Circular RNA (circRNA) is a type of noncoding RNA that can interact with miRNAs to regulate gene expression. However, little is known concerning circRNA, which is crucial in the pathogenesis of lung ...cancer. To date, limited studies have explored the role of circ_0044516 in lung cancer progression. Recently, we observed that circ_0044516 expression levels were obviously elevated in lung cancer tissues and cells. A549 and SPCA1 cells were transfected with circ_0044516 siRNA. We observed that knockdown of circ_0044516 dramatically repressed cell proliferation, increased cell apoptosis, and repressed the cell cycle. Moreover, A549 and SPCA1 cell migration and invasion abilities were greatly repressed by circ_0044516 siRNA. Due to accumulating evidence demonstrating the vital role of cancer stem cells, their mechanism of involvement has drawn increasing attention in tumor progression and metastasis research. We also found that cancer stem cell properties were restrained by silencing circ_0044516 in A549 and SPC-A1 cells. Moreover, in vivo xenograft experiments showed that circ_0044516 downregulation reduced tumor growth. Mechanistically, in lung cancer and using bioinformatics, we demonstrated that circ_0044516 sponges miR-136 targeting MAT2A. Furthermore, rescue assays were carried out to identify that circ_0044516 modulates cell proliferation, invasion, and stemness by regulating miR-136 and MAT2A in lung cancer. In summary, our study revealed that the circ_0044516/miR-136/MAT2A axis is involved in lung cancer progression. Our findings may provide novel targets for diagnosis and therapeutic intervention in lung cancer patients.
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•The performance was evaluated in RMO (micro-oxygen bioanode MFC).•6-fold higher phenol removal and 4-fold higher current were achieved in RMO.•Micro-oxygen promoted more biomass ...production through stimulating bacterial growth.•The enhancement of RMO could be related to metabolites, biomass and microflora.
It is controversial to introduce oxygen into anode chamber as oxygen would decrease the CE (Coulombic efficiency) while it could also enhance the degradation of aromatics in microbial fuel cell (MFCs). Therefore, it is important to balance the pros and cons of oxygen in aromatics driven MFCs. A RMO (micro-oxygen bioanode MFC) was designed to determine the effect of oxygen on electricity output and phenol degradation. The RMO showed 6-fold higher phenol removal efficiency, 4-fold higher current generation than the RAN (anaerobic bioanode MFC) at a cost of 26.9% decline in CE. The Zoogloea and Geobacter, which account for phenol degradation and current generation, respectively, were dominated in the RMO bioanode biofilm. The biomass also showed great difference between RMO and RAN (114.3 ± 14.1 vs. 2.2 ± 0.5 nmol/g). Therefore, different microbial community, higher biomass as well as the different degradation pathway were suggested as reasons for the better performance in RMO.
Serum albumin level is a crucial nutritional indicator for patients on dialysis. Approximately one-third of patients on hemodialysis (HD) have protein malnutrition. Therefore, the serum albumin level ...of patients on HD is strongly correlated with mortality.
In study, the data sets were obtained from the longitudinal electronic health records of the largest HD center in Taiwan from July 2011 to December 2015, included 1,567 new patients on HD who met the inclusion criteria. Multivariate logistic regression was performed to evaluate the association of clinical factors with low serum albumin, and the grasshopper optimization algorithm (GOA) was used for feature selection. The quantile g-computation method was used to calculate the weight ratio of each factor. Machine learning and deep learning (DL) methods were used to predict the low serum albumin. The area under the curve (AUC) and accuracy were calculated to determine the model performance.
Age, gender, hypertension, hemoglobin, iron, ferritin, sodium, potassium, calcium, creatinine, alkaline phosphatase, and triglyceride levels were significantly associated with low serum albumin. The AUC and accuracy of the GOA quantile g-computation weight model combined with the Bi-LSTM method were 98% and 95%, respectively.
The GOA method was able to rapidly identify the optimal combination of factors associated with serum albumin in patients on HD, and the quantile g-computation with DL methods could determine the most effective GOA quantile g-computation weight prediction model. The serum albumin status of patients on HD can be predicted by the proposed model and accordingly provide patients with better a prognostic care and treatment.
