Prediction of High-Altitude Cardiorespiratory Fitness Impairment Using a Combination of Physiological Parameters During Exercise at Sea Level and Genetic Information in an Integrated Risk Model

Insufficient cardiorespiratory compensation is closely associated with acute hypoxic symptoms and high-altitude (HA) cardiovascular events. To avoid such adverse events, predicting HA cardiorespiratory fitness impairment (HA-CRFi) is clinically important. However, to date, there is insufficient information regarding the prediction of HA-CRFi. In this study, we aimed to formulate a protocol to predict individuals at risk of HA-CRFi. We recruited 246 volunteers who were transported to Lhasa (HA, 3,700 m) from Chengdu (the sea level [SL], <500 m) through an airplane. Physiological parameters at rest and during post-submaximal exercise, as well as cardiorespiratory fitness at HA and SL, were measured. Logistic regression and receiver operating characteristic (ROC) curve analyses were employed to predict HA-CRFi. We analyzed 66 pulmonary vascular function and hypoxia-inducible factor- (HIF-) related polymorphisms associated with HA-CRFi. To increase the prediction accuracy, we used a combination model including physiological parameters and genetic information to predict HA-CRFi. The oxygen saturation (SpO2) of post-submaximal exercise at SL and EPAS1 rs13419896-A and EGLN1 rs508618-G variants were associated with HA-CRFi (SpO2, area under the curve (AUC) = 0.736, cutoff = 95.5%, p < 0.001; EPAS1 A and EGLN1 G, odds ratio [OR] = 12.02, 95% CI = 4.84–29.85, p < 0.001). A combination model including the two risk factors—post-submaximal exercise SpO2 at SL of <95.5% and the presence of EPAS1 rs13419896-A and EGLN1 rs508618-G variants—was significantly more effective and accurate in predicting HA-CRFi (OR = 19.62, 95% CI = 6.42–59.94, p < 0.001). Our study employed a combination of genetic information and the physiological parameters of post-submaximal exercise at SL to predict HA-CRFi. Based on the optimized prediction model, our findings could identify individuals at a high risk of HA-CRFi in an early stage and reduce cardiovascular events.

Deformation Prediction of a Deep Foundation Pit Based on the Combination Model of Wavelet Transform and Gray BP Neural Network

The purpose of this study was to predict the deformation of a deep foundation pit based on a combination model of wavelet transform and gray BP neural network. Using a case of a deep foundation pit, a combination model of wavelet transform and gray BP neural network was used to predict the deformation of the deep foundation pit. The results show that compared with the traditional gray BP neural network model, the relative error of the combination model of wavelet transform and gray BP neural network was reduced by 2.38%. This verified that the combined model has high accuracy and reliability in the prediction of foundation pit deformation and also conforms to the actual situation of the project. The research results can provide a valuable reference for foundation pit deformation monitoring.

The Rank of Silaturrahmi-Assimilated Collaboration Parameter Based on Core Drive Using Octalysis Gamification Framework and Fuzzy AHP

This study aims to build SME collaboration parameters from the assimilation of the "silaturrahmi" culture that considers user motivation in collaborating. Parameter validation involves linear regression statistical methods, followed by determining the ranking of collaboration parameters using a combination model of Octalysis and Fuzzy AHP frameworks. The test resulted in 4 collaboration parameters from the assimilation of "Silaturrahmi" ranked based on the user's level of motivation (core drive) with details: Relationship Building-RB (52.86%), Reciprocal Sustainment-RS (25.44%), Reciprocal Assistant-RA (21.77%) and Active Support- US (0.83%). It can be concluded that the four parameters potentially measure the performance of SME collaboration. The combination model can determine the user's motivation (core drive) for collaboration through these parameters. The ranking results serve as a reference for developing a collaborative framework by prioritizing activities related to the highest weight percentage parameters and evaluating the lowest weight percentage.

Dual Targeting by Inhibition of Phosphoinositide-3-Kinase and Mammalian Target of Rapamycin Attenuates the Neuroinflammatory Responses in Murine Hippocampal Cells and Seizures in C57BL/6 Mice

