Identification of symptomatic carotid artery plaque: a predictive model combining angiography with optical coherence tomography.

Objective: Symptomatic carotid artery disease is indicative of an elevated likelihood of experiencing a subsequent stroke, with the morphology of plaque and its specific features being closely linked to the risk of stroke occurrence. Our study based on the characteristics of carotid plaque assessed by optical coherence tomography (OCT), the plaque morphology evaluated by digital subtraction angiography (DSA) and clinical laboratory indicators were combined, develop a combined predictive model to identify symptomatic carotid plaque. Methods: Patients diagnosed with carotid atherosclerotic stenosis who underwent whole-brain DSA and OCT examination at the Affiliated Hospital of Jining Medical University from January 2021 to November 2023 were evaluated. Clinical features, as well as DSA and OCT plaque characteristics, were analyzed for differences between symptomatic and asymptomatic cohorts. An analysis of logistic regression was carried out to identify factors associated with the presence of symptomatic carotid plaque. A multivariate binary logistic regression equation was established with the odds ratio (OR) serving as the risk assessment parameter. The receiver operating characteristic curve was utilized to assess the combined predictive model and independent influencing factors. Results: A total of 52 patients were included in the study (symptomatic: 44.2%, asymptomatic: 55.8%). Symptomatic carotid stenosis was significantly linked to four main factors: low-density lipoprotein-cholesterol >3.36 mmol/L [OR, 6.400; 95% confidence interval (CI), 1.067-38.402; p = 0.042], irregular plaque (OR, 6.054; 95% CI, 1.016-36.083; p = 0.048), ruptured plaque (OR, 6.077; 95% CI, 1.046-35.298; p = 0.048), and thrombus (OR, 6.773; 95% CI, 1.194-38.433; p = 0.044). The combined predictive model generated using four indicators showed good discrimination (Area Under Curve, 0.924; 95% CI, 0.815-0. 979). The p value was <0.05 with 78.26% sensitivity and 93.10% specificity. Conclusion: OCT is valuable in evaluating the plaque characteristics of carotid atherosclerotic stenosis. The combined predictive model comprising low-density lipoprotein-cholesterol >3.36 mmol/L, irregular plaque, ruptured plaque, and thrombus could help in the detection of symptomatic carotid plaque. Further research conducted on additional independent cohorts is necessary to confirm the clinical significance of the predictive model for symptomatic carotid plaque.

New insights into the stromal interaction molecule 2 function and its impact on the immunomodulation of tumor microenvironment.

Immune cells-enhanced immunotherapy exhibits unprecedented overall survival-prolongation even curable in some cancer patients. Although so, most of the patients show no response. Tumor microenvironment (TME) where immune cells settle down has multi-faceted influences, but usually creates an immunosuppressive niche that facilitating tumor cells escape from immune attack. The metabolites and malnutrition of TME exert enormous effects on the resident immune cells, but the underlying mechanism is largely unknown. The stromal interaction molecules 2 (STIM2) is an endoplasmic reticulum (ER) calcium (Ca2+) sensor to maintain Ca2+ homeostasis. Notably, the cytosol STIM2 C-terminus is long with various domains that are available for the combination or/and molecular modification. This distinct structure endows STIM2 with a high susceptibility to numerous permeable physico-chemical molecules or protein interactions. STIM2 and its variants are extensively expressed in various immune cells, especially in T immune cells. STIM2 was reported closely correlated with the function of immune cells via regulating Ca2+ signaling, energy metabolism and cell fitness. Herein, we sum the latest findings on the STIM2 structure, focusing on its distinct characteristics and profound effect on the regulation of Ca2+ homeostasis and multi-talented functionality. We also outline the advancements on the underlying mechanism how STIM2 anomalies influence the function of immune cells and on the turbulent expression or/and amenably modification of STIM2 within the tumor niches. Then we discuss the translation of these researches into antitumor approaches, emphasizing the potential of STIM2 as a therapeutic target for direct inhibition of tumor cells or more activation towards immune cells driving to flare TME. This review is an update on STIM2, aiming to rationalize the potential of STIM2 as a therapeutic target for immunomodulation, engaging immune cells to exert the utmost anti-tumor effect.

