Radioiodine refractory (RAIR) patients do not benefit from iodine-131 therapy. Thus, timely identification of RAIR patients is critical for avoiding ineffective radioactive iodine therapy. In addition, determining the causes of iodine resistance will facilitate the development of novel treatment strategies. This study was comprised of 20 RAIR and 14 non-radioiodine refractory (non-RAIR) thyroid cancer patients. Liquid chromatography-mass spectrometry was used to identify differences in the serum metabolites of RAIR and non-RAIR patients. In addition, chemical assays were performed to determine the effects of the differential metabolites on iodine uptake. Metabolic pathway enrichment analysis of the differential metabolites revealed significant differences in the phenylalanine and tyrosine metabolic pathways. Notably, quinate and shikimic acid, metabolites of the tyrosine pathway, were significantly increased in the RAIR group. In contrast, the phenylalanine pathway metabolites, hippuric acid and 2-phenylacetamide, were markedly decreased in the RAIR group. Thyroid peroxidase plays an important role in catalyzing the iodination of tyrosine residues, while the ionic state of iodine promotes the iodination reaction. Quinate, shikimic acid, hippuric acid, and 2-phenylacetamide were found to be involved in the iodination of tyrosine, which is a key step in thyroid hormone synthesis. Specifically, quinate and shikimic acid were found to inhibit iodination, while hippuric acid and 2-phenylacetamide promoted iodination. Abnormalities in phenylalanine and tyrosine metabolic pathways are closely associated with iodine resistance. Tyrosine is required for thyroid hormone synthesis and could be a potential cause of iodine resistance.
Iodine Radioisotopes , Metabolomics , Thyroid Neoplasms , Humans , Thyroid Neoplasms/metabolism , Thyroid Neoplasms/radiotherapy , Female , Male , Middle Aged , Metabolomics/methods , Adult , Iodine/metabolism , Metabolic Networks and Pathways/drug effects , Aged , Metabolome
Variations in functional traits serve as measures of plants' ability to adapt to environment. Exploring the patterns of functional traits of desert plants along elevational gradients is helpful to understand the responses and adaptation strategies of species to changing environments. However, it is unknown whether the relationship between functional traits and elevation is affected by differences in the species' elevational distributions (elevation preference and species' range). Importantly, most researches have concerned with differences in mean trait values and ignored intraspecific trait variation. Here, we measured functional traits of desert plants along a wide elevational gradient in the Tibetan Plateau and adjacent areas and explored functional trait patterns over elevation in species with different elevational distributions. We decomposed trait variation and further investigated characterizations of intraspecific variation. Ultimately, the main drivers of trait variation were identified using redundancy analysis. We found that species' elevational distributions significantly influenced the relationship of functional traits such as plant height, leaf dry matter content, leaf thickness, leaf nitrogen and carbon content with elevation. Species with a lower elevational preference showed greater trait variation than species with a higher elevational preference, suggesting that species that prefer high elevation are more conservative facing environmental changes. We provide evidence that interspecific trait variation in leaf thickness and leaf carbon content decreased with increasing species' range, indicating that increased variations in resistance traits within species make greater responsiveness to environmental changes, enabling species a wider range. Elevation, temperature and precipitation were the main drivers of trait variation in species with a low elevational preference, while the effect of precipitation on trait variation in species with a high elevational preference was not significant. This study sheds new insights on how plants with different elevational distributions regulate their ecological strategies to cope with changing environments.
Altitude , Desert Climate , Tibet , Plant Leaves/physiology , Plant Leaves/anatomy & histology
Electrochemical synthesis of ammonia (NH3) in aqueous electrolyte has long been suffered from poor nitrogen (N2) supply owing to its low solubility and sluggish diffusion kinetics. Therefore, creating a N2 rich microenvironment around catalyst surface may potentially improve the efficiency of nitrogen reduction reaction (NRR). Herein, a delicately designed N2 filtering membrane consisted of polydimethylsiloxane is covered on catalyst surface via superspreading. Because this membrane let the dissolved N2 molecules be accessible to the catalyst but block excess water, the designed N2 rich microenvironment over catalyst leads to an optimized Faradaic efficiency of 39.4% and an NH3 yield rate of 109.2 µg h-1 mg-1, which is superior to those of the most report metal-based catalysts for electrochemical NRR. This study offers alternative strategy for enhancing NRR performance.
