BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices

Introduction

Social media platforms serve as a valuable resource for users to share health-related information, aiding in the monitoring of adverse events linked to medications and treatments in drug safety surveillance. However, extracting drug-related adverse events accurately and efficiently from social media poses challenges in both natural language processing research and the pharmacovigilance domain.

Method

Recognizing the lack of detailed implementation and evaluation of Bidirectional Encoder Representations from Transformers (BERT)-based models for drug adverse event extraction on social media, we developed a BERT-based language model tailored to identifying drug adverse events in this context. Our model utilized publicly available labeled adverse event data from the ADE-Corpus-V2. Constructing the BERT-based model involved optimizing key hyperparameters, such as the number of training epochs, batch size, and learning rate. Through ten hold-out evaluations on ADE-Corpus-V2 data and external social media datasets, our model consistently demonstrated high accuracy in drug adverse event detection.

Result

The hold-out evaluations resulted in average F1 scores of 0.8575, 0.9049, and 0.9813 for detecting words of adverse events, words in adverse events, and words not in adverse events, respectively. External validation using human-labeled adverse event tweets data from SMM4H further substantiated the effectiveness of our model, yielding F1 scores 0.8127, 0.8068, and 0.9790 for detecting words of adverse events, words in adverse events, and words not in adverse events, respectively.

Discussion

This study not only showcases the effectiveness of BERT-based language models in accurately identifying drug-related adverse events in the dynamic landscape of social media data, but also addresses the need for the implementation of a comprehensive study design and evaluation. By doing so, we contribute to the advancement of pharmacovigilance practices and methodologies in the context of emerging information sources like social media.

Towards a Light-mediated Gene Therapy for the Eye using Caged Ethinylestradiol and the Inducible Cre/lox System

Increasingly, retinal pathologies are being treated with virus-mediated gene therapies. To be able to target viral transgene expression specifically to the pathological regions of the retina with light, we established an in vivo photoactivated gene expression paradigm for retinal tissue. Based on the inducible Cre/lox system, we discovered that ethinylestradiol is a suitable alternative to Tamoxifen as ethinylestradiol is more amenable to modification with photosensitive protecting compounds, i.e., "caging." Identification of ethinylestradiol as a ligand for the mutated human estradiol receptor was supported by in silico binding studies showing the reduced binding of caged ethinylestradiol. Caged ethinylestradiol was injected into the eyes of double transgenic GFAP-CreERT2 mice with a Cre-dependent tdTomato reporter transgene followed by irradiation with light of 450 nm. Photoactivation significantly increased retinal tdTomato expression compared to controls. We thus demonstrated a first step towards the development of a targeted, light-mediated gene therapy for the eyes.

[Research on epidemiological characteristics of pneumonia and correlative factors of length of hospitalization in the elderly aged 60 years and older in Ningbo]

Objective: To investigate the epidemiological characteristics of pneumonia and the related factors of the length of hospitalization of pneumonia in the elderly aged 60 years and older in Ningbo in 2019. Methods: Data on hospitalized cases of pneumonia in the elderly aged 60 years and older in Ningbo in 2019 were collected through the regional health information platform, and the population data of Ningbo in 2019 were obtained through the Zhejiang Provincial Bureau of Statistics. A descriptive epidemiological analysis was conducted on hospitalized cases of pneumonia in the elderly population, and factors related to the length of hospitalization were explored. Results: A total of 15 956 hospitalized cases of pneumonia aged 60 years and older were reported in Ningbo in 2019, and the incidence of pneumonia requiring hospitalization was 1.02% (15 956/1 571 431). The incidence was 1.13% (8 613/760 357) in males and 0.83% (6 759/811 074) in females, and the ratio of male to female cases was 1.27∶1. The highest incidence was found in the ≥80 age group (2.52%), and the lowest incidence was found in the 60-69 age group (0.58%). March, February, and January were the peak period of pneumonia hospitalization. The main types of pneumonia diagnosed were not specified (65.12%), followed by bacterial pneumonia (34.60%). The M(Q1, Q3) of hospitalized patients with pneumonia was 9 (7, 13) days. The results of multivariate logistic regression analysis showed that gender (female: OR=0.911, 95%CI: 0.849-0.978) and older age (70-79 years old: OR=1.211, 95%CI: 1.111-1.321; ≥80 years old group: OR=1.486, 95%CI: 1.365-1.617), settlement method (self-payment: OR=0.567, 95%CI: 0.464-0.691), higher level of hospitals (Grade Ⅱ: OR=1.902,95%CI:1.723-2.100; Grade Ⅲ: OR=1.546,95%CI:1.407-1.698) were associated with the length of hospitalization for pneumonia in people aged 60 years and older in Ningbo. Conclusions: Hospitalization with pneumonia in people aged 60 years and older was high in winter and spring, men and older adults were in high-risk groups in Ningbo in 2019. Gender, age, billing method, and level of hospitals may be related factors to the length of hospitalization for pneumonia.

