To explore the mechanism of tobacco addiction using structural and functional MRI: a preliminary study of the role of the cerebellum-striatum circuit.

Previous studies have found that the striatum and the cerebellum played important roles in nicotine dependence, respectively. In heavy smokers, however, the effect of resting-state functional connectivity of cerebellum-striatum circuits in nicotine dependence remained unknown. This study aimed to explore the role of the circuit between the striatum and the cerebellum in addiction in heavy smokers using structural and functional magnetic resonance imaging. The grey matter volume differences and the resting-state functional connectivity differences in cerebellum-striatum circuits were investigated between 23 heavy smokers and 23 healthy controls. The cigarette dependence in heavy smokers and healthy controls were evaluated by using Fagerström Test. Then, we applied mediation analysis to test whether the resting-state functional connectivity between the striatum and the cerebellum mediates the relationship between the striatum morphometry and the nicotine dependence in heavy smokers. Compared with healthy controls, the heavy smokers' grey matter volumes decreased significantly in the cerebrum (bilateral), and increased significantly in the caudate (bilateral). Seed-based resting-state functional connectivity analysis showed significantly higher resting-state functional connectivity among the bilateral caudate, the left cerebellum, and the right middle temporal gyrus in heavy smokers. The cerebellum-striatum resting-state functional connectivity fully mediated the relationship between the striatum morphometry and the nicotine dependence in heavy smokers. Heavy smokers showed abnormal interactions and functional connectivity between the striatum and the cerebellum, which were associated with the striatum morphometry and nicotine dependence. Such findings could provide new insights into the neural correlates of nicotine dependence in heavy smokers.

Deep learning radiomic analysis of DCE-MRI combined with clinical characteristics predicts pathological complete response to neoadjuvant chemotherapy in breast cancer.

Objective: The aim of this study was to develop and validate a deep learning-based radiomic (DLR) model combined with clinical characteristics for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer. For early prediction of pCR, the DLR model was based on pre-treatment and early treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data. Materials and methods: This retrospective study included 95 women (mean age, 48.1 years; range, 29-77 years) who underwent DCE-MRI before (pre-treatment) and after two or three cycles of NAC (early treatment) from 2018 to 2021. The patients in this study were randomly divided into a training cohort (n=67) and a validation cohort (n=28) at a ratio of 7:3. Deep learning and handcrafted features were extracted from pre- and early treatment DCE-MRI contoured lesions. These features contribute to the construction of radiomic signature RS1 and RS2 representing information from different periods. Mutual information and least absolute shrinkage and selection operator regression were used for feature selection. A combined model was then developed based on the DCE-MRI features and clinical characteristics. The performance of the models was assessed using the area under the receiver operating characteristic curve (AUC) and compared using the DeLong test. Results: The overall pCR rate was 25.3% (24/95). One radiomic feature and three deep learning features in RS1, five radiomic features and 11 deep learning features in RS2, and five clinical characteristics remained in the feature selection. The performance of the DLR model combining pre- and early treatment information (AUC=0.900) was better than that of RS1 (AUC=0.644, P=0.068) and slightly higher that of RS2 (AUC=0.888, P=0.604) in the validation cohort. The combined model including pre- and early treatment information and clinical characteristics showed the best ability with an AUC of 0.925 in the validation cohort. Conclusion: The combined model integrating pre-treatment, early treatment DCE-MRI data, and clinical characteristics showed good performance in predicting pCR to NAC in patients with breast cancer. Early treatment DCE-MRI and clinical characteristics may play an important role in evaluating the outcomes of NAC by predicting pCR.

Diversity of the T cell receptor ß chain complementarity-determining region 3 in peripheral blood of neonates with sepsis: an analysis based on immune repertoire sequencing. / å ç–«ç»„åº“æµ‹åºåˆ†æžæ–°ç”Ÿå„¿è„“æ¯’ç—‡æ‚£è€ å¤–å‘¨è¡€T细胞受体ß链CDR3çš„å¤šæ ·æ€§.

OBJECTIVES: To investigate the diversity of peripheral blood T cell receptor (TCR) ß chain complementarity-determining region 3 (CDR3) based on immune repertoire sequencing in neonates with sepsis and the possible pathogenesis of neonatal sepsis. METHODS: A total of 12 neonates with sepsis were enrolled as the case group, and 9 healthy full-term infants, matched for gestational age, birth weight, and age, were enrolled as the control group. Omega nucleic acid purification kit (SQ blood DNA Kit II) was used to extract DNA from peripheral blood samples, TCR ß chain CDR3 was amplified by multiplex PCR, and then high-throughput sequencing was performed for the products to analyze the diversity of TCR ß chain CDR3 and the difference in expression. RESULTS: The length and type of TCR ß chain CDR3 were similar between the case and control groups, and Gaussian distribution was observed in both groups. With D50 and Shannon-Wiener index as the evaluation indices for diversity, the case group had a significantly lower diversity of TCR ß chain CDR3 than the control group (P<0.05). The frequency of 48 genes in TCR ß chain V segment was compared, and the results showed that compared with the control group, the case group had significantly higher frequencies of TRBV10-3, TRBV2, and TRBV20-1 (P<0.05). The frequency of 13 genes in TCR ß chain J segment were compared, and the results showed that compared with the control group, the case group had significantly higher frequencies of TRBJ2-3, TRBJ2-5, and TRBJ2-7 (P<0.05). CONCLUSIONS: There is a significant change in the diversity of TCR ß chain CDR3 in the peripheral blood of neonates with sepsis, suggesting that it might be associated with the immune pathogenesis of neonatal sepsis.