Effects of Separation and Purification Methods on Antioxidation, Hypoglycemic and DNA Protection Activity of Fenugreek Polysaccharide.

Six low molecular weight fenugreek polysaccharides (FP) were isolated and purified by ethanol stepwise precipitation (EFP-20, EFP-40, and EFP-60) and DEAE-52 cellulose column method (DFP-0, DFP-0.15, and DFP-0.3), respectively. The effects of different separation and purification techniques on the preliminary properties and biological activities of fenugreek polysaccharides were compared. The results showed that the DEAE-52 cellulose-eluted fractions had a higher total sugar content and displayed a looser structure. The molecular weights of all six fractions were in the range of 4-19 kDa, with significant changes in the ratio of galactose to mannose. All six fractions contained α-D-galactopyranose and ß-D-mannopyranose structures. Activity tests showed that all six fractions possessed antioxidant, hypoglycemic and DNA-protective activities. Among them, the DFP-0 fraction showed the highest activity. Overall, different isolation and purification methods lead to changes in the properties and bioactivities of FP, which provides a theoretical basis for the development and application of FP in functional foods and drugs.

P-NiFe2O4/N-doped carbon nanotubes/NiFe multi-phase heterojunctions for overall water splitting and urea electrolysis.

The design and construction of highly efficient electrocatalysts for overall water splitting and urea electrolysis are significantly important for promoting energy conversion and realizing green hydrogen production. In this work, we constructed a multi-phase heterojunction through a simple hydrothermal and phosphorization process. The P-doped NiFe2O4 (P-NiFe2O4) nanoparticles were uniformly anchored on the bamboo-like N-doped carbon nanotubes (NCNTs) grown via a NiFe-alloy autocatalysis. The electronic structure and coordination environment of active species were optimized by the synergistic action of P doping, well-dispersed ultrafine NiFe2O4, and NCNTs matrix with good conductivity, enhancing their quantity and activity for electrocatalysis. Consequently, the P-NiFe2O4/NCNTs/NiFe exhibits excellent HER and OER activities with an overpotential of 111 and 266 mV at 10 mA cm-2 in 1 M KOH, respectively. The symmetrical overall water-splitting cell using P-NiFe2O4/NCNTs/NiFe as both anode and cathode delivers 10 mA cm-2 at a voltage of 1.604 V in 1 M KOH. Notably, the two-electrode cell requires a low voltage of 1.467 V to achieve a current density of 10 mA cm-2 in 1 M KOH solution with 0.6 M urea. This designed catalysts display outstanding reaction kinetics and catalytic stability. This work provides useful guidance for applying transition metal-based catalysts for hydrogen production.

OliTag-seq enhances in cellulo detection of CRISPR-Cas9 off-targets.

The potential for off-target mutations is a critical concern for the therapeutic application of CRISPR-Cas9 gene editing. Current detection methodologies, such as GUIDE-seq, exhibit limitations in oligonucleotide integration efficiency and sensitivity, which could hinder their utility in clinical settings. To address these issues, we introduce OliTag-seq, an in-cellulo assay specifically engineered to enhance the detection of off-target events. OliTag-seq employs a stable oligonucleotide for precise break tagging and an innovative triple-priming amplification strategy, significantly improving the scope and accuracy of off-target site identification. This method surpasses traditional assays by providing comprehensive coverage across various sgRNAs and genomic targets. Our research particularly highlights the superior sensitivity of induced pluripotent stem cells (iPSCs) in detecting off-target mutations, advocating for using patient-derived iPSCs for refined off-target analysis in therapeutic gene editing. Furthermore, we provide evidence that prolonged Cas9 expression and transient HDAC inhibitor treatments enhance the assay's ability to uncover off-target events. OliTag-seq merges the high sensitivity typical of in vitro assays with the practical application of cellular contexts. This approach significantly improves the safety and efficacy profiles of CRISPR-Cas9 interventions in research and clinical environments, positioning it as an essential tool for the precise assessment and refinement of genome editing applications.

