understanding of prognosis facilitates precise disease risk assessment and advances the selection of strategies for risk-adaptive management. These findings broaden the landscape of carcinogenic lncRNAs in breast cancer, providing insights into the biological significance and clinical application of lncRNAs in breast cancer.

Deciphering the mechanism of Indirubin and its derivatives in the inhibition of Imatinib resistance using a "drug target prediction-gene microarray analysis-protein network construction" strategy.

BACKGROUND: The introduction of imatinib revolutionized the treatment of chronic myeloid leukaemia (CML), substantially extending patient survival. However, imatinib resistance is currently a clinical problem for CML. It is very importantto find a strategy to inhibit imatinib resistance. METHODS: (1) We Identified indirubin and its derivatives and predicted its putative targets; (2) We downloaded data of the gene chip GSE2810 from the Gene Expression Omnibus (GEO) database and performed GEO2R analysis to obtain differentially expressed genes (DEGs); and (3) we constructed a P-P network of putative targets and DEGs to explore the mechanisms of action and to verify the results of molecular docking. RESULT: We Identified a total of 42 small-molecule compounds, of which 15 affected 11 putative targets, indicating the potential to inhibit imatinib resistance; the results of molecular docking verified these results. Six biomarkers of imatinib resistance were characterised by analysing DEGs. CONCLUSION: The 15 small molecule compounds inhibited imatinib resistance through the cytokine-cytokine receptor signalling pathway, the JAK-stat pathway, and the NF-KB signalling pathway. Indirubin and its derivatives may be new drugsthat can combat imatinib resistance.

MicroRNA expression in cervical cancer: Novel diagnostic and prognostic biomarkers.

Growing evidence has shown that a large number of miRNAs are abnormally expressed in cervical cancer (CC) tissues and play irreplaceable roles in tumorigenesis, progression, and metastasis. This study aimed to identify new biomarkers and pivotal genes associated with CC prognosis through comprehensive bioinformatics analysis. At first, the data of gene expression microarray (GSE30656) was downloaded from GEO database and differential miRNAs were obtained. Additionally, 4 miRNAs associated with the survival time of patients with CC were screened through TCGA differential data analysis, Kaplan-Meier, and Landmark analysis. Among them, the low expression of miR-188 and high expression of miR-223 correlated with the short survival of CC patients, while the down-regulation of miR-99a and miR-125b was closely related to the 5-year survival rate of patients. Then, based on the correspondence between the differentially expressed genes (DEGs) in CC from the TCGA data and the 4 miRNAs target genes, 58 target genes were screened to perform the analysis of function enrichment and the visualization of protein-protein interaction (PPI) networks. The seven pivotal genes of the PPI network as the target genes of four miRNAs related to prognosis, they were directly or indirectly involved in the development of CC. In this study, based on high-throughput data mining, differentially expressed miRNAs and related target genes were analyzed to provide an effective bioinformatics basis for further understanding of the pathogenesis and prognosis of CC. And the results may be a promising biomarker for the early screening of high-risk populations and early diagnosis of cervical cancer.

Developing DNA methylation-based prognostic biomarkers of acute myeloid leukemia.

Acute myeloid leukemia (AML) is a heterogeneous clonal neoplasm characterized by complex genomic alterations. The incidence of AML increases with age, and most cases experience serious illness and poor prognosis. To explore the relationship between abnormal DNA methylation and the occurrence and development of AML based on the Gene Expression Database (GEO), this study extracted data related to methylation in AML and identified a methylated CpG site that was significantly different in terms of expression and distribution between the primary cells of AML patients, and hematopoietic stem/progenitor cells from normal bone marrow. To further investigate the differences caused by the dysfunction of methylation sites, bioinformatics analysis was used to screen methylation-related biomarkers, and the potential prognostic genes were selected by univariate and multivariate Cox proportional hazards regressions. Finally, five independent prognostic indicators were identified. In addition, these results provide new insight into the molecular mechanisms of methylation.

Exploration of methylation-driven genes for monitoring and prognosis of patients with lung adenocarcinoma.

