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Metabolites in the tumor microenvironment are a critical factor for tumor progression. However, the lack of knowledge about the metabolic profile in the bone marrow (BM) microenvironment of multiple myeloma (MM) limits our understanding of MM progression. Here, we show that the glycine concentration in the BM microenvironment is elevated due to bone collagen degradation mediated by MM cell-secreted matrix metallopeptidase 13 (MMP13), while the elevated glycine level is linked to MM progression. MM cells utilize the channel protein solute carrier family 6 member 9 (SLC6A9) to absorb extrinsic glycine subsequently involved in the synthesis of glutathione (GSH) and purines. Inhibiting glycine utilization via SLC6A9 knockdown or the treatment with betaine suppresses MM cell proliferation and enhances the effects of bortezomib on MM cells. Together, we identify glycine as a key metabolic regulator of MM, unveil molecular mechanisms governing MM progression, and provide a promising therapeutic strategy for MM treatment.

The discrepancy between worker self- and supervisor-assessment of worker safety performance, thus, suggests the importance of including alternative measurements of safety performance in addition to self-reflection. Lower levels of participation behavior in both raters suggest more research on the motivators of participatory behavior. Practical applications The discrepancy between different raters can lead to negative reactions of ratees, suggesting that managers should be aware of that difference. Assigning experienced supervisors as raters can be effective at mitigating interrater discrepancy and conflicts in the assessment of compliance behavior.

Purpose Previous research has little specific guidance on how to improve large infrastructures’ risk analysis. This paper aims to propose a practical risk analysis framework across the project lifecycle with Bayesian Networks (BNs). Design/methodology/approach The framework includes three phases. In the qualitative phase, primary risks were identified by literature reviews and interviews; questionnaires were used to determine key risks at each project stage and causal relationships between stage-related risks. In the quantitation, brainstorming and questionnaires, and techniques of ranked nodes/paths, risk map and Bayesian truth serum were adopted. Then, a BN-based risk assessment model was developed, and risk analysis was conducted with AgenaRisk software. Findings Twenty key risks across the lifecycle were determined: some risks were recurring and different risks emerged at various stages with the construction and feasibility most risky. Results showed that previous stages’ risks significantly amplified subsequent stages’ risks. Based on the causality of stage-related risks, a qualitative model was easily constructed. Ranked nodes/paths facilitated the quantification by requiring less statistical knowledge and fewer parameters than traditional BNs. As articulated by a case, this model yielded very simple and easy-to-understand representations of risks and risk propagation pathways. Originality/value Rare research has developed a BN risk assessment model from the perspective of project stages. A structured model, a propagation network among individual risks, stage-related risks, and the final adverse consequence, has been designed. This research provides practitioners with a realistic risk assessment approach and further understanding of dynamic and stage-related risks throughout large infrastructures’ lifecycle. The framework can be modified and used in other real-world risk analysis where risks are complex and develop in stages.

Early prediction of sepsis is critical in clinical practicesince each hour of delayed treatment has been associated with an increase in mortality due to irreversible organ damage. This study aimed to develop an algorithm for accurately predicting the onset of sepsis in the proceeding of six hours. Firstly, we selected 37 available variates features after data pre-processing, and then extracted three kinds of features as well in this paper, including 62 missing value features, 8 scoring quantified features and 61 time series features. After that, a multi-feature fusion based XGBoost classification model was developed and was further improved by a Bayesian optimizer and an ensemble learning framework. Analysis was performed on the PhysioNet/Computing in Cardiology Challenge 2019, which provided a publicly available sepsis data sourced from 40,336 ICU patients. Finally, after searching an optimized predicted risk threshold of 0.522 through the official submissions, our team "SailOcean" applied the developed model on the full hidden test set of 24,819 ICU patients from three hospital systems and obtained a final Unormalized score (U-Score) defined by the organizers of 0.364, which was the highest unofficial score.