NIR molecule induced self-assembled nanoparticles for synergistic in vivo chemo-photothermal therapy of bladder cancer

Bladder cancer (BC) is one of the commonest malignancies in the urinary system. Bladder cancer is divided into non-muscle invasive bladder cancer (NMIBC) and muscle invasive bladder cancer (MIBC) according to the depth of invasion. Besides, the prognosis of MIBC remains poor. Surgical resection combined with radiotherapy or chemotherapy is the standard treatment for MIBC. However, the major obstacle that hinders successful chemotherapy is its lack of tumor targeting. Here, we fabricated nanoparticles that respond to near-infrared laser irradiation in order to increase the drug accumulation at the tumor sites and combine chemotherapy with photothermal therapy to overcome challenges of bladder cancer treatment. IR780 and Doxorubicin (DOX)were loaded into albumin nanoparticles (IR780-DOX@Albumin NPs). In the process of IR780-DOX@Albumin NPs synthesis, the near-infrared molecule IR780 was used as the assembly molecular bridge. Under irradiation, the nanoparticles were decomposed due to the degradation of IR780 while the release of DOX increased. Nanoparticles can be ingested by tumor cells in a short time. The IR780- DOX@Albumin NPs were sensitive to near-infrared laser irradiation. Near-infrared laser irradiation can promote the release of the drugs from the nanoparticles and induce a photothermal effect, thus destroying the tumor cells. Given the excellent tumor-targeting ability and negligible toxicity to normal tissue, IR780-DOX@Albumin NPs can greatly increase the concentration of chemotherapeutic drugs in tumor cells. This study combines photothermal therapy and chemotherapy to treat MIBC, so as to avoid chemotherapy resistance, reduce the toxicity to normal cells, and achieve the purpose of improving the treatment of MIBC

Deep reinforcement learning based direct torque control strategy for distributed drive electric vehicles considering active safety and energy saving performance

Distributed drive electric vehicles are regarded as a broadly promising transportation tool owing to their convenience and maneuverability. However, reasonable and efficient allocation of torque demand to four wheels is a challenging task. In this paper, a deep reinforcement learning-based torque distribution strategy is proposed to guarantee the active safety and energy conservation. The torque distribution task is explicitly formulated as a Markov decision process, in which the vehicle dynamic characteristics can be approximated. The actor-critic networks are utilized to approximate the action value and policy functions for a better control effect. To guarantee continuous torque output and further stabilize the learning process, a twin delayed deep deterministic policy gradient algorithm is deployed. The motor efficiency is incorporated into the cumulative reward to reduce the energy consumption. The results of double lane change demonstrate that the proposed strategy results in better handling stability performance. In addition, it can improve the vehicle transient response and eliminate the static deviation in the step steering maneuver test. For typical steering maneuvers, the proposed direct torque distribution strategy significantly improves the average motor efficiency and reduces the energy loss by 5.25%–10.51%. Finally, a hardware-in-loop experiment was implemented to validate the real-time executability of the proposed torque distribution strategy. This study provides a foundation for the practical application of intelligent safety control algorithms in future vehicles

TGFβƒ1 Promotes Gemcitabine Resistance Through Regulating the LncRNA-LET/NF90/miR-145 Signaling Axis in Bladder Cancer

High tumor recurrence is frequently observed in patients with urinary bladder cancers (UBCs), with the need for biomarkers of prognosis and drug response. Chemoresistance and subsequent recurrence of cancers are driven by a subpopulation of tumor initiating cells, namely cancer stem-like cells (CSCs). However, the underlying molecular mechanism in chemotherapy-induced CSCs enrichment remains largely unclear. In this study, we found that during gemcitabine treatment lncRNA-Low Expression in Tumor (lncRNA-LET) was downregulated in chemoresistant UBC, accompanied with the enrichment of CSC population. Knockdown of lncRNA-LET increased UBC cell stemness, whereas forced expression of lncRNA-LET delayed gemcitabine-induced tumor recurrence. Furthermore, lncRNA-LET was directly repressed by gemcitabine treatment-induced overactivation of TGFβ/SMAD signaling through SMAD binding element (SBE) in the lncRNA-LET promoter. Consequently, reduced lncRNA-LET increased the NF90 protein stability, which in turn repressed biogenesis of miR-145 and subsequently resulted in accumulation of CSCs evidenced by the elevated levels of stemness markers HMGA2 and KLF4. Treatment of gemcitabine resistant xenografts with LY2157299, a clinically relevant specific inhibitor of TGFβRI, sensitized them to gemcitabine and significantly reduced tumorigenecity in vivo. Notably, overexpression of TGFβ1, combined with decreased levels of lncRNA-LET and miR-145 predicted poor prognosis in UBC patients. Collectively, we proved that the dysregulated lncRNA-LET/NF90/miR-145 axis by gemcitabine-induced TGFβ1 promotes UBC chemoresistance through enhancing cancer cell stemness. The combined changes in TGFβ1/lncRNA-LET/miR-145 provide novel molecular prognostic markers in UBC outcome. Therefore, targeting this axis could be a promising therapeutic approach in treating UBC patients

Design and validation of a battery management system for solar-assisted electric vehicles