Accumulating studies have demonstrated close relationships between long non-coding RNAs (lncRNAs) and diseases. Identification of new lncRNA-disease associations (LDAs) enables us to better understand disease mechanisms and further provides promising insights into cancer targeted therapy and anti-cancer drug design. Here, we present an LDA prediction framework called GEnDDn based on deep learning. GEnDDn mainly comprises two steps: First, features of both lncRNAs and diseases are extracted by combining similarity computation, non-negative matrix factorization, and graph attention auto-encoder, respectively. And each lncRNA-disease pair (LDP) is depicted as a vector based on concatenation operation on the extracted features. Subsequently, unknown LDPs are classified by aggregating dual-net neural architecture and deep neural network. Using six different evaluation metrics, we found that GEnDDn surpassed four competing LDA identification methods (SDLDA, LDNFSGB, IPCARF, LDASR) on the lncRNADisease and MNDR databases under fivefold cross-validation experiments on lncRNAs, diseases, LDPs, and independent lncRNAs and independent diseases, respectively. Ablation experiments further validated the powerful LDA prediction performance of GEnDDn. Furthermore, we utilized GEnDDn to find underlying lncRNAs for lung cancer and breast cancer. The results elucidated that there may be dense linkages between IFNG-AS1 and lung cancer as well as between HIF1A-AS1 and breast cancer. The results require further biomedical experimental verification. GEnDDn is publicly available at https://github.com/plhhnu/GEnDDn.
BACKGROUND: While the daily rhythm of allergic rhinitis (AR) has long been recognized, the molecular mechanism underlying this phenomenon remains enigmatic. OBJECTIVE: We aim to investigate the role of circadian clock in AR development and to clarify the mechanism by which the daily rhythm of AR is generated. METHODS: AR was induced in mice using the ovalbumin method. Toluidine blue staining, LC-MS/MS analysis, qPCR, and immunoblotting were performed with AR and control mice. RESULTS: Ovalbumin-induced AR is diurnally rhythmic and associated with clock gene disruption in nasal mucosa. In particular, Rev-erbα is generally down-regulated, and its rhythm retained but with a near 12-h phase shift. Furthermore, global knockout of the core clock gene Bmal1 or Rev-erbα increases the susceptibility of mice to AR and blunts AR rhythmicity. Importantly, nasal SCCs (solitary chemosensory cells) are rhythmically activated, and inhibition of the SCC pathway leads to attenuated AR and a loss of its rhythm. Moreover, rhythmic activation of SCCs is accounted for by diurnal expression of ChAT (an enzyme responsible for the synthesis of acetylcholine) and temporal generation of the neurotransmitter acetylcholine. Mechanistically, REV-ERBα trans-represses Chat through direct binding to a specific response element, generating a diurnal oscillation in this target gene. CONCLUSION: These findings identify SCCs, under the control of REV-ERBα, as a driver of AR rhythmicity, and suggest targeting SCCs as a new avenue for AR management.
