The Ontology of Adverse Events (OAE) was launched in 2011 to define, standardize and integrate various adverse events (AEs) arising after medical interventions. The terminological framework of OAE has undergone consistent expansion since its inception, driven by its successful implementation in numerous AE investigations. In this paper, we document substantial ontological extensions addressing patient anatomic regions and clinical manifestations, encompassing symptoms, physical signs, and pathological processes. Current statistical analysis reveals that OAE has 10,829 formally defined terms with unique identifiers. Compared to the 3,088 ontology terms included in the last OAE publication in 2014, 7,741 new terms have been added to OAE, which represents significant progress of the ontology in clinical granularity and domain coverage. The OAE framework enables structured representation of critical determinants influencing clinical outcomes, including but not limited to administration routes, dosage parameters, and demographic variables such as patient age. Through its standardized semantic architecture, OAE provides an integrative platform for cross-disciplinary analysis of AE patterns, etiological factors, and outcome trajectories in clinical interventions.

Susceptibility genes, including single-nucleotide polymorphisms (SNPs) in the DNA sequences, genetically predispose certain individuals to developing adverse events (AEs) following vaccination. Such AEs are often undetected in initial clinical safety trials during vaccine licensing evaluations. Therefore, a comprehensive understanding of susceptibility genes is crucial for vaccine development, safety monitoring, and precision immunization. VaegenDB is a web-based centralized database and analysis system designed for managing, storing, and analyzing susceptibility genes associated with vaccine AEs. Basic information on these genes and supporting evidence are curated from peer-reviewed literature, while more detailed gene, AE, and vaccine data are automatically extracted from existing databases such as RefSeq and VIOLIN using in-house scripts. Currently, VaegenDB contains information on 160 susceptibility genes linked to 151 AEs and 86 vaccines. The system offers a user-friendly web interface that enables interactive querying and visualization of susceptibility genes. Bioinformatics analyses using VaegenDB reveal that a single susceptibility gene may harbor multiple genetic variations, one vaccine can be associated with several AEs, and a single AE may be influenced by multiple genes or SNPs. In addition, KEGG and GO enrichment analyses were employed to identify gene signatures—including functional annotations, mutation types, and expression patterns—associated with adverse reactions. The construction of this database and subsequent bioinformatics analyses help clarify enriched gene profiles and underlying mechanisms of vaccine-related AEs, thereby supporting rational vaccine design and advances in precision medicine.

Since the beginning of the COVID-19 pandemic, vaccines have been developed to mitigate the spread of SARS-CoV-2, the virus that causes COVID-19. These vaccines have been effective in reducing the rate and severity of COVID-19 infection but also have been associated with various adverse events (AEs). In this study, data from the Vaccine Adverse Event Reporting System (VAERS) was queried and analyzed via the Cov19VaxKB vaccine safety statistical analysis tool to identify statistically significant (i.e., enriched) AEs for the three currently FDA-authorized or approved COVID-19 vaccines. An ontology-based classification and literature review were conducted for these enriched AEs. Using VAERS data as of 31 December 2021, 96 AEs were found to be statistically significantly associated with the Pfizer-BioNTech, Moderna, and/or Janssen COVID-19 vaccines. The Janssen COVID-19 vaccine had a higher crude reporting rate of AEs compared to the Moderna and Pfizer COVID-19 vaccines. Females appeared to have a higher case report frequency for top adverse events compared to males. Using the Ontology of Adverse Event (OAE), these 96 adverse events were classified to different categories such as behavioral and neurological AEs, cardiovascular AEs, female reproductive system AEs, and immune system AEs. Further statistical comparison between different ages, doses, and sexes was also performed for three notable AEs: myocarditis, GBS, and thrombosis. The Pfizer vaccine was found to have a closer association with myocarditis than the other two COVID-19 vaccines in VAERS, while the Janssen vaccine was more likely to be associated with thrombosis and GBS AEs. To support standard AE representation and study, we have also modeled and classified the newly identified thrombosis with thrombocytopenia syndrome (TTS) AE and its subclasses in the OAE by incorporating the Brighton Collaboration definition. Notably, severe COVID-19 vaccine AEs (including myocarditis, GBS, and TTS) rarely occur in comparison to the large number of COVID-19 vaccinations administered in the United States, affirming the overall safety of these COVID-19 vaccines.

