BACKGROUND: Accumulating evidence suggests that latent infection with Toxoplasma gondii is associated with a variety of neuropsychiatric and behavioral conditions. This research aims to explore the potential correlation between T. gondii antibody positivity and neuropsychiatric disorders through a comprehensive prospective cohort study. METHODS: The cohort study utilized the UK Biobank database to recruit 8814 individuals with no prior diagnosis of neuropsychiatric disorders. Cox proportional hazards models were employed to investigate the associations between T. gondii P22 antibody seropositivity (P22+) and the development of various types of neuropsychiatric disorders. RESULTS: Of the population, 14.65â¯% tested positive for T. gondii P22 antibody. The presence of T. gondii P22 antibody showed a slight inverse association with epilepsy (HR: 0.28; 95â¯% CI: 0.10-0.77), while it was positively associated with an increased risk of developing anxiety disorders (HR: 1.38; 95â¯% CI: 1.04-1.83). LIMITATIONS: The study sample consisted mostly of white British individuals aged 40 to 69â¯years old. Although we adjusted for potential confounders, there may be other unmeasured and residual confounding factors that could have influenced our reported associations. CONCLUSIONS: The findings suggested an increased risk of anxiety and potential evidence of epilepsy associated with T. gondii P22+. However, our analysis did not reveal an increased risk of several other neuropsychiatric conditions including Alzheimer's disease, dementia, substance abuse disorders, depression, and neurodegenerative disorders, associated with P22 antibody seropositivity.
BACKGROUND: The prevalence of different types/subtypes varies across seasons and countries for seasonal influenza viruses, indicating underlying interactions between types/subtypes. The global interaction patterns and determinants for seasonal influenza types/subtypes need to be explored. METHODS: Influenza epidemiological surveillance data, as well as multidimensional data that include population-related, environment-related, and virus-related factors from 55 countries worldwide were used to explore type/subtype interactions based on Spearman correlation coefficient. The machine learning method Extreme Gradient Boosting (XGBoost) and interpretable framework SHapley Additive exPlanation (SHAP) were utilized to quantify contributing factors and their effects on interactions among influenza types/subtypes. Additionally, causal relationships between types/subtypes were also explored based on Convergent Cross-mapping (CCM). RESULTS: A consistent globally negative correlation exists between influenza A/H3N2 and A/H1N1. Meanwhile, interactions between influenza A (A/H3N2, A/H1N1) and B show significant differences across countries, primarily influenced by population-related factors. Influenza A has a stronger driving force than influenza B, and A/H3N2 has a stronger driving force than A/H1N1. CONCLUSION: The research elucidated the globally complex and heterogeneous interaction patterns among influenza type/subtypes, identifying key factors shaping their interactions. This sheds light on better seasonal influenza prediction and model construction, informing targeted prevention strategies and ultimately reducing the global burden of seasonal influenza.
Flexible supercapacitors are potential to power next-generation flexible electronics. However, the mechanical and electrochemical stability of flexible supercapacitors under different flexible conditions is limited by the weak bonding between adjacent layers, posing a significant hindrance to their practical applicability. Herein, based on the uninterrupted 3D network during the growth of bacterial cellulose (BC), w e have cultivated a flexible all-in-one supercapacitor through a continuous biosynthesis process. This strategy ensures the continuity of the 3D network of BC throughout the material, thereby forming a continuous electrode-separator-electrode structure. Benefitting from this bioinspired structure, the all-in-one supercapacitor not only achieves a high areal capacitance (3.79 F cm-2) of electrodes but also demonstrates the integration of high tensile strength (2.15 MPa), high shear strength (more than 54.6 kPa), and high bending resistance, indicating a novel pathway towards high-performance flexible power sources. This article is protected by copyright. All rights reserved.
