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This paper explores cut-offs in the oral narrations of Spanish native speakers in their mother tongue (L1), and in their language-under-study, English (L2). Fluency in the L2 varies with proficiency, and so cut-offs offer a possible means of evaluating this. However, there are certain aspects of fluency which might be common to the L1 and the L2, suggesting that the L1 and L2 share cognitive factors that lead to similar disfluency patterns. To determine if cut-offs are reliable markers of L2 fluency, independent of those occurring in the L1, we assessed the cut-off patterns in the L1 and L2 narrations of this group of speakers, following a multimodal approach. We observed similarities in both languages, potentially indicating that speakers use comparable cut-off-gesture patterns in the L1 and L2. We conclude that using speakers' cut-offs in an L2 to gauge proficiency is meaningful only if the L1 cut-off behaviour is known. [ABSTRACT FROM AUTHOR]

Alpha1-antitrypsin deficiency (AATD) is an inherited condition that predisposes individuals to an increased risk of developing lung and liver disease. Even though AATD is one of the most widespread inherited diseases in Caucasian populations, only a minority of affected individuals has been detected. Whereas methods have been validated for AATD testing, there is no universally-established algorithm for the detection and diagnosis of the disorder. In order to compare different methods for diagnosing AATD, we carried out a systematic review of the literature on AATD diagnostic algorithms. Complete biochemical and molecular analyses of 5,352 samples processed in our laboratory were retrospectively studied using each of the selected algorithms. When applying the diagnostic algorithms to the same samples, the frequency of False Negatives varied from 1.94 to 12.9%, the frequency of True Negatives was 62.91% for each algorithm and the frequency of True Positives ranged from 24.19 to 35.15%. We, therefore, highlighted some differences among Negative Predictive Values, ranging from 0.83 to 0.97. Accordingly, the sensitivity of each algorithm ranged between 0.61 and 0.95. We also postulated 1.108 g/L as optimal AAT cut-off value, in absence of inflammatory status, which points to the possible presence of genetic AATD. The choice of the diagnostic algorithm has a significant impact on the correct diagnosis of AATD, which is essential for appropriate treatment and medical care. The fairly large number of possible false negative diagnoses revealed by the present paper should also warn clinicians of negative results in patients with clinically-suspected AATD. [ABSTRACT FROM AUTHOR]