NEDD4 ameliorates myocardial reperfusion injury by preventing macrophages pyroptosis.

OBJECTIVES: The inflammatory cascade and cell death post-myocardial ischemia reperfusion (MI/R) are very complex. Despite the understanding that macrophage inflammation has a pivotal role in the pathophysiology of MI/R, the contribution of macrophage inflammatory signals in tailoring the function of vascular endothelium remains unknown. MATERIALS AND METHODS: In the present study, we analyzed the effects of NEDD4 on the NLRP3 inflammasome activation-mediated pyroptosis in vitro after an acute pro-inflammatory stimulus and in vivo in a MI/R mouse model. TTC and Evan's blue dye, Thioflavin S, immunohistochemistry staining, and ELISA were performed in wild-type and NEDD4 deficiency mice. THP-1 cells were transfected with si-NEDD4 or si-SF3A2. HEK293T cells were transfected with NEDD4 or SF3A2 overexpression plasmid. ELISA analyzed the inflammatory cytokines in the cell supernatant. The levels of NEDD4, SF3A2, and NLRP3/GSDMD pathway were determined by Western blot. Protein interactions were evaluated by immunoprecipitation. The protein colocalization in cells was monitored using a fluorescence microscope. RESULTS: NEDD4 inhibited NLRP3 inflammasome activation and pyroptosis in THP-1 cells treated with lipopolysaccharide (LPS) and nigericin (Nig). Mechanistically, NEDD4 maintained the stability of NLRP3 through direct interaction with the SF3A2, whereas the latter association with NLRP3 indirectly interacted with NEDD4 promoting proteasomal degradation of NLRP3. Deletion of NLRP3 expression further inhibited the caspase cascade to induce pyroptosis. Interestingly, inhibiting NLRP3 inflammasome activation in THP-1 cells could prevent cardiac microvascular endothelial cells (CMECs) injury. In addition, NEDD4 deficiency decreased animal survival and increased myocardial infarct size, no-reflow area, and promoted macrophages infiltration post-MI/R. CONCLUSIONS: NEDD4 could be a potential therapeutic target in microvascular injury following myocardial reperfusion. Video Abstract.

JSQE: Joint Surveillance Quality and Energy Conservation for Barrier Coverage in WSNs.

Barrier coverage is a fundamental issue in wireless sensor networks (WSNs). Most existing works have developed centralized algorithms and applied the Boolean Sensing Model (BSM). However, the critical characteristics of sensors and environmental conditions have been neglected, which leads to the problem that the developed mechanisms are not practical, and their performance shows a large difference in real applications. On the other hand, the centralized algorithms also lack scalability and flexibility when the topologies of WSNs are dynamically changed. Based on the Elfes Sensing Model (ESM), this paper proposes a distributed Joint Surveillance Quality and Energy Conservation mechanism (JSQE), which aims to satisfy the requirements of the desired surveillance quality and minimize the number of working sensors. The proposed JSQE first evaluates the sensing probability of each sensor and identifies the location of the weakest surveillance quality. Then, the JSQE further schedules the sensor with the maximum contribution to the bottleneck location to improve the overall surveillance quality. Extensive experiment results show that our proposed JSQE outperforms the existing studies in terms of surveillance quality, the number of working sensors, and the efficiency and fairness of surveillance quality. In particular, the JSQE improves the surveillance quality by 15% and reduces the number of awake sensors by 22% compared with the relevant TOBA.

Analysis of Characteristics of Patients with Non-ST-Segment Elevation Myocardial Infarction by Cardiac Magnetic Resonance Imaging.

