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Polypharmacy is a promising approach for treating diseases, especially those with complex symptoms. However, it can lead to unexpected drug-drug interactions (DDIs), potentially reducing efficacy and triggering adverse drug reactions (ADRs). Predicting the risk of DDIs is crucial for ensuring safe drug use, particularly by identifying the types of DDIs and the mechanisms involved. Therefore, this study used biomedical literature to proposed hierarchical attention-based deep learning models to predict DDIs and their types. The proposed model consists of two components: drug embedding and DDI prediction. The drug embedding module extracts representation vectors that effectively capture drug properties using sentence and sequence embedding methods. For sentence embedding, a pre-trained biomedical language model is used to map drug-related sentences into vector space. For sequence embedding, sentence embedding vectors are sequentially fed into bidirectional long short-term memory with a hierarchical attention network, enabling the analysis of sentences relevant to DDI prediction while accounting for the order of the sentences. Finally, DDI prediction is performed using a deep neural network based on the sequence embedding vectors of a drug pair. Our model achieved high performances in the accuracy (0.85-0.90), AUROC (0.98-0.99), and AUPR (0.63-0.95) performance across 164 DDI types. Additionally, the proposed model showed improvements in up to 11 % in AUROC, and 8 % in AUPR. Furthermore, model interprets predictions by leveraging attention mechanisms and drug similarity. The results indicated that the model considered various factors beyond similarity to predict DDIs. These findings may help prevent unforeseen medical accidents and reduce healthcare costs by predicting detailed drug interaction types., Competing Interests: Declaration of competing interest None Declared., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Accurate imputation of tissue-specific gene expression can be a powerful tool for understanding the biological mechanisms underlying human complex traits. Existing imputation methods can be grouped into two categories according to the types of predictors used. The first category uses genotype data, while the second category uses whole-blood expression data. Both data types can be easily collected from blood, avoiding invasive tissue biopsies. In this study, we attempted to build an optimal predictive model for imputing tissue-specific gene expression by combining the genotype and whole-blood expression data. We first evaluated the imputation performance of each standalone model (using genotype data [GEN model] and using whole-blood expression data [WBE model]) using their respective data types across 47 human tissues. The WBE model outperformed the GEN model in most tissues by a large gain. Then, we developed several combined models that leverage both types of predictors to further improve imputation performance. We tried various strategies, including utilizing a merged dataset of the two data types (MERGED models) and integrating the imputation outcomes of the two standalone models (inverse variance-weighted [IVW] models). We found that one of the MERGED models noticeably outperformed the standalone models. This model involved a fixed ratio between the two regularization penalty factors for the two predictor types so that the contribution of the whole-blood transcriptome is upweighted compared with the genotype. Our study suggests that one can improve the imputation of tissue-specific gene expression by combining the genotype and whole-blood expression, but the improvement can be largely dependent on the combination strategy chosen., Competing Interests: B.H. is the CTO of Genealogy Inc., (© 2023 The Authors.)

Cataracts are a prevalent ophthalmic disease worldwide, and research on the risk factors for cataracts occurrence is actively being conducted. This study aimed to investigate the relationship between nutrient intake and cataracts in the older adult population in Korea. We analyzed data from Korean adults over the age of 60 years (cataract: 2137, non-cataract: 3497) using the Korean National Health and Nutrition Examination Survey. We performed univariate simple and multiple logistic regressions, adjusting for socio-demographic, medical history, and lifestyle, to identify the associations between nutrient intake and cataracts. A higher intake of vitamin B1 in the male group was associated with a lower incidence of cataracts. A lower intake of polyunsaturated fatty acids and vitamin A, and a higher intake of vitamin B2 in the female group were associated with a higher incidence of cataracts. Our study demonstrated that polyunsaturated fatty acids, vitamin A, and vitamin B2 could affect the incidence of cataracts according to sex. The findings could be used to control nutrient intake for cataract prevention.