Porcine parvovirus (PPV) is one of the most important pathogens that cause reproductive failure in pigs. However, the pathogenesis of PPV infection remains unclear. Proteomics is a powerful tool to understand the interaction between virus and host cells. In the present study, we analyzed the proteomics of PPV-infected PK-15 cells. A total of 32 and 345 proteins were differentially expressed at the early and replication stages, respectively. Subsequent gene ontology annotation and Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed these differentially expressed proteins were significantly enriched in pathways including toll-like receptor signaling pathway, tumor necrosis factor signaling pathway, and viral carcinogenesis. The expression of poly (rC) binding protein 1 (PCBP1) was observed to decrease after PPV infection. Overexpressed or silenced PCBP1 expression inhibited or promoted PPV infection. Our studies established a foundation for further exploration of the multiplication mechanism of PPV. IMPORTANCE: Porcine parvovirus (PPV) is a cause of reproductive failure in the swine industry. Our knowledge of PPV remains limited, and there is no effective treatment for PPV infection. Proteomics of PPV-infected PK-15 cells was conducted to identify differentially expressed proteins at 6 hours post-infection (hpi) and 36 hpi. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed that various pathways participate in PPV infection. Poly (rC) binding protein 1 was confirmed to inhibit PPV replication, which provided potential targets for anti-PPV infection. Our findings improve the understanding of PPV infection and pave the way for future research in this area.
OBJECT: To enable high-quality physics-guided deep learning (PG-DL) reconstruction of large-scale 3D non-Cartesian coronary MRI by overcoming challenges of hardware limitations and limited training data availability. MATERIALS AND METHODS: While PG-DL has emerged as a powerful image reconstruction method, its application to large-scale 3D non-Cartesian MRI is hindered by hardware limitations and limited availability of training data. We combine several recent advances in deep learning and MRI reconstruction to tackle the former challenge, and we further propose a 2.5D reconstruction using 2D convolutional neural networks, which treat 3D volumes as batches of 2D images to train the network with a limited amount of training data. Both 3D and 2.5D variants of the PG-DL networks were compared to conventional methods for high-resolution 3D kooshball coronary MRI. RESULTS: Proposed PG-DL reconstructions of 3D non-Cartesian coronary MRI with 3D and 2.5D processing outperformed all conventional methods both quantitatively and qualitatively in terms of image assessment by an experienced cardiologist. The 2.5D variant further improved vessel sharpness compared to 3D processing, and scored higher in terms of qualitative image quality. DISCUSSION: PG-DL reconstruction of large-scale 3D non-Cartesian MRI without compromising image size or network complexity is achieved, and the proposed 2.5D processing enables high-quality reconstruction with limited training data.
In this article, we investigate self-supervised 3D scene flow estimation and class-agnostic motion prediction on point clouds. A realistic scene can be well modeled as a collection of rigidly moving parts, therefore its scene flow can be represented as a combination of rigid motion of these individual parts. Building upon this observation, we propose to generate pseudo scene flow labels for self-supervised learning through piecewise rigid motion estimation, in which the source point cloud is decomposed into local regions and each region is treated as rigid. By rigidly aligning each region with its potential counterpart in the target point cloud, we obtain a region-specific rigid transformation to generate its pseudo flow labels. To mitigate the impact of potential outliers on label generation, when solving the rigid registration for each region, we alternately perform three steps: establishing point correspondences, measuring the confidence for the correspondences, and updating the rigid transformation based on the correspondences and their confidence. As a result, confident correspondences will dominate label generation, and a validity mask will be derived for the generated pseudo labels. By using the pseudo labels together with their validity mask for supervision, models can be trained in a self-supervised manner. Extensive experiments on FlyingThings3D and KITTI datasets demonstrate that our method achieves new state-of-the-art performance in self-supervised scene flow learning, without any ground truth scene flow for supervision, even performing better than some supervised counterparts. Additionally, our method is further extended to class-agnostic motion prediction and significantly outperforms previous state-of-the-art self-supervised methods on nuScenes dataset.