MTS-LOF: Medical Time-Series Representation Learning via Occlusion-Invariant Features.

Medical time series data are indispensable in healthcare, providing critical insights for disease diagnosis, treatment planning, and patient management. The exponential growth in data complexity, driven by advanced sensor technologies, has presented challenges related to data labeling. Self-supervised learning (SSL) has emerged as a transformative approach to address these challenges, eliminating the need for extensive human annotation. In this study, we introduce a novel framework for Medical Time Series Representation Learning, known as MTS-LOF. MTS-LOF leverages the strengths of Joint-Embedding SSL and Masked Autoencoder (MAE) methods, offering a unique approach to representation learning for medical time series data. By combining these techniques, MTS-LOF enhances the potential of healthcare applications by providing more sophisticated, context-rich representations. Additionally, MTS-LOF employs a multi-masking strategy to facilitate occlusion-invariant feature learning. This approach allows the model to create multiple views of the data by masking portions of it. By minimizing the discrepancy between the representations of these masked patches and the fully visible patches, MTS-LOF learns to capture rich contextual information within medical time series datasets. The results of experiments conducted on diverse medical time series datasets demonstrate the superiority of MTS-LOF over other methods. These findings hold promise for significantly enhancing healthcare applications by improving representation learning. Furthermore, our work delves into the integration of Joint-Embedding SSL and MAE techniques, shedding light on the intricate interplay between temporal and structural dependencies in healthcare data. This understanding is crucial, as it allows us to grasp the complexities of healthcare data analysis.

Knowledge distillation under ideal joint classifier assumption.

Knowledge distillation constitutes a potent methodology for condensing substantial neural networks into more compact and efficient counterparts. Within this context, softmax regression representation learning serves as a widely embraced approach, leveraging a pre-established teacher network to guide the learning process of a diminutive student network. Notably, despite the extensive inquiry into the efficacy of softmax regression representation learning, the intricate underpinnings governing the knowledge transfer mechanism remain inadequately elucidated. This study introduces the 'Ideal Joint Classifier Knowledge Distillation' (IJCKD) framework, an overarching paradigm that not only furnishes a lucid and exhaustive comprehension of prevailing knowledge distillation techniques but also establishes a theoretical underpinning for prospective investigations. Employing mathematical methodologies derived from domain adaptation theory, this investigation conducts a comprehensive examination of the error boundary of the student network contingent upon the teacher network. Consequently, our framework facilitates efficient knowledge transference between teacher and student networks, thereby accommodating a diverse spectrum of applications.

A transformer-based diffusion probabilistic model for heart rate and blood pressure forecasting in Intensive Care Unit.

BACKGROUND AND OBJECTIVE: Vital sign monitoring in the Intensive Care Unit (ICU) is crucial for enabling prompt interventions for patients. This underscores the need for an accurate predictive system. Therefore, this study proposes a novel deep learning approach for forecasting Heart Rate (HR), Systolic Blood Pressure (SBP), and Diastolic Blood Pressure (DBP) in the ICU. METHODS: We extracted 24,886 ICU stays from the MIMIC-III database which contains data from over 46 thousand patients, to train and test the model. The model proposed in this study, Transformer-based Diffusion Probabilistic Model for Sparse Time Series Forecasting (TDSTF), merges Transformer and diffusion models to forecast vital signs. The TDSTF model showed state-of-the-art performance in predicting vital signs in the ICU, outperforming other models' ability to predict distributions of vital signs and being more computationally efficient. The code is available at https://github.com/PingChang818/TDSTF. RESULTS: The results of the study showed that TDSTF achieved a Standardized Average Continuous Ranked Probability Score (SACRPS) of 0.4438 and a Mean Squared Error (MSE) of 0.4168, an improvement of 18.9% and 34.3% over the best baseline model, respectively. The inference speed of TDSTF is more than 17 times faster than the best baseline model. CONCLUSION: TDSTF is an effective and efficient solution for forecasting vital signs in the ICU, and it shows a significant improvement compared to other models in the field.

