Delivery-mediated exosomal therapeutics in ischemia-reperfusion injury: advances, mechanisms, and future directions.

Ischemia-reperfusion injury (IRI) poses significant challenges across various organ systems, including the heart, brain, and kidneys. Exosomes have shown great potentials and applications in mitigating IRI-induced cell and tissue damage through modulating inflammatory responses, enhancing angiogenesis, and promoting tissue repair. Despite these advances, a more systematic understanding of exosomes from different sources and their biotransport is critical for optimizing therapeutic efficacy and accelerating the clinical adoption of exosomes for IRI therapies. Therefore, this review article overviews the administration routes of exosomes from different sources, such as mesenchymal stem cells and other somatic cells, in the context of IRI treatment. Furthermore, this article covers how the delivered exosomes modulate molecular pathways of recipient cells, aiding in the prevention of cell death and the promotions of regeneration in IRI models. In the end, this article discusses the ongoing research efforts and propose future research directions of exosome-based therapies.

Neuronal innervation regulates the secretion of neurotrophic myokines and exosomes from skeletal muscle.

Myokines and exosomes, originating from skeletal muscle, are shown to play a significant role in maintaining brain homeostasis. While exercise has been reported to promote muscle secretion, little is known about the effects of neuronal innervation and activity on the yield and molecular composition of biologically active molecules from muscle. As neuromuscular diseases and disabilities associated with denervation impact muscle metabolism, we hypothesize that neuronal innervation and firing may play a pivotal role in regulating secretion activities of skeletal muscles. We examined this hypothesis using an engineered neuromuscular tissue model consisting of skeletal muscles innervated by motor neurons. The innervated muscles displayed elevated expression of mRNAs encoding neurotrophic myokines, such as interleukin-6, brain-derived neurotrophic factor, and FDNC5, as well as the mRNA of peroxisome-proliferator-activated receptor γ coactivator 1α, a key regulator of muscle metabolism. Upon glutamate stimulation, the innervated muscles secreted higher levels of irisin and exosomes containing more diverse neurotrophic microRNAs than neuron-free muscles. Consequently, biological factors secreted by innervated muscles enhanced branching, axonal transport, and, ultimately, spontaneous network activities of primary hippocampal neurons in vitro. Overall, these results reveal the importance of neuronal innervation in modulating muscle-derived factors that promote neuronal function and suggest that the engineered neuromuscular tissue model holds significant promise as a platform for producing neurotrophic molecules.

Differential contributions of G protein- or arrestin subtype-mediated signalling underlie urocortin 3-induced somatostatin secretion in pancreatic δ cells.

BACKGROUND AND PURPOSE: Pancreatic islets are modulated by cross-talk among different cell types and paracrine signalling plays important roles in maintaining glucose homeostasis. Urocortin 3 (UCN3) secreted by pancreatic ß cells activates the CRF2 receptor (CRF2R) and downstream pathways mediated by different G protein or arrestin subtypes in δ cells to cause somatostatin (SST) secretion, and constitutes an important feedback circuit for glucose homeostasis. EXPERIMENTAL APPROACH: Here, we used Arrb1-/-, Arrb2-/-, Gsfl/fl and Gqfl/fl knockout mice, the G11-shRNA-GFPfl/fl lentivirus, as well as functional assays and pharmacological characterization to study how the coupling of Gs, G11 and ß-arrestin1 to CRF2R contributed to UCN3-induced SST secretion in pancreatic δ cells. KEY RESULTS: Our study showed that CRF2R coupled to a panel of G protein and arrestin subtypes in response to UCN3 engagement. While RyR3 phosphorylation by PKA at the S156, S2706 and S4697 sites may underlie the Gs-mediated UCN3- CRF2R axis for SST secretion, the interaction of SYT1 with ß-arrestin1 is also essential for efficient SST secretion downstream of CRF2R. The specific expression of the transcription factor Stat6 may contribute to G11 expression in pancreatic δ cells. Furthermore, we found that different UCN3 concentrations may have distinct effects on glucose homeostasis, and these effects may depend on different CRF2R downstream effectors. CONCLUSIONS AND IMPLICATIONS: Collectively, our results provide a landscape view of signalling mediated by different G protein or arrestin subtypes downstream of paracrine UCN3- CRF2R signalling in pancreatic ß-δ-cell circuits, which may facilitate the understanding of fine-tuned glucose homeostasis networks.