BACKGROUND: As one of the most common malignant tumors in humans, lung cancer has experienced a gradual increase in morbidity and mortality. This study examined prognosis-related methylation-driven genes specific to lung adenocarcinoma (LUAD) to provide a basis for prognosis prediction and personalized targeted therapy for LUAD patients. METHODS: The methylation and survival time data from LUAD patients in the TCGA database were downloaded. The MethylMix algorithm was used to identify the differential methylation status of LUAD and adjacent tissues based on the ß-mixture model to obtain disease-related methylation-driven genes. A COX regression model was then used to screen for LUAD prognosis-related methylation-driven genes, and a linear risk model based on five methylation-driven gene expression profiles was constructed. A methylation and gene expression combined survival analysis was performed to further explore the prognostic value of 5 genes independently. RESULTS: There were 118 differentially expressed methylation-driven genes in the LUAD tissues and adjacent tissues. Five of the genes, CCDC181, PLAU, S1PR1, ELF3, and KLHDC9, were used to construct a prognostic risk model. Overall, the survival time was significantly lower in the high-risk group compared with that in the low-risk group (P < 0.05). In addition, the methylation and gene expression combined survival analysis found that the combined expression levels of the genes CCDC181, PLAU, and S1PR1 as well as KLHDC9 alone can be used as independent prognostic markers or drug targets. CONCLUSION: Our findings provide an important bioinformatic basis and relevant theoretical basis for guiding subsequent LUAD early diagnosis and prognosis assessments.

Deciphering Key Pharmacological Pathways of Qingdai Acting on Chronic Myeloid Leukemia Using a Network Pharmacology-Based Strategy.

Qingdai, a traditional Chinese medicine (TCM) used for the treatment of chronic myeloid leukemia (CML) with good efficacy, has been used in China for decades. However, due to the complexity of traditional Chinese medicinal compounds, the pharmacological mechanism of Qingdai needs further research. In this study, we investigated the pharmacological mechanisms of Qingdai in the treatment of CML using network pharmacology approaches. First, components in Qingdai that were selected by pharmacokinetic profiles and biological activity predicted putative targets based on a combination of 2D and 3D similarity measures with known ligands. Then, an interaction network of Qingdai putative targets and known therapeutic targets for the treatment of chronic myeloid leukemia was constructed. By calculating the 4 topological features (degree, betweenness, closeness, and coreness) of each node in the network, we identified the candidate Qingdai targets according to their network topological importance. The composite compounds of Qingdai and the corresponding candidate major targets were further validated by a molecular docking simulation. Seven components in Qingdai were selected and 32 candidate Qingdai targets were identified; these were more frequently involved in cytokine-cytokine receptor interaction, cell cycle, p53 signaling pathway, MAPK signaling pathway, and immune system-related pathways, which all play important roles in the progression of CML. Finally, the molecular docking simulation showed that 23 pairs of chemical components and candidate Qingdai targets had effective binding. This network-based pharmacology study suggests that Qingdai acts through the regulation of candidate targets to interfere with CML and thus regulates the occurrence and development of CML.

A Systems Biology-Based Approach to Uncovering Molecular Mechanisms Underlying Effects of Traditional Chinese Medicine Qingdai in Chronic Myelogenous Leukemia, Involving Integration of Network Pharmacology and Molecular Docking Technology.

BACKGROUND The method of multiple targets overall control is increasingly used to predict the main active ingredient and potential target group of Chinese traditional medicines and to determine the mechanisms involved in their curative effects. Qingdai is the main traditional Chinese medicine used in the treatment of chronic myelogenous leukemia (CML), but the complex active ingredients and antitumor targets in treatment of CML have not been clearly defined in previous studies. MATERIAL AND METHODS We constructed a protein-protein interaction network diagram of CML with 638 nodes (proteins) and 1830 edges, based on the biological function of chronic myelocytic leukemia by use of Cytoscape, and we determined 19 key gene nodes in the CML molecule by network topological properties analysis in a data bank. Then, we used the Surflex-dock plugin in SYBYL7.3 docking and acquired the protein crystal structures of key genes involved in CML from the chemical composition of the traditional Chinese medicine Qingdai with key proteins in CML networks. RESULTS According to the score and the spatial structure, the pharmacodynamically active ingredients of Qingdai are Isdirubin, Isoindigo, N-phenyl-2-naphthylamine, and Isatin, among which Isdirubin is the most important. We further screened the most effective activity key protein structures of CML to find the best pharmacodynamically active ingredients of Qingdai, according to the binding interactions of the inhibitors at the catalytic site performed in best docking combinations. CONCLUSIONS The results suggest that Isdirubin plays a role in resistance to CML by altering the expressions of PIK3CA, MYC, JAK2, and TP53 target proteins. Network pharmacology and molecular docking technology can be used to search for possible reactive molecules in traditional chinese medicines (TCM) and to elucidate their molecular mechanisms.

