Biomaterials based on noncovalent interactions of small molecules

Unlike conventional materials that covalent bonds connecting atoms as the major force to hold the materials together, supramolecular biomaterials rely on noncovalent intermolecular interactions to assemble. The reversibility and biocompatibility of supramolecular biomaterials render them with diverse range of functions and lead to rapid development in the past two decades. This review focuses on the noncovalent and enzymatic control of supramolecular biomaterials, with the introduction to various triggering mechanism to initiate self-assembly. Representative applications of supramolecular biomaterials are highlighted in four categories: tissue engineering, cancer therapy, drug delivery, and molecular imaging. By introducing various applications, we intend to show enzymatic control and noncovalent interactions as a powerful tool for achieving spatiotemporal control of biomaterials both in vitro and in vivo for biomedicine

Comparison Study of Wide Bandgap Polymer (PBDB-T) and Narrow Bandgap Polymer (PBDTTT-EFT) as Donor for Perylene Diimide Based Polymer Solar Cells

Perylene diimide (PDI) derivatives as a kind of promising non-fullerene-based acceptor (NFA) have got rapid development. However, most of the relevant developmental work has focused on synthesizing novel PDI-based structures, and few paid attentions to the selection of the polymer donor in PDI-based solar cells. Wide bandgap polymer (PBDB-T) and narrow bandgap polymer (PBDTTT-EFT) are known as the most efficient polymer donors in polymer solar cells (PSCs). While PBDB-T is in favor with non-fullerene acceptors achieving power conversion efficiency (PCE) more than 12%, PBDTTT-EFT is one of the best electron donors with fullerene acceptors with PCE up to 10%. Despite the different absorption profiles, the working principle of these benchmark polymer donors with a same electron acceptor, specially PDI-based acceptors, was rarely compared. To this end, we used PBDB-T and PBDTTT-EFT as the electron donors, and 1,1′-bis(2-methoxyethoxyl)-7,7′-(2,5-thienyl) bis-PDI (Bis-PDI-T-EG) as the electron acceptor to fabricate PSCs, and systematically compared their differences in device performance, carrier mobility, recombination mechanism, and film morphology

Identification of Potential Biomarkers and Metabolic Profiling of Serum in Ovarian Cancer Patients Using UPLC/Q-TOF MS

Background/Aims: Ovarian cancer (OC) is a malignant neoplasm of the female reproductive system with a high mortality rate. Identifying useful biomarkers and clarifying the molecular pathogenesis of OC are critical for early diagnosis and treatment. The aim of the study was to identify candidate biomarkers and explore metabolic changes of OC. Methods: A two-stage design was used in our study, with a discovery cohort of OC cases (n = 30) and controls (n = 30) and an independent cohort of cases (n = 17) and controls (n = 18) for validation. The serum metabolic profiling was investigated by ultra-performance liquid chromatography and quadrupole time-of-fight mass spectrometry with positive electrospray ionization. Results: A total of 18 metabolites closely related to OC were identified in the discovery stage, of which 12 were confirmed in the validation cohort. Metabolic pathways in OC related to these biomarkers included fatty acid β-oxidation, phospholipid metabolism, and bile acid metabolism, which are closely related to the proliferation, invasion, and metastasis of cancer cells. Multiple logistic regression analysis of these metabolites showed that 2-piperidinone and 1-heptadecanoylglycerophosphoethanolamine were potential biomarkers of OC, with high sensitivity (96.7%), specificity (66.7%), and area under the receiver operating characteristic curve value (0.894). Conclusion: These findings provide insight into the pathogenesis pathogenesis of OC and may be useful for clinical diagnosis and treatment

Dietary Protein Consumption and the Risk of Type 2 Diabetes: A Systematic Review and Meta-Analysis of Cohort Studies

Recently, some studies have focused on the relationship between dietary protein intake and the risk of type 2 diabetes mellitus (T2DM), but the conclusions have been inconsistent. Therefore, in this paper, a systematic review and meta-analysis of cohort studies regarding protein consumption and T2DM risk are conducted in order to present the association between them. We searched the PubMed and Embase databases for cohort studies on dietary protein, high-protein food consumption and risk of T2DM, up to July 2017. A summary of relative risks was compiled by the fixed-effect model or random-effect model. Eleven cohort studies regarded protein intake and T2DM (52,637 cases among 483,174 participants). The summary RR and 95% CI (Confidence Interval) of T2DM was 1.12 (1.08–1.17) in all subjects, 1.13 (1.04–1.24) in men, and 1.09 (1.04–1.15) in women for total protein;1.14 (1.09–1.19) in all subjects, 1.23 (1.09–1.38) in men, and 1.11 (1.03–1.19) in women for animal protein; 0.96 (0.88–1.06) in all subjects, 0.98 (0.72–1.34) in men, and 0.92 (0.86–0.98) in women for plant protein. We also compared the association between different food sources of protein and the risk of T2DM. The summary RR (Relative Risk) and 95% CI of T2DM was 1.22 (1.09–1.36) for red meat, 1.39 (1.29–1.49) for processed meat, 1.03 (0.89–1.17) for fish, 1.03 (0.64–1.67) for egg, 0.89 (0.84–0.94) for total dairy products, 0.87 (0.78–0.96) for whole milk, 0.83 (0.70–0.98) for yogurt, 0.74 (0.59–0.93) in women for soy. This meta-analysis shows that total protein and animal protein could increase the risk of T2DM in both males and females, and plant protein decreases the risk of T2DM in females. The association between high-protein food types and T2DM are also different. Red meat and processed meat are risk factors of T2DM, and soy, dairy and dairy products are the protective factors of T2DM. Egg and fish intake are not associated with a decreased risk of T2DM. This research indicates the type of dietary protein and food sources of protein that should be considered for the prevention of diabetes

