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Complex signaling pathways are believed to be responsible for drug resistance. Drug combinations perturbing multiple signaling targets have the potential to reduce drug resistance. The large-scale multi-omic datasets and experimental drug combination synergistic score data are valuable resources to study mechanisms of synergy (MoS) to guide the development of precision drug combinations. However, signaling patterns of MoS are complex and remain unclear, and thus it is challenging to identify synergistic drug combinations in clinical. Herein, we proposed a novel integrative and interpretable graph AI model, DeepSignalingFlow, to uncover the MoS by integrating and mining multi-omic data. The major innovation is that we uncover MoS by modeling the signaling flow from multi-omic features of essential disease proteins to the drug targets, which has not been introduced by the existing models. The model performance was assessed utilizing four distinct drug combination synergy evaluation datasets, i.e., NCI ALMANAC, O'Neil, DrugComb, and DrugCombDB. The comparison results showed that the proposed model outperformed existing graph AI models in terms of synergy score prediction, and can interpret MoS using the core signaling flows. The code is publicly accessible via Github: https://github.com/FuhaiLiAiLab/DeepSignalingFlow [ABSTRACT FROM AUTHOR]

Although vaccines have been evaluated and approved for SARS-CoV-2 infection prevention, there remains a lack of effective treatments to reduce the mortality of COVID-19 patients already infected with SARS-CoV-2. The global data on COVID-19 showed that men have a higher mortality rate than women. We further observed that the proportion of mortality of females increases starting from around the age of 55 significantly. Thus, sex is an essential factor associated with COVID-19 mortality, and sex related genetic factors could be interesting mechanisms and targets for COVID-19 treatment. However, the associated sex factors and signaling pathways remain unclear. Here, we propose to uncover the potential sex associated factors using systematic and integrative network analysis. The unique results indicated that estrogens, e.g., estrone and estriol, (1) interacting with ESR1/2 receptors, (2) can inhibit SARS-CoV-2 caused inflammation and immune response signaling in host cells; and (3) estrogens are associated with the distinct fatality rates between male and female COVID-19 patients. Specifically, a high level of estradiol protects young female COVID-19 patients, and estrogens drop to an extremely low level in females after about 55 years of age causing the increased fatality rate of women. In conclusion, estrogen, interacting with ESR1/2 receptors, is an essential sex factor that protects COVID-19 patients from death by inhibiting inflammation and immune response caused by SARS-CoV-2 infection. Moreover, medications boosting the down-stream signaling of ESR1/ESR2, or inhibiting the inflammation and immune-associated targets on the signaling network can be potentially effective or synergistic combined with other existing drugs for COVID-19 treatment. [ABSTRACT FROM AUTHOR]

For some pressurized water reactors, 110mAg colloids deposite on the piping surface and auxiliary systems of the primary loop during the oxidation operation process, becoming one of the most important radionuclides. The decreasing concentration of hydrogen peroxide (H2O2) during the oxidation operation process may be the cause of the deposition of 110mAg colloids. In this work, Ag colloids with particle size of 20–60 nm were first synthesized. Then the effects of reaction temperature, concentration of H2O2 and reaction time on the oxidation and removal of Ag colloids were studied. The results showed that at least 5 ppm H2O2 was needed to achieve significant oxidation and removal of Ag colloids at 80 °C. The results of this work were then applied during the shutdown of a PWR. The oxidation operation process was improved to maintain the concentration of H2O2 at 4–8 ppm during the whole process. The deposition of 110mAg colloids was then effectively mitigated, and the RCV radiation index was reduced by 28.2%. This work demonstrates the importance of H2O2 in reducing the deposition of Ag colloids during the oxidation operation process, providing a new idea and methodology to reduce shutdown dose rate of PWRs. [ABSTRACT FROM AUTHOR]