BACKGROUND In this study, cardiac magnetic resonance imaging was used to investigate the characteristics of patients who have total coronary occlusion but manifest with non-ST-segment elevation myocardial infarction (NSTEMI), and we assessed the extent of infarct transmurality and myocardial necrosis size in NSTEMI patients. MATERIAL AND METHODS We enrolled all patients diagnosed at our hospital with subtotal or total occlusion of the culprit artery (TOCA), based on the coronary angiography, who successfully underwent PCI within 12 h of admission, and who had CMR imaging performed within 2 days after the PCI. RESULTS Based on 12-lead ECG findings, 48% of patients were categorized as having STEMI and 52% as having NSTEMI. TOCA was detected by coronary angiography in 43% of NSTEMI patients, and in 60% and 33% of normal ST segment and ST-segment depression MI patients, respectively. The transmural segments were found in 78% of STEMI patients and 31% of NSTEMI patients (P<0.05). Transmural infarction segments were found in 64% of NSTEMI patients with TOCA and in 8% of NTOCA patients (P<0.05). Moreover, the number of transmural segments in ST-segment depression MI patients was the lowest (P<0.05). Infarct size in STEMI patients was significantly larger than in patients with NSTEMI (P<0.05), whereas there was no statistically significant difference in patients with normal ST segment and ST-segment depression MI patients (P>0.05). CONCLUSIONS Identification TOCA by coronary angiography and transmural infarction by DE-MRI can be challenging in AMI patients with non-ST-segment elevation. In approximately 30% of non-ST-segment elevation MI patients, transmural infarction was detected by DE-MRI. Therefore, TOCA accompanied by transmural infarction in non-ST-segment-elevation MI patients is not uncommon.

Towards a Clustering Guided Hierarchical Framework for Sensor-Based Activity Recognition.

Human activity recognition plays a prominent role in numerous applications like smart homes, elderly healthcare and ambient intelligence. The complexity of human behavior leads to the difficulty of developing an accurate activity recognizer, especially in situations where different activities have similar sensor readings. Accordingly, how to measure the relationships among activities and construct an activity recognizer for better distinguishing the confusing activities remains critical. To this end, we in this study propose a clustering guided hierarchical framework to discriminate on-going human activities. Specifically, we first introduce a clustering-based activity confusion index and exploit it to automatically and quantitatively measure the confusion between activities in a data-driven way instead of relying on the prior domain knowledge. Afterwards, we design a hierarchical activity recognition framework under the guidance of the confusion relationships to reduce the recognition errors between similar activities. Finally, the simulations on the benchmark datasets are evaluated and results show the superiority of the proposed model over its competitors. In addition, we experimentally evaluate the key components of the framework comprehensively, which indicates its flexibility and stability.

Cooperative Data Collection Mechanism Using Multiple Mobile Sinks in Wireless Sensor Networks.

Data collection problems have received much attention in recent years. Many data collection algorithms that constructed a path and adopted one or more mobile sinks to collect data along the paths have been proposed in wireless sensor networks (WSNs). However, the efficiency of the established paths still can be improved. This paper proposes a cooperative data collection algorithm (CDCA), which aims to prolong the network lifetime of the given WSNs. The CDCA initially partitions the n sensor nodes into k groups and assigns each mobile sink acting as the local mobile sink to collect data generated by the sensors of each group. Then the CDCA selects an appropriate set of data collection points in each group and establishes a separate path passing through all the data collection points in each group. Finally, a global path is constructed and the rendezvous time points and the speed of each mobile sink are arranged for collecting data from k local mobile sinks to the global mobile sink. Performance evaluations reveal that the proposed CDCA outperforms the related works in terms of rendezvous time, network lifetime, fairness index as well as efficiency index.

An Efficient Wireless Recharging Mechanism for Achieving Perpetual Lifetime of Wireless Sensor Networks.

Energy recharging has received much attention in recent years. Several recharging mechanisms were proposed for achieving perpetual lifetime of a given Wireless Sensor Network (WSN). However, most of them require a mobile recharger to visit each sensor and then perform the recharging task, which increases the length of the recharging path. Another common weakness of these works is the requirement for the mobile recharger to stop at the location of each sensor. As a result, it is impossible for recharger to move with a constant speed, leading to inefficient movement. To improve the recharging efficiency, this paper takes "recharging while moving" into consideration when constructing the recharging path. We propose a Recharging Path Construction (RPC) mechanism, which enables the mobile recharger to recharge all sensors using a constant speed, aiming to minimize the length of recharging path and improve the recharging efficiency while achieving the requirement of perpetual network lifetime of a given WSN. Performance studies reveal that the proposed RPC outperforms existing proposals in terms of path length and energy utilization index, as well as visiting cycle.

