Bedaquiline and clofazimine resistance in Mycobacterium tuberculosis: an in-vitro and in-silico data analysis

Autor: Sonnenkalb, L, Carter, JJ, Spitaleri, A, Iqbal, Z, Hunt, M, Malone, KM, Utpatel, C, Cirillo, DM, Rodrigues, C, Nilgiriwala, KS, Fowler, PW, Merker, M, Niemann, S, Barilar, I, Battaglia, S, Borroni, E, Brandao, AP, Brankin, A, Cabibbe, AM, Carter, J, Claxton, P, Clifton, DA, Cohen, T, Coronel, J, Crook, DW, Dreyer, V, Earle, SG, Escuyer, V, Ferrazoli, L, Fu Gao, G, Gardy, J, Gharbia, S, Ghisi, KT, Ghodousi, A, Gibertoni Cruz, AL, Grandjean, L, Grazian, C, Groenheit, R, Guthrie, JL, He, W, Hoffmann, H, Hoosdally, SJ, Ismail, NA, Jarrett, L, Joseph, L, Jou, R, Kambli, P, Khot, R, Knaggs, J, Koch, A, Kohlerschmidt, D, Kouchaki, S, Lachapelle, AS, Lalvani, A, Grandjean Lapierre, S, Laurenson, IF, Letcher, B, Lin, W-H, Liu, C, Liu, D, Mandal, A, Mansjö, M, Matias, D, Meintjes, G, De Freitas Mendes, F, Mihalic, M, Millard, J, Miotto, P, Mistry, N, Moore, D, Musser, KA, Ngcamu, D, Hoang, NN, Nimmo, C, Okozi, N, Oliveira, RS, Omar, SV, Paton, N, Peto, TEA, Watanabe Pinhata, JM, Plesnik, S, Puyen, ZM, Rabodoarivelo, MS, Rakotosamimanana, N, Rancoita, PMV, Rathod, P, Rodger, G, Rodwell, TC, Roohi, E, Santos-Lazaro, D, Shah, S, Kohl, TA, Smith, G, Solano, W, Supply, P, Surve, U, Tahseen, S, Thuong, NTT, Thwaites, G, Todt, K, Trovato, A, Van Rie, A, Vijay, S, Walker, TM, Walker, SA, Warren, R, Werngren, J, Wijkander, M, Wilkinson, RJ, Wilson, DJ, Wintringer, P, Yu, XX, Yang, Y, Zhao, Y, Yao, S-Y, Zhu, B

Publikováno v: e368
e358

Background Bedaquiline is a core drug for the treatment of multidrug-resistant tuberculosis; however, the understanding of resistance mechanisms is poor, which is hampering rapid molecular diagnostics. Some bedaquiline-resistant mutants are also cros

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c5d2c6f478af9f451ce02ba6c6ec516b
https://doi.org/10.1016/s2666-5247(23)00002-2

Global SARS-CoV-2 seroprevalence from January 2020 to April 2022: a systematic review and meta-analysis of standardized population-based studies

Autor: Bergeri, I, Whelan, MG, Ware, H, Subissi, L, Nardone, A, Lewis, HC, Li, Z, Ma, X, Valenciano, M, Cheng, B, Al Ariqi, L, Rashidian, A, Okeibunor, J, Azim, T, Wijesinghe, P, Le, L-V, Vaughan, A, Pebody, R, Vicari, A, Yan, T, Yanes-Lane, M, Cao, C, Clifton, DA, Cheng, MP, Papenburg, J, Buckeridge, D, Bobrovitz, N, Arora, RK, Van Kerkhove, MD

Background Our understanding of the global scale of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection remains incomplete: Routine surveillance data underestimate infection and cannot infer on population immunity; there is a predo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9bad2e23a8f50188ef4d3254fef71d58
https://doi.org/10.1371/journal.pmed.1004107

Dynamic inter-treatment information sharing for heterogeneous treatment effects estimation

Autor: Chauhan, VK, Zhou, J, Molaei, S, Ghosheh, G, Clifton, DA

Existing heterogeneous treatment effects learners, also known as conditional average treatment effects (CATE) learners, lack a general mechanism for end-to-end inter-treatment information sharing, and data have to be split among potential outcome fun

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7ad5a16f66de7d884cec9a5d7a74daaa
https://ora.ox.ac.uk/objects/uuid:671eccff-143a-45dd-b78b-5220f435869f

An adversarial training framework for mitigating algorithmic biases in clinical machine learning

Autor: Yang, J, Soltan, AAS, Eyre, DW, Yang, Y, Clifton, DA

Publikováno v: npj Digital Medicine. 6

Machine learning is becoming increasingly prominent in healthcare. Although its benefits are clear, growing attention is being given to how these tools may exacerbate existing biases and disparities. In this study, we introduce an adversarial trainin

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::abe2e94f0a94577707129dc938bf13fa
https://doi.org/10.1038/s41746-023-00805-y

Continuous Patient State Attention Models

Autor: Chauhan, VK, Rohanian, O, Thakur, A, O’Donoghue, O, Clifton, DA

Irregular time-series (ITS) are prevalent in the electronic health records (EHR) as the data is recorded in EHR system as per the clinical guidelines/requirements but not for research and also depends on the patient health status. ITS present challen

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c306be9b496cb3079ea71eee4b0bc914
https://doi.org/10.1101/2022.12.23.22283908