RESUMO
Perioperative neurocognitive dysfunction is a significant concern for population health, impacting postoperative recovery and increasing the financial burden on patients. With an increasing number of surgical procedures being performed, the prevention and management of perioperative neurocognitive dysfunction have garnered significant attention. While factors such as age, lifestyle, genetics, and education are known to influence the development of cognitive dysfunction, recent research has highlighted the role of the gut microbiota in neurological health. An increased abundance of pro-inflammatory gut microbiota can trigger and worsen neuroinflammation, neuronal cell damage, and impaired cellular autophagy. Moreover, the inflammation-promoting gut microbiota can disrupt immune function, impair neuroautophagy, and affect the production and circulation of extracellular vesicles and neurotransmitters. These factors collectively play a role in the onset and advancement of cognitive impairment. This narrative review delves into the molecular mechanisms through which gut microbiota and their derivatives contribute to cognitive impairment, focusing on the impact of anesthesia surgery, changes in gut microbial populations, and perioperative cognitive impairment associations. The study suggests that alterations in the abundance of various bacterial species and their metabolites pre- and post-surgery may be linked to postoperative cognitive impairment. Furthermore, the potential of probiotics or prebiotics in addressing cognitive impairment is discussed, offering a promising avenue for investigating the treatment of perioperative neurocognitive disorders.
RESUMO
Background: Conventionally, the judgment of whether small pulmonary nodules are invasive is mainly made by thoracic surgeons according to the chest computed tomography (CT) features of patients. However, there are limits to how much useful information can be obtained from this approach. A large number of feature information was extracted from CT images by CT radiomics. The machine learning algorithm was used to construct models based on radiomic characteristics to predict the invasiveness of lung adenocarcinoma (LUAD) with a good prediction accuracy. Methods: A total of 416 patients with pathologically confirmed preinvasive lesions and LUAD after video-assisted thoracoscopic surgery (VATS) in the Department of Thoracic Surgery of the First People's Hospital of Yunnan Province from February 2020 to February 2022 were retrospectively analyzed. According to random classification, patients were divided into 2 groups. The RadCloud platform was used to extract radiomics features, and the most relevant radiomics features were selected by continuous dimension reduction method. Then, 6 machine learning algorithms were used to establish and verify the prediction model of small lung nodular adenocarcinoma invasiveness. Receiver operating characteristic (ROC) curve and area under curve (AUC) were used to evaluate the predictive performance. Results: There were 78 cases of pre-invasive lesions and 226 cases of invasive lesions in the training group, and 34 cases of pre-invasive lesions and 78 cases of invasive lesions in the validation group. In the training group, the AUC values of the 6 models were all more than 0.914, the 95% confidence interval (CI) was 0.857-1.00, the sensitivity was equal or more than 0.87, and the specificity was equal or more than 0.85. In the validation group, the AUC values of the 6 models were all equal or more than 0.732, the 95% CI was 0.651-1.00, the sensitivity was equal or more than 0.7, and the specificity was more than 0.77. Conclusions: Machine learning algorithms were used to construct models to predict the invasiveness of small nodular LUAD based on radiomics features, which it could provide more evidence for doctors to make diagnoses and more personalized treatment plans for patients.