Expanding the travel mileage of power batteries is of great significance for electric vehicles (EVs). The solar battery pack is considered as a promising supplement to the battery management system (BMS) of EVs but integrating solar power into EVs remains a challenge. This paper proposes a BMS that coordinates the solar panels and the lithium battery system. The proposed BMS mainly involves three aspects. Firstly, an equivalent second-order resistance-capacitance model is established and afterwards is identified by using an improved recursive least squares algorithm. Then, the maximum power prediction strategy is developed based on the advanced state of charge (SOC) algorithm and the available solar energy estimation algorithm. Thirdly, a multi-stage constant current charging strategy based on the adaptive genetic algorithm is designed to optimize the battery temperature rise and charging time simultaneously. The proposed BMS is validated by the experiment on a real-world solar-assisted EV. The results indicate that the proposed power prediction strategy can accurately estimate the available power for EVs. Compared with the widely-used charging method, the developed optimal charging strategy reduces the charging time and temperature rise by 7%–11% and 36%–45%, respectively

High-quality genome assembly and comparative genomic profiling of yellowhorn (Xanthoceras sorbifolia) revealed environmental adaptation footprints and seed oil contents variations

Yellowhorn (Xanthoceras sorbifolia) is a species of deciduous tree that is native to Northern and Central China, including Loess Plateau. The yellowhorn tree is a hardy plant, tolerating a wide range of growing conditions, and is often grown for ornamental purposes in parks, gardens, and other landscaped areas. The seeds of yellowhorn are edible and contain rich oil and fatty acid contents, making it an ideal plant for oil production. However, the mechanism of its ability to adapt to extreme environments and the genetic basis of oil synthesis remains to be elucidated. In this study, we reported a high-quality and near gap-less yellowhorn genome assembly, containing the highest genome continuity with a contig N50 of 32.5 Mb. Comparative genomics analysis showed that 1,237 and 231 gene families under expansion and the yellowhorn-specific gene family NB-ARC were enriched in photosynthesis and root cap development, which may contribute to the environmental adaption and abiotic stress resistance of yellowhorn. A 3-ketoacyl-CoA thiolase (KAT) gene (Xso_LG02_00600) was identified under positive selection, which may be associated with variations of seed oil content among different yellowhorn cultivars. This study provided insights into environmental adaptation and seed oil content variations of yellowhorn to accelerate its genetic improvement

Second generation androgen receptor antagonist, TQB3720 abrogates prostate cancer growth via AR/GPX4 axis activated ferroptosis

Purpose: Prostate cancer (PCa) poses a great threat to humans. The study aimed to evaluate the potential of TQB3720 in promoting ferroptosis to suppress prostate cancer, providing a theoretical basis for PCa therapy.Methods: PCa cells and nude mice models were divided into TQB3720, enzalutamide (ENZ), and control groups. Sulforhodamine B assay, colony formation assessment, organoids culture system, and the CCK8 assay were used for detecting proliferation. Western blot assay was processed to detect the expression of androgen receptor (AR), ferroptosis, and apoptosis-related genes. Flow cytometry was applied to measure the intracellular ROS levels. ELISA was performed to determine the cellular oxidized glutathione (GSSG) and malondialdehyde (MDA) levels. RT-qPCR was conducted to detect the mRNA expression of genes in AR signaling. BODIPYTM™ 581/591 was processed for detection of intracellular lipid peroxidation levels. The interaction of AR with other translational factor complex proteins was explored using Co-immunoprecipitation (Co-IP), and the chromatin immunoprecipitation (ChIP) assay was performed to detect the binding of AR-involved translational complex to downstream genes promoter. Luciferase reporter assay was conducted to examine the translation activity of GPX4 promoter, and immunohistochemistry (IHC) was conducted to analyze the levels of c-MYC, Ki-67 and AR in TQB3720-treated cancer tissues.Results: Here, we found TQB3720 inhibits the growth of prostate cancer in vitro and in vivo. TQB3720 treatment induced intracellular levels of GSSG and MDA significantly, by which hints AR antagonist caused ferroptosis-related cell death. Moreover, molecular evidence shown TQB3720 regulates downstream of AR signaling by binding AR resulting in inhibition of AR entry into the nucleus. Additional, we also proved that TQB3720 abrogates the interaction between AR and SP1 and leads to decrease GPX4 transcription.Conclusion: TQB3720 promotes ferroptosis in prostate cancer cells by reducing the AR/SP1 transcriptional complex binding to GPX4 promoter. As a result, it is suggested to be a potential drug for clinic prostate cancer treatment

Electron microscopy investigations of changes in morphology and conductivity of LiFePO4/C electrodes

AbstractIn this work we study the structural degradation of a laboratory Li-ion battery LiFePO4/Carbon Black (LFP/CB) cathode by various electron microscopy techniques including low kV Focused Ion Beam (FIB)/Scanning Electron Microscopy (SEM) 3D tomography. Several changes are observed in FIB/SEM images of fresh and degraded cathodes, including cracks in the LFP particles, secondary disconnected particles, and agglomeration of CB. Low voltage (1 kV) SEM images show that the CB agglomerates have a different brightness than the fresh CB, due to charging effects. This suggests that the electronic conductivity of the CB agglomerates is low compared to that of the fresh CB particles. HRTEM analysis shows that fresh CB particles are quasi crystalline, whereas the LFP/CB interface in the degraded electrode shows amorphous carbon surrounding the LFP particles. The presence of the amorphous carbon is known to impede the electronic conductivity and thereby decreasing percolation in the cathode and reducing the electrode capacity