Moisturizing and aroma-enhancing effects of low molecular weight fenugreek polysaccharides in cigarettes.

Polysaccharides possess excellent moisturizing effects due to their abundance of hydrophilic groups and film-forming properties. Additionally, they can produce a refreshing aroma during the pyrolysis process. However, there is scarce research on their application in the tobacco field. Herein, we investigated the effects of low molecular weight fenugreek polysaccharide (FP) obtained through ethanol fractionation and DEAE-52 cellulose column chromatography on moisture retention and aroma enhancement in tobacco. The moisture retention test revealed that the addition of FP increased the moisture retention index (MRI) of tobacco by 11.72 %-16.69 %, indicating that the hydrophilic nature of polysaccharides facilitated the migration of free water in tobacco to bound water, resulting in reduced water activity. Moreover, the contact angle between polysaccharide and tobacco was <90°, enabling better infiltration into tobacco and slowing down tobacco shrinkage caused by water loss. Among all the components, EFP-20 and EFP-40 demonstrated superior performance. Furthermore, FP exhibited excellent thermal stability below 200 °C and can decomposed to produce aromatic substances at high temperatures. It also demonstrated the ability to adsorb ethyl heptanoate and thermally decompose to produce a substantial amount of heptanoic acid. Consequently, the incorporation of FP in tobacco demonstrated favorable effects on both moisturization and aroma enhancement.

Continual Learning with Deep Neural Networks in Physiological Signal Data: A Survey.

Deep-learning algorithms hold promise in processing physiological signal data, including electrocardiograms (ECGs) and electroencephalograms (EEGs). However, healthcare often requires long-term monitoring, posing a challenge to traditional deep-learning models. These models are generally trained once and then deployed, which limits their ability to adapt to the dynamic and evolving nature of healthcare scenarios. Continual learning-known for its adaptive learning capabilities over time-offers a promising solution to these challenges. However, there remains an absence of consolidated literature, which reviews the techniques, applications, and challenges of continual learning specific to physiological signal analysis, as well as its future directions. Bridging this gap, our review seeks to provide an overview of the prevailing techniques and their implications for smart healthcare. We delineate the evolution from traditional approaches to the paradigms of continual learning. We aim to offer insights into the challenges faced and outline potential paths forward. Our discussion emphasizes the need for benchmarks, adaptability, computational efficiency, and user-centric design in the development of future healthcare systems.

CD147: an integral and potential molecule to abrogate hallmarks of cancer.

CD147 also known as EMMPRIN, basigin, and HAb18G, is a single-chain type I transmembrane protein shown to be overexpressed in aggressive human cancers of CNS, head and neck, breasts, lungs, gastrointestinal, genitourinary, skin, hematological, and musculoskeletal. In these malignancies, the molecule is integral to the diverse but complimentary hallmarks of cancer: it is pivotal in cancerous proliferative signaling, growth propagation, cellular survival, replicative immortality, angiogenesis, metabolic reprogramming, immune evasion, invasion, and metastasis. CD147 also has regulatory functions in cancer-enabling characteristics such as DNA damage response (DDR) and immune evasion. These neoplastic functions of CD147 are executed through numerous and sometimes overlapping molecular pathways: it transduces signals from upstream molecules or ligands such as cyclophilin A (CyPA), CD98, and S100A9; activates a repertoire of downstream molecules and pathways including matrix metalloproteinases (MMPs)-2,3,9, hypoxia-inducible factors (HIF)-1/2α, PI3K/Akt/mTOR/HIF-1α, and ATM/ATR/p53; and also functions as an indispensable chaperone or regulator to monocarboxylate, fatty acid, and amino acid transporters. Interestingly, induced loss of functions to CD147 prevents and reverses the acquired hallmarks of cancer in neoplastic diseases. Silencing of Cd147 also alleviates known resistance to chemoradiotherapy exhibited by malignant tumors like carcinomas of the breast, lung, pancreas, liver, gastric, colon, ovary, cervix, prostate, urinary bladder, glioblastoma, and melanoma. Targeting CD147 antigen in chimeric and induced-chimeric antigen T cell or antibody therapies is also shown to be safer and more effective. Moreover, incorporating anti-CD147 monoclonal antibodies in chemoradiotherapy, oncolytic viral therapy, and oncolytic virus-based-gene therapies increases effectiveness and reduces on and off-target toxicity. This study advocates the expedition and expansion by further exploiting the evidence acquired from the experimental studies that modulate CD147 functions in hallmarks of cancer and cancer-enabling features and strive to translate them into clinical practice to alleviate the emergency and propagation of cancer, as well as the associated clinical and social consequences.