PURPOSE: This study aims to assess ultrashort-TE magnetization transfer (UTE-MT) imaging of collagen degradation using an in vitro model of rotator cuff tendinopathy. METHODS: Thirty-six supraspinatus tendon specimens were divided into three groups and treated with 600 U collagenase (Group 1), 150 U collagenase (Group 2), and phosphate buffer saline (Group 3). UTE-MT imaging was performed to assess changes in macromolecular fraction (MMF), macromolecule transverse relaxation time (T2m), water longitudinal relaxation rate constant (R1m), the magnetization exchange rate from the macromolecular to water pool (Rm0 w) and from water to the macromolecular pool (Rm0 m), and magnetization transfer ratio (MTR) at baseline and following digestion and their differences between groups. Biochemical and histological studies were conducted to determine the extent of collagen degradation. Correlation analyses were performed with MMF, T2m, R1m, Rm0 w, Rm0 m, and MTR, respectively. Univariate and multivariate linear regression analyses were performed to evaluate combinations of UTE-MT parameters to predict collagen degradation. RESULTS: MMF, T2m, R1m, Rm0 m, and MTR decreased after digestion. MMF (r = -0.842, p < 0.001), MTR (r = -0.78, p < 0.001), and Rm0 m (r = -0.662, p < 0.001) were strongly negatively correlated with collagen degradation. The linear regression model of differences in MMF and Rm0 m before and after digestion explained 68.9% of collagen degradation variation in the tendon. The model of postdigestion in MMF and T2m and the model of MTR explained 54.2% and 52.3% of collagen degradation variation, respectively. CONCLUSION: This study highlighted the potential of UTE-MT parameters for evaluation of supraspinatus tendinopathy.
Identifying the association between miRNA and diseases is helpful for disease prevention, diagnosis and treatment. It is of great significance to use computational methods to predict potential human miRNA disease associations. Considering the shortcomings of existing computational methods, such as low prediction accuracy and weak generalization, we propose a new method called SCPLPA to predict miRNA-disease associations. First, a heterogeneous disease similarity network was constructed using the disease semantic similarity network and the disease Gaussian interaction spectrum kernel similarity network, while a heterogeneous miRNA similarity network was constructed using the miRNA functional similarity network and the miRNA Gaussian interaction spectrum kernel similarity network. Then, the estimated miRNA-disease association scores were evaluated by integrating the outcomes obtained by implementing label propagation algorithms in the heterogeneous disease similarity network and the heterogeneous miRNA similarity network. Finally, the spatial consistency projection algorithm of the network was used to extract miRNA disease association features to predict unverified associations between miRNA and diseases. SCPLPA was compared with four classical methods (MDHGI, NSEMDA, RFMDA and SNMFMDA), and the results of multiple evaluation metrics showed that SCPLPA exhibited the most outstanding predictive performance. Case studies have shown that SCPLPA can effectively identify miRNAs associated with colon neoplasms and kidney neoplasms. In summary, our proposed SCPLPA algorithm is easy to implement and can effectively predict miRNA disease associations, making it a reliable auxiliary tool for biomedical research.
Algorithms , Computational Biology , MicroRNAs , MicroRNAs/genetics , Humans , Computational Biology/methods , Genetic Predisposition to Disease , Gene Regulatory Networks
Precision mapping techniques coupled with high resolution image acquisition of the mouse brain permit the study of the spatial organization of gene expression and their mutual interaction for a comprehensive view of salient structural/functional relationships. Such research is facilitated by standardized anatomical coordinate systems, such as the well-known Allen Common Coordinate Framework (AllenCCFv3), and the ability to spatially map to such standardized spaces. The Advanced Normalization Tools Ecosystem is a comprehensive open-source software toolkit for generalized quantitative imaging with applicability to multiple organ systems, modalities, and animal species. Herein, we illustrate the utility of ANTsX for generating precision spatial mappings of the mouse brain and potential subsequent quantitation. We describe ANTsX-based workflows for mapping domain-specific image data to AllenCCFv3 accounting for common artefacts and other confounds. Novel contributions include ANTsX functionality for velocity flow-based mapping spanning the spatiotemporal domain of a longitudinal trajectory which we apply to the Developmental Common Coordinate Framework. Additionally, we present an automated structural morphological pipeline for determining volumetric and cortical thickness measurements analogous to the well-utilized ANTsX pipeline for human neuroanatomical structural morphology which illustrates a general open-source framework for tailored brain parcellations.