Lead halide molecular ferroelectrics represent an important class of luminescent ferroelectrics, distinguished by their high chemical and structural tunability, excellent processability and distinctive luminescent characteristics. However, their inherent instability, prone to decomposition upon exposure to moisture and light, hinders their broader ferroelectric applications. Herein, for the first time, we present a series of isoreticular metal-organic framework (MOF)-type lead halide luminescent ferroelectrics, demonstrating exceptional robustness under ambient conditions for at least 15 months and even when subjected to aqueous boiling conditions. Unlike conventional metal-oxo secondary building units (SBUs) in MOFs adopting highly centrosymmetric structure with limited structural distortion, our lead halide-based MOFs occupy structurally deformable [Pb2X]+ (X=Cl-/Br-/I-) SBUs that facilitate a c-axis-biased displacement of Pb2+ centers and substantially contribute to thermoinducible structural transformation. Importantly, this class of MOF-type lead halide ferroelectrics undergo ferroelectric-to-paraelectric phase transitions with remarkably high Curie temperature of up to 505 K, superior to most of molecular ferroelectrics. Moreover, the covalent bonding between phosphorescent organic component and the light-harvesting inorganic component achieves efficient spin-orbit coupling and intersystem crossing, resulting in long-lived afterglow emission. The compelling combination of high stability, ferroelectricity and afterglow emission exhibited by lead halide MOFs opens up many potential opportunities in energy-conversion applications.
BACKGROUND: Mounting evidences shows that the ubiquitinâproteasome pathway plays a pivotal role in tumor progression. The expression of 26S proteasome non-ATPase regulatory subunit 9 (PSMD9) is correlated with recurrence and radiotherapy resistance in several tumor types. However, the role and mechanism of PSMD9 in hepatocellular carcinoma (HCC) progression remain largely unclear. METHODS: PSMD9 was identified as a prognosis-related biomarker for HCC based on analysis of clinical characteristics and RNA-seq data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and the JP Project of the International Cancer Genome Consortium (ICGC-LIRI-JP). PSMD9 expression was analyzed in cancer tissues and adjacent noncancerous tissues via immunohistochemistry and Western blotting. Multiple in vivo and in vitro experimental techniques (such as CCK-8, colony formation, EdU, and Transwell assays; flow cytometry; Western blotting; quantitative RT-PCR; Coimmunoprecipitation assay and immunofluorescence confocal imaging) were used to assess the functions of PSMD9 in the pathogenesis of HCC. RESULTS: We found that the expression of PSMD9 was upregulated and associated with a poor prognosis in HCC patients. PSMD9 promoted HCC cell proliferation, migration, invasion and metastasis. Knockdown of PSMD9 significantly inhibited HCC cell proliferation by inducing G1/S cell cycle arrest and apoptosis. Mechanistically, we demonstrated that PSMD9 promoted HCC cell proliferation and metastasis via direct interaction with the E3 ubiquitin ligase c-Cbl, suppresses EGFR ubiquitination, influenced EGFR endosomal trafficking and degradation and subsequently activated ERK1/2 and Akt signaling. In addition, we showed that PSMD9 knockdown sensitized HCC cells to the tyrosine kinase inhibitor erlotinib in vitro and in vivo. CONCLUSIONS: Collectively, our results indicate that PSMD9 drives HCC progression and erlotinib resistance by suppressing c-Cbl mediated EGFR ubiquitination and therefore can be a potential therapeutic target for HCC.
Carcinoma, Hepatocellular , Disease Progression , ErbB Receptors , Liver Neoplasms , Proto-Oncogene Proteins c-cbl , Signal Transduction , Humans , Carcinoma, Hepatocellular/metabolism , Carcinoma, Hepatocellular/pathology , Carcinoma, Hepatocellular/genetics , Liver Neoplasms/metabolism , Liver Neoplasms/pathology , Liver Neoplasms/genetics , Proto-Oncogene Proteins c-cbl/metabolism , Proto-Oncogene Proteins c-cbl/genetics , ErbB Receptors/metabolism , ErbB Receptors/genetics , Mice , Animals , Male , Female , Cell Line, Tumor , Proteasome Endopeptidase Complex/metabolism , Cell Proliferation , Prognosis , Mice, Nude , Apoptosis , Middle Aged , Cell Movement
Aptamers are widely used molecular recognition tools in targeted therapy, but their ability to effectively penetrate deep into solid tumors remains a significant challenge, leading to suboptimal treatment efficacy. Here, we developed a polyfluoroalkyl (PFA) decoration strategy to enhance aptamer recognition, cell internalization, and solid tumor penetration. Our results indicate that PFA with around 11 fluorine atoms significantly improves aptamer internalization both in vitro and in vivo settings. However, we also observed that the use of PFA tags containing 19 and 23 fluorine atoms on aptamers resulted in nonspecific cell anchoring in control cell lines, affecting the specificity of aptamers. Overall, we found that using a chemical modification strategy could enhance the deep tumor penetration ability of aptamers and validate their effectiveness in vivo. This approach has significant practical applications in targeted drug delivery for cancer treatment.
