Cardiac Fibroblasts Enhance MMP2 Activity to Suppress Gap Junction Function in Cardiomyocytes.

Although it is crucial to promptly restore blood perfusion to revive the ischemic myocardium, reperfusion itself can paradoxically contribute to the electrical instability and arrhythmias of the myocardium. Several studies have revealed that cardiac fibroblasts can impact cardiac electrophysiology through various mechanisms including the deposition of extracellular matrix, release of chemical mediators, and direct electrical coupling with myocytes. Previously, we have shown that hypoxia/reoxygenation (H/R)-treated rat fibroblasts conditional medium (H/R-FCM) could decrease the spontaneous beating frequency of rat neonatal cardiomyocytes and downregulate the expression of gap junction proteins. However, the specific mechanism by which H/R-FCM affects the gap junctions requires further investigation. H/R-FCM was obtained by culturing confluent rat cardiac fibroblasts (RCF) for 4 h under hypoxic conditions. Gap junction function, hemichannel activity, and expression of Cx43 were examined upon treatment with H/R-FCM. Gelatin zymography was performed to detect matrix metalloproteinase (MMP) activity in the conditioned medium. The effect of H/R-FCM and MMP2 inhibitors on cardiac electrophysiology and arrhythmias was investigated with an isolated rat ischemia/reperfusion (I/R) model. H/R-FCM treatment impaired gap junction function, downregulated Cx43 expression, and increased hemichannel activity in rat cardiomyocytes (H9c2). The adverse effect of H/R-FCM on gap junction, which was confirmed by the cardiomyocyte H/R model, was involved in the activation of MMP2. MMP2 inhibition could partially attenuate the detrimental effects of I/R on myocardial electrophysiological indices and arrhythmia susceptibility. Our study indicates that inhibition of MMP2 may be a promising therapeutic target for the treatment of reperfusion arrhythmia.

Mapping the awareness and knowledge about patient and public versions of guidelines among stakeholders in China: a cross-sectional survey.

OBJECTIVE: Patient and public versions of guidelines (PVGs) have gradually gained wide recognition and attention from the public and the society due to their scientific, professional and authoritative characteristics. This study aims to survey the awareness and knowledge of PVGs among stakeholders in China. STUDY DESIGN AND SETTING: This was a cross-sectional survey among stakeholder (guideline developers, clinicians, journal editors, patients and the public) in China. We self-designed the questionnaire and distributed it through the Questionnaire Star platform. The primary outcomes were awareness of PVGs, and opinions about the development methodology, writing, dissemination and implementation of PVGs. The Kruskal Wallis H test and post hoc multiple comparison tests were used to compare the levels of awareness of PVGs between different subgroups of respondents. RESULTS: A total of 1319 valid questionnaires were collected: 722 from guideline developers, 136 from clinicians, 83 from journal editors, 284 from patients and 94 from members of the public. Of all respondents, 253 (19.2%) had not heard of PVGs, 349 (26.5%) had heard of PVGs but had no further knowledge, 475 (36.0%) had some knowledge of PVGs, and 242 (18.3%) were familiar with or had participated in the development of PVGs. Guideline developers, clinicians and journal editors had higher scores for awareness than patients and the public. Higher education and older age also correlated with higher awareness of PVGs. Over half (52.9%) of guideline developers considered that both rewriting of the source guidelines and direct development as independent documents were appropriate methods for developing PVGs. The survey respondents agreed that clinicians (97.3%), guideline methodologists (76.6%), representatives of patients and the public (74.5%) and medical editors or writers (63.4%) should participate in the development of PVGs. More than 80% of the respondents agreed that the quality of evidence and strength of recommendations should be presented, however, there was no consensus on the form of presentation. CONCLUSIONS: The level of awareness of PVGs among stakeholders in China is relatively low and differs between different stakeholder groups, but the majority of key stakeholders have a positive attitude towards PVGs. The collection of the perspectives and opinions on the development methods, writing, dissemination and implementation provides a key reference and basis for the future optimization and improvement of PVGs development.

Synapses tagged, memories kept: synaptic tagging and capture hypothesis in brain health and disease.