High-throughput calculations and machine learning modeling of 17O NMR in non-magnetic oxides.

The only NMR-active oxygen isotope, oxygen-17 (17O), serves as a sensitive probe due to its large chemical shift range, the electric field gradient at the oxygen site, and the quadrupolar interaction. Consequently, 17O solid-state NMR offers unique insights into local structures and finds significant applications in the studies of disorder, reactivity, and host-guest chemistry. Despite recent advances in sensitivity enhancement, isotopic labeling, and NMR crystallography, the application of 17O solid-state NMR is still hindered by low natural abundance, costly enrichment, and challenges in handling spectrum signals. Density functional theory calculations and machine learning techniques offer an alternative approach to mapping the local crystal structures to NMR parameters. However, the lack of high-quality data remains a challenge, despite the establishment of some datasets. In this study, we implement and execute a high-throughput workflow combining AiiDA and CASTEP to evaluate the NMR parameters. Focusing on non-magnetic oxides, we have chosen over 7100 binary, ternary, and quaternary compounds from the Materials Project database and performed calculations. Furthermore, using various descriptors for the local crystalline environments, we model the 17O NMR parameters using machine learning techniques, further enhancing our ability to predict and understand 17O NMR parameters in oxide crystals.

Enhanced Effects of Intermittent Fasting by Magnetic Fields in Severe Diabetes.

Intermittent fasting (IF) is a convenient dietary intervention for multiple diseases, including type 2 diabetes. However, whether it can be used as a long-term antidiabetic approach is still unknown. Here, we confirm that IF alone is beneficial for both moderate and severe diabetic mice, but its antidiabetic effects clearly diminish at later stages, especially for severe diabetic db/db mice, which have obviously impaired autophagy. We found that static magnetic fields can directly promote actin assembly and boost IF-induced autophagy. Consequently, the pancreatic islet and liver were improved, and the antidiabetic effects of IF were boosted. In fact, at later stages, combined static magnetic field and IF could reduce the blood glucose level of moderate type 2 diabetic mice by 40.5% (P < 0.001) and severe type 2 diabetes by 34.4% (P < 0.05), when IF alone no longer has significant blood glucose reduction effects. Therefore, although IF is generally beneficial for diabetes, our data reveal its insufficiency for late-stage diabetes, which can be compensated by a simple, noninvasive, long-lasting, and nonpharmacological strategy for effective long-term diabetic control.

Human umbilical cord-derived mesenchymal stem cells alleviate valproate-induced immune stress and social deficiency in rats.

Introduction: Autism spectrum disorders (ASD) are a set of heterogeneous neurodevelopmental disorders characterized by impaired social interactions and stereotypic behaviors. Current clinical care is palliative at the most and there remains huge unmet medical need to fully address the core symptoms of ASD. Human umbilical cord-derived mesenchymal stem cells (hUC-MSCs) are emerging as a promising candidate for ASD treatment, but the precise mechanism remains controversial. Methods: In vitro studies we performed the transwell migration assay to explore the interaction between hUC-MSCs and the primary-cultured cortical neurons. Then we determined the therapeutic effects of intravenous administration of hUC-MSCs in rats challenged with valproic acid (VPA) during gestation, a well-defined rat model of autism. Results: Our studies showed that hUC-MSCs promoted the growth of primary-cultured cortical neurons. Furthermore, our results demonstrated that hUC-MSCs significantly alleviated microglial activation in the brain, especially in the anterior cingulate cortex, and effectively improved the sociability of the VPA-exposed rats. Discussion: These results offer valuable insights for clinical translation and further research on the mechanisms of hUC-MSCs in psychiatric disorders characterized by microglial activation, particularly in cases of autism, shall be warranted.