Multicellular organisms have dense affinity with the coordination of cellular activities, which severely depend on communication across diverse cell types. Cell-cell communication (CCC) is often mediated via ligand-receptor interactions (LRIs). Existing CCC inference methods are limited to known LRIs. To address this problem, we developed a comprehensive CCC analysis tool SEnSCA by integrating single cell RNA sequencing and proteome data. SEnSCA mainly contains potential LRI acquisition and CCC strength evaluation. For acquiring potential LRIs, it first extracts LRI features and reduces the feature dimension, subsequently constructs negative LRI samples through K-means clustering, finally acquires potential LRIs based on Stacking ensemble comprising support vector machine, 1D-convolutional neural networks and multi-head attention mechanism. During CCC strength evaluation, SEnSCA conducts LRI filtering and then infers CCC by combining the three-point estimation approach and single cell RNA sequencing data. SEnSCA computed better precision, recall, accuracy, F1 score, AUC and AUPR under most of conditions when predicting possible LRIs. To better illustrate the inferred CCC network, SEnSCA provided three visualization options: heatmap, bubble diagram and network diagram. Its application on human melanoma tissue demonstrated its reliability in CCC detection. In summary, SEnSCA offers a useful CCC inference tool and is freely available at https://github.com/plhhnu/SEnSCA.
Cell Communication , Single-Cell Analysis , Humans , Ligands , Single-Cell Analysis/methods , Software , Computational Biology/methods , Algorithms , Support Vector Machine , Sequence Analysis, RNA/methods , Melanoma/metabolism , Melanoma/pathology , Melanoma/genetics , Proteome/metabolism , Neural Networks, Computer
OBJECTIVE: To make predictions about the risk of MVA (Malignant Ventricular Arrhythmia) after primary PCI (Percutaneous Coronary Intervention) in patients with AMI (Acute Myocardial Infarction) through constructing and validating the Nomogram model. METHODS: 311 AMI patients who suffered from emergency PCI in Hefei Second People's Hospital from January 2020 to May 2023 were selected as the training set; 253 patients suffering from the same symptom in Hefei First People's Hospital during the same period were selected as the validation set. Risk factors were further screened by means of multivariate logistic and stepwise regression. The nomogram model was constructed, and then validated by using C-index, ROC curve, decision curve and calibration curve. RESULTS: Multivariate logistic analysis revealed that urea, systolic pressure, hypertension, Killip class II-IV, as well as LVEF (Left Ventricular Ejection Fraction) were all unrelated hazards for MVA after emergency PCI for AMI (P<0.05); a risk prediction nomogram model was constructed. The C-index was calculated to evaluate the predictive ability of the model. Result showed that the index of the training and the validation set was 0.783 (95% CI: 0.726-0.84) and 0.717 (95% CI: 0.65-0.784) respectively, which suggested that the model discriminated well. Meanwhile, other tools including ROC curve, calibration curve and decision curve also proved that this nomogram plays an effective role in forecasting the risk for MVA after PCI in AMI patients. CONCLUSIONS: The study successfully built the nomogram model and made predictions for the development of MVA after PCI in AMI patients.
The dearomatization at the hydrophobic tail of the boscalid was carried out to construct a series of novel pyrazole-4-carboxamide derivatives containing an oxime ether fragment. By using fungicide-likeness analyses and virtual screening, 24 target compounds with theoretical strong inhibitory effects against fungal succinate dehydrogenase (SDH) were designed and synthesized. Antifungal bioassays showed that the target compound E1 could selectively inhibit the in vitro growth of R. solani, with the EC50 value of 1.1 µg/mL that was superior to that of the agricultural fungicide boscalid (2.2 µg/mL). The observations by scanning electron microscopy (SEM) and transmission electron microscopy (TEM) demonstrated that E1 could reduce mycelial density and significantly increase the mitochondrial number in mycelia cytoplasm, which was similar to the phenomenon treated with boscalid. Enzyme activity assay showed that the E1 had the significant inhibitory effect against the SDH from R. solani, with the IC50 value of 3.3 µM that was superior to that of boscalid (7.9 µM). The mode of action of the target compound E1 with SDH was further analyzed by molecular docking and molecular dynamics simulation studies. Among them, the number of hydrogen bonds was significantly more in the SDH-E1 complex than that in the SDH-boscalid complex. This research on the dearomatization strategy of the benzene ring for constructing pyrazole-4-carboxamides containing an oxime ether fragment provides a unique thought to design new antifungal drugs targeting SDH.
