OBJECTIVE: To employ machine learning (ML) and stacked ensemble learning (EL) methods in predicting short- and long-term mortality in Asian patients diagnosed with NSTEMI/UA and to identify the associated features, subsequently evaluating these findings against established risk scores.
METHODS: We analyzed data from the National Cardiovascular Disease Database for Malaysia (2006-2019), representing a diverse NSTEMI/UA Asian cohort. Algorithm development utilized in-hospital records of 9,518 patients, 30-day data from 7,133 patients, and 1-year data from 7,031 patients. This study utilized 39 features, including demographic, cardiovascular risk, medication, and clinical features. In the development of the stacked EL model, four base learner algorithms were employed: eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF), with the Generalized Linear Model (GLM) serving as the meta learner. Significant features were chosen and ranked using ML feature importance with backward elimination. The predictive performance of the algorithms was assessed using the area under the curve (AUC) as a metric. Validation of the algorithms was conducted against the TIMI for NSTEMI/UA using a separate validation dataset, and the net reclassification index (NRI) was subsequently determined.
RESULTS: Using both complete and reduced features, the algorithm performance achieved an AUC ranging from 0.73 to 0.89. The top-performing ML algorithm consistently surpassed the TIMI risk score for in-hospital, 30-day, and 1-year predictions (with AUC values of 0.88, 0.88, and 0.81, respectively, all p < 0.001), while the TIMI scores registered significantly lower at 0.55, 0.54, and 0.61. This suggests the TIMI score tends to underestimate patient mortality risk. The net reclassification index (NRI) of the best ML algorithm for NSTEMI/UA patients across these periods yielded an NRI between 40-60% (p < 0.001) relative to the TIMI NSTEMI/UA risk score. Key features identified for both short- and long-term mortality included age, Killip class, heart rate, and Low-Molecular-Weight Heparin (LMWH) administration.
CONCLUSIONS: In a broad multi-ethnic population, ML approaches outperformed conventional TIMI scoring in classifying patients with NSTEMI and UA. ML allows for the precise identification of unique characteristics within individual Asian populations, improving the accuracy of mortality predictions. Continuous development, testing, and validation of these ML algorithms holds the promise of enhanced risk stratification, thereby revolutionizing future management strategies and patient outcomes.
Methods: The cytotoxicity of the Ligno TG-K against human breast (MCF7), prostate (PC3) and lung (A549) adenocarcinoma cell lines was evaluated using MTT cytotoxicity assay. The cytotoxic mechanisms of the active high molecular weight proteins (HMWp) fraction were investigated through detection of caspases activity and apoptotic-related proteins expression by Western blotting. The in vivo antitumor activity of the isolated HMWp was examined using MCF7 mouse xenograft model. Shotgun LC-MS/MS analysis was performed to identify the proteins in the HMWp.
Results and Discussion: Cold water extract of the sclerotia inhibited proliferation of MCF7, A549 and PC3 cells with IC50 ranged from 28.9 to 95.0 µg/mL. Bioassay guided fractionation of the extract revealed that HMWp exhibited selective cytotoxicity against MCF7 cells via induction of cellular apoptosis by the activation of extrinsic and intrinsic signaling pathways. HMWp activated expression of caspase-8 and -9 enzymes, and pro-apoptotic Bax protein whilst inhibiting expression of tumor survivor protein, Bcl-2. HMWp induced tumor-cell apoptosis and suppressed growth of tumor in MCF-7 xenograft mice. Lectins, serine proteases, RNase Gf29 and a 230NA deoxyribonuclease are the major cytotoxic proteins that accounted for 55.93% of the HMWp.
Conclusion: The findings from this study provided scientific evidences to support the traditional use of the L. tigris sclerotia for treatment of breast cancer. Several cytotoxic proteins with high abundance have been identified in the HMWp of the sclerotial extract and these proteins have potential to be developed into new anticancer agents or as adjunct cancer therapy.