Wearable bio-adhesive metal detector array (BioMDA) for spinal implants.

Dynamic tracking of spinal instrumentation could facilitate real-time evaluation of hardware integrity and in so doing alert patients/clinicians of potential failure(s). Critically, no method yet exists to continually monitor the integrity of spinal hardware and by proxy the process of spinal arthrodesis; as such hardware failures are often not appreciated until clinical symptoms manifest. Accordingly, herein, we report on the development and engineering of a bio-adhesive metal detector array (BioMDA), a potential wearable solution for real-time, non-invasive positional analyses of osseous implants within the spine. The electromagnetic coupling mechanism and intimate interfacial adhesion enable the precise sensing of the metallic implants position without the use of radiation. The customized decoupling models developed facilitate the precise determination of the horizontal and vertical positions of the implants with incredible levels of accuracy (e.g., <0.5 mm). These data support the potential use of BioMDA in real-time/dynamic postoperative monitoring of spinal implants.

Field-of-view extension for brain diffusion MRI via deep generative models.

Purpose: In brain diffusion magnetic resonance imaging (dMRI), the volumetric and bundle analyses of whole-brain tissue microstructure and connectivity can be severely impeded by an incomplete field of view (FOV). We aim to develop a method for imputing the missing slices directly from existing dMRI scans with an incomplete FOV. We hypothesize that the imputed image with a complete FOV can improve whole-brain tractography for corrupted data with an incomplete FOV. Therefore, our approach provides a desirable alternative to discarding the valuable brain dMRI data, enabling subsequent tractography analyses that would otherwise be challenging or unattainable with corrupted data. Approach: We propose a framework based on a deep generative model that estimates the absent brain regions in dMRI scans with an incomplete FOV. The model is capable of learning both the diffusion characteristics in diffusion-weighted images (DWIs) and the anatomical features evident in the corresponding structural images for efficiently imputing missing slices of DWIs in the incomplete part of the FOV. Results: For evaluating the imputed slices, on the Wisconsin Registry for Alzheimer's Prevention (WRAP) dataset, the proposed framework achieved PSNR b 0 = 22.397 , SSIM b 0 = 0.905 , PSNR b 1300 = 22.479 , and SSIM b 1300 = 0.893 ; on the National Alzheimer's Coordinating Center (NACC) dataset, it achieved PSNR b 0 = 21.304 , SSIM b 0 = 0.892 , PSNR b 1300 = 21.599 , and SSIM b 1300 = 0.877 . The proposed framework improved the tractography accuracy, as demonstrated by an increased average Dice score for 72 tracts ( p < 0.001 ) on both the WRAP and NACC datasets. Conclusions: Results suggest that the proposed framework achieved sufficient imputation performance in brain dMRI data with an incomplete FOV for improving whole-brain tractography, thereby repairing the corrupted data. Our approach achieved more accurate whole-brain tractography results with an extended and complete FOV and reduced the uncertainty when analyzing bundles associated with Alzheimer's disease.

A novel diagnosis method combined dual-channel SE-ResNet with expert features for inter-patient heartbeat classification.

As the number of patients with cardiovascular diseases (CVDs) increases annually, a reliable and automated system for detecting electrocardiogram (ECG) abnormalities is becoming increasingly essential. Scholars have developed numerous methods of arrhythmia classification using machine learning or deep learning. However, the issue of low classification rates of individual classes in inter-patient heartbeat classification remains a challenge. This study proposes a method for inter-patient heartbeat classification by fusing dual-channel squeeze-and-excitation residual neural networks (SE-ResNet) and expert features. In the preprocessing stage, ECG heartbeats extracted from both leads of ECG signals are filtered and normalized. Additionally, nine features representing waveform morphology and heartbeat contextual information are selected to be fused with the deep neural networks. Using different filter and kernel sizes for each block, the SE-residual block-based model can effectively learn long-term features between heartbeats. The divided ECG heartbeats and extracted features are then input to the improved SE-ResNet for training and testing according to the inter-patient scheme. The focal loss is utilized to handle the heartbeat of the imbalance category. The proposed arrhythmia classification method is evaluated on three open-source databases, and it achieved an overall F1-score of 83.39 % in the MIT-BIH database. This system can be applied in the scenario of daily monitoring of ECG and plays a significant role in diagnosing arrhythmias.