Aptamers, Nucleotide , Receptor Protein-Tyrosine Kinases , Aptamers, Nucleotide/chemistry , Humans , Animals , Receptor Protein-Tyrosine Kinases/antagonists & inhibitors , Receptor Protein-Tyrosine Kinases/metabolism , Cell Line, Tumor , Mice , Cell Adhesion Molecules/metabolism , Cell Adhesion Molecules/antagonists & inhibitors , Neoplasms/drug therapy , Neoplasms/metabolism , Drug Delivery Systems/methods
Microbial production of L-malic acid from renewable carbon sources has attracted extensive attention. The reduced cofactor NADPH plays a key role in biotransformation because it participates in both biosynthetic reactions and cellular stress responses. In this study, NADPH or its precursors nicotinamide and nicotinic acid were added to the fermentation medium of Aspergillus niger RG0095, which significantly increased the yield of malic acid by 11%. To further improve the titer and productivity of L-malic acid, we increased the cytoplasmic NADPH levels of A. niger by upregulating the NAD kinases Utr1p and Yef1p. Biochemical analyses demonstrated that overexpression of Utr1p and Yef1p reduced oxidative stress, while also providing more NADPH to catalyze the conversion of glucose into malic acid. Notably, the strain overexpressing Utr1p reached a malate titer of 110.72 ± 1.91 g L-1 after 108 h, corresponding to a productivity of 1.03 ± 0.02 g L-1 h-1. Thus, the titer and productivity of malate were increased by 24.5% and 44.7%, respectively. The strategies developed in this study may also be useful for the metabolic engineering of fungi to produce other industrially relevant bulk chemicals.
Aspergillus niger , Fermentation , Malates , Metabolic Engineering , NADP , Aspergillus niger/metabolism , Aspergillus niger/genetics , Malates/metabolism , Metabolic Engineering/methods , NADP/metabolism , Fungal Proteins/genetics , Fungal Proteins/metabolism , Glucose/metabolism , Phosphotransferases (Alcohol Group Acceptor)/genetics , Phosphotransferases (Alcohol Group Acceptor)/metabolism
Pulmonary hypertension (PH) is a progressive fatal disease with no cure. Canagliflozin (CANA), a novel medication for diabetes, has been found to have remarkable cardiovascular benefits. However, few studies have addressed the effect and pharmacological mechanism of CANA in the treatment of PH. Therefore, our study aimed to investigate the effect and pharmacological mechanism of CANA in treating PH. First, CANA suppressed increased pulmonary artery pressure, right ventricular hypertrophy, and vascular remodeling in both mouse and rat PH models. Network pharmacology, transcriptomics, and biological results suggested that CANA could ameliorate PH by suppressing excessive oxidative stress and pulmonary artery smooth muscle cell proliferation partially through the activation of PPARγ. Further studies demonstrated that CANA inhibited phosphorylation of PPARγ at Ser225 (a novel serine phosphorylation site in PPARγ), thereby promoting the nuclear translocation of PPARγ and increasing its ability to resist oxidative stress and proliferation. Taken together, our study not only highlighted the potential pharmacological effect of CANA on PH but also revealed that CANA-induced inhibition of PPARγ Ser225 phosphorylation increases its capacity to counteract oxidative stress and inhibits proliferation. These findings may stimulate further research and encourage future clinical trials exploring the therapeutic potential of CANA in PH treatment.
