The Quality of Short Videos as a Source of Coronary Heart Disease Information on TikTok: Cross-Sectional Study.

BACKGROUND: Coronary heart disease (CHD) is a leading cause of death worldwide and imposes a significant economic burden. TikTok has risen as a favored platform within the social media sphere for disseminating CHD-related information and stands as a pivotal resource for patients seeking knowledge about CHD. However, the quality of such content on TikTok remains largely unexplored. OBJECTIVE: This study aims to assess the quality of information conveyed in TikTok CHD-related videos. METHODS: A comprehensive cross-sectional study was undertaken on TikTok videos related to CHD. The sources of the videos were identified and analyzed. The comprehensiveness of content was assessed through 6 questions addressing the definition, signs and symptoms, risk factors, evaluation, management, and outcomes. The quality of the videos was assessed using 3 standardized evaluative instruments: DISCERN, the Journal of the American Medical Association (JAMA) benchmarks, and the Global Quality Scale (GQS). Furthermore, correlative analyses between video quality and characteristics of the uploaders and the videos themselves were conducted. RESULTS: The search yielded 145 CHD-related videos from TikTok, predominantly uploaded by health professionals (n=128, 88.3%), followed by news agencies (n=6, 4.1%), nonprofit organizations (n=10, 6.9%), and for-profit organizations (n=1, 0.7%). Content comprehensiveness achieved a median score of 3 (IQR 2-4). Median values for the DISCERN, JAMA, and GQS evaluations across all videos stood at 27 (IQR 24-32), 2 (IQR 2-2), and 2 (IQR 2-3), respectively. Videos from health professionals and nonprofit organizations attained significantly superior JAMA scores in comparison to those of news agencies (P<.001 and P=.02, respectively), whereas GQS scores for videos from health professionals were also notably higher than those from news agencies (P=.048). Within health professionals, cardiologists demonstrated discernibly enhanced performance over noncardiologists in both DISCERN and GQS assessments (P=.02). Correlative analyses unveiled positive correlations between video quality and uploader metrics, encompassing the positive correlations between the number of followers; total likes; average likes per video; and established quality indices such as DISCERN, JAMA, or GQS scores. Similar investigations relating to video attributes showed correlations between user engagement factors-likes, comments, collections, shares-and the aforementioned quality indicators. In contrast, a negative correlation emerged between the number of days since upload and quality indices, while a longer video duration corresponded positively with higher DISCERN and GQS scores. CONCLUSIONS: The quality of the videos was generally poor, with significant disparities based on source category. The content comprehensiveness coverage proved insufficient, casting doubts on the reliability and quality of the information relayed through these videos. Among health professionals, video contributions from cardiologists exhibited superior quality compared to noncardiologists. As TikTok's role in health information dissemination expands, ensuring accurate and reliable content is crucial to better meet patients' needs for CHD information that conventional health education fails to fulfill.

MATI: Multimodal Adaptive Tracking Integrator for Robust Visual Object Tracking.

Visual object tracking, pivotal for applications like earth observation and environmental monitoring, encounters challenges under adverse conditions such as low light and complex backgrounds. Traditional tracking technologies often falter, especially when tracking dynamic objects like aircraft amidst rapid movements and environmental disturbances. This study introduces an innovative adaptive multimodal image object-tracking model that harnesses the capabilities of multispectral image sensors, combining infrared and visible light imagery to significantly enhance tracking accuracy and robustness. By employing the advanced vision transformer architecture and integrating token spatial filtering (TSF) and crossmodal compensation (CMC), our model dynamically adjusts to diverse tracking scenarios. Comprehensive experiments conducted on a private dataset and various public datasets demonstrate the model's superior performance under extreme conditions, affirming its adaptability to rapid environmental changes and sensor limitations. This research not only advances visual tracking technology but also offers extensive insights into multisource image fusion and adaptive tracking strategies, establishing a robust foundation for future enhancements in sensor-based tracking systems.

Analysis of Glucose Responsive Glucagon Therapeutics using Computational Models of the Glucoregulatory System.