AlphaFold2-guided engineering of split-GFP technology enables labeling of endogenous tubulins across species while preserving function.

Dynamic properties are essential for microtubule (MT) physiology. Current techniques for in vivo imaging of MTs present intrinsic limitations in elucidating the isotype-specific nuances of tubulins, which contribute to their versatile functions. Harnessing the power of the AlphaFold2 pipeline, we engineered a strategy for the minimally invasive fluorescence labeling of endogenous tubulin isotypes or those harboring missense mutations. We demonstrated that a specifically designed 16-amino acid linker, coupled with sfGFP11 from the split-sfGFP system and integration into the H1-S2 loop of tubulin, facilitated tubulin labeling without compromising MT dynamics, embryonic development, or ciliogenesis in Caenorhabditis elegans. Extending this technique to human cells and murine oocytes, we visualized MTs with the minimal background fluorescence and a pathogenic tubulin isoform with fidelity. The utility of our approach across biological contexts and species set an additional paradigm for studying tubulin dynamics and functional specificity, with implications for understanding tubulin-related diseases known as tubulinopathies.

Impact of COVID-19 Lockdown on NO2 Pollution and the Associated Health Burden in China: A Comparison of Different Approaches.

So far, a large number of studies have quantified the effect of COVID-19 lockdown measures on air quality in different countries worldwide. However, few studies have compared the influence of different approaches on the estimation results. The present study aimed to utilize a random forest machine learning approach as well as a difference-to-difference approach to explore the effect of lockdown policy on nitrogen dioxide (NO2) concentration during COVID-19 outbreak period in mainland China. Datasets from 2017 to 2019 were adopted to establish the random forest models, which were then applied to predict the NO2 concentrations in 2020, representing a scenario without the lockdown effect. The results showed that random forest models achieved remarkable predictive accuracy for predicting NO2 concentrations, with index of agreement values ranging between 0.34 and 0.76. Compared with the modelled NO2 concentrations, on average, the observed NO2 concentrations decreased by approximately 16 µg/m3 in the lockdown period in 2020. The difference-to-difference approach tended to underestimate the influence of COVID-19 lockdown measures. Due to the improvement of NO2 pollution, around 3722 non-accidental premature deaths were avoided in the studied population. The presented machine learning modelling framework has a great potential to be transferred to other short-term events with abrupt pollutant emission changes.

An Enhanced Autoencoder-based Anomaly Detection Model for time series Data from Wearable Medical Devices.

In today's era of rapidly advancing technologies, such as sensors and the Internet of Things (IoT), and the increasing focus on promoting healthy lifestyles, smart wearable devices play a crucial role in real-time detection and diagnosis of physical health conditions. Through analyzing the multi-featured time series data captured by these devices with multiple sensors, we can uncover hidden diseases and provide timely treatment. Therefore, it is imperative to study an anomaly detection model with robust feature learning and anomaly diagnosis capabilities. To address this need, this paper proposes an enhanced autoencoder-based anomaly detection model for time series data obtained from wearable medical devices. Initially, the model utilizes a convolutional neural network to learn the correlations between multiple features. Subsequently, a long and short-term memory network is employed to capture the sequence correlations, and an multi-head attention mechanism is used to mitigate the performance degradation caused by increasing the sequence length. The residual loss is also used to effectively mitigate the vanishing gradient problem. Finally, the model is evaluated using two widely recognized public datasets: the HeartDisease dataset, which contains information on patients with heart conditions, and the MIMIC dataset, a comprehensive database of de-identified health data related to critical care. The experimental results demonstrate that our model can achieve an accuracy of 95.37% and 95.56% on the two datasets, respectively. Compared to the best performing baseline methods, our model improves 8.6% and 12.3% on the two datasets, respectively. Overall, our model enables efficient analysis of sequential data, effectively captures long-term dependencies, and significantly improves the success rate of early health diagnosis for individuals.