The pore structure of alkali-activated fly ash mortar (AAFAM) under different mix ratios and curing system was measured by mercury intrusion porosimetry (MIP) and scanning electron microscopy (SEM). Based on the fractal theory, the integral shape dimension of the pore surface of the AAFAM is obtained and discussed its relationship with total porosity, proportion of gel pores, transition pore, capillary pores and macropores, average pore diameter, median pore diameter, pore surface area, pore size distribution, and the influence of mass ratio of alkali activators, curing temperature, and curing age on pore structures. In addition, data regression analysis (DRA) is a statistical tool and applied to study the relationship between microscopic pore parameters and the fractal dimension of the AAFAM mixtures. The results show that the fractal dimension of the AAFAM is between 2.6 and 2.9, and the pore size distribution has a linear relationship with the fractal dimension, and the internal pore diameter of the AAFAM will gradually decrease and AAFAM's matrix gradually becomes denser and the microstructure integrity is more stable as the curing age and temperature increases, and the mechanical strength will be higher. The AAFAM mixture composed of Na2SiO3/NAOH with a mass ratio of 1.0 and 100% fly ash is considered the most suitable mixture. AAFAM can make full use of industrial by-products (fly ash) and seems to be a suitable candidate for green environmental protection materials for engineering applications. [ABSTRACT FROM AUTHOR]

Mortality remains an exceptional burden of extremely preterm birth. Current clinical mortality prediction scores are calculated using a few static variable measurements, such as gestational age, birth weight, temperature, and blood pressure at admission. While these models do provide some insight, numerical and time-series vital sign data are also available for preterm babies admitted to the NICU and may provide greater insight into outcomes. Computational models that predict the mortality risk of preterm birth in the NICU by integrating vital sign data and static clinical variables in real time may be clinically helpful and potentially superior to static prediction models. However, there is a lack of established computational models for this specific task. In this study, we developed a novel deep learning model, DeepPBSMonitor (Deep Preterm Birth Survival Risk Monitor), to predict the mortality risk of preterm infants during initial NICU hospitalization. The proposed deep learning model can effectively integrate time-series vital sign data and fixed variables while resolving the influence of noise and imbalanced data. The proposed model was evaluated and compared with other approaches using data from 285 infants. Results showed that the DeepPBSMonitor model outperforms other approaches, with an accuracy, recall, and AUC score of 0.888, 0.780, and 0.897, respectively. In conclusion, the proposed model has demonstrated efficacy in predicting the real-time mortality risk of preterm infants in initial NICU hospitalization. [ABSTRACT FROM AUTHOR]

Mortality is an unfortunately common outcome of extremely and very preterm birth. Existing clinical prediction models capture mortality risk at birth but fail to account for the remainder of the hospital course. Infants born < 32 weeks gestation with complete physiologic and clinical data were included in this retrospective study. Mortality risk was quantified by conventional means (clinical factors) using the CRIB-II score and the optimal logistic regression model. A random forest (RF) model was trained using a subset of the cohort, labeling data within 6 h of death as "worry." The model was then tested using the remaining infants. A total of 275 infants were included in the study with a mean gestational age of 27 weeks, mean birth weight of 929 g, 49% female, and a mortality rate of 21%. The CRIB-II and logistic regression models had acceptable performance with sensitivities of 71% and 80% AUC scores of 0.78 and 0.84, respectively. The RF model had superior performance with a sensitivity of 88% and an AUC of 0.93. A random forest model which incorporates fixed clinical factors with the influence of aberrancies in subsequent physiology has superior performance for mortality prediction compared to conventional models. [ABSTRACT FROM AUTHOR]

To investigate the influence of curing conditions and raw materials on the performance of engineered cement-based composites (ECCs), polypropylene fiber engineered cement-based composites (PP-ECCs) were designed and manufactured. The mathematical relationships between the fly ash content, curing temperature and PP-ECC tensile properties were explored by a uniaxial tensile test and by scanning electron microscopy. The results show the following: 1) with the increase in fly ash content and curing temperature, the ultimate tensile strain, ultimate tensile stress and tensile toughness index of the PP-ECC show a trend of first increasing and then decreasing, while the modulus of elasticity increases. 2) The optimum temperature is 20°C for the hydration reaction of the cementitious material inside the PP-ECC matrix. 3) When the fly ash content is 40% and the curing temperature is 20°C, the tensile mechanical parameters, ductility and toughness index of the PP-ECC are the best. This set of variables is recommended as a reference for PP-ECC engineering applications. 4) A mathematical relationship among the tensile strength, tensile strain and elastic modulus of the PP-ECC and coupling variables (fly ash content and curing temperature) is established. [ABSTRACT FROM AUTHOR]

