Exploring the Appropriate Price of Semaglutide for Type 2 Diabetes Patients Based on Cost-Utility Analysis in China

Introduction: Semaglutide is the first and only oral version of a glucagon-like peptide-1 analogue approved by the FDA for the treatment of type 2 diabetes (T2D). This research was designed to explore the appropriate price of once-weekly (OW) semaglutide for T2D patients in China based on cost-utility analysis.Methods: The baseline patient cohorts of OW semaglutide and once-daily (OD) empagliflozin were sourced from a patient-level meta-analysis integrating the SUSTAIN 2, SUSTAIN 3, SUSTAIN 8 and PIONEER 2 trials. The long-term health and economic outcomes were simulated using the United Kingdom Prospective Diabetes Study Outcome Model 2 from the Chinese healthcare provider’s perspective. The appropriate price of semaglutide was explored by binary search. One-way sensitivity analysis (one-way SA), probabilistic sensitivity analysis and scenario analysis were applied to solve the uncertainty.Results: Under the assumption that the annual cost of semaglutide is equal to that of OD empagliflozin, OW semaglutide was superior to OD empagliflozin due to its higher quality adjusted life years and lower total costs. After binary search, the incremental cost-utility ratio of OW semaglutide vs. OD empagliflozin was approximately equal to 3λ with an annual cost of semaglutide of $1,007.18 and approximately equal to λ with an annual cost of semaglutide of$708.11. Subsequently, the incremental cost-utility ratio of OW semaglutide vs. OD empagliflozin was approximately 3λ and λ, with annual costs of semaglutide of $877.43 and$667.04, respectively, adjusted by one-way SA. Ultimately, the cost-utility results with annual costs of semaglutide of $877.43 and$667.04 were robust to probabilistic sensitivity analysis and scenario analysis.Conclusion: In conclusion, the annual cost of semaglutide appears to be appropriate between $667.04 and$877.43 for T2D patients in China

Public health insurance and cancer‐specific mortality risk among patients with breast cancer: A prospective cohort study in China

We thank all staff members working on the Breast Cancer Information Management System (BCIMS) for their contributions to data collection and management. We also thank Dr Bo Fu, Mr Yan Li and Mr Pei Liu at the University of Electronic Science and Technology of China for data cleaning and zip code mapping. Our study was supported by the Key Research and Development Project of Sichuan Province of China (grant number: 2017SZ00005) and Swedish Research Council (grant number: 2018‐00648).Little is known about how health insurance policies, particularly in developing countries, influence breast cancer prognosis. Here, we examined the association between individual health insurance and breast cancer-specific mortality in China. We included 7436 women diagnosed with invasive breast cancer between 2009 and 2016, at West China Hospital, Sichuan University. The health insurance plan of patient was classified as either urban or rural schemes and was also categorized as reimbursement rate (ie, the covered/total charge) below or above the median. Breast cancer-specific mortality was the primary outcome. Using Cox proportional hazards models, we calculated hazard ratios (HRs) for cancer-specific mortality, contrasting rates among patients with a rural insurance scheme or low reimbursement rate to that of those with an urban insurance scheme or high reimbursement rate, respectively. During a median follow-up of 3.1 years, we identified 326 deaths due to breast cancer. Compared to patients covered by urban insurance schemes, patients covered by rural insurance schemes had a 29% increased cancer-specific mortality (95% CI 0%-65%) after adjusting for demographics, tumor characteristics and treatment modes. Reimbursement rate below the median was associated with a 42% increased rate of cancer-specific mortality (95% CI 11%-82%). Every 10% increase in the reimbursement rate is associated with a 7% (95% CI 2%-12%) reduction in cancer-specific mortality risk, particularly in patients covered by rural insurance schemes (26%, 95% CI 9%-39%). Our findings suggest that underinsured patients face a higher risk of breast cancer-specific mortality in developing countries.VetenskapsrådetPeer Reviewe

Low-Density Lipoprotein Has an Enormous Capacity To Bind (E)-4-Hydroxynon-2-enal (HNE): Detection and Characterization of Lysyl and Histidyl Adducts Containing Multiple Molecules of HNE

