Natural plant medications for the treatment of retinal diseases: The blood-retinal barrier as a clue.

BACKGROUND: Retinal diseases significantly contribute to the global burden of visual impairment and blindness. The occurrence of retinal diseases is often accompanied by destruction of the blood‒retinal barrier, a vital physiological structure responsible for maintaining the stability of the retinal microenvironment. However, detailed summaries of the factors damage the blood‒retinal barrier and treatment methods involving natural plant medications are lacking. PURPOSE: To comprehensively summarize and analyze the protective effects of active substances in natural plant medications on damage to the blood-retina barrier that occurs when retinal illnesses, particularly diabetic retinopathy, and examine their medicinal value and future development prospects. METHODS: In this study, we searched for studies published in the ScienceDirect, PubMed, and Web of Science databases. The keywords used included natural plant medications, plants, natural herbs, blood retinal barrier, retinal diseases, diabetic retinopathy, age-related macular degeneration, and uveitis. Chinese herbal compound articles, non-English articles, warning journals, and duplicates were excluded from the analysis. RESULTS: The blood‒retinal barrier is susceptible to high glucose, aging, immune responses, and other factors that destroy retinal homeostasis, resulting in pathological changes such as apoptosis and increased vascular permeability. Existing studies have shown that the active compounds or extracts of many natural plants have the effect of repairing blood-retinal barrier dysfunction. Notably, berberine, puerarin, and Lycium barbarum polysaccharides exhibited remarkable therapeutic effects. Additionally, curcumin, astragaloside IV, hesperidin, resveratrol, ginsenoside Rb1, luteolin, and Panax notoginseng saponins can effectively protect the blood‒retinal barrier by interfering with distinct pathways. The active ingredients found in natural plant medications primarily repair the blood‒retinal barrier by modulating pathological factors such as oxidative stress, inflammation, pyroptosis, and autophagy, thereby alleviating retinal diseases. CONCLUSION: This review summarizes a series of plant extracts and plant active compounds that can treat retinal diseases by preventing and treating blood‒retinal barrier damage and provides reference for the research of new drugs for treating retinal diseases.

Pharmacological mechanism and clinical study of Qiming granules in treating diabetic retinopathy based on network pharmacology and literature review.

ETHNOPHARMACOLOGY RELEVANCE: Diabetic retinopathy (DR) is a general complication of diabetes, which has become a serious threat to human health worldwide. However, the best treatment is still under development. Qiming (QM) granules are mainly composed of Astragalus membranaceus, Pueraria lobata, Rehmannia Glutinosa, Lycium barbarum, Cassiae Semen, Fructus Leonuri, Pollen Typhae, and Leech, which are beneficial to qi, nourish liver and kidney, and clear collaterals and eyes. The treatment of DR is a comprehensive application based on the above traditional Chinese medicine. In recent years, satisfactory results have been achieved for DR. AIM OF THE STUDY: Through the traditional application analysis, network pharmacology analysis and clinical research summary of QM granules, to review the effectiveness and advantages of QM granules in the treatment of DR comprehensively. MATERIALS AND METHODS: Analysis of main active components of QM granules by network pharmacology and prediction of mechanism of action of QM granules on DR. Further, PubMed, Web of Science, and China National Knowledge Infrastructure were used to search literature, using the keywords of "Qiming granules", "diabetic retinopathy", "clinical research" and their combinations, mainly from 1999 to 2022. RESULTS: Traditional pharmacological analysis, Network pharmacological analysis, animal experiments, and clinical studies have confirmed that QM granules plays a role in the treatment of DR through multiple components, multiple targets, and multiple channels. CONCLUSIONS: This systematic review for the first time provides meaningful information for the further study of the pharmacodynamic material basis and pharmacological mechanism of QM granules, and also provides a basis for further study of quality markers and quality control of QM granules.

Secure Decentralized IoT Service Platform Using Consortium Blockchain.

