High-Performance PM6:Y6-Based Ternary Solar Cells with Enhanced Open Circuit Voltage and Balanced Mobilities via Doping a Wide-Band-Gap Amorphous Acceptor.

Great progress has been made in organic solar cells (OSCs) in recent years, especially after the report of the highly efficient small-molecule electron acceptor Y6. However, the relatively low open circuit voltage (VOC) and unbalanced charge mobilities remain two issues that need to be resolved for further improvement in the performance of OSCs. Herein, a wide-band-gap amorphous acceptor IO-4Cl, which possessed a shallower lowest unoccupied molecular orbital (LUMO) energy level than Y6, was introduced into the PM6:Y6 binary system to construct a ternary device. The mechanism study revealed that the introduced IO-4Cl was alloyed with Y6 to prevent the overaggregation of Y6 and offer dual channels for effective hole transportation, resulting in balanced hole and electron mobilities. Taking these advantages, an enhanced VOC of 0.894 V and an improved fill factor of 75.58% were achieved in the optimized PM6:Y6:IO-4Cl-based ternary device, yielding a promising power conversion efficiency (PCE) of 17.49%, which surpassed the 16.72% efficiency of the PM6:Y6 binary device. This work provides an alternative solution to balance the charge mobilities of PM6:Y6-based devices by incorporating an amorphous high-performance LUMO A-D-A small molecule as the third compound.

MTX-PEG-modified CG/DMMA polymeric micelles for targeted delivery of doxorubicin to induce synergistic autophagic death against triple-negative breast cancer.

The chemotherapy of triple-negative breast cancer based on doxorubicin (DOX) regimens suffers from great challenges on toxicity and autophagy raised off-target. In this study, a conjugate methotrexate-polyethylene glycol (shorten as MTX-PEG)-modified CG/DMMA polymeric micelles were prepared to endue DOX tumor selectivity and synergistic autophagic flux interference to reduce systematic toxicity and to improve anti-tumor capacity. The micelles could effectively promote the accumulation of autophagosomes in tumor cells and interfere with the degradation process of autophagic flux, collectively inducing autophagic death of tumor cells. In vivo and in vitro experiments showed that the micelles could exert improved anti-tumor effect and specificity, as well as reduced accumulation and damage of chemotherapeutic drugs in normal organs. The potential mechanism of synergistic autophagic death exerted by the synthesized micelles in MDA-MB-231 cells has been performed by autophagic flux-related pathway.

Hyaluronic Acid Hydrogels Hybridized With Au-Triptolide Nanoparticles for Intraarticular Targeted Multi-Therapy of Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a chronic inflammatory disease, characterized by synovial inflammation in multiple joints. Triptolide (TP) is a disease-modifying anti-rheumatic drug (DMARD) highly effective in patients with RA and has anti-inflammatory properties. However, its clinical application has been limited owing to practical disadvantages. In the present study, hyaluronic acid (HA) hydrogel-loaded RGD-attached gold nanoparticles (AuNPs) containing TP were synthesized to alleviate the toxicity and increase therapeutic specificity. The hydrogels can be applied for targeted photothermal-chemo treatment and in vivo imaging of RA. Hydrogel systems with tyramine-modified HA (TA-HA) conjugates have been applied to artificial tissue models as surrogates of cartilage to investigate drug transport and release properties. After degradation of HA chains, heat was locally generated at the inflammation region site due to near-infrared resonance (NIR) irradiation of AuNPs, and TP was released from nanoparticles, delivering heat and drug to the inflamed joints simultaneously. RA can be penetrated with NIR light. Intraarticular administration of the hydrogels containing low dosage of TP with NIR irradiation improved the inflamed conditions in mice with collagen-induced arthritis (CIA). Additionally, in vitro experiments were applied to deeply verify the antirheumatic mechanisms of TP-PLGA-Au@RGD/HA hydrogels. TP-PLGA-Au@RGD/HA hydrogel treatment significantly reduced the migratory and invasive capacities of RA fibroblast-like synoviocytes (RA-FLS) in vitro, through the decrease of phosphorylation of mTOR and its substrates, p70S6K1, thus inhibiting the mTOR pathway.

The naturally occurring flavonoid nobiletin reverses methotrexate resistance via inhibition of P-glycoprotein synthesis.

