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.

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.

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.

Recent water-level fluctuations, future trends and their eco-environmental impacts on Lake Qinghai.

The water level of Lake Qinghai, the largest lake on the Qinghai-Tibetan Plateau, has increased continuously, at an average speed of 0.21 m per year since 2005, causing a rapid expansion of the lake area. We investigated the hydrological processes of Lake Qinghai and the surrounding watershed that have influenced water level and lake area from 1956 to 2019. Relationships among water level, climate change and human activities were also assessed. Water level and lake area were positively correlated with precipitation and runoff into the lake, and negatively correlated with evaporation. Climate change factors including precipitation and runoff were the primary causes of lake level change, whereas human activities, including variation in a human footprint index, land use, and grassland irrigation, were secondary factors. A time series model forecasted that from 2020 to 2050 water levels will increase further by 2.45 m. Although this increase in water level may have some benefits, such as reduced local desertification, the expansion of lake area will continue to flood low beaches, pasture lands, near shore infrastructure and roads, and impact tourism locations. However, continued water level rise may also have negative ecological effects, such as reduce habitat of seasonal birds and reduced water quality due to erosion and sediment resuspension in shallow nearshore lake areas. Local stakeholders, government authorities, and scientists should give greater attention to anticipated changes in water level, and further ecological studies and infrastructure adaptation measures should be implemented.

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.

The prognostic value of the preoperative inflammatory index on the survival of glioblastoma patients.

OBJECTIVES: The growth and development of tumors are closely related to the initiation and amplification of the inflammatory response. Various inflammatory biomarkers had attained growing attention for nearly two decades and were discovered strongly associated with cancer patients' prognosis, indicating that systemic inflammatory response is possibly essential to cancer progression. However, little was known about the sensitive biomarkers associated with the detection, persistence, treatment, and prognosis of GBM. Hence, the retrospective research endeavored to evaluate the prognostic value of preoperative inflammatory biomarkers in patients with GBM who initially received standardized treatment. METHODS: The 232 glioblastoma patients eligible who were admitted to Qilu Hospitals in Shandong Province from January 2014 to January 2018 were collected for this analysis. Inflammatory markers, including the systemic immune-inflammation index (SII), systemic immune response index (SIRI), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), monocyte-lymphocyte ratio (MLR), and albumin/globulin ratio (AGR), were designed. Progression-free survival (PFS) and overall survival (OS) were estimated using the Kaplan-Meier method, and we calculated the area under the ROC curve to determine the AUC value. Besides, we used the Cox proportional hazard model to estimate the relationship between variables and PFS and OS. The statistical differences between variables and PFS and OS were tested through the log-rank test. What is more, the LR method was used to perform Cox multiple regression analysis. The results were represented by hazard ratio (HR), 95% CI, any 2-tailed P < 0.01 was accepted as statistically different. RESULTS: The multivariate Cox proportional hazard model presented that SII ≥ 659.1 was an independent risk factor affecting OS (HR = 2.238, 95% CI = 1.471-3.406, P < 0.001) and postoperative PFS (HR = 2.000, 95% CI = 1.472-2.716, P < 0.001) in GBM patients. The 1-, 3-, and 5-year OS of the SII < 659.1 group was 70.8%, 26.9%, and 14.1%, respectively, while the 1- and 3-year OS of the SII ≥ 659.1 group was 37.5% and 11.5% (P < 0.001). The 1-, 3-, and 5-year PFS of the SII < 659.1 group was 36.3%, 19.6%, and 13%, respectively, while the 1-year PFS of the SII ≥ 659.1 group was 11.3% (P < 0.001). Results of patients' clinical and pathological characteristics paraded that in comparison to the lower SII group, the higher SII group had significantly inferior Karnofsky Performance Scale (KPS) scores (P < 0.001) and more frequent cystic changes of the tumors (P < 0.001), whereas the values of SIRI, NLR, PLR, MLR, and AGR were low. CONCLUSIONS: SII is an independent inflammatory indicator for predicting the prognosis of GBM patients after receiving initially standardized treatments.

