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  1. Article ; Online: Reply.

    Perkins, Griffith / Troelnikov, Alexander / Hissaria, Pravin

    The Journal of allergy and clinical immunology

    2021  Volume 148, Issue 3, Page(s) 902–903

    Language English
    Publishing date 2021-06-26
    Publishing country United States
    Document type Letter ; Research Support, Non-U.S. Gov't ; Comment
    ZDB-ID 121011-7
    ISSN 1097-6825 ; 1085-8725 ; 0091-6749
    ISSN (online) 1097-6825 ; 1085-8725
    ISSN 0091-6749
    DOI 10.1016/j.jaci.2021.06.003
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Neither cancer nor myositis are common in patients testing positive for anti-TIF1γ by line blot in real-world laboratory settings.

    Troelnikov, Alexander / Choo, Xin Jing / Beroukas, Dimitra / Limaye, Vidya

    International journal of rheumatic diseases

    2022  Volume 26, Issue 3, Page(s) 586–590

    MeSH term(s) Humans ; Myositis ; Neoplasms ; Autoantibodies
    Chemical Substances Autoantibodies
    Language English
    Publishing date 2022-12-30
    Publishing country England
    Document type Letter
    ZDB-ID 2426924-4
    ISSN 1756-185X ; 1756-1841
    ISSN (online) 1756-185X
    ISSN 1756-1841
    DOI 10.1111/1756-185X.14552
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Machine learning models automate classification of penicillin adverse drug reaction labels.

    Inglis, Joshua M / Bacchi, Stephen / Troelnikov, Alexander / Smith, William / Shakib, Sepehr

    Internal medicine journal

    2023  Volume 53, Issue 8, Page(s) 1485–1488

    Abstract: There is a growing interest in the appropriate evaluation of penicillin adverse drug reaction (ADR) labels. We have developed machine learning models for classifying penicillin ADR labels using free-text reaction descriptions, and here report external ... ...

    Abstract There is a growing interest in the appropriate evaluation of penicillin adverse drug reaction (ADR) labels. We have developed machine learning models for classifying penicillin ADR labels using free-text reaction descriptions, and here report external and practical validation. The models performed comparably with expert criteria for the categorisation of allergy or intolerance and identification of high-risk allergies. These models have practical applications in detecting individuals suitable for penicillin ADR evaluation. Implementation studies are required.
    MeSH term(s) Humans ; Drug-Related Side Effects and Adverse Reactions/diagnosis ; Drug-Related Side Effects and Adverse Reactions/epidemiology ; Hypersensitivity ; Machine Learning ; Penicillins/adverse effects
    Chemical Substances Penicillins
    Language English
    Publishing date 2023-08-15
    Publishing country Australia
    Document type Journal Article
    ZDB-ID 2045436-3
    ISSN 1445-5994 ; 1444-0903
    ISSN (online) 1445-5994
    ISSN 1444-0903
    DOI 10.1111/imj.16194
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: The choice of anti-LEDGF/DFS70 assay matters: a comparative study of six assays.

    Troelnikov, Alexander / Hender, Lauren / Lester, Susan / Gordon, Thomas Paul / Hughes, Tiffany / Beroukas, Dimitra

    Pathology

    2022  Volume 54, Issue 7, Page(s) 910–916

    Abstract: Lens-epithelial derived growth factor (LEDGF/DFS70) autoantibodies result in the commonly observed dense fine speckled (DFS) pattern by anti-nuclear antibody (ANA) assay. However, there is no consensus approach for confirmation of this autoantibody ... ...