Methotrexate (MTX) is the first-line treatment for rheumatoid arthritis (RA). However, after long-term treatment, some patients develop resistance. P-glycoprotein (P-gp), as an indispensable drug transporter, is essential for mediating this MTX resistance. In addition, nobiletin (NOB), a naturally occurring polymethoxylated flavonoid, has also been shown to reverse P-gp-mediated MTX resistance in RA groups; however, the precise role of NOB in this process is still unclear. Here, we administered MTX and NOB alone or in combination to collagen II-induced arthritic (CIA) mice and evaluated disease severity using the arthritis index, synovial histopathological changes, immunohistochemistry, and P-gp expression. In addition, we used conventional RNA-seq to identify targets and possible pathways through which NOB reverses MTX-induced drug resistance. We found that NOB in combination with MTX could enhance its performance in synovial tissue and decrease P-gp expression in CIA mice compared to MTX treatment alone. In vitro, in MTX-resistant fibroblast-like synoviocytes from CIA cells (CIA-FLS/MTX), we show that NOB treatment downregulated the PI3K/AKT/HIF-1α pathway, thereby reducing the synthesis of the P-gp protein. In addition, NOB significantly inhibited glycolysis and metabolic activity of CIA-FLS/MTX cells, which could reduce the production of ATP and block P-gp, ultimately decreasing the efflux of MTX and maintaining its anti-RA effects. In conclusion, this study shows that NOB overcomes MTX resistance in CIA-FLS/MTX cells through the PI3K/AKT/HIF-1α pathway, simultaneously influencing metabolic processes and inhibiting P-gp-induced drug efflux.

The endoplasmic reticulum participated in drug metabolic toxicity.

Covalent binding of reactive metabolites formed by drug metabolic activation with biological macromolecules is considered to be an important mechanism of drug metabolic toxicity. Recent studies indicate that the endoplasmic reticulum (ER) could play an important role in drug toxicity by participating in the metabolic activation of drugs and could be a primarily attacked target by reactive metabolites. In this article, we summarize the generation and mechanism of reactive metabolites in ER stress and their associated cell death and inflammatory cascade, as well as the systematic modulation of unfolded protein response (UPR)-mediated adaptive pathways.

Improving MRI-based analysis of brain structural changes in patients with hypertension via a privileged information learning algorithm.

Hypertension can lead to changes in the brain structure and function, and different blood pressure levels (2017ACC/AHA) have different effects on brain structure. It is important to analyze these changes by machine learning methods, and various characteristics can provide rich information for the analysis of these changes. However, multiple feature extraction involves complex data processing. How to make a single feature achieve the same diagnosis effect as multiple features do is worth of study. Kernel ridge regression (KRR) is a kind of machine learning method, which shows faster learning speed and generalization ability in classification tasks. In order to knowledge transfer, we use privileged information (PI) to transfer information of multiple types of feature to single feature. This allows only one feature type to be used during the test stage. In the process of feature fusion, we need to consider all the samples' attribution making the classifier better. In this work, we propose a multi-kernel KRR+ framework based on self-paced learning to analyze the changes of the brain structure in patients with different blood pressure levels. Specifically, one kind of a feature is taken as main feature, and other features are input into the multi-kernel KRR as PI. These two inputs are fed into the final KRR classifier together. In addition, a self-paced learning method is introduced into sample selecting to avoid training the classifier using samples with a large loss value firstly, which improves the generalization performance of the classifier. Experimental results show that the proposed method can make full use of the information of various features and achieve better classification performance. This shows self-paced learning based KRR can help analyze brain structure of patients with different blood pressure levels. The discriminative features may help clinicians to make judgments of hypertension degrees on brain MRI images.

Recent advances of nanotechnology-based tumor vessel-targeting strategies.