Cognitive dysfunctions are one of the key symptoms of schizophrenia (SZ) and major depressive disorder (MDD), which exist not only during the onset of diseases but also before the onset, even after the remission of psychiatric symptoms. With the development of neuroimaging techniques, these non-invasive approaches provide valuable insights into the underlying pathogenesis of psychiatric disorders and information of cognitive remediation interventions. This review synthesizes existing neuroimaging studies to examine domains of cognitive impairment, particularly processing speed, memory, attention, and executive function in SZ and MDD patients. First, white matter (WM) abnormalities are observed in processing speed deficits in both SZ and MDD, with distinct neuroimaging findings highlighting WM connectivity abnormalities in SZ and WM hyperintensity caused by small vessel disease in MDD. Additionally, the abnormal functions of prefrontal cortex and medial temporal lobe are found in both SZ and MDD patients during various memory tasks, while aberrant amygdala activity potentially contributes to a preference to negative memories in MDD. Furthermore, impaired large-scale networks including frontoparietal network, dorsal attention network, and ventral attention network are related to attention deficits, both in SZ and MDD patients. Finally, abnormal activity and volume of the dorsolateral prefrontal cortex (DLPFC) and abnormal functional connections between the DLPFC and the cerebellum are associated with executive dysfunction in both SZ and MDD. Despite these insights, longitudinal neuroimaging studies are lacking, impeding a comprehensive understanding of cognitive changes and the development of early intervention strategies for SZ and MDD. Addressing this gap is critical for advancing our knowledge and improving patient prognosis.
Small ubiquitin-like modifiers (SUMOs) are tiny but important protein regulators involved in orchestrating a broad spectrum of biological processes, either by covalently modifying protein substrates or by noncovalently interacting with other proteins. Here, we report an updated server, GPS-SUMO 2.0, for the prediction of SUMOylation sites and SUMO-interacting motifs (SIMs). For predictor training, we adopted three machine learning algorithms, penalized logistic regression (PLR), a deep neural network (DNN), and a transformer, and used 52 404 nonredundant SUMOylation sites in 8262 proteins and 163 SIMs in 102 proteins. To further increase the accuracy of predicting SUMOylation sites, a pretraining model was first constructed using 145 545 protein lysine modification sites, followed by transfer learning to fine-tune the model. GPS-SUMO 2.0 exhibited greater accuracy in predicting SUMOylation sites than did other existing tools. For users, one or multiple protein sequences or identifiers can be input, and the prediction results are shown in a tabular list. In addition to the basic statistics, we integrated knowledge from 35 public resources to annotate SUMOylation sites or SIMs. The GPS-SUMO 2.0 server is freely available at https://sumo.biocuckoo.cn/. We believe that GPS-SUMO 2.0 can serve as a useful tool for further analysis of SUMOylation and SUMO interactions.
The dysregulated levels of branched chain amino acids (BCAA) contribute to renal fibrosis in chronic kidney disease (CKD), yet specific analysis of BCAA contents and how they are regulated still remain unclear. It is therefore of great scientific interest to understand BCAA catabolism in CKD and develop a sensitive method for simultaneous determination of individual BCAA and their metabolites branched chain α-ketoacids (BCKA). In this work, the important role of BCAA metabolism that drives renal fibrosis in the process of CKD was first revealed by using transcriptomics. The key target genes controlling BCAA metabolism were then validated, that is, mRNA levels of BCKDHA and BCKDHB, the regulating rate-limiting enzymes during BCAA metabolism were abnormally reduced by quantitative PCR (qPCR), and a similar drop-off trend of protein expression of BCKDH, HIBCH and MCCC2 that are closely related to BCAA metabolism was also confirmed by western blotting. Furthermore, we established a novel strategy that simultaneously determines 6 individual BCAA and BCKA in serum and tissue. The method based on dansylhydrazine derivatization and ultra-high performance liquid chromatography-tandem triple quadrupole mass spectrometry (UHPLC-QQQ-MS) achieved to simultaneously determine the contents of BCAA and BCKA, which is efficient and stable. Compared with normal rats, levels of BCAA including leucine, isoleucine and valine in serum and kidney of CKD rats was decreased, while BCKA including α-ketoisocaproic acid, α-ketomethylvaleric acid and α-ketoisovaleric acid was increased. Together, these findings revealed the abnormality of BCAA metabolism in driving the course of kidney fibrosis and CKD. Our current study sheds new light on changes in BCAA metabolism during CKD, and may facilitate development of drugs to treat CKD and renal fibrosis.