Male hermaphroditism with rare malignant transformation of gonadal organs: a case report.

A 27-year-old man was admitted to the hospital after a year of marriage due to infertility. During laparoscopic exploratory surgery, the presence of a retrovesical uterus was clearly observed, and the gonadal organs were visible on both sides. However, the testicles or ovaries were not identifiable, nor were the spermatic vessels and fallopian tubes at the joint. Intraoperative bilateral gonad biopsy was performed. Cryopreservation of the right gonadal gland revealed gonadoblastoma and malignant germinoma (asexual tumor/seminoma) with sclerosis and atrophy of testicular tissue. No proliferation of germ cells and sertoli cells was observed in spermatic tubule. The left gonad was diagnosed as a gonadoblastoma. Finally, total hysterectomy and bilateral gonadal tumor organectomy were performed to seal the vaginal stump. Local radiotherapy was administered after surgery. In general, tumors were found on both sides of the gonads, especially gonadoblastoma and malignant germinoma on the right side and gonadoblastoma on the left side.

Ortho-digital dynamics: Exploration of advancing digital health technologies in musculoskeletal disease management.

Background: Musculoskeletal (MSK) disorders, affecting billions of people worldwide, pose significant challenges to the healthcare system and require effective management models. The rapid development of digital healthcare technologies (DHTs) has revolutionized the healthcare industry. DHT-based interventions have shown promising clinical benefits in managing MSK disorders, alleviating pain, and improving functional impairment. There is, however, no bibliometric analysis of the overall trends on this topic. Methods: We extracted all relevant publications from the Web of Science Core Collection (WoSCC) database until April 30, 2023. We performed bibliometric analysis and visualization using CiteSpace, VOSviewer, and R software. Annual trends of publications, countries/regions distributions, funding agencies, institutions, co-cited journals, author contributions, references, core journals, and keywords and research hotspots were analyzed. Results: A total of 6810 papers were enrolled in this study. Publications have increased drastically from 16 in 1995 to 1198 in 2022, with 4067 articles published in the last five years. In all, 53 countries contributed with publications to this research area. The United States, the United Kingdom, and China were the most productive countries. Harvard University was the most contributing institution. Regarding keywords, research focuses include artificial intelligence, deep learning, machine learning, telemedicine, rehabilitation, and robotics. Conclusion: The COVID-19 pandemic has further accelerated the adoption of DHTs, highlighting the need for remote care options. The analysis reveals the positive impact of DHTs on improving physician productivity, enhancing patient care and quality of life, reducing healthcare expenditures, and predicting outcomes. DHTs are a hot topic of research not only in the clinical field but also in the multidisciplinary intersection of rehabilitation, nursing, education, social and economic fields. The analysis identifies four promising hotspots in the integration of DHTs in MSK pain management, biomechanics assessment, MSK diagnosis and prediction, and robotics and tele-rehabilitation in arthroplasty care.

Crossmodal sensory neurons based on high-performance flexible memristors for human-machine in-sensor computing system.