    Abstract Lens-epithelial derived growth factor (LEDGF/DFS70) autoantibodies result in the commonly observed dense fine speckled (DFS) pattern by anti-nuclear antibody (ANA) assay. However, there is no consensus approach for confirmation of this autoantibody specificity. To evaluate current approaches, we examined inter-assay agreement between six anti-LEDGF/DFS70 assays. A total of 395 consecutive sera samples from routine ANA diagnostics were obtained, tested by routine ANA, anti-ENA line immunoblot assay (LIA) and anti-dsDNA assay and with six anti-DFS/LEDGF assays: the EuroLine-LIA (Euro-LIA), Medical and Biological Laboratories ELISA (MBL-ELISA), Phadia-EliA (EliA), QUANTA Flash CLIA, EuroImmun ELISA (Euro-ELISA) and Immco-Diagnostics HEp-2 ELITE/DFS-Knockout (HEp-2KO). Of 395 sera, 108 tested positive by at least one assay. Despite general good concordance between all assays across the cohort (Gwet's AC1=0.89), within the target DFS-ANA pattern group inter-assay agreement was poor (AC1=0.59). Euro-LIA, CLIA and MBL-ELISA assays were most concordant, but CLIA and Euro-LIA were also most likely to identify discordant positive results. EliA and Euro-ELISA had poorer agreement, which could be attributable to ill-matched cut-offs between assays. HEp-2KO was frequently discordant with all other assays tested. Euro-LIA, CLIA and MBL-ELISA were most concordant at manufacturer's specifications and are suited for use in clinical laboratories. Modified assay thresholds are required to ensure comparative results for Euro-ELISA and EliA. HEp-2KO assay is frequently discordant with all other assays, making it less suited for routine diagnostics. The study highlights the importance of considering inter-assay variability when developing a diagnostic strategy for anti-LEDGF/DFS70 autoantibodies in clinical laboratories.
    MeSH term(s) Humans ; Autoimmune Diseases/diagnosis ; Adaptor Proteins, Signal Transducing/metabolism ; Transcription Factors/metabolism ; Antibodies, Antinuclear ; Autoantibodies ; Intercellular Signaling Peptides and Proteins/metabolism ; Fluorescent Antibody Technique, Indirect/methods
    Chemical Substances Adaptor Proteins, Signal Transducing ; Transcription Factors ; Antibodies, Antinuclear ; Autoantibodies ; Intercellular Signaling Peptides and Proteins
    Language English
    Publishing date 2022-09-14
    Publishing country England
    Document type Journal Article
    ZDB-ID 7085-3
    ISSN 1465-3931 ; 0031-3025
    ISSN (online) 1465-3931
    ISSN 0031-3025
    DOI 10.1016/j.pathol.2022.07.010
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: A case of urethrovaginal fistula caused by granulomatosis with polyangiitis mimicking malignancy.

    Liu, Jianliang / Troelnikov, Alexander / Wang, Yong Gang / Couchman, Ashani / Pravin, Hissaria

    ANZ journal of surgery

    2021  Volume 91, Issue 12, Page(s) 2833–2835

    MeSH term(s) Granulomatosis with Polyangiitis/complications ; Granulomatosis with Polyangiitis/diagnosis ; Humans ; Neoplasms ; Urethral Diseases/diagnosis ; Urethral Diseases/etiology ; Urinary Fistula/diagnostic imaging ; Urinary Fistula/etiology
    Language English
    Publishing date 2021-04-01
    Publishing country Australia
    Document type Case Reports ; Journal Article
    ZDB-ID 2050749-5
    ISSN 1445-2197 ; 1445-1433 ; 0004-8682
    ISSN (online) 1445-2197
    ISSN 1445-1433 ; 0004-8682
    DOI 10.1111/ans.16794
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Vaccine-induced immune thrombotic thrombocytopenia is mediated by a stereotyped clonotypic antibody.

    Wang, Jing Jing / Armour, Bridie / Chataway, Tim / Troelnikov, Alexander / Colella, Alex / Yacoub, Olivia / Hockley, Simon / Tan, Chee Wee / Gordon, Tom Paul

    Blood

    2022  Volume 140, Issue 15, Page(s) 1738–1742

    MeSH term(s) Antibodies/adverse effects ; Humans ; Purpura, Thrombocytopenic, Idiopathic/chemically induced ; Thrombocytopenia/chemically induced ; Vaccines/adverse effects
    Chemical Substances Antibodies ; Vaccines
    Language English
    Publishing date 2022-06-05
    Publishing country United States
    Document type Journal Article
    ZDB-ID 80069-7
    ISSN 1528-0020 ; 0006-4971
    ISSN (online) 1528-0020
    ISSN 0006-4971
    DOI 10.1182/blood.2022016474
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Automation of penicillin adverse drug reaction categorisation and risk stratification with machine learning natural language processing.

