Machine Learning-Based Prediction of Intraoperative Red Blood Cell Transfusion in Aortic Valve Replacement Surgery.

BACKGROUND: Blood shortage is a global challenge, impacting elective surgeries with high bleeding risk. Predicting intraoperative blood use, optimizing resource allocation, and ensuring safe elective surgery are vital. This study targets identifying key bleeding risk factors in Aortic Valve Replacement (AVR) through machine learning. METHODS: Data from 702 AVR patients were split into 70% training and 30% test sets. Thirteen models predicted RBC transfusion. SHapley Additive exPlanations (SHAP) analyzed risk factors. RESULTS: Logistic Regression excelled, with Area Under Curve (AUC) 0.872 and 81.0% accuracy on the test set. Notably, female gender, Hemoglobin (HGB) < 131.91 g/L, Hematocrit (HCT) < 0.41L/L, weight < 59.49 kg, age > 54.47 year, Mean Corpuscular Hemoglobin (MCH) < 29.15 pg, Total Protein (TP) > 69.7 g/L, FIB > 2.61 g/L, height < 160 cm, and type of operation is Surgical Aortic Valve Replacement (SAVR) were significant RBC transfusion predictors. CONCLUSIONS: The study's model accurately forecasts AVR-related RBC transfusions. This informs presurgery blood preparations, reducing resource waste and aiding clinicians in optimizing patient care.

Increased adipose tissue is associated with improved overall survival, independent of skeletal muscle mass in non-small cell lung cancer.

BACKGROUND: The prognostic significance of non-cancer-related prognostic factors, such as body composition, has gained extensive attention in oncological research. Compared with sarcopenia, the prognostic significance of adipose tissue for overall survival in non-small cell lung cancer remains unclear. We investigated the prognostic value of skeletal muscle and adipose tissue in patients with non-small cell lung cancer. METHODS: This retrospective study included 4434 patients diagnosed with non-small cell lung cancer between January 2014 and December 2016. Cross-sectional areas of skeletal muscle and subcutaneous fat were measured, and the pericardial fat volume was automatically calculated. The skeletal muscle index and subcutaneous fat index were calculated as skeletal muscle area and subcutaneous fat area divided by height squared, respectively, and the pericardial fat index was calculated as pericardial fat volume divided by body surface area. The association between body composition and outcomes was evaluated using Cox proportional hazards model. RESULTS: A total of 750 patients (501 males [66.8%] and 249 females [33.2%]; mean age, 60.9 ± 9.8 years) were included. Sarcopenia (60.8% vs. 52.7%; P < 0.001), decreased subcutaneous fat index (51.4% vs. 25.2%; P < 0.001) and decreased pericardial fat index (55.4% vs. 16.5%; P < 0.001) were more commonly found in the deceased group than survived group. In multivariable Cox regression analysis, after adjusting for clinical variables, increased subcutaneous fat index (hazard ratio [HR] = 0.56, 95% confidence interval [CI]: 0.47-0.66, P < 0.001) and increased pericardial fat index (HR = 0.47, 95% CI: 0.40-0.56, P < 0.001) were associated with longer overall survival. For stage I-III patients, increased subcutaneous fat index (HR = 0.62, 95% CI: 0.48-0.76, P < 0.001) and increased pericardial fat index (HR = 0.43, 95% CI: 0.34-0.54, P < 0.001) were associated with better 5-year overall survival rate. Similar results were recorded in stage IV patients. For patients with surgery, the prognostic value of increased subcutaneous fat index (HR = 0.60, 95% CI: 0.44-0.80, P = 0.001) and increased pericardial fat index (HR = 0.51, 95% CI: 0.38-0.68, P < 0.001) remained and predicted favourable overall survival. Non-surgical patients showed similar results as surgical patients. No association was noted between sarcopenia and overall survival (P > 0.05). CONCLUSIONS: Increased subcutaneous fat index and pericardial fat index were associated with a higher 5-year overall survival rate, independent of sarcopenia, in non-small cell lung cancer and may indicate a reduced risk of non-cancer-related death.

3D convolutional neural network model from contrast-enhanced CT to predict spread through air spaces in non-small cell lung cancer.

