Exerkine FNDC5/irisin-enriched exosomes promote proliferation and inhibit ferroptosis of osteoblasts through interaction with Caveolin-1.

Postmenopausal osteoporosis is a prevalent metabolic bone disorder characterized by a decrease in bone mineral density and deterioration of bone microstructure. Despite the high prevalence of this disease, no effective treatment for osteoporosis has been developed. Exercise has long been considered a potent anabolic factor that promotes bone mass via upregulation of myokines secreted by skeletal muscle, exerting long-term osteoprotective effects and few side effects. Irisin was recently identified as a novel myokine that is significantly upregulated by exercise and could increase bone mass. However, the mechanisms underlying exercise-induced muscle-bone crosstalk remain unclear. Here, we identified that polyunsaturated fatty acids (arachidonic acid and docosahexaenoic acid) are increased in skeletal muscles following a 10-week treadmill exercise programme, which then promotes the expression and release of FNDC5/irisin. In osteoblasts, irisin binds directly to Cav1, which recruits and interacts with AMP-activated protein kinase α (AMPKα) to activate the AMPK pathway. Nrf2 is the downstream target of the AMPK pathway and increases the transcription of HMOX1 and Fpn. HMOX1 is involved in regulating the cell cycle and promotes the proliferation of osteoblasts. Moreover, upregulation of Fpn in osteoblasts enhanced iron removal, thereby suppressing ferroptosis in osteoblasts. Additionally, we confirmed that myotube-derived exosomes are involved in the transportation of irisin and enter osteoblasts through caveolae-mediated endocytosis. In conclusion, our findings highlight the crucial role of irisin, present in myotube-derived exosomes, as a crucial regulator of exercise-induced protective effects on bone, which provides novel insights into the mechanisms underlying exercise-dependent treatment of osteoporosis.

Temperature Self-Compensating Intelligent Wireless Measuring Contact Lens for Quantitative Intraocular Pressure Monitoring.

Flexible bioelectronic devices that can perform real-time and accurate intraocular pressure (IOP) monitoring in both clinical and home settings hold significant implications for the diagnosis and treatment of glaucoma, yet they face challenges due to the open physiological environment of the ocular. Herein, we develop an intelligent wireless measuring contact lens (WMCL) incorporating a dual inductor-capacitor-resistor (LCR) resonant system to achieve temperature self-compensation for quantitative IOP monitoring in different application environments. The WMCL utilizes a compact circuitry design, which enables the integration of low-frequency and high-frequency resonators within a single layer of a sensing circuit without causing visual impairment. Mechanically guided microscale 3D encapsulation strategy combined with flexible circuit printing techniques achieves the surface-adaptive fabrication of the WMCL. The specific design of frequency separation imparts distinct temperature response characteristics to the dual resonators, and the linear combination of the dual resonators can eliminate the impact of temperature variations on measurement accuracy. The WMCL demonstrates outstanding sensitivity and linearity in monitoring the IOP of porcine eyes in vitro while maintaining satisfactory measurement accuracy even with internal temperature variations exceeding 10 °C. Overcoming the impact of temperature variations on IOP monitoring from the system level, the WMCL showcases immense potential as the next generation of all-weather IOP monitoring devices.

Fusion of biomedical imaging studies for increased sample size and diversity: a case study of brain MRI.

Data collection, curation, and cleaning constitute a crucial phase in Machine Learning (ML) projects. In biomedical ML, it is often desirable to leverage multiple datasets to increase sample size and diversity, but this poses unique challenges, which arise from heterogeneity in study design, data descriptors, file system organization, and metadata. In this study, we present an approach to the integration of multiple brain MRI datasets with a focus on homogenization of their organization and preprocessing for ML. We use our own fusion example (approximately 84,000 images from 54,000 subjects, 12 studies, and 88 individual scanners) to illustrate and discuss the issues faced by study fusion efforts, and we examine key decisions necessary during dataset homogenization, presenting in detail a database structure flexible enough to accommodate multiple observational MRI datasets. We believe our approach can provide a basis for future similarly-minded biomedical ML projects.