The synaptic tagging and capture (STC) hypothesis lays the framework on the synapse-specific mechanism of protein synthesis-dependent long-term plasticity upon synaptic induction. Activated synapses will display a transient tag that will capture plasticity-related products (PRPs). These two events, tag setting and PRP synthesis, can be teased apart and have been studied extensively-from their electrophysiological and pharmacological properties to the molecular events involved. Consequently, the hypothesis also permits interactions of synaptic populations that encode different memories within the same neuronal population-hence, it gives rise to the associativity of plasticity. In this review, the recent advances and progress since the experimental debut of the STC hypothesis will be shared. This includes the role of neuromodulation in PRP synthesis and tag integrity, behavioural correlates of the hypothesis and modelling in silico. STC, as a more sensitive assay for synaptic health, can also assess neuronal aberrations. We will also expound how synaptic plasticity and associativity are altered in ageing-related decline and pathological conditions such as juvenile stress, cancer, sleep deprivation and Alzheimer's disease. This article is part of a discussion meeting issue 'Long-term potentiation: 50 years on'.

A study on the potential of digital economy in reducing agricultural carbon emissions.

Agriculture is a significant source of carbon emissions, which have a substantial environmental impact. The digital economy plays a vital role in mitigating these emissions through innovative digital solutions. As a leading agricultural nation, China faces substantial pressure to reduce its agricultural carbon emissions(ACE). This paper aims to thoroughly examine the relationship between the growth of the rural digital economy and ACE. To achieve this, we utilize an extensive panel dataset covering China's provinces from 2011 to 2020, analyzing the dynamic and spatial effects of digital economy development on ACE. The key findings of this research are as follows: (1) The rapid expansion of the digital economy significantly reduces ACE. (2) The impact of digital economic development on lowering ACE varies spatially, with a clear progression from eastern to western regions. (3) The digital economy helps reduce ACE through three specific channels: fostering technological innovation, enhancing scale efficiency management, and providing agricultural financial incentives. Based on these findings, this study proposes policy recommendations to improve digital infrastructure, promote balanced regional development in the digital economy, and optimize the management of agricultural science and technology. These policy insights aim to transform agriculture and achieve the goal of reducing ACE, thereby contributing to broader environmental sustainability.

Seismic hazard prediction of the Hunhe Fault in the Shen-Fu New District.

Earthquake prevention and disaster mitigation are crucial aspects of social welfare that significantly impact national public security. This paper presents a seismic risk assessment and hazard prediction of the Hunhe Fault in the Shengyang-Fushun (Shen-Fu) New District. The target area is at risk of seismic damage due to two major branch ruptures, namely, F9 and F1; these ruptures have the potential to generate maximum earthquakes with a magnitude of 6.0 in the next 50 to 100 years. A three-dimensional underground velocity structure and asperity source model were established for the target faults. Subsequently, a hybrid technique combining deterministic and empirical approaches was employed to simulate the broadband strong ground motion of the target region in anticipation of the occurrence of expected scenario earthquakes. The distributions of peak ground acceleration (PGA), peak ground velocity (PGV) and peak ground displacement (PGD) for the area are provided, and the results indicate that densely populated urban areas could experience PGA values close to 280 cm/s2 along the fault traces. This study provides a reliable basis for engineering construction and urban planning in the Shen-Fu New District.

Potential Roles of Large Language Models in the Production of Systematic Reviews and Meta-Analyses.

Large language models (LLMs) such as ChatGPT have become widely applied in the field of medical research. In the process of conducting systematic reviews, similar tools can be used to expedite various steps, including defining clinical questions, performing the literature search, document screening, information extraction, and language refinement, thereby conserving resources and enhancing efficiency. However, when using LLMs, attention should be paid to transparent reporting, distinguishing between genuine and false content, and avoiding academic misconduct. In this viewpoint, we highlight the potential roles of LLMs in the creation of systematic reviews and meta-analyses, elucidating their advantages, limitations, and future research directions, aiming to provide insights and guidance for authors planning systematic reviews and meta-analyses.

Targeted Therapy of Tumors and Cancer Stem Cells based on Oxidant-Regulated Redox Pathway and its Mechanism.