    Inglis, Joshua M / Bacchi, Stephen / Troelnikov, Alexander / Smith, William / Shakib, Sepehr

    International journal of medical informatics

    2021  Volume 156, Page(s) 104611

    Abstract: Background: The penicillin adverse drug reaction (ADR) label is common in electronic health records (EHRs). However, there is significant misclassification between allergy and intolerance within the EHR and most patients can be delabelled after an ... ...

    Abstract Background: The penicillin adverse drug reaction (ADR) label is common in electronic health records (EHRs). However, there is significant misclassification between allergy and intolerance within the EHR and most patients can be delabelled after an immunologic assessment. Machine learning natural language processing may be able to assist with the categorisation and risk stratification of penicillin ADRs.
    Objective: The aim of this study was to use text entered into an EHR to derive and evaluate machine learning models to classify penicillin ADRs and assess the risk of true allergy.
    Methods: Machine learning natural language processing was applied to free-text penicillin ADR data extracted from a public health system EHR. The model was developed by training on labelled dataset. ADR entries were split into training and testing datasets and used to develop and test a variety of machine learning models. These were compared to categorisation with a simple algorithm using keyword search.
    Results: The best performing model for the classification of penicillin ADRs as being consistent with allergy or intolerance was the artificial neural network (AUC 0.994, sensitivity 0.99, specificity 0.96). The artificial neural network also achieved the highest AUC in the classification of high- or low-risk of true allergy (AUC 0.988, sensitivity 0.99, specificity 0.99). All ADR labels were able to be classified using these machine learning models, whereas a small proportion were unclassifiable using the simple algorithm as they contained no keywords.
    Conclusion: Machine learning natural language processing performed similarly to expert criteria in classifying and risk stratifying penicillin ADRs labels. These models outperformed simpler algorithms in their ability to interpret free-text data contained in the EHR. The automated evaluation of penicillin ADR labels may allow real-time risk stratification to facilitate delabelling and improve the specificity of prescribing alerts.
    MeSH term(s) Algorithms ; Automation ; Drug-Related Side Effects and Adverse Reactions/diagnosis ; Electronic Health Records ; Humans ; Machine Learning ; Natural Language Processing ; Penicillins/adverse effects ; Risk Assessment
    Chemical Substances Penicillins
    Language English
    Publishing date 2021-10-05
    Publishing country Ireland
    Document type Journal Article
    ZDB-ID 1466296-6
    ISSN 1872-8243 ; 1386-5056
    ISSN (online) 1872-8243
    ISSN 1386-5056
    DOI 10.1016/j.ijmedinf.2021.104611
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Immunoglobulin repertoire restriction characterizes the serological responses of patients with predominantly antibody deficiency.

    Troelnikov, Alexander / Armour, Bridie / Putty, Trishni / Aggarwal, Anupriya / Akerman, Anouschka / Milogiannakis, Vanessa / Chataway, Tim / King, Jovanka / Turville, Stuart G / Gordon, Tom P / Wang, Jing Jing

    The Journal of allergy and clinical immunology

    2023  Volume 152, Issue 1, Page(s) 290–301.e7

    Abstract: Background: Predominantly antibody deficiency (PAD) is the most common category of inborn errors of immunity and is underpinned by impaired generation of appropriate antibody diversity and quantity. In the clinic, responses are interrogated by ... ...