Triboelectric-Responsive Drug Delivery Hydrogel for Accelerating Infected Wound Healing.

Electrotherapy is of great interest in the field of tissue repair as an effective, well-tolerated, and noninvasive treatment. Triboelectric nanogenerator (TENG) has shown advantages in promoting wound healing due to its peak output characteristic and low Joule heating effect. However, it is limited in infected wound healing due to poor antimicrobial capacity. Here, a wearable triboelectric stimulator (WTS) is developed that consists of a flexible TENG (F-TENG) and a triboelectric-responsive drug delivery hydrogel (TR-DDH) for healing of bacterium-infected wounds. F-TENG can generate pulsed current to wounds by converting mechanical energy from body movements. Polypyrrole is prone to reduction and volume contraction under electrical stimulation, resulting in desorption of curcumin nanoparticles (CUR NPs) from the polypyrrole in TR-DDH. Therefore, the highly efficient and controllable release of CUR NPs can be achieved by triboelectric stimulation. According to the in vitro and in vivo experiments, WTS has the greatest antimicrobial effect and the fastest promotion of infected wound healing compared to treatment with electrical stimulation or curcumin. Finally, the safety assessment demonstrates that the WTS has excellent tissue safety for chronic wound healing. Synergistic therapy with WTS provides an efficient strategy for chronic wound healing and smart-responsive drug delivery systems.

Isoperimetric Constraint Inference for Discrete-Time Nonlinear Systems Based on Inverse Optimal Control.

In this article, the problem of inferring unknown isoperimetric constraints is considered given optimal state and control trajectories that solve the optimal control problem with isoperimetric constraints. By exploiting Pontryagin's principle, the recovery equations for unknown isoperimetric constraints are established. Under verifiable dimensionality condition and matrix rank condition, the proposed method is guaranteed to infer the unknown isoperimetric constraints exactly. Furthermore, the proposed method is extended to multiple trajectory setting. Finally, the effectiveness of the proposed method is illustrated by two simulation examples with various settings.

Identification of structural origins of complex charge heterogeneity in therapeutic ACE2Fc fusion protein facilitated by free-flow isoelectric focusing.

Fc Fusion protein represents a versatile molecular platform with considerable potential as protein therapeutics of which the charge heterogeneity should be well characterized according to regulatory guidelines. Angiotensin-converting enzyme 2 Fc fusion protein (ACE2Fc) has been investigated as a potential neutralizing agent to various coronaviruses, including the lingering SARS-CoV-2, as this coronavirus must bind to ACE2 to allow for its entry into host cells. ACE2Fc, an investigational new drug developed by Henlius (Shanghai China), has passed the Phase I clinical trial, but its huge amount of charge isoforms and complicated charge heterogeneity posed a challenge to charge variant investigation in pharmaceutical development. We employed offline free-flow isoelectric focusing (FF-IEF) fractionation, followed by detailed characterization of enriched ACE2Fc fractions, to unveil the structural origins of charge heterogeneity in ACE2Fc expressed by recombinant CHO cells. We adopted a well-tuned 3-component separation medium for ACE2Fc fractionation, the highest allowable voltage to maximize the FF-IEF separation window and a mild Protein A elution method for preservation of protein structural integrity. Through peptide mapping and other characterizations, we revealed that the intricate profiles of ACE2Fc charge heterogeneity are mainly caused by highly sialylated multi-antenna N-glycosylation. In addition, based on fraction characterization and in silico glycoprotein model analysis, we discovered that the large acidic glycans at N36, N73, and N305 of ACE2Fc were able to decrease the binding activity towards Spike (S) protein of SARS-CoV-2. Our study exemplifies the value of FF-IEF in highly complex fusion protein characterization and revealed a quantitative sialylation-activity relationship in ACE2Fc.