Glucose-responsive glucagon (GRG) therapeutics are a promising technology for reducing the risk of severe hypoglycemia as a complication of diabetes mellitus. Herein, the performance of candidate GRGs in the literature by modeling the kinetics of activation and connecting them as input into physiological glucoregulatory models is evaluated and projected the two distinct GRG designs, experimental results reported in Wu et al. (GRG-I) and Webber et al. (GRG-II) is considered. Both are evaluated using a multi-compartmental glucoregulatory model (IMPACT) and used to compare in-vivo experimental data of therapeutic performance in rats and mice. For GRG-I and GRG-II, the total integrated glucose material balances are overestimated by 41.5% ± 14% and underestimated by 24.8% ± 16% compared to in-vivo time-course data, respectively. These large differences to the relatively simple computational descriptions of glucagon dynamics in the model, which underscores the urgent need for improved glucagon models is attributed. Additionally, therapeutic insulin and glucagon infusion pumps are modeled for type 1 diabetes mellitus (T1DM) human subjects to extend the results to additional datasets. These observations suggest that both the representative physiological and non-physiological models considered in this work require additional refinement to successfully describe clinical data that involve simultaneous, coupled insulin, glucose, and glucagon dynamics.

Prospective Role of Foundation Models in Advancing Autonomous Vehicles.

With the development of artificial intelligence and breakthroughs in deep learning, large-scale foundation models (FMs), such as generative pre-trained transformer (GPT), Sora, etc., have achieved remarkable results in many fields including natural language processing and computer vision. The application of FMs in autonomous driving holds considerable promise. For example, they can contribute to enhancing scene understanding and reasoning. By pre-training on rich linguistic and visual data, FMs can understand and interpret various elements in a driving scene, and provide cognitive reasoning to give linguistic and action instructions for driving decisions and planning. Furthermore, FMs can augment data based on the understanding of driving scenarios to provide feasible scenes of those rare occurrences in the long tail distribution that are unlikely to be encountered during routine driving and data collection. The enhancement can subsequently lead to improvement in the accuracy and reliability of autonomous driving systems. Another testament to the potential of FMs' applications lies in world models, exemplified by the DREAMER series, which showcases the ability to comprehend physical laws and dynamics. Learning from massive data under the paradigm of self-supervised learning, world models can generate unseen yet plausible driving environments, facilitating the enhancement in the prediction of road users' behaviors and the off-line training of driving strategies. In this paper, we synthesize the applications and future trends of FMs in autonomous driving. By utilizing the powerful capabilities of FMs, we strive to tackle the potential issues stemming from the long-tail distribution in autonomous driving, consequently advancing overall safety in this domain.

Bilateral first rib fractures with multi-organ complications: A case report and literature review.

Background: Bilateral first rib fractures are rare. This article presented the diagnosis and treatment of a case of bilateral first rib fractures with multi-organ complications and discussed the injury mechanism. Case presentation: A 15-year-old girl fell off a motorcycle. She complained of right neck root pain and right upper limb weakness. The myodynamia of the right upper limb was grade 0, and the sensation disappeared below the level of the elbow joint. The computed tomography (CT) showed bilateral first rib fractures and transverse process fracture of the 6th cervical vertebra. Chest CT revealed a massive hemothorax in the right thoracic cavity, and head magnetic resonance imaging showed bilateral cerebellar infarction. Cervical computed tomography angiography (CTA) revealed a lumen occlusion at the origin of the right subclavian artery. The patient underwent an emergency thoracoscopy, and a re-examination of chest CT indicated that no obvious pleural effusion was found after the hemothorax was cleared. The patient underwent right subclavian arteriography and interventional endovascular thrombolysis, and the right subclavicular artery was patency postoperative. Bilateral first rib fractures and cerebellar infarction were treated conservatively. The brachial plexus injury did not show any signs of recovery after conservative treatment, and she was recommended to be transferred to a superior hospital for surgical treatment. Conclusions: The injury mechanism of bilateral first rib fractures with multi-organ complications was closely related to the initial factor of the right neck root colliding with a bulge on the ground. We believe that the fractures occur as a result of a combination including a high energy trauma from direct impact and a low-energy mechanism from violent muscle contraction caused by neck hyperextension. This case report was helpful for clinicians to understand bilateral first rib fractures and their complications.