Although many studies have been devoted to integrating blockchain into IoT device management, access control, data integrity, security, and privacy, blockchain-facilitated IoT communication is still much less studied. Blockchain has great potential in decentralizing and securing IoT communications. In this paper, we propose an innovative IoT service platform powered by the consortium blockchain technology. The proposed platform abstracts machine-to-machine (M2M) and human-to-machine (H2M) communications into services provided by IoT devices. Then, it materializes the data exchange of the IoT network through smart contracts and blockchain transactions. Additionally, we introduce the auxiliary storage layer to the proposed platform to address various off-chain data storage needs. Our proof-of-concept implementation was tested against various workloads and connection sizes under different block configurations to evaluate the platform's transaction throughput, latency, and hardware utilization. The experimental results demonstrate that our solution can maintain high performance with a throughput of approximately 800 reads per second (RPS), 50-80 transactions per second (TPS), and a latency of 50 ms-2 s under light to moderate workloads. Our extensive evaluation of the performance impact of batch size, batch timeout, and connection size also provides valuable insights into the optimization of blockchain configuration for achieving high performance.

Investigation on the Q-markers of Bushen Huoxue Prescriptions for DR treatment based on chemometric methods and spectrum-effect relationship.

ETHNOPHARMACOLOGICAL RELEVANCE: Diabetic retinopathy (DR) is a kind of complex complication of late diabetes mellitus with high incidence and risk of blindness. Bushen Huoxue Prescription (BHP), which consists of Rehmanniae radix (RR), Salviae miltiorrhizae radix et rhizoma (SMRR), Ginseng radix et rhizome (GRR) and Puerariae lobatae radix (PLR), has an active effect on the treatment of DR. However, the quality markers (Q-markers) of BHP are not entirely clear. PURPOSE: This study aimed to screen the Q-markers of BHP for DR treatment based on the establishment of spectrum-effect relationship and verified experiment. MATERIALS AND METHODS: In this study, 12 BHP samples (S1-S12) for fingerprint analysis and pharmacological evaluation were prepared according to a four-factor and twelve-level uniform design. High performance liquid chromatography-ultraviolet detector-evaporative light scattering detector (HPLC-UV-ELSD) was employed to analyze the fingerprint on the basis of the characteristics of BHP components. The evaluation of sample similarity was carried out by similarity analysis (SA) and hierarchical cluster analysis (HCA). The pharmacological indicators, including expression of vascular endothelial growth factor (VEGF) and hypoxia-inducible factor-1α (HIF-1α) in the retina of Sprague Dawley (SD) rats induced by streptozotocin (STZ), were detected by enzyme-linked immunosorbent assay (ELISA). Besides, the spectrum-effect relationship between common peaks of fingerprints and the pharmacological results was investigated by partial least squares regression (PLSR) and canonical correlation analysis (CCA). The results of spectrum-effect relationship were verified by the expression of VEGF and HIF-1α on primary culture retinal Müller cells induced by hyperglycemia and hypoxia. RESULTS: In the HPLC-UV-ELSD fingerprint, 23 common peaks in UV and 14 common peaks in ELSD were identified. The pharmacological results indicated that the expression of VEGF and HIF-1α in the retina of SD rats was inhibited by 12 BHP samples to varying degrees compared with the model group. Based on SA and heatmap of HCA, S4 and S8 were clearly distinguished from other samples. The results of PLSR and CCA revealed that the contents of puerarin, daidzin, salvianolic acid B and ginsenoside Rb1 were inversely correlated with the expression of VEGF and HIF-1α. Hence, the four compounds may be the main active components to prevent and treat DR. The results of intervention on primary culture retinal Müller cells showed that puerarin, daidzin, salvianolic acid B, and ginsenoside Rb1 can significantly inhibit the expression of VEGF and HIF-1α. CONCLUSIONS: The spectrum-effect relationship of BHP was successfully established, and the Q-markers of BHP for the prevention and treatment of DR were preliminarily confirmed. It provides a feasible method for the research of quality control.

Simultaneous determination of six main components in Bushen Huoxue prescription by HPLC-CAD.