    Abstract Background: Predominantly antibody deficiency (PAD) is the most common category of inborn errors of immunity and is underpinned by impaired generation of appropriate antibody diversity and quantity. In the clinic, responses are interrogated by assessment of vaccination responses, which is central to many PAD diagnoses. However, the composition of the generated antibody repertoire is concealed from traditional quantitative measures of serological responses. Leveraging modern mass spectrometry-based proteomics (MS-proteomics), it is possible to elaborate the molecular features of specific antibody repertoires, which may address current limitations of diagnostic vaccinology.
    Objectives: We sought to evaluate serum antibody responses in patients with PAD following vaccination with a neo-antigen (severe acute respiratory syndrome coronavirus-2 vaccination) using MS-proteomics.
    Methods: Following severe acute respiratory syndrome coronavirus-2 vaccination, serological responses in individuals with PAD and healthy controls (HCs) were assessed by anti-S1 subunit ELISA and neutralization assays. Purified anti-S1 subunit IgG and IgM was profiled by MS-proteomics for IGHV subfamily usage and somatic hypermutation analysis.
    Results: Twelve patients with PAD who were vaccine-responsive were recruited with 11 matched vaccinated HCs. Neutralization and end point anti-S1 titers were lower in PAD. All subjects with PAD demonstrated restricted anti-S1 IgG antibody repertoires, with usage of <5 IGHV subfamilies (median: 3; range 2-4), compared to ≥5 for the 11 HC subjects (P < .001). IGHV3-7 utilization was far less common in patients with PAD than in HCs (2 of 12 vs 10 of 11; P = .001). Amino acid substitutions due to somatic hypermutation per subfamily did not differ between groups. Anti-S1 IgM was present in 64% and 50% of HC and PAD cohorts, respectively, and did not differ significantly between HCs and patients with PAD.
    Conclusions: This study demonstrates the breadth of anti-S1 antibodies elicited by vaccination at the proteome level and identifies stereotypical restriction of IGHV utilization in the IgG repertoire in patients with PAD compared with HC subjects. Despite uniformly pauci-clonal antibody repertoires some patients with PAD generated potent serological responses, highlighting a possible limitation of traditional serological techniques. These findings suggest that IgG repertoire restriction is a key feature of antibody repertoires in PAD.
    MeSH term(s) Humans ; COVID-19 ; Amino Acid Substitution ; Biological Assay ; Primary Immunodeficiency Diseases ; Vaccination ; Immunoglobulin G ; Immunoglobulin M ; Antibodies, Viral
    Chemical Substances Immunoglobulin G ; Immunoglobulin M ; Antibodies, Viral
    Language English
    Publishing date 2023-03-23
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 121011-7
    ISSN 1097-6825 ; 1085-8725 ; 0091-6749
    ISSN (online) 1097-6825 ; 1085-8725
    ISSN 0091-6749
    DOI 10.1016/j.jaci.2023.02.033
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Basophil reactivity to BNT162b2 is mediated by PEGylated lipid nanoparticles in patients with PEG allergy.

    Troelnikov, Alexander / Perkins, Griffith / Yuson, Chino / Ahamdie, Aida / Balouch, Summaya / Hurtado, Plinio R / Hissaria, Pravin

    The Journal of allergy and clinical immunology

    2021  Volume 148, Issue 1, Page(s) 91–95

    Abstract: Background: The mechanisms underpinning allergic reactions to the BNT162b2 (Pfizer) COVID-19 vaccine remain unknown, with polyethylene glycol (PEG) contained in the lipid nanoparticle suspected as being the cause.: Objective: Our aim was to evaluate ... ...