Tumor vessels can provide oxygen and nutrition for solid tumor tissue, create abnormal tumor microenvironment (TME), and play a vital role in the development, immune escape, metastasis and drug resistance of tumor. Tumor vessel-targeting therapy has become an important and promising direction in anti-tumor therapy, with the development of five anti-tumor therapeutic strategies, including vascular disruption, anti-angiogenesis, vascular blockade, vascular normalization and breaking immunosuppressive TME. However, the insufficient drug accumulation and severe side effects of vessel-targeting drugs limit their development in clinical application. Nanotechnology offers an excellent platform with flexible modified surface that can precisely deliver diverse cargoes, optimize efficacy, reduce side effects, and realize the combined therapy. Various nanomedicines (NMs) have been developed to target abnormal tumor vessels and specific TME to achieve more efficient vessel-targeting therapy. The article reviews tumor vascular abnormalities and the resulting abnormal microenvironment, the application of NMs in the tumor vessel-targeting strategies, and how NMs can improve these strategies and achieve multi-strategies combination to maximize anti-tumor effects.

Self-Paced Learning and Privileged Information based Cascaded Multi-column Classification algorithm for ASD diagnosis.

Autism spectrum disorder (ASD) is one of the most serious mental disorder in children. Machine learning based computer aided diagnosis (CAD) on resting-state functional magnetic resonance imaging (rs-fMRI) for ASD has attracted widespread attention. In recent years, learning using privileged information (LUPI), a supervised transfer learning method, has been generally used on multi-modality cases, which can transfer knowledge from source domain to target domain in order to improve the prediction capability on the target domain. However, multi-modality data is difficult to collect in clinical cases. LUPI method without introducing additional imaging modality images is worth further study. Random vector function link network plus (RVFL+) is a LUPI diagnosis algorithm, which has been proven to be effective for classification tasks. In this work, we proposed a self-paced learning based cascaded multi-column RVFL+ algorithm (SPL-cmcRVFL+) for ASD diagnosis. Initial classification model is trained using RVFL on the single-modal data (e.g. rs-fMRI). The output of the initial layer is then sent as privileged information (PI) to train the next layer of classification model. During this process, samples are selected using self-paced learning (SPL), which can adaptively select simple to difficult samples according to the loss value. The procedure is repeated until all samples are included. Experimental results show that our proposed method can accurately identify ASD and normal control, and outperforms other methods by a relatively higher classification accuracy.

The Role of miRNA in Tumor Immune Escape and miRNA-Based Therapeutic Strategies.

Tumor immune escape is a critical step in the malignant progression of tumors and one of the major barriers to immunotherapy, making immunotherapy the most promising therapeutic approach against tumors today. Tumor cells evade immune surveillance by altering the structure of their own, or by causing abnormal gene and protein expression, allowing for unrestricted development and invasion. These genetic or epigenetic changes have been linked to microRNAs (miRNAs), which are important determinants of post-transcriptional regulation. Tumor cells perform tumor immune escape by abnormally expressing related miRNAs, which reduce the killing effect of immune cells, disrupt the immune response, and disrupt apoptotic pathways. Consequently, there is a strong trend toward thoroughly investigating the role of miRNAs in tumor immune escape and utilizing them in tumor treatment. However, because of the properties of miRNAs, there is an urgent need for a safe, targeted and easily crossed biofilm vehicle to protect and deliver them in vivo, and exosomes, with their excellent biological properties, have successfully beaten traditional vehicles to provide strong support for miRNA therapy. This review summarizes the multiple roles of miRNAs in tumor immune escape and discusses their potential applications as an anti-tumor therapy. Also, this work proposes exosomes as a new opportunity for miRNA therapy, to provide novel ideas for the development of more effective tumor-fighting therapeutic approaches based on miRNAs.

Experimental study on enrichment of heavy metals by intercalation-exfoliation modified kaolin during coal combustion.

An intercalation-exfoliation method is applied to modify the natural kaolin mineral, so that to improve the enrichment effects on heavy metals (Zn, Pb, Cr & Cd) during coal combustion. The modified kaolin is scanned by electron microscope (SEM), X-ray diffraction (XRD), Fourier Transform infrared spectroscopy (FTIR) and Brunner-Emmett-Teller (BET), which indicate that the natural kaolin is peeled off to form fine flakes and the interlayer spacing is significantly increased. The coal-kaolin combustion tests were performed in a tube furnace from 900°C to 1300°C. It is found that the enrichment of heavy metals is enhanced obviously during the coal combustion, especially when the raw kaolin has high activity. Besides, the adsorption effects on the above four heavy metals are different. To be specific, the kaolin modified by potassium acetate has a better performance for Zn and Pb, but that intercalated by dimethyl sulfoxide shows better influences on Cd and Cr. The modified kaolin can provide more active sites for the adsorption of heavy metals, enhance chemical adsorption, and fix heavy metals in the form of aluminosilicates, silicates and aluminates. These founding could reduce the pollutant emissions of coal combustion in industrial applications.

