Development and validation of a nomogram for predicting pulmonary complications in elderly patients undergoing thoracic surgery.

BACKGROUND: Postoperative pulmonary complications (PPCs) remain a prevalent concern among elderly patients undergoing surgery, with a notably higher incidence observed in elderly patients undergoing thoracic surgery. This study aimed to develop a nomogram to predict the risk of PPCs in this population. METHODS: A total of 2963 elderly patients who underwent thoracic surgery were enrolled and randomly divided into a training cohort (80%, n = 2369) or a validation cohort (20%, n = 593). Univariate and multivariate logistic regression analyses were conducted to identify risk factors for PPCs, and a nomogram was developed based on the findings from the training cohort. The validation cohort was used to validate the model. The predictive accuracy of the model was evaluated by receiver operating characteristic (ROC) curve, area under ROC (AUC), calibration curve, and decision curve analysis (DCA). RESULTS: A total of 918 (31.0%) patients reported PPCs. Nine independent risk factors for PPCs were identified: preoperative presence of chronic obstructive pulmonary disease (COPD), elevated leukocyte count, higher partial pressure of arterial carbon dioxide (PaCO2) level, surgical site, thoracotomy, intraoperative hypotension, blood loss > 100 mL, surgery duration > 180 min, and malignant tumor. The AUC value for the training cohort was 0.739 (95% CI: 0.719-0.762), and it was 0.703 for the validation cohort (95% CI: 0.657-0.749). The P-values for the Hosmer-Lemeshow test were 0.633 and 0.144 for the training and validation cohorts, respectively, indicating a notable calibration curve fit. The DCA curve indicated that the nomogram could be applied clinically if the risk threshold was between 12% and 84%, which was found to be between 8% and 82% in the validation cohort. CONCLUSION: This study highlighted the pressing need for early detection of PPCs in elderly patients undergoing thoracic surgery. The nomogram exhibited promising predictive efficacy for PPCs in elderly patients undergoing thoracic surgery, enabling the identification of high-risk patients and consequently aiding in the implementation of preventive interventions.

Tryptophan-rich diet and its effects on Htr7+ Tregs in alleviating neuroinflammation and cognitive impairment induced by lipopolysaccharide.

BACKGROUND: Neuroinflammation is a vital pathogenic mechanism for neurodegenerative diseases such as Alzheimer's, schizophrenia, and age-related cognitive decline. Regulatory T cells (Tregs) exhibit potent anti-inflammatory properties and can modulate neurodegenerative diseases arising from central nervous system inflammatory responses. However, the role of Tregs in neuroinflammation-related cognitive dysfunction remains unclear. It is highly plausible that Htr7+ Tregs expressing unique genes associated with the nervous system, including the Htr7 gene encoding the serotonin receptor 5-HT7, play a pivotal role. METHODS: Mice were given a tryptophan-rich diet (with a tryptophan content of 0.6%) or a normal diet (with a tryptophan content of 0.16%). The neuroinflammation-mediated cognitive dysfunction model was established by intracerebroventricular injection of lipopolysaccharide (LPS) in 8-week-old C57BL/6J mice. The activation and infiltration of Tregs were measured using flow cytometry. Primary Tregs were cocultured separately with primary CD8+ T cells and primary microglia for in vitro validation of the impact of 5-HT and 5-HT7 receptor on Tregs. Prior to their transfer into recombination activating gene 1 (Rag1-/-) mice, Tregs were ex vivo transfected with lentivirus to knock down the expression of Htr7. RESULTS: In this study, the tryptophan-rich diet was found to reverse LPS-induced cognitive impairment and reduce the levels of 5-HT in peripheral blood. The tryptophan-rich diet led to increased levels of 5-HT in peripheral blood, which in turn promoted the proliferation and activation of Htr7+ Tregs. Additionally, the tryptophan-rich diet was also shown to attenuate LPS-mediated neuroinflammation by activating Htr7+ Tregs. Furthermore, 5-HT and 5-HT7 receptor were found to enhance the immunosuppressive effect of Tregs on CD8+ T cells and microglia. In Rag1-/- mice, Htr7+ Tregs were shown to alleviate LPS-induced neuroinflammation and cognitive impairment. CONCLUSIONS: Our research revealed the ability of Htr7+ Tregs to mitigate neuroinflammation and prevent neuronal damage by suppressing the infiltration of CD8+ T cells into the brain and excessive activation of microglia, thereby ameliorating LPS-induced cognitive impairment. These insights may offer novel therapeutic targets involving Tregs for neuroinflammation and cognitive impairment.

