Hydrolysis Mechanism of Multimodular Endoglucanases with Distinctive Domain Composition in the Saccharification of Cellulosic Substrates.

Two multimodular endoglucanases in glycoside hydrolase family 5, ReCel5 and ElCel5, share 73% identity and exhibit similar modular structures: family 1 carbohydrate-binding module (CBM1); catalytic domain; CBMX2; module of unknown function. However, they differed in their biochemical properties and catalytic performance. ReCel5 showed optimal activity at pH 4.0 and 70 °C, maintaining stability at 70 °C (>80% activity). Conversely, ElCel5 is optimal at pH 3.0 and 50 °C (>50% activity at 50 °C). ElCel5 excels in degrading CMC-Na (256 U/mg vs 53 U/mg of ReCel5). Five domain-truncated (TM1-TM5) and four domain-replaced (RM1-RM4) mutants of ReCel5 with the counterparts of ElCel5 were constructed, and their enzymatic properties were compared with those of the wild type. Only RM1, with ElCel5-CBM1, displayed enhanced thermostability and activity. The hydrolysis of pretreated corn stover was reduced in most TM and RM mutants. Molecular dynamics simulations revealed interdomain interactions within the multimodular endoglucanase, potentially affecting its structural stability and complex biological catalytic processes.

Analyzing the Prevalence of Depression and Its Influencing Factors in Elderly Patients With Obstructive Sleep Apnea: A Machine Learning Approach.

Objective: Depressive symptoms are prevalent and detrimental in elderly patients with obstructive sleep apnea (OSA). Understanding the factors influencing these symptoms is crucial. This study aims to use machine learning algorithms to identify the contributing factors in this population. Method: The National Health and Nutrition Examination Survey database provided the data for this study. The study includes elderly patients who are eligible for diagnostic evaluation for OSA. Logistic regression was used to screen their influencing factors, and random forest (RF), extreme gradient boosting (XGB), artificial neural network (ANN), and support vector machine (SVM) were utilized to 4 algorithms were used to construct depressive symptoms classification models, and the best model performance was selected for feature importance ranking. Influential factors included demographics (age, gender, education, etc.), chronic disease status (diabetes, hypertension, etc.), and laboratory findings (white blood cells, C-reactive protein, cholesterol, etc.). Result: Ultimately, we chose 1538 elderly OSA patients for the study, out of which 528 (34.4%) suffered from depressive symptoms. Logistic regression initially identified 17 influencing factors and then constructed classification models based on those 17 using RF, XGB, ANN, and SVM. We selected the best-performing SVM model [area under the curve (AUC) = 0.746] based on the AUC values of 0.73, 0.735, 0.742, and 0.746 for the 4 models. We ranked the variables in order of importance: General health status, sleep disorders, gender, frequency of urinary incontinence, liver disease, physical activity limitations, education, moisture, eosinophils, erythrocyte distribution width, and hearing loss. Conclusion: Elderly OSA patients experience a high incidence of depressive symptoms, influenced by various objective and subjective factors. The situation is troubling, and healthcare institutions and policymakers must prioritize their mental health. We should implement targeted initiatives to improve the mental health of high-risk groups in multiple dimensions.

Risk factors for mild cognitive impairment in patients with age-related hearing loss: a meta-analysis.