HPCDNet: Hybrid position coding and dual-frquency domain transform network for low-light image enhancement.

Low-light image enhancement (LLIE) improves lighting to obtain natural normal-light images from images captured under poor illumination. However, existing LLIE methods do not effectively utilize positional and frequency domain image information. To address this limitation, we proposed an end-to-end low-light image enhancement network called HPCDNet. HPCDNet uniquely integrates a hybrid positional coding technique into the self-attention mechanism by appending hybrid positional codes to the query and key, which better retains spatial positional information in the image. The hybrid positional coding can adaptively emphasize important local structures to improve modeling of spatial dependencies within low-light images. Meanwhile, frequency domain image information lost under low-light is recovered via discrete wavelet and cosine transforms. The resulting two frequency domain feature types are weighted and merged using a dual-attention module. More effective use of frequency domain information enhances the network's ability to recreate details, improving visual quality of enhanced low-light images. Experiments demonstrated that our approach can heighten visibility, contrast and color properties of low-light images while better preserving details and textures than previous techniques.

Research on the relationship between CEO career variety, digital knowledge base extension, and digital transformation in the context of digital merger and acquisition: The case of China's new generation of information technology firms.

This study examines the relationship between CEO career variety, digital knowledge base extension, and digital transformation in a digital M&A context. An empirical test was conducted using regression analysis with the digital M&A events of the new generation of information technology firms in China as the research sample. The results reveal that CEO career variety has a positive effect on digital transformation in the digital M&A context and that digital knowledge-base extension plays a mediating role. Moreover, the heterogeneity impact analysis indicated that the moderating effects of geographical distance, knowledge disparity, and cultural difference between target and acquirer firms on the above relationships vary greatly: geographical distance has a negative moderating effect, cultural difference has a positive moderating effect, and the moderating effects of both geographical distance and cultural difference are realized through mediating effects, but none of the moderating effects of knowledge disparity are significant.

SCMEA: A stacked co-enhanced model for entity alignment based on multi-aspect information fusion and bidirectional contrastive learning.

Entity alignment refers to discovering the entity pairs with the same realistic meaning in different knowledge graphs. This technology is of great significance for completing and fusing knowledge graphs. Recently, methods based on knowledge representation learning have achieved remarkable achievements in entity alignment. However, most existing approaches do not mine hidden information in the knowledge graph as much as possible. This paper suggests SCMEA, a novel cross-lingual entity alignment framework based on multi-aspect information fusion and bidirectional contrastive learning. SCMEA initially adopts diverse representation learning models to embed multi-aspect information of entities and integrates them into a unified embedding space with an adaptive weighted mechanism to overcome the missing information and the problem of different-aspect information are not uniform. Then, we propose a stacked relation-entity co-enhanced model to further improve the representations of entities, wherein relation representation is modeled using an Entity Collector with Global Entity Attention. Finally, a combined loss function based on improved bidirectional contrastive learning is introduced to optimize model parameters and entity representation, effectively mitigating the hubness problem and accelerating model convergence. We conduct extensive experiments to evaluate the alignment performance of SCMEA. The overall experimental results, ablation studies, and analysis performed on five cross-lingual datasets demonstrate that our model achieves varying degrees of performance improvement and verifies the effectiveness and robustness of the model.

Multidomain interventions based on a life-course model to prevent dementia in at-risk Chinese older adults: A randomized controlled trial.