Antimicrobial resistance poses a serious threat to human health due to the high morbidity and mortality caused by drug-resistant microbial infections. Therefore, the development of rapid, sensitive and selective identification methods is key to improving the survival rate of patients. In this paper, a sandwich-type electrochemical DNA biosensor based on a polyadenine-DNA tetrahedron probe was constructed. The key experimental conditions were optimized, including the length of polyadenine, the concentration of the polyadenine DNA tetrahedron, the concentration of the signal probe and the hybridization time. At the same time, poly-avidin-HRP80 was used to enhance the electrochemical detection signal. Finally, excellent biosensor performance was achieved, and the detection limit for the synthetic DNA target was as low as 1 fM. In addition, we verified the practicability of the system by analyzing E. coli with the MCR-1 plasmid and realized multi-channel detection of the drug resistance genes MCR-1, blaNDM, blaKPC and blaOXA. With the ideal electrochemical interface, the polyA-based biosensor exhibits excellent stability, which provides powerful technical support for the rapid detection of antibiotic-resistant strains in the field.
PURPOSE: The efficacy of induction chemotherapy (IC) as a primary treatment for advanced nasopharyngeal carcinoma (NPC) remains a topic of debate, with a lack of dependable biomarkers for predicting its efficacy. This study seeks to establish a predictive classifier utilizing plasma metabolomics profiling. EXPERIMENTAL DESIGN: A total of 166 NPC patients enrolled in the clinical trial NCT05682703 and undergoing IC were included in the study. Plasma lipoprotein profiles were obtained using 1H-NMR before and after IC treatment. An AI-assisted radiomics method was developed to effectively evaluate the efficacy. Metabolic biomarkers were identified through a machine learning approach based on a discovery cohort and subsequently validated in a validation cohort that mimicked the most unfavorable scenario in real-world. RESULTS: Our research findings indicate that the effectiveness of IC varies among individual patients, with a correlation observed between efficacy and changes in metabolite profiles. Utilizing machine learning techniques, it was determined that the XGB model exhibited notable efficacy, attaining an Area Under the Curve (AUC) value of 0.792 (95% CI, 0.668-0.913). In the validation cohort, the model exhibited strong stability and generalizability with an AUC of 0.786 (95%CI, 0.533-0.922). CONCLUSION: In this study, we found that dysregulation of plasma lipoprotein may result in resistance to IC in NPC patients. The prediction model constructed based on the plasma metabolites' profile as good predictive capabilities and potential for real-world generalization. This discovery has implications for the development of treatment strategies and may offer insight into potential targets for enhancing the effectiveness of IC.
Point scene instance mesh reconstruction is a challenging task since it requires both scene-level instance segmentation and instance-level mesh reconstruction from partial observations simultaneously. Previous works either adopt a detection backbone or a segmentation one, and then directly employ a mesh reconstruction network to produce complete meshes from incomplete instance point clouds. To further boost the mesh reconstruction quality with both local details and global smoothness, in this work, we propose JIMR, a joint framework with two cascaded stages for semantic and geometry understanding. In the first stage, we propose to perform both instance segmentation and object detection simultaneously. By making both tasks promote each other, this design facilitates subsequent mesh reconstruction by providing more precisely-segmented instance points and better alignment benefiting from predicted complete bounding boxes. In the second stage, we propose a complete-then-reconstruct procedure, where the completion module explicitly disentangles completion from reconstruction, and enables the usage of pre-trained weights of existing powerful completion and reconstruction networks. Moreover, we propose a comprehensive confidence score to filter proposals considering the quality of instance segmentation, bounding box detection, semantic classification, and mesh reconstruction at the same time. Experiments show that our proposed JIMR outperforms state-of-the-art methods regarding instance reconstruction qualitatively and quantitatively.