Purpose:Three licensed human papillomavirus (HPV) vaccines (Cervarix, Gardasil, and Gardasil 9) have been effectively used to prevent infection with oncogenic HPV types; however, many adverse events (AEs) have also been reported following their vaccinations. We assessed AE profiles after receiving the HPV vaccines based on the reported data from Vaccine Adverse Event Reporting System (VAERS). Methods:The AE data associated with Cervarix, Gardasil, and Gardasil 9 were retrieved from VAERS database respectively. The combinatorial biomedical statistical methods were used to identify the statistically significant AEs. The Gamma-Poisson Shrinker (GPS) model with gender/age stratification was applied to ascertain the serious adverse events (SAEs) related to the three licensed HPV vaccines. The AE profiles were classified and represented by the Ontology of Adverse Events (OAE) for further analysis. Results:As of July 31, 2020, VAERS recorded 3,112, 31,606, and 6,872 AE case reports for Cervarix, Gardasil, and Gardasil 9, respectively. Our Frequentist statistical methods identified 135 Cervarix-enriched AEs, 55 Gardasil-enriched AEs, and 17 Gardasil 9-enriched AEs. Based on the OAE hierarchical classification, these AEs were clustered in the AEs related to behavioral and neurological conditions, immune system, nervous system, and reproductive system. Combined with GPS modeling, 46 unique statistically significant SAEs were founded to be associated with at least one of the three vaccines. Conclusions:Our study led to the better understanding of the AEs associated with the licensed HPV vaccines. The hypotheses on the cause and effect relationships between the HPV vaccination and specific AEs deserve further epidemiological investigations as well as clinical trial studies.

Yeast is an integral part of our environment. The detection of yeast is of great significance in many fields. The impedance-based sensor with interdigital microelectrodes is a promising method to establish a simple and portable detection system. However, the positions of yeast cells greatly influence the impedance variation and eventually the sensitivity of detection. In this study, an impedimetric biosensor with ultrasonic-assisted cell alignment for yeast detection was proposed. In order to verify the feasibility of this biosensor, finite element modeling was conducted using the software COMSOL. The acoustic pressure field and the acoustic radiation force exerted on yeast cells were investigated. Cell positions in the microfluidic channel were determined using the fluid particle tracking module. After that, the impedance between the microelectrodes was calculated. Yeast suspensions with different cell concentrations were used as the tested samples for the simulation. The proposed sensor showed a higher sensitivity than the conventional impedimetric biosensor on which the cells were randomly located. It can be used for the detection of microorganisms. This finite element modeling provided an effective approach for the design of biosensors.

Our systematic literature collection and annotation identified 106 chemical drugs and 31 antibodies effective against the infection of at least one human coronavirus (including SARS-CoV, SAR-CoV-2, and MERS-CoV) in vitro or in vivo in an experimental or clinical setting. A total of 163 drug protein targets were identified, and 125 biological processes involving the drug targets were significantly enriched based on a Gene Ontology (GO) enrichment analysis. The Coronavirus Infectious Disease Ontology (CIDO) was used as an ontological platform to represent the anti-coronaviral drugs, chemical compounds, drug targets, biological processes, viruses, and the relations among these entities. In addition to new term generation, CIDO also adopted various terms from existing ontologies and developed new relations and axioms to semantically represent our annotated knowledge. The CIDO knowledgebase was systematically analyzed for scientific insights. To support rational drug design, a “Host-coronavirus interaction (HCI) checkpoint cocktail” strategy was proposed to interrupt the important checkpoints in the dynamic HCI network, and ontologies would greatly support the design process with interoperable knowledge representation and reasoning.

