Pesticide carrier systems are highly desirable in achieving the effective utilization of pesticides and reduction of their loss. In order to increase utilization and enhance pesticide adhesion to harmful targets, adhesive and stimulus-responsive nanocomposites were prepared using graphene oxide (GO) and polydopamine (PDA). The results demonstrated that graphene oxide with a layer of PDA had a high hymexazol-loading capacity. The release curve of hymexazol from the nanocomposite showed that the release was NIR-laser-dependent and pH-dependent. The adhesion-performance investigation demonstrated that Hy-GO@PDA exhibited greater hymexazol persistence than a hymexazol solution after a simulated-rainwash experiment, and it also left more hymexazol residue than a hymexazol solution with a surfactant under high concentrations. Finally, the bioactivity of the prepared hymexazol-loaded nanocomposite was measured against Fusarium oxysporum f. sp. cucumebrium Owen, and it showed an inhibition activity similar to that of the hymexazol solution. All of these revealed that GO with a PDA layer could serve as pesticide carrier to solve low-utilization and wash-off problems, especially for water-soluble pesticides.

Pesticide formulation is highly desirable for effective utilization of pesticide and environmental pollution reduction. Studies of pesticide delivery system such as microcapsules are developing prosperously. In this work, we chose polymeric nanoparticles as a pesticide delivery system and metolachlor was used as a hydrophobic pesticide model to study water-based mPEG-PLGA nanoparticle formulation. Preparation, characterization results showed that the resulting nanoparticles enhanced "water solubility" of hydrophobic metolachlor and contained no organic solvent or surfactant, which represent one of the most important sources of pesticide pollution. After the release study, absorption of Cy5-labeled nanoparticles into rice roots suggested a possible transmitting pathway of this metolachlor formulation and increased utilization of metolachlor. Furthermore, the bioassay test demonstrated that this nanoparticle showed higher effect than non-nano forms under relatively low concentrations on Oryza sativa, Digitaria sanguinalis. In addition, a simple cytotoxicity test involving metolachlor and metolachlor-loaded nanoparticles was performed, indicating toxicity reduction of the latter to the preosteoblast cell line. All of these results showed that those polymeric nanoparticles could serve as a pesticide carrier with lower environmental impact, comparable effect, and effective delivery.

Background Differentiating between ulcerative colitis (UC), Crohn’s disease (CD) and intestinal tuberculosis (ITB) using endoscopy is challenging. We aimed to realize automatic differential diagnosis among these diseases through machine learning algorithms. Methods A total of 6399 consecutive patients (5128 UC, 875 CD and 396 ITB) who had undergone colonoscopy examinations in the Peking Union Medical College Hospital from January 2008 to November 2018 were enrolled. The input was the description of the endoscopic image in the form of free text. Word segmentation and key word filtering were conducted as data preprocessing. Random forest (RF) and convolutional neural network (CNN) approaches were applied to different disease entities. Three two-class classifiers (UC and CD, UC and ITB, and CD and ITB) and a three-class classifier (UC, CD and ITB) were built. Results The classifiers built in this research performed well, and the CNN had better performance in general. The RF sensitivities/specificities of UC-CD, UC-ITB, and CD-ITB were 0.89/0.84, 0.83/0.82, and 0.72/0.77, respectively, while the values for the CNN of CD-ITB were 0.90/0.77. The precisions/recalls of UC-CD-ITB when employing RF were 0.97/0.97, 0.65/0.53, and 0.68/0.76, respectively, and when employing the CNN were 0.99/0.97, 0.87/0.83, and 0.52/0.81, respectively. Conclusions Classifiers built by RF and CNN approaches had excellent performance when classifying UC with CD or ITB. For the differentiation of CD and ITB, high specificity and sensitivity were achieved as well. Artificial intelligence through machine learning is very promising in helping unexperienced endoscopists differentiate inflammatory intestinal diseases. Conference The abstract of this article has won the first prize of the Young Investigator Award during the Asian Pacific Digestive Week (APDW) 2019 held in Kolkata, India.

In drug delivery, the poor tumor perfusion results in disappointing therapeutic efficacy. Nanomedicines for photodynamic therapy (PDT) greatly need deep tumor penetration due to short lifespan and weak diffusion of the cytotoxic reactive oxygen species (ROS). The damage of only shallow cells can easily cause invasiveness and metastasis. Moreover, even if the nanomedicines enter into deeper lesion, the effectiveness of PDT is limited due to the hypoxic microenvironment. Here, a deep penetrating and oxygen self-sufficient PDT nanoparticle is developed for balanced ROS distribution within tumor and efficient cancer therapy. The designed nanoparticles (CNPs/IP) are doubly emulsified (W/O/W) from poly(ethylene glycol)-poly(ε-caprolactone) copolymers doped with photosensitizer IR780 in the O layer and oxygen depot perfluorooctyl bromide (PFOB) inside the core, and functionalized with the tumor penetrating peptide Cys-Arg-Gly-Asp-Lys (CRGDK). The CRGDK modification significantly improves penetration depth of CNPs/IP and makes the CNPs/IP arrive at both the periphery and hypoxic interior of tumors where the PFOB releases oxygen, effectively alleviating hypoxia and guaranteeing efficient PDT performance. The improved intratumoral distribution of photosensitizer and adequate oxygen supply augment the sensitivity of tumor cells to PDT and significantly improve PDT efficiency. Such a nanosystem provides a potential platform for improved therapeutic index in anticancer therapy.