Modeling the Autonomic and Metabolic Effects of Obstructive Sleep Apnea: A Simulation Study

Long-term exposure to intermittent hypoxia and sleep fragmentation introduced by recurring obstructive sleep apnea (OSA) has been linked to subsequent cardiovascular disease and Type 2 diabetes. The underlying mechanisms remain unclear, but impairment of the normal interactions among the systems that regulate autonomic and metabolic function is likely involved. We have extended an existing integrative model of respiratory, cardiovascular, and sleep–wake state control, to incorporate a sub-model of glucose–insulin–fatty acid regulation. This computational model is capable of simulating the complex dynamics of cardiorespiratory control, chemoreflex and state-related control of breath-to-breath ventilation, state-related and chemoreflex control of upper airway potency, respiratory and circulatory mechanics, as well as the metabolic control of glucose–insulin dynamics and its interactions with the autonomic control. The interactions between autonomic and metabolic control include the circadian regulation of epinephrine secretion, epinephrine regulation on dynamic fluctuations in glucose and free-fatty acid in plasma, metabolic coupling among tissues and organs provided by insulin and epinephrine, as well as the effect of insulin on peripheral vascular sympathetic activity. These model simulations provide insight into the relative importance of the various mechanisms that determine the acute and chronic physiological effects of sleep-disordered breathing. The model can also be used to investigate the effects of a variety of interventions, such as different glucose clamps, the intravenous glucose tolerance test, and the application of continuous positive airway pressure on OSA subjects. As such, this model provides the foundation on which future efforts to simulate disease progression and the long-term effects of pharmacological intervention can be based

Research on Green Design Strategy of Office Building

Green building design refers to reducing the energy consumption of buildings through the use of energy-saving and environmentally-friendly technologies during the design and construction of buildings. The consumption of water resources and electric energy in office buildings is significantly higher than that of ordinary residential buildings, which has a greater impact on the environment during the construction process. Therefore, the use of green building design in the design of office buildings plays an important role in reducing building energy consumption. This article takes an office building in Chongqing as an example to explain the green optimization design and provide support for subsequent related constructions

Two heads are better than one: current landscape of integrating QSP and machine learning

Quantitative systems pharmacology (QSP) modeling is applied to address essential questions in drug development, such as the mechanism of action of a therapeutic agent and the progression of disease. Meanwhile, machine learning (ML) approaches also contribute to answering these questions via the analysis of multi-layer ‘omics’ data such as gene expression, proteomics, metabolomics, and high-throughput imaging. Furthermore, ML approaches can also be applied to aspects of QSP modeling. Both approaches are powerful tools and there is considerable interest in integrating QSP modeling and ML. So far, a few successful implementations have been carried out from which we have learned about how each approach can overcome unique limitations of the other. The QSP ? ML working group of the International Society of Pharmacometrics QSP Special Interest Group was convened in September, 2019 to identify and begin realizing new opportunities in QSP and ML integration. The working group, which comprises 21 members representing 18 academic and industry organizations, has identified four categories of current research activity which will be described herein together with case studies of applications to drug development decision making. The working group also concluded that the integration of QSP and ML is still in its early stages of moving from evaluating available technical tools to building case studies. This paper reports on this fast-moving field and serves as a foundation for future codification of best practices

Identification of Alternatively-Activated Pathways between Primary Breast Cancer and Liver Metastatic Cancer Using Microarray Data

Alternatively-activated pathways have been observed in biological experiments in cancer studies, but the concept had not been fully explored in computational cancer system biology. Therefore, an alternatively-activated pathway identification method was proposed and applied to primary breast cancer and breast cancer liver metastasis research using microarray data. Interestingly, the results show that cytokine-cytokine receptor interaction and calcium signaling were significantly enriched under both conditions. TGF beta signaling was found to be the hub in network topology analysis. In total, three types of alternatively-activated pathways were recognized. In the cytokine-cytokine receptor interaction pathway, four active alteration patterns in gene pairs were noticed. Thirteen cytokine-cytokine receptor pairs with inverse activity changes of both genes were verified by the literature. The second type was that some sub-pathways were active under only one condition. For the third type, nodes were significantly active in both conditions, but with different active genes. In the calcium signaling and TGF beta signaling pathways, node E2F5 and E2F4 were significantly active in primary breast cancer and metastasis, respectively. Overall, our study demonstrated the first time using microarray data to identify alternatively-activated pathways in breast cancer liver metastasis. The results showed that the proposed method was valid and effective, which could be helpful for future research for understanding the mechanism of breast cancer metastasis

The association between diabetes status and latent-TB IGRA levels from a cross-sectional study in eastern China

BackgroundThere is a debate regarding the sensitivity of the QuantiFERON-TB Gold In-Tube (QFT) among people with diabetes, and prior studies have shown heterogeneous results. We evaluated whether the QFT TB antigen was modified among persons with differing diabetes status and other related risk factors.MethodsA cross-sectional study of 5,302 people was conducted to screen latent tuberculosis infection (LTBI) in eastern China. The QFT assay was performed as an indicator of LTBI. Fasting plasma glucose (FPG) was collected from each participant; the definition of diabetes followed the guidelines from the American Diabetes Association. Participants were classified into normoglycemia, prediabetes, undiagnosed diabetes, and previously diagnosed diabetes to evaluate the relationship between the QFT TB antigen and distinct diabetes status.ResultsTB antigen values from the QFT were statistically different among participants with differing diabetes status (P = 0.008). Persons with undiagnosed diabetes had a higher TB antigen value (0.96 ± 0.20) than persons with normoglycemia (0.50 ± 0.02, P < 0.05). However, the TB antigen values demonstrated no significant difference among the four different diabetic groups when stratified by the standard cutoff for the QFT (P = 0.492 for the positive group and P = 0.368 for the negative group). In a linear regression model, we found that FPG, age, and smoking were positively associated with the QFT TB antigen value (P = 0.017, P < 0.001, and P < 0.001).ConclusionsDiabetes status had little influence on the level of QFT TB antigen response among IGRA-positive persons. However, FPG, old age, and smoking were important risk factors for increasing levels of QFT TB antigen

Identification and characterization of a potential strain for the production of polyhydroxyalkanoate from glycerol

While poly (3-hydroxybutyrate) (PHB) holds promise as a bioplastic, its commercial utilization has been hampered by the high cost of raw materials. However, glycerol emerges as a viable feedstock for PHB production, offering a sustainable production approach and substantial cost reduction potential. Glycerol stands out as a promising feedstock for PHB production, offering a pathway toward sustainable manufacturing and considerable cost savings. The identification and characterization of strains capable of converting glycerol into PHB represent a pivotal strategy in advancing PHB production research. In this study, we isolated a strain, Ralstonia sp. RRA (RRA). The strain exhibits remarkable proficiency in synthesizing PHB from glycerol. With glycerol as the carbon source, RRA achieved a specific growth rate of 0.19 h−1, attaining a PHB content of approximately 50% within 30 h. Through third-generation genome and transcriptome sequencing, we elucidated the genome composition and identified a total of eight genes (glpR, glpD, glpS, glpT, glpP, glpQ, glpV, and glpK) involved in the glycerol metabolism pathway. Leveraging these findings, the strain RRA demonstrates significant promise in producing PHB from low-cost renewable carbon sources