Genome-wide analysis and expression profiling of the HD-ZIP gene family in kiwifruit.

The homeodomain-leucine zipper (HD-Zip) gene family plays a pivotal role in plant development and stress responses. Nevertheless, a comprehensive characterization of the HD-Zip gene family in kiwifruit has been lacking. In this study, we have systematically identified 70 HD-Zip genes in the Actinidia chinensis (Ac) genome and 55 in the Actinidia eriantha (Ae) genome. These genes have been categorized into four subfamilies (HD-Zip I, II, III, and IV) through rigorous phylogenetic analysis. Analysis of synteny patterns and selection pressures has provided insights into how whole-genome duplication (WGD) or segmental may have contributed to the divergence in gene numbers between these two kiwifruit species, with duplicated gene pairs undergoing purifying selection. Furthermore, our study has unveiled tissue-specific expression patterns among kiwifruit HD-Zip genes, with some genes identified as key regulators of kiwifruit responses to bacterial canker disease and postharvest processes. These findings not only offer valuable insights into the evolutionary and functional characteristics of kiwifruit HD-Zips but also shed light on their potential roles in plant growth and development.

Association of subjective and objective sleep disturbance with the spectrum of gastroesophageal reflux disease.

STUDY OBJECTIVES: The relationship between obstructive sleep apnea (OSA) and gastroesophageal reflux disease (GERD) is complex. We aim to determine the association of subjective and objective sleep parameters with diverse manifestations of the GERD spectrum. METHODS: We prospectively recruited 561 subjects who underwent an electrocardiogram-based cardiopulmonary coupling (CPC) for OSA screening during a health check-up. All subjects received the Reflux Disease Questionnaire (RDQ) and an upper endoscopy to determine the presence of troublesome reflux symptoms and erosive esophagitis (EE). Sleep quality was evaluated by the Pittsburgh Sleep Quality Index (PSQI) and sleep dysfunction was defined as a PSQI > 5. OSA was defined as a CPC-derived apnea/hypopnea index exceeding 15 events per hour. Comparisons were made between subjects on the GERD spectrum with respect to their various subjective and objective sleep parameters. RESULTS: Among the 277 subjects with GERD (49.4%), 198 (35.3%) had EE. Subjects with GERD had higher scores of PSQI (6.99 ± 3.97 vs. 6.07 ± 3.73, P = 0.005) and a higher prevalence of sleep dysfunction (60.6% vs. 49.6%, P = 0.009). Subjects with EE had a higher prevalence of OSA (42.9% vs. 33.9%, P = 0.034). Along the GERD spectrum, symptomatic EE subjects had the highest PSQI scores and prevalence of sleep dysfunction (70.7%), while asymptomatic EE subjects had the highest prevalence of OSA (44%). CONCLUSIONS: Our findings indicate a high prevalence of sleep dysfunction among individuals with GERD. Furthermore, patients on the GERD spectrum are prone to experiencing a range of subjective and objective sleep disturbances.

Separated collaborative learning for semi-supervised prostate segmentation with multi-site heterogeneous unlabeled MRI data.

