Nitrogen-rich covalent organic framework as a practical coating for effective determinations of polycyclic aromatic hydrocarbons.

Polycyclic aromatic hydrocarbons (PAHs) are high-profile organic pollutants to be poisonous, carcinogenic, and mutagenic, and widely distributed at trace levels in the environment. In order to effectively enrich PAHs, two stable covalent organic frameworks (COFs, TAPT-OMe-PDA and TPB-DMTP) were prepared by combining 2,4,6-tri(4-aminophenyl)-1,3,5-triazine (TAPT) and 1,3,5-tri(4-aminophenyl) benzene (TAPB) with 2,5-dimethoxy-phenyl-1,4-diformaldehyde (OMe-PDA), respectively. Even though the surface area of TAPT-OMe-PDA was much lower than that of TPB-DMTP, it still demonstrated much better extraction efficiencies towards PAHs as the solid phase microextraction (SPME) coating. Therefore, the TAPT-OMe-PDA coated fiber was coupled with gas chromatography-mass spectrometry (GC-MS) to establish a practical and sensitive method, after the extraction parameters (extraction time, extraction temperature, desorption temperature, desorption time, salt concentration and pH) were optimized. This developed analytical method showed wide linear ranges, low limits of detection, good repeatability and reproducibility. Finally, five PAHs in three water samples were detected and quantified precisely (2.72-38.7 ng·L-1) with satisfactory recoveries (88.3%-118%).

Core-Shell Reactor Partitioning Enzyme and Prodrug by ZIF-8 for NADPH-Sensitive In Situ Prodrug Activation.

Enzyme-prodrug therapies have shown unique advantages in efficiency, selectivity, and specificity of in vivo prodrug activation. However, precise spatiotemporal control of both the enzyme and its substrate at the target site, preservation of enzyme activity, and in situ substrate depletion due to low prodrug delivery efficiency continue to be great challenges. Here, we propose a novel core-shell reactor partitioning enzyme and prodrug by ZIF-8, which integrates an enzyme with its substrate and increases the drug loading capacity (DLC) using a prodrug as the building ligand to form a Zn-prodrug shell. Cytochrome P450 (CYP450) is immobilized in ZIF-8, and the antitumor drug dacarbazine (DTIC) is coordinated and deposited in its outer layer with a high DLC of 43.6±0.8 %. With this configuration, a much higher prodrug conversion efficiency of CYP450 (36.5±1.5 %) and lower IC50 value (26.3±2.6 µg/mL) are measured for B16-F10 cells with a higher NADPH concentration than those of L02 cells and HUVECs. With the tumor targeting ability of hyaluronic acid, this core-shell enzyme reactor shows a high tumor suppression rate of 96.6±1.9 % and provides a simple and versatile strategy for enabling in vivo biocatalysis to be more efficient, selective, and safer.

Classifying the lifestyle status for Alzheimer's disease from clinical notes using deep learning with weak supervision.

BACKGROUND: Since no effective therapies exist for Alzheimer's disease (AD), prevention has become more critical through lifestyle status changes and interventions. Analyzing electronic health records (EHRs) of patients with AD can help us better understand lifestyle's effect on AD. However, lifestyle information is typically stored in clinical narratives. Thus, the objective of the study was to compare different natural language processing (NLP) models on classifying the lifestyle statuses (e.g., physical activity and excessive diet) from clinical texts in English. METHODS: Based on the collected concept unique identifiers (CUIs) associated with the lifestyle status, we extracted all related EHRs for patients with AD from the Clinical Data Repository (CDR) of the University of Minnesota (UMN). We automatically generated labels for the training data by using a rule-based NLP algorithm. We conducted weak supervision for pre-trained Bidirectional Encoder Representations from Transformers (BERT) models and three traditional machine learning models as baseline models on the weakly labeled training corpus. These models include the BERT base model, PubMedBERT (abstracts + full text), PubMedBERT (only abstracts), Unified Medical Language System (UMLS) BERT, Bio BERT, Bio-clinical BERT, logistic regression, support vector machine, and random forest. The rule-based model used for weak supervision was tested on the GSC for comparison. We performed two case studies: physical activity and excessive diet, in order to validate the effectiveness of BERT models in classifying lifestyle status for all models were evaluated and compared on the developed Gold Standard Corpus (GSC) on the two case studies. RESULTS: The UMLS BERT model achieved the best performance for classifying status of physical activity, with its precision, recall, and F-1 scores of 0.93, 0.93, and 0.92, respectively. Regarding classifying excessive diet, the Bio-clinical BERT model showed the best performance with precision, recall, and F-1 scores of 0.93, 0.93, and 0.93, respectively. CONCLUSION: The proposed approach leveraging weak supervision could significantly increase the sample size, which is required for training the deep learning models. By comparing with the traditional machine learning models, the study also demonstrates the high performance of BERT models for classifying lifestyle status for Alzheimer's disease in clinical notes.