A comprehensive epigenetic network can influence the occurrence of thyroid-associated ophthalmopathy by affecting immune and inflammatory response.

The primary objective of this study is to understand the regulatory role of epigenetics in thyroid-associated ophthalmopathy (TAO) using multi-omics sequencing data. We utilized tRFs sequencing data, DNA methylation sequencing data, and lncRNA/circRNA/mRNA sequencing data, as well as several RNA methylation target prediction websites, to analyze the regulatory effect of DNA methylation, non-coding RNA, and RNA methylation on TAO-associated genes. Through differential expression analysis, we identified 1019 differentially expressed genes, 985 differentially methylated genes, and 2601 non-coding RNA. Functional analysis showed that differentially expressed genes were mostly associated with the PI3K signaling pathway and the IL17 signaling pathway. Genes regulated by DNA epigenetic regulatory networks were mainly related to the Cytokine-cytokine receptor interaction pathway, whereas genes regulated by RNA epigenetic regulatory networks were primarily related to the T cell receptor signaling pathway. Finally, our integrated regulatory network analysis revealed that epigenetics mainly impacts the occurrence of TAO through its effects on key pathways such as cell killing, cytokine production, and immune response. In summary, this study is the first to reveal a new mechanism underlying the development of TAO and provides new directions for future TAO research.

EPAT: a user-friendly MATLAB toolbox for EEG/ERP data processing and analysis.

Background: At the intersection of neural monitoring and decoding, event-related potential (ERP) based on electroencephalography (EEG) has opened a window into intrinsic brain function. The stability of ERP makes it frequently employed in the field of neuroscience. However, project-specific custom code, tracking of user-defined parameters, and the large diversity of commercial tools have limited clinical application. Methods: We introduce an open-source, user-friendly, and reproducible MATLAB toolbox named EPAT that includes a variety of algorithms for EEG data preprocessing. It provides EEGLAB-based template pipelines for advanced multi-processing of EEG, magnetoencephalography, and polysomnogram data. Participants evaluated EEGLAB and EPAT across 14 indicators, with satisfaction ratings analyzed using the Wilcoxon signed-rank test or paired t-test based on distribution normality. Results: EPAT eases EEG signal browsing and preprocessing, EEG power spectrum analysis, independent component analysis, time-frequency analysis, ERP waveform drawing, and topological analysis of scalp voltage. A user-friendly graphical user interface allows clinicians and researchers with no programming background to use EPAT. Conclusion: This article describes the architecture, functionalities, and workflow of the toolbox. The release of EPAT will help advance EEG methodology and its application to clinical translational studies.

Cadmium promoted LPS-induced inflammation through TLR4/IκBα/NFκ-B signaling by increasing ROS-mediated incomplete autophagy.

Cadmium (Cd) exposure is considered as non-infectious stressor to human and animal health. Recent studies suggest that the immunotoxicity of low dose Cd is not directly apparent, but disrupts the immune responses when infected with some bacteria or virus. But how Cd alters the adaptive immunity organ and cells remains unclear. In this study, we applied lipopolysaccharide (LPS, infectious stressor) to induced inflammation in spleen tissues and T cells, and investigated the effects after Cd exposure and the underlying mechanism. Cd exposure promoted LPS-induced the expressions of the inflammatory factors, induced abnormal initiation of autophagy, but blocked autophagic flux. The effects Cd exposure under LPS activation were reversed by the autophagy promoter Rapamycin. Under LPS activation conditions, Cd also induced oxidative stress by increasing the levels of reactive oxygen species (ROS) and malondialdehyde (MDA), and reducing total antioxidant capacity (T-AOC) activity. The increased superoxide dismutase (SOD) activity after Cd exposure might be a negative feedback or passive adaptive regulation of oxidative stress. Cd-increased autophagic flux inhibition and TNF-α expression were reversed by ROS scavenger α-tocopherol (TCP). Furthermore, under LPS activation condition, Cd promoted activation of toll-like receptor 4 (TLR4)/IκBα/NFκ-B signaling pathway and increased TLR4 protein stability, which were abolished by the pretreatment of Rapamycin. The present study confirmed that, by increasing ROS-mediated inhibiting autophagic degradation of TLR4, Cd promoted LPS-induced inflammation in spleen T cells. This study identified the mechanism of autophagy in Cd-aggravated immunotoxicity under infectious stress, which could arouse public attention to synergistic toxicity of Cd and bacterial or virus infection.