BACKGROUND: Dementia is a leading cause of disability in people older than 65 years worldwide. However, diagnosing dementia in its earliest symptomatic stages remains challenging. This study combined specific questions from the AD8 scale with comprehensive health-related characteristics, and used machine learning (ML) to construct diagnostic models of cognitive impairment (CI). METHODS: The study was based on the Shenzhen Healthy Ageing Research (SHARE) project, and we recruited 823 participants aged 65 years and older, who completed a comprehensive health assessment and cognitive function assessments. Permutation importance was used to select features. Five ML models using BalanceCascade were applied to predict CI: a support vector machine (SVM), multilayer perceptron (MLP), AdaBoost, gradient boosting decision tree (GBDT), and logistic regression (LR). An AD8 score ≥ 2 was used to define CI as a baseline. SHapley Additive exPlanations (SHAP) values were used to interpret the results of ML models. RESULTS: The first and sixth items of AD8, platelets, waist circumference, body mass index, carcinoembryonic antigens, age, serum uric acid, white blood cells, abnormal electrocardiogram, heart rate, and sex were selected as predictive features. Compared to the baseline (AUC = 0.65), the MLP showed the highest performance (AUC: 0.83 ± 0.04), followed by AdaBoost (AUC: 0.80 ± 0.04), SVM (AUC: 0.78 ± 0.04), GBDT (0.76 ± 0.04). Furthermore, the accuracy, sensitivity and specificity of four ML models were higher than the baseline. SHAP summary plots based on MLP showed the most influential feature on model decision for positive CI prediction was female sex, followed by older age and lower waist circumference. CONCLUSIONS: The diagnostic models of CI applying ML, especially the MLP, were substantially more effective than the traditional AD8 scale with a score of ≥ 2 points. Our findings may provide new ideas for community dementia screening and to promote such screening while minimizing medical and health resources.
Dementia , Machine Learning , Mass Screening , Humans , Aged , Male , Female , China , Dementia/diagnosis , Mass Screening/methods , Aged, 80 and over , Cognitive Dysfunction/diagnosis
The temperature factor is an important factor affecting the intercomponent forces while maintaining the physical stability of solid-liquid mixed fuels. Through self-designed experimental equipment, feedback was provided on the fuel stratification and density distribution uniformity with solid-liquid volume ratios of 1.25:1 and 1:1 under different temperature conditions. As the viscosity of the liquid increased with decreasing temperature, the ability of the fuel to overcome particle deposition was enhanced. Although none of the three fuel ratios with a solid-liquid volume ratio of 1.25:1 showed stratification, the differences in the liquid bridging forces of the components resulted in an increasingly uneven distribution of density with increasing surface tension of the liquid components. By analyzing the imaging results and measuring the liquid bridge force, it was found that the fuel with a nitromethane mass ratio of 40% had the lowest temperature effect on the solid-liquid contact area and the most uniform density distribution. Properly reducing the surface tension of liquid components could effectively resist the influence of the temperature on the liquid bridge force while maintaining the physical stability of the fuel.