PURPOSE: The purpose of this study was to compare the efficacy of five non-invasive models, including three-dimensional (3D) convolutional neural network (CNN) model, to predict the spread through air spaces (STAS) status of non-small cell lung cancer (NSCLC), and to obtain the best prediction model to provide a basis for clinical surgery planning. MATERIALS AND METHODS: A total of 203 patients (112 men, 91 women; mean age, 60 years; age range 22-80 years) with NSCLC were retrospectively included. Of these, 153 were used for training cohort and 50 for validation cohort. According to the image biomarker standardization initiative reference manual, the image processing and feature extraction were standardized using PyRadiomics. The logistic regression classifier was used to build the model. Five models (clinicopathological/CT model, conventional radiomics model, computer vision (CV) model, 3D CNN model and combined model) were constructed to predict STAS by NSCLC. Area under the receiver operating characteristic curves (AUC) were used to validate the capability of the five models to predict STAS. RESULTS: For predicting STAS, the 3D CNN model was superior to the clinicopathological/CT model, conventional radiomics model, CV model and combined model and achieved satisfactory discrimination performance, with an AUC of 0.93 (95% CI: 0.70-0.82) in the training cohort and 0.80 (95% CI: 0.65-0.86) in the validation cohort. Decision curve analysis indicated that, when the probability of the threshold was over 10%, the 3D CNN model was beneficial for predicting STAS status compared to either treating all or treating none of the patients within certain ranges of risk threshold CONCLUSION: The 3D CNN model can be used for the preoperative prediction of STAS in patients with NSCLC, and was superior to the other four models in predicting patients' risk of developing STAS.

Intermediate metabolites of the pyrimidine metabolism pathway extend the lifespan of C. elegans through regulating reproductive signals.

The pyrimidine metabolism pathway has important biological functions; it not only maintains appropriate pyrimidine pools but also produces bioactive intermediate metabolites. In a previous study, we identified that the pyrimidine metabolism pathway is associated with aging regulation. However, the molecular mechanism by which the pyrimidine metabolism pathway regulates aging remains unclear. Here, we investigated the longevity effect of pyrimidine intermediates on Caenorhabditis elegans (C. elegans). Our results demonstrated that the supplementation of some pyrimidine intermediates could extend the lifespan of C. elegans. In addition, the RNAi knockdown of essential enzymes involved in pyrimidine metabolism could also significantly affect lifespan. We further investigated the molecular mechanism by which a representative intermediate metabolite, thymine, extends the lifespan of worms and found that thymine-induced longevity required the nuclear receptors DAF-12 and NHR-49, and the transcription factor DAF-16/FOXO. Further pathway analysis revealed that the longevity effect of thymine depended on the inhibition of reproductive signals. Additionally, we found that other pyrimidine intermediates functioned in a manner similar to thymine to prolong lifespan in C. elegans. Taken together, our results revealed that pyrimidine intermediates increased lifespan by inhibiting reproductive signals and subsequently inducing the function of DAF-12, NHR-49 and DAF-16 in C. elegans.

Ganoderma lucidum polysaccharide improves rat DSS-induced colitis by altering cecal microbiota and gene expression of colonic epithelial cells.

BACKGROUND: The effects of ß-glucan on colitis mice are contradictory in previous reports. As a result, it is still unclear whether there is an anti-colitis effect in Ganoderma lucidum polysaccharide (GLP), which is mainly composed of ß-glucan. Moreover, the association between GLP function and gut microbiota remains to be elucidated. OBJECTIVE: This study aimed to investigate whether GLP consumption improved rat dextran sodium sulfate (DSS)-induced colitis by regulating gut microbiota and altering colonic epithelial expression. DESIGN: The disease activity index (DAI) scores and the cecal short chain fatty acid (SCFA) levels of DSS-induced colitis rats fed with a GLP diet (Group GLP, n = 6) and a control diet (Group Con, n = 6) were investigated and analyzed. Moreover, the profiles of gut microbiota and colonic epithelial expression were analyzed using metagenomics and transcriptomics. RESULTS: GLP consumption significantly lowered animal DAI scores by producing more SCFAs by increasing SCFA-producing bacteria such as Ruminococcus_1 and reducing pathogens such as Escherichia-Shigella in both the small intestine and cecum of rat. Moreover, GLP consumption regulated 11 genes, including six upregulated (Ccl5, Cd3e, Cd8a, Il21r, Lck, and Trbv) and five downregulated (Ccl3, Gro, Il11, Mhc2, and Ptgs) genes enriched in six inflammation-related Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, resulting in enhancement of immunity and reduction of inflammatory response and colonic cancer risk. CONCLUSIONS: GLP consumption alleviated DSS-induced colitis and may have potential for ulcerative colitis relief.