Complementary and Integrative Health Lexicon (CIHLex) and Entity Recognition in the Literature

Objective: Our study aimed to construct an exhaustive Complementary and Integrative Health (CIH) Lexicon (CIHLex) to better represent the often underrepresented physical and psychological CIH approaches in standard terminologies. We also intended to apply advanced Natural Language Processing (NLP) models such as Bidirectional Encoder Representations from Transformers (BERT) and GPT-3.5 Turbo for CIH named entity recognition, evaluating their performance against established models like MetaMap and CLAMP. Materials and Methods: We constructed the CIHLex by integrating various resources, compiling and integrating data from biomedical literature and relevant knowledge bases. The Lexicon encompasses 198 unique concepts with 1090 corresponding unique terms. We matched these concepts to the Unified Medical Language System (UMLS). Additionally, we developed and utilized BERT models and compared their efficiency in CIH named entity recognition to that of other models such as MetaMap, CLAMP, and GPT3.5-turbo. Results: From the 198 unique concepts in CIHLex, 62.1% could be matched to at least one term in the UMLS. Moreover, 75.7% of the mapped UMLS Concept Unique Identifiers (CUIs) were categorized as "Therapeutic or Preventive Procedure." Among the models applied to CIH named entity recognition, BLUEBERT delivered the highest macro average F1-score of 0.90, surpassing other models. Conclusion: Our CIHLex significantly augments representation of CIH approaches in biomedical literature. Demonstrating the utility of advanced NLP models, BERT notably excelled in CIH entity recognition. These results highlight promising strategies for enhancing standardization and recognition of CIH terminology in biomedical contexts

Effect of Heat Treatment on the Microstructure and Mechanical Properties of Additive Manufactured Ti-6.5Al-2Zr-1Mo-1V Alloy

Ti-6.5Al-2Zr-1Mo-1V (TA15), widely used in the aerospace industry, is a medium- to high-strength, near-α titanium alloy with high aluminium equivalent value. The TA15 fabricated via laser powder bed fusion (L-PBF) normally presents a typical brittle appearance in as-built status, with high strength and low ductility. In this study, the microstructure and properties of L-PBF TA15 were engineered by various heat treatments below the β-transus temperature (1022 °C). After heat treatment, the original acicular martensite gradually transforms into a typical lamellar α + β dual-phase structure. Withannealing temperature increases, the lamellar α phase thickened with a decreased aspect ratio. Globularisation of the α grain can be noticed when annealing above 800 °C, which leads to a balance between strength and ductility. After heat treatment between 800–900 °C, the desired combination of strength and ductility can be achieved, with elongation of about 12.5% and ultimate tensile strength of about 1100 Mpa

Identification of Cancer Dysfunctional Subpathways by Integrating DNA Methylation, Copy Number Variation, and Gene-Expression Data

A subpathway is defined as the local region of a biological pathway with specific biological functions. With the generation of large-scale sequencing data, there are more opportunities to study the molecular mechanisms of cancer development. It is necessary to investigate the potential impact of DNA methylation, copy number variation (CNV), and gene-expression changes in the molecular states of oncogenic dysfunctional subpathways. We propose a novel method, Identification of Cancer Dysfunctional Subpathways (ICDS), by integrating multi-omics data and pathway topological information to identify dysfunctional subpathways. We first calculated gene-risk scores by integrating the three following types of data: DNA methylation, CNV, and gene expression. Second, we performed a greedy search algorithm to identify the key dysfunctional subpathways within pathways for which the discriminative scores were locally maximal. Finally, a permutation test was used to calculate the statistical significance level for these key dysfunctional subpathways. We validated the effectiveness of ICDS in identifying dysregulated subpathways using datasets from liver hepatocellular carcinoma (LIHC), head-neck squamous cell carcinoma (HNSC), cervical squamous cell carcinoma, and endocervical adenocarcinoma. We further compared ICDS with methods that performed the same subpathway identification algorithm but only considered DNA methylation, CNV, or gene expression (defined as ICDS_M, ICDS_CNV, or ICDS_G, respectively). With these analyses, we confirmed that ICDS better identified cancer-associated subpathways than the three other methods, which only considered one type of data. Our ICDS method has been implemented as a freely available R-based tool (https://cran.r-project.org/web/packages/ICDS)

VS-4718 Antagonizes Multidrug Resistance in ABCB1- and ABCG2-Overexpressing Cancer Cells by Inhibiting the Efflux Function of ABC Transporters