A malignant tumor is a frequent and common disease that severely threatens human health. Many mechanisms, such as cell signaling pathway, anti-apoptosis mechanism, cell stemness, metabolism, and cell phenotype, have been studied to explain the reasons for chemotherapy, radioresistance, and tumor recurrences in antitumor treatment. Cancer stem cells (CSCs) are important tumor cell subclasses that can potentially organize and regulate stem cell properties. Growing evidence suggests that CSCs can initiate tumors and constitute a significant factor in metastasis, recurrence, and treatment resistance. The inability to completely target and remove CSCs is a considerable obstacle in tumor treatment. Therefore, drugs and therapeutic strategies that can effectively intervene with CSCs are essential for the treatment of different tumor types. However, the current strategies and efficacy of targeted elimination of CSCs are very limited. Oxidative stress has been recognized to play a crucial role in cancer pathophysiology. Moreover, reactive oxygen species (ROS) production and imbalance of the built-in cellular antioxidant defense system are hallmarks of tumor and cancer etiology. The current paper will focus on the regulation and mechanism behind oxidative stress in tumors and cancer stem cells and its tumor therapy applications. Additionally, the article discusses the role of CSCs in causing tumor treatment resistance and recurrence based on a redox perspective. The study also emphasizes that targeted modulation of oxidative stress in CSCs has great potential in tumor therapy, providing novel prospects for tumor therapy.

Research on process parameter optimization of irregular coal face.

Based on the engineering background of 1353 working face in Daizhuang Coalmine, the paper identifies three primary issues with the working face mining process: conventional pressure relief means are limited, risk of impact and the length of working face changes, It also proposes comprehensive control measures to improve the blasting roof cutting scheme, optimize mining speed and the location of the stopping line. The three improvement measures mentioned above are simulated numerically, and the effects of the drilling and blasting plan, mining speed, and stopping line location on stress distribution are determined. The results show that by implementing the three improvement measures, the stress variation interval can be efficiently controlled and the working face's production safety can be increased. Finally, it is determined that the 1353 working face of Daizhuang Coalmine adopts the pressure relief method of drilling on one side and cutting the roof 20 m deep on the other side, and the mining speed of the working face is 3 m/day and the length of the stopping line is 85 m. Based on the on-site monitoring results, the implementation of comprehensive treatment measures can effectively improve the surrounding rock state of 1353 working face, which has certain guiding significance for the mining of irregular working face.

Bispecific antibodies with broad neutralization potency against SARS-CoV-2 variants of concern.

The ongoing emergence of SARS-CoV-2 variants of concern (VOCs) that reduce the effectiveness of antibody therapeutics necessitates development of next-generation antibody modalities that are resilient to viral evolution. Here, we characterized N-terminal domain (NTD) and receptor binding domain (RBD)-specific monoclonal antibodies previously isolated from COVID-19 convalescent donors for their activity against emergent SARS-CoV-2 VOCs. Among these, the NTD-specific antibody C1596 displayed the greatest breadth of binding to VOCs, with cryo-EM structural analysis revealing recognition of a distinct NTD epitope outside of the site i antigenic supersite. Given C1596's favorable binding profile, we designed a series of bispecific antibodies (bsAbs) termed CoV2-biRNs, that featured both NTD and RBD specificities. Notably, two of the C1596-inclusive bsAbs, CoV2-biRN5 and CoV2-biRN7, retained potent in vitro neutralization activity against all Omicron variants tested, including XBB.1.5, EG.5.1, and BA.2.86, contrasting the diminished potency of parental antibodies delivered as monotherapies or as a cocktail. Furthermore, prophylactic delivery of CoV2-biRN5 significantly reduced the viral load within the lungs of K18-hACE2 mice following challenge with SARS-CoV-2 XBB.1.5. In conclusion, our NTD-RBD bsAbs offer promising potential for the design of resilient, next-generation antibody therapeutics against SARS-CoV-2 VOCs.

Innovative methods for microplastic characterization and detection: Deep learning supported by photoacoustic imaging and automated pre-processing data.

