Association between genetically proxied glucosamine and risk of cancer and non-neoplastic disease: A Mendelian randomization study

IntroductionObservational investigations have examined the impact of glucosamine use on the risk of cancer and non-neoplastic diseases. However, the findings from these studies face limitations arising from confounding variables, reverse causation, and conflicting reports. Consequently, the establishment of a causal relationship between habitual glucosamine consumption and the risk of cancer and non-neoplastic diseases necessitates further investigation.MethodsFor Mendelian randomization (MR) investigation, we opted to employ single-nucleotide polymorphisms (SNPs) as instruments that exhibit robust associations with habitual glucosamine consumption. We obtained the corresponding effect estimates of these SNPs on the risk of cancer and non-neoplastic diseases by extracting summary data for genetic instruments linked to 49 varied cancer types amounting to 378,284 cases and 533,969 controls, as well as 20 non-neoplastic diseases encompassing 292,270 cases and 842,829 controls. Apart from the primary analysis utilizing inverse-variance weighted MR, we conducted two supplementary approaches to account for potential pleiotropy (MR-Egger and weighted median) and assessed their respective MR estimates. Furthermore, the results of the leave-one-out analysis revealed that there were no outlying instruments.ResultsOur results suggest divergence from accepted biological understanding, suggesting that genetically predicted glucosamine utilization may be linked to an increased vulnerability to specific illnesses, as evidenced by increased odds ratios and confidence intervals (95% CI) for diseases, such as malignant neoplasm of the eye and adnexa (2.47 [1.34–4.55]), benign neoplasm of the liver/bile ducts (2.12 [1.32–3.43]), benign neoplasm of the larynx (2.01 [1.36–2.96]), melanoma (1.74 [1.17–2.59]), follicular lymphoma (1.50 [1.06–2.11]), autoimmune thyroiditis (2.47 [1.49–4.08]), and autoimmune hyperthyroidism (1.93 [1.17–3.18]). In contrast to prior observational research, our genetic investigations demonstrate a positive correlation between habitual glucosamine consumption and an elevated risk of sigmoid colon cancer, lung adenocarcinoma, and benign neoplasm of the thyroid gland.ConclusionCasting doubt on the purported purely beneficial association between glucosamine ingestion and prevention of neoplastic and non-neoplastic diseases, habitual glucosamine ingestion exhibits dichotomous effects on disease outcomes. Endorsing the habitual consumption of glucosamine as a preventative measure against neoplastic and non-neoplastic diseases cannot be supported

Modeling and simulations of the cascading failure of multiple interdependent R&D networks under risk propagation

In this paper, we study the robustness of multiple interrelated R&D networks under risk propagation. Firstly, using a bi-partite graph to represent the interrelated R&D networks is emphasized and proposed. Secondly, a risk propagation model is built by defining risk load and risk capacity of each enterprise on a specific R&D network, Thirdly, we use simulations to study risk propagation in interrelated R&D networks. Our results indicate that there exist three critical thresholds to quantify the robustness of R&D networks. Risk propagation in R&D networks is highly affected by the heterogeneity of all enterprises' scales and risk capacities

Locating Facilities under Competition and Market Expansion: Formulation, Optimization, and Implications

As the ongoing battle between brick-and-mortar stores and e-commerce shops escalates, managers of the former are becoming more cautious regarding their strategic store site selection and decoration decisions, particularly if foreseeable competition from rival companies exists. This article investigates a bilevel competitive facility location problem (BCFL), where two companies, a leader and a follower, plan to enter a market sequentially. Each company has a budget to open and design facilities. The goal is to maximize expected revenue that is forecasted through a discrete choice model. To reflect a practical environment, we further consider a situation with elastic demand, explaining the market expansion effect when customers are offered better service due to open new facilities. We formulate the problem as a nonlinear 0-1 bilevel program. Because of the bilevel structure and the market expansion effect, this problem is such challenging that we are unaware of any exact algorithms in the literature. Motivated by this gap, we develop an exact framework that leverages the state-of-the-art value-function-based approach. However, this framework requires solving a mixed-integer non-convex optimization problem (MINOP) at each iteration, which is computationally prohibitive even for medium-scale instances. To mitigate the intractability, we propose a new framework that avoids MINOP and tackles instances with hundreds of location variables. Finally, we conduct extensive computational studies to show the efficiency and effectiveness of our method as well as provide insightful guidance for managers to have win-win/dominate outcomes and choose an appropriate market size function when dealing with expansion decisions in chained business operations

Accounting Method for Carbon Emission Obligation on Load Side of Power System Based on Peak Load Characteristics

To accurately estimate the carbon emission obligation of the user side of a new power system and promote the continuous development of low-carbon electricity， this study uses the node carbon intensity calculation method to characterize the carbon flow distribution of the system and preliminarily calculates the carbon emission obligation on the load side. Moreover， under the premise that the total carbon emission obligation allocation amount remains unchanged before and after correction， the initial carbon emission obligation on the load side is modified and analyzed based on an innovative combination of the comprehensive load method， where the peak load characteristics of the user at the current stage are fully considered. Next， considering the long-term changes in electricity demand and emission reduction capabilities on the load side， a carbon emission obligation allocation interval reflecting the long-term electricity consumption characteristics of the users was constructed. Based on this， the satisfaction of each load node with the carbon emission obligation accounting results was accurately evaluated， providing a direction for the formulation of low-carbon transformation strategies and the construction of demand-side response capabilities in the power system. The application results in the 14-node power system show that compared to the initial accounting results of the node carbon intensity allocation method， the load-side carbon emission obligation accounting results based on the user electricity consumption characteristics of the power system are both reasonable and accurate， providing data support for the clean and low-carbon development of the power industry， and have a certain reference value