Co-administration of combretastatin A4 nanoparticles and anti-PD-L1 for synergistic therapy of hepatocellular carcinoma.

BACKGROUND: According to data estimated by the WHO, primary liver cancer is currently the fourth most common malignant tumor and the second leading cause of death around the world. Hepatocellular carcinoma (HCC) is one of the most common primary liver malignancies, so effective therapy is highly desired for HCC. RESULTS: In this study, the use of poly(L-Aspartic acid)-poly(ethylene glycol)/combretastatin A4 (CA4-NPs) was aimed to significantly disrupt new blood vessels in tumor tissues for targeted hepatic tumor therapy. Here, PEG-b-PAsp-g-CA4 showed significantly prolonged retention in plasma and tumor tissue. Most importantly, CA4-NPs were mainly distributed at the tumor site because of the triple target effects-enhanced permeability and retention (EPR) effect, acid-sensitive (pH = 5.5) effect to the tumor microenvironment (TME), and good selectivity of CA4 for central tumor blood vessel. Considering that CA4-NPs might induce severe hypoxic conditions resulting in high expression of HIF-1α in tumor tissues, which could induce the overexpression of PD-L1, herein we also used a programmed death-ligand 1 antibody (aPD-L1) to prevent immunosuppression. This way of complementary combination is able to achieve an ideal treatment effect in tumor site where CA4-NPs and aPD-L1 could respond to the inner area and peripheral area, respectively. As a result, a significant decrease in tumor volume and weight was observed in the combination group of CA4-NPs plus aPD-L1 compared with CA4-NPs or aPD-L1 monotherapy in subcutaneous Hepa1-6 hepatic tumor models. CONCLUSIONS: We presented a new idea that co-administration of CA4-NPs and aPD-L1 possessed notable anti-tumor efficacy for HCC treatment.

A model of conceptual bootstrapping in human cognition.

To tackle a hard problem, it is often wise to reuse and recombine existing knowledge. Such an ability to bootstrap enables us to grow rich mental concepts despite limited cognitive resources. Here we present a computational model of conceptual bootstrapping. This model uses a dynamic conceptual repertoire that can cache and later reuse elements of earlier insights in principled ways, modelling learning as a series of compositional generalizations. This model predicts systematically different learned concepts when the same evidence is processed in different orders, without any extra assumptions about previous beliefs or background knowledge. Across four behavioural experiments (total n = 570), we demonstrate strong curriculum-order and conceptual garden-pathing effects that closely resemble our model predictions and differ from those of alternative accounts. Taken together, this work offers a computational account of how past experiences shape future conceptual discoveries and showcases the importance of curriculum design in human inductive concept inferences.

On the Influence of Fabrication Methods and Materials for mRNA-LNP Production: From Size and Morphology to Internal Structure and mRNA Delivery Performance In Vitro and In Vivo.

Lipid nanoparticle (LNP) remains the most advanced platform for messenger RNA (mRNA) delivery. To date, mRNA LNPs synthesis is mostly performed by mixing lipids and mRNA with microfluidics. In this study, a cost-effective microfluidic setup for synthesizing mRNA LNPs is developed. It allows to fine-tune the LNPs characteristics without compromising LNP properties. It is compared with a commercial device (NanoAssemblr) and ethanol injection and the influence of manufacturing conditions on the performance of mRNA LNPs is investigated. LNPs prepared by ethanol injection exhibit broader size distributions and more inhomogeneous internal structure (e.g., bleb-like substructures), while other LNPs show uniform structure with dense cores. Small angel X-ray scattering (SAXS) data indicate a tighter interaction between mRNA and lipids within LNPs synthesized by custom device, compared to LNPs produced by NanoAssemblr. Interestingly, the better transfection efficiency of polysarcosine (pSar)-modified LNPs correlates with a higher surface roughness than that of PEGylated ones. The manufacturing approach, however, shows modest influence on mRNA expression in vivo. In summary, the home-developed cost-effective microfluidic device can synthesize LNPs and represents a potent alternative to NanoAssemblr. The preparation methods show notable effect on LNPs' structure but a minor influence on mRNA delivery in vitro and in vivo.

Local Search and the Evolution of World Models.

An open question regarding how people develop their models of the world is how new candidates are generated for consideration out of infinitely many possibilities. We discuss the role that evolutionary mechanisms play in this process. Specifically, we argue that when it comes to developing a global world model, innovation is necessarily incremental, involving the generation and selection among random local mutations and recombinations of (parts of) one's current model. We argue that, by narrowing and guiding exploration, this feature of cognitive search is what allows human learners to discover better theories, without ever grappling directly with the problem of finding a "global optimum," or best possible world model. We suggest this aspect of cognitive processing works analogously to how blind variation and selection mechanisms drive biological evolution. We propose algorithms developed for program synthesis provide candidate mechanisms for how human minds might achieve this. We discuss objections and implications of this perspective, finally suggesting that a better process-level understanding of how humans incrementally explore compositional theory spaces can shed light on how we think, and provide explanatory traction on fundamental cognitive biases, including anchoring, probability matching, and confirmation bias.

How Do People Generalize Causal Relations over Objects? A Non-parametric Bayesian Account.