Plastic products' widespread applications and their non-biodegradable nature have resulted in the continuous accumulation of microplastic waste, emerging as a significant component of ecological environmental issues. In the field of microplastic detection, the intricate morphology poses challenges in achieving rapid visual characterization of microplastics. In this study, photoacoustic imaging technology is initially employed to capture high-resolution images of diverse microplastic samples. To address the limited dataset issue, an automated data processing pipeline is designed to obtain sample masks while effectively expanding the dataset size. Additionally, we propose Vqdp2, a generative deep learning model with multiple proxy tasks, for predicting six forms of microplastics data. By simultaneously constraining model parameters through two training modes, outstanding morphological category representations are achieved. The results demonstrate Vqdp2's excellent performance in classification accuracy and feature extraction by leveraging the advantages of multi-task training. This research is expected to be attractive for the detection classification and visual characterization of microplastics.

Isobavachin attenuates osteoclastogenesis and periodontitis-induced bone loss by inhibiting cellular iron accumulation and mitochondrial biogenesis.

As bone-resorbing cells rich in mitochondria, osteoclasts require high iron uptake to promote mitochondrial biogenesis and maintain a high-energy metabolic state for active bone resorption. Given that abnormal osteoclast formation and activation leads to imbalanced bone remodeling and osteolytic bone loss, osteoclasts may be crucial targets for treating osteolytic diseases such as periodontitis. Isobavachin (IBA), a natural flavonoid compound, has been confirmed to be an inhibitor of receptor activator of nuclear factor κB ligand (RANKL)-induced osteoclast differentiation from bone marrow-derived macrophages (BMMs). However, its effects on periodontitis-induced bone loss and the potential mechanism of its anti-osteoclastogenesis effect remain unclear. Our study demonstrated that IBA suppressed RANKL-induced osteoclastogenesis in BMMs and RAW264.7 cells and inhibited osteoclast-mediated bone resorption in vitro. Transcriptomic analysis indicated that iron homeostasis and reactive oxygen species (ROS) metabolic process were enriched among the differentially expressed genes following IBA treatment. IBA exerted its anti-osteoclastogenesis effect by inhibiting iron accumulation in osteoclasts. Mechanistically, IBA attenuated iron accumulation in RANKL-induced osteoclasts by inhibiting the mitogen-activated protein kinase (MAPK) pathway to upregulate ferroportin1 (Fpn1) expression and promote Fpn1-mediated intracellular iron efflux. We also found that IBA inhibited mitochondrial biogenesis and function, and reduced RANKL-induced ROS generation in osteoclasts. Furthermore, IBA attenuated periodontitis-induced bone loss by reducing osteoclastogenesis in vivo. Overall, these results suggest that IBA may serve as a promising therapeutic strategy for bone diseases characterized by osteoclastic bone resorption.

Manipulation of Luminescence via Surface Site Occupation in Ln3+-Doped Nanocrystals.

Ln3+-doped (Ln = lanthanide) nanocrystals are garnering strong interest for their potential as optical materials in various applications. For that reason, a thorough understanding of photophysical processes and ways to tune them in these materials is of great importance. This study, using Eu3+-doped Sr2YF7 as a well-suited model system, underscores the (not unexpected) significance of surface site occupation of Ln3+ and also challenges the prevailing views about their contribution to the luminescence of the system. High-temperature cation exchange and epitaxial shell growth allow nanocrystals to exclusively feature Eu3+ residing at the surface or in the interior, thereby separating their spectral responses. Meticulous experiments reveal that nanocrystals with high doping concentrations exhibit luminescence primarily from surface Eu3+, in contrast to the popular belief that luminescence from surface Ln3+ is largely negligible. The present study shows, on the one hand, the necessity to revise common ideas and also reveals the potential for manipulating the luminescence of such materials through an, until now, unperceived way of surface engineering.

Attention-guided variational graph autoencoders reveal heterogeneity in spatial transcriptomics.

The latest breakthroughs in spatially resolved transcriptomics technology offer comprehensive opportunities to delve into gene expression patterns within the tissue microenvironment. However, the precise identification of spatial domains within tissues remains challenging. In this study, we introduce AttentionVGAE (AVGN), which integrates slice images, spatial information and raw gene expression while calibrating low-quality gene expression. By combining the variational graph autoencoder with multi-head attention blocks (MHA blocks), AVGN captures spatial relationships in tissue gene expression, adaptively focusing on key features and alleviating the need for prior knowledge of cluster numbers, thereby achieving superior clustering performance. Particularly, AVGN attempts to balance the model's attention focus on local and global structures by utilizing MHA blocks, an aspect that current graph neural networks have not extensively addressed. Benchmark testing demonstrates its significant efficacy in elucidating tissue anatomy and interpreting tumor heterogeneity, indicating its potential in advancing spatial transcriptomics research and understanding complex biological phenomena.