OBJECTIVES: One of the most common sensory impairments in the elderly is age-related hearing loss, and individuals with this condition have a higher risk of mild cognitive impairment than the overall aged population. The purpose of this study was to conduct a systematic review of the literature in order to evaluate the evidence supporting the hypothesis that mild cognitive impairment may be developed in patients with age-related hearing loss. METHODS: The PRISMA principles were followed when searching the databases of the China Knowledge Network, Wanfang, China Biomedical Literature Database, Pub Med, Cochrane Library, Embase, and Web of Science. Two investigators independently carried out the quality assessment, data extraction, and literature review of the eligible studies. Stata 17.0 was used to finish the statistical analysis and descriptive results. RESULTS: A total of 13 articles containing 2,222,036 individuals who were evaluated for demographic traits, factors associated with age-related hearing loss, vascular neurologic factors, and psychological factors were included after 2166 search records were found in the database. In patients with age-related hearing loss, eleven factors were found to be risk factors for the development of mild cognitive impairment: age (OR = 1.63; 95% CI 1.09-2.43), male (OR = 1.29; 95% CI 1.14-1.47), degree of hearing loss (OR = 1.35; 95% CI 1.03-1.75), not wearing hearing aids (OR = 1.56; 95% CI 1.37-1.79), cerebrovascular disease (OR = 1.41; 95% CI 1.17-1.69), cardiovascular disease (OR = 1.29; 95% CI 1.07-1.55), diabetes mellitus (OR = 1.28; 95% CI 1.20-1.35), head injury (OR = 1.22; 95% CI 1.13-1.33), alcohol consumption (OR = 1.28; 95% CI 1.14-1.43), and tobacco use (OR = 1.19; 95% CI 1.14-1.25), and depression (OR = 1.63; 95% CI 1.47-1.81). CONCLUSION: Caregivers can customize care strategies to decrease the occurrence of mild cognitive impairment in elderly deaf patients by considering demographic traits, factors associated with age-related hearing loss, vascular-neurologic factors, and psychological factors.

Distinct metabolic profiles and pathway alterations in myocardial infarction and unstable angina revealed by metabolomics.

BACKGROUND AND AIMS: Myocardial infarction (MI) and unstable angina (UA) exhibit overlapping symptoms, yet they require distinct management approaches. Identifying the metabolic differences between MI and UA may facilitate more precise diagnosis and treatment. MATERIALS AND METHODS: Metabolomic analysis was conducted on 95 patients, comprising 33 UA patients, 38 MI patients, and 24 normal controls. Serum metabolites were profiled using tandem mass spectrometry coupled with liquid chromatography. RESULTS: Metabolic analysis revealed notable differences in several metabolites, including xylidine, hydroxycaproic acid, butylbenzenesulfonamide, octanetriol, phosphocholine, and medronic acid, between MI and UA. These metabolites displayed promising diagnostic capabilities for distinguishing between MI and UA. Pathway analysis identified connections with cardiac hypertrophy, Wnt signaling, and fatty acid oxidation. CONCLUSION: Potential metabolite biomarkers and pathways differentially altered in MI compared to UA were identified in this metabolomics study. The results provide new insights into the metabolic signatures of these ischemic heart diseases. With further confirmation, improved early diagnosis and personalized treatment approaches could be facilitated.

CAR-T therapy for ovarian cancer: Recent advances and future directions.

Ovarian cancer (OC) is a common gynecological tumor with high mortality, which is difficult to control its progression with conventional treatments and is prone to recurrence. Recent studies have identified OC as an immunogenic tumor that can be recognized by the host immune system. Immunotherapy for OC is being evaluated, but approaches such as immune checkpoint inhibitors have limited efficacy, adoptive cell therapy is an alternative therapy, in which CAR(chimeric antigen receptor)-T therapy has been applied to the clinical treatment of hematological malignancies. In addition, CAR-NK and CAR-macrophage (CAR-M) have also shown great potential in the treatment of solid tumors. Here, we discuss recent advances in preclinical and clinical studies of CAR-T for OC treatment, introduce the efforts made by researchers to modify the structure of CAR in order to achieve effective OC immunotherapy, as well as the research status of CAR-NK and CAR-M, and highlight emerging therapeutic opportunities that can be utilized to improve the survival of patients with OC using CAR-based adoptive cell therapy.

Effect of holographic Guasha on sleep quality of older adults with hypertension in the community: A prospective study.