Enhancing cord blood stem cell-derived NK cell growth and differentiation through hyperosmosis.

BACKGROUND: Natural killer (NK) cells hold great promise in treating diverse hematopoietic and solid tumors. Despite their availability from peripheral blood and cord blood, stem cell-derived NK cells offer an 'off-the-shelf' solution. Hematopoietic stem and progenitor cells (HSPCs) derived from cord blood pose no risk to the newborn or mother and are virtually ideal sources for NK cell differentiation. METHODS: We developed a modified protocol to differentiate HSPCs to NK cells under serum-free conditions using defined factors. The HSPC-derived NK (HSC-NK) cells could be expanded in a K562 feeder cell-dependent manner. Furthermore, using lentivirus transduction, chimeric antigen receptor (CAR)-modified HSPCs could be differentiated into NK cells, leading to the establishment of CAR-NK cells. RESULTS: The efficiency of NK cell differentiation from HSPCs was increased through the simple modulation of osmotic pressure by the addition of sodium chloride or glucose. Furthermore, the hyperosmosis-primed HSC-NK cells exhibited enhanced proliferation capacity and maintained normal functional characteristics, including transcriptome and antitumor efficacy. The optimized protocol yielded approximately 1.8 million NK cells from a single CD34-positive cell within a 28-day cycle, which signifies more than a ten-fold increase in efficiency relative to the conventional methods. This optimized protocol was also suitable for generating CAR-NK cells with high yields compared to standard conditions. CONCLUSIONS: The results of this study establish high osmotic pressure as a simple yet powerful adjustment that significantly enhances the efficiency and functionality of HSC-NK cells, including CAR-NK cells. This optimized protocol could lead to cost-effective, high-yield NK cell therapies, potentially revolutionizing cancer immunotherapy strategies.

Fast CU Partition Algorithm for Intra Frame Coding Based on Joint Texture Classification and CNN.

High-efficiency video coding (HEVC/H.265) is one of the most widely used video coding standards. HEVC introduces a quad-tree coding unit (CU) partition structure to improve video compression efficiency. The determination of the optimal CU partition is achieved through the brute-force search rate-distortion optimization method, which may result in high encoding complexity and hardware implementation challenges. To address this problem, this paper proposes a method that combines convolutional neural networks (CNN) with joint texture recognition to reduce encoding complexity. First, a classification decision method based on the global and local texture features of the CU is proposed, efficiently dividing the CU into smooth and complex texture regions. Second, for the CUs in smooth texture regions, the partition is determined by terminating early. For the CUs in complex texture regions, a proposed CNN is used for predictive partitioning, thus avoiding the traditional recursive approach. Finally, combined with texture classification, the proposed CNN achieves a good balance between the coding complexity and the coding performance. The experimental results demonstrate that the proposed algorithm reduces computational complexity by 61.23%, while only increasing BD-BR by 1.86% and decreasing BD-PSNR by just 0.09 dB.