Advancing microplastic surveillance through photoacoustic imaging and deep learning techniques.

Microplastic contamination presents a significant global environmental threat, yet scientific understanding of its morphological distribution within ecosystems remains limited. This study introduces a pioneering method for comprehensive microplastic assessment and environmental monitoring, integrating photoacoustic imaging and advanced deep learning techniques. Rigorous curation of diverse microplastic datasets enhances model training, yielding a high-resolution imaging dataset focused on shape-based discrimination. The introduction of the Vector-Quantized Variational Auto Encoder (VQVAE2) deep learning model signifies a substantial advancement, demonstrating exceptional proficiency in image dimensionality reduction and clustering. Furthermore, the utilization of Vector Quantization Microplastic Photoacoustic imaging (VQMPA) with a proxy task before decoding enhances feature extraction, enabling simultaneous microplastic analysis and discrimination. Despite inherent limitations, this study lays a robust foundation for future research, suggesting avenues for enhancing microplastic identification precision through expanded sample sizes and complementary methodologies like spectroscopy. In conclusion, this innovative approach not only advances microplastic monitoring but also provides valuable insights for future environmental investigations, highlighting the potential of photoacoustic imaging and deep learning in bolstering sustainable environmental monitoring efforts.

Evaluation of the prognostic performance of different cutoff values of lymph node ratio staging system for stage III colorectal cancer.

This study attempted to compare the prognostic performance of lymph node ratio (LNR) staging system with different cutoff values relative to American Joint Committee on Cancer (AJCC) pN staging system in stage III colorectal cancer (CRC). Overall, 45,069 patients from the SEER dataset and 69 patients from the Second Affiliated Hospital of Nanjing Medical University (the External set) who underwent surgical resection of the primary tumor and were diagnosed with stage III CRC by postoperative pathology were included. Patients were divided into three subgroups based on the LNR cutoff used in previous studies, Kaplan-Meier curves were plotted, and log-rank test was used to compare the differences among groups in terms of cancer-specific survival (CSS). Cox regression model was applied for survival analysis. To evaluate the discriminatory power of different lymph node staging systems, Harrell's C statistic(C-index) and Akaike's Information Criterion (AIC) were applied. A set of optimal cutoff values (0.11; 0.36; 0.66) of LNR staging system with the most considerable discriminatory power to the prognosis in patients with stage III CRC (SEER set: C-index = 0.714; AIC = 58,942.46, External set: C-index = 0.809; AIC = 164.36) were obtained, and both were superior to the AJCC pN staging system (SEER set: C-index = 0.708; AIC = 59,071.20, External set: C-index = 0.788; AIC = 167.06). For evaluating the prognostic efficacy of patients with stage III colorectal cancer, the cutoff value (0.11; 0.36; 0.66) of LNR staging system had the best discrimination and prognostic ability, which was superior to LNR staging system under other cutoff values and AJCC pN staging system.

Enhancing subcellular protein localization mapping analysis using Sc2promap utilizing attention mechanisms.

BACKGROUND: Aberrant protein localization is a prominent feature in many human diseases and can have detrimental effects on the function of specific tissues and organs. High-throughput technologies, which continue to advance with iterations of automated equipment and the development of bioinformatics, enable the acquisition of large-scale data that are more pattern-rich, allowing for the use of a wider range of methods to extract useful patterns and knowledge from them. METHODS: The proposed sc2promap (Spatial and Channel for SubCellular Protein Localization Mapping) model, designed to proficiently extract meaningful features from a vast repository of single-channel grayscale protein images for the purposes of protein localization analysis and clustering. Sc2promap incorporates a prediction head component enriched with supplementary protein annotations, along with the integration of a spatial-channel attention mechanism within the encoder to enables the generation of high-resolution protein localization maps that encapsulate the fundamental characteristics of cells, including elemental cellular localizations such as nuclear and non-nuclear domains. RESULTS: Qualitative and quantitative comparisons were conducted across internal and external clustering evaluation metrics, as well as various facets of the clustering results. The study also explored different components of the model. The research outcomes conclusively indicate that, in comparison to previous methods, Sc2promap exhibits superior performance. CONCLUSIONS: The amalgamation of the attention mechanism and prediction head components has led the model to excel in protein localization clustering and analysis tasks. GENERAL SIGNIFICANCE: The model effectively enhances the capability to extract features and knowledge from protein fluorescence images.