Prolonged duration of repolarization and decreased conduction velocity in the atrial myocardium after hypothermic ischemia-reperfusion may be related to expressions of inward rectifier potassium channel 2.1 protein and connexin 40.

OBJECTIVES: The study aimed to determine the role of inward rectifier potassium channel 2.1 protein and connexin 40 expressions in regulating the duration of repolarization and conduction velocity of right atrial myocardium in rats following hypothermic ischemia-reperfusion. METHODS: The Langendorff isolated rat cardiac perfusion models were divided into control (C) and hypothermic ischemia-reperfusion groups, with 8 models in group C and 16 models in group ischemia-reperfusion. Depending on the incidence of atrial arrhythmia after reperfusion, the models in group ischemia-reperfusion were further divided into reperfusion non-atrial arrhythmia or reperfusion atrial arrhythmia subgroup. Right atrial monophasic action potential duration at 50% and 90% of repolarization after 30 minutes of continuous perfusion in group C and group ischemia-reperfusion (T0), 105 minutes of continuous perfusion in group C or after 15 minutes of reperfusion in group ischemia-reperfusion (T1) and 120 minutes of continuous perfusion in group C or 30 minutes of reperfusion in group ischemia-reperfusion (T2) were recorded. Right atrial conduction velocity and effective refractory period were recorded at T2. Then, the expressions of inward rectifier potassium channel 2.1 protein and connexin 40 in the right atrial myocardium were detected. RESULTS: Monophasic action potential duration at 50% and 90% were higher at T1 and T2 than those at T0 in subgroup reperfusion atrial arrhythmia (p < 0.05); monophasic action potential duration at 50% in subgroup reperfusion atrial arrhythmia were larger than group C and subgroup reperfusion non-atrial arrhythmia at T1 and T2 (p < 0.05); monophasic action potential duration at 90% in subgroup reperfusion atrial arrhythmia were larger than group C and subgroup reperfusion non-atrial arrhythmia at T1 and T2 (p < 0.05); effective refractory period in subgroup reperfusion atrial arrhythmia was greater than that in group C and subgroup reperfusion non-atrial arrhythmia, and the conduction velocity and the expressions of inward rectifier potassium channel 2.1 protein and connexin 40 were significantly lower than group C and subgroup reperfusion non-atrial arrhythmia (p < 0.05). CONCLUSIONS: The prolonged duration of repolarization and a decrease in conduction velocity of the atrial myocardium occur in rats after hypothermic ischemia-reperfusion. These observed effects may be related to the downregulated expressions of connexin 40 and inward rectifier potassium channel 2.1.

Effects of different concentrations of desflurane on the index of cardiac electrophysiological balance in gynecologic surgery patients.

The objectives were to observe the effects of different concentrations of desflurane on QT, QTc, Tp-e, Tp-e/QT, and the index of cardiac electrophysiological balance (iCEB). Sixty patients were randomly divided into group D1, group D2, and group D3 by using a random number table, 20 in each group. After entering the operating room, patients received 10 mL/kg hydroxyethyl starch, 0.1 mg/kg midazolam, 0.1 mg/kg vecuronium, 3 µg/kg fentanyl, and 0.3 mg/kg etomidate intravenously and then accepted intubation and mechanical ventilation. The desflurane evaporator was opened. The concentrations of desflurane in the D1, D2, and D3 groups were maintained at 0.6, 1.3, and 2.0 minimum alveolar concentration (MAC), respectively. Twelve-lead ECGs were recorded at time before induction (T1) and at 20 min after desflurane reached the required concentration (T2). HR and MAP were recorded measure and the QT interval, QTc interval, Tp-e interval, Tp-e/QT ratio, and iCEB were calculated. Compared with before inhalation (T1), the QTc interval was prolonged in the D1, D2, and D3 groups after inhalation of different concentrations of desflurane for 20 min (T2) (P < 0.05) and the Tp-e/QT ratio decreased in the D1 and D2 groups at T2 (P < 0.05). Compared with the D1 and D2 groups, the Tp-e/QT ratio of the D3 group increased at T2 (P < 0.05). There was no significant difference in Tp-e interval and iCEB at any time (P > 0.05). The study suggested that inhalation of desflurane at a normal concentration cannot cause arrhythmogenic characteristics and affect the cardiac electrophysiological stability.