Multifunctional Intelligent Reconfigurable Metasurface.

The emergent reconfigurable metasurfaces (RMs) have attracted a lot of attention due to their potential in broad applications. As a general platform, RMs are able to control the reflection (or refraction) of incident waves with predefined functionalities. Nevertheless, the operation of RMs is highly dependent on the arrival direction of incidence. The self-adaptive design of an RM, so that it can respond to varied incident waves automatically, is highly requested in practical implementation, which is actually challenging. This study reports the realization of an intelligent RM (IRM) system, which can detect the arrival direction of impinging waves and respond to the incidence with a predefined functionality accordingly. This IRM system is constructed by integrating a direction of the arrival estimation module, a frontend by the varactor-based metasurface, and a central control unit. In experiments, an IRM system designed for TM polarization is demonstrated to perform various functions, i.e., retroreflection, directional reflection, and fixed-point energy focusing, which are highly requested by edge communication and sensing. The measured results imply that this IRM system responds quite well within a wide incident range from -60° to 60° in a frequency range from 9 to 9.5 GHz. The proposed IRM can be a good candidate for boosting 5G communication and Internet of Things applications, including beam shaping/steering, RCS manipulation, object imaging, and sensor recharging.

Association of GSDMD with microvascular-ischemia reperfusion injury after ST-elevation myocardial infarction.

Objectives: Little is known about the clinical prognosis of gasdermin D (GSDMD) in patients with ST-elevation myocardial infarction (STEMI). The purpose of this study was to investigate the association of GSDMD with microvascular injury, infarction size (IS), left ventricular ejection fraction (LVEF), and major adverse cardiac events (MACEs), in STEMI patients with primary percutaneous coronary intervention (pPCI). Methods: We retrospectively analyzed 120 prospectively enrolled STEMI patients (median age 53 years, 80% men) treated with pPCI between 2020 and 2021 who underwent serum GSDMD assessment and cardiac magnetic resonance (CMR) within 48 h post-reperfusion; CMR was also performed at one year follow-up. Results: Microvascular obstruction was observed in 37 patients (31%). GSDMD concentrations ≧ median (13 ng/L) in patients were associated with a higher risk of microvascular obstruction and IMH (46% vs. 19%, P = 0.003; 31% vs. 13%, P = 0.02, respectively), as well as with a lower LVEF both in the acute phase after infarction (35% vs. 54%, P < 0.001) and in the chronic phase (42% vs. 56%, P < 0.001), larger IS in the acute (32% vs. 15%, P < 0.001) and in the chronic phases (26% vs. 11%, P < 0.001), and larger left ventricular volumes (119 ± 20 vs. 98 ± 14, P = 0.003) by CMR. Univariable and multivariable Cox regression analysis results showed that patients with GSDMD concentrations ≧ median (13 ng/L) had a higher incidence of MACE (P < 0.05). Conclusions: High GSDMD concentrations in STEMI patients are associated with microvascular injury (including MVO and IMH), which is a powerful MACE predictor. Nevertheless, the therapeutic implications of this relation need further research.

A Hierarchical Ensemble Deep Learning Activity Recognition Approach with Wearable Sensors Based on Focal Loss.