To explore the effect of holographic Guasha therapy on the Pittsburg Sleep Quality Index (PSQI) and Hamilton Anxiety Rating Scale (HAMA) in older adults with hypertension living in the community. This prospective study was conducted from July 2019 to December 2020. Older adults with hypertension (systolic pressure ≥ 140 mm Hg, diastolic pressure ≥ 90 mm Hg) were divided into the control and Guasha groups. The PSQI and HAMA were assessed before and after 4 weeks of intervention. 62 patients were enrolled, with 31/group (Guasha: 72.4 ±â€…6.9 years, 23.0 ±â€…3.1 kg/m2; control: 71.4 ±â€…6.3 years; 22.9 ±â€…2.9 kg/m2). The total PSQI score did not decrease in the control group after 4 weeks (from 14.8 ±â€…1.2 to 14.8 ±â€…1.8, P = .498) but decreased in the Guasha group (from 14.9 ±â€…1.1 to 6.8 ±â€…3.5, P < .001). All PSQI subscores decreased in the Guasha group after 4 weeks of Guasha intervention (all P < .05), except for the use of sleep medication, since the use of such drugs was an exclusion criterion. The HAMA index scores did not change in the control or Guasha group (both P > .05). Holographic Guasha appears to achieve better sleep outcomes than conventional treatment in improving the sleep quality of older adults with hypertension living in the community. The participants were not randomly assigned to the treatments, and the results should be confirmed in a formal trial.

Development of a Machine Learning Algorithm to Forecast the Likelihood of Postoperative Neurological Complications in Patients With Parotid Tumors.

Objective: The objective of this study was to create and verify a machine learning-driven predictive model to forecast the likelihood of facial nerve impairment in patients with parotid tumors following surgery. Methods: We retrospectively collected data from patients with parotid tumors between 2013 and 2023 to develop a prediction model for postoperative facial nerve dysfunction using 5 ML techniques: Logistic Regression (Logit), Random Forest (RF), XGBoost (XGB), Artificial Neural Network (ANN), and Support Vector Machine (SVM). Predictor variables were screened using binomial-LASSO regression. Results: The study had a total of 403 participants, out of which 56 individuals encountered facial nerve damage after the surgery. By employing binomial-LASSO regression, we have successfully identified 8 crucial predictive variables: tumor kind, tumor pain, surgeon's experience, tumor volume, basophil percentage, red blood cell count, partial thromboplastin time, and prothrombin time. The models utilizing ANN and Logit achieved higher area under the curve (AUC) values, namely 0.829, which was significantly better than the SVM model that had an AUC of 0.724. There were no noticeable disparities in the AUC values between the ANN and Logit models, as well as between these models and other techniques like RF and XGB. Conclusion: Using machine learning, our prediction model accurately predicts the likelihood that patients with parotid tumors may experience facial nerve damage following surgery. By using this model, doctors can assess patients' risks more accurately before to surgery, and it may also help optimize postoperative treatment techniques. It is anticipated that this tool would enhance patients' quality of life and therapeutic outcomes.

Mulberry leaf and its effects against obesity: A systematic review of phytochemistry, molecular mechanisms and applications.

BACKGROUND: Obesity and hyperlipidemia can induce a variety of diseases, and have become major health problems worldwide. How to effectively prevent and control obesity has become one of the hot-spots of contemporary research. Mulberry leaf is the dried leaf of Morus alba L., which is approved by the Ministry of Health as a "homology of medicine and food", rich in diverse active constituents and with a variety of health effects including anti-obesity and anti-hyperlipidemia activities. PURPOSE: The review attempts to summarize and provide the molecular basis, mechanism, safety and products for further exploration and application of mulberry leaf on the treatment on the control of weight gain and obesity. METHODS: This review is conducted by using ScienceDirect, PubMed, CNKI and Web of Science databases following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). RESULTS: Based on the research progress of domestic and foreign scholars, the effective phytochemicals, molecular mechanisms and product applications of mulberry leaf in the prevention and treatment of obesity and related metabolic diseases were summarized. CONCLUSION: Mulberry leaf has excellent medicinal and health care value in obesity treatment. However, its pharmacodynamic substance basis and molecular mechanisms need to be further studied.

Successful surgical treatment of impending paradoxical embolism with pulmonary embolism and myocardial infarction.