美国密西根大学"本科生研究机会计划"(UROP)实施多年、成熟可靠,大大提高了该校的人才培养质量.在科学思想传播方面,UROP实现了基于使命和愿景的科学价值观塑造;运行管理方面,实现了选题精细化、管理专业化、运行信息化;校企合作方面,注重引入社会团体参与,准确把握科研人才的流向;考核评估方面,评价手段灵活丰富,注重学生自我展示.UROP对我国完善高校本科生科研训练体系的启示是:强化科研德育教育,培养理性科学价值观;加强师生及企业互联互通,构筑科研创新共同体;建立常态化管理机构,形成信息化管理模式;完善科研评价体系,引导学生积极参与.

近年来,互联网与物联网等技术的快速发展为探寻精准医学与人工智能之间的关联应用提供了战略机遇。然而,如何组织、集成和共享复杂异构的生物医学数据业已成为阻碍该领域发展的严重技术挑战。本体因其能够提供知识与元数据层面的语义基础而推动了生物医学人工智能的发展,而基于互操作性的本体对异构知识与数据的整合和分析发挥着关键性的作用。将人工智能与精准医疗的有机集合称之为智能精准医疗,并提出一个"河马假设",用以阐明互操作性本体与智能精准医疗之间的正相关关系及如何发挥协同增效作用;同时,提出和展示了使用可扩展本体开发的原理和工具来实现本体的互操作性,进而支持智能精准医疗的应用与发展。此外,还对国内外互操作性本体的研究现状、本体中国的成立与发展以及医学伦理在智能精准医疗中的重要性等进行了综述和深入探讨。

Purpose Wrist-cuff oscillometric blood pressure monitors are very popular in the portable medical device market. However, its accuracy has always been controversial. In addition to the oscillatory pressure pulse wave, the finger photoplethysmography (PPG) can provide information on blood pressure changes. A blood pressure measurement system integrating the information of pressure pulse wave and the finger PPG may improve measurement accuracy. Additionally, a neural network can synthesize the information of different types of signals and approximate the complex nonlinear relationship between inputs and outputs. The purpose of this study is to verify the hypothesis that a wrist-cuff device using a neural network for blood pressure estimation from both the oscillatory pressure pulse wave and PPG signal may improve the accuracy. Design/methodology/approach A PPG sensor was integrated into a wrist blood pressure monitor, so the finger PPG and the oscillatory pressure wave could be detected at the same time during the measurement. After the peak detection, curves were fitted to the data of pressure pulse amplitude and PPG pulse amplitude versus time. A genetic algorithm-back propagation neural network was constructed. Parameters of the curves were inputted into the neural network, the outputs of which were the measurement values of blood pressure. Blood pressure measurements of 145 subjects were obtained using a mercury sphygmomanometer, the developed device with the neural network algorithm and an Omron HEM-6111 blood pressure monitor for comparison. Findings For the systolic blood pressure (SBP), the difference between the proposed device and the mercury sphygmomanometer is 0.0062 ± 2.55 mmHg (mean ± SD) and the difference between the Omron device and the mercury sphygmomanometer is 1.13 ± 9.48 mmHg. The difference in diastolic blood pressure between the mercury sphygmomanometer and the proposed device was 0.28 ± 2.99 mmHg. The difference in diastolic blood pressure between the mercury sphygmomanometer and Omron HEM-6111 was −3.37 ± 7.53 mmHg. Originality/value Although the difference in the SBP error between the proposed device and Omron HEM-6111 was not remarkable, there was a significant difference between the proposed device and Omron HEM-6111 in the diastolic blood pressure error. The developed device showed an improved performance. This study was an attempt to enhance the accuracy of wrist-cuff oscillometric blood pressure monitors by using the finger PPG and the neural network. The hardware framework constructed in this study can improve the conventional wrist oscillometric sphygmomanometer and may be used for continuous measurement of blood pressure.