Risk Factor Analysis and a Predictive Model of Postoperative Depressive Symptoms in Elderly Patients Undergoing Video-Assisted Thoracoscopic Surgery.

Among the elderly, depression is one of the most common mental disorders, which seriously affects their physical and mental health and quality of life, and their suicide rate is particularly high. Depression in the elderly is strongly associated with surgery. In this study, we aimed to explore the risk factors and establish a predictive model of depressive symptoms 1 month after video-assisted thoracoscopic surgery (VATS) in elderly patients. The study participants included 272 elderly patients (age > 65 years) undergoing VATS from April 2020 to May 2021 at 1 of 18 medical centers in China. The patients were divided into a depression group and a nondepression group according to the Chinese version of the nine-item Patient Health Questionnaire (PHQ-9). The patients' pre- and postoperative characteristics and questionnaires were collected and compared. Then, binary logistic regression was used to determine the risk factors that affect postoperative depressive symptoms, and the predictive model was constructed. The prediction efficiency of the model was evaluated by drawing the receiver operating characteristic curve (ROC), and the area under the curve (AUC) was calculated to evaluate the value of the predictive model. Among all of the included patients, 16.54% (45/272) suffered from depressive symptoms after VATS. The results of the univariate analysis showed that body mass index (BMI), chronic pain, leukocyte count, fibrinogen levels, prothrombin time, ASA physical status, infusion volume, anxiety, sleep quality, and postoperative pain were related to postoperative depressive symptoms (all p < 0.05). The results of multivariate logistic regression analysis showed that a high fibrinogen level (OR = 2.42), postoperative anxiety (OR = 12.05), poor sleep quality (OR = 0.61), and pain (OR = 2.85) were risk factors of postoperative depressive symptoms. A predictive model was constructed according to the regression coefficient of each variable, the ROC curve was drawn, and the AUC value was calculated to be 0.889. The prediction model may help medical personnel identify older patients at risk of developing depressive disorders associated with VATS and may be useful for clinical purposes.

Small-Data-Driven Temporal Convolutional Capsule Network for Locomotion Mode Recognition of Robotic Prostheses.

Locomotion mode recognition has been shown to substantially contribute to the precise control of robotic lower-limb prostheses under different walking conditions. In this study, we proposed a temporal convolutional capsule network (TCCN) which integrates the spatial-temporal-based, dilation-convolution-based, dyna- mic routing and vector-based features for recognizing locomotion mode recognition with small data rather than big-data-based neural networks for robotic prostheses. TCCN proposed in this study has four characteristics, which extracts the (1) spatial-temporal information in the data and then makes (2) dilated convolution to deal with small data, and uses (3) dynamic routing, which produces some similarities to the human brain to process the data as a (4) vector, which is different from other scalar-based networks, such as convolutional neural network (CNN). By comparison with a traditional machine learning, e.g., support vector machine(SVM) and big-data-driven neural networks, e.g., CNN, recurrent neural network(RNN), temporal convolutional network(TCN) and capsule network(CN). The accuracy of TCCN is 4.1% higher than CNN under 5-fold cross-validation of three-locomotion-mode and 5.2% higher under the 5-fold cross-validation of five-locomotion modes. The main confusion we found appears in the transition state. The results indicate that TCCN may handle small data balancing global and local information which is closer to the way how the human brain works, and the capsule layer allows for better processing vector information and retains not only magnitude information, but also direction information.