Evidence regarding the relationship between diffuse myocardial fibrosis and the prognosis of heart failure with reduced ejection fraction (HFrEF) was limited. Therefore, this study set out to investigate whether diffuse myocardial fibrosis was independently related to the prognosis of failure with reduced ejection fraction in Chinese patients after adjusting for other covariates. The present study was a cohort study. A total of 45 consecutive HFrEF patients were involved in Zhongshan Hospital of Fudan University in China from 1/9/2015 to 31/12/2016. The target-independent variable was extracellular volume (ECV) quantified by cardiac magnetic resonance T1 mapping using the modified Look-Locker inversion recovery (MOLLI) sequence at baseline. To assess the prognostic impact of MOLLI-ECV, its association with hospitalization for heart failure/cardiac death was tested by multivariable Cox regression analysis. Covariates involved in this study included age, gender, body mass index, heart rate, systolic blood pressure diastolic blood pressure, smoking, hypertension, diabetes mellitus, etiology, NYHA functional class, blood urea nitrogen, creatinine, serum uric acid, total bilirubin, and growth stimulation-expressed gene 2. Ten age- and sex-matched healthy participants with no history of cardiovascular disease served as a control group. Mean MOLLI-ECV was significantly higher in HFrEF patients versus healthy controls (29.55 ± 1.46% vs. 23.17 ± 1.93%, P < 0.001). Patients were followed for 9 months, during which the primary outcome (cardiac death or first heart failure hospitalization) occurred in 15 patients. By Kaplan-Meier analysis, patients with high MOLLI-ECV ≥ 30.10% had shorter event-free survival than the middle (MOLLI-ECV between 30.10 and 28.60) and low (MOLLI-ECV < 28.60) MOLLI-ECV patients (log-rank, P = 0.0035). Result of fully-adjusted multivariable Cox regression analysis showed MOLLI-ECV was positively associated with the composite outcome of HFrEF patients after adjusting confounders hazard ratio (HR) 2.57, 95% CI (1.09, 6.04). By subgroup analysis, a stronger association was seen in patients who with NYHA functional class III-IV, hematocrit < 39.8%, left atrial diameter ≥ 53.5 mm, or without the medical history of MRA or diuretics other than MRA. The P for interaction was < 0.05. In HFrEF patients, the relationship between MOLLI-ECV determined by CMR and the composite outcome is linear. High MOLLI-ECV was associated with a higher rate of cardiac mortality and first HF hospitalization in the short term follow up. [ABSTRACT FROM AUTHOR]

Thermal barrier coatings that are widely used to protect the turbine blades of gas turbines from failure are subjected to hot corrosion conditions. The present study aims to improve their hot corrosion resistance by modifying the plasma-sprayed yttria-stabilized zirconia topcoat by laser remelting and Al deposition. First, a dense remelted layer with some vertical cracks was obtained by laser remelting. Then, Al film was deposited on the surface of laser-remelted coatings by magnetron sputtering, and the dense α-Al2O3 overlay was in situ synthesized through the reaction of Al and ZrO2 during vacuum heat treatment. Hot corrosion tests of all coatings were conducted in a mixture of 55 wt.% V2O5 and 45 wt.% Na2SO4 corrosive salts at 1000 °C for 30 h. The defects such as pores and microcracks in the as-sprayed coatings provided paths for molten salt penetration, which led to the worst hot corrosion resistance of the as-sprayed coatings. The minimal surface roughness of the dense laser-remelted coatings was the main reason for improving the hot corrosion resistance. However, the resistance of laser-remelted coatings to hot corrosion was significantly improved when they were modified by Al deposition, which is mainly due to the reduced reaction surface between molten salts and yttria stabilizer. The corresponding hot corrosion mechanism was discussed. [ABSTRACT FROM AUTHOR]

