Metabolic syndrome, characterized by central obesity, hypertension, and hyperlipidemia, increases the morbidity and mortality of cardiovascular disease, type 2 diabetes, nonalcoholic fatty liver ...disease, and other metabolic diseases. It is well known that insulin resistance, especially hepatic insulin resistance, is a risk factor for metabolic syndrome. Current research has shown that hepatic fatty acid accumulation can cause hepatic insulin resistance through increased gluconeogenesis, lipogenesis, chronic inflammation, oxidative stress and endoplasmic reticulum stress, and impaired insulin signal pathway. Mitochondria are the major sites of fatty acid β-oxidation, which is the major degradation mechanism of fatty acids. Mitochondrial dysfunction has been shown to be involved in the development of hepatic fatty acid–induced hepatic insulin resistance. Mitochondrial autophagy (mitophagy), a catabolic process, selectively degrades damaged mitochondria to reverse mitochondrial dysfunction and preserve mitochondrial dynamics and function. Therefore, mitophagy can promote mitochondrial fatty acid oxidation to inhibit hepatic fatty acid accumulation and improve hepatic insulin resistance. Here, we review advances in our understanding of the relationship between mitophagy and hepatic insulin resistance. Additionally, we also highlight the potential value of mitophagy in the treatment of hepatic insulin resistance and metabolic syndrome.
The coronavirus disease 2019 (COVID-19) pandemic has resulted in millions of confirmed cases and deaths globally. The purpose of this study was to investigate the therapeutic effect of airway ...clearance technology combined with prone ventilation on patients infected with COVID-19.
38 patients with COVID-19 (severe) who were treated in the intensive rehabilitation group of Shengli Oilfield Central Hospital. They were randomly divided into a control group and an observation group. The control group received prone position ventilation intervention, and the observation group received airway clearance technology combined with prone position ventilation intervention. The changes of oxygen and index, procalcitonin (PCT), interleukin-6 (IL-6) and chest X-ray image indexes were compared between the two groups.
There was no significant difference in age, gender and other general data between the control group and the observation group. The results showed that oxygen index, PCT, IL-6 and chest X-ray image index in the observation group were better than that indexes in the control group.
Airway clearance technology combined with prone ventilation intervention in patients with COVID-19 can improve the total effective rate and oxygenation index, improve the inflammatory indicators and respiratory function of patients. And it may be widely promoted and used in the treatment of patients with COVID-19 (severe).
Residential loads have great potential to enhance the efficiency and reliability of electricity systems via demand response (DR) programs. One major challenge in residential DR is how to learn and ...handle unknown and uncertain customer behaviors. In this letter, we consider the residential DR problem where the load service entity (LSE) aims to select an optimal subset of customers to optimize some DR performance, such as maximizing the expected load reduction with a financial budget or minimizing the expected squared deviation from a target reduction level. To learn the uncertain customer behaviors influenced by various time-varying environmental factors, we formulate the residential DR as a contextual multi-armed bandit (MAB) problem, and develop an online learning and selection (OLS) algorithm based on Thompson sampling to solve it. This algorithm takes the contextual information into consideration and is applicable to complicated DR settings. Numerical simulations are performed to demonstrate the learning effectiveness of the proposed algorithm.
Improving health-related quality of life (HRQoL) and exercise capacity is an important goal of treatment in heart failure (HF). However, evidence for the effects of sodium-glucose cotransporter-2 ...(SGLT-2) inhibitors on the improvement of HRQoL and exercise capacity seems to be conflicted. We performed a systematic review and meta-analysis to evaluate the effects of SGLT-2 inhibitors on HRQL and exercise capacity in patients with heart failure and reduced ejection fraction (HFrEF).
All studies (up to March 20, 2021) evaluating the effects of SGLT-2 inhibitors on HRQoL and exercise capacity in patients with HFrEF were initially searched from four electronic search engines: PubMed, Web of Science, Cochrane Library, and SinoMed. All statistical analyses were performed with RevMan 5.4.
We included 9 articles describing 7 trials with 9428 patients. SGLT-2 inhibitors group exhibited significant improvement in HRQoL assessed by Kansas City Cardiomyopathy Questionnaires (KCCQ) (MD: 2.13, 95% CI: 1.11 to 3.14, p < 0.001) and the rate of KCCQ-overall summary score improvement≥5 points (RR 1.15, 95%CI 1.08 to 1.21, P < 0.001) compared with placebo. No significant difference was observed in exercise capacity assessed by 6-min walk test distance between SGLT-2 inhibitors and placebo (MD 24.45, 95%CI -22.82 to 71.72, P = 0.31).
Our meta-analysis demonstrates that SGLT-2 inhibitors significantly improve HRQoL, and supports the concept that SGLT-2 inhibitors do not significantly improve exercise capacity in patients with HFrEF. Studies with larger sample sizes and longer follow-up duration are needed to determine whether the treatment with SGLT-2 inhibitors may improve exercise ability.
PROSPERO: CRD42021248346.
•Despite successful therapies that improve morbidity and mortality, most patients with HF have troublesome symptoms.•Evidence for the effects of SGLT-2 inhibitors on the improvement of HRQoL and exercise capacity seems to be conflicted.•SGLT-2 inhibitors significantly improve HRQoL as measured by KCCQ in patients with HFrEF.•SGLT-2 inhibitors do not significantly improve exercise capacity as measured by 6MWTD and accelerometry.