BACKGROUND: Due to the time-dependent effect of specific risk factors for dementia, multidomain interventions based on a life-course model might achieve optimal preventive effects against dementia. OBJECTIVE: The purpose of this study was to investigate the effectiveness of multidomain interventions based on a life-course model of modifiable risk factors for dementia in at-risk Chinese older adults. DESIGN: This was a two-arm, proof-of-concept, randomized controlled trial. SETTING AND PARTICIPANTS: We randomly assigned 96 community-dwelling at-risk adults aged 60 years or older in a 1:1 ratio to either the 6-month multidomain intervention group (dementia literacy, physical activity, cognitive training, social activity and optional modules) or the control group (health education). METHODS: The primary outcomes were the dementia risk score and cognitive composite Z score. The secondary outcomes included the individual components of the dementia risk score (protective and risk factors) and cognitive composite Z score (global cognition, memory, executive function and language), social isolation (loneliness, social contact, and social participation), dementia literacy and prevention belief. Linear mixed models with maximum likelihood estimation were used to calculate the outcomes between the groups over time. RESULTS: The primary analyses showed that the dementia risk score was significantly lower (p < 0.001) and that the cognitive composite Z score was significantly higher (p = 0.013) in the multidomain intervention group than in the control group. Baseline characteristics did not modify the effects of the multidomain interventions (p value for interaction > 0.05). For secondary outcomes, statistically significant group × time interactions were observed for the protective (p = 0.001) and risk factors (p = 0.049), as well as in executive function (p = 0.020), loneliness (p = 0.029), dementia literacy (p < 0.001) and prevention belief (p < 0.001). CONCLUSIONS: Multidomain interventions based on a life-course model are feasible and have the potential to reduce dementia risk and improve cognitive function in at-risk Chinese older adults. REGISTRATION: The trial was registered at the Chinese Clinical Trials Registry (ChiCTR2100053417).

Detection of peach soluble solids based on near-infrared spectroscopy with High Order Spatial Interaction network.

BACKGROUND: Due to the scalability of deep learning technology, researchers have applied it to the non-destructive testing of peach internal quality. In addition, the soluble solids content (SSC) is an important internal quality indicator that determines the quality of peaches. Peaches with high SSC have a sweeter taste and better texture, making them popular in the market. Therefore, SSC is an important indicator for measuring peach internal quality and making harvesting decisions. RESULTS: This article presents the High Order Spatial Interaction Network (HOSINet), which combines the Position Attention Module (PAM) and Channel Attention Module (CAM). Additionally, a feature wavelength selection algorithm similar to the Group-based Clustering Subspace Representation (GCSR-C) is used to establish the Position and Channel Attention Module-High Order Spatial Interaction (PC-HOSI) model for peach SSC prediction. The accuracy of this model is compared with traditional machine learning and traditional deep learning models. Finally, the permutation algorithm is combined with deep learning models to visually evaluate the importance of feature wavelengths. Increasing the order of the PC-HOSI model enhances its ability to learn spatial correlations in the dataset, thus improving its predictive performance. CONCLUSION: The optimal model, PC-HOSI model, performed well with an order of 3 (PC-HOSI-3), with a root mean square error of 0.421 °Brix and a coefficient of determination of 0.864. Compared with traditional machine learning and deep learning algorithms, the coefficient of determination for the prediction set was improved by 0.07 and 0.39, respectively. The permutation algorithm also provided interpretability analysis for the predictions of the deep learning model, offering insights into the importance of spectral bands. These results contribute to the accurate prediction of SSC in peaches and support research on interpretability of neural network models for prediction. © 2024 Society of Chemical Industry.

Ocular biomarkers of cognitive decline based on deep-learning retinal vessel segmentation.