Computational Nanotoxicology Models for Environmental Risk Assessment of Engineered Nanomaterials

Although engineered nanomaterials (ENMs) have tremendous potential to generate technological benefits in numerous sectors, uncertainty on the risks of ENMs for human health and the environment may impede the advancement of novel materials. Traditionally, the risks of ENMs can be evaluated by experimental methods such as environmental field monitoring and animal-based toxicity testing. However, it is time-consuming, expensive, and impractical to evaluate the risk of the increasingly large number of ENMs with the experimental methods. On the contrary, with the advancement of artificial intelligence and machine learning, in silico methods have recently received more attention in the risk assessment of ENMs. This review discusses the key progress of computational nanotoxicology models for assessing the risks of ENMs, including material flow analysis models, multimedia environmental models, physiologically based toxicokinetics models, quantitative nanostructure-activity relationships, and meta-analysis. Several challenges are identified and a perspective is provided regarding how the challenges can be addressed.

RxNorm for drug name normalization: a case study of prescription opioids in the FDA adverse events reporting system

Numerous studies have been conducted on the US Food and Drug Administration (FDA) Adverse Events Reporting System (FAERS) database to assess post-marketing reporting rates for drug safety review and risk assessment. However, the drug names in the adverse event (AE) reports from FAERS were heterogeneous due to a lack of uniformity of information submitted mandatorily by pharmaceutical companies and voluntarily by patients, healthcare professionals, and the public. Studies using FAERS and other spontaneous reporting AEs database without drug name normalization may encounter incomplete collection of AE reports from non-standard drug names and the accuracies of the results might be impacted. In this study, we demonstrated applicability of RxNorm, developed by the National Library of Medicine, for drug name normalization in FAERS. Using prescription opioids as a case study, we used RxNorm application program interface (API) to map all FDA-approved prescription opioids described in FAERS AE reports to their equivalent RxNorm Concept Unique Identifiers (RxCUIs) and RxNorm names. The different names of the opioids were then extracted, and their usage frequencies were calculated in collection of more than 14.9 million AE reports for 13 FDA-approved prescription opioid classes, reported over 17 years. The results showed that a significant number of different names were consistently used for opioids in FAERS reports, with 2,086 different names (out of 7,892) used at least three times and 842 different names used at least ten times for each of the 92 RxNorm names of FDA-approved opioids. Our method of using RxNorm API mapping was confirmed to be efficient and accurate and capable of reducing the heterogeneity of prescription opioid names significantly in the AE reports in FAERS; meanwhile, it is expected to have a broad application to different sets of drug names from any database where drug names are diverse and unnormalized. It is expected to be able to automatically standardize and link different representations of the same drugs to build an intact and high-quality database for diverse research, particularly postmarketing data analysis in pharmacovigilance initiatives.

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery

Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein-ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure-activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure-activity relationships in β2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein-ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.