: Vaccination is one of the most important innovations in human history. It has also become a hot research area in a new application - the development of new vaccines against non-infectious diseases such as cancers. However, effective and safe vaccines still do not exist for many diseases, and where vaccines exist, their protective immune mechanisms are often unclear. Although licensed vaccines are generally safe, various adverse events, and sometimes severe adverse events, still exist for a small population. Precision medicine tailors medical intervention to the personal characteristics of individual patients or sub-populations of individuals with similar immunity-related characteristics. Precision vaccinology is a new strategy that applies precision medicine to the development, administration, and post-administration analysis of vaccines. Several conditions contribute to make this the right time to embark on the development of precision vaccinology. First, the increased level of research in vaccinology has generated voluminous “big data” repositories of vaccinology data. Secondly, new technologies such as multi-omics and immunoinformatics bring new methods for investigating vaccines and immunology. Finally, the advent of AI and machine learning software now makes possible the marriage of Big Data to the development of new vaccines in ways not possible before. However, something is missing in this marriage, and that is a common language that facilitates the correlation, analysis, and reporting nomenclature for the field of vaccinology. Solving this bioinformatics problem is the domain of applied biomedical ontology. Ontology in the informatics field is human- and machine-interpretable representation of entities and the relations among entities in a specific domain. The Vaccine Ontology (VO) and Ontology of Vaccine Adverse Events (OVAE) have been developed to support the standard representation of vaccines, vaccine components, vaccinations, host responses, and vaccine adverse events. Many other biomedical ontologies have also been developed and can be applied in vaccine research. Here, we review the current status of precision vaccinology and how ontological development will enhance this field, and propose an ontology-based precision vaccinology strategy to support precision vaccine research and development.

Current COVID-19 pandemic and previous SARS/MERS outbreaks have caused a series of major crises to global public health We must integrate the large and exponentially growing amount of heterogeneous coronavirus data to better understand coronaviruses and associated disease mechanisms, in the interest of developing effective and safe vaccines and drugs Ontologies have emerged to play an important role in standard knowledge and data representation, integration, sharing, and analysis We have initiated the development of the community-based Coronavirus Infectious Disease Ontology (CIDO) As an Open Biomedical Ontology (OBO) library ontology, CIDO is an open source and interoperable with other existing OBO ontologies In this article, the general architecture and the design patterns of the CIDO are introduced, CIDO representation of coronaviruses, phenotypes, anti-coronavirus drugs and medical devices (e g ventilators) are illustrated, and an application of CIDO implemented to identify repurposable drug candidates for effective and safe COVID-19 treatment is presented Copyright © 2020 for this paper by its authors

本文阐述了数据结构与算法课程的重要性;分析目前该课程教学中存在的问题;在"以学为中心"的理念指导下,结合自身的教学经验从教学内容、教学方法和课程考核三个方面提出改革思路;通过实践此次教学改革,基本解决了我校数据结构与算法课程现存的问题,取得了理想的教学效果.

Pansharpening is normally utilized to take full advantage of all the available spectral and spatial information that are derived from a low-spatial-resolution multispectral (MS) image and its associated high-spatial-resolution (HR) panchromatic (PAN) image, respectively, producing a fused MS image with high spectral and spatial resolutions. Many methods have been recently developed based on convolutional neural networks (CNNs) for the pansharpening task, but most of them still have some drawbacks: 1) The information cannot efficiently flow in their simple stacked convolutional architectures, thereby hindering the representation ability of the networks. 2) They are commonly trained using supervised learning, which does not only require an extra effort to produce the simulated training data, but can also lead to scale-related problems in the fusion results. In this article, we propose a novel unsupervised CNN-based pansharpening method to overcome these limitations. Specifically, we design an iterative network architecture, in which a PAN-guided strategy and a set of skip connections are adopted to continuously extract and fuse the features from the input, thus enhancing the information reuse and transmission. Besides, we propose a new loss function for unsupervised training in which the relationships between the input MS and PAN images and the fused MS image are used to design the spatial constrains and spectral consistency, respectively. The typical quality index with no-reference is also added to this function to further adjust the spectral and spatial qualities. The designed loss function allows the network to be learned only on input images, without any hand-crafted labels (reference HR MS image). We evaluated the effectiveness of our designed network architecture and the combined loss function, and the experiments testify that our unsupervised strategy can also obtain promising results with minor spectral and spatial distortions compared with other traditional and supervised methods.