Heterogeneous bacterial communities affected by phytoplankton community turnover and microcystins in plateau lakes of Southwestern China.

Both phytoplankton and bacteria are fundamental organisms with key ecological functions in lake ecosystems. However, the mechanistic interactions through which phytoplankton community change and bacterial communities interact remain poorly understood. Here, the responses of bacterial communities to the community structure, resource-use efficiency (RUE), and community turnover of phytoplankton and microcystins (MCs) were investigated in Lake Dianchi, Lake Xingyun, and Lake Erhai of Southwestern China across two seasons (May and October 2020). Among phytoplankton, Cyanobacteria was the dominant species in all three lakes and attained greater dominance in October than in May due to variation in the RUE of nitrogen and phosphorus and environmental changes. The production of MCs, including MC_LR, MC_RR and MC_YR, was the result of the massive Cyanobacteria. Decreases in diversity and increases in heterogeneity were observed in the bacterial community structure. Nutrient levels, environmental factors and MCs (especially MC_YR) jointly affected the bacterial community in lakes, namely its diversity and community assembly. The cascading effects in lakes mediated by environmental conditions, phytoplankton community composition, RUE, community turnover, and MCs on bacterial communities were revealed in this study. These findings underscore the importance of relating phytoplankton community change and MCs to the bacterial community, which is fundamental for better understanding the lake ecosystem functioning and potential risks of MCs.

Psychoneurological symptoms and inflammatory markers in patients with glioma in China: a network analysis.

PURPOSE: Anxiety, depression, sleep disorder, fatigue, and pain develop as common psychoneurological symptoms in patients with glioma, and their occurrence and development are potentially related to inflammatory factors. However, this theory has not been proven within the context of glioma. This study aimed to estimate interconnections among psychoneurological symptoms and inflammatory biomarkers by a network analysis. PATIENTS AND METHODS: We selected 203 patients with stage I-IV glioma from a tertiary hospital in China using convenient sampling method. Patients completed the self-made questionnaires, Hamilton Anxiety Scale-14 (HAMA-14), Hamilton Depression Scale-24 (HAMD-24), Pittsburgh Sleep Quality Index (PSQI), Multidimensional Fatigue Inventory-20 (MFI-20), and pain Numerical Rating Scale (NRS). The plasma inflammatory cytokines were examined. Partial correlation network analysis was performed to illustrate interactions of symptoms and inflammatory biomarkers. RESULTS: Among the 203 included patients, all psychoneurological symptoms, except for depression and pain, exhibited significant connections with each other. Depression, anxiety, fatigue, interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α) with higher strength centrality indices were identified as the most central node within the symptom-biomarker network. CONCLUSIONS: Depression, anxiety, fatigue, IL-6, and TNF-α play a significant role in the symptom-biomarker network in patients with glioma. Medical staff should strengthen the dynamic evaluation of the involved symptoms and inflammatory cytokines and take effective measures to alleviate the burden of symptoms and improve the quality of life of patients.

N-acetylcysteine alleviates oxidative stress and apoptosis and prevents skeletal muscle atrophy in type 1 diabetes mellitus through the NRF2/HO-1 pathway.