Organic–inorganic hybrid materials have drawn increasing attention as photothermal agents in tumor therapy due to the advantages of green synthesis, high loading efficiency of hydrophobic drugs, ...facile incorporation of theranostic iron, and excellent photothermal efficiency without inert components or additives. Herein, we proposed a strategy for biomimetic engineering-mediated enhancement of photothermal performance in the tumor microenvironment (TME). This strategy is based on the specific characteristics of organic–inorganic hybrid materials and endows these materials with homologous targeting ability and photothermal stability in the TME. The hybrid materials perform the functions of cancer cells to target homolytic tumors (acting as “artificial nanotargeted cells (ANTC)”). Inspired by the pH-dependent disassembly behaviors of tannic acid (TA) and ferric ion (FeIII) and subsequent attenuation of photothermal performance, cancer cell membranes were self-deposited onto the surfaces of protoporphyrin-encapsulated TA and FeIII nanoparticles to achieve ANTC with TME-stable photothermal performance and tumor-specific phototherapy. The resulting ANTC can be used as contrast agents for concurrent photoacoustic imaging, magnetic resonance imaging, and photothermal imaging to guide the treatment. Importantly, the high loading efficiency of protoporphyrin enables the initiation of photodynamic therapy to enhance photothermal therapeutic efficiency, providing antitumor function with minimal side effects.
The tourism industry has become one of the most important economic sectors for governments worldwide. Accurately forecasting tourism demand is crucial because it provides useful information to ...related industries and governments, enabling stakeholders to adjust plans and policies. To develop a forecasting tool for the tourism industry, this study proposes a method that combines feature selection (FS) and support vector regression (SVR) with particle swarm optimization (PSO), named FS–PSOSVR. To ensure high forecast accuracy, FS and a PSO algorithm are employed to, respectively, select reliable input variables and to identify the optimal initial parameters of SVR. The proposed method was tested using a data set of monthly tourist arrivals to Taiwan from January 2006 to December 2016. The results reveal that the errors obtained using FS–PSOSVR are comparatively smaller than those obtained using other methods, indicating that FS–PSOSVR is an effective method for forecasting tourism demand.
Exposure to heavy metals could lead to adverse health effects by oxidative reactions or inflammation. Some essential elements are known as reactors of anti-inflammatory enzymes or coenzymes. The ...relationship between tumor necrosis factor alpha (TNF-α) and heavy metal exposures was reported. However, the interaction between toxic metals and essential elements in the inflammatory response remains unclear. This study aimed to explore the association between arsenic (As), cadmium (Cd), lead (Pb), cobalt (Co), copper (Cu), selenium (Se), and zinc (Zn) in blood and TNF-α as well as kidney function. We enrolled 421 workers and measured the levels of these seven metals/metalloids and TNF-α in blood; kidney function was calculated by CKD-EPI equation. We applied weighted quantile sum (WQS) regression and group WQS regression to assess the effects of metal/metalloid mixtures to TNF-α and kidney function. We also approached the relationship between metals/metalloids and TNF-α by generalized additive models (GAM). The relationship of the exposure−response curve between Pb level and TNF-α in serum was found significantly non-linear after adjusting covariates (p < 0.001). Within the multiple-metal model, Pb, As, and Zn were associated with increased TNF-α levels with effects dedicated to the mixture of 50%, 31%, and 15%, respectively. Grouped WQS revealed that the essential metal group showed a significantly negative association with TNF-α and kidney function. The toxic metal group found significantly positive associations with TNF-α, serum creatinine, and WBC but not for eGFR. These results suggested Pb, As, Zn, Se, and mixtures may act on TNF-α even through interactive mechanisms. Our findings offer insights into what primary components of metal mixtures affect inflammation and kidney function during co-exposure to metals; however, the mechanisms still need further research.