Investigating quantitative approach for microalgal biomass using deep convolutional neural networks and image recognition.

The effective monitoring of microalgae cultivation is crucial for optimizing their energy utilization efficiency. In this paper, a quantitative analysis method, using microalgae images based on two convolutional neural networks, EfficientNet (EFF) and residual network (RES), is proposed. Suspension samples prepared from two types of dried microalgae powders, Rhodophyta (RH) and Spirulina (SP), were used to mimic real microalgae cultivation settings. The method's prediction accuracy of the algae concentration ranges from 0.94 to 0.99. RH, with a distinctively pronounced red-green-blue value shift, achieves a higher prediction accuracy than SP. The prediction results of the two algorithms were significantly superior to those of a linear regression. Additionally, RES outperforms EFF in terms of its generalization ability and robustness, which is attributable to its distinct residual block architecture. The RES provides a viable approach for the image-based quantitative analysis.

Rotenone-induced PINK1/Parkin-mediated mitophagy: establishing a silkworm model for Parkinson's disease potential.

Parkinson's disease (PD), ranking as the second most prevalent neurodegenerative disorder globally, presents a pressing need for innovative animal models to deepen our understanding of its pathophysiology and explore potential therapeutic interventions. The development of such animal models plays a pivotal role in unraveling the complexities of PD and investigating promising treatment avenues. In this study, we employed transcriptome sequencing on BmN cells treated with 1 µg/ml rotenone, aiming to elucidate the underlying toxicological mechanisms. The investigation brought to light a significant reduction in mitochondrial membrane potential induced by rotenone, subsequently triggering mitophagy. Notably, the PTEN induced putative kinase 1 (PINK1)/Parkin pathway emerged as a key player in the cascade leading to rotenone-induced mitophagy. Furthermore, our exploration extended to silkworms exposed to 50 µg/ml rotenone, revealing distinctive motor dysfunction as well as inhibition of Tyrosine hydroxylase (TH) gene expression. These observed effects not only contribute valuable insights into the impact and intricate mechanisms of rotenone exposure on mitophagy but also provide robust scientific evidence supporting the utilization of rotenone in establishing a PD model in the silkworm. This comprehensive investigation not only enriches our understanding of the toxicological pathways triggered by rotenone but also highlights the potential of silkworms as a valuable model organism for PD research.

Potential Strategy to Control the Organic Components of Condensable Particulate Matter: A Critical Review.

More and more attention has been paid to condensable particulate matter (CPM) since its emissions have surpassed that of filterable particulate matter (FPM) with the large-scale application of ultralow-emission reform. CPM is a gaseous material in the flue stack but instantly turns into particles after leaving the stack. It is composed of inorganic and organic components. Organic components are an important part of CPM, and they are an irritant, teratogenic, and carcinogenic, which triggers photochemical smog, urban haze, and acid deposition. CPM organic components can aggravate air pollution and climate change; therefore, consideration should be given to them. Based on existing methods for removing atmospheric organic pollutants and combined with the characteristics of CPM organic components, we provide a critical overview from the aspects of (i) fundamental cognition of CPM, (ii) common methods to control CPM organic components, and (iii) catalytic oxidation of CPM organic components. As one of the most encouraging methods, catalytic oxidation is discussed in detail, especially in combination with selective catalytic reduction (SCR) technology, to meet the growing demands for multipollutant control (MPC). We believe that this review is inspiring for a fuller understanding and deeper exploration of promising approaches to control CPM organic components.

A novel digital intermediate frequency module for hyperspectral microwave radiometers based on the parallel fast Fourier transform algorithm.