Emerging evidence suggests the association of seizures and inflammation; however, underlying cell signaling mechanisms are still not fully understood. Overactivation of phosphoinositide-3-kinases is associated with both neuroinflammation and seizures. Herein, we speculate the PI3K/Akt/mTOR pathway as a promising therapeutic target for neuroinflammation-mediated seizures and associated neurodegeneration. Firstly, we cultured HT22 cells for detection of the downstream cell signaling events activated in a lipopolysaccharide (LPS)-primed pilocarpine (PILO) model. We then evaluated the effects of 7-day treatment of buparlisib (PI3K inhibitor, 25 mg/kg p.o.), dactolisib (PI3K/mTOR inhibitor, 25 mg/kg p.o.), and rapamycin (mTORC1 inhibitor, 10 mg/kg p.o.) in an LPS-primed PILO model of seizures in C57BL/6 mice. LPS priming resulted in enhanced seizure severity and reduced latency. Buparlisib and dactolisib, but not rapamycin, prolonged latency to seizures and reduced neuronal loss, while all drugs attenuated seizure severity. Buparlisib and dactolisib further reduced cellular redox, mitochondrial membrane potential, cleaved caspase-3 and p53, nuclear integrity, and attenuated NF-κB, IL-1β, IL-6, TNF-α, and TGF-β1 and TGF-β2 signaling both in vitro and in vivo post-PILO and LPS+PILO inductions; however, rapamycin mitigated the same only in the PILO model. Both drugs protected against neuronal cell death demonstrating the contribution of this pathway in the seizure-induced neuronal pyknosis; however, rapamycin showed resistance in a combination model. Furthermore, LPS and PILO exposure enhanced pAkt/Akt and phospho-p70S6/total-p70S6 kinase activity, while buparlisib and dactolisib, but not rapamycin, could reduce it in a combination model. Partial rapamycin resistance was observed possibly due to the reactivation of the pathway by a functionally different complex of mTOR, i.e., mTORC2. Our study substantiated the plausible involvement of PI3K-mediated apoptotic and inflammatory pathways in LPS-primed PILO-induced seizures and provides evidence that its modulation constitutes an anti-inflammatory mechanism by which seizure inhibitory effects are observed. We showed dual inhibition by dactolisib as a promising approach. Targeting this pathway at two nodes at a time may provide new avenues for antiseizure therapies.

Risk prediction of biomarkers for early multiple organ dysfunction in critically ill patients

Background Shock and organ damage occur in critically ill patients in the emergency department because of biological responses to invasion, and cytokines play an important role in their development. It is important to predict early multiple organ dysfunction (MOD) because it is useful in predicting patient outcomes and selecting treatment strategies. This study examined the accuracy of biomarkers, including interleukin (IL)-6, in predicting early MOD in critically ill patients compared with that of quick sequential organ failure assessment (qSOFA). Methods This was a multicenter observational sub-study. Five universities from 2016 to 2018. Data of adult patients with systemic inflammatory response syndrome who presented to the emergency department or were admitted to the intensive care unit were prospectively evaluated. qSOFA score and each biomarker (IL-6, IL-8, IL-10, tumor necrosis factor-α, C-reactive protein, and procalcitonin [PCT]) level were assessed on Days 0, 1, and 2. The primary outcome was set as MOD on Day 2, and the area under the curve (AUC) was analyzed to evaluate qSOFA scores and biomarker levels. Results Of 199 patients, 38 were excluded and 161 were included. Patients with MOD on Day 2 had significantly higher qSOFA, SOFA, and Acute Physiology and Chronic Health Evaluation II scores and a trend toward worse prognosis, including mortality. The AUC for qSOFA score (Day 0) that predicted MOD (Day 2) was 0.728 (95% confidence interval [CI]: 0.651–0.794). IL-6 (Day 1) showed the highest AUC among all biomarkers (0.790 [95% CI: 0.711–852]). The combination of qSOFA (Day 0) and IL-6 (Day 1) showed improved prediction accuracy (0.842 [95% CI: 0.771–0.893]). The combination model using qSOFA (Day 1) and IL-6 (Day 1) also showed a higher AUC (0.868 [95% CI: 0.799–0.915]). The combination model of IL-8 and PCT also showed a significant improvement in AUC. Conclusions The addition of IL-6, IL-8 and PCT to qSOFA scores improved the accuracy of early MOD prediction.

Research on Population Development Trend in Huizhou of China Forecast Based on Optimal Weighted Combination Method and Fractional Grey Model

In this paper, the optimal weighted combination model and fractional grey model are constructed. The coefficients of the optimal weighted combination model are determined by minimizing the sum of squares of resists of each model. On the other hand, the optimal conformable fractional order and dynamic background value coefficient are determined by the quantum inspired evolutionary algorithm (QIEA). Taking the resident population from 2008 to 2018 as the research object, the optimal weighted combination model and fractional grey model were used to study the estimated and predicted values. The results are compared and analyzed. The results show that the fractional grey model is better than the optimal weighted combination model in the estimation of the values. The optimal weighted combination model is better than the fractional grey model in predicting. Meanwhile, the fractional grey model is found to be very suitable for the data values that are large, and the changes between the data are relatively small. The research results expand the application of the fractional grey model and have important implications for the policy implementation activities of Huizhou government according to the population growth trend in Huizhou.