Segmenting prostate from magnetic resonance imaging (MRI) is a critical procedure in prostate cancer staging and treatment planning. Considering the nature of labeled data scarcity for medical images, semi-supervised learning (SSL) becomes an appealing solution since it can simultaneously exploit limited labeled data and a large amount of unlabeled data. However, SSL relies on the assumption that the unlabeled images are abundant, which may not be satisfied when the local institute has limited image collection capabilities. An intuitive solution is to seek support from other centers to enrich the unlabeled image pool. However, this further introduces data heterogeneity, which can impede SSL that works under identical data distribution with certain model assumptions. Aiming at this under-explored yet valuable scenario, in this work, we propose a separated collaborative learning (SCL) framework for semi-supervised prostate segmentation with multi-site unlabeled MRI data. Specifically, on top of the teacher-student framework, SCL exploits multi-site unlabeled data by: (i) Local learning, which advocates local distribution fitting, including the pseudo label learning that reinforces confirmation of low-entropy easy regions and the cyclic propagated real label learning that leverages class prototypes to regularize the distribution of intra-class features; (ii) External multi-site learning, which aims to robustly mine informative clues from external data, mainly including the local-support category mutual dependence learning, which takes the spirit that mutual information can effectively measure the amount of information shared by two variables even from different domains, and the stability learning under strong adversarial perturbations to enhance robustness to heterogeneity. Extensive experiments on prostate MRI data from six different clinical centers show that our method can effectively generalize SSL on multi-site unlabeled data and significantly outperform other semi-supervised segmentation methods. Besides, we validate the extensibility of our method on the multi-class cardiac MRI segmentation task with data from four different clinical centers.

ABCC4 suppresses glioblastoma progression and recurrence by restraining cGMP-PKG signalling.

BACKGROUND: Cyclic nucleotides are critical mediators of cellular signalling in glioblastoma. However, the clinical relevance and mechanisms of regulating cyclic nucleotides in glioblastoma progression and recurrence have yet to be thoroughly explored. METHODS: In silico, mRNA, and protein level analyses identified the primary regulator of cyclic nucleotides in recurrent human glioblastoma. Lentiviral and pharmacological manipulations examined the functional impact of cyclic nucleotide signalling in human glioma cell lines and primary glioblastoma cells. An orthotopic xenograft mice model coupled with aspirin hydrogels verified the in vivo outcome of targeting cyclic nucleotide signalling. RESULTS: Elevated intracellular levels of cGMP, instead of cAMP, due to a lower substrate efflux from ATP-binding cassette sub-family C member 4 (ABCC4) is engaged in the recurrence of glioblastoma. ABCC4 gene expression is negatively associated with recurrence and overall survival outcomes in glioblastoma specimens. ABCC4 loss-of-function activates cGMP-PKG signalling, promoting malignancy in glioblastoma cells and xenografts. Hydrogels loaded with aspirin, inhibiting glioblastoma progression partly by upregulating ABCC4 expressions, augment the efficacy of standard-of-care therapies in orthotopic glioblastoma xenografts. CONCLUSION: ABCC4, repressing the cGMP-PKG signalling pathway, is a tumour suppressor in glioblastoma progression and recurrence. Aspirin hydrogels impede glioblastoma progression through ABCC4 restoration and constitute a viable translational approach.

Transcutaneous electrical acupoint stimulation for prevention of postoperative urinary retention: A systematic review.

Introduction: Transcutaneous electrical acupoint stimulation (TEAS) has been proposed for postoperative urinary retention (POUR). This meta-analysis evaluated the effect of TEAS in preventing POUR. Methods: Databases were searched until February 6, 2023. Randomized controlled trials (RCTs) about TEAS for preventing POUR were included. The primary concern was the incidence of POUR, with post-void residual urine volume as a secondary outcome. Results: Fourteen studies with 2865 participants were identified. TEAS reduced the incidence of POUR (RR = 0.44, 95%CI = 0.33 to 0.58, P < 0.00001) and decreased the post-void residual urine volume (MD = -75.41 mL, 95%CI = -118.76 to -32.06, P = 0.0007). The preventive effect on POUR was found in patients receiving anorectal, gynecologic, orthopedic and biliary surgery, but not urinary surgery. Dilatational- and continuous-wave TEAS had a great outcome in preventing POUR. Intraoperative TEAS, preoperative and intraoperative TEAS, and postoperative TEAS were beneficial, and TEAS was more beneficial when compared with sham TEAS and blank control. It is nevertheless difficult to rule out publication bias. Conclusions: TEAS could prevent POUR. Due to insufficient evidence, multicenter, large-sample and high-quality RCTs should be conducted. (Registration:INPLASY202320095).

Thermal-Assisted Multiscale Patterning of Nonplanar Colloidal Nanostructures for Multi-Modal Anti-Counterfeiting.