BACKGROUND: Paradoxical embolism is a rare cause of acute arterial occlusion. This phenomenon arises when embolic material travels from the venous system crosses an abnormal shunt such as patent foramen ovale, atrial septal defects, ventricular septal defects, or pulmonary arteriovenous malformations, into the arterial system. Impending paradoxical embolism refers to the presence of an entrapped thrombus in the patent foramen ovale. CASE PRESENTATION: We report a case of a 68-year-old female patient who presented with an impending paradoxical embolism, alongside both concomitant pulmonary embolism and myocardial infarction with ST-segment elevation. Swiftly addressed through emergency cardiac surgery and systemic anticoagulation, the patient's condition was effectively treated. CONCLUSIONS: While the ideal treatment strategy for impending paradoxical embolism remains a topic of debate due to limited and inconclusive evidence, emergent open surgery should be contemplated in patients as it signifies a critical clinical emergency.

SPARK: A High-Efficiency Black-Box Domain Adaptation Framework for Source Privacy-Preserving Drowsiness Detection.

Developing an effective and efficient electroencephalography (EEG)-based drowsiness monitoring system is crucial for enhancing road safety and reducing the risk of accidents. For general usage, cross-subject evaluation is indispensable. Despite progress in unsupervised domain adaptation (UDA) and source-free domain adaptation (SFDA) methods, these often rely on the availability of labeled source data or white-box source models, posing potential privacy risks. This study explores a more challenging setting of UDA for EEG-based drowsiness detection, termed black-box domain adaptation (BBDA). In BBDA, adaptation in the target domain relies solely on a black-box source model, without access to the source data or parameters of the source model. Specifically, we propose a framework called Self-distillation and Pseudo-labelling for Ensemble Deep Random Vector Functional Link (edRVFL)-based Black-box Knowledge Adaptation (SPARK). SPARK employs entropy-based selection of high-confidence samples, which are then pseudo-labeled to train a student edRVFL network. Subsequently, ensemble self-distillation is performed to extract knowledge by training the edRVFL using refined labels introduced by ensemble learning. This process further improves the robustness of the student edRVFL network. The features of the edRVFL are beneficial for improving the computational efficiency of the framework, making it more suitable for tasks involving small datasets. The proposed SPARK framework is evaluated on two publicly available driver drowsiness datasets. Experimental results demonstrate its superior performance over strong baselines, while significantly reducing training time. These findings underscore the potential for practical integration of the proposed framework into drowsiness monitoring systems, thereby contributing substantially to the privacy preservation of source subjects.

Non-IRC Mechanism of Bimolecular Reactions with Submerged Barriers: A Case Study of Si+ + H2O Reaction.

Chemical reactions with submerged barriers may feature interesting dynamic behaviors that are distinct from those with substantial barriers or those entirely dominated by capture. The Si+ + H2O reaction is a prototypical example, involving even two submerged saddle points along the reaction path: one for the direct dissociation of H (H-dissociation SP) and another for H migration from the O-side to the Si-side (H-migration SP). We investigated the intricacies of this process by employing quasi-classical trajectory calculations on an accurate, full-dimensional ab initio potential energy surface. Through careful trajectory analysis, an interesting nonintrinsic reaction coordinate mechanism was found to play an important role in producing SiOH+ and H. This new pathway is featured as that the H atoms do not form HSiOH+ complexes along the minimum-energy path but directly dissociate into the products after passing through the H-migration SP. Furthermore, based on artificially scaled potential energy surfaces (PESs), the impact of barrier height on the reaction is also explored. This work provides new insights into the dynamics of the Si+ + H2O reaction and enriches our understanding of reactions with submerged barriers.

Discovery of cyperenoic acid as a potent and novel entry inhibitor of influenza A virus.