Human leukocyte antigen (HLA) gene variants in the major histocompatibility complex (MHC) region are associated with numerous complex human diseases and quantitative traits. Previous phenome-wide association studies (PheWAS) for this region demonstrated that HLA association patterns to the phenome have both population-specific and population-shared components. We performed MHC PheWAS in the Korean population by analyzing associations between phenotypes and genetic variants in the MHC region using the Korea Biobank Array project data samples from the Korean Genome and Epidemiology Study cohorts. Using this single-population dataset, we curated and analyzed 82 phenotypes for 125 673 Korean individuals after imputing HLA using CookHLA, a recently developed imputation framework. More than one-third of these phenotypes showed significant associations, confirming 56 known associations and discovering 13 novel association signals that were not reported previously. In addition, we analyzed heritability explained by the variants in the MHC region and genetic correlations among phenotypes based on the MHC variants., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

The observation of histopathology using optical microscope is an essential procedure for examination of tissue biopsies or surgically excised specimens in biological and clinical laboratories. However, slide-based microscopic pathology is not suitable for visualizing the large-scale tissue and native 3D organ structure due to its sampling limitation and shallow imaging depth. Here, we demonstrate serial optical coherence microscopy (SOCM) technique that offers label-free, high-throughput, and large-volume imaging of ex vivo mouse organs. A 3D histopathology of whole mouse brain and kidney including blood vessel structure is reconstructed by deep tissue optical imaging in serial sectioning techniques. Our results demonstrate that SOCM has unique advantages as it can visualize both native 3D structures and quantitative regional volume without introduction of any contrast agents.

Enhanced hydrogen sensing performance of Pt Schottky diodes on ZnO single crystal wafers in humid ambient conditions is reported using a polymethylmethacrylate (PMMA) membrane layer. ZnO diode sensors showed little change in forward current when switching to wet ambient H 2 conditions with 100% relative humidity. This sensitivity drop in the presence of water vapor can be attributed to surface coverage of hydroxyl groups on the Pt surface in humid ambient conditions. The hydrogen sensitivity of PMMA-coated diode sensors recovered up to 805% in wet H 2 ambient conditions at room temperature. The PMMA layer can selectively filter water vapor and allow H 2 molecules to pass through the membrane layer. It is clear that the PMMA layer can effectively serve as a moisture barrier because of low water vapor permeability and its hydrophobicity. In both dry and wet conditions, ZnO diodes exhibited relatively fast and stable on/off switching in each cycle with good repeatability., Competing Interests: The authors declare no conflict of interest.

Tissue clearing has gained attention as a pioneering research tool for imaging of large tissue samples. This technique improves light transmission by reducing light scattering within tissues, either by removing lipids or by replacing water with a high refractive index solution. Although various clearing techniques have been developed, quantitative assessments on clearing efficacy depending on tissue properties are rare. In this study, we developed the quantitative mapping of regional clearing efficacy using mean free path in optical coherence tomography (OCT) and proton density in magnetic resonance imaging (MRI), and demonstrated its feasibility in the brain sample with four representative clearing techniques (benzyl alcohol and benzyl benzoate [BABB], Clear T , Scale, and passive CLARITY technique [PACT]). BABB (solvent-based clearing), involving both refractive index matching and lipid removal, exhibited best optical clearing performance with the highest proton density reduction both in gray and white matter. Lipid-removing techniques such as Scale (aqueous hyperhydration) and PACT (hydrogel embedding) showed higher clearing efficiency in white matter than gray matter in accordance with larger proton density increase in white matter. For Clear T (aqueous-based simple immersion), we observed lowest clearing efficiency in the white matter as well as poor lipid removal reflected in low proton density reduction. Our results showed the feasibility of the regional mapping of clearing efficacy and correlating optical transparency and proton density changes using OCT and MRI from existing tissue clearing techniques. This novel quantitative mapping of clearing efficacy depending on tissue types and clearing methods may be helpful in the development of optimized clearing methods for different biological samples.

The imaging capability of optical coherence microscopy (OCM) has great potential to be used in neuroscience research because it is able to visualize anatomic features of brain tissue without labeling or external contrast agents. However, the field of view of OCM is still narrow, which dilutes the strength of OCM and limits its application. In this study, we present fully automated wide-field OCM for mosaic imaging of sliced mouse brains. A total of 308 segmented OCM images were acquired, stitched, and reconstructed as an en-face brain image after intensive imaging processing. The overall imaging area was 11.2×7.0  mm (horizontal×vertical), and the corresponding pixel resolution was 1.2×1.2  μm. OCM images were compared to traditional histology stained with Nissl and Luxol fast blue (LFB). In particular, the orientation of the fibers was analyzed and quantified in wide-field OCM.