    Abstract Background: The mechanisms underpinning allergic reactions to the BNT162b2 (Pfizer) COVID-19 vaccine remain unknown, with polyethylene glycol (PEG) contained in the lipid nanoparticle suspected as being the cause.
    Objective: Our aim was to evaluate the performance of skin testing and basophil activation testing to PEG, polysorbate 80, and the BNT162b2 (Pfizer) and AZD1222 (AstraZeneca) COVID-19 vaccines in patients with a history of PEG allergy.
    Methods: Three known individuals with PEG allergy and 3 healthy controls were recruited and evaluated for hypersensitivity to the BNT162b2 and AZD1222 vaccines, and to related compounds by skin testing and basophil activation, as measured by CD63 upregulation using flow cytometry.
    Results: We found that the BNT162b2 vaccine induced positive skin test results in patients with PEG allergy, whereas the result of traditional PEG skin testing was negative in 2 of 3 patients. One patient was found to be cosensitized to both the BNT162b2 and AZD1222 vaccines because of cross-reactive PEG and polysorbate allergy. The BNT162b2 vaccine, but not PEG alone, induced dose-dependent activation of all patients' basophils ex vivo. Similar basophil activation could be induced by PEGylated liposomal doxorubicin, suggesting that PEGylated lipids within nanoparticles, but not PEG in its native state, are able to efficiently induce degranulation.
    Conclusions: Our findings implicate PEG, as covalently modified and arranged on the vaccine lipid nanoparticle, as a potential trigger of anaphylaxis in response to BNT162b2, and highlight shortcomings of current skin testing protocols for allergy to PEGylated liposomal drugs.
    MeSH term(s) Adult ; Anaphylaxis/immunology ; Basophils/immunology ; COVID-19/immunology ; COVID-19 Vaccines/immunology ; Cell Degranulation ; Cells, Cultured ; Doxorubicin/adverse effects ; Doxorubicin/analogs & derivatives ; Doxorubicin/chemistry ; Drug Hypersensitivity/immunology ; Female ; Humans ; Lipids/chemistry ; Male ; Middle Aged ; Nanoparticles/adverse effects ; Nanoparticles/chemistry ; Polyethylene Glycols/adverse effects ; Polyethylene Glycols/chemistry ; SARS-CoV-2/physiology ; Skin Tests ; Young Adult
    Chemical Substances COVID-19 Vaccines ; Lipids ; liposomal doxorubicin ; Polyethylene Glycols (3WJQ0SDW1A) ; Doxorubicin (80168379AG) ; ChAdOx1 COVID-19 vaccine (B5S3K2V0G8) ; BNT162 vaccine (N38TVC63NU)
    Language English
    Publishing date 2021-05-12
    Publishing country United States
    Document type Journal Article
    ZDB-ID 121011-7
    ISSN 1097-6825 ; 1085-8725 ; 0091-6749
    ISSN (online) 1097-6825 ; 1085-8725
    ISSN 0091-6749
    DOI 10.1016/j.jaci.2021.04.032
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article: Virtual Global Transplant Laboratory Standard Operating Protocol for Donor Alloantigen-specific Interferon-gamma ELISPOT Assay.

    Carroll, Robert / Troelnikov, Alexander / Chong, Anita S

    Transplantation direct

    2016  Volume 2, Issue 11, Page(s) e111

    Abstract: The quantification of frequency of IFN-γ-producing T cells responding to donor alloantigen using the IFN-γ enzyme linked immunosorbent spot (ELISPOT) holds potential for pretransplant and posttransplant immunological risk stratification. The ... ...

    Abstract The quantification of frequency of IFN-γ-producing T cells responding to donor alloantigen using the IFN-γ enzyme linked immunosorbent spot (ELISPOT) holds potential for pretransplant and posttransplant immunological risk stratification. The effectiveness of this assay, and the ability to compare results generated by different studies, is dependent on the utilization of a standardized operating procedure (SOP). Key factors in assay standardization include the identification of primary and secondary antibody pairs, and the reading of the ELISPOT plate with a standardized automated algorithm. Here, we describe in detail, an SOP that should provide low coefficient of variation results. For multicenter trials, it is recommended that groups perform the ELISPOT assays locally but use a centralized ELISPOT reading facility, as this has been shown to be beneficial in reducing coefficient of variation between laboratories even when the SOP is strictly adhered to.
    Language English
    Publishing date 2016-10-20
    Publishing country United States
    Document type Journal Article
    ISSN 2373-8731
    ISSN 2373-8731
    DOI 10.1097/TXD.0000000000000621
    Database MEDical Literature Analysis and Retrieval System OnLINE

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