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.
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.
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.
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
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
Background: Gut microbiota is an important factor affecting host health. With the further study of the mechanism of gut microbiota, significant progress has been made in the study of the link between gut microbiota and epigenetics. This study visualizes the body of knowledge and research priorities between the gut microbiota and epigenetics through bibliometrics. Methods: Publications related to gut microbiota and epigenetics were searched in the Web of Science Core Collection (WoSCC) database. Vosviewer 1.6.17 and CiteSpace 6.1.R2 were used for bibliometric analysis. Results: WoSCC includes 460 articles from 71 countries. The number of publications on gut microbiota and epigenetics has increased each year since 2011. The USA, PEOPLES R CHINA, and ITALY are at the center of this field of research. The University of California System, Harvard University, and the University of London are the main research institutions. Li, X, Yu, Q, Zhang, S X are the top authors in this research field. We found that current research hotspots and frontiers include short-chain fatty acids (SCFA) play an important role in gut microbiota and epigenetic mechanisms, gut microbiota and epigenetics play an important role in host obesity, diet, and metabolism. Gut microbiota and epigenetics are closely related to colorectal cancer, breast cancer, and inflammatory bowel disease. At the same time, we found that gut microbiota regulates epigenetics through the gut-brain axis and has an impact on psychiatric diseases. Therefore, probiotics can regulate gut microbiota, improve lifestyle, and reduce the occurrence and development of diseases. Conclusion: This is the first comprehensive and in-depth bibliometric study of trends and developments in the field of gut microbiota and epigenetics research. This study helps to guide the direction of research scholars in their current field of study.
Bibliometrics , Epigenesis, Genetic , Gastrointestinal Microbiome , Humans , Animals , Biomedical Research
The flaviviral NS2B/NS3 protease is a conserved enzyme required for flavivirus replication. Its highly dynamic conformation poses major challenges but also offers opportunities for antiviral inhibition. Here, we established a nanopore tweezers-based platform to monitor NS2B/NS3 conformational dynamics in real-time. Molecular simulations coupled with electrophysiology revealed that the protease could be captured in the middle of the ClyA nanopore lumen, stabilized mainly by dynamic electrostatic interactions. We designed a new Salmonella typhi ClyA nanopore with enhanced nanopore/protease interaction that can resolve the open and closed states at the single-molecule level for the first time. We demonstrated that the tailored ClyA could track the conformational transitions of the West Nile NS2B/NS3 protease and unravel the conformational energy landscape of various protease constructs through population and kinetic analysis. The new ClyA-protease platform paves a way to high-throughput screening strategies for discovering new allosteric inhibitors that target the NS2B and NS3 interface.
Temozolomide (TMZ) stands as the primary chemotherapeutic drug utilized in clinical glioma treatment, particularly for high-grade glioblastoma (GBM). However, the emergence of TMZ resistance in GBM poses a significant hurdle to its clinical efficacy. Our objective was to elucidate the role of deubiquitinating enzymes (DUBs) in GBM cell resistance to TMZ. We employed the broad-spectrum DUBs inhibitor G5 to investigate the function of DUBs in TMZ cytotoxicity against GBM cells. Eighty-two GBM cell lines with specified DUBs knockout were generated and subjected to CCK-8 assays to assess cell proliferation and TMZ resistance. Furthermore, the association between DUBs and TMZ resistance in GBM cells, along with the modulation of autophagic flux, was examined. The pan-DUBs inhibitor G5 demonstrated the ability to induce cell death and enhance TMZ toxicity in GBM cells. Subsequently, we identified potential DUBs involved in regulating GBM cell proliferation and TMZ resistance. The impact of DUBs knockout on TMZ cytotoxicity was found to be associated with their regulation of TMZ-induced autophagy. In summary, our study provides primary insights into the role of DUBs in GBM cell proliferation and TMZ resistance, and contributes to a deeper understanding of the complex function of DUBs genes underlying TMZ resistance in GBM cells.
[This corrects the article DOI: 10.1039/D4RA00793J.].
Radiotherapy demonstrates a synergistic effect with immunotherapy by inducing a transformation of "immune cold" tumors into "immune hot" tumors in triple negative breast cancer (TNBC). Nevertheless, the effectiveness of immunotherapy is constrained by low expression of tumor-exposed antigens, inadequate inflammation, and insufficient tumor infiltrating lymphocyte (TILs). To address this predicament, novel lutecium-based rare earth nanoparticles (RENPs) are synthesized with the aim of amplifying radiation effect and tumor immune response. The nanoprobe is characterized by neodymium-based down-conversion fluorescence, demonstrating robust photostability, biocompatibility, and targetability. The conjugation of RENPs with a CXCR4 targeted drug enables precise delineation of breast tumors using a near-infrared imaging system and improves radiation efficacy via lutetium-based radio-sensitizer in vivo. Furthermore, the study shows a notable enhancement of immune response through the induction of immunogenic cell death and recruitment of TILs, resulting in the inhibition of tumor progression both in vitro and in vivo models following the administration of nanoparticles. Hence, the novel multifunctional nanoprobes incorporating various lanthanide elements offer the potential for imaging-guided tumor delineation, radio-sensitization, and immune activation post-radiation, thus presenting an efficient radio-immunotherapeutic approach for TNBC.