Microwave radiometers, possessing all-day and all-weather operational capabilities, are extensively utilized in the exploration of planetary atmospheres and surfaces. The potential of the hyperspectral detection technology to enhance the precision and resolution of microwave radiometer detection has made it a crucial research focus. This paper introduces an intermediate frequency (IF) module for hyperspectral microwave radiometers. The IF module is constructed with two 6.4 gigabit samples per second sampling-rate analog-to-digital converter (ADC) chips and a Xilinx Virtex-7 field programmable gate array. By implementing a parallel fast Fourier transform algorithm and a pipeline architecture, the IF module can efficiently process all ADC sampling data in real-time, generating 512 channels of output for each ADC. The test results, including linearity, sensitivity, and flatness, are presented and thoroughly analyzed for the IF module. Furthermore, the module is interfaced with the radio frequency front-end of the microwave radiometer to measure hot and cold calibration sources and assess its sensitivity. When combined with a suitable front-end receiver, the IF module can form a radiometer system suitable for various applications of microwave remote sensing.

Eco-evolutionary evidence for the global diversity pattern of Cycas (Cycadaceae).

The evolution of the latitudinal diversity gradient (LDG), characterized by a peak in diversity toward the tropics, has captured significant attention in evolutionary biology and ecology. However, the inverse LDG (i-LDG) mechanism, wherein species richness increases toward the poles, remains inadequately explored. Cycads are among one of the oldest lineages of extant seed plants and have undergone extensive diversification in the tropics. Intriguingly, the extant cycad abundance exhibits an i-LDG pattern, and the underlying causes for this phenomenon remain largely elusive. Here, using 1,843 nuclear genes from a nearly complete sampling, we conducted comprehensive phylogenomic analyses to establish a robust species-level phylogeny for Cycas, the largest genus within cycads. We then reconstructed the spatial-temporal dynamics and integrated global environmental data to evaluate the roles of species ages, diversification rates, contemporary environment, and conservatism to ancestral niches in shaping the i-LDG pattern. We found Cycas experienced decreased diversification rates, coupled with the cooling temperature since its origin in the Eocene from continental Asia. Different regions have distinctively contributed to the formation of i-LDG for Cycas, with the northern hemisphere acting as evolutionary museums and the southern hemisphere serving as cradles. Moreover, water-related climate variables, specifically precipitation seasonality and potential evapotranspiration, were identified as paramount factors constraining Cycas species richness in the rainforest biome near the equator. Notably, the adherence to ancestral monsoonal climates emerges as a critical factor in sustaining the diversity pattern. This study underscores the imperative of integrating both evolutionary and ecological approaches to comprehensively unravel the mechanisms underpinning global biodiversity patterns.

From ferroptosis to cuproptosis, and calcicoptosis, to find more novel metals-mediated distinct form of regulated cell death.

Regulated cell death (RCD), also known as programmed cell death (PCD), plays a critical role in various biological processes, such as tissue injury/repair, development, and homeostasis. Dysregulation of RCD pathways can lead to the development of many human diseases, such as cancer, neurodegenerative disorders, and cardiovascular diseases. Maintaining proper metal ion homeostasis is critical for human health. However, imbalances in metal levels within cells can result in cytotoxicity and cell death, leading to a variety of diseases and health problems. In recent years, new types of metal overload-induced cell death have been identified, including ferroptosis, cuproptosis, and calcicoptosis. This has prompted us to examine the three defined metal-dependent cell death types, and discuss other metals-induced ferroptosis, cuproptosis, and disrupted Ca2+ homeostasis, as well as the roles of Zn2+ in metals' homeostasis and related RCD. We have reviewed the connection between metals-induced RCD and various diseases, as well as the underlying mechanisms. We believe that further research in this area will lead to the discovery of novel types of metal-dependent RCD, a better understanding of the underlying mechanisms, and the development of new therapeutic strategies for human diseases.

Recycling selectable markers via Cre/loxP system for constructing Komagataella phaffii strains co-expressing multiple proteins.