AIMS: Type 1 diabetes mellitus (T1DM) has been linked to the occurrence of skeletal muscle atrophy. Insulin monotherapy may lead to excessive blood glucose fluctuations. N-acetylcysteine (NAC), a clinically employed antioxidant, possesses cytoprotective, anti-inflammatory, and antioxidant properties. The objective of our study was to evaluate the viability of NAC as a supplementary treatment for T1DM, specifically regarding its therapeutic and preventative impacts on skeletal muscle. MAIN METHODS: Here, we used beagles as T1DM model for 120d to explore the mechanism of NRF2/HO-1-mediated skeletal muscle oxidative stress and apoptosis and the therapeutic effects of NAC. Oxidative stress and apoptosis related factors were analyzed by immunohistochemistry, immunofluorescence, western blotting, and RT-qPCR assay. KEY FINDINGS: The findings indicated that the co-administration of NAC and insulin led to a reduction in creatine kinase levels, preventing weight loss and skeletal muscle atrophy. Improvement in the reduction of muscle fiber cross-sectional area. The expression of Atrogin-1, MuRF-1 and MyoD1 was downregulated, while Myh2 and MyoG were upregulated. In addition, CAT and GSH-Px levels were increased, MDA levels were decreased, and redox was maintained at a steady state. The decreased of key factors in the NRF2/HO-1 pathway, including NRF2, HO-1, NQO1, and SOD1, while KEAP1 increased. In addition, the apoptosis key factors Caspase-3, Bax, and Bak1 were found to be downregulated, while Bcl-2, Bcl-2/Bax, and CytC were upregulated. SIGNIFICANCE: Our findings demonstrated that NAC and insulin mitigate oxidative stress and apoptosis in T1DM skeletal muscle and prevent skeletal muscle atrophy by activating the NRF2/HO-1 pathway.

Biocatalytic Fluoroalkylation Using Fluorinated S-Adenosyl-l-methionine Cofactors.

Modification of organic molecules with fluorine functionalities offers a critical approach to develop new pharmaceuticals. Here, we report a multienzyme strategy for biocatalytic fluoroalkylation using S-adenosyl-l-methionine (SAM)-dependent methyltransferases (MTs) and fluorinated SAM cofactors prepared from ATP and fluorinated l-methionine analogues by an engineered human methionine adenosyltransferase hMAT2AI322A. This work introduces the first example of biocatalytic 3,3-difluoroallylation. Importantly, this strategy can be applied to late-stage site-selective fluoroalkylation of complex molecule vancomycin with conversions up to 99%.

Sequential targeting biomimetic nano platform for enhanced mild photothermal therapy and chemotherapy of tumor.

Tumor targeting drug delivery is of significant importance for the treatment of triple negative breast cancer (TNBC) considering the presence of appreciable amount of tumor matrix and the absence of effective targets on the tumor cells. Hence in this study, a new therapeutic multifunctional nanoplatform with improved TNBC targeting ability and efficacy was constructed and used for therapy of TNBC. Specifically, curcumin loaded mesoporous polydopamine (mPDA/Cur) nanoparticles were synthesized. Thereafter, manganese dioxide (MnO2) and a hybrid of cancer-associated fibroblasts (CAFs) membranes as well as cancer cell membranes were sequentially coated on the surface of mPDA/Cur to obtain mPDA/Cur@M/CM. It was found that two distinct kinds of cell membranes were able to endow the nano platform with homologous targeting ability, thereby achieving accurate delivery of drugs. Nanoparticles gathered in the tumor matrix can loosen the tumor matrix via the photothermal effect mediated by mPDA to rupture the physical barrier of tumor, which is conducive to the penetration and targeting of drugs to tumor cells in the deep tissues. Moreover, the existence of curcumin, MnO2 and mPDA was able to promote the apoptosis of cancer cells by promoting increased cytotoxicity, enhanced Fenton-like reaction, and thermal damage, respectively. Overall, both in vitro and in vivo results showed that the designed biomimetic nanoplatform could significantly inhibit the tumor growth and thus provide an efficient novel therapeutic strategy for TNBC.

Synthesis and Evaluation of Novel Nitric Oxide-Donating Ligustrazine Derivatives as Potent Antiplatelet Aggregation Agents.

Antiplatelet aggregation agents have demonstrated clinical benefits in the treatment of ischemic stroke. In our study, a series of novel nitric oxide (NO)-donating ligustrazine derivatives were designed and synthesized as antiplatelet aggregation agents. They were evaluated for the inhibitory effect on 5'-diphosphate (ADP)-induced and arachidonic acid (AA)-induced platelet aggregation in vitro. The results showed that compound 15d displayed the best activity in both ADP-induced and AA-induced assays, and compound 14a also showed quite better activity than ligustrazine. The preliminary structure-activity relationships of these novel NO-donating ligustrazine derivatives were discussed. Moreover, these compounds were docked with the thromboxane A2 receptor to study the structure-activity relationships. These results suggested that the novel NO-donating ligustrazine derivatives 14a and 15d deserve further study as potent antiplatelet aggregation agents.