BACKGROUND: The Bushen Huoxue prescription is a traditional Chinese medicine formula treating diabetic retinopathy, which was developed by our research group. Catalpol, puerarin, salvianolic acid B, ginsenoside Rg1, ginsenoside Rb1 and ginsenoside Rd are six of main effective components, which could be partly representative of this prescription. The corona charged aerosol detector (corona CAD) is one kind of universal detectors equipped with the high performance liquid chromatography (HPLC). The CAD has many advantages for the analysis of complex mixtures, but too few applications in traditional Chinese medicine compounds. OBJECTIVE: The aims of this study are to establish a method for the determination of six components in Bushen Huoxue prescription, and to increase the use of the CAD in traditional Chinese medicine compounds. METHODS: HPLC-CAD analysis was performed on an Inertsil ODS-SP (4.6 mm × 250 mm, 5 µm) with a mobile phase consisting of 0.5 % formic acid solution(A)-acetonitrile(B) at a flow rate of 1 mL/min (0-7 min, 1 % B; 7-12 min, 1 %-12 % B; 12-22 min, 12 %-19 % B; 22-40 min, 19 %-28 % B; 40-43 min, 28 %-33 % B; 43-50 min, 33 % B; 50-65 min, 33 %-42 % B). The column temperature maintained at 30 ℃, the injection volume was 20 µL, the atomization temperature mode was LOW, the filtration constant (filter) was 3.6 and data collection rate was 10 Hz. The methodology was examined and the linearity of regression of different functions was compared. Sixteen batches of samples were prepared and their contents were determined. RESULTS: The six compounds showed a better linearity (R2 > 0.9990) in their concentration ranges when using the linear function. The average recoveries were 99.18 %-101.30 %. Although the RSD value of puerarin and ginsenoside Rg1 was slightly out of 3 % during the average recovery investigation, all the other methodological investigations of the six components were within 3 %. The precision, stability and repeatability of the method were good. In sixteen batches of Bushen Huoxue prescription samples, the contents of six components were 0.3138 %-0.6042 % for catalpol, 0.8095 %-1.2917 % for puerarin, 0.7416 %-1.1189 % for salvianolic acid B, 0.0231 %-0.0418 % for ginsenoside Rg1, 0.0702 %-0.1724 % for ginsenoside Rb1, 0.0384 %-0.1196 % for ginsenoside Rd. CONCLUSION: In this experiment, a method for the determination of six components in Bushen Huoxue prescription based on HPLC-CAD was established with high accuracy, good repeatability and simple operation, and it can provide references for the improvement of quality standard of the Bushen Huoxue prescription. It is reasonable and accessible for the CAD application in the determination of traditional Chinese medicine compound prescriptions.

The effects of Bushen Yiqi Huoxue prescription and its disassembled prescriptions on a diabetic retinopathy model in Sprague Dawley rats.

BACKGROUND: Diabetic retinopathy (DR) is one of the most serious complications in the late stages of diabetes, with a complex mechanism. As a complication affecting local lesions, few studies have compared differences of cytokine expression in the serum and retina. Owing to the specific value of traditional Chinese medicine (TCM) to complex diseases, TCM research has recently boomed in the prevention and treatment of diabetes. Bushen Yiqi Huoxue (BYH) prescription is a Chinese herbal compound that has been independently developed by our research group and has been proved to have a positive effect on DR; however, its specific mechanism and compatibility rule remain to be further explored. OBJECTIVE: To construct a DR model of Sprague Dawley (SD) rats, simultaneously detect multiple factor expression in the serum and retina of rats, explore the effect of BYH prescription and its disassembled prescriptions on DR, and discuss the influence of various compatibility combinations. METHODS: BYH prescription was disassembled into two new compatibilities in the absence of Rehmanniae Radix (Yiqi Huoxue prescription, YH prescription) or Ginseng Radix et Rhizoma (Bushen Huoxue prescription, BH prescription). Male SD rats were induced using streptozotocin + high-fat and high-sugar diet to establish DR models and were divided into groups, then the intragastric administration and sampling. The body weight and fasting blood glucose of rats were continuously recorded during feeding; pathophysiological status observation of the retina by haematoxylin and eosin (HE) staining; advanced glycation end products (AGEs) and haemoglobin A1c (HbA1c) level detection in the rat serum by enzyme-linked immunosorbent assay; and the Luminex technique was used to detect the ICAM-1, IL-1ß, IL-6, TNF-α and vascular endothelial growth factor (VEGF) expression concentrations in the retinal tissue and serum. RESULTS: The results of blood glucose, body weight and HE staining proved that the model was successfully constructed, and the three combinations could reduce the retinal injury in DR rats. Serum AGEs and HbA1c levels of the model group increased compared with the control group (CG). Compared with the DR model group, only AGEs decreased in the BYH group, while the AGEs and HbA1c levels were significantly inhibited in the YH and BH groups, showing a significant correlation between the expression of AGEs and HbA1c in the serum of DR rats. In the serum of rats, IL-1ß, IL-6, TNF-α and VEGF concentrations in the DR model group increased, although no statistical difference was observed in the ICAM-1 data compared with the CG. Compared with the DR model group, the IL-1ß, IL-6 and TNF-α expression decreased in the BYH group. Moreover, the IL-6 and TNF-α expression decreased in the YH group and only the IL-6 expression decreased in the BH group. In the retina tissue, the model group had higher ICAM-1, IL-1ß, IL-6, TNF-α and VEGF levels than the CG. Compared with the DR model group, TNF-α in the BYH group rats decreased, and the ICAM-1, IL-6 and TNF-α concentrations decreased in the YH and BH groups. Furthermore, differences in the ICAM-1 and VEGF expression in the serum and retina existed. CONCLUSION: BYH compound and its disassembled prescriptions could improve the DR model rats induced with streptozotocin + high-fat and high-sugar diet, respectively, by inhibiting chronic blood glucose, AGEs, or inflammation response. The expression level and location of each factor are different, confirming that the effect of TCM prescriptions is not the simple addition of each single drug or its chemical components, but the rationality of its internal compatibility combination. Further, ICAM-1 and VEGF have exactly different expression levels, suggesting more attention to be paid by other researchers or doctors in future studies.