BACKGROUND: The current literature shows a strong relationship between retinal neuronal and vascular alterations in dementia. The purpose of the study was to use NFN+ deep learning models to analyze retinal vessel characteristics for cognitive impairment (CI) recognition. METHODS: We included 908 participants from a community-based cohort followed for over 15 years (the prospective KaiLuan Study) who underwent brain magnetic resonance imaging (MRI) and fundus photography between 2021 and 2022. The cohort consisted of both cognitively healthy individuals (N = 417) and those with cognitive impairment (N = 491). We employed the NFN+ deep learning framework for retinal vessel segmentation and measurement. Associations between Retinal microvascular parameters (RMPs: central retinal arteriolar / venular equivalents, arteriole to venular ratio, fractal dimension) and CI were assessed by Pearson correlation. P < 0.05 was considered statistically significant. The correlation between the CI and RMPs were explored, then the correlation coefficients between CI and RMPs were analyzed. Random Forest nonlinear classification model was used to predict whether one having cognitive decline or not. The assessment criterion was the AUC value derived from the working characteristic curve. RESULTS: The fractal dimension (FD) and global vein width were significantly correlated with the CI (P < 0.05). Age (0.193), BMI (0.154), global vein width (0.106), retinal vessel FD (0.099), and CRAE (0.098) were the variables in this model that were ranked in order of feature importance. The AUC values of the model were 0.799. CONCLUSIONS: Establishment of a predictive model based on the extraction of vascular features from fundus images has a high recognizability and predictive power for cognitive function and can be used as a screening method for CI.

The association of global vessel width with cognitive decline and cerebral small vessel disease burden in the KaiLuan study.

Background: As the retinal microvasculature shares similarities with the cerebral microvasculature, numerous studies have shown that retinal vascular is associated with cognitive decline. In addition, several population-based studies have confirmed the association between retinal vascular and cerebral small vessel disease (CSVD) burden. However, the association of retinal vascular with CSVD burden as well as cognitive function has not been explored simultaneously. This study investigated the relations of retinal microvascular parameters (RMPs) with CSVD burden and cognitive function. Methods: We conducted a cross-sectional study of participants in the KaiLuan study. Data were collected from subjects aged ≥18 years old who could complete retinal photography and brain magnetic resonance imaging (MRI) between December 2020 to October 2021 in the Kailuan community of Tangshan. RMPs were evaluated using a deep learning system. The cognitive function was measured using the Montreal Cognitive Assessment (MoCA). We conducted logistic regression models, and mediation analysis to evaluate the associations of RMPs with CSVD burden and cognitive decline. Results: Of the 905 subjects (mean age: 55.42±12.02 years, 54.5% female), 488 (53.9%) were classified with cognitive decline. The fractal dimension (FD) [odds ratio (OR), 0.098, 95% confidence interval (CI): 0.015-0.639, P=0.015] and global vein width (OR: 1.010, 95% CI: 1.005-1.015, P<0.001) were independent risk factors for cognitive decline after adjustment for potential confounding factors. The global artery width was significantly associated with severe CSVD burden (OR: 0.985, 95% CI: 0.974-0.997, P=0.013). The global vein width was sightly associated with severe CSVD burden (OR: 1.005, 95% CI: 1.000-1.010, P=0.050) after adjusting for potential confounders. The multivariable-adjusted odds ratios (95% CI) in highest tertile versus lowest tertile of global vein width were 1.290 (0.901-1.847) for cognitive decline and 1.546 (1.004-2.290) for severe CSVD burden, respectively. Moreover, CSVD burden played a partial mediating role in the association between global vein width and cognitive function (mediating effect 6.59%). Conclusions: RMPs are associated with cognitive decline and the development of CSVD. A proportion of the association between global vein width and cognitive decline may be attributed to the presence of CSVD burden.

Genome-wide analysis of the U-box E3 ligases gene family in potato (Solanum tuberosum L.) and overexpress StPUB25 enhance drought tolerance in transgenic Arabidopsis.

