Cells derived from iPSC can be immunogenic — Yes or No?

The induced pluripotent stem cells (iPSCs), derived by ectopic expression of reprogramming factors in somatic cells, can potentially provide unlimited autologous cells for regenerative medicine. In theory, the autologous cells derived from patient iPSCs should be immune tolerant by the host without any immune rejections. However, our recent studies have found that even syngeneic iPSC-derived cells can be immunogenic in syngeneic hosts by using a teratoma transplantation model (Nature 474:212–215, 2011). Recently two research groups differentiated the iPSCs into different germ layers or cells, transplanted those cells to the syngeneic hosts, and evaluated the immunogenicity of those cells. Both of the two studies support our conclusions that some certain but not all tissues derived from iPSCs can be immunogenic, although they claimed either “negligible” or “lack of” immunogenicity in iPSC derivatives (Nature 494:100–104, 2013; Cell Stem Cell 12:407–412, 2013). To test the immunogenicity of clinically valuable cells differentiated from human iPSCs are emergently required for translation of iPSC technology to clinics

State of health prediction of lithium-ion batteries based on SSA optimized hybrid neural network model.

The accurate state of health (SOH) estimation of lithium-ion batteries (LIBs) is crucial for the operation and maintenance of new energy electric vehicles. To address this current problem, an improved hybrid neural network model for SOH prediction based on a sparrow search algorithm (SSA) optimized convolutional bi-directional long short-term memory neural network (CNN-Bi-LSTM) is proposed. Firstly, by analyzing the battery aging data, several feature indicators with highly correlated battery life degradation are constructed. Secondly, the CNN-Bi-LSTM model is used to extract the battery aging data features and the latent timing laws. Finally, the SSA optimizes the parameters to improve the model accuracy. Experimental results based on the NASA-Pcoe battery dataset show that the SSA-CNN-Bi-LSTM model outperforms other models, and the root-mean-square errors of the SOH prediction results are all less than 0.6%. It indicates that the proposed SSA-CNN-Bi-LSTM model is capable of predicting SOH accurately and with high precision

High-precision joint estimation of the state of charge and state of energy for new energy electric vehicle lithium-ion batteries based on improved singular value decomposition-adaptive embedded cubature Kalman filtering.

Accurate online estimation of the state of charge (SOC) and state of energy (SOE) of lithium-ion batteries are essential for efficient and reliable energy management of new energy electric vehicles (EVs). To improve the accuracy and stability of the joint estimation of SOC and SOE of lithium-ion batteries for EVs, based on a dual-polarization (DP) equivalent circuit model and time-varying forgetting factor recursive least squares (TVFFRLS) algorithm for online parameter identification, a joint estimation method based on singular value decomposition with adaptive embedded cubature Kalman filtering (SVD-AECKF) algorithm is proposed. The algorithm adopts the embedded cubature criterion and singular value decomposition method to improve filtering efficiency, accuracy, and numerical stability. Meanwhile, combining the idea of adaptive covariance matching for real-time adaptive updating of system noise to improve joint estimation accuracy. Finally, the results under different initial errors and complex operating conditions show that the SVD-AECKF algorithm improves the convergence time of SOC estimation by at least 26.3% compared to that before optimization. The SOE estimation error is reduced by at least 12.0% compared to that before optimization. This indicates that the SVD-AECKF algorithm has good joint SOC and SOE estimation accuracy, convergence, and stability

Improved backward smoothing square root cubature Kalman filtering and fractional order—battery equivalent modeling for adaptive state of charge estimation of lithium-ion batteries in electric vehicles.

The accuracy of lithium-ion battery state of charge (SOC) estimation affects the driving distance, battery life, and safety performance of electric vehicles. Herein, the polarization reaction inside the battery is modeled using a second-order fractional-order equivalent circuit model and uses an adaptive genetic algorithm for model parameter identification. Then, an improved adaptive fractional-order backward smoothing square root cubature Kalman filtering algorithm (AFOBS-SRCKF) is proposed by integrating Sage Husa adaptive filtering and backward smoothing processes to optimize the square root cubature Kalman filter for improving the accuracy and adaptability of real-time estimation of SOC in a complex environment. Finally, the algorithm is compared with the integer-order SRCKF, fractional-order SRCKF through simulation, and fractional-order backward smoothing SRCKF through simulation. Under complex operating conditions, the error sum of SOC estimation of the AFOBS-SRCKF algorithm is controlled within 1.0% and the convergence speed is improved by at least 30%. The results show that the AFOBS-SRCKF algorithm effectively improves the accuracy, stability, and convergence of SOC estimation

The effects of diverse microbial community structures, driven by arbuscular mycorrhizal fungi inoculation, on carbon release from a paddy field