The false discovery rate (FDR) and the false non-discovery rate (FNR), defined as the expected false discovery proportion (FDP) and the false non-discovery proportion (FNP), are the most popular ...benchmarks for multiple testing. Despite the theoretical and algorithmic advances in recent years, the optimal tradeoff between the FDR and the FNR has been largely unknown except for certain restricted classes of decision rules, e.g., separable rules, or for other performance metrics, e.g., the marginal FDR and the marginal FNR (mFDR and mFNR). In this paper, we determine the asymptotically optimal FDR-FNR tradeoff under the two-group random mixture model when the number of hypotheses tends to infinity. Distinct from the optimal mFDR-mFNR tradeoff, which is achieved by separable decision rules, the optimal FDR-FNR tradeoff requires compound rules even in the large-sample limit and for models as simple as the Gaussian location model. This suboptimality of separable rules also holds for other objectives, such as maximizing the expected number of true discoveries. Finally, to address the limitation of the FDR which only controls the expectation but not the fluctuation of the FDP, we also determine the optimal tradeoff when the FDP is controlled with high probability and show it coincides with that of the mFDR and the mFNR. Extensions to models with a fixed non-null proportion are also obtained.
The authors give an approximation method for Bayesian inference in arena model, which is focused on paired comparisons with eliminations and bifurcations. The approximation method simplifies the ...inference by reducing parameters and introducing normal distribution functions into the computation of posterior distribution, which is largely based on an important property of normal random variables. Maximum a posteriori probability (MAP) and Bayesian prediction are then used to mine the information from the past pairwise comparison data, such as an individual's strength or volatility and his possible future results. We conduct a simulation to show the accuracy and stability of the approximation method and demonstrate the algorithm on nonlinear parameter inference as well as prediction problem arising in the FIFA World Cup.
Residential loads have great potential to enhance the efficiency and reliability of electricity systems via demand response (DR) programs. One major challenge in residential DR is to handle the ...unknown and uncertain customer behaviors. Previous works use learning techniques to predict customer DR behaviors, while the influence of time-varying environmental factors is generally neglected, which may lead to inaccurate prediction and inefficient load adjustment. In this paper, we consider the residential DR problem where the load service entity (LSE) aims to select an optimal subset of customers to maximize the expected load reduction with a financial budget. To learn the uncertain customer behaviors under the environmental influence, we formulate the residential DR as a contextual multi-armed bandit (MAB) problem, and the online learning and selection (OLS) algorithm based on Thompson sampling is proposed to solve it. This algorithm takes the contextual information into consideration and is applicable to complicated DR settings. Numerical simulations are performed to demonstrate the learning effectiveness of the proposed algorithm.
Abstract
Rapid growth of single-cell transcriptomic data provides unprecedented opportunities for close scrutinizing of dynamical cellular processes. Through investigating epithelial-to-mesenchymal ...transition (EMT), we develop an integrative tool that combines unsupervised learning of single-cell transcriptomic data and multiscale mathematical modeling to analyze transitions during cell fate decision. Our approach allows identification of individual cells making transition between all cell states, and inference of genes that drive transitions. Multiscale extractions of single-cell scale outputs naturally reveal intermediate cell states (ICS) and ICS-regulated transition trajectories, producing emergent population-scale models to be explored for design principles. Testing on the newly designed single-cell gene regulatory network model and applying to twelve published single-cell EMT datasets in cancer and embryogenesis, we uncover the roles of ICS on adaptation, noise attenuation, and transition efficiency in EMT, and reveal their trade-off relations. Overall, our unsupervised learning method is applicable to general single-cell transcriptomic datasets, and our integrative approach at single-cell resolution may be adopted for other cell fate transition systems beyond EMT.
Abstract
In response to DNA double-strand breaks (DSBs), repair proteins are recruited to the damaged sites. Ubiquitin signaling plays a critical role in coordinating protein recruitment during the ...DNA damage response. Here, we find that the microRNA biogenesis factor DGCR8 promotes tumor resistance to X-ray radiation independently of its Drosha-binding ability. Upon radiation, the kinase ATM and the deubiquitinase USP51 mediate the activation and stabilization of DGCR8 through phosphorylation and deubiquitination. Specifically, radiation-induced ATM-dependent phosphorylation of DGCR8 at serine 677 facilitates USP51 to bind, deubiquitinate, and stabilize DGCR8, which leads to the recruitment of DGCR8 and DGCR8’s binding partner RNF168 to MDC1 and RNF8 at DSBs. This, in turn, promotes ubiquitination of histone H2A, repair of DSBs, and radioresistance. Altogether, these findings reveal the non-canonical function of DGCR8 in DSB repair and suggest that radiation treatment may result in therapy-induced tumor radioresistance through ATM- and USP51-mediated activation and upregulation of DGCR8.
This paper presents a comprehensive optimization control scheme to improve efficiency and dynamic response of dual-active-bridge (DAB) dc-dc converters. Unified-phase-shift (UPS) control is widely ...used to increase the efficiency of DAB dc-dc converters by minimizing the peak current, but dynamic performance of the converters needs to be further enhanced. In this paper, to gain superior dynamic performance of DAB dc-dc converters, an equivalent power-balancing (PB) model is employed, which is capable of predicting dynamic behavior of converter output voltages due to input voltage fluctuation and load disturbance. And then, combining the UPS control and the PB control, a comprehensive UPS and PB (UPS-PB) scheme is proposed to improve the efficiency and dynamic performance simultaneously. This work also includes the detailed inductance parameter sensitivity of the proposed UPS-PB scheme and a zonal voltage control strategy to further improve dynamic responses of the output voltage under the start-up process or a large step change of the output voltage reference. Moreover, the variant of UPS-PB scheme for constant power load is analyzed in the experimental part. Finally, experimental results have verified the excellent performance of the proposed UPS-PB scheme and correctness of theoretical analysis in this work.
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