Eculizumab in paraxysmal nocturnal haemogloburinia and atypical haemolytic syndrome 10-year pharmacovigilance analysis

Eculizumab is the first and only medication approved for paroxysmal nocturnal haemoglobinuria (PNH) and atypical haemolytic uraemic syndrome (aHUS) treatment. However, eculizumab safety based on long‐term pharmacovigilance is unknown. This analysis summarises safety data collected from spontaneous and solicited sources from 16 March 2007 through 1 October 2016. Cumulative exposure to eculizumab was 28 518 patient‐years (PY) (PNH, 21 016 PY; aHUS, 7502 PY). Seventy‐six cases of meningococcal infection were reported (0·25/100 PY), including eight fatal PNH cases (0·03/100 PY). Susceptibility to meningococcal infections remained the key risk in patients receiving eculizumab. The meningococcal infection rate decreased over time; related mortality remained steady. The most commonly reported serious nonmeningococcal infections were pneumonia (11·8%); bacteraemia, sepsis and septic shock (11·1%); urinary tract infection (4·1%); staphylococcal infection (2·6%); and viral infection (2·5%). There were 434 reported cases of eculizumab exposure in pregnant women; of 260 cases with known outcomes, 70% resulted in live births. Reporting rates for solid tumours (≈0·6/100 PY) and haematological malignancies (≈0·74/100 PY) remained stable over time. No new safety signals affecting the eculizumab benefit‐risk profile were identified. Continued awareness and implementation of risk mitigation protocols are essential to minimise risk of meningococcal and other Neisseria infections in patients receiving eculizumab

The processing of pseudoword form and meaning in production and comprehension: A computational modeling approach using linear discriminative learning

Pseudowords have long served as key tools in psycholinguistic investigations of the lexicon. A common assumption underlying the use of pseudowords is that they are devoid of meaning: Comparing words and pseudowords may then shed light on how meaningful linguistic elements are processed differently from meaningless sound strings. However, pseudowords may in fact carry meaning. On the basis of a computational model of lexical processing, linear discriminative learning (LDL Baayen et al., Complexity, 2019, 1-39, 2019), we compute numeric vectors representing the semantics of pseudowords. We demonstrate that quantitative measures gauging the semantic neighborhoods of pseudowords predict reaction times in the Massive Auditory Lexical Decision (MALD) database (Tucker et al., 2018). We also show that the model successfully predicts the acoustic durations of pseudowords. Importantly, model predictions hinge on the hypothesis that the mechanisms underlying speech production and comprehension interact. Thus, pseudowords emerge as an outstanding tool for gauging the resonance between production and comprehension. Many pseudowords in the MALD database contain inflectional suffixes. Unlike many contemporary models, LDL captures the semantic commonalities of forms sharing inflectional exponents without using the linguistic construct of morphemes. We discuss methodological and theoretical implications for models of lexical processing and morphological theory. The results of this study, complementing those on real words reported in Baayen et al., (Complexity, 2019, 1-39, 2019), thus provide further evidence for the usefulness of LDL both as a cognitive model of the mental lexicon, and as a tool for generating new quantitative measures that are predictive for human lexical processing