Influenza therapeutics with new targets and modes of action are urgently needed due to the frequent emergence of mutants resistant to currently available anti-influenza drugs. Here we report the in vitro and in vivo anti-influenza A virus activities of cyperenoic acid, a natural compound, which was isolated from a Chinese medicine Croton crassifolius Geise. Cyperenoic acid could potently suppress H1N1, H3N2 and H9N2 virus replication with IC50 values ranging from 0.12 to 15.13 µM, and showed a low cytotoxicity against MDCK cells (CC50 = 939.2 ± 60.0 µM), with selectivity index (SI) values ranging from 62 to 7823. Oral or intraperitoneal treatment of cyperenoic acid effectively protected mice against a lethal influenza virus challenge, comparable to the efficacy of Tamiflu. Additionally, cyperenoic acid also significantly reduced lung virus titers and alleviated influenza-induced acute lung injury in infected mice. Mechanism-of-action studies revealed that cyperenoic acid exhibited its anti-influenza activity during the entry stage of viral replication by inhibiting HA-mediated viral fusion. Simulation docking analyses of cyperenoic acid with the HA structures implied that cyperenoic acid binds to the stalk domain of HA in a cavity near the fusion peptide. Collectively, these results demonstrate that cyperenoic acid is a promising lead compound for the anti-influenza drug development and this research provides a useful small-molecule probe for studying the HA-mediated viral entry process.

Mobile genetic elements affect the dissemination of antibiotic resistance genes (ARGs) of clinical importance in the environment.

The prevalence of antibiotic resistance genes (ARGs) in the environment is a quintessential One Health issue that threats both human and ecosystem health; however, the source and transmission of ARGs, especially clinically important ARGs (CLIARGs), in the environment have not yet been well studied. In the present study, shotgun metagenomic approaches were used to characterize the microbiome, resistome, and mobilome composition in human feces and six different environment sample types in South China. Overall, the resistome harbored 157 CLIARGs, with specific ARG hotspots (e.g., human feces, wastewater treatment plants, livestock manure and wastewater) excreting significantly higher abundance of CLIARGs compared with the natural environment. A redundancy analysis (RDA) was performed and revealed that the bacterial community compositions and mobile genetic elements (MGEs) explained 55.08% and 34.68% of the variations in ARG abundance, respectively, indicating that both bacterial community and MGEs are key contributors to the maintenance and dissemination of CLIARGs in the environment. The network analysis revealed non-random co-occurrence patterns between 200 bacterial genera and 147 CLIARGs, as well as between 135 MGEs and 123 CLIARGs. In addition to numerous co-shared CLIARGs among different sample types, the source tracking program based on the FEAST probabilistic model was used to estimate the relative contributions of the CLIARGs from potential sources to the natural environment. The source tracking analysis results delineated that mobilome, more than microbiome, contributed CLIARG transmission from those ARG hotspots into natural environment, and the MGEs in WWTPs seem to play the most significant role in the spread of CLIARGs to the natural environment (average contribution 32.9%-46.4%). Overall, this study demonstrated the distribution and dissemination of CLIARGs in the environment, and aimed to better inform strategies to control the spread of CLIARGs into the natural environment.

Benchmarking EEG-based Cross-dataset Driver Drowsiness Recognition with Deep Transfer Learning.

It usually takes a long time to collect data for calibration when using electroencephalography (EEG) for driver drowsiness monitoring. Cross-dataset recognition is desirable since it can significantly save the calibration time when an existing dataset is used. However, the recognition accuracy is affected by the distribution drift problem caused by different experimental environments when building different datasets. In order to solve the problem, we propose a deep transfer learning model named Entropy-Driven Joint Adaptation Network (EDJAN), which can learn useful information from source and target domains simultaneously. An entropy-driven loss function is used to promote clustering of target-domain representations and an individual-level domain adaptation technique is proposed to alleviate the distribution discrepancy problem of test subjects. We use two public driving datasets SEEG-VIG and SADT to test the model on the cross-dataset setting. The proposed model achieved an accuracy of 83.3% when SADT is used as source domain and SEED-VIG is used as target domain and 76.7% accuracy on the reverse setting, which is higher than the other SOTA methods. The results are further analyzed with both global and local interpretation methods. Our work illuminates a promising direction of using EEG for calibration-free driver drowsiness recognition.

Towards best practice of interpreting deep learning models for EEG-based brain computer interfaces.