Abnormal activity in daily life is a relatively common symptom of chronic diseases, such as dementia. There will probably be a variety of repetitive activities in dementia patients' daily life, such as repeated handling of objects and repeated packing of clothes. It is particularly important to recognize the daily activities of the elderly, which can be further used to predict and monitor chronic diseases. In this paper, we propose a hierarchical ensemble deep learning activity recognition approach with wearable sensors based on focal loss. Seven basic everyday life activities including cooking, keyboarding, reading, brushing teeth, washing one's face, washing dishes and writing are considered in order to show its performance. Based on hold-out cross-validation results on a dataset collected from elderly volunteers, the average accuracy, precision, recall and F1-score of our approach are 98.69%, 98.05%, 98.01% and 97.99%, respectively, in identifying the activities of daily life for the elderly.

Network pharmacology-based and molecular docking prediction of the active ingredients and mechanism of ZaoRenDiHuang capsules for application in insomnia treatment.

BACKGROUND: The ZaoRenDiHuang (ZRDH) capsule is widely used in clinical practice and has significant therapeutic effects on insomnia. However, its active ingredients and mechanisms of action for insomnia remain unknown. In this study, network pharmacology was employed to elucidate the potential anti-insomnia mechanisms of ZRDH. METHODS: The potential active ingredients of ZRDH were obtained from the Traditional Chinese Medicine Systems Pharmacology Database. Possible targets were predicted using SwissTargetPrediction tools. The insomnia-related targets were identified using the therapeutic target database, Drugbank database, Online Mendelian Inheritance in Man database, and gene-disease associations database. A compound-target-disease network was constructed using Cytoscape for visualization. Additionally, the protein functional annotation and identification of signaling pathways of potential targets were performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses using the Metascape platform. RESULTS: In this study, 61 anti-insomnia components and 65 anti-insomnia targets of ZRDH were filtered through database mining. The drug-disease network was constructed with five key components. Sixty-five key targets were identified using topological analysis. Docking studies indicated that bioactive compounds could stably bind to the pockets of target proteins. Through data mining and network analysis, the GO terms and KEGG annotation suggested that the neuroactive ligand-receptor interaction, serotonergic synapse CAMP signaling, HIF-1a signaling, and toll-like receptor signaling pathways play vital roles against insomnia. CONCLUSION: The potential mechanisms of ZRDH treatment for insomnia involve multiple components, targets, and pathways. These findings provide a reference for further investigations into the mechanisms underlying ZRDH treatment of insomnia.

The Potential Effect of Rhizoma coptidis on Polycystic Ovary Syndrome Based on Network Pharmacology and Molecular Docking.

BACKGROUND: Rhizoma coptidis (RC) showed a significant effect on PCOS, but its mechanism in PCOS remains unclear. METHODS: The components of RC were searched by TCMSP. The Smiles number of the active ingredients was queried through PubChem, and the predicted targets were obtained from the SwissTargetPrediction database. The DrugBank, GeneCards, and DisGeNET databases were retrieved to acquire the related targets of PCOS. Then, the network of compound-target was constructed. The core targets were analyzed using protein-protein interaction (PPI) analysis, and the binding activities were verified by molecular docking. The enriched pathways of key targets were examined by GO and KEGG. RESULTS: 13 components and 250 targets of RC on PCOS were screened. The core network was filtered based on topological parameters, and the key components were palmatine, berberine, berberrubine, quercetin, and epiberberine. The key targets included DRD2, SLC6A4, CDK2, DPP4, ESR1, AKT2, PGR, and AKT1. Molecular docking displayed that the active ingredients of RC had good binding activities with potential targets of PCOS. After enrichment analysis, 30 functional pathways were obtained, including neuroactive ligand-receptor interaction, dopaminergic synapse, and cAMP signaling pathway. CONCLUSION: In summary, this study clarified the potential effect of RC on PCOS, which is helpful to provide references for clinical practice. It is also conducive to the secondary development of RC and its monomer components.

The three syndromes and six Chinese patent medicine study during the recovery phase of COVID-19.

The coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), first broke out in Wuhan, China, in 2019. SARS-CoV-2 develops many types of mutations (such as B.1.1.7), making diagnosis and treatment challenging. Although we now have a preliminary understanding of COVID-19, including pathological changes, clinical manifestations, and treatment measures, we also face new difficulties. The biggest problem is that most COVID-19 patients might face sequelae (e.g., fatigue, sleep disturbance, pulmonary fibrosis) during the recovery phase. We aimed to test six Chinese patent medicines to treat three major abnormal symptoms in COVID-19 patients during the recovery phase, including cardiopulmonary function, sleep disturbance, and digestive function. We launched the "three syndromes and six Chinese patent medicines" randomized, double-blind, placebo-controlled, multicenter clinical trial on April 10, 2020. The results showed that Jinshuibao tablets and Shengmaiyin oral liquid significantly improved the cardiopulmonary function of recovering COVID-19 patients. Shumian capsules, but not Xiaoyao capsules, significantly improved patients' sleep disorders. This might be because the indication of Xiaoyao capsules is liver qi stagnation rather than psychological or emotional problems. Xiangsha Liujun pills and Ludangshen oral liquid significantly improved digestive function. Our research provides a guideline for treating COVID-19 sequelae in patients during the recovery period based on high-quality evidence.

Potential Mechanism Prediction of Herbal Medicine for Pulmonary Fibrosis Associated with SARS-CoV-2 Infection Based on Network Analysis and Molecular Docking.

Background: Coronavirus Disease 2019 (COVID-19) is still a relevant global problem. Although some patients have recovered from COVID-19, the sequalae to the SARS-CoV-2 infection may include pulmonary fibrosis, which may contribute to considerable economic burden and health-care challenges. Convalescent Chinese Prescription (CCP) has been widely used during the COVID-19 recovery period for patients who were at high risk of pulmonary fibrosis and is recommended by the Diagnosis and Treatment Protocol for COVID-19 (Trial Version sixth, seventh). However, its underlying mechanism is still unclear. Methods: In this study, an integrated pharmacology approach was implemented, which involved evaluation of absorption, distribution, metabolism and excretion of CCP, data mining of the disease targets, protein-protein interaction (PPI) network construction, and analysis, enrichment analysis, and molecular docking simulation, to predict the bioactive components, potential targets, and molecular mechanism of CCP for pulmonary fibrosis associated with SARS-CoV-2 infection. Results: The active compound of CCP and the candidate targets, including pulmonary fibrosis targets, were obtained through database mining. The Drug-Disease network was constructed. Sixty-five key targets were identified by topological analysis. The findings of Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation suggested that the VEGF, Toll-like 4 receptor, MAPK signaling pathway, and TGF-ß1 signaling pathways may be involved in pulmonary fibrosis. In the molecular docking analyses, VEGF, TNF-α, IL-6, MMP9 exhibited good binding activity. Findings from our study indicated that CCP could inhibit the expression of VEGF, TNF-α, IL-6, MMP9, TGF-ß1 via the VEGF, Toll-like 4 receptor, MAPK, and TGF-ß1 signaling pathways. Conclusion: Potential mechanisms involved in CCP treatment for COVID-19 pulmonary fibrosis associated with SARS-CoV-2 infection involves multiple components and multiple target points as well as multiple pathways. These findings may offer a profile for further investigations of the anti-fibrotic mechanism of CCP.

Gut microbiota as the critical correlation of polycystic ovary syndrome and type 2 diabetes mellitus.

Gut microbiota forms a symbiotic relationship with the host and maintains the ecological balance of the internal and external environment of the human body. However, dysbiosis of the gut microbiota and immune deficiency, as well as environmental changes, can destroy the host-microbial balance, leading to the occurrence of a variety of diseases, such as polycystic ovary syndrome (PCOS), type 2 diabetes mellitus (T2DM), and obesity. Meanwhile, diseases can also affect gut microbiota, forming a vicious cycle. The role of the intestinal microbiota in different diseases have been proven by several studies; however, as a common target of PCOS and T2DM, there are few reports on the treatment of different diseases through the regulation of intestinal microbiota as the critical correlation. This review analyzed the common mechanisms of intestinal microbiota in PCOS and T2DM, including the dysbiosis of gut microbiota, endotoxemia, short-chain fatty acids, biotransformation of bile acids, and synthesis of amino acid in regulating insulin resistance, obesity, chronic inflammation, and mitochondrial dysfunction. The possible therapeutic effects of probiotics and/or prebiotics, fecal microbiota transplantation, bariatric surgery, dietary intervention, drug treatment, and other treatments targeted at regulating intestinal microbiota were also elucidated.