Inspired by the unique structures and shapes of biological organisms, thermal barrier coatings (TBCs) with different shapes including dot, striation and grid were modified by laser remelting. NiCrAlY/ZrO2-7 wt.%Y2O3 double-layer-structured TBCs were prepared. The microstructure, microhardness, phase composition and thermal shock behaviors of the as-sprayed and laser-treated specimens with different shapes were examined. The results indicated that the unit was characterized by the dense columnar crystal structure and the high microhardness. The thermal cycle lifetime of the dotted specimen was about twice that of the as-sprayed specimen. On the one hand, due to the elimination of defects and higher hardness after laser remelting, the dotted unit could resist thermal crack propagation. On the other hand, the columnar grains and segmented cracks in the dotted units were beneficial to increase the strain tolerance. However, due to more continuous segmented cracks and transverse cracks, the striated and grid specimens had relatively poor thermal shock resistance. [ABSTRACT FROM AUTHOR]

Ras GTPases are powerful drivers for tumorigenesis, but directly targeting Ras for treating cancer remains challenging. The growth and transforming activity of the aggressive basal-like breast cancer (BLBC) are driven by N-Ras. To target N-Ras in BLBC, this study screened existing pharmacologically active compounds for the new ability to induce N-Ras degradation, which led to the identification of flunarizine (FLN), previously approved for treating migraine and epilepsy. The FLN-induced N-Ras degradation was not affected by a 26S-proteasome inhibitor. Rather, it was blocked by autophagy inhibitors. Furthermore, N-Ras can be seen co-localized with active autophagosomes upon FLN treatment, suggesting that FLN alters the autophagy pathway to degrade N-Ras. Importantly, FLN treatment recapitulated the effect of N-RAS silencing in vitro by selectively inhibiting the growth of BLBC cells, but not that of breast cancer cells of other subtypes. In addition, in vivo FLN inhibited tumor growth of a BLBC xenograft model. In conclusion, this proof-of-principle study presents evidence that the autophagy pathway can be coerced by small molecule inhibitors, such as FLN, to degrade Ras as a strategy to treat cancer. FLN has low toxicity and should be further investigated to enrich the toolbox of cancer therapeutics. [ABSTRACT FROM AUTHOR]

The effects of Li2ZnTi3O8 addition on the sintering behavior, phase formation, microstructure and microwave dielectric properties of 0.94MgTiO3–0.06CaTiO3 ceramics have been investigated. These results suggested that Li2ZnTi3O8 addition not only contributes in decreasing the sintering temperature, but also enhances grain growth. A new microwave dielectric ceramic, 0.94MgTiO3–0.06CaTiO3–0.015Li2ZnTi3O8 sintered at 1220 °C had optimal dielectric properties (εr = 20.97, Q × f = 70,832 GHz, τf = 3.45 ppm/°C), which satisfies microwave applications in resonators, filters and antenna substrates. [ABSTRACT FROM AUTHOR]

To satisfy the upsurging demand for energy storage in modern society, anode materials which can deliver high capacity have been intensively researched for the next generation lithium ion batteries. Typically, the binary MnCoO with a characteristic coupled metal cations showed promising potential due to its high theoretical capacity and low cost. Here, by means of a well-designed synthesis control, we demonstrated a scalable process to achieve a hierarchical structure of MnCoO, which existed as uniform microspheres with embedded mesopores, showing favorable structural characters for high performance during a fast charge/discharge process. Our synthesis highlighted the importance of sodium salicylate as an essential additive to control the precipitation of the two involved metal cations. It was proved that a dual role was played sodium salicylate which cannot only facilitate the formation of microspheric shape, but also act as an effective precursor for the creation of inner mesopores. We confirmed that the hierarchically-structured MnCoO showed outstanding performance when it was tested as an anode material in lithium ion batteries as revealed by its extraordinary cycling stability and high rate capability. [ABSTRACT FROM AUTHOR]