Introduction: As deep learning has achieved state-of-the-art performance for many tasks of EEG-based BCI, many efforts have been made in recent years trying to understand what have been learned by the models. This is commonly done by generating a heatmap indicating to which extent each pixel of the input contributes to the final classification for a trained model. Despite the wide use, it is not yet understood to which extent the obtained interpretation results can be trusted and how accurate they can reflect the model decisions. Methods: We conduct studies to quantitatively evaluate seven different deep interpretation techniques across different models and datasets for EEG-based BCI. Results: The results reveal the importance of selecting a proper interpretation technique as the initial step. In addition, we also find that the quality of the interpretation results is inconsistent for individual samples despite when a method with an overall good performance is used. Many factors, including model structure and dataset types, could potentially affect the quality of the interpretation results. Discussion: Based on the observations, we propose a set of procedures that allow the interpretation results to be presented in an understandable and trusted way. We illustrate the usefulness of our method for EEG-based BCI with instances selected from different scenarios.

The application of HER2 and CD47 CAR-macrophage in ovarian cancer.

BACKGROUND: The chimeric antigen receptor (CAR)-T therapy has a limited therapeutic effect on solid tumors owing to the limited CAR-T cell infiltration into solid tumors and the inactivation of CAR-T cells by the immunosuppressive tumor microenvironment. Macrophage is an important component of the innate and adaptive immunity, and its unique phagocytic function has been explored to construct CAR macrophages (CAR-Ms) against solid tumors. This study aimed to investigate the therapeutic application of CAR-Ms in ovarian cancer. METHODS: In this study, we constructed novel CAR structures, which consisted of humanized anti-HER2 or CD47 scFv, CD8 hinge region and transmembrane domains, as well as the 4-1BB and CD3ζ intracellular domains. We examined the phagocytosis of HER2 CAR-M and CD47 CAR-M on ovarian cancer cells and the promotion of adaptive immunity. Two syngeneic tumor models were used to estimate the in vivo antitumor activity of HER2 CAR-M and CD47 CAR-M. RESULTS: We constructed CAR-Ms targeting HER2 and CD47 and verified their phagocytic ability to ovarian cancer cells in vivo and in vitro. The constructed CAR-Ms showed antigen-specific phagocytosis of ovarian cancer cells in vitro and could activate CD8+ cytotoxic T lymphocyte (CTL) to secrete various anti-tumor factors. For the in vivo model, mice with human-like immune systems were used. We found that CAR-Ms enhanced CD8+ T cell activation, affected tumor-associated macrophage (TAM) phenotype, and led to tumor regression. CONCLUSIONS: We demonstrated the inhibition effect of our constructed novel HER2 CAR-M and CD47 CAR-M on target antigen-positive ovarian cancer in vitro and in vivo, and preliminarily verified that this inhibitory effect is due to phagocytosis, promotion of adaptive immunity and effect on tumor microenvironment.

Construction of the Gal-NH2/mulberry leaf polysaccharides-lysozyme/luteolin nanoparticles and the amelioration effects on lipid accumulation.

Luteolin is a kind of natural flavonoid with great potential for lipid accumulation intervention. However, the poor water solubility and non-targeted release greatly diminish its efficiency. In this study, 4-aminophenyl ß-D-galactopyranoside (Gal-NH2)/mulberry leaf polysaccharides- lysozyme/luteolin nanoparticles (Gal-MPL/Lut) were fabricated via amide reaction, self-assembly process and electrostatic interaction. The nanoparticles could hepatic-target of Lut and enhance action on liver tissue by specific recognition of asialoglycoprotein receptor (ASGPR). Physicochemical characterization of the nanoparticles showed a spherical shape with a uniform particle size distribution (77.8 ± 2.6 nm) with a polydispersity index (PDI) of 0.22 ± 0.06. Subsequently, in HepG2 cells model, administration with hepatic-targeted Gal-MPL/Lut nanoparticles promoted the cellular uptake of Lut, and regulated lipid metabolism manifested by remarkably inhibiting total cholesterol (TC) and triglyceride (TG) expression levels through the modulation of PI3K/SIRT-1/FAS/CEBP-α signaling pathway. This study provides a promising strategy for a highly hepatic-targeted therapy to ameliorate lipid accumulation using natural medicines facilitated by nano-technology.