Constructing crossmodal in-sensor processing system based on high-performance flexible devices is of great significance for the development of wearable human-machine interfaces. A bio-inspired crossmodal in-sensor computing system can perform real-time energy-efficient processing of multimodal signals, alleviating data conversion and transmission between different modules in conventional chips. Here, we report a bio-inspired crossmodal spiking sensory neuron (CSSN) based on a flexible VO2 memristor, and demonstrate a crossmodal in-sensor encoding and computing system for wearable human-machine interfaces. We demonstrate excellent performance in the VO2 memristor including endurance (>1012), uniformity (0.72% for cycle-to-cycle variations and 3.73% for device-to-device variations), speed (<30 ns), and flexibility (bendable to a curvature radius of 1 mm). A flexible hardware processing system is implemented based on the CSSN, which can directly perceive and encode pressure and temperature bimodal information into spikes, and then enables the real-time haptic-feedback for human-machine interaction. We successfully construct a crossmodal in-sensor spiking reservoir computing system via the CSSNs, which can achieve dynamic objects identification with a high accuracy of 98.1% and real-time signal feedback. This work provides a feasible approach for constructing flexible bio-inspired crossmodal in-sensor computing systems for wearable human-machine interfaces.

Establishment and evaluation of a stable and reliable rat model of peritoneal adhesions.

BACKGROUND: In this study, we aimed to establish a stable and standardized animal model of peritoneal adhesions. METHODS: Forty-eight male Sprague-Dawley rats were randomly divided (n = 12 each) into blank control, classic cecum sidewall, ischemic button, and cecum-sidewall suture groups. The modified American Fertility Society adhesion score was used on postoperative day 7 to evaluate adhesions. Sixty male Sprague-Dawley rats were used to dynamically observe the adhesion characteristics of cecum-sidewall ischemic injury suture model at different time points (n = 60, randomly divided into groups a-e with 12 rats each). The modified American Fertility Society and Zühlke histologic scoring systems, hematoxylin-eosin staining, Masson staining, and computed tomography of the abdomen were used to evaluate adhesions on postoperative days 1, 3, 5, 7, and 14. RESULTS: No peritoneal adhesions were observed in the blank control group on postoperative day 7. In the classic cecum sidewall group, 8 rats had inconsistent adhesions, which had a modified American Fertility Society adhesion score of 2.25 ± 1.96. All rats in the ischemic button and cecum-sidewall suture groups developed significant adhesions with modified American Fertility Society scores of 3.08 ± 1.31 and 4.67 ± 0.78, respectively. When the modified American Fertility Society score was used, statistically significant differences were observed between the classic cecum sidewall groups and cecum-sidewall suture groups and between the ischemic button groups and cecum-sidewall suture groups. All animals in groups a-e developed adhesions; adhesion scores increased gradually with time. CONCLUSIONS: The cecum-sidewall ischemic injury suture model is a stable and standardized animal model of peritoneal adhesions.

Network Pharmacology-Based Virtual Screening of Chemical Constituents from Vitexnegundo Linn's Discovered Novel Molecular Targets for Breast Cancer Treatment.

BACKGROUND: The Chinese chaste tree Vitexnegundo (VN) is a popular herb in South and Southeast Asia that has several health benefits, including the ability to inhibit tumor growth and induce apoptosis in multiple tumors. Literature revealed scanty research on breast cancer, with little focus on the molecular mechanism of the disease and an emphasis on targets, biological networks, and active components. Exploring natural compounds as possible therapeutic options is an old but still promising approach for drug discovery and development. This study used a thorough computational and statistical method to screen potential drug candidates. METHODS: The active ingredients and targets of VN were identified using SwissADME, SwissTargetPrediction, STITCH, IMPPAT database, KNapSAcK database, and literature. The OMIM and GeneCards databases were searched for possible targets related to breast cancer. The PASS online server was used to check the probability of active metabolite (Pa) against breast cancer. To build protein-protein interactions (PPI) networking, the intersection of disease and drug targets was uploaded to the STITCH database. Cytoscape software was used to analyze the topology parameters of networking to identify hub targets. Gene Ontology (GO) was analyzed using Metascape and ShinyGO, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed using the David database and SR plot, and the site of expression and protein domain were studied using FunRich. We employed AutoDockvina, Discovery Studio, and UCSF ChimeraX software and auxiliary tools for molecular docking and analysis. Zincpharmer was used for pharmacophore mapping. ADMET analysis was conducted using ADMETsar, Swiss ADME, ADMETLab servers, and mypresto using GROMACS for molecular dynamics simulation (MDS). RESULTS: A total of 65 targets and 21 active ingredients were identified. Further investigation was conducted on 20 hub targets selected through PPI networking construction. The enrichment analysis results indicated that the key factors were P, amyloid-beta response, cellular response to amyloid- beta, Pos. reg. of G2/M transition of the mitotic cell cycle, and response to a toxic substance. The molecular docking, pharmacophore mapping, and MD simulation results indicated that apigenin, kaempferol, and luteolin positively interacted with CDK1 and CDK6 proteins. CONCLUSION: This study is the first to use network pharmacology, molecular docking, pharmacophore mapping, and MD simulation to identify the active ingredients, molecular targets, and critical biological pathways responsible for VN anti-breast cancer. The study provides a theoretical basis for further research in this area.