In mammals, gonadal somatic cell lineage differentiation determines the development of the bipotential gonad into either the ovary or testis. Sertoli cells, the only somatic cells in the spermatogenic tubules, support spermatogenesis during gonadal development. During embryonic Sertoli cell lineage differentiation, relevant genes, including WT1, GATA4, SRY, SOX9, AMH, PTGDS, SF1, and DMRT1, are expressed at specific times and in specific locations to ensure the correct differentiation of the embryo toward the male phenotype. The dysregulated development of Sertoli cells leads to gonadal malformations and male fertility disorders. Nevertheless, the molecular pathways underlying the embryonic origin of Sertoli cells remain elusive. By reviewing recent advances in research on embryonic Sertoli cell genesis and its key regulators, this review provides novel insights into sex determination in male mammals as well as the molecular mechanisms underlying the genealogical differentiation of Sertoli cells in the male reproductive ridge.
Cell Differentiation , Cell Lineage , Sertoli Cells , Sertoli Cells/cytology , Sertoli Cells/metabolism , Sertoli Cells/physiology , Male , Humans , Animals , Reproduction/physiology , Spermatogenesis/physiology , Sex Determination Processes/physiology
Leaf traits are essential for understanding many physiological and ecological processes. Partial least squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time, and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47 393 records for > 700 species and eight PFTs at 101 globally distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water, and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R2 (0.12-0.49, 0.15-0.42, and 0.25-0.56) and increased NRMSE (3.58-18.24%, 6.27-11.55%, and 7.0-33.12%) compared with nonspatial random cross-validation. The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs, and time due to biased validation schemes, and provide guidance for future field sampling strategies and remote sensing applications.
Traditional Chinese medicine (TCM) formulae have been studied extensively in various human diseases and have proven to be effective due to their multi-component, multi-target advantage. However, its active metabolites are not clear and the specific mechanisms are not well established, which limits its scientific application. Recently, combination therapies are attracting increasing attention from the scientific community in the past few years and are considered as the next paradigm in drug discovery. Here, we tried to define a new concept of "active metabolites combination therapies (AMCT)" rules to elucidate how the bioactive metabolites from TCMs to produce their synergistic effects in this review. The AMCT rules integrate multidisciplinary technologies like molecular biology, biochemistry, pharmacology, analytical chemistry and pharmacodynamics, etc. Meanwhile, emerging technologies such as multi-omics combined analysis, network analysis, artificial intelligence conduce to better elucidate the mechanisms of these combination therapies in disease treatment, which provides new insights for the development of novel active metabolites combination drugs. AMCT rules will hopefully further guide the development of novel combination drugs that will promote the modernization and international needs of TCM.
We report the experimental measurement of millijoule terahertz (THz) radiation emitted in the backward direction from laser wakefields driven by a femtosecond laser pulse of few joules interacting with a gas target. By utilizing frequency-resolved energy measurement, it is found that the THz spectrum exhibits two peaks located at about 4.5 and 9.0 THz, respectively. In particular, the high frequency component emerges when the drive laser energy exceeds 1.26 J, at which electron acceleration in the forward direction is detected simultaneously. Theoretical analysis and particle-in-cell simulations indicate that the THz radiation is generated via mode conversion from the laser wakefields excited in plasma with an up-ramp profile, where radiations both at the local electron plasma frequency and its harmonics are produced. Such intense THz sources may find many applications in ultrafast science, e.g., manipulating the transient states of matter.
What is already known about this topic?: Foodborne diseases are a growing public health concern with a notable disease burden in China. What is added by this report?: Two children with diarrhea visited a healthcare facility within 24 hours on August 1 and 2, 2023. Salmonella Grumpensis was detected in their stool samples by the public health laboratory. Whole genome sequencing (WGS) analysis revealed characteristics typical of outbreak strains. Although the origin of the outbreak was unknown, the possibility of a hidden shared infection was deemed feasible. What are the implications for public health practice?: It underscores the importance of thorough genomic surveillance to promptly detect emerging pathogens. Public health laboratories play a crucial role by utilizing advanced genomic technologies for accurate pathogen identification and timely warning systems.