Urine metabolomics study of Bushen Huoxue Prescription on diabetic retinopathy rats by UPLC-Q-exactive Orbitrap-MS.

Diabetic retinopathy (DR) is a common microvascular complication of diabetes and remains one of the leading causes of blindness worldwide. Previous studies have shown that Bushen Huoxue Prescription (BP) possesses an effect on preventing and treating DR, but the mechanisms of action are not entirely clear. In order to clarify the mechanisms, the pharmacodynamical assessments of BP were investigated. Combining the pharmacodynamical studies, it was found that BP could inhibit high expression of VEGF and HIF-1α, indicating significant therapeutic effects on DR. In order to further investigate the mechanism of BP in treating DR, a urine metabolomics method based on ultrahigh-performance liquid chromatography coupled with Q-exactive quadrupole-electrostatic field Orbitrap mass spectrometry was established to observe the metabolic variations in DR rats and investigate the therapeutic effect of BP on DR. As a result, nine potential biomarkers associated with DR were found. The metabolic pathways related to these compounds were explored, and the results showed that these biomarkers were mainly associated with gut microbial metabolism, lipid metabolism and tryptophan metabolism. The results for the pharmacodynamics and metabolomics provided a theoretical basis for clarifying the mechanism of BP in the treatment of DR.

Developing and Validating a Computable Phenotype for the Identification of Transgender and Gender Nonconforming Individuals and Subgroups.

Transgender and gender nonconforming (TGNC) individuals face significant marginalization, stigma, and discrimination. Under-reporting of TGNC individuals is common since they are often unwilling to self-identify. Meanwhile, the rapid adoption of electronic health record (EHR) systems has made large-scale, longitudinal real-world clinical data available to research and provided a unique opportunity to identify TGNC individuals using their EHRs, contributing to a promising routine health surveillance approach. Built upon existing work, we developed and validated a computable phenotype (CP) algorithm for identifying TGNC individuals and their natal sex (i.e., male-to-female or female-to-male) using both structured EHR data and unstructured clinical notes. Our CP algorithm achieved a 0.955 F1-score on the training data and a perfect F1-score on the independent testing data. Consistent with the literature, we observed an increasing percentage of TGNC individuals and a disproportionate burden of adverse health outcomes, especially sexually transmitted infections and mental health distress, in this population.

Exploring the Association Between Self-Reported Asthma Impact and Fitbit-Derived Sleep Quality and Physical Activity Measures in Adolescents.

BACKGROUND: Smart wearables such as the Fitbit wristband provide the opportunity to monitor patients more comprehensively, to track patients in a fashion that more closely follows the contours of their lives, and to derive a more complete dataset that enables precision medicine. However, the utility and efficacy of using wearable devices to monitor adolescent patients' asthma outcomes have not been established. OBJECTIVE: The objective of this study was to explore the association between self­reported sleep data, Fitbit sleep and physical activity data, and pediatric asthma impact (PAI). METHODS: We conducted an 8­week pilot study with 22 adolescent asthma patients to collect: (1) weekly or biweekly patient­reported data using the Patient-Reported Outcomes Measurement Information System (PROMIS) measures of PAI, sleep disturbance (SD), and sleep­related impairment (SRI) and (2) real-time Fitbit (ie, Fitbit Charge HR) data on physical activity (F-AM) and sleep quality (F­SQ). To explore the relationship among the self-reported and Fitbit measures, we computed weekly Pearson correlations among these variables of interest. RESULTS: We have shown that the Fitbit-derived sleep quality F-SQ measure has a moderate correlation with the PROMIS SD score (average r=-.31, P=.01) and a weak but significant correlation with the PROMIS PAI score (average r=-.18, P=.02). The Fitbit physical activity measure has a negligible correlation with PAI (average r=.04, P=.62). CONCLUSIONS: Our findings support the potential of using wrist-worn devices to continuously monitor two important factors-physical activity and sleep-associated with patients' asthma outcomes and to develop a personalized asthma management platform.