Fast Real-Time Brain Tumor Detection Based on Stimulated Raman Histology and Self-Supervised Deep Learning Model.

In intraoperative brain cancer procedures, real-time diagnosis is essential for ensuring safe and effective care. The prevailing workflow, which relies on histological staining with hematoxylin and eosin (H&E) for tissue processing, is resource-intensive, time-consuming, and requires considerable labor. Recently, an innovative approach combining stimulated Raman histology (SRH) and deep convolutional neural networks (CNN) has emerged, creating a new avenue for real-time cancer diagnosis during surgery. While this approach exhibits potential, there exists an opportunity for refinement in the domain of feature extraction. In this study, we employ coherent Raman scattering imaging method and a self-supervised deep learning model (VQVAE2) to enhance the speed of SRH image acquisition and feature representation, thereby enhancing the capability of automated real-time bedside diagnosis. Specifically, we propose the VQSRS network, which integrates vector quantization with a proxy task based on patch annotation for analysis of brain tumor subtypes. Training on images collected from the SRS microscopy system, our VQSRS demonstrates a significant speed enhancement over traditional techniques (e.g., 20-30 min). Comparative studies in dimensionality reduction clustering confirm the diagnostic capacity of VQSRS rivals that of CNN. By learning a hierarchical structure of recognizable histological features, VQSRS classifies major tissue pathological categories in brain tumors. Additionally, an external semantic segmentation method is applied for identifying tumor-infiltrated regions in SRH images. Collectively, these findings indicate that this automated real-time prediction technique holds the potential to streamline intraoperative cancer diagnosis, providing assistance to pathologists in simplifying the process.

Severe Acute Respiratory Syndrome Coronavirus 2 Omicron Subvariant Neutralization Following a Primary Vaccine Series of NVX-CoV2373 and BNT162b2 Monovalent Booster Vaccine.

We evaluated the immunologic response to a novel vaccine regimen that included 2 doses of NVX-CoV2373 (Novavax) followed by 1 dose of BNT162b2 (Pfizer-BioNTech) monovalent booster vaccine. A durable neutralizing antibody response to Omicron BA.4/BA.5 and BA.1 variants was observed at month 6 after the booster, while immune escape was noted for the XBB.1.5 variant.

Non-pharmacological interventions for asthma prevention and management across the life course: Umbrella review.

BACKGROUND: The impact of non-pharmacological interventions (NPIs) on asthma prevention and management is insufficiently examined. We aim to comprehensively evaluate and synthesize existing evidence regarding the effectiveness of various NPIs throughout the life course. METHODS: We conducted a systematic search and screening of reviews that examined the effectiveness of various NPIs on asthma prevention and control in the Cochrane Library, PubMed, Embase, and Ovid databases. Data extraction was performed by considering the type of NPIs and the life course stages of the target population. Recommendations were provided by considering the quality of review assessed using the AMSTAR2 tool and the consistency of findings across reviews. RESULTS: We identified 145 reviews and mapped the evidence on the impact of 25 subtypes of NPIs on asthma prevention and control based on five stages of life course. Reviews indicated a shift of focus and various impacts of major NPIs on asthma prevention and control across life courses, while a few types of NPIs, such as physical exercise, appeared to be beneficial in children, adolescents and adults. Consistent and high-level evidence was observed only for psychological intervention on asthma control and quality of life among adults and older adults. Potential benefit with high-level evidence was reported on certain NPIs, such as vitamin D in reducing risk of developing asthma in offsprings in the prenatal stage, digital health interventions in improving asthma control from childhood to older adulthood, and breathing exercise in improving quality of life, asthma-related symptoms and lung function in adulthood and older adulthood. CONCLUSION: This study emphasizes the significance of delivering NPIs to improve asthma prevention and management and highlights the heterogeneity regarding the impact of NPIs across life courses. High-quality research is urgently needed to further strengthen the evidence base of NPIs and tailored interventions should be considered in guideline development.