BACKGROUND: Plant U-box (PUB) E3 ubiquitin ligases have vital effects on various biological processes. Therefore, a comprehensive and systematic identification of the members of the U-box gene family in potato will help to understand the evolution and function of U-box E3 ubiquitin ligases in plants. RESULTS: This work identified altogether 74 PUBs in the potato (StPUBs) and examined their gene structures, chromosomal distributions, and conserved motifs. There were seventy-four StPUB genes on ten chromosomes with diverse densities. As revealed by phylogenetic analysis on PUBs within potato, Arabidopsis, tomato (Solanum lycopersicum), cabbage (Brassica oleracea), rice (Oryza sativa), and corn (Zea mays), were clustered into eight subclasses (C1-C8). According to synteny analysis, there were 40 orthologous StPUB genes to Arabidopsis, 58 to tomato, 28 to cabbage, 7 to rice, and 8 to corn. In addition, RNA-seq data downloaded from PGSC were utilized to reveal StPUBs' abiotic stress responses and tissue-specific expression in the doubled-monoploid potato (DM). Inaddition, we performed RNA-seq on the 'Atlantic' (drought-sensitive cultivar, DS) and the 'Qingshu NO.9' (drought-tolerant cultivar, DT) in early flowering, full-blooming, along with flower-falling stages to detect genes that might be involved in response to drought stress. Finally, quantitative real-time PCR (qPCR) was carried out to analyze three candidate genes for their expression levels within 100 mM NaCl- and 10% PEG 6000 (w/v)-treated potato plantlets for a 24-h period. Furthermore, we analyzed the drought tolerance of StPUB25 transgenic plants and found that overexpression of StPUB25 significantly increased peroxidase (POD) activity, reduced ROS (reactive oxygen species) and MDA (malondialdehyde) accumulation compared with wild-type (WT) plants, and enhancing drought tolerance of the transgenic plants. CONCLUSION: In this study, three candidate genes related to drought tolerance in potato were excavated, and the function of StPUB25 under drought stress was verified. These results should provide valuable information to understand the potato StPUB gene family and investigate the molecular mechanisms of StPUBs regulating potato drought tolerance.

Hyperspectral Imaging Using a Convolutional Neural Network with Transformer for the Soluble Solid Content and pH Prediction of Cherry Tomatoes.

Cherry tomatoes are cultivated worldwide and favored by consumers of different ages. The soluble solid content (SSC) and pH are two of the most important quality attributes of cherry tomatoes. The rapid and non-destructive measurement of the SSC and pH of cherry tomatoes is of great significance to their production and consumption. In this research, hyperspectral imaging combined with a convolutional neural network with Transformer (CNN-Transformer) was utilized to analyze the SSC and pH of cherry tomatoes. Conventional machine learning and deep learning models were established for the determination of the SSC and pH. The findings demonstrated that CNN-Transformer yielded outstanding results in predicting the SSC, with the coefficient of determination of calibration (R2C), validation (R2V), and prediction (R2P) for the SSC being 0.83, 0.87, and 0.83, respectively. Relatively worse results were obtained for the pH value prediction, with R2C, R2V, and R2P values of 0.74, 0.68, and 0.60, respectively. Furthermore, the visualization of the CNN-Transformer model revealed the wavelength weight distributions, indicating that the 1380-1650 nm range served as the characteristic band for the SSC, while the spectral range at 945-1280 nm was the characteristic band for pH. In conclusion, integrating spectral information features with the attention mechanism of Transformer through a convolutional neural network can enhance the accuracy of predicting the SSC and pH for cherry tomatoes.

Delving Into the Devils of Bird's-Eye-View Perception: A Review, Evaluation and Recipe.

Learning powerful representations in bird's-eye-view (BEV) for perception tasks is trending and drawing extensive attention both from industry and academia. Conventional approaches for most autonomous driving algorithms perform detection, segmentation, tracking, etc., in a front or perspective view. As sensor configurations get more complex, integrating multi-source information from different sensors and representing features in a unified view come of vital importance. BEV perception inherits several advantages, as representing surrounding scenes in BEV is intuitive and fusion-friendly; and representing objects in BEV is most desirable for subsequent modules as in planning and/or control. The core problems for BEV perception lie in (a) how to reconstruct the lost 3D information via view transformation from perspective view to BEV; (b) how to acquire ground truth annotations in BEV grid; (c) how to formulate the pipeline to incorporate features from different sources and views; and (d) how to adapt and generalize algorithms as sensor configurations vary across different scenarios. In this survey, we review the most recent works on BEV perception and provide an in-depth analysis of different solutions. Moreover, several systematic designs of BEV approach from the industry are depicted as well. Furthermore, we introduce a full suite of practical guidebook to improve the performance of BEV perception tasks, including camera, LiDAR and fusion inputs. At last, we point out the future research directions in this area. We hope this report will shed some light on the community and encourage more research effort on BEV perception.