In Situ Thermal Polymerization of a Succinonitrile-Based Gel Polymer Electrolyte for Lithium-Oxygen Batteries

For lithium-oxygen batteries (LOBs), the leakage and volatilization of a liquid electrolyte and its poor electrochemical performance are the main reasons for the slow industrial advancement. Searching for more stable electrolyte substrates and reducing the use of liquid solvents are crucial to the development of LOBs. In this work, a well-designed succinonitrile-based (SN) gel polymer electrolyte (GPE-SLFE) is prepared by in situ thermal cross-linking of an ethoxylate trimethylolpropane triacrylate (ETPTA) monomer. The continuous Li+ transfer channel, formed by the synergistic effect of an SN-based plastic crystal electrolyte and an ETPTA polymer network, endows the GPE-SLFE with a high room-temperature ionic conductivity (1.61 mS cm–1 at 25 °C), a high lithium-ion transference number (tLi+ = 0.489), and excellent long-term stability of the Li/GPE-SLFE/Li symmetric cell at a current density of 0.1 mA cm–2 for over 220 h. Furthermore, cells with the GPE-SLFE exhibit a high discharge specific capacity of 4629.7 mAh g–1 and achieve 40 cycles

MicroRNA-137 reduces stemness features of pancreatic cancer cells by targeting KLF12

Abstract Background Cancer stem cells (CSCs) play an important role in the development of pancreatic cancer. We previously showed that the microRNA miR-137 is downregulated in clinical samples of pancreatic cancer, and its expression negatively regulates the proliferation and invasiveness of pancreatic cancer cells. Methods The stemness features of pancreatic cancer cells was detected by flow cytometry, immunofluorescence and sphere formation assay. Xenograft mouse models were used to assess the role of miR-137 in stemness features of pancreatic cancer cells in vivo. Dual-luciferase reporter assays were used to determine how miR-137 regulates KLF12. Bioinformatics and Chromatin immunoprecipitation analysis of KLF12 recruitment to the DVL2 promoters. Involvement of the Wnt/β-catenin pathways was investigated by western blot and Immunohistochemistry. Results miR-137 inhibits pancreatic cancer cell stemness in vitro and vivo. KLF12 as miR-137 target inhibits CSC phenotype in pancreatic cancer cells. Suppression of KLF12 by miR-137 inhibits Wnt/β-catenin signalling. KLF12 expression correlates with DVL2 and canonical Wnt pathway in clinical pancreatic cancer. Conclusion Our results suggest that miR-137 reduces stemness features of pancreatic cancer cells by Targeting KLF12-associated Wnt/β-catenin pathways and may identify new diagnostic and therapeutic targets in pancreatic cancer

General Strategy for Doping Impurities (Ge, Si, Mn, Sn, Ti) in Hematite Nanocrystals

The doping of foreign atoms is critical in tailoring the properties and potential applications of semiconductor nanocrystals. A general strategy for successfully incorporating various impurities (e.g., Ge, Si, Mn, Sn, Ti) inside the regular crystal lattice of hematite (α-Fe₂O₃), a promising candidate for water splitting and environmental protection, is developed. Liquid-phase laser ablation-derived colloidal clusters are used as doping precursors for the metastable growth of doped hematite nanocrystals, thereby avoiding surfactants and hazardous liquid byproducts. The doping percentage, morphology, and structure of the hematite nanocrystals are greatly affected by the type and amount of the colloidal precursors used. High-resolution transmission electron microscopy and the corresponding component analysis reveal that the dopant atoms either form superlattice structures (Ge and Si) or distribute as disordered solid solutions (Mn, Sn, Ti) inside the crystal lattice of hematite. The optical absorption spectra and the resulting band gaps of the doped-hematite nanocrystals are investigated. Typical electronic transitions consisting of ligand to metal charge transitions, Fe³⁺ d–d transitions, and pair excitations distinctly occur in the optical spectra. The simultaneous incorporation of impurities and preferential growth mechanism of hematite nanocrystals are also further elaborated

Comparative Evaluation of a Newly Developed Trunk-Based Tree Detection/Localization Strategy on Leaf-Off LiDAR Point Clouds with Varying Characteristics

LiDAR data acquired by various platforms provide unprecedented data for forest inventory and management. Among its applications, individual tree detection and segmentation are critical and prerequisite steps for deriving forest structural metrics, especially at the stand level. Although there are various tree detection and localization approaches, a comparative analysis of their performance on LiDAR data with different characteristics remains to be explored. In this study, a new trunk-based tree detection and localization approach (namely, height-difference-based) is proposed and compared to two state-of-the-art strategies—DBSCAN-based and height/density-based approaches. Leaf-off LiDAR data from two unmanned aerial vehicles (UAVs) and Geiger mode system with different point densities, geometric accuracies, and environmental complexities were used to evaluate the performance of these approaches in a forest plantation. The results from the UAV datasets suggest that DBSCAN-based and height/density-based approaches perform well in tree detection (F1 score > 0.99) and localization (with an accuracy of 0.1 m for point clouds with high geometric accuracy) after fine-tuning the model thresholds; however, the processing time of the latter is much shorter. Even though our new height-difference-based approach introduces more false positives, it obtains a high tree detection rate from UAV datasets without fine-tuning model thresholds. However, due to the limitations of the algorithm, the tree localization accuracy is worse than that of the other two approaches. On the other hand, the results from the Geiger mode dataset with low point density show that the performance of all approaches dramatically deteriorates. Among them, the proposed height-difference-based approach results in the greatest number of true positives and highest F1 score, making it the most suitable approach for low-density point clouds without the need for parameter/threshold fine-tuning