Investigation on gaseous pollutants emissions during co-combustion of coal and wheat straw in a fluidized bed combustor.

Co-combustion of coal and wheat straw (WS) was conducted in a lab-scale BFB combustor. Fuel composition (coal, 70%coal+30%WS), temperature (750, 800, 850, 900, 950 °C), secondary air ratio (0, 10%, 20%, 30%) were varied to on the release of gaseous pollutant was studied. CO, NOx and SO2 concentration in flue gas (FG) were measured on-line by a flue gas analyzer. Fly ash (FA), bottom slag (BS) and bed material (BM) were collected, digested and analyzed by ICP-OES to determine the distribution of heavy metals (e.g. Pb, Zn, Cr and Cd). Results indicated that co-combustion could improve the combustion of coal alone by reducing CO, NOx and SO2 emission and carbon content in fly ash effectively. In co-combustion the increasing secondary air could reduce CO emission and SO2 by enhancing disturbance and promoting sulfation respectively while the minimum NO emission was reached at the ratio of 20%. Co-combustion restrained the release of Zn, Cd and Pb compared with coal combustion alone. In co-combustion, high temperature increased their portion in the flue gas. For Zn, Pb and Cd, their content in the bottom solids increased while the portion of Cr decreased. Secondary air decreased their content in fly ash and transferred into flue gas significantly and in bottom solids content of Zn and Pb decreased while Cd increased.

Analyzing brain structural differences associated with categories of blood pressure in adults using empirical kernel mapping-based kernel ELM.

BACKGROUND: Hypertension increases the risk of angiocardiopathy and cognitive disorder. Blood pressure has four categories: normal, elevated, hypertension stage 1 and hypertension stage 2. The quantitative analysis of hypertension helps determine disease status, prognosis assessment, guidance and management, but is not well studied in the framework of machine learning. METHODS: We proposed empirical kernel mapping-based kernel extreme learning machine plus (EKM-KELM+) classifier to discriminate different blood pressure grades in adults from structural brain MR images. ELM+ is the extended version of ELM, which integrates the additional privileged information about training samples in ELM to help train a more effective classifier. In this work, we extracted gray matter volume (GMV), white matter volume, cerebrospinal fluid volume, cortical surface area, cortical thickness from structural brain MR images, and constructed brain network features based on thickness. After feature selection and EKM, the enhanced features are obtained. Then, we select one feature type as the main feature to feed into KELM+, and the rest of the feature types are PI to assist the main feature to train 5 KELM+ classifiers. Finally, the 5 KELM+ classifiers are ensemble to predict classification result in the test stage, while PI is not used during testing. RESULTS: We evaluated the performance of the proposed EKM-KELM+ method using four grades of hypertension data (73 samples for each grade). The experimental results show that the GMV performs observably better than any other feature types with a comparatively higher classification accuracy of 77.37% (Grade 1 vs. Grade 2), 93.19% (Grade 1 vs. Grade 3), and 95.15% (Grade 1 vs. Grade 4). The most discriminative brain regions found using our method are olfactory, orbitofrontal cortex (inferior), supplementary motor area, etc. CONCLUSIONS: Using region of interest features and brain network features, EKM-KELM+ is proposed to study the most discriminative regions that have obvious structural changes in different blood pressure grades. The discriminative features that are selected using our method are consistent with the existing neuroimaging studies. Moreover, our study provides a potential approach to take effective interventions in the early period, when the blood pressure makes minor impacts on the brain structure and function.

Cascaded Multi-Column RVFL+ Classifier for Single-Modal Neuroimaging-Based Diagnosis of Parkinson's Disease.