Enhanced Horizontal Dipole Orientation by Novel Penta-Helicene Anthracene-Based Host for Efficient Blue Fluorescent OLEDs.

Due to the relatively low photoluminescence quantum yield (PLQY) and horizontal dipole orientation of doped films, anthracene-based fluorescent organic light-emitting diodes (F-OLEDs) have faced a great challenge to achieve high external quantum efficiency (EQE). Herein, a novel approach is introduced by incorporating penta-helicene into anthracene, presented as linear-shaped 3-(4-(10-phenylanthracen-9-yl)phenyl)dibenzo[c,g]phenanthrene (BABH) and 3-(4-(10-(naphthalen-2-yl)anthracen-9-yl)phenyl)dibenzo[c,g]phenanthrene (NABH). These blue hosts exhibit minimal intermolecular overlap of π-π stacking, effectively suppressing excimer formation, which facilitates the effective transfer of singlet energy to the fluorescent dopant for PLQY as high as 90%. Additionally, the as-obtained two hosts of BABH and NABH have effectively demonstrated major horizontal components transition dipole moments (TDM) and high thermal stability with glass transitional temperature (Tg ) surpassing 188 °C, enhancing the horizontal dipole orientation of their doped films to be 89% and 93%, respectively. The OLEDs based on BABH and NABH exhibit excellent EQE of 10.5% and 12.4% at 462 nm and device lifetime up to 90% of the initial luminance over 4500 h at 100 cd m-2 , which has firmly established them as among the most efficient blue F-OLEDs based on anthracene to date to the best knowledge. This work provides an instructive strategy to design an effective host for highly efficient and stable F-OLEDs.

Inhibition of PDGFRß alleviates endothelial cell apoptotic injury caused by DRP-1 overexpression and mitochondria fusion failure after mitophagy.

Kawasaki disease (KD), described as "mucocutaneous lymph node syndrome", affects infants and toddlers. Patients with KD suffer from an inflammatory cascade leading to vasculitis with a predilection for coronary arteries. While the symptoms and pathogenesis of KD have received more and more attention, the precise mechanisms are still debated. Researches show that endothelial dysfunction process in KD leads to arterial damage and affect clinical outcome. In this study, we constructed a Candida albicans water soluble fraction (CAWS)-induced KD murine model and penetrated investigating the mechanisms behind endothelial dysfunction. CAWS-induced mice presented remarkably elevated vascular endothelial cell growth factor (VEGF) levels. Abundant expression of VEGF was documented in all vessels that showed edema from acute KD. It has been reported that Platelet-derived growth factor (PDGF) co-expression normalizes VEGF-induced aberrant angiogenesis. Hyperexpression of PDGFRß was induced in the thickened medial layer and vascular endothelium of KD mice. Masitinib (Mas) is an oral tyrosine kinase inhibitor of numerous targets, which can selectively target PDGFR signaling. We set out to explore whether Mas could regulate coronary pathology in KD. Mas administration significantly reduced the VEGF-induced endothelial cells migration. NOX4 was activated in vascular endothelial cells to produce more ROS. Mitochondrial dysregulated fission and mitophagy caused by DRP-1 overexpression precipitated the arterial endothelial cells injury. Here, mitophagy seemed to work as the driving force of DRP-1/Bak/BNIP3-dependent endothelial cells apoptosis. In summary, how mitophagy is regulated by DRP-1 under pathologic status is critical and complex, which may contribute to the development of specific therapeutic interventions in cardiovascular diseases patients, for example Masatinib, the inhibitor of PDGFRß. FACTS AND QUESTIONS: Kawasaki disease causing systemic vasculitis, affects infants and toddlers. Coronary artery injury remains the major causes of morbidity and mortality. DRP-1 overexpression induces DRP-1/Bak/BNIP3-dependent endothelial cells apoptosis. PDGFRß was high-expressed in the thickened medial layer of CAWS-induced KD mice. Inhibition of PDGFRß signaling alleviates arterial endothelial cells injury.