The role of epithelial-to-mesenchymal transition (EMT) in metastasis is a longstanding source of debate, largely owing to an inability to monitor transient and reversible EMT phenotypes in vivo. Here we establish an EMT lineage-tracing system to monitor this process in mice, using a mesenchymal-specific Cre-mediated fluorescent marker switch system in spontaneous breast-to-lung metastasis models. We show that within a predominantly epithelial primary tumour, a small proportion of tumour cells undergo EMT. Notably, lung metastases mainly consist of non-EMT tumour cells that maintain their epithelial phenotype. Inhibiting EMT by overexpressing the microRNA miR-200 does not affect lung metastasis development. However, EMT cells significantly contribute to recurrent lung metastasis formation after chemotherapy. These cells survived cyclophosphamide treatment owing to reduced proliferation, apoptotic tolerance and increased expression of chemoresistance-related genes. Overexpression of miR-200 abrogated this resistance. This study suggests the potential of an EMT-targeting strategy, in conjunction with conventional chemotherapies, for breast cancer treatment. [ABSTRACT FROM AUTHOR]

The way in which cells adopt different morphologies is not fully understood. Cell shape could be a continuous variable or restricted to a set of discrete forms. We developed quantitative methods to describe cell shape and show that Drosophila haemocytes in culture are a heterogeneous mixture of five discrete morphologies. In an RNAi screen of genes affecting the morphological complexity of heterogeneous cell populations, we found that most genes regulate the transition between discrete shapes rather than generating new morphologies. In particular, we identified a subset of genes, including the tumour suppressor PTEN, that decrease the heterogeneity of the population, leading to populations enriched in rounded or elongated forms. We show that these genes have a highly conserved function as regulators of cell shape in both mouse and human metastatic melanoma cells. [ABSTRACT FROM AUTHOR]

mTOR inhibitor rapamycin and its analogs are lipophilic, demonstrate blood-brain barrier penetration, and have shown promising antitumor effects in several types of refractory tumors. We thus try to explore the therapeutic effects of mTOR inhibitors on brain metastasis models. We examined the effects of different dose of mTOR inhibitors (rapamycin, Temsirolimus-CCI-779) on cell invasion in two brain metastatic breast cancer cell lines (MDA-MB231-BR and CN34-BrM2). Antibody microarray and immunoblotting were applied to detect signaling pathways underlying the dose differential drug effects. The in vivo effects of single drug (CCI-779), and drug combination of CCI-779 with SL327 (a brain penetrant MEK inhibitor) to eliminate the unfavorable activation of MAPK pathway were evaluated in MDA-MB231-BR brain metastases xenograft mice. The two mTOR inhibitors, rapamycin and CCI-779, inhibited the invasion of brain metastatic cells only at a moderate concentration level, which was lost at higher concentrations secondary to activation of the MAPK signaling pathway. Pharmacological inhibition of ERK1/2 by PD98059 and SL327 restored the anti-invasion effects of mTOR inhibition in vitro. In vivo, a significant decrease was noted in the average number of micro and large metastatic lesions as well as the whole brain GFP expression in the CCI-779 1 mg/kg/day treated group compared with that in the vehicle group ( P < 0.05). However, 10 mg/kg CCI-779 treatment did not show significant anti-metastasis effect on the animal model. High-dose CCI-779 eliciting the ERK MAPK activation in the brain metastatic lesion was corroborated. Combined with the brain penetrant MEK inhibitor SL327, high-dose CCI-779 significantly reduces the brain metastasis, and the combination treatment prohibited perivascular invasion of tumor cells and inhibits tumor angiogenesis in vivo. This study provides evidence on the potential value of CCI-779 as well as CCI-779 + SL327 in prohibiting breast cancer brain metastasis. [ABSTRACT FROM AUTHOR]

