RESUMO
OBJECTIVE: Addiction is a serious spiral where negative events or relationships trigger a craving even when the situation is caused by the addiction in the first place. Nucleus accumbens is identified as an important hub for the neural pathways involved in the addictive behavior. Stimulation of this structure was demonstrated to be beneficial for addiction previously, but radioneuromodulation was never investigated until today. This study aimed to investigate if radioneuromodulation of the nucleus accumbens has any effect on alcohol addiction. METHODS: An addiction model was used on 36 Long-Evans rats (18 females/18 males), via a 2-bottle intermittent access protocol, and the trial group received 100 Gy of gamma irradiation to their bilateral nucleus accumbens. Rats were followed up for an additional 15 weeks. Multiple sets of a behavioral test battery, a 4-week abstinence period, and quinine adulteration challenges were used to evaluate responses. RESULTS: The experiment showed that the intervention reduced alcohol preference in the presence of aversive stimuli in female rats, compared with the nonirradiated control rats, because the trial group showed a 9.83-point decrease in alcohol preference rate under high-dose quinine adulteration compared with baseline, whereas the control group did not show any decrease. There were also implications of additional benefits regarding weight control in females and behavioral tests in males. No evident adverse effect was observed with the treatment. CONCLUSIONS: This study indicates that nucleus accumbens radioneuromodulation, although not significantly affecting baseline consumption, reduces intake when an aversive stimulus is involved, implying improved self-control.
RESUMO
BACKGROUND: Vestibular schwannomas (VSs) are benign tumors often monitored over time, with measurement techniques for assessing growth rates subject to significant interobserver variability. Automatic segmentation of these tumors could provide a more reliable and efficient for tracking their progression, especially given the irregular shape and growth patterns of VS. METHODS: Various studies and segmentation techniques employing different Convolutional Neural Network architectures and models, such as U-Net and convolutional-attention transformer segmentation, were analyzed. Models were evaluated based on their performance across diverse datasets, and challenges, including domain shift and data sharing, were scrutinized. RESULTS: Automatic segmentation methods offer a promising alternative to conventional measurement techniques, offering potential benefits in precision and efficiency. However, these methods are not without challenges, notably the "domain shift" that occurs when models trained on specific datasets underperform when applied to different datasets. Techniques such as domain adaptation, domain generalization, and data diversity were discussed as potential solutions. CONCLUSIONS: Accurate measurement of VS growth is a complex process, with volumetric analysis currently appearing more reliable than linear measurements. Automatic segmentation, despite its challenges, offers a promising avenue for future investigation. Robust well-generalized models could potentially improve the efficiency of tracking tumor growth, thereby augmenting clinical decision-making. Further work needs to be done to develop more robust models, address the domain shift, and enable secure data sharing for wider applicability.