Mining Twitter to Assess the Public Perception of the "Internet of Things".

Social media analysis has shown tremendous potential to understand public's opinion on a wide variety of topics. In this paper, we have mined Twitter to understand the public's perception of the Internet of Things (IoT). We first generated the discussion trends of the IoT from multiple Twitter data sources and validated these trends with Google Trends. We then performed sentiment analysis to gain insights of the public's attitude towards the IoT. As anticipated, our analysis indicates that the public's perception of the IoT is predominantly positive. Further, through topic modeling, we learned that public tweets discussing the IoT were often focused on business and technology. However, the public has great concerns about privacy and security issues toward the IoT based on the frequent appearance of related terms. Nevertheless, no unexpected perceptions were identified through our analysis. Our analysis was challenged by the limited fraction of tweets relevant to our study. Also, the user demographics of Twitter users may not be strongly representative of the population of the general public.

SegAuth: A Segment-based Approach to Behavioral Biometric Authentication.

Many studies have been conducted to apply behavioral biometric authentication on/with mobile devices and they have shown promising results. However, the concern about the verification accuracy of behavioral biometrics is still common given the dynamic nature of behavioral biometrics. In this paper, we address the accuracy concern from a new perspective-behavior segments, that is, segments of a gesture instead of the whole gesture as the basic building block for behavioral biometric authentication. With this unique perspective, we propose a new behavioral biometric authentication method called SegAuth, which can be applied to various gesture or motion based authentication scenarios. SegAuth can achieve high accuracy by focusing on each user's distinctive gesture segments that frequently appear across his or her gestures. In SegAuth, a time series derived from a gesture/motion is first partitioned into segments and then transformed into a set of string tokens in which the tokens representing distinctive, repetitive segments are associated with higher genuine probabilities than those tokens that are common across users. An overall genuine score calculated from all the tokens derived from a gesture is used to determine the user's authenticity. We have assessed the effectiveness of SegAuth using 4 different datasets. Our experimental results demonstrate that SegAuth can achieve higher accuracy consistently than existing popular methods on the evaluation datasets.

Mining Twitter as a First Step toward Assessing the Adequacy of Gender Identification Terms on Intake Forms.

The Institute of Medicine (IOM) recommends that health care providers collect data on gender identity. If these data are to be useful, they should utilize terms that characterize gender identity in a manner that is 1) sensitive to transgender and gender non-binary individuals (trans* people) and 2) semantically structured to render associated data meaningful to the health care professionals. We developed a set of tools and approaches for analyzing Twitter data as a basis for generating hypotheses on language used to identify gender and discuss gender-related issues across regions and population groups. We offer sample hypotheses regarding regional variations in the usage of certain terms such as 'genderqueer', 'genderfluid', and 'neutrois' and their usefulness as terms on intake forms. While these hypotheses cannot be directly validated with Twitter data alone, our data and tools help to formulate testable hypotheses and design future studies regarding the adequacy of gender identification terms on intake forms.

CollaborationViz: interactive visual exploration of biomedical research collaboration networks.

Social network analysis (SNA) helps us understand patterns of interaction between social entities. A number of SNA studies have shed light on the characteristics of research collaboration networks (RCNs). Especially, in the Clinical Translational Science Award (CTSA) community, SNA provides us a set of effective tools to quantitatively assess research collaborations and the impact of CTSA. However, descriptive network statistics are difficult for non-experts to understand. In this article, we present our experiences of building meaningful network visualizations to facilitate a series of visual analysis tasks. The basis of our design is multidimensional, visual aggregation of network dynamics. The resulting visualizations can help uncover hidden structures in the networks, elicit new observations of the network dynamics, compare different investigators and investigator groups, determine critical factors to the network evolution, and help direct further analyses. We applied our visualization techniques to explore the biomedical RCNs at the University of Arkansas for Medical Sciences--a CTSA institution. And, we created CollaborationViz, an open-source visual analytical tool to help network researchers and administration apprehend the network dynamics of research collaborations through interactive visualization.