Solid surfaces submerged in liquid in natural environments alter bacterial swimming behavior and serve as platforms for bacteria to form biofilms. In the initial stage of biofilm formation, bacteria detect surfaces and increase the intracellular level of the second messenger c-di-GMP, leading to a reduction in swimming speed. The impact of this speed reduction on bacterial surface swimming remains unclear. In this study, we utilized advanced microscopy techniques to examine the effect of swimming speed on bacterial surface swimming behavior. We found that a decrease in swimming speed reduces the cell-surface distance and prolongs the surface trapping time. Both these effects would enhance bacterial surface sensing and increase the likelihood of cells adhering to the surface, thereby promoting biofilm formation. We also examined the surface-escaping behavior of wild-type Escherichia coli and Pseudomonas aeruginosa, noting distinct surface-escaping mechanisms between the two bacterial species. IMPORTANCE: In the early phase of biofilm formation, bacteria identify surfaces and increase the intracellular level of the second messenger c-di-GMP, resulting in a decrease in swimming speed. Here, we utilized advanced microscopy techniques to investigate the impact of swimming speed on bacterial surface swimming, focusing on Escherichia coli and Pseudomonas aeruginosa. We found that an increase in swimming speed led to an increase in the radius of curvature and a decrease in surface detention time. These effects were explained through hydrodynamic modeling as a result of an increase in the cell-surface distance with increasing swimming speed. We also observed distinct surface-escaping mechanisms between the two bacterial species. Our study suggests that a decrease in swimming speed could enhance the likelihood of cells adhering to the surface, promoting biofilm formation. This sheds light on the role of reduced swimming speed in the transition from motile to sedentary bacterial lifestyles.
BACKGROUND: The tendon-bone interface (TBI) in the rotator cuff has a poor intrinsic capacity for healing, which increases the risk of retear after rotator cuff repair (RCR). However, facilitating regeneration of the TBI still remains a great clinical challenge. Herein, the authors established a novel strategy based on magnetic seeding to enhance the TBI regeneration. HYPOTHESIS: Magnetic seeding bone marrow mesenchymal stem cells labeled with superparamagnetic iron oxide (SPIO-BMSCs) into a biphasic scaffold can promote tendon-bone healing after RCR. STUDY DESIGN: Controlled laboratory study. METHODS: BMSCs were labeled with SPIOs. Prussian blue staining, CCK-8 tests, Western blot, and quantitative reverse transcription polymerase chain reaction (PCR) were used to determine the optimal effect concentration of SPIOs on cell bioactivities and abilities. Then SPIO-BMSCs were magnetically seeded into a biphasic scaffold under a magnetic field. The seeding efficacy was assessed by a scanning electron microscope, and the potential mechanism in chondrogenic differentiation after seeding SPIO-BMSCs into the scaffold was evaluated by Western blot and PCR. Furthermore, the effect of SPIO-BMSC/biphasic scaffold on tendon-bone healing after RCR using a rat model was examined using histological analysis, enzyme-linked immunosorbent assay, and biomechanical evaluation. RESULTS: BMSCs labeled with 100 µg/mL SPIO had no effect on cell bioactivities and the ability of chondrogenic differentiation. SPIO-BMSCs were magnetically seeded into a biphasic scaffold, which offered a high seeding efficacy to enhance chondrogenic differentiation of SPIO-BMSCs via the CDR1as/miR-7/FGF2 pathway for TBI formation in vitro. Furthermore, in vivo application of the biphasic scaffold with magnetically seeded SPIO-BMSCs showed their regenerative potential, indicating that they could significantly accelerate and promote TBI healing with superior biomechanical properties after RCR in a rat rotator cuff tear model. CONCLUSION: Magnetically seeding SPIO-BMSCs into a biphasic scaffold enhanced seeding efficacy to promote cell distribution and condensation. This construct enhanced the chondrogenesis process via the CDR1as/miR-7/FGF2 pathway and further promoted tendon-bone healing after RCR in a rat rotator cuff tear model. CLINICAL RELEVANCE: This study provides an alternative strategy for improving TBI healing after RCR.
Herein, we present a novel strategy for synthesizing polyfluorinated compounds by the fluorotrifluoromethylation of olefins, which was achieved through a new trifluoromethyl-iodine(III) reagent TFNI-1. TFNI-1 was readily synthesized via a three-step process, and its structure was characterized by NMR spectroscopy and X-ray crystallography. It is shown by radical trapping and radical clock experiments that the reaction involves the CF3 radical intermediate.