Coronavirus-infected diseases have posed great threats to human health. In past years, highly infectious coronavirus-induced diseases, including COVID-19, SARS, and MERS, have resulted in world-wide severe infections. Our literature annotations identified 72 chemical drugs and 27 antibodies effective against at least one human coronavirus infection in vitro or in vivo. Many of these drugs inhibit viral entry to cells and viral replication inside cells or modulate host immune responses. Many antimicrobial drugs, including antimalarial (e.g., chloroquine and mefloquine) and antifungal (e.g., terconazole and rapamycin) drugs as well as antibiotics (e.g., teicoplanin and azithromycin) were associated with anti-coronavirus activity. A few drugs, including remdesivir, chloroquine phosphate, favipiravir, and tocilizumab, have already been reported to be effective in treating COVID-19. After mapping our identified drugs to three ontologies ChEBI, NDF-RT, and DrON, many features such as roles and mechanisms of action (MoAs) of these drugs were identified and categorized. For example, out of 35 drugs with MoA annotations in NDF-RT, 34 have MoAs of different types of inhibitors and antagonists. Two clustering analyses, one based on ChEBI-based semantic similarity, the other based on drug chemical similarity, were performed to cluster over 60 drugs to new categories. Moreover, PCA analysis of anti-coronavirus drugs found differences in physicochemical properties between those inhibiting viral entry and viral replication. A total of 137 host genes were identified as the targets of 47 anti-coronavirus drugs, resulting in a network of 370 interactions among these drugs and targets. Chlorpromazine, dasatinib, and anisomycin are the hubs of the drug-target network with the highest number of connected target proteins. Many enriched pathways such as calcium signaling and neuroactive ligand-receptor interaction pathways were identified. These findings may be used to facilitate drug repurposing against COVID-19.

Brucella abortus strain 19 (S19), Brucella melitensis Rev 1 (Rev1), and B abortus strain RB51 (RB51) are the three licensed animal brucellosis vaccines, and they have been most commonly and successfully used in prevent brucellosis in animals However, many adverse events (AEs) have been associated with these three vaccines after their administering to animals or being accidentally exposed to humans In this study, 27 peerreviewed publications containing animal and human AE reports associated with these three brucellosis vaccines were manually annotated from the PubMed database Our meta analysis identified 20 animal AEs and 46 human AEs associated with the three vaccines Based on the Ontology of Adverse Events OAE) hierarchical classification, these animal AEs were enriched in the immune and reproductive systems that might eventually result in the occurrence of abortion or infertility The human AEs were concentrated in the behavioral and neurological conditions, and these AEs showed flu-like symptoms that are consistent with human brucellosis Furthermore, an analysis of variance (ANOVA) statistics analysis with linear model fits was used to determine the major variables that might affect the occurrence of abortion AE in animals The ANOVA results indicated that three variables (P-value < 0 05) are significantly associated with the occurrence of abortion AE animal species, vaccination dose, and vaccination route The other two variables (i e , vaccine type and animal age at vaccination) did not significantly (P-value -> 0 05) associated with the occurrence of abortion AE Overall, this study represents the first ontology-based meta-analysis of adverse events associated with animal vaccines The results of such a study led to the better understanding of brucellosis vaccine AEs, facilitating rational design of more secure and effective vaccines.

