ARPC4 gene silencing inhibits T24 cell invasion and metastasis via a mechanism involving Arp2/3/cofilin-1 signaling pathway

Purpose: To study the influence of ARPC4 gene silencing on human urinary bladder cancer (T24) cell proliferation, invasiveness and migration, and the mechanism(s) involved.Methods: Short interfering RNA (siRNA) ARPC4 silencing fragment was transfected into T24 cells. Transfection efficiency was measured with qRT-PCR. Cell proliferation, invasiveness and migratory potential were determined with CCK-8, Transwell invasion assay, and immunofluorescence assay,respectively. Protein expressions of ARPC4 and cofilin-1 were assayed using Western blotting.Results: Short interfering RNA (siRNA) silencing of ARPC4 gene led to the downregulation of mRNA and protein expressions of ARPC4 (t = 14.898, p < 0.05; t = 7.686, p < 0.05). It also significantly downregulated cofilin-1 protein, while inhibiting proliferative capacity, invasiveness and pseudopodiaformation capacity of T24 cells (t = 8.042, p < 0.05).Conclusion: The results obtained suggest that ARPC4 gene silencing inhibits T24 cell invasion and metastasis via a mechanism involving regulation of the Arp2/3/cofilin-1 signaling route. This provides new leads for gene therapy. Keywords: ARPC4, Bladder carcinoma, Gene silencing, Invasiveness, Cell proliferatio

Generalization bound for estimating causal effects from observational network data

Estimating causal effects from observational network data is a significant but challenging problem. Existing works in causal inference for observational network data lack an analysis of the generalization bound, which can theoretically provide support for alleviating the complex confounding bias and practically guide the design of learning objectives in a principled manner. To fill this gap, we derive a generalization bound for causal effect estimation in network scenarios by exploiting 1) the reweighting schema based on joint propensity score and 2) the representation learning schema based on Integral Probability Metric (IPM). We provide two perspectives on the generalization bound in terms of reweighting and representation learning, respectively. Motivated by the analysis of the bound, we propose a weighting regression method based on the joint propensity score augmented with representation learning. Extensive experimental studies on two real-world networks with semi-synthetic data demonstrate the effectiveness of our algorithm

Estimating Long-term Heterogeneous Dose-response Curve: Generalization Bound Leveraging Optimal Transport Weights

Long-term causal effect estimation is a significant but challenging problem in many applications. Existing methods rely on ideal assumptions to estimate long-term average effects, e.g., no unobserved confounders or a binary treatment,while in numerous real-world applications, these assumptions could be violated and average effects are unable to provide individual-level suggestions.In this paper,we address a more general problem of estimating the long-term heterogeneous dose-response curve (HDRC) while accounting for unobserved confounders. Specifically, to remove unobserved confounding in observational data, we introduce an optimal transport weighting framework to align the observational data to the experimental data with theoretical guarantees. Furthermore,to accurately predict the heterogeneous effects of continuous treatment, we establish a generalization bound on counterfactual prediction error by leveraging the reweighted distribution induced by optimal transport. Finally, we develop an HDRC estimator building upon the above theoretical foundations. Extensive experimental studies conducted on multiple synthetic and semi-synthetic datasets demonstrate the effectiveness of our proposed method

A Systematic, Integrated Study on the Neuroprotective Effects of Hydroxysafflor Yellow A Revealed by<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mrow><mml:mtext>H</mml:mtext></mml:mrow><mml:mrow><mml:mtext>1</mml:mtext></mml:mrow></mml:mrow></mml:math>NMR-Based Metabonomics and the NF-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mi mathvariant="bold">κ</mml:mi></mml:mrow></mml:math>B Pathway

Hydroxysafflor yellow A (HSYA) is the main active component of the Chinese herbCarthamus tinctoriusL.. Purified HSYA is used as a neuroprotective agent to prevent cerebral ischemia. Injectable safflor yellow (50 mg, containing 35 mg HSYA) is widely used to treat patients with ischemic cardiocerebrovascular disease. However, it is unknown how HSYA exerts a protective effect on cerebral ischemia at the molecular level. A systematical integrated study, including histopathological examination, neurological evaluation, blood-brain barrier (BBB), metabonomics, and the nuclear factor-κB (NF-κB) pathway, was applied to elucidate the pathophysiological mechanisms of HSYA neuroprotection at the molecular level. HSYA could travel across the BBB, significantly reducing the infarct volume and improving the neurological functions of rats with ischemia. Treatment with HSYA could lead to relative corrections of the impaired metabolic pathways through energy metabolism disruption, excitatory amino acid toxicity, oxidative stress, and membrane disruption revealed by1H NMR-based metabonomics. Meanwhile, HSYA treatment inhibits the NF-κB pathway via suppressing proinflammatory cytokine expression and p65 translocation and binding activity while upregulating an anti-inflammatory cytokine.</jats:p

Efficacy and Safety of Electroacupuncture on Treating Depression Related Sleep Disorders: Study Protocol of a Randomized Controlled Trial