Arbuscular mycorrhizal fungi (AMF) play a key role in regulating the carbon cycle in terrestrial ecosystems. However, there is little information on how AMF inoculation affects the carbon fluxes of paddy fields, which are major sources of global carbon emissions. We, therefore, designed an experiment to study the effects of AMF inoculation on methane and carbon dioxide emissions from a paddy field. Results showed that: (1) Among the tested factors, the C/N ratio was the main environmental determinant of microbial community structure in the investigated soil; (2) compared with traditional fertilisation (control), the soil C/N ratio increased by 2.1~15.2% and 1.4~10.5% as a result of AMF application alone (M) or in combination with mineral fertiliser (FM) throughout the growing season, respectively. This change shifted microbial community composition to higher G+/G- bacterial and fungal/bacterial ratios; (3) the microbial community change favoured soil carbon retention. Methane (CH4) emission peaks were reduced by 59.4% and 76.0% versus control in the M treatment and by 52.5% and 29.4% in the FM treatment in the midseason and end-of-season drainage periods, and CO2 emission peaks were reduced by 70.1% and 52.3% in the M plots and by 55.4% and 66.4% in the FM plots

Improved Antitumor Efficacy and Pharmacokinetics of Bufalin via PEGylated Liposomes

Abstract Bufalin was reported to show strong pharmacological effects including cardiotonic, antiviral, immune-regulation, and especially antitumor effects. The objective of this study was to determine the characterization, antitumor efficacy, and pharmacokinetics of bufalin-loaded PEGylated liposomes compared with bufalin entity, which were prepared by FDA-approved pharmaceutical excipients. Bufalin-loaded PEGylated liposomes and bufalin-loaded liposomes were prepared reproducibly with homogeneous particle size by the combination of thin film evaporation method and high-pressure homogenization method. Their mean particle sizes were 127.6 and 155.0 nm, mean zeta potentials were 2.24 and − 18.5 mV, and entrapment efficiencies were 76.31 and 78.40%, respectively. In vitro release profile revealed that the release of bufalin in bufalin-loaded PEGylated liposomes was slower than that in bufalin-loaded liposomes. The cytotoxicity of blank liposomes has been found within acceptable range, whereas bufalin-loaded PEGylated liposomes showed enhanced cytotoxicity to U251 cells compared with bufalin entity. In vivo pharmacokinetics indicated that bufalin-loaded PEGylated liposomes could extend or eliminate the half-life time of bufalin in plasma in rats. The results suggested that bufalin-loaded PEGylated liposomes improved the solubility and increased the drug concentration in plasma

Comparison of transcatheter arterial chemoembolization combined with radiofrequency ablation or microwave ablation for the treatment of unresectable hepatocellular carcinoma: a systemic review and meta-analysis

Background Transcatheter arterial chemoembolization (TACE), radiofrequency ablation (RFA), and microwave ablation (MWA) are regarded as effective therapies for treating unresectable hepatocellular carcinoma (HCC). We conducted this study to compare the efficiency and safety of TACE combined with RFA (TR group) or MWA (TM group). Method PubMed, the Cochrane Library, Ovid Medline, Web of Science, Scopus, Embase, ScienceDirect, and Google Scholar were searched. The primary endpoints were overall survival (OS), progression-free survival (PFS), response rates, and complications. Result Eight cohort studies and one randomized controlled trial were included. The TM group had better OS (Hazard ratio [HR]: 1.55; 95% confidence interval [CI]: 1.09–2.21, p = 0.01) and a better 2- and 3-year OS rate, 24-month PFS rate (Risk ratio [RR]: 0.67; 95% CI: 0.46–0.96, p = 0.03), and complete response rate (RR: 0.87; 95% CI: 0.79–0.96, p = 0.003) than the TR group. Furthermore, the TM and TR groups did not show significant differences in PFS, the disease control rate or complications. The advantage of TM was mainly reflected in younger patients (50–60 years old) compared with patients aged 60–70 years, as well as in patients with larger tumors (≥3 cm) compared with patients with tumors <3 cm. Moreover, patients treated with conventional TACE (cTACE) in the TM group showed longer OS, while patients treated with drug-eluting bead transarterial chemoembolization (DEB-TACE) in the TR group showed a higher overall response rate. Conclusion TM seems to be a more effective therapy than TR for unresectable HCC, with better survival and similar safety

Cinnamaldehyde in a Novel Intravenous Submicrometer Emulsion: Pharmacokinetics, Tissue Distribution, Antitumor Efficacy, and Toxicity

The purpose of our research is to find a new lipid emulsion to deliver a low water-soluble compound, cinnamaldehyde (CA). Its characteristics, pharmacokinetics, antitumor efficacy, and toxicity were evaluated. The mean particle size, zeta potential, and encapsulation efficiency of the submicromemter emulsion of CA (SME-CA) were 130 ± 5.92 nm, −25.7 ± 6.00 mV, and 99.5 ± 0.25%, respectively. The area under the curve from 0 h to termination time (AUC_0–<i>t</i>) of SME-CA showed a significantly higher value than that of CA (589 ± 59.2 vs 375 ± 83.5 ng h/L, <i>P</i> < 0.01). Tissue distribution study showed various changes; among them, a 27% higher concentration was found in brain tissue when using SME-CA at 15 min after administration. For the efficacy evaluation, SME-CA exhibited 8- and 11-fold antitumor activity in the depression of HeLa and A549 cell lines with the IC₅₀ decreasing to 0.003 and 0.001 mmol/L, respectively. The LD₅₀ values of CA and SME-CA in mice were 74.8 and 125 mg/kg, suggesting increased safety from the new formulation. The new formulation exhibited lower toxicity, higher antitumor activity, and a more satisfactory pharmacokinetic property, which displayed great potential for future pharmacological application