Background: Aiming to understand cellular responses to different perturbations, the NIH Common Fund Library of Integrated Network-based Cellular Signatures (LINCS) program involves many institutes and laboratories working on over a thousand cell lines. The community-based Cell Line Ontology (CLO) is selected as the default ontology for LINCS cell line representation and integration. Results: CLO has consistently represented all 1097 LINCS cell lines and included information extracted from the LINCS Data Portal and ChEMBL. Using MCF 10A cell line cells as an example, we demonstrated how to ontologically model LINCS cellular signatures such as their non tumorigenic epithelial cell type, three dimensional growth, latrunculin-A-induced actin depolymerization and apoptosis, and cell line transfection. A CLO subset view of LINCS cell lines, named LINCS-CLOview, was generated to support systematic LINCS cell line analysis and queries. In summary, LINCS cell lines are currently associated with 43 cell types, 131 tissues and organs, and 121 cancer types. The LINCS-CLO view information can be queried using SPARQL scripts. Conclusions: CLO was used to support ontological representation, integration, and analysis of over a thousand LINCS cell line cells and their cellular responses.

Vaccine is the one of the greatest inventions of modern medicine that has contributed most to the relief of human misery and the exciting increase in life expectancy. In 1796, an English country physician, Edward Jenner, discovered that inoculating mankind with cowpox can protect them from smallpox (Riedel S, Edward Jenner and the history of smallpox and vaccination. Proceedings (Baylor University. Medical Center) 18(1):21, 2005). Based on the vaccination worldwide, we finally succeeded in the eradication of smallpox in 1977 (Henderson, Vaccine 29:D7-D9, 2011). Other disabling and lethal diseases, like poliomyelitis and measles, are targeted for eradication (Bonanni, Vaccine 17:S120-S125, 1999).Although vaccine development and administration are tremendously successful and cost-effective practices to human health, no vaccine is 100% safe for everyone because each person reacts to vaccinations differently given different genetic background and health conditions. Although all licensed vaccines are generally safe for the majority of people, vaccinees may still suffer adverse events (AEs) in reaction to various vaccines, some of which can be serious or even fatal (Haber et al., Drug Saf 32(4):309-323, 2009). Hence, the double-edged sword of vaccination remains a concern.To support integrative AE data collection and analysis, it is critical to adopt an AE normalization strategy. In the past decades, different controlled terminologies, including the Medical Dictionary for Regulatory Activities (MedDRA) (Brown EG, Wood L, Wood S, et al., Drug Saf 20(2):109-117, 1999), the Common Terminology Criteria for Adverse Events (CTCAE) (NCI, The Common Terminology Criteria for Adverse Events (CTCAE). Available from: http://evs.nci.nih.gov/ftp1/CTCAE/About.html . Access on 7 Oct 2015), and the World Health Organization (WHO) Adverse Reactions Terminology (WHO-ART) (WHO, The WHO Adverse Reaction Terminology - WHO-ART. Available from: https://www.umc-products.com/graphics/28010.pdf ), have been developed with a specific aim to standardize AE categorization. However, these controlled terminologies have many drawbacks, such as lack of textual definitions, poorly defined hierarchies, and lack of semantic axioms that provide logical relations among terms. A biomedical ontology is a set of consensus-based and computer and human interpretable terms and relations that represent entities in a specific biomedical domain and how they relate each other. To represent and analyze vaccine adverse events (VAEs), our research group has initiated and led the development of a community-based ontology: the Ontology of Adverse Events (OAE) (He et al., J Biomed Semant 5:29, 2014). The OAE has been found to have advantages to overcome the drawbacks of those controlled terminologies (He et al., Curr Pharmacol Rep :1-16. doi:10.1007/s40495-016-0055-0, 2014). By expanding the OAE and the community-based Vaccine Ontology (VO) (He et al., VO: vaccine ontology. In The 1st International Conference on Biomedical Ontology (ICBO-2009). Nature Precedings, Buffalo. http://precedings.nature.com/documents/3552/version/1 ; J Biomed Semant 2(Suppl 2):S8; J Biomed Semant 3(1):17, 2009; Ozgur et al., J Biomed Semant 2(2):S8, 2011; Lin Y, He Y, J Biomed Semant 3(1):17, 2012), we have also developed the Ontology of Vaccine Adverse Events (OVAE) to represent known VAEs associated with licensed vaccines (Marcos E, Zhao B, He Y, J Biomed Semant 4:40, 2013).In this book chapter, we will first introduce the basic information of VAEs, VAE safety surveillance systems, and how to specifically query and analyze VAEs using the US VAE database VAERS (Chen et al., Vaccine 12(10):960-960, 1994). In the second half of the chapter, we will introduce the development and applications of the OAE and OVAE. Throughout this chapter, we will use the influenza vaccine Flublok as the vaccine example to launch the corresponding elaboration (Huber VC, McCullers JA, Curr Opin Mol Ther 10(1):75-85, 2008). Flublok is a recombinant hemagglutinin influenza vaccine indicated for active immunization against disease caused by influenza virus subtypes A and type B. On January 16, 2013, Flublok was approved by the FDA for the prevention of seasonal influenza in people 18 years and older in the USA. Now, more than 3 years later, an exploration of the reported AEs associated with this vaccine is urgently needed.