Overexpression of ATP-binding cassette (ABC) transporters is one of the most important mechanisms responsible for multi-drug resistance (MDR). VS-4718, a tyrosine kinase inhibitor targeting focal adhesion kinase (FAK) with a potential anticancer effect, is currently evaluated in clinical trials. In this study, we investigated whether VS-4718 could reverse MDR mediated by ABC transporters, including ABCB1, ABCG2, and ABCC1. The results showed that VS-4718 significantly reversed ABCB1- and ABCG2-mediated MDR, but not MDR mediated by ABCC1. Treatment of VS-4718 did not alter the protein level and subcellular localization of ABCB1 or ABCG2. Mechanism studies indicated that the reversal effects of VS-4718 were related to attenuation of the efflux activity of ABCB1 and ABCG2 transporters. ATPase analysis indicated that VS-4718 stimulated the ATPase activity of ABCB1 and ABCG2. Docking study showed that VS-4718 interacted with the substrate-binding sites of both ABCB1 and ABCG2, suggesting that VS-4718 may affect the activity of ABCB1 and ABCG2 competitively. This study provided a novel insight for MDR cancer treatment. It indicated that combination of VS-4718 with antineoplastic drugs could attenuate MDR mediated by ABCB1 or ABCG2 in ABCB1- or ABCG2-overexpressing cancer cells

Glesatinib, a c-MET/SMO Dual Inhibitor, Antagonizes P-glycoprotein Mediated Multidrug Resistance in Cancer Cells

Multidrug resistance (MDR) is one of the leading causes of treatment failure in cancer chemotherapy. One major mechanism of MDR is the overexpressing of ABC transporters, whose inhibitors hold promising potential in antagonizing MDR. Glesatinib is a dual inhibitor of c-Met and SMO that is under phase II clinical trial for non-small cell lung cancer. In this work, we report the reversal effects of glesatinib to P-glycoprotein (P-gp) mediated MDR. Glesatinib can sensitize paclitaxel, doxorubicin, colchicine resistance to P-gp overexpressing KB-C2, SW620/Ad300, and P-gp transfected Hek293/ABCB1 cells, while has no effect to their corresponding parental cells and negative control drug cisplatin. Glesatinib suppressed the efflux function of P-gp to [3H]-paclitaxel and it didn't impact both the expression and cellular localization of P-gp based on Western blot and immunofluorescent analysis. Furthermore, glesatinib can stimulate ATPase in a dose-dependent manner. The docking study indicated that glesatinib interacted with human P-gp through several hydrogen bonds. Taken together, c-Met/SMO inhibitor glesatinib can antagonize P-gp mediated MDR by inhibiting its cell membrane transporting functions, suggesting new application in clinical trials

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

The impact of low-carbon emission reduction policies on economic growth

As the development of a green and low-carbon economy has received great attention from governments around the world, carbon peaking and carbon neutrality have become important issues raised by China. As a major energy consuming country, government has actively formulated and implemented various carbon emission reduction policies in order to curb carbon emissions. Whether these policies achieve economic growth in the process of energy conservation and emission reduction, and promote China’s green and low-carbon development transition is the focus of this paper. This paper selects data from 30 provinces in China from 2010 to 2019, establishes a model, and empirically analysis the impact of carbon emission reduction policy tools on economic growth. The results show that there is a significant negative correlation between mandatory carbon emission reduction policies and economic growth, while market-based carbon emission reduction policies enhance the economic strength of the region. In addition, this paper empirically tests that after the establishment of the carbon market in 2013, market-based carbon emission reduction policies have significantly promoted economic growth, and the impact of carbon emission reduction policies on economic growth have regional heterogeneity

Virtual optofluidic time-stretch quantitative phase imaging

Optofluidic time-stretch quantitative phase imaging (OTS-QPI) is a potent tool for biomedical applications as it enables high-throughput QPI of numerous cells for large-scale single-cell analysis in a label-free manner. However, there are a few critical limitations that hinder OTS-QPI from being widely applied to diverse applications, such as its costly instrumentation and inherent phase-unwrapping errors. Here, to overcome the limitations, we present a QPI-free OTS-QPI method that generates "virtual" phase images from their corresponding bright-field images by using a deep neural network trained with numerous pairs of bright-field and phase images. Specifically, our trained generative adversarial network model generated virtual phase images with high similarity (structural similarity index >0.7) to their corresponding real phase images. This was also supported by our successful classification of various types of leukemia cells and white blood cells via their virtual phase images. The virtual OTS-QPI method is highly reliable and cost-effective and is therefore expected to enhance the applicability of OTS microscopy in diverse research areas, such as cancer biology, precision medicine, and green energy