OBJECTIVE: A convenient strategy was developed to recycle selectable markers using Cre/loxP system for constructing Komagataella phaffii strains co-expressing multiple proteins. RESULTS: A plasmid in this strategy was generated from pPICZαA with integration of lox71-Sh ble-lox66. Firstly, the plasmid was inserted with one target protein gene and then transformed into K. phaffii KM71. Secondly, the auxiliary plasmid pPICZαA/cre/his4 containing CRE recombinase gene was further chromosomally inserted to Sh ble gene therein. Finally, methanol induction was conducted to produce CRE for Cre/loxP-mediated recombination, and consequently, the sequence between lox71 and lox66 was deleted, leading to recycling of ZeoR and His- markers. Then the resulted strain expressing the one target protein was used as the host to which another target protein gene could be inserted by the same procedures. CONCLUSIONS: With easy manipulation, the method was effective in recycling of the selectable markers, and consequently two protein genes were sequential integrated chromosomally and successfully co-expressed in the yeast.

An integrated transcriptomics and network pharmacology approach to explore the mechanism of Wang-Bi tablet against SAPHO syndrome.

BACKGROUND: SAPHO syndrome is recognized as a rare entity with damage to skin and bones due to inflammation. Currently, the treatment for SAPHO syndrome is still a challenge in clinical practice. In this study, an integrated transcriptomics and network pharmacology approach was applied to explore the therapeutic effect and mechanism of Wang-Bi tablet (WBT) on SAPHO syndrome. METHODS: The main components of WBT and their targets, as well as the targets of SAPHO syndrome, were collected from databases. Network visualization was performed using Cytoscape software. The GO and KEGG enrichment analysis was executed by David dataset. Then, the molecular mechanism of WBT improving SAPHO syndrome was validated by transcriptomics of peripheral blood neutrophils in SAPHO syndrome. Finally, the above results were validated by molecular docking. RESULTS: The Network Pharmacology results showed there are 152 core targets for WBT treatment on SAPHO syndrome. RNA-seq data showed 442 differentially expressed genes (DEGs) in peripheral blood neutrophils of SAPHO patients. Intriguingly, NIK/NF-kappaB-, MyD88-dependent toll-like receptor-, and MAPK pathway were included in the enrichment results of network pharmacology and RNA-seq. Moreover, we verified that the core components of WBT have good affinity with the core targets of NIK/NF-kappaB-, MyD88-dependent toll-like receptor-, and MAPK pathway by molecular docking. CONCLUSIONS: This study illustrated that the possible mechanisms of WBT against SAPHO syndrome may be related to NIK/NF-kappaB-, MyD88-dependent toll-like receptor-, and MAPK pathway, and further experiments are needed to prove these predictions.

Data pyramid structure for optimizing EUS-based GISTs diagnosis in multi-center analysis with missing label.

This study introduces the Data Pyramid Structure (DPS) to address data sparsity and missing labels in medical image analysis. The DPS optimizes multi-task learning and enables sustainable expansion of multi-center data analysis. Specifically, It facilitates attribute prediction and malignant tumor diagnosis tasks by implementing a segmentation and aggregation strategy on data with absent attribute labels. To leverage multi-center data, we propose the Unified Ensemble Learning Framework (UELF) and the Unified Federated Learning Framework (UFLF), which incorporate strategies for data transfer and incremental learning in scenarios with missing labels. The proposed method was evaluated on a challenging EUS patient dataset from five centers, achieving promising diagnostic performance. The average accuracy was 0.984 with an AUC of 0.927 for multi-center analysis, surpassing state-of-the-art approaches. The interpretability of the predictions further highlights the potential clinical relevance of our method.

Correlation between the gut microbiome and neurodegenerative diseases: a review of metagenomics evidence.

A growing body of evidence suggests that the gut microbiota contributes to the development of neurodegenerative diseases via the microbiota-gut-brain axis. As a contributing factor, microbiota dysbiosis always occurs in pathological changes of neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. High-throughput sequencing technology has helped to reveal that the bidirectional communication between the central nervous system and the enteric nervous system is facilitated by the microbiota's diverse microorganisms, and for both neuroimmune and neuroendocrine systems. Here, we summarize the bioinformatics analysis and wet-biology validation for the gut metagenomics in neurodegenerative diseases, with an emphasis on multi-omics studies and the gut virome. The pathogen-associated signaling biomarkers for identifying brain disorders and potential therapeutic targets are also elucidated. Finally, we discuss the role of diet, prebiotics, probiotics, postbiotics and exercise interventions in remodeling the microbiome and reducing the symptoms of neurodegenerative diseases.