Polysaccharides regulate Th1/Th2 balance: A new strategy for tumor immunotherapy.

T helper (Th) cells have received extensive attention owing to their indispensable roles in anti-tumor immune responses. Th1 and Th2 cells are two key subsets of Th cells that exist in relative equilibrium through the secretion of cytokines that suppress their respective immune response. When the type of cytokine in the tumor microenvironment is altered, this equilibrium may be disrupted, leading to a shift from Th1 to Th2 immune response. Th1/Th2 imbalance is one of the decisive factors in the development of malignant tumors. Therefore, focusing on the balance of Th1/Th2 anti-tumor immune responses may enable future breakthroughs in cancer immunotherapy. Polysaccharides can regulate the imbalance between Th1 and Th2 cells and their characteristic cytokine profiles, thereby improving the tumor immune microenvironment. To our knowledge, this study is the most comprehensive assessment of the regulation of the tumor Th1/Th2 balance by polysaccharides. Herein, we systematically summarized the intrinsic molecular mechanisms of polysaccharides in the regulation of Th1 and Th2 cells to provide a new perspective and potential target drugs for improved anti-tumor immunity and delayed tumor progression.

Construction of semiconductor nanocomposites for room-temperature gas sensors.

Gas sensors are essential for ensuring public safety and improving quality of life. Room-temperature gas sensors are notable for their potential economic benefits and low energy consumption, and their expected integration with wearable electronics, making them a focal point of contemporary research. Advances in nanomaterials and low-dimensional semiconductors have significantly contributed to the enhancement of room-temperature gas sensors. These advancements have focused on improving sensitivity, selectivity, and response/recovery times, with nanocomposites offering distinct advantages. The discussion here focuses on the use of semiconductor nanocomposites for gas sensing at room temperature, and provides a review of the latest synthesis techniques for these materials. This involves the precise adjustment of chemical compositions, microstructures, and morphologies. In addition, the design principles and potential functional mechanisms are examined. This is crucial for deepening the understanding and enhancing the operational capabilities of sensors. We also highlight the challenges faced in scaling up the production of nanocomposite materials. Looking ahead, semiconductor nanocomposites are expected to drive innovation in gas sensor technology due to their carefully crafted design and construction, paving the way for their extensive use in various sectors.

Electrochemical protein biosensors for disease marker detection: progress and opportunities.

The development of artificial intelligence-enabled medical health care has created both opportunities and challenges for next-generation biosensor technology. Proteins are extensively used as biological macromolecular markers in disease diagnosis and the analysis of therapeutic effects. Electrochemical protein biosensors have achieved desirable specificity by using the specific antibody-antigen binding principle in immunology. However, the active centers of protein biomarkers are surrounded by a peptide matrix, which hinders charge transfer and results in insufficient sensor sensitivity. Therefore, electrode-modified materials and transducer devices have been designed to increase the sensitivity and improve the practical application prospects of electrochemical protein sensors. In this review, we summarize recent reports of electrochemical biosensors for protein biomarker detection. We highlight the latest research on electrochemical protein biosensors for the detection of cancer, viral infectious diseases, inflammation, and other diseases. The corresponding sensitive materials, transducer structures, and detection principles associated with such biosensors are also addressed generally. Finally, we present an outlook on the use of electrochemical protein biosensors for disease marker detection for the next few years.