Enhancement of Corrosion and Wear Resistance of Ni-P Coatings Stems from the Synergistic Effects of Cr3C2 and Heat Treatment.

This study describes the preparation of Ni-P-Cr3C2 composite coatings using pulsed electrodeposition, with varying Cr3C2 concentrations (0, 1, 2, 3, 4, and 5 g/L). Subsequently, the Ni-P-Cr3C2 composite coatings are heat-treated at different temperatures (200, 400, and 600 °C) using the characteristic of Cr3C2 oxidizing to Cr2O3 at high temperatures. The Ni-P coatings, Ni-P-Cr3C2 composite coatings, and heat-treated-state Ni-P-Cr3C2 composite coatings are compared and discussed. The results show that the hardness, wear resistance, and corrosion resistance of the composite coatings are optimized when the Cr3C2 content is 3 g/L and the heat-treatment temperature is 400 °C. This is due to the presence of oxides such as Cr2O3 on the surface of the composite coatings after heat treatment at 400 °C. By efficiently enhancing the coating's densification to the substrate, these oxides raise the composite coating's resistance to corrosion and wear. The Ni-P-Cr3C2 composite coating in its heat-treated makeup at 400 °C is found to have long-term corrosion resistance in the 3.5 wt % NaCl solution immersion test. This study provides a new idea in the field of corrosion.

Multi-functional adhesive hydrogel as bio-interface for wireless transient pacemaker.

Traditional temporary cardiac pacemakers (TCPs), which employ transcutaneous leads and external wired power systems are battery-dependent and generally non-absorbable with rigidity, thereby necessitating surgical retrieval after therapy and resulting in potentially severe complications. Wireless and bioresorbable transient pacemakers have, hence, emerged recently, though hitting a bottleneck of unfavorable tissue-device bonding interface subject to mismatched mechanical modulus, low adhesive strength, inferior electrical performances, and infection risks. Here, to address such crux, we develop a multifunctional interface hydrogel (MIH) with superior electrical performance to facilitate efficient electrical exchange, comparable mechanical strength to natural heart tissue, robust adhesion property to enable stable device-tissue fixation (tensile strength: ∼30 kPa, shear strength of ∼30 kPa, and peel-off strength: ∼85 kPa), and good bactericidal effect to suppress bacterial growth. Through delicate integration of this versatile MIH with a leadless, battery-free, wireless, and transient pacemaker, the entire system exhibits stable and conformal adhesion to the beating heart while enabling precise and constant electrical stimulation to modulate the cardiac rhythm. It is envisioned that this versatile MIH and the proposed integration framework will have immense potential in overcoming key limitations of traditional TCPs, and may inspire the design of novel bioelectronic-tissue interfaces for next-generation implantable medical devices.

SS-31 mitigates oxidative stress and restores mitochondrial function in cigarette smoke-damaged oral epithelial cells via PINK1-mediated mitophagy.