The neuroimaging-based computer-aided diagnosis for Parkinson's disease (PD) has attracted considerable attention in recent years, where the classifier plays a critical role. Random vector functional link network (RVFL) has shown its effectiveness for classification task, while its extended version, namely RVFL plus (RVFL+), integrates the additional privileged information (PI) about training samples in RVFL to help training a more effective classifier. On the other hand, it is still a popular way to adopt only a single neuroimaging modality for PD diagnosis in a clinical practice. In this work, we construct a novel cascaded multi-column RVFL+ (cmcRVFL+) framework for the single-modal neuroimaging-based PD diagnosis without the additional neuroimaging modality as PI. Specifically, the predicted values of RVFL+ classifiers in the current layers are used as the PI for the following classifiers, and therefore, the PI features are self-generated without additional modality. Furthermore, only the multi-column RVFL+ classifiers in the last layer of cmcRVFL+ are finally ensembled to generate the predictive result in the test stage. The experimental results on both the transcranial sonography data set and the magnetic resonance imaging data set for PD show that the proposed cmcRVFL+ algorithm achieves superior performance to all the compared algorithms. It suggests that the proposed cmcRVFL+ has the potential to be flexibly applied to various single-modal imaging based CAD.

Transcranial Sonography Based Diagnosis Of Parkinson's Disease Via Cascaded Kernel RVFL.

The transcranial sonography (TCS) based computer-aided diagnosis (CAD) for Parkinson's disease (PD) has attracted considerable attention. The learning using privileged information (LUPI) is a new learning paradigm, in which, the privileged information (PI) is only available for model training, but unavailable in the testing stage. The Random vector functional link network plus (RVFL+) algorithm is a newly proposed LUPI algorithm, which has shown its effectiveness for classification task. Moreover, the kernel-based RVFL+ (KRVFL+) has been proposed to overcome the randomness in RVFL+. In this work, we propose a cascaded KRVFL+ (cKRVFL+) algorithm for the single-modal TCS-based PD diagnosis. The predicted value of the former KRVFL+ classifier is adopted as the PI for the current KRVFL+, and only the KRVFL+ in the last layer is finally used as classifiers during the testing stage. This cascaded structure progressively promotes the discrimination performance of KRVFL+ classifier. The experimental results show the effectiveness of the cascaded LUPI classifier framework for single-modality TCS based diagnosis of PD, and the proposed cKRVFL+ algorithm achieves the best performance.

Upgraded bio-oil production via catalytic fast co-pyrolysis of waste cooking oil and tea residual.

Catalytic fast co-pyrolysis (co-CFP) offers a concise and effective process to achieve an upgraded bio-oil production. In this paper, co-CFP experiments of waste cooking oil (WCO) and tea residual (TR) with HZSM-5 zeolites were carried out. The influences of pyrolysis reaction temperature and H/C ratio on pyrolytic products distribution and selectivities of aromatics were performed. Furthermore, the prevailing synergetic effect of target products during co-CFP process was investigated. Experimental results indicated that H/C ratio played a pivotal role in carbon yields of aromatics and olefins, and with H/C ratio increasing, the synergetic coefficient tended to increase, thus led to a dramatic growth of aromatics and olefins yields. Besides, the pyrolysis temperature made a significant contribution to carbon yields, and the yields of aromatics and olefins increased at first and then decreased at the researched temperature region. Note that 600°C was an optimum temperature as the maximum yields of aromatics and olefins could be achieved. Concerning the transportation fuel dependence and security on fossil fuels, co-CFP of WCO and TR provides a novel way to improve the quality and quantity of pyrolysis bio-oil, and thus contributes bioenergy accepted as a cost-competitive and promising alternative energy.

Catalytic fast pyrolysis of mushroom waste to upgraded bio-oil products via pre-coked modified HZSM-5 catalyst.

In this paper, HZSM-5 catalyst was modified by pre-coked to cover the strong external acid sites by methanol to olefins reaction, and the modified catalysts were then applied to conduct the catalyst fast pyrolysis of mushroom waste for upgraded bio-fuel production. Experiment results showed that the strong external acid sites and specific surface area decreased with pre-coked percentage increasing from 0% to 5.4%. Carbon yields of hydrocarbons increased at first and then decreased with a maximum value of 53.47%. While the obtained oxygenates presented an opposite variation tendency, and the minimum values could be reached when pre-coked percentage was 2.7%. Among the achieved hydrocarbons, toluene and p-xylene were found to be the main products, and the selectivity of p-xylene increased at first and then decreased with a maximum value of 34.22% when the pre-coked percentage was 1.3%, and the selectivity of toluene showed the opposite tendency with a minimum value of 25.47%.