With increased usage of cardiovascular drugs (CVDs) for treating cardiovascular diseases, it is important to analyze CVD-associated adverse events (AEs). In this study, we systematically collected package insert-reported AEs associated with CVDs used in China, and developed and analyzed an Ontology of Cardiovascular Drug AEs (OCVDAE). Extending the Ontology of AEs (OAE) and NDF-RT, OCVDAE includes 194 CVDs, CVD ingredients, mechanisms of actions (MoAs), and CVD-associated 736 AEs. An AE-specific drug class effect is defined to exist when all the drugs (drug chemical ingredients or drug products) in a drug class are associated with an AE, which is formulated as a new proportional class level ratio (“PCR”) = 1. Our PCR-based heatmap analysis identified many class level drug effects on different AE classes such as behavioral and neurological AE and digestive system AE. Additional drug-AE correlation tests (i.e., class-level PRR, Chi-squared, and minimal case reports) were also modified and applied to further detect statistically significant drug class effects. Two drug ingredient classes and three CVD MoA classes were found to have statistically significant class effects on 13 AEs. For example, the CVD Active Transporter Interactions class (including reserpine, indapamide, digoxin, and deslanoside) has statistically significant class effect on anorexia and diarrhea AEs.

M. bovis strain Bacillus Calmette-Guérin (BCG) has been the only licensed live attenuated vaccine against tuberculosis (TB) for nearly one century and has also been approved as a therapeutic vaccine for bladder cancer treatment since 1990. During its long time usage, different adverse events (AEs) have been reported. However, the AEs associated with the BCG preventive TB vaccine and therapeutic cancer vaccine have not been systematically compared. In this study, we systematically collected various BCG AE data mined from the US VAERS database and PubMed literature reports, identified statistically significant BCG-associated AEs, and ontologically classified and compared these AEs related to these two types of BCG vaccine. From 397 VAERS BCG AE case reports, we identified 64 AEs statistically significantly associated with the BCG TB vaccine and 14 AEs with the BCG cancer vaccine. Our meta-analysis of 41 peer-reviewed journal reports identified 48 AEs associated with the BCG TB vaccine and 43 AEs associated with the BCG cancer vaccine. Among all identified AEs from VAERS and literature reports, 25 AEs belong to serious AEs. The Ontology of Adverse Events (OAE)-based ontological hierarchical analysis indicated that the AEs associated with the BCG TB vaccine were enriched in immune system (e.g., lymphadenopathy and lymphadenitis), skin (e.g., skin ulceration and cyanosis), and respiratory system (e.g., cough and pneumonia); in contrast, the AEs associated with the BCG cancer vaccine mainly occurred in the urinary system (e.g., dysuria, pollakiuria, and hematuria). With these distinct AE profiles detected, this study also discovered three AEs (i.e., chills, pneumonia, and C-reactive protein increased) shared by the BCG TB vaccine and bladder cancer vaccine. Furthermore, our deep investigation of 24 BCG-associated death cases from VAERS identified the important effects of age, vaccine co-administration, and immunosuppressive status on the final BCG-associated death outcome.