The direct evidence and mechanism of traditional Chinese medicine treatment of COVID-19.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the third coronavirus causing serious human disease to spread across the world in the past 20 years, after SARS and Middle East respiratory syndrome. As of mid-September 2020, more than 200 countries and territories have reported 30 million cases of coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2, including 950,000 deaths. Supportive treatment remains the mainstay of therapy for COVID-19. The World Health Organization reported that four candidate drugs, including remdesivir, are ineffective or have little effect on COVID-19. According to China News, 90 % of Chinese patients with COVID-19 use traditional Chinese medicine (TCM), with an effectiveness rate of 80 %, and no deterioration in patient condition. We have compiled the direct evidence of TCM treatment for COVID-19 as of December 31, 2020. We describe the advantages of TCM in the treatment of COVID-19 based on clinical evidence and the required methods for its clinical use. TCM can inhibit virus replication and transcription, prevent the combination of SARS-CoV-2 and the host, and attenuate the cytokine storm and immune deficiency caused by the virus infection. The cooperation of many countries is required to establish international guidelines regarding the use of TCM in patients with severe COVID-19 from other regions and of different ethnicities. Studies on the psychological abnormalities in patients with COVID-19, and medical staff, is lacking; it is necessary to provide a complete chain of evidence to determine the efficacy of TCM in the related prevention, treatment, and recovery. This study aims to provide a reference for the rational use of TCM in the treatment of COVID-19.

Traditional Chinese Medicine Decoctions Significantly Reduce the Mortality in Severe and Critically Ill Patients with COVID-19: A Retrospective Cohort Study.

Coronavirus disease (COVID-19) is a new infectious disease associated with high mortality, and traditional Chinese medicine decoctions (TCMDs) have been widely used for the treatment of patients with COVID-19 in China; however, the impact of these decoctions on severe and critical COVID-19-related mortality has not been evaluated. Therefore, we aimed to address this gap. In this retrospective cohort study, we included inpatients diagnosed with severe/critical COVID-19 at the Tongren Hospital of Wuhan University and grouped them depending on the recipience of TCMDs (TCMD and non-TCMD groups). We conducted a propensity score-matched analysis to adjust the imbalanced variables and treatments and used logistic regression methods to explore the risk factors associated with in-hospital death. Among 282 patients with COVID-19 who were discharged or died, 186 patients (66.0%) received TCMD treatment (TCMD cohort) and 96 (34.0%) did not (non-TCMD cohort). After propensity score matching at a 1:1 ratio, 94 TCMD users were matched to 94 non-users, and there were no significant differences in baseline clinical variables between the two groups of patients. The all-cause mortality was significantly lower in the TCMD group than in the non-TCMD group, and this trend remained valid even after matching (21.3% [20/94] vs. 39.4% [37/94]). Multivariable logistic regression model showed that disease severity (odds ratio: 0.010; 95% CI: 0.003, 0.037; [Formula: see text]¡ 0.001) was associated with increased odds of death and that TCMD treatment significantly decreased the odds of in-hospital death (odds ratio: 0.115; 95% CI: 0.035, 0.383; [Formula: see text]¡ 0.001), which was related to the duration of TCMD treatment. Our findings show that TCMD treatment may reduce the mortality in patients with severe/critical COVID-19.

Implicit Irregularity Detection Using Unsupervised Learning on Daily Behaviors.