Nanotransfer printing of colloidal nanoparticles is a promising technique for the fabrication of functional materials and devices. However, patterning nonplanar nanostructures pose a challenge due to weak adhesion from the extremely small nanostructure-substrate contact area. Here, the study proposes a thermal-assisted nonplanar nanostructure transfer printing (NP-NTP) strategy for multiscale patterning of polystyrene (PS) nanospheres. The printing efficiency is significantly improved from ≈3.1% at low temperatures to ≈97.2% under the glass transition temperature of PS. Additionally, the arrangement of PS nanospheres transitioned from disorder to long-range order. The mechanism of printing efficiency enhancement is the drastic drop of Young's modulus of nanospheres, giving rise to an increased contact area, self-adhesive effect, and inter-particle necking. To demonstrate the versatility of the NP-NTP strategy, it is combined with the intaglio transfer printing technique, and multiple patterns are created at both micro and macro scales at a 4-inch scale with a resolution of ≈2757 pixels per inch (PPI). Furthermore, a multi-modal anti-counterfeiting concept based on structural patterns at hierarchical length scales is proposed, providing a new paradigm of imparting multiscale nanostructure patterning into macroscale functional devices.

Uncovering the Neural Mechanisms of Inter-Hemispheric Balance Restoration in Chronic Stroke Through EMG-Driven Robot Hand Training: Insights From Dynamic Causal Modeling.

EMG-driven robot hand training can facilitate motor recovery in chronic stroke patients by restoring the interhemispheric balance between motor networks. However, the underlying mechanisms of reorganization between interhemispheric regions remain unclear. This study investigated the effective connectivity (EC) between the ventral premotor cortex (PMv), supplementary motor area (SMA), and primary motor cortex (M1) using Dynamic Causal Modeling (DCM) during motor tasks with the paretic hand. Nineteen chronic stroke subjects underwent 20 sessions of EMG-driven robot hand training, and their Action Reach Arm Test (ARAT) showed significant improvement ( ß =3.56, [Formula: see text]). The improvement was correlated with the reduction of inhibitory coupling from the contralesional M1 to the ipsilesional M1 (r=0.58, p=0.014). An increase in the laterality index was only observed in homotopic M1, but not in the premotor area. Additionally, we identified an increase in resting-state functional connectivity (FC) between bilateral M1 ( ß =0.11, p=0.01). Inter-M1 FC demonstrated marginal positive relationships with ARAT scores (r=0.402, p=0.110), but its changes did not correlate with ARAT improvements. These findings suggest that the improvement of hand functions brought about by EMG-driven robot hand training was driven explicitly by task-specific reorganization of motor networks. Particularly, the restoration of interhemispheric balance was induced by a reduction in interhemispheric inhibition from the contralesional M1 during motor tasks of the paretic hand. This finding sheds light on the mechanistic understanding of interhemispheric balance and functional recovery induced by EMG-driven robot training.

Phlorizin, a novel caloric restriction mimetic, stimulates hypoxia and protects cardiomyocytes through activating autophagy via modulating the Hif-1α/Bnip3 axis in sepsis-induced myocardial dysfunction.