Background. Depression is frequently accompanied by sleep disturbances including insomnia. Insomnia may persist even after mood symptoms have been adequately treated. Acupuncture is considered to be beneficial to adjust the state of body and mind and restore the normal sleep-awake cycle. This trial is aimed at evaluating the efficacy and safety of electroacupuncture on treating insomnia in patients with depression. Methods. We describe a protocol for a randomized, single-blinded, sham controlled trial. Ninety eligible patients will be randomly assigned to one of 3 treatment groups: treatment group (acupuncture), control A group (superficial acupuncture at sham points), and control B group (sham acupuncture). All treatment will be given 3 times per week for 8 weeks. The primary outcome is the Pittsburgh Sleep Quality Index (PSQI). The secondary outcomes are sleep parameters recorded in the Actigraphy, Hamilton Rating Scale for Depression (HAMD), and Self-Rating Depression Scale (SDS). All adverse effects will be accessed by the Treatment Emergent Symptom Scale (TESS). Outcomes will be evaluated at baseline, 4 weeks after treatment, 8 weeks after treatment, and 4 weeks of follow-up. Ethics. This trial has been approved by the Ethics Committee of Shanghai Municipal Hospital of Traditional Chinese Medicine (2015SHL-KY-21) and is registered with ChiCTR-IIR-16008058

Short-Term Energy Consumption Prediction of Large Public Buildings Combined with Data Feature Engineering and Bilstm-Attention

Accurate building energy consumption prediction is a crucial condition for the sustainable development of building energy management systems. However, the highly nonlinear nature of data and complex influencing factors in the energy consumption of large public buildings often pose challenges in improving prediction accuracy. In this study, we propose a combined prediction model that combines signal decomposition, feature screening, and deep learning. First, we employ the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose energy consumption data. Next, we propose the Maximum Mutual Information Coefficient (MIC)-Fast Correlation Based Filter (FCBF) combined feature screening method for feature selection on the decomposed components. Finally, the selected input features and corresponding components are fed into the Bi-directional Long Short-Term Memory Attention Mechanism (BiLSTMAM) model for prediction, and the aggregated results yield the energy consumption forecast. The proposed approach is validated using energy consumption data from a large public building in Shaanxi Province, China. Compared with the other five comparison methods, the RMSE reduction of the CEEMDAN-MIC-FCBF-BiLSTMAM model proposed in this study ranged from 57.23% to 82.49%. Experimental results demonstrate that the combination of CEEMDAN, MIC-FCBF, and BiLSTMAM modeling markedly improves the accuracy of energy consumption predictions in buildings, offering a potent method for optimizing energy management and promoting sustainability in large-scale facilities

Dam Deformation Interpretation and Prediction Based on a Long Short-Term Memory Model Coupled with an Attention Mechanism

An accurate dam deformation prediction model is vital to a dam safety monitoring system, as it helps assess and manage dam risks. Most traditional dam deformation prediction algorithms ignore the interpretation and evaluation of variables and lack qualitative measures. This paper proposes a data processing framework that uses a long short-term memory (LSTM) model coupled with an attention mechanism to predict the deformation response of a dam structure. First, the random forest (RF) model is introduced to assess the relative importance of impact factors and screen input variables. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) method is used to identify and filter the equipment based abnormal values to reduce the random error in the measurements. Finally, the coupled model is used to focus on important factors in the time dimension in order to obtain more accurate nonlinear prediction results. The results of the case study show that, of all tested methods, the proposed coupled method performed best. In addition, it was found that temperature and water level both have significant impacts on dam deformation and can serve as reliable metrics for dam management.</jats:p

ARPC4 gene silencing inhibits T24 cell invasion and metastasis via a mechanism involving Arp2/3/cofilin-1 signaling pathway

Purpose: To study the influence of ARPC4 gene silencing on human urinary bladder cancer (T24) cell proliferation, invasiveness and migration, and the mechanism(s) involved.Methods: Short interfering RNA (siRNA) ARPC4 silencing fragment was transfected into T24 cells. Transfection efficiency was measured with qRT-PCR. Cell proliferation, invasiveness and migratory potential were determined with CCK-8, Transwell invasion assay, and immunofluorescence assay,respectively. Protein expressions of ARPC4 and cofilin-1 were assayed using Western blotting.Results: Short interfering RNA (siRNA) silencing of ARPC4 gene led to the downregulation of mRNA and protein expressions of ARPC4 (t = 14.898, p &lt; 0.05; t = 7.686, p &lt; 0.05). It also significantly downregulated cofilin-1 protein, while inhibiting proliferative capacity, invasiveness and pseudopodiaformation capacity of T24 cells (t = 8.042, p &lt; 0.05).Conclusion: The results obtained suggest that ARPC4 gene silencing inhibits T24 cell invasion and metastasis via a mechanism involving regulation of the Arp2/3/cofilin-1 signaling route. This provides new leads for gene therapy.&#x0D; Keywords: ARPC4, Bladder carcinoma, Gene silencing, Invasiveness, Cell proliferation </jats:p