Calcium ions (Ca2+) play a fundamental role in a variety of physiological functions in many cell types by acting as a secondary messenger. Variation of intracellular Ca2+ concentration ([Ca2+]i) is often observed when the cell is stimulated. However, it is a challenging task to automatically quantify intracellular [Ca2+]i in a population of cells. In this study, we present a workflow including specific algorithms for the automated intracellular calcium signal analysis using high-content, time-lapse cellular images. The experimental validations indicate the effectiveness of the proposed workflow and algorithms. We applied the workflow to analyze the intracellular calcium signals induced by different concentrations of H2O2 in the cell lines transfected by presenilin-1 (PS-1) that is known to be closely related to the familial Alzheimer’s disease (FAD). The analysis results imply an important role of mutant PS-1, but not normal human PS-1 and mutant human amyloid precursor protein (APP), in enhancing intracellular calcium signaling induced by H2O2. [ABSTRACT FROM AUTHOR]

Systems biology perspectives are crucial for understanding the pathophysiology of complex diseases, and therefore hold great promise for the discovery of novel treatment strategies. Drug combinations have been shown to improve durability and reduce resistance to available first-line therapies in a variety of cancers; however, traditional drug discovery approaches are prohibitively cost and labor-intensive to evaluate large-scale matrices of potential drug combinations. Computational methods are needed to efficiently model complex interactions of drug target pathways and identify mechanisms underlying drug combination synergy. In this study, we employ a computational approach, SynGeNet (Synergy from Gene expression and Network mining), which integrates transcriptomics-based connectivity mapping and network centrality analysis to analyze disease networks and predict drug combinations. As an exemplar of a disease in which combination therapies demonstrate efficacy in genomic-specific contexts, we investigate malignant melanoma. We employed SynGeNet to generate drug combination predictions for each of the four major genomic subtypes of melanoma (BRAF, NRAS, NF1, and triple wild type) using publicly available gene expression and mutation data. We validated synergistic drug combinations predicted by our method across all genomic subtypes using results from a high-throughput drug screening study across. Finally, we prospectively validated the drug combination for BRAF-mutant melanoma that was top ranked by our approach, vemurafenib (BRAF inhibitor) + tretinoin (retinoic acid receptor agonist), using both in vitro and in vivo models of BRAF-mutant melanoma and RNA-sequencing analysis of drug-treated melanoma cells to validate the predicted mechanisms. Our approach is applicable to a wide range of disease domains, and, importantly, can model disease-relevant protein subnetworks in precision medicine contexts. Systems Biology: Computational method predicts drug combinations for melanoma Existing drugs have the potential to learn "new tricks" to fight cancer when working together in combinations. Researchers from The Ohio State University and Washington University in St. Louis developed a computational approach to predict synergistic drug combinations among thousands of potential pairs based upon how cancer cells alter certain genes and connections between genes. They found that drug predictions were more accurate when this method was applied to specific molecular subtypes of melanoma. The top drug combination predicted for melanoma with BRAF gene mutations included a known melanoma drug, vemurafenib, in combination with another agent not currently used for melanoma, tretinoin. This combination could kill melanoma cells and reduce tumor size in mice. Future applications include other examples of "precision medicine" to deliver the right drugs to the right patients at the right time. [ABSTRACT FROM AUTHOR]

Small molecule compounds promoting the neuronal differentiation of stem/progenitor cells are of pivotal importance to regenerative medicine. We carried out a high-content screen to systematically characterize known bioactive compounds, on their effects on the neuronal differentiation and the midbrain dopamine (mDA) neuron specification of neural progenitor cells (NPCs) derived from the ventral mesencephalon of human fetal brain. Among the promoting compounds three major pharmacological classes were identified including the statins, TGF-βRI inhibitors, and GSK-3 inhibitors. The function of each class was also shown to be distinct, either to promote both the neuronal differentiation and mDA neuron specification, or selectively the latter, or promote the former but suppress the latter. We then carried out initial investigation on the possible mechanisms underlying, and demonstrated their applications on NPCs derived from human pluripotent stem cells (PSCs). Our study revealed the potential of several small molecule compounds for use in the directed differentiation of human NPCs. The screening result also provided insight into the signaling network regulating the differentiation of human NPCs. [ABSTRACT FROM AUTHOR]

The article announces the retraction of the article "Ginsenoside-Rg1 enhances angiogenesis and ameliorates ventricular remodeling in a rat model of myocardial infarction," by Huiqiu Yin and colleagues that was published in the 2011 issue.