Detoxification of Aflatoxin B1 by Phytochemicals in Agriculture and Food Science.

Aflatoxin B1 (AFB1), the most toxic and harmful mycotoxin, has a high likelihood of occurring in animal feed and human food, which seriously affects agriculture and food safety and endangers animal and human health. Recently, natural plant products have attracted widespread attention due to their low toxicity, high biocompatibility, and simple composition, indicating significant potential for resisting AFB1. The mechanisms by which these phytochemicals resist toxins mainly involve antioxidative, anti-inflammatory, and antiapoptotic pathways. Moreover, these substances also inhibit the genotoxicity of AFB1 by directly influencing its metabolism in vivo, which contributes to its elimination. Here, we review various phytochemicals that resist AFB1 and their anti-AFB1 mechanisms in different animals, as well as the common characteristics of phytochemicals with anti-AFB1 function. Additionally, the shortcomings of current research and future research directions will be discussed. Overall, this comprehensive summary contributes to the better application of phytochemicals in agriculture and food safety.

Overall survival time prediction for glioblastoma using multimodal deep KNN.

Glioblastoma (GBM) is a severe malignant brain tumor with bad prognosis, and overall survival (OS) time prediction is of great clinical value for customized treatment. Recently, many deep learning (DL) based methods have been proposed, and most of them build deep networks to directly map pre-operative images of patients to the OS time. However, such end-to-end prediction is sensitive to data inconsistency and noise. In this paper, inspired by the fact that clinicians usually evaluate patient prognosis according to previously encountered similar cases, we propose a novel multimodal deep KNN based OS time prediction method. Specifically, instead of the end-to-end prediction, for each input patient, our method first search itsKnearest patients with known OS time in a learned metric space, and the final OS time of the input patient is jointly determined by theKnearest patients, which is robust to data inconsistency and noise. Moreover, to take advantage of multiple imaging modalities, a new inter-modality loss is introduced to encourage learning complementary features from different modalities. The in-house single-center dataset containing multimodal MR brain images of 78 GBM patients is used to evaluate our method. In addition, to demonstrate that our method is not limited to GBM, a public multi-center dataset (BRATS2019) containing 211 patients with low and high grade gliomas is also used in our experiment. As benefiting from the deep KNN and the inter-modality loss, our method outperforms all methods under evaluation in both datasets. To the best of our knowledge, this is the first work, which predicts the OS time of GBM patients in the strategy of KNN under the DL framework.

Hydrolysis and Solvolysis as Benign Routes for the End-of-Life Management of Thermoset Polymer Waste.

The production of thermoset polymers is increasing globally owing to their advantageous properties, particularly when applied as composite materials. Though these materials are traditionally used in more durable, longer-lasting applications, ultimately, they become waste at the end of their usable lifetimes. Current recycling practices are not applicable to traditional thermoset waste, owing to their network structures and lack of processability. Recently, researchers have been developing thermoset polymers with the right functionalities to be chemically degraded under relatively benign conditions postuse, providing a route to future management of thermoset waste. This review presents thermosets containing hydrolytically or solvolytically cleavable bonds, such as esters and acetals. Hydrolysis and solvolysis mechanisms are discussed, and various factors that influence the degradation rates are examined. Degradable thermosets with impressive mechanical, thermal, and adhesion behavior are discussed, illustrating that the design of material end-of-life need not limit material performance.

Recent Advances in Conjugated Furans.

Thiophene is one of the most ubiquitous moieties in organic conjugated materials; however, furan, its oxygen congener, and furan derivatives have received comparatively less attention. This is primarily due to the intrinsic instability of furan and its tendency to decompose in the presence of oxygen and light. Incorporating furan into conjugated systems can confer many benefits, including increases in conjugation, improved solubility, and better transport properties. In this Concept Article, advances in furan-containing conjugated materials are presented. The impact of furan on the properties of conjugated materials is discussed, recent advances in synthetic methods are overviewed, and strategies for improving the stability of conjugated furans are detailed.

Mono- and Bis-Terpyridine-Based Dimer and Metallo-Organic Polymers as Ionic Templates for Preparation of Multi-Metallic Au Nanocluster and Nanowires.

The preparation of multi-metallic Au nanocluster and nanowires has been achieved using terpyridine-based metallo-organic polymers as multi-ionic templates through a straightforward counterion exchange with aqueous NaAuCl4 followed by a mild reduction in-situ with sodium citrate. The mild reduction of the [TpyFeTpy]2+ x 2[AuCl4]- complex, derived from [TpyFeTpy]2+ x 2Cl- 1 (tpy = 2,2':6',2"-terpyridine), led to the formation of Au nanoclusters (Au NC) with diameters ranging from 7.5-88 nm. Each Au NC alone contained multiple nanoparticles, with diameters ranging from 2.5-4.5 nm. 1,4-bis-terpyridine based metallo-oraganic polymer [-TpyFeTpy-TpyFeTpy-]n(2n+) x [Cl]2n- 2 was found to generate a multi-ionic metallo-polymer with AuCl4- as the counterion, after mild reduction with sodium citrate, resulting in irregular zigzag shaped Au nanowires (Au NW). The prepared Au NW from the di-metallic complex 3 should find applications within electronic devices. Both Au NC and NW were also found to possess excellent catalytic properties.