An amino-functionalized zirconium-based metal-organic framework of type UiO-66-NH2 covered with a molecularly imprinted polymer as a sorbent for the extraction of aflatoxins AFB1, AFB2, AFG1 and AFG2 from grain.

A surface imprinted polymer of type UiO-66-NH2@MIP was prepared by combining molecular imprinted polymers (MIPs) and an amino-functionalized zirconium-based metal-organic framework. Quercetin is used as the virtual template, UiO-66-NH2 acts as the carrier to which the monomer acrylamide can be copolymerized. The material was characterized by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, and Fourier transform infrared spectroscopy. It was used as a sorbent in a solid-phase extraction column. The extraction conditions were optimized. The adsorption capacities for aflatoxins AFB1, AFB2, AFG1 and AFG2 by this SPE and by the commercial SPE were compared. The method was successfully applied to quantify the aflatoxins in grain. Figures of merit include (a) good linearity (range from 0.20-45 µg·kg-1) with R2 (range from 0.9986-0.9994), (b) low detection limits (90-130 ng·kg-1), (c) acceptable reproducibility (1.0-5.9%; for n = 6), and (d) relatively satisfactory recovery rates (74.3-98.6%). The new sorbent has good selectivity and reusability. Graphical abstractUiO-66-NH2@MIPs were synthesized with modified UIO-66-NH2 as core and quercetin as pseudo template. A cartridge was prepared with the polymers as the sorbent, and its performance was compared with different commercial SPE cartridges.

Inhibitive effects of chlortetracycline on performance of the nitritation-anaerobic ammonium oxidation (anammox) process and strategies for recovery.

The short- and long-term effects of chlortetracycline (CTC) on the nitritation- anaerobic ammonium oxidation (anammox) process were evaluated. The half maximal inhibitory concentration of CTC in the batch tests of the nitritation-anammox process was 278.91mg/L at an exposure time of 12hr. The long-term effects of CTC on the process were examined in a continuous-flow nitritation-anammox reactor. Within 14days, the nitrogen removal rate significantly decreased from 0.61 to 0.25kgN/m3/day with 60 mg/L CTC in the influent. The performance suppressed by CTC barely recovered, even after CTC was removed from the influent. Furthermore, the inhibition of CTC also reduced the relative abundance of ammonium oxidizing bacteria (AOB) and anaerobic ammonium oxidizing bacteria (AnAOB) in the reactor, resulting in both a decreased amount of and an imbalance between AOB and AnAOB. When fresh anammox sludge was reseeded into the nitritation-anammox reactor, the nitrogen removal rate recovered to 0.09 ± 0.03 kg N/m3/day.

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.

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.

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.

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.

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.

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.

Soluble Tim-3 serves as a tumor prognostic marker and therapeutic target for CD8+ T cell exhaustion and anti-PD-1 resistance.

Resistance to PD-1 blockade in onco-immunotherapy greatly limits its clinical application. T cell immunoglobulin and mucin domain containing-3 (Tim-3), a promising immune checkpoint target, is cleaved by ADAM10/17 to produce its soluble form (sTim-3) in humans, potentially becoming involved in anti-PD-1 resistance. Herein, serum sTim-3 upregulation was observed in non-small cell lung cancer (NSCLC) and various digestive tumors. Notably, serum sTim-3 is further upregulated in non-responding patients undergoing anti-PD-1 therapy for NSCLC and anti-PD-1-resistant cholangiocarcinoma patients. Furthermore, sTim-3 overexpression facilitates tumor progression and confers anti-PD-1 resistance in multiple tumor mouse models. Mechanistically, sTim-3 induces terminal T cell exhaustion and attenuates CD8+ T cell response to PD-1 blockade through carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM-1). Moreover, the ADAM10 inhibitor GI254023X, which blocks sTim-3 production, reduces tumor progression in Tim-3 humanized mice and reverses anti-PD-1 resistance in human tumor-infiltrating lymphocytes (TILs). Overall, human sTim-3 holds great predictive and therapeutic potential in onco-immunotherapy.

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.

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.