Anterior Capsulotomy for Refractory Obsessive-Compulsive Disorder: A Tractography and Lesion Geometry study.

INTRODUCTION: A bilateral anterior capsulotomy effectively treats refractory obsessive-compulsive disorder (OCD). We investigated the geometry of lesions and disruption of white matter pathways within the anterior limb of the internal capsule (ALIC) in patients with different outcomes. METHODS: In this retrospective study, we analyzed data from 18 patients with refractory OCD who underwent capsulotomies. Patients were grouped into "responders" and "nonresponders" based on the percentage of decrease in the Yale-Brown Obsessive-Compulsive Scale (YBOCS) after surgery. We investigated neurobehavioral adverse effects and analyzed the overlap between lesions and the ventromedial prefrontal (vmPFC) and dorsolateral prefrontal (dlPFC) pathways. Probabilistic maps were constructed to investigate the relationship between lesion location and clinical outcomes. RESULTS: Of the 18 patients who underwent capsulotomies, 12 were responders (>35% improvement in YBOCS), and six were nonresponders. The vmPFC pathway was more involved than the dlPFC pathway in responders (p = 0.01), but no significant difference was observed in nonresponders (p = 0.10). The probabilistic voxel-wise efficacy map showed a relationship between ventral voxels within the ALIC with symptom improvement. Weight gains occurred in 11/18 (61%) patients and could be associated with medial voxels within the ALIC. CONCLUSION: The optimal outcome after capsulotomy in refractory OCD is linked to vmPFC disruption in the ALIC. Medial voxels within the ALIC could be associated with weight gains following capsulotomy.

Dermal effects and pharmacokinetic evaluation of the lidocaine/prilocaine cream in healthy Chinese volunteers.

BACKGROUND: EMLA cream is a local anesthetic. The pharmacokinetics and dermal effects of a topical anesthetic formulation has not been evaluated in healthy Chinese volunteers. MATERIALS AND METHODS: The Pharmacokinetics of the lidocaine/prilocaine test (T) or reference (R, EMLA) cream were evaluated in a fasting, single-dose, two-period crossover bioequivalent study conducted in 40 healthy Chinese volunteers. Meanwhile, the dermal effects including blanching, erythema, temperature sensation, edema, and skin rash were also evaluated during the study. RESULTS: After applied 15 g of the cream for 4 h to a 100 cm2 area under plastic occlusive film on the skin of the thigh of healthy volunteers, the results of the pharmacokinetic study showed that the active components absorbed in skin from topical products was relatively low compared with most system absorption drugs. After the removal of the residual anesthetic cream, there was a vascular biphasic response with initial transient blanching which reaches a peak at 4.5 h and later more persisting period erythema. The change of temperature sensory sensitivity reached the peak value at 4.5-6 h.There was no statistically significant difference of the changes after application the lidocaine/prilocaine T or R cream in subjects. In general, the lidocaine/prilocaine T or R cream was well tolerated. CONCLUSION: The method described a model for investigations of pharmacokinetics and pharmacodynamics of topical lidocaine/prilocaine cream. Except the plasma drug level indicator, these pharmacodynamics data should also be evaluated in the anesthetic transdermal pharmacokinetics study. CLINICAL TRIAL REGISTRATION: CTR20211544; registered in http://www.chinadrugtrials.org.cn/ at September 2021.