Smoking is a well-established risk factor for several oral diseases, including oral cancer, oral leukoplakia and periodontitis, primarily related to reactive oxygen species (ROS). SS-31, a mitochondria-targeting tetrapeptide, has exhibited demonstrable efficacy in medical conditions by attenuating mitochondrial ROS production. However, its potential in the treatment of oral diseases remains underexplored. The aim of this study was to investigate the therapeutic potential of SS-31 in mitigating smoking-induced oral epithelial injury. Through in vitro experiments, our results indicate that SS-31 plays a protective role against cigarette smoke extract (CSE) by reducing oxidative stress, attenuating inflammatory response, and restoring mitochondrial function. Furthermore, we found that mitophagy, regulated by PINK1 (PTEN-induced putative kinase 1)/Parkin (Parkin RBR E3 ubiquitin-protein ligase), was critical for the protective role of SS-31. Our findings offer valuable insights into SS-31's therapeutic potential in mitigating CSE-induced oxidative stress, inflammatory response, and mitochondrial dysfunction in oral epithelial cells. This study provides novel intervention targets for smoking-related oral diseases.

AMPK Attenuation of ß-Adrenergic Receptor-Induced Cardiac Injury via Phosphorylation of ß-Arrestin-1-ser330.

BACKGROUND: ß-adrenergic receptor (ß-AR) overactivation is a major pathological cue associated with cardiac injury and diseases. AMPK (AMP-activated protein kinase), a conserved energy sensor, regulates energy metabolism and is cardioprotective. However, whether AMPK exerts cardioprotective effects via regulating the signaling pathway downstream of ß-AR remains unclear. METHODS: Using immunoprecipitation, mass spectrometry, site-specific mutation, in vitro kinase assay, and in vivo animal studies, we determined whether AMPK phosphorylates ß-arrestin-1 at serine (Ser) 330. Wild-type mice and mice with site-specific mutagenesis (S330A knock-in [KI]/S330D KI) were subcutaneously injected with the ß-AR agonist isoproterenol (5 mg/kg) to evaluate the causality between ß-adrenergic insult and ß-arrestin-1 Ser330 phosphorylation. Cardiac transcriptomics was used to identify changes in gene expression from ß-arrestin-1-S330A/S330D mutation and ß-adrenergic insult. RESULTS: Metformin could decrease cAMP/PKA (protein kinase A) signaling induced by isoproterenol. AMPK bound to ß-arrestin-1 and phosphorylated Ser330 with the highest phosphorylated mass spectrometry score. AMPK activation promoted ß-arrestin-1 Ser330 phosphorylation in vitro and in vivo. Neonatal mouse cardiomyocytes overexpressing ß-arrestin-1-S330D (active form) inhibited the ß-AR/cAMP/PKA axis by increasing PDE (phosphodiesterase) 4 expression and activity. Cardiac transcriptomics revealed that the differentially expressed genes between isoproterenol-treated S330A KI and S330D KI mice were mainly involved in immune processes and inflammatory response. ß-arrestin-1 Ser330 phosphorylation inhibited isoproterenol-induced reactive oxygen species production and NLRP3 (NOD-like receptor protein 3) inflammasome activation in neonatal mouse cardiomyocytes. In S330D KI mice, the ß-AR-activated cAMP/PKA pathways were attenuated, leading to repressed inflammasome activation, reduced expression of proinflammatory cytokines, and mitigated macrophage infiltration. Compared with S330A KI mice, S330D KI mice showed diminished cardiac fibrosis and improved cardiac function upon isoproterenol exposure. However, the cardiac protection exerted by AMPK was abolished in S330A KI mice. CONCLUSIONS: AMPK phosphorylation of ß-arrestin-1 Ser330 potentiated PDE4 expression and activity, thereby inhibiting ß-AR/cAMP/PKA activation. Subsequently, ß-arrestin-1 Ser330 phosphorylation blocks ß-AR-induced cardiac inflammasome activation and remodeling.