Mitochondria and cell membrane play important roles in maintaining cellular activity and stability. Here, a single-agent self-delivery chimeric peptide based nanoparticle (designated as M-ChiP) was ...developed for mitochondria and plasma membrane dual-targeted photodynamic tumor therapy. Without additional carrier, M-ChiP possessed high drug loading efficacy as well as the excellent ability of producing reactive oxygen species (ROS). Moreover, the dual-targeting property facilitated the effective subcellular localization of photosensitizer protoporphyrin IX (PpIX) to generate ROS in situ for enhanced photodynamic therapy (PDT). Notably, plasma membrane-targeted PDT would enhance the membrane permeability to improve the cellular delivery of M-ChiP, and even directly disrupt the cell membrane to induce cell necrosis. Additionally, mitochondria-targeted PDT would decrease mitochondrial membrane potential and significantly promote the cell apoptosis. Both in vitro and in vivo investigations indicated that this combinatorial PDT in mitochondria and plasma membrane could achieve the therapeutic effect maximization with reduced side effects. The single-agent self-delivery system with dual-targeting strategy was demonstrated to be a promising nanoplatform for synergistic tumor therapy.
In this study, we investigated the association of time-varying serum albumin levels with mortality over a 5-year period in one cohort of patients undergoing long-term peritoneal dialysis (PD) ...therapy.
The participants in this study enrolled 302 patients who underwent long-term PD at a single PD center in Taiwan. We reviewed medical records from 2011 to 2015 retrospectively. Time-averaged albumin level and serum albumin reach rate (defined as the percentage of serum albumin measurements that reached ≥3.5 g/dL) were applied as the predictor variables in the first 2 years (2011-2012). All-cause mortality was used as the outcome variable in the subsequent 3 years (2013-2015). Hazard function of all-cause mortality in the study participants was examined by using Cox proportional hazard regression models .
Patients with different albumin reach rates (75-< 100%, 50-< 75%, 1-< 50%) did not exhibit a significantly increased risk for all-cause mortality. Patients with a 0% albumin reach rate exhibited a significantly increased risk for all-cause mortality (hazard ratio HR 7.59, 95% confidence interval CI, 2.38-24.21) by fully adjusted analysis. Patients with time-averaged albumin levels of < 3.5 g/dL (HR 15.49, 95% CI 1.74-137.72) exhibited a higher risk for all-cause mortality than those with serum albumin levels ≥4.0 g/dL.
This study demonstrated that higher serum albumin reach rates and higher time-averaged serum albumin levels are associated with a lower mortality rate over a 5-year period among patients undergoing long-term PD.
Tide is a phenomenon of water level change caused by gravity. Tidal level forecasting is not only a key theoretical topic but also crucial in coastal and ocean engineering applications. The waiting ...time before a cargo ship enters a port affects the efficiency of cargo transportation, the tidal difference affects the establishment of turbine generators, and an excessive tidal water level reduces vessel safety. With the proliferation of information technology, the application of deep learning models in the analysis and study of hydrological problems has become increasingly common. This study proposed a deep learning model to predict the tidal water level. A forecasting model was developed on the basis of the long short-term memory (LSTM) recurrent neural network for predicting the water levels of 17 harbors in Taiwan. Tidal water level data for 21 years were collected from different observation stations. To objectively evaluate model performance, the developed model was compared with six other forecasting models in terms of the mean absolute percentage error (MAPE) and root mean square error (RMSE) of the forecasting results. The results indicated that the LSTM model had the lowest forecasting error for the tidal water level for up to 30 days. The average MAPE and RMSE values for the developed model were 6.97% and 0.049 m, respectively; thus, the model could effectively reduce the overlapping problems caused by machine learning methods in continuous forecasting.
Reported is a modular strategy for the preparation of the unique benzofused dioxabicycle scaffolds involving a Catellani reaction of aryl iodides, epoxides, and terminal alkynes and an ...oxa-cyclization. This is a mild, scalable, chemoselective, and atom-economical protocol, compatible with various functionalized aryl iodides, epoxides, and terminal alkynes. With the ability to build up the molecular complexity rapidly and efficiently from feedstock chemicals, this method will have wide applications in organic synthesis.
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