CLARA: an integrated clinical research administration system.

Administration of human subject research is complex, involving not only the institutional review board but also many other regulatory and compliance entities within a research enterprise. Its efficiency has a direct and substantial impact on the conduct and management of clinical research. In this paper, we report on the Clinical Research Administration (CLARA) platform developed at the University of Arkansas for Medical Sciences. CLARA is a comprehensive web-based system that can streamline research administrative tasks such as submissions, reviews, and approval processes for both investigators and different review committees on a single integrated platform. CLARA not only helps investigators to meet regulatory requirements but also provides tools for managing other clinical research activities including budgeting, contracting, and participant schedule planning.

Social network analysis of biomedical research collaboration networks in a CTSA institution.

BACKGROUND: The popularity of social networks has triggered a number of research efforts on network analyses of research collaborations in the Clinical and Translational Science Award (CTSA) community. Those studies mainly focus on the general understanding of collaboration networks by measuring common network metrics. More fundamental questions about collaborations still remain unanswered such as recognizing "influential" nodes and identifying potential new collaborations that are most rewarding. METHODS: We analyzed biomedical research collaboration networks (RCNs) constructed from a dataset of research grants collected at a CTSA institution (i.e., University of Arkansas for Medical Sciences (UAMS)) in a comprehensive and systematic manner. First, our analysis covers the full spectrum of a RCN study: from network modeling to network characteristics measurement, from key nodes recognition to potential links (collaborations) suggestion. Second, our analysis employs non-conventional model and techniques including a weighted network model for representing collaboration strength, rank aggregation for detecting important nodes, and Random Walk with Restart (RWR) for suggesting new research collaborations. RESULTS: By applying our models and techniques to RCNs at UAMS prior to and after the CTSA, we have gained valuable insights that not only reveal the temporal evolution of the network dynamics but also assess the effectiveness of the CTSA and its impact on a research institution. We find that collaboration networks at UAMS are not scale-free but small-world. Quantitative measures have been obtained to evident that the RCNs at UAMS are moving towards favoring multidisciplinary research. Moreover, our link prediction model creates the basis of collaboration recommendations with an impressive accuracy (AUC: 0.990, MAP@3: 1.48 and MAP@5: 1.522). Last but not least, an open-source visual analytical tool for RCNs is being developed and released through Github. CONCLUSIONS: Through this study, we have developed a set of techniques and tools for analyzing research collaboration networks and conducted a comprehensive case study focusing on a CTSA institution. Our findings demonstrate the promising future of these techniques and tools in understanding the generative mechanisms of research collaborations and helping identify beneficial collaborations to members in the research community.

USign--a security enhanced electronic consent model.

Electronic consent becomes increasingly popular in the healthcare sector given the many benefits it provides. However, security concerns, e.g., how to verify the identity of a person who is remotely accessing the electronic consent system in a secure and user-friendly manner, also arise along with the popularity of electronic consent. Unfortunately, existing electronic consent systems do not pay sufficient attention to those issues. They mainly rely on conventional password based authentication to verify the identity of an electronic consent user, which is far from being sufficient given that identity theft threat is real and significant in reality. In this paper, we present a security enhanced electronic consent model called USign. USign enhances the identity protection and authentication for electronic consent systems by leveraging handwritten signatures everyone is familiar with and mobile computing technologies that are becoming ubiquitous. We developed a prototype of USign and conducted preliminary evaluation on accuracy and usability of signature verification. Our experimental results show the feasibility of the proposed model.

A probabilistic model of functional brain connectivity network for discovering novel biomarkers.

Graph theoretical analyses of functional brain connectivity networks have been limited to a static view of brain activities over the entire timeseries. In this paper, we propose a new probabilistic model of the functional brain connectivity network, the strong-edge model, which incorporates the temporal fluctuation of neurodynamics. We also introduce a systematic approach to identifying biomarkers based on network characteristics that quantitatively describe the organization of the brain network. The evaluation results of the proposed strong-edge network model is quite promising. The biomarkers derived from the strong-edge model have achieved much higher prediction accuracy of 89% (ROCAUC: 0.96) in distinguishing depression subjects from healthy controls in comparison with the conventional network model (accuracy: 76%, ROC-AUC: 0.87). These novel biomarkers have the high potential of being applied clinically in diagnosing neurological and psychiatric brain diseases with noninvasive neuroimaging technologies.