Mechanical failure and chemical degradation of device heterointerfaces can strongly influence the long-term stability of perovskite solar cells (PSCs) under thermal cycling and damp heat conditions. We report chirality-mediated interfaces based on R-/S-methylbenzyl-ammonium between the perovskite absorber and electron-transport layer to create an elastic yet strong heterointerface with increased mechanical reliability. This interface harnesses enantiomer-controlled entropy to enhance tolerance to thermal cycling-induced fatigue and material degradation, and a heterochiral arrangement of organic cations leads to closer packing of benzene rings, which enhances chemical stability and charge transfer. The encapsulated PSCs showed retentions of 92% of power-conversion efficiency under a thermal cycling test (-40°C to 85°C; 200 cycles over 1200 hours) and 92% under a damp heat test (85% relative humidity; 85°C; 600 hours).
In this study, we investigated the prevalence of Bartonella in deer from Qilian County, Qinghai Province, China. Blood samples were collected from 69 red deer, 40 white-lipped deer, and 27 sika deer. The detection of Bartonella spp. has been conducted. The overall prevalence of Bartonella was 33.6% (46/135). Species-specific prevalence was 50.72% in red deer (35/69), 20.00% in white-lipped deer (8/40), and 11.11% in sika deer (3/27). There were significant differences in the prevalence rates among the different species of deer. The amplicon sequence comparison revealed a high homology of the ruminant-associated Bartonella spp. Nanopore sequencing further confirmed the results. Bartonella reads were presented in each of the qPCR-positive samples. Phylogenetic analysis indicated that the Bartonella sequences detected in deer blood were closely related to ruminant-borne Bartonella spp. In summary, we reported the Bartonella prevalence of different deer species in Qinghai, and there were at least one species of ruminant-associated Bartonella, B. schoenbuchensis. IMPORTANCE: This is the first report about Bartonella infections in the deer population from China. We found that there were two species of Bartonella and an unidentified species of Bartonella among the unculturing strains carried by these deer populations. We first used Nanopore sequencing to detect Bartonella from deer blood samples and indicated that Nanopore sequencing is beneficial to detect pathogens due to its advantage of real-time and high sensitivity.
The remediation of high-concentration thallium (Tl+) contaminated wastewater is a critical environmental concern. Current research emphasizes the effectiveness of adsorption and oxidation methods for Tl+ treatment, yet challenges persist in enhancing their performance. This study explores the feasibility of emergency Tl+ wastewater treatment and elucidates the mechanisms of Tl+ incorporation into mineral structures, with a focus on the struvite mineral as a framework for Tl+ integration via NH4+ ion exchange. To assess the efficacy and mechanisms of Tl+ immobilization, we utilized comprehensive analytical techniques, including X-ray Diffraction (XRD), X-ray Photoelectron Spectroscopy (XPS), Fourier-Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (SEM-EDS), Thermogravimetric Analysis (TG), and Density Functional Theory (DFT) calculations. The findings reveal that struvite adsorbs Tl+ onto its surface, followed by an ion exchange process between monovalent cations (NH4+/K+) within the structure and Tl+. Ultimately, Tl+ is incorporated in the form of a (NH4,Tl)MgPO4 solid solution within the structure, achieving a remarkable maximum incorporation capacity of 320.56 mg/g, which significantly surpasses the capacity of typical adsorbents. The findings demonstrate significant Tl+ incorporation, validating the approach for emergency wastewater treatment and suggesting the potential of mineralogy in environmental remediation. This research contributes to advancing heavy metal wastewater treatment strategies, offering a foundation for further investigation.
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
In this work, a magnetic adsorption material based on metal-organic framework (Fe3O4@ZnAl-LDH@MIL-53(Al)) was synthesized and used as an adsorbent in the process of magnetic solid phase extraction. Then, a high-performance liquid chromatograph was used to quantitatively detect triazole fungicides in samples. In order to verify the successful preparation of the material, a series of characterization analyses were carried out. Besides, the key parameters that may affect the extraction efficiency have been optimized, and under optimal conditions the three triazole fungicides showed good linearity in the range of 10-1000 µg/L (R2 ≥ 0.9796); Limit of detections were ranged from 0.013 to 0.030 µg/mL. Finally, the established method was applied to the detection of triazole fungicides in four fresh juice samples. The results showed that the target analyte was not detected in all the test samples. By detecting the recoveries (73.3-104.3%) and coefficient variation (RSD ≤ 6.8%) of triazole fungicides in fortified samples, it proved that this established method meets the requirements of pesticide residue analysis and showed excellent application potential.