The irregularity detection of daily behaviors for the elderly is an important issue in homecare. Plenty of mechanisms have been developed to detect the health condition of the elderly based on the explicit irregularity of several biomedical parameters or some specific behaviors. However, few research works focus on detecting the implicit irregularity involving the combination of diverse behaviors, which can assess the cognitive and physical wellbeing of elders but cannot be directly identified based on sensor data. This paper proposes an Implicit IRregularity Detection (IIRD) mechanism that aims to detect the implicit irregularity by developing the unsupervised learning algorithm based on daily behaviors. The proposed IIRD mechanism identifies the distance and similarity between daily behaviors, which are important features to distinguish the regular and irregular daily behaviors and detect the implicit irregularity of elderly health condition. Performance results show that the proposed IIRD outperforms the existing unsupervised machine-learning mechanisms in terms of the detection accuracy and irregularity recall.

BIA: Behavior Identification Algorithm Using Unsupervised Learning Based on Sensor Data for Home Elderly.

Behavior identification plays an important role in supporting homecare for the elderly living alone. In literature, plenty of algorithms have been designed to identify behaviors of the elderly by learning features or extracting patterns from sensor data. However, most of them adopted probabilistic models or supervised learning to identify behaviors based on labeled sensor data. This paper proposes a behavior identification algorithm (BIA) using unsupervised learning based on unlabeled sensor data for the elderly living alone in smart home. This paper presents the observation of elder behaviors with three features: Event Order, Time Length Similarity and Time Interval Similarity features. Based on these features of behavior observations, two properties of behaviors, including the Event Shift and Histogram Shape Similarity properties, are presented. According to these properties, the proposed BIA is developed. Finally, performance results show that the proposed BIA outperforms the existing unsupervised machine learning mechanisms in terms of the behavior identification precision and recall.

Direct and indirect therapeutic effect of traditional Chinese medicine as an add-on for non-proliferative diabetic retinopathy: a systematic review and meta-analysis.

BACKGROUND: Diabetic retinopathy (DR) is the leading cause of blindness in many countries. The current treatment for non-proliferative DR (NPDR) using Western medicine (WM) alone is insufficient. At present, the combination of NPDR treatment with traditional Chinese medicine (TCM) and WM is universally applied. We aimed to evaluate the effectiveness and safety of TCM as an add-on for NPDR using a systematic review and meta-analysis. METHOD: Data from randomized controlled trials (RCTs) of TCM for NPDR treatment along with WM before July 6, 2019, were collected from the China National Knowledge Infrastructure, Wanfang Database, China Biomedical Database, Pubmed, Embase, and Cochrane Library. Relevant data were extracted by two reviewers. I 2 statistics was adopted to appraise heterogeneity. If I 2 < 50% the fixed-effects model was employed, otherwise a random-effect model was employed. (PROSPERO: CRD42019134947). RESULT: Eighteen RCTs (1522 patients) were included based on the inclusion and exclusion criteria. The results showed that compared with WM alone, TCM (including Compound Xueshuantong Capsule, Qiming Granule, and others) combined with WM for NPDR could improve the overall effiicacy [n = 1686, RR 1.24 (1.18,1.30), P < 0.00001, I 2 = 0%], and reduce the influence of risk factors related to NPDR, such as glycated hemoglobin level [n = 360, MD - 0.85 (- 1.28, - 0.41), P = 0.0001, I 2 = 72%], triglyceride (P < 0.00001), and total cholesterol (P = 0.0008). Moreover, no serious adverse events were reported. CONCLUSION: Compared with WM alone, TCM + WM could significantly improve NPDR and also reduce the correlation levels of risk factors, such as hyperglycemia, dyslipidemia. However, the small sample included in the study might lead to a publication bias, and therefore, our results should be treated with caution.

Corrigendum: Basis and Design of a Randomized Clinical Trial to Evaluate the Effect of Jinlida Granules on Metabolic Syndrome in Patients With Abnormal Glucose Metabolism.

[This corrects the article DOI: 10.3389/fendo.2020.00415.].