[Epidemiological characteristics and spatiotemporal distribution of schizophrenia in Ningbo, 2018-2022]

Objective: To explore the temporal and spatial distribution characteristics of reported incidence of schizophrenia in Ningbo from 2018 to 2022 and to provide a scientific basis for rational allocation of mental health resources and comprehensive prevention and treatment of schizophrenia. Methods: The reported incidence data of schizophrenia from 2018 to 2022 were collected from Ningbo's mental health information management system, and the reported incidence was calculated by township. The spatial correlation analysis and the spatiotemporal scan analysis were used to study the spatiotemporal distribution of schizophrenia. Results: The reported incidence of schizophrenia decreased from 2018 to 2022, with 4 133 new cases reported, and the annual average reported incidence was 9.76/100 000. Global and local spatial autocorrelation analysis showed positive spatial correlations and hot spots in 2018-2020. The space-time scan analysis showed an incidence cluster in Dongqiao Town, Haishu District, during 2018-2019. The RR was 2.46, and the log-likelihood ratio was 256.89. Conclusions: The reported incidence of schizophrenia in Ningbo has obvious temporal and spatial aggregation, and the high incidence area explored can provide clues for further research on the correlation between environmental factors and the incidence of schizophrenia and has certain guiding significance for the rational allocation of mental health resources in Ningbo.

Quartet DNA reference materials and datasets for comprehensively evaluating germline variant calling performance

BACKGROUND

Genomic DNA reference materials are widely recognized as essential for ensuring data quality in omics research. However, relying solely on reference datasets to evaluate the accuracy of variant calling results is incomplete, as they are limited to benchmark regions. Therefore, it is important to develop DNA reference materials that enable the assessment of variant detection performance across the entire genome.

RESULTS

We established a DNA reference material suite from four immortalized cell lines derived from a family of parents and monozygotic twins. Comprehensive reference datasets of 4.2 million small variants and 15,000 structural variants were integrated and certified for evaluating the reliability of germline variant calls inside the benchmark regions. Importantly, the genetic built-in-truth of the Quartet family design enables estimation of the precision of variant calls outside the benchmark regions. Using the Quartet reference materials along with study samples, batch effects are objectively monitored and alleviated by training a machine learning model with the Quartet reference datasets to remove potential artifact calls. Moreover, the matched RNA and protein reference materials and datasets from the Quartet project enables cross-omics validation of variant calls from multiomics data.

CONCLUSIONS

The Quartet DNA reference materials and reference datasets provide a unique resource for objectively assessing the quality of germline variant calls throughout the whole-genome regions and improving the reliability of large-scale genomic profiling.

Machine learning and deep learning for brain tumor MRI image segmentation

Brain tumors are often fatal. Therefore, accurate brain tumor image segmentation is critical for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic resonance imaging (MRI) is a commonly used imaging technique for capturing brain images. Both machine learning and deep learning techniques are popular in analyzing MRI images. This article reviews some commonly used machine learning and deep learning techniques for brain tumor MRI image segmentation. The limitations and advantages of the reviewed machine learning and deep learning methods are discussed. Even though each of these methods has a well-established status in their individual domains, the combination of two or more techniques is currently an emerging trend.

Assessments of tumor mutational burden estimation by targeted panel sequencing: A comprehensive simulation analysis

Tumor mutational burden (TMB), when at a high level, is an emerging indicative factor of sensitivity to immune checkpoint inhibitors. Previous studies have shown that the more affordable and accurate targeted panels can be used to measure TMB as a substitute for whole exome sequencing (WES). However, additional processes, such as hotspot mutations exclusion and TMB adjustment, are usually required to deal with the effect of the limited panel sizes. A comprehensive investigation of the effective factors is needed for accurate TMB estimation by targeted panels. In this study, we quantitatively evaluated the variances of TMB values calculated by WES and targeted panels using 10,000 simulated targeted panels with panel sizes ranging from 0.2 to 3.1 million bases. With The Cancer Genome Atlas (TCGA) cancer samples and mutation profiles, we fixed regressions on WES-TMBs and panel-TMBs to assess the performance of a given targeted panel. Panel size was found as one of the major effective factors of TMB estimation. Meanwhile, by investigating the well-performing small panels that reported TMB values similar to those of WES, we demonstrated the evidence of the cancer type-specific impacts of genes on TMB estimation and identified high-impact gene sets for different cancer types based on the TCGA data. This study revealed the quantitative correlations between TMB variance and panel size, and the potential impacts of individual genes on TMB estimation. Our results suggested that for cancer patients diagnosed using targeted panels, it would be highly beneficial to have the capability to directly measure TMB from the targeted sequencing data. This would greatly assist in making decisions regarding the use of immunotherapies.

