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Purpose: This research aims to analyze the relationship between emotional regulation and the symbolic process in autobiographical narratives of a group of individuals diagnosed with restrictive anorexia nervosa (AN), compared to a non-clinical group. The study is framed within multiple code theory (MCT) (Bucci, 1997; 2021), which considers mind-body integration. The purposes of this study are to investigate whether participants of the AN group will show greater alexithymia and emotional dysregulation than the non-clinical group; and whether the specific linguistic and symbolic features, such as somato-sensory words, affect words, and difficulty in the symbolizing process will predict the AN group., Methods: Twenty-nine female participants hospitalized with AN during an acute phase (mean age 19.8 ± 4.1) and 36 non-clinical female participants (mean age 21 ± 2.4) were selected through snow-ball sampling. The participants completed the Toronto Alexithymia Scale (TAS-20), the Profile of Mood of State (POMS), the Emotion Regulation Questionnaire (ERQ), and the Relationship Anecdotes Paradigm Interview (RAP). The RAP interview was audio-recorded and transcribed to apply the Referential Process (RP) Linguistic Measures. A T test for paired samples and a logistic binary regression was performed., Results: AN presented a significantly higher emotional dysregulation through the ERQ, TAS20 and POMS measures. Specifically, AN showed higher ER expression/suppression strategies, fewer functional cognitive strategies, higher alexithymia, and higher mood dysregulation. Specific linguistic features such as sensory-somatic, word affect, and difficulty in RP symbolizing predict the AN group (R2 = 0.349; χ2 = 27,929; df = 3; p = .001)., Conclusions: Emotional dysregulation is connected to AN symptoms and autobiographical narratives. The results can help a clinical assessment phase showing specific linguistic features in AN patients., Level of Evidence: Level II, controlled trial without randomization., (© 2022. The Author(s).)

Rapid Automatized Naming (RAN) is considered a universal marker of developmental dyslexia (DD) and could also be helpful to identify a reading deficit in minority-language children (MLC), in which it may be hard to disentangle whether the reading difficulties are due to a learning disorder or a lower proficiency in the language of instruction. We tested reading and rapid naming skills in monolingual Good Readers (mGR), monolingual Poor Readers (mPR), and MLC, by using our new version of RAN, the RAN-Shapes, in 127 primary school students (from 3rd to 5th grade). In line with previous research, MLC showed, on average, lower reading performances as compared to mGR. However, the two groups performed similarly to the RAN-Shapes task. On the contrary, the mPR group underperformed both in the reading and the RAN tasks. Our findings suggest that reading difficulties and RAN performance can be dissociated in MLC; consequently, the performance at the RAN-Shapes may contribute to the identification of children at risk of a reading disorder without introducing any linguistic bias, when testing MLC., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Carioti, Stucchi, Toneatto, Masia, Broccoli, Carbonari, Travellini, Del Monte, Riccioni, Marcelli, Vernice, Guasti and Berlingeri.)

Ovarian cancer (OC) is the most deadly gynecological malignancy worldwide. OC patients undergo debulking surgery followed by platinum/taxane-based chemotherapy; however, despite recent development of new therapeutic approaches based on combination of chemotherapy and innovative targeted-therapies, most of them relapse due to chemoresistance. Many studies have been carried out to decipher the high heterogeneity of ovarian cancer cells that drives tumor treatment failure. Here, we describe our experience in the characterization of ovarian cancer cell subsets through a high-resolution technology in multiparametric analysis, such as mass cytometry (MC)., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)