Hyperhomocysteinemia activates NLRP3 inflammasome to cause hepatic steatosis and insulin resistance via MDM2-mediated ubiquitination of HSF1.

Hyperhomocysteinemia (HHcy) is associated with nonalcoholic fatty liver disease (NAFLD) and insulin resistance (IR). However, the underlying mechanism is still unknown. Recent studies have demonstrated that NLRP3 inflammasome activation plays a vital role in NAFLD and IR. Our study aimed to explore whether NLRP3 inflammasome contributed to HHcy-induced NAFLD and IR as well as dissected the underlying mechanism. C57BL/6 mice were fed a high-methionine diet (HMD) for 8 weeks to establish the HHcy mouse model. Compared with a chow diet, HMD induced hepatic steatosis (HS) and IR as well as activation of hepatic NLRP3 inflammasome. Moreover, HHcy-induced NAFLD and IR characterization disclosed that NLRP3 inflammasome activation occurred in liver tissue of HMD-fed mice, but was very marginal in either NLRP3-/- or Caspase-1-/- mice. Mechanistically, high levels of homocysteine (Hcy) up-regulated the expression of mouse double minute 2 homolog (MDM2), which directly ubiquitinates heat shock transcription factor 1 (HSF1) and consequently activated hepatic NLRP3 inflammasome in vivo and in vitro. In addition, in vitro experiments showed P300-mediated HSF1 acetylation at K298 hindered MDM2-mediated ubiquitination of HSF1 at K372, which plays important role in determining the HSF1 level. Importantly, either inhibition of MDM2 by JNJ-165 or activation of HSF1 by HSF1A reversed HMD-induced hepatic NLRP3 inflammasome, and consequently alleviated HS and IR in mice. This study demonstrates that NLRP3 inflammasome activation contributes to HHcy-induced NAFLD and IR, and further identified that HSF1 as a new substrate of MDM2 and its decrease on MDM2-mediated ubiquitination at K372 modulates NLRP3 inflammasome activation. These findings may provide novel therapeutic strategies aimed at halting HS or IR.

DeScoD-ECG: Deep Score-Based Diffusion Model for ECG Baseline Wander and Noise Removal.

OBJECTIVE: Electrocardiogram (ECG) signals commonly suffer noise interference, such as baseline wander. High-quality and high-fidelity reconstruction of the ECG signals is of great significance to diagnosing cardiovascular diseases. Therefore, this paper proposes a novel ECG baseline wander and noise removal technology. METHODS: We extended the diffusion model in a conditional manner that was specific to the ECG signals, namely the Deep Score-Based Diffusion model for Electrocardiogram baseline wander and noise removal (DeScoD-ECG). Moreover, we deployed a multi-shots averaging strategy that improved signal reconstructions. We conducted the experiments on the QT Database and the MIT-BIH Noise Stress Test Database to verify the feasibility of the proposed method. Baseline methods are adopted for comparison, including traditional digital filter-based and deep learning-based methods. RESULTS: The quantities evaluation results show that the proposed method obtained outstanding performance on four distance-based similarity metrics with at least 20% overall improvement compared with the best baseline method. CONCLUSION: This paper demonstrates the state-of-the-art performance of the DeScoD-ECG for ECG baseline wander and noise removal, which has better approximations of the true data distribution and higher stability under extreme noise corruptions. SIGNIFICANCE: This study is one of the first to extend the conditional diffusion-based generative model for ECG noise removal, and the DeScoD-ECG has the potential to be widely used in biomedical applications.