BACKGROUND: Sepsis is a systemic inflammatory syndrome that can lead to multiple organ dysfunction and life-threatening complications. Sepsis-induced myocardial dysfunction (SIMD) has been confirmed to be present in half of patients with septic shock, increasing their mortality rate to 70-90%. The pathogenesis of SIMD is complex, and no specific clinical treatment has yet been developed. Caloric restriction mimetics (CRM), compounds that simulate the biochemical and functional properties of CR, can improve cardiovascular injury by activating autophagy. This study investigated the effect of a new type of CRM which can induce hypoxia, the SGLT nonspecific inhibitor phlorizin on SIMD. MATERIALS AND METHODS: In vivo, phlorizin was administered at 1 mg/kg/day intragastrically for 28 days. In vitro, AC16 was treated with 120 µM phlorizin for 48 h. Echocardiography was used to assess cardiac function. Myocardial injury markers were detected in serum and cell supernatant. Western blotting was employed to detect changed proteins associated with apoptosis and autophagy. Immunofluorescence, immunohistochemistry, co-immunoprecipitation, molecular docking, and other methods were also used to illustrate cellular changes. RESULTS: In vivo, phlorizin significantly improved the survival rate and cardiac function after sepsis injury, reduced markers of myocardial injury, inhibited myocardial apoptosis and oxidative stress, and promoted autophagy. In vitro, phlorizin alleviated the apoptosis of AC16, as well as inhibited oxidative stress and apoptotic enzyme activity. Phlorizin acts on autophagy at multiple sites through low energy (activation of AMPK) and hypoxia (release of Beclin-1 by Hif-1α/Bnip3 axis), promoting the formation and degradation of autophagosomes. CONCLUSION: We indicated for the first time that phlorizin could inhibit glucose uptake via GLUT-1 and conforms to the metabolic characteristics of CRM, it can induce the hypoxic transcriptional paradigm. In addition, it inhibits apoptosis and improves SIMD by promoting autophagy generation and unobstructing autophagy flux. Moreover, it affects autophagy by releasing Beclin-1 through the Hif-1α/Bnip3 axis.

Research progress of visualization methods and localization techniques of the cardiac conduction system / 中国胸心血管外科临床杂志

@#The cardiac conduction system (CCS) is a set of specialized myocardial pathways that spontaneously generate and conduct impulses transmitting throughout the heart, and causing the coordinated contractions of all parts of the heart. A comprehensive understanding of the anatomical characteristics of the CCS in the heart is the basis of studying cardiac electrophysiology and treating conduction-related diseases. It is also the key of avoiding damage to the CCS during open heart surgery. How to identify and locate the CCS has always been a hot topic in researches. Here, we review the histological imaging methods of the CCS and the specific molecular markers, as well as the exploration for localization and visualization of the CCS. We especially put emphasis on the clinical application prospects and the future development directions of non-destructive imaging technology and real-time localization methods of the CCS that have emerged in recent years.

Integrative pharmacology reveals the mechanisms of Erzhi pills, A traditional Chinese formulation, stimulating melanogenesis.

ETHNOPHARMACOLOGICAL RELEVANCE: Erzhi pills (EZP), a traditional Chinese medicine formula prescribed for the treatment of vitiligo, has shown promising efficacy. However, the oral bioactive components and mechanisms underlying the promotion of melanogenesis by EZP remain unclear. AIM OF THE STUDY: This study aimed to investigate the pharmacological basis and mechanism of EZP in promoting melanogenesis. MATERIALS AND METHODS: UHPLC-TOF-MS analysis was used to identify absorbed phytochemicals in serum after oral administration of EZP. Network pharmacology methods were used to predict potential targets and pathways involved in the melanogenic activity of EZP, resulting in the construction of a "compound-target-pathway" network. Zebrafish and B16F10 cells were used to evaluate the effects of EZP on tyrosinase activity and melanin content. Western blot and ELISA analyses were used to validate the effects of EZP on melanogenesis-related proteins, including MITF, TYR, CREB, p-CREB, and cAMP. RESULTS: UHPLC-TOF-MS analysis identified 36 compounds derived from EZP in serum samples. Network pharmacology predictions revealed 89 target proteins associated with the identified compounds and closely related to vitiligo. GO and KEGG analyses indicated the involvement of the cAMP/PKA signaling pathway in the promotion of melanogenesis by EZP. Experimental results showed that EZP increased tyrosinase activity and melanin content in zebrafish and B16F10 cells without inducing toxicity. Western blot and ELISA results suggested that the melanogenic effect of EZP may be related to the activation of the cAMP/PKA signaling pathway. These results confirm the feasibility of combining serum pharmacological and network pharmacological approaches. CONCLUSIONS: EZP have the potential to increase tyrosinase activity and melanin content in zebrafish and cells possibly through activation of the cAMP/PKA pathway.

Mind In Vitro Platforms: Versatile, Scalable, Robust, and Open Solutions to Interfacing with Living Neurons.

