Recent Progress of Crystalline Porous Frameworks for Intermediate-Temperature Proton Conduction.

Proton exchange membranes (PEMs), especially for work under intermediate temperatures (100-200 °C), have attracted great interest because of the high CO toleration and facial water management of the corresponding proton exchange membrane fuel cells (PEMFCs). Traditional polymer PEMs faced challenges of low stability and proton carrier leaking. Crystalline porous materials, such as metal-organic frameworks (MOFs) and covalent organic frameworks (COFs), are promising to overcome these issues contributed by nanometer-sized channels. Herein we summarized the recent development of MOF/COF-based intermediate-temperature proton conductors. The strategies of framework engineering and pore impregnation were introduced in detail for raising proton conductivity. The proton-conducting mechanism was described as well. This spotlight will provide new insight into the fabrication of MOF/COF proton conductors under intermediate-temperature and anhydrous conditions.

Recent advancements in visible-light-driven carboxylation with carbon dioxide.

Carbon dioxide as a classic C1 source has long been investigated in organic synthetic chemistry. Diverse catalytic methods for CO2 activation have been reported over the past several decades. In this minireview, we mainly focus on detailing recent advances (since 2020) in utilizing carbon dioxide to attain the carboxylation of organic motifs via photoredox and metallaphotoredox catalysis.

ABCA1 Variants rs1800977 (C69T) and rs9282541 (R230C) Are Associated with Susceptibility to Type 2 Diabetes.

OBJECTIVE: Accumulated evidence suggests that ATP-binding cassette A1 transporter (ABCA1) contributes to secreting insulin in pancreatic ß-cells and amyloid beta formation. This study aimed to investigate the association between three single nucleotide polymorphisms (SNPs) of ABCA1 and susceptibility to type 2 diabetes mellitus (T2DM) in a Han Chinese population. METHODS: A total of 996 T2DM patients and 1,002 controls were included in the study. Three SNPs in the ABCA1 gene, i.e., rs2230806 (R219K), rs1800977 (C69T), and rs9282541 (R230C), were genotyped by SNaPshot. A genotype model, an allele model, a dominant model, and a recessive model were used to assess susceptibility to T2DM. RESULTS: There were significant associations between rs1800977 and T2DM in different genetic models (TT vs. CC, OR = 0.591 [0.446-0.793], p < 0.001; T vs. C, OR = 0.835 [0.735-0.949], p = 0.006; recessive model, OR = 0.583 [0.449-0.756], p < 0.001). There were also significant associations between rs9282541 and T2DM in different genetic models (CT vs. CC, OR = 1.690 [0.807-1.005], p = 0.048; T vs. C, OR = 1.756 [0.694-1.060], p = 0.029; dominant model, OR = 1.735 [0.715-1.034], p = 0.037). CONCLUSION: Our case-control study showed that the two SNPs rs1800977 and rs9282541 in the ABCA1 gene are significantly associated with susceptibility to T2DM in our Han Chinese population. Study of further mechanisms should be performed before application to clinical therapy.

Solvothermal Synthesis of Humic Acid-Supported CeO2 Nanosheets Composite as High Performance Adsorbent for Congo Red Removal.

Surface properties and structures of materials are essential for their adsorption of pollutants in water. Humic acids (HA)-supported CeO2 nanosheet composites are synthesised by solvothermal method. The size of CeO2 nanosheets are approximately 100-500 nm. The obtained composite exhibits superior adsorption ability for Congo Red (CR) in water, which can be attributed to its unique structure and highly dispersed CeO2 nanosheet. The composite's adsorption behaviour of CR follows a pseudo-second-order mode and Langmuir adsorption model well, and the maximum adsorptive capacity for CR achieves 260 mg g-1. The presence of CeO2 nanosheets enhances surface area and enriches the mesoporous structure of the composites, thereby promoting CR adsorption capacity.

Bilingual term alignment from comparable corpora in English discharge summary and Chinese discharge summary.

BACKGROUND: Electronic medical record (EMR) systems have become widely used throughout the world to improve the quality of healthcare and the efficiency of hospital services. A bilingual medical lexicon of Chinese and English is needed to meet the demand for the multi-lingual and multi-national treatment. We make efforts to extract a bilingual lexicon from English and Chinese discharge summaries with a small seed lexicon. The lexical terms can be classified into two categories: single-word terms (SWTs) and multi-word terms (MWTs). For SWTs, we use a label propagation (LP; context-based) method to extract candidates of translation pairs. For MWTs, which are pervasive in the medical domain, we propose a term alignment method, which firstly obtains translation candidates for each component word of a Chinese MWT, and then generates their combinations, from which the system selects a set of plausible translation candidates. RESULTS: We compare our LP method with a baseline method based on simple context-similarity. The LP based method outperforms the baseline with the accuracies: 4.44% Acc1, 24.44% Acc10, and 62.22% Acc100, where AccN means the top N accuracy. The accuracy of the LP method drops to 5.41% Acc10 and 8.11% Acc20 for MWTs. Our experiments show that the method based on term alignment improves the performance for MWTs to 16.22% Acc10 and 27.03% Acc20. CONCLUSIONS: We constructed a framework for building an English-Chinese term dictionary from discharge summaries in the two languages. Our experiments have shown that the LP-based method augmented with the term alignment method will contribute to reduction of manual work required to compile a bilingual sydictionary of clinical terms.

Multi-label classification for colon cancer using histopathological images.

Colon cancer classification has a significant guidance value in clinical diagnoses and medical prognoses. The classification of colon cancers with high accuracy is the premise of efficient treatment. Our task is to build a system for colon cancer detection and classification based on slide histopathological images. Some former researches focus on single label classification. Through analyzing large amount of colon cancer images, we found that one image may contain cancer regions of multiple types. Therefore, we reformulated the task as multi-label problem. Four kinds of features (Color Histogram, Gray-Level Co-occurrence Matrix, Histogram of Oriented Gradients and Euler number) were introduced to compose our discriminative feature set, extracted from our dataset that includes six single categories and four multi-label categories. In order to evaluate the performance and make comparison with our multi-label model, three commonly used multi-classification methods were designed in our experiment including one-against-all SVM (OAA), one-against-one SVM (OAO) and multi-structure SVM. Four indicators (Precision, Recall, F-measure, and Accuracy) under 3-fold cross-validation were used to validate the performance of our approach. Experiment results show that the precision, recall and F-measure of multi-label method as 73.7%, 68.2%, and 70.8% with all features, which are higher than the other three classifiers. These results demonstrate the effectiveness and efficiency of our method on colon histopathological images analysis.