(E)-4-Hydroxynon-2-enal (HNE), an electrophilic bifunctional cytotoxic lipid peroxidation product, forms covalent adducts with nucleophilic side chains of amino acid residues. HNE-derived adducts have been implicated in many pathophysiological processes including atherosclerosis, diabetes, and Alzheimer’s disease. Tritium- and deuterium-labeled HNE (d4-HNE) were used orthogonally to study adduction with proteins and individual nucleophilic groups of histidyl, lysyl, and cysteine residues. Using tritium-labeled HNE, we detected the binding of 486 molecules of HNE per low-density lipoprotein (LDL) particle, significantly more than the total number of all reactive nucleophiles in the LDL particle. This suggests the formation of adducts that incorporate multiple molecules of HNE with some nucleophilic amino acid side chains. We also found that the reaction of a 1:1 mixture of d4-HNE and d0-HNE with N-acetylhistidine, N-acetyl-Gly-Lys-OMe, or N-acetyl cysteine generates 1:1, 2:1, and 3:1 adducts, which exhibit unique mass spectral signatures that aid in structural characterization. A domino-like reaction of initial 1:1 HNE Michael adducts of histidyl or lysyl nucleophiles with multiple additional HNE molecules forms 2:1 and 3:1 adducts that were structurally characterized by tandem mass spectrometry

Prioritizing multiple therapeutic targets in parallel using automated DNA-encoded library screening

AbstractThe identification and prioritization of chemically tractable therapeutic targets is a significant challenge in the discovery of new medicines. We have developed a novel method that rapidly screens multiple proteins in parallel using DNA-encoded library technology (ELT). Initial efforts were focused on the efficient discovery of antibacterial leads against 119 targets from Acinetobacter baumannii and Staphylococcus aureus. The success of this effort led to the hypothesis that the relative number of ELT binders alone could be used to assess the ligandability of large sets of proteins. This concept was further explored by screening 42 targets from Mycobacterium tuberculosis. Active chemical series for six targets from our initial effort as well as three chemotypes for DHFR from M. tuberculosis are reported. The findings demonstrate that parallel ELT selections can be used to assess ligandability and highlight opportunities for successful lead and tool discovery.</jats:p

A gene expression-based risk model reveals prognosis of gastric cancer

Background The prognosis of gastric cancer is difficult to determine, although clinical indicators provide valuable evidence. Methods In this study, using screened biomarkers of gastric cancer in combination with random forest variable hunting and multivariable Cox regression, a risk score model was developed to predict the survival of gastric cancer. Survival difference between high/low-risk groups were compared. The relationship between risk score and other clinicopathological indicators was evaluated. Gene set enrichment analysis (GSEA) was used to identify pathways associated with risk scores. Results The patients with high risk scores (median overall survival: 20.2 months, 95% CI [16.9–26.0] months) tend to exhibit early events compared with those with low risk scores (median survival: 70.0 months, 95% CI [46.9–101] months, p = 1.80e–5). Further validation was implemented in another three independent datasets (GSE15459, GSE26253, GSE62254). Correlation analyses between clinical observations and risk scores were performed, and the results indicated that the risk score was not significantly associated with gender, age and primary tumor size but was significantly associated with grade and tumor stage. In addition, the risk score was also not influenced by radiation therapy. Cox multivariate regression and three-year survival nomogram suggest that the risk score is an important indicator of gastric cancer prognosis. GSEA was used to identified KEGG pathways significantly associated with risk score, and signaling pathways involved in focal adhesion and the TGF-beta signaling pathway were identified. Conclusion The risk score model successfully predicted the survival of 1,294 gastric cancer samples from four independent datasets and is among the most important indicators in clinical clinicopathological information for the prognosis of gastric cancer. To our knowledge, it is the first report to predict the survival of gastric cancer using optimized expression panel

Predicting lncRNA-disease associations using multiple metapaths in hierarchical graph attention networks

Abstract Background Many biological studies have shown that lncRNAs regulate the expression of epigenetically related genes. The study of lncRNAs has helped to deepen our understanding of the pathogenesis of complex diseases at the molecular level. Due to the large number of lncRNAs and the complex and time-consuming nature of biological experiments, applying computer techniques to predict potential lncRNA-disease associations is very effective. To explore information between complex network structures, existing methods rely mainly on lncRNA and disease information. Metapaths have been applied to network models as an effective method for exploring information in heterogeneous graphs. However, existing methods are dominated by lncRNAs or disease nodes and tend to ignore the paths provided by intermediate nodes. Methods We propose a deep learning model based on hierarchical graphical attention networks to predict unknown lncRNA-disease associations using multiple types of metapaths to extract features. We have named this model the MMHGAN. First, the model constructs a lncRNA-disease–miRNA heterogeneous graph based on known associations and two homogeneous graphs of lncRNAs and diseases. Second, for homogeneous graphs, the features of neighboring nodes are aggregated using a multihead attention mechanism. Third, for the heterogeneous graph, metapaths of different intermediate nodes are selected to construct subgraphs, and the importance of different types of metapaths is calculated and aggregated to obtain the final embedded features. Finally, the features are reconstructed using a fully connected layer to obtain the prediction results. Results We used a fivefold cross-validation method and obtained an average AUC value of 96.07% and an average AUPR value of 93.23%. Additionally, ablation experiments demonstrated the role of homogeneous graphs and different intermediate node path weights. In addition, we studied lung cancer, esophageal carcinoma, and breast cancer. Among the 15 lncRNAs associated with these diseases, 15, 12, and 14 lncRNAs were validated by the lncRNA Disease Database and the Lnc2Cancer Database, respectively. Conclusion We compared the MMHGAN model with six existing models with better performance, and the case study demonstrated that the model was effective in predicting the correlation between potential lncRNAs and diseases

