Background Violent behavior in patients with schizophrenia (SCZ) is a major social problem. The early identification of SCZ patients with violence can facilitate implementation of targeted intervention. Methods A total of 57 male SCZ patients were recruited into this study. The general linear model was utilized to compare differences in structural magnetic resonance imaging (sMRI) including gray matter volume, cortical surface area, and cortical thickness between 30 SCZ patients who had exhibited violence and 27 SCZ patients without a history of violence. Based on machine learning algorithms, the different sMRI features between groups were integrated into the models for prediction of violence in SCZ patients. Results After controlling for the whole brain volume and age, the general linear model showed significant reductions in right bankssts thickness, inferior parietal thickness as well as left frontal pole volume in the patients with SCZ and violence relative to those without violence. Among seven machine learning algorithms, Support Vector Machine (SVM) have better performance in differentiating patients with violence from those without violence, with its balanced accuracy and area under curve (AUC) reaching 0.8231 and 0.841, respectively. Conclusions Patients with SCZ who had a history of violence displayed reduced cortical thickness and volume in several brain regions. Based on machine learning algorithms, structural MRI features are useful to improve predictive ability of SCZ patients at particular risk of violence.
The genetic events occurring in recurrent nasopharyngeal carcinoma (rNPC) are poorly understood. Here, we performed whole-genome and whole-exome sequencing in 55 patients with rNPC and 44 primarily diagnosed NPC (pNPC), with 7 patients having paired rNPC and pNPC samples. Previously published pNPC exome data were integrated for analysis. rNPC and pNPC tissues had similar mutational burdens, however, the number of clonal mutations was increased in rNPC samples. TP53 and three NF-kB pathway components (TRAF3, CYLD, and NFKBIA) were significantly mutated in both pNPC and rNPC. Notably, mutations in TRAF3, CYLD, and NFKBIA were all clonal in rNPC, however, 55.6% to 57.9% of them were clonal in pNPC. In general, the number of clonal mutations in NF-kB pathway-associated genes was significantly higher in rNPC than in pNPC. The NF-kB mutational clonality was selected and/or enriched during NPC recurrence. The amount of NF-kB translocated to the nucleus in samples with clonal NF-kB mutants was significantly higher than that in samples with subclonal NF-kB mutants. Moreover, the nuclear abundance of NF-kB protein was significantly greater in pNPC samples with locoregional relapse than in those without relapse. Furthermore, high nuclear NF-kB levels were an independent negative prognostic marker for locoregional relapsefree survival in pNPC. Finally, inhibition of NF-kB enhanced both radiosensitivity and chemosensitivity in vitro and in vivo.In conclusion, NF-kB pathway activation by clonal mutations plays an important role in promoting the recurrence of NPC. Moreover, nuclear accumulation of NF-kB is a prominent biomarker for predicting locoregional relapse-free survival.Significance: This study uncovers genetic events that promote the progression and recurrence of nasopharyngeal carcinoma and has potential prognostic and therapeutic implications.See related commentary by Sehgal and Barbie, p. 5915
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