Modulation of the catalytic activity and thermostability of a thermostable GH7 endoglucanase by engineering the key loop B3.

The loop B3 of glycoside hydrolase family 7 (GH7) endoglucanases is confined into long and short types. TtCel7 is a thermophilic GH7 endoglucanase from Thermothelomyces thermophilus ATCC 42464 with a long loop B3. TtCel7 was distinct for the excellent thermostability (>30 % residual activity after 1-h incubation at 90 °C). The catalytic efficiency was reduced by removing the disulfide bond in loop B3 (C220A) and truncated the loop B3 (B3cut). However, B3cut exhibited improved thermostability, the remaining enzyme activity increased by 39 %-171 % compared toTtCel7 when treated at 70-90 °C for 1-h. Based on the analysis of molecular dynamics simulation, both loops B1 and A3 of B3cut swing toward the catalytic center, which contributed to the reduced cleft-space and increased structure-rigidity. Conversely, the deletion of disulfide bond resulted in a reduction of structural rigidity in C220A. Through structure-directed enzyme modulation, this study has identified two structural elements that are related to the catalysis and thermostability of TtCel7. The loop B3 of TtCel7 possibly stretches the catalytic pocket, thereby increases the openness of the catalytic tunnel and enhancing flexibility for efficient catalysis. Additionally, the disulfide bond within loop B3 serves to enhance structural stability and maintain a heightened level of activity.

Online dynamic ensemble deep random vector functional link neural network for forecasting.

This paper proposes a three-stage online deep learning model for time series based on the ensemble deep random vector functional link (edRVFL). The edRVFL stacks multiple randomized layers to enhance the single-layer RVFL's representation ability. Each hidden layer's representation is utilized for training an output layer, and the ensemble of all output layers forms the edRVFL's output. However, the original edRVFL is not designed for online learning, and the randomized nature of the features is harmful to extracting meaningful temporal features. In order to address the limitations and extend the edRVFL to an online learning mode, this paper proposes a dynamic edRVFL consisting of three online components, the online decomposition, the online training, and the online dynamic ensemble. First, an online decomposition is utilized as a feature engineering block for the edRVFL. Then, an online learning algorithm is designed to learn the edRVFL. Finally, an online dynamic ensemble method, which can measure the change in the distribution, is proposed for aggregating all layers' outputs. This paper evaluates and compares the proposed model with state-of-the-art methods on sixteen time series.

CCR2 antagonist represses fibroblast-like synoviocyte-mediated inflammation in patients with rheumatoid arthritis.

Rheumatoid arthritis (RA) is a common autoimmune disease with a global incidence of approximately 1%. Its complex pathogenesis makes the development of RA-related therapeutics very difficult. Existing drugs for RA have many side effects and are prone to drug resistance. One potential target for RA drugs includes C-Cchemokinereceptortype2 (CCR2), which belongs to the G protein-coupled receptor family. A series of RA drugs targeting CCR2 have been developed; however, the pre-clinical and clinical research results for CCR2 antagonists are inconsistent. We found that CCR2 was also expressed in primary Fibroblast-like synoviocyte (FLS) from patients with RA. CCR2 antagonists can inhibit inflammatory cytokines and matrix metalloproteinases released by RA-FLS but do not affect the proliferation and migration ability of RA-FLS. In addition, CCR2 antagonist-treated RA-FLS indirectly repressed macrophage-mediated inflammation and rescued the viability of chondrocytes. Finally, a CCR2 antagonist ameliorated the collagen-induced arthritic (CIA). CCR2 antagonists may exert anti-inflammatory effects on RA-FLS by inhibiting the JAK-STAT pathway. In summary, a CCR2 antagonist can exert anti-inflammatory effects by acting on RA-FLS. This study provides a new experimental basis for the use of CCR2 antagonists in the development of RA drugs.