Demand-Oriented Train Timetabling Integrated with Passenger Train-Booking Decisions

In recent years, with the global energy shortage and severe environmental deterioration, railway transport has begun to attract great interest as a green transportation mode. One of the vital means to realize social sustainable development is to improve railway transportation systems, in which providing a demand-oriented train timetable with a higher service level is the most viable method. A demand-oriented train timetable problem generally deals with passengers&rsquo; train-choice decisions according to the queue principle, but it is not adapted to rail systems, such as China&rsquo;s, where passengers usually book tickets a few days in advance by telephone or online instead of going to stations. This paper is devoted to modeling and solving the demand-oriented train timetabling problem integrated with passengers&rsquo; train-booking decisions. Firstly, a bi-level programming model is formulated for their integrated optimization on a rail network. Its upper-level model is to optimize train arrival and departure times at each visited station with the aim of reducing passengers&rsquo; total travel cost, while its lower-level model aims to determine passengers&rsquo; train-booking behavior using the user equilibrium theory. Then, a priority-based heuristic algorithm is designed to solve this model. It has two main steps at each iteration: one is to determine the number of passengers booking each train with a given train timetable, and the other is to improve the current train timetable based on the valuable information of passenger train-booking decisions. The performance, convergence, and practicability of the proposed method were analyzed based on the Changsha&ndash;Zhuzhou&ndash;Xiangtan intercity rail in China. Experimental results show the proposed method can effectively reduce the travel cost for passengers, creating a greater passenger demand for railway travel, which is beneficial to the sustainable development of railway systems and even society

Pore water salinity effect on the intrinsic compression behaviour of artificial soft soils

International audienceThe framework of the intrinsic compression behaviour of normally consolidated soft clay proposed by Burland (1990) is widely adopted in engineering practices. However, further investigations should be conducted on its validity in the coastal and offshore environments to verify the effect of pore water salinity. In the present work, oedometer tests were performed on remoulded artificial soft clays (mixtures of kaolin and bentonite with 0%, 5%, 10%, and 20% of bentonite by mass). The artificial clays were salinized with sodium chloride solutions at concentrations 0 mol/L (distilled water), 0.17 mol/L, 0.51 mol/L, 0.86 mol/L, and 1.70 mol/L at water contents equal to 1.0–1.5 times of the liquid limits. The results showed that the ‘intrinsic’ properties of clays, including the compression index Cc⁎(100–1000 kPa), void ratio e100*, and compression line based on the void index Iv, changed with the pore water salinity. Following the empirical correlation proposed by previous researchers, the decrease in Cc⁎ caused by pore water salinity could be generally characterised by the liquid limit and void ratio at the liquid limit (eL). The dispersed correlation between the predicted e100⁎ and the experimental results in this study were caused by the significant changes in e0/eL controlled by pore water salinity. The relationships between void index Iv and vertical stress σv’ deviated from the intrinsic compression line (ICL) under the saline environment. For the artificial clay rich in smectite, the slopes of Iv–log σv’ before yielding increased with pore water salinity at the initial water content. Pore water salinity affected the intrinsic compression behaviour of soft clays primarily composed of kaolinite to a lesser extent

untitled

Borehole Stability Analysis in Deepwater Shallow Sediments Deepwater shallow sediment is less-consolidated, with a rock mechanical behavior similar to saturated soil. It is prone to borehole shrinkage and downhole leakage. Assume the deepwater shallow sediments are homogeneous, isotropic, and ideally elastoplastic materials, and formation around the borehole is divided into elastic and plastic zone. The theories of small deformation and large deformation are, respectively, adopted in the elastic and plastic zone. In the plastic zone, Mohr-Coulomb strength criterion is selected. The stress and deformation distributions in these two zones, and the radius of plastic zone are derived. The collapse pressure calculation formula of deepwater shallow sediments under the control of different shrinkage rates is obtained. With the introduction of excess pore pressure theory in soil mechanics, the distribution rule of excess pore pressure in these two zones is obtained. Combined with hydraulic fracturing theory, the fracture mechanism of shallow sediments is analyzed and the theoretical formula of fracture pressure is given. The calculation results are quite close to the practically measured results. So the reliability of the theory is confirmed