Motivated by the unexplored potential of in vitro neural systems for computing and by the corresponding need of versatile, scalable interfaces for multimodal interaction, an accurate, modular, fully customizable, and portable recording/stimulation solution that can be easily fabricated, robustly operated, and broadly disseminated is presented. This approach entails a reconfigurable platform that works across multiple industry standards and that enables a complete signal chain, from neural substrates sampled through micro-electrode arrays (MEAs) to data acquisition, downstream analysis, and cloud storage. Built-in modularity supports the seamless integration of electrical/optical stimulation and fluidic interfaces. Custom MEA fabrication leverages maskless photolithography, favoring the rapid prototyping of a variety of configurations, spatial topologies, and constitutive materials. Through a dedicated analysis and management software suite, the utility and robustness of this system are demonstrated across neural cultures and applications, including embryonic stem cell-derived and primary neurons, organotypic brain slices, 3D engineered tissue mimics, concurrent calcium imaging, and long-term recording. Overall, this technology, termed "mind in vitro" to underscore the computing inspiration, provides an end-to-end solution that can be widely deployed due to its affordable (>10× cost reduction) and open-source nature, catering to the expanding needs of both conventional and unconventional electrophysiology.

Synergistic Label-Stability Learning for Semi-supervised Left Atrium Segmentation.

Recent semi-supervised learning approaches appealingly advance medical image segmentation for their effectiveness in alleviating the need for a large amount of expert-demanding annotations. However, most of them have two limitations: (i) neglect of the intra-class variation caused by different patients and scanning protocols, which makes the pixel-level label propagation difficult; (ii) non-selective stability learning (a.k.a., consistency regularization), resulting in distraction by the redundant easy regions. To address these, in this work, we propose a novel synergistic label-stability learning (SLSL) framework for semi-supervised medical image segmentation. Specifically, our method is built upon the teacher-student framework. Then, the label learning process includes the typical pseudo label learning that reinforces confirmation of well-classified easy regions and the cyclic real label learning that takes advantage of real labels and class prototypes to regularize the distribution of intra-class features from unlabeled data to facilitate label propagation. In addition, the difficulty-selective stability learning aims to regularize the perturbed stability only at the high-entropy (can be regarded as difficult) regions, rather than being distracted by the less-informative easy regions. Extensive experiments on left atrium segmentation from MRI show that our method can effectively exploit the unlabeled data and outperform other semi-supervised medical image segmentation methods.Clinical relevance- The proposed method can help develop a high-performance automatic left atrium segmentation model for treating atrial fibrillation under limited expert-demanding annotation budgets.

Development of a Deep Learning Model for Diagnosing Lumbar Spinal Stenosis Based on CT Images.

STUDY DESIGN: Retrospective study. OBJECTIVES: This study aimed to develop an initial deep learning model based on CT scans for diagnosing lumbar spinal stenosis. SUMMARY OF BACKGROUND DATA: MRI is commonly used for diagnosing lumbar spinal stenosis due to its high soft tissue resolution, but CT is more portable, cost-effective, and has wider regional coverage. Using deep learning models to improve the accuracy of CT diagnosis can effectively reduce missed diagnoses and misdiagnoses in clinical practice. METHODS: Axial lumbar spine CT scans obtained between March 2022 and September 2023 were included. The dataset was divided into a training set (62.3%), a validation set (22.9%), and a control set (14.8%). All data were labeled by two spine surgeons using the widely accepted grading system for lumbar spinal stenosis. The training and validation sets were used to annotate the ROIs by the two spine surgeons. First, an ROI detection model and a CNN classifier were trained using the training set. After training, the model was preliminarily evaluated using a validation set. Finally, the performance of the deep learning model was evaluated on the control set, and a comparison was made between the model and classification performance of specialists with varying levels of experience. RESULTS: The central stenosis grading accuracies of DL Model Version 1 and DL Model Version 2 were 88% and 83%, respectively. The lateral recess grading accuracies of DL Model Version 1 and DL Model Version 2 were 75% and 71%, respectively. CONCLUSIONS: Our preliminarily developed deep learning system for assessing the degree of lumbar spinal stenosis in CT, including the central canal and lateral recess, has shown similar accuracy to experienced specialist physicians. This holds great value for further development and clinical application.