How do people decide how general a causal relationship is, in terms of the entities or situations it applies to? What features do people use to decide whether a new situation is governed by a new causal law or an old one? How can people make these difficult judgments in a fast, efficient way? We address these questions in two experiments that ask participants to generalize from one (Experiment 1) or several (Experiment 2) causal interactions between pairs of objects. In each case, participants see an agent object act on a recipient object, causing some changes to the recipient. In line with the human capacity for few-shot concept learning, we find systematic patterns of causal generalizations favoring simpler causal laws that extend over categories of similar objects. In Experiment 1, we find that participants' inferences are shaped by the order of the generalization questions they are asked. In both experiments, we find an asymmetry in the formation of causal categories: participants preferentially identify causal laws with features of the agent objects rather than recipients. To explain this, we develop a computational model that combines program induction (about the hidden causal laws) with non-parametric category inference (about their domains of influence). We demonstrate that our modeling approach can both explain the order effect in Experiment 1 and the causal asymmetry, and outperforms a naïve Bayesian account while providing a computationally plausible mechanism for real-world causal generalization.

Recent advances in nano-drug delivery systems for synergistic antitumor immunotherapy.

Immunotherapy has demonstrated great clinical success in the field of oncology in comparison with conventional cancer therapy. However, cancer immunotherapy still encounters major challenges that limit its efficacy against different types of cancers and the patients show minimal immune response to the immunotherapy. To overcome these limitations, combinatorial approaches with other therapeutics have been applied in the clinic. Simultaneously, nano-drug delivery system has played an important role in increasing the antitumor efficacy of various treatments and has been increasingly utilized for synergistic immunotherapy to further enhance the immunogenicity of the tumors. Specifically, they can promote the infiltration of immune cells within the tumors and create an environment that is more sensitive to immunotherapy, particularly in solid tumors, by accelerating tumor accumulation and permeability. Herein, this progress report provides a brief overview of the development of nano-drug delivery systems, classification of combinatory cancer immunotherapy and recent progress in tumor immune synergistic therapy in the application of nano-drug delivery systems.

Highly Efficient Mesoporous Core-Shell Structured Ag@SiO2 Nanosphere as an Environmentally Friendly Catalyst for Hydrogenation of Nitrobenzene.

The size-uniformed mesoporous Ag@SiO2 nanospheres' catalysts were prepared in one-pot step via reducing AgNO3 by different types of aldehyde, which could control the size of Ag@SiO2 NPs and exhibit excellent catalytic activity for the hydrogenation of nitrobenzene. The results showed that the Ag core size, monitored by different aldehydes with different reducing abilities, together with the ideal monodisperse core-shell mesoporous structure, was quite important to affect its superior catalytic performances. Moreover, the stability of Ag fixed in the core during reaction for 6 h under 2.0 MPa, 140 °C made this type of Ag@SiO2 catalyst separable and environmentally friendly compared with those conventional homogeneous catalysts and metal NPs catalysts. The best catalyst with smaller Ag cores was prepared by strong reducing agents such as CH2O. The conversion of nitrobenzene can reach 99.9%, the selectivity was 100% and the catalyst maintained its activity after several cycles, and thus, it is a useful novel candidate for the production of aniline.

Detection of peripherally inserted central catheter (PICC) in chest X-ray images: A multi-task deep learning model.

BACKGROUND AND OBJECTIVE: Peripherally inserted central catheter (PICC) is a novel drug delivery mode which has been widely used in clinical practice. However, long-term retention and some improper actions of patients may cause some severe complications of PICC, such as the drift and prolapse of its catheter. Clinically, the postoperative care of PICC is mainly completed by nurses. However, they cannot recognize the correct position of PICC from X-ray chest images as soon as the complications happen, which may lead to improper treatment. Therefore, it is necessary to identify the position of the PICC catheter as soon as these complications occur. Here we proposed a novel multi-task deep learning framework to detect PICC automatically through X-ray images, which could help nurses to solve this problem. METHODS: We collected 348 X-ray chest images from 326 patients with visible PICC. Then we proposed a multi-task deep learning framework for line segmentation and tip detection of PICC catheters simultaneously. The proposed deep learning model is composed of an extraction structure and three routes, an up-sampling route for segmentation, an RPNs route, and an RoI Pooling route for detection. We further compared the effectiveness of our model with the models previously proposed. RESULTS: In the catheter segmentation task, 300 X-ray images were utilized for training the model, then 48 images were tested. In the tip detection task, 154 X-ray images were used for retraining and 20 images were used in the test. Our model achieved generally better results among several popular deep learning models previously proposed. CONCLUSIONS: We proposed a multi-task deep learning model that could segment the catheter and detect the tip of PICC simultaneously from X-ray chest images. This model could help nurses to recognize the correct position of PICC, and therefore, to handle the potential complications properly.

Correction: People making deontological judgments in the Trapdoor dilemma are perceived to be more prosocial in economic games than they actually are.

[This corrects the article DOI: 10.1371/journal.pone.0205066.].

People making deontological judgments in the Trapdoor dilemma are perceived to be more prosocial in economic games than they actually are.

Why do people make deontological decisions, although they often lead to overall unfavorable outcomes? One account is receiving considerable attention: deontological judgments may signal commitment to prosociality and thus may increase people's chances of being selected as social partners-which carries obvious long-term benefits. Here we test this framework by experimentally exploring whether people making deontological judgments are expected to be more prosocial than those making consequentialist judgments and whether they are actually so. In line with previous studies, we identified deontological choices using the Trapdoor dilemma. Using economic games, we take two measures of general prosociality towards strangers: trustworthiness and altruism. Our results procure converging evidence for a perception gap according to which Trapdoor-deontologists are believed to be more trustworthy and more altruistic towards strangers than Trapdoor-consequentialists, but actually they are not so. These results show that deontological judgments are not universal, reliable signals of prosociality.