Pesticide residues in agricultural products pose a significant threat to human health. Herein, a sensitive fluorescence method employing upconversion nanoparticles was developed for detecting organophosphorus pesticides (OPs) based on the principle of enzyme inhibition and copper-triggered o-phenylenediamine (OPD) oxidation. Copper ions (Cu2+) oxidized the colorless OPD to a yellow 2,3-diaminophenazine (oxOPD). The yellow solution oxOPD quenched the fluorescence of upconversion nanoparticles due to the fluorescence resonance energy transfer. The high affinity of Cu2+ for thiocholine reduced the level of oxOPD, resulting in almost no fluorescence quenching. The addition of dimethoate led to the inhibition of acetylcholinesterase activity and thus prevented the formation of thiocholine. Subsequently, Cu2+ oxidized OPD to form oxOPD, which attenuated the fluorescence signal of the system. The detection system has a good linear range of 0.01 ng/mL to 50 ng/mL with a detection limit of 0.008 ng/mL, providing promising applications for rapid detection of dimethoate.
Here we presented an electrophysiological dataset collected from layer V of the primary motor cortex (M1) and the corresponding behavior dataset from normal and hemi-parkinson rats over 5 consecutive weeks. The electrophysiological dataset was constituted by the raw wideband signal, neuronal spikes, and local field potential (LFP) signal. The open-field test was done and recorded to evaluate the behavior variation of rats among the entire experimental cycle. We conducted technical validation of this dataset through sorting the spike data to form action potential waveforms and analyzing the spectral power of LFP data, then based on these findings a closed-loop DBS protocol was developed by the oscillation activity response of M1 LFP signal. Additionally, this protocol was applied to the hemi-parkinson rat for five consecutive days while simultaneously recording the electrophysiological data. This dataset is currently the only publicly available dataset that includes longitudinal closed-loop DBS recordings, which can be utilized to investigate variations of neuronal activity within the M1 following long-term closed-loop DBS, and explore additional reliable biomarkers.
Deep Brain Stimulation , Motor Cortex , Animals , Rats , Motor Cortex/physiology , Parkinson Disease/physiopathology , Parkinson Disease/therapy , Action Potentials , Behavior, Animal , Electrophysiological Phenomena , Neurons/physiology
BACKGROUND: In-hospital mortality following hip fractures is a significant concern, and accurate prediction of this outcome is crucial for appropriate clinical management. Nonetheless, there is a lack of effective prediction tools in clinical practice. By utilizing artificial intelligence and machine learning techniques, this study aims to develop a predictive model that can assist clinicians in identifying geriatric hip fracture patients at a higher risk of in-hospital mortality. METHODS: A total of 52,707 geriatric hip fracture patients treated with surgery from 90 hospitals were included in this study. The primary outcome was postoperative in-hospital mortality. The patients were randomly divided into two groups, with a ratio of 7:3. The majority of patients, assigned to the training cohort, were used to develop the AI models. The remaining patients, assigned to the validation cohort, were used to validate the models. Various machine learning algorithms, including logistic regression (LR), decision tree (DT), naïve Bayesian (NB), neural network (NN), eXGBoosting machine (eXGBM), and random forest (RF), were employed for model development. A comprehensive scoring system, incorporating 10 evaluation metrics, was developed to assess the prediction performance, with higher scores indicating superior predictive capability. Based on the best machine learning-based model, an AI application was developed on the Internet. In addition, a comparative testing of prediction performance between doctors and the AI application. FINDINGS: The eXGBM model exhibited the best prediction performance, with an AUC of 0.908 (95% CI: 0.881-0.932), as well as the highest accuracy (0.820), precision (0.817), specificity (0.814), and F1 score (0.822), and the lowest Brier score (0.120) and log loss (0.374). Additionally, the model showed favorable calibration, with a slope of 0.999 and an intercept of 0.028. According to the scoring system incorporating 10 evaluation metrics, the eXGBM model achieved the highest score (56), followed by the RF model (48) and NN model (41). The LR, DT, and NB models had total scores of 27, 30, and 13, respectively. The AI application has been deployed online at https://in-hospitaldeathinhipfracture-l9vhqo3l55fy8dkdvuskvu.streamlit.app/ , based on the eXGBM model. The comparative testing revealed that the AI application's predictive capabilities significantly outperformed those of the doctors in terms of AUC values (0.908 vs. 0.682, P <0.001). CONCLUSIONS: The eXGBM model demonstrates promising predictive performance in assessing the risk of postoperative in-hospital mortality among geriatric hip fracture patients. The developed AI model serves as a valuable tool to enhance clinical decision-making.
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.