Review of machine learning and deep learning models for toxicity prediction

The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional in vitro and in vivo toxicity assays are complicated, costly, and time-consuming and may face ethical issues. These constraints raise the need for alternative methods for assessing the toxicity of chemicals. Recently, due to the advancement of machine learning algorithms and the increase in computational power, many toxicity prediction models have been developed using various machine learning and deep learning algorithms such as support vector machine, random forest, k-nearest neighbors, ensemble learning, and deep neural network. This review summarizes the machine learning- and deep learning-based toxicity prediction models developed in recent years. Support vector machine and random forest are the most popular machine learning algorithms, and hepatotoxicity, cardiotoxicity, and carcinogenicity are the frequently modeled toxicity endpoints in predictive toxicology. It is known that datasets impact model performance. The quality of datasets used in the development of toxicity prediction models using machine learning and deep learning is vital to the performance of the developed models. The different toxicity assignments for the same chemicals among different datasets of the same type of toxicity have been observed, indicating benchmarking datasets is needed for developing reliable toxicity prediction models using machine learning and deep learning algorithms. This review provides insights into current machine learning models in predictive toxicology, which are expected to promote the development and application of toxicity prediction models in the future.

A systematic analysis and data mining of opioid-related adverse events submitted to the FAERS database

The opioid epidemic has become a serious national crisis in the United States. An indepth systematic analysis of opioid-related adverse events (AEs) can clarify the risks presented by opioid exposure, as well as the individual risk profiles of specific opioid drugs and the potential relationships among the opioids. In this study, 92 opioids were identified from the list of all Food and Drug Administration (FDA)-approved drugs, annotated by RxNorm and were classified into 13 opioid groups: buprenorphine, codeine, dihydrocodeine, fentanyl, hydrocodone, hydromorphone, meperidine, methadone, morphine, oxycodone, oxymorphone, tapentadol, and tramadol. A total of 14,970,399 AE reports were retrieved and downloaded from the FDA Adverse Events Reporting System (FAERS) from 2004, Quarter 1 to 2020, Quarter 3. After data processing, Empirical Bayes Geometric Mean (EBGM) was then applied which identified 3317 pairs of potential risk signals within the 13 opioid groups. Based on these potential safety signals, a comparative analysis was pursued to provide a global overview of opioid-related AEs for all 13 groups of FDA-approved prescription opioids. The top 10 most reported AEs for each opioid class were then presented. Both network analysis and hierarchical clustering analysis were conducted to further explore the relationship between opioids. Results from the network analysis revealed a close association among fentanyl, oxycodone, hydrocodone, and hydromorphone, which shared more than 22 AEs. In addition, much less commonly reported AEs were shared among dihydrocodeine, meperidine, oxymorphone, and tapentadol. On the contrary, the hierarchical clustering analysis further categorized the 13 opioid classes into two groups by comparing the full profiles of presence/absence of AEs. The results of network analysis and hierarchical clustering analysis were not only consistent and cross-validated each other but also provided a better and deeper understanding of the associations and relationships between the 13 opioid groups with respect to their adverse effect profiles.

Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment

The coronavirus disease 2019 (COVID-19) global pandemic resulted in millions of people becoming infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and close to seven million deaths worldwide. It is essential to further explore and design effective COVID-19 treatment drugs that target the main protease of SARS-CoV-2, a major target for COVID-19 drugs. In this study, machine learning was applied for predicting the SARS-CoV-2 main protease binding of Food and Drug Administration (FDA)-approved drugs to assist in the identification of potential repurposing candidates for COVID-19 treatment. Ligands bound to the SARS-CoV-2 main protease in the Protein Data Bank and compounds experimentally tested in SARS-CoV-2 main protease binding assays in the literature were curated. These chemicals were divided into training (516 chemicals) and testing (360 chemicals) data sets. To identify SARS-CoV-2 main protease binders as potential candidates for repurposing to treat COVID-19, 1188 FDA-approved drugs from the Liver Toxicity Knowledge Base were obtained. A random forest algorithm was used for constructing predictive models based on molecular descriptors calculated using Mold2 software. Model performance was evaluated using 100 iterations of fivefold cross-validations which resulted in 78.8% balanced accuracy. The random forest model that was constructed from the whole training dataset was used to predict SARS-CoV-2 main protease binding on the testing set and the FDA-approved drugs. Model applicability domain and prediction confidence on drugs predicted as the main protease binders discovered 10 FDA-approved drugs as potential candidates for repurposing to treat COVID-19. Our results demonstrate that machine learning is an efficient method for drug repurposing and, thus, may accelerate drug development targeting SARS-CoV-2.

The Quartet Data Portal: integration of community-wide resources for multiomics quality control

The Quartet Data Portal facilitates community access to well-characterized reference materials, reference datasets, and related resources established based on a family of four individuals with identical twins from the Quartet Project. Users can request DNA, RNA, protein, and metabolite reference materials, as well as datasets generated across omics, platforms, labs, protocols, and batches. Reproducible analysis tools allow for objective performance assessment of user-submitted data, while interactive visualization tools support rapid exploration of reference datasets. A closed-loop "distribution-collection-evaluation-integration" workflow enables updates and integration of community-contributed multiomics data. Ultimately, this portal helps promote the advancement of reference datasets and multiomics quality control.

Deep Learning Methods for Omics Data Imputation

One common problem in omics data analysis is missing values, which can arise due to various reasons, such as poor tissue quality and insufficient sample volumes. Instead of discarding missing values and related data, imputation approaches offer an alternative means of handling missing data. However, the imputation of missing omics data is a non-trivial task. Difficulties mainly come from high dimensionality, non-linear or non-monotonic relationships within features, technical variations introduced by sampling methods, sample heterogeneity, and the non-random missingness mechanism. Several advanced imputation methods, including deep learning-based methods, have been proposed to address these challenges. Due to its capability of modeling complex patterns and relationships in large and high-dimensional datasets, many researchers have adopted deep learning models to impute missing omics data. This review provides a comprehensive overview of the currently available deep learning-based methods for omics imputation from the perspective of deep generative model architectures such as autoencoder, variational autoencoder, generative adversarial networks, and Transformer, with an emphasis on multi-omics data imputation. In addition, this review also discusses the opportunities that deep learning brings and the challenges that it might face in this field.

Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project

Quantitative structure-activity relationship (QSAR) models are powerful in silico tools for predicting the mutagenicity of unstable compounds, impurities and metabolites that are difficult to examine using the Ames test. Ideally, Ames/QSAR models for regulatory use should demonstrate high sensitivity, low false-negative rate and wide coverage of chemical space. To promote superior model development, the Division of Genetics and Mutagenesis, National Institute of Health Sciences, Japan (DGM/NIHS), conducted the Second Ames/QSAR International Challenge Project (2020-2022) as a successor to the First Project (2014-2017), with 21 teams from 11 countries participating. The DGM/NIHS provided a curated training dataset of approximately 12,000 chemicals and a trial dataset of approximately 1,600 chemicals, and each participating team predicted the Ames mutagenicity of each trial chemical using various Ames/QSAR models. The DGM/NIHS then provided the Ames test results for trial chemicals to assist in model improvement. Although overall model performance on the Second Project was not superior to that on the First, models from the eight teams participating in both projects achieved higher sensitivity than models from teams participating in only the Second Project. Thus, these evaluations have facilitated the development of QSAR models.

Morin protects chicks with T-2 toxin poisoning by decreasing heterophil extracellular traps, oxidative stress and inflammatory response

1. Fusarium tritici widely exists in a variety of grain feeds. The T-2 toxin is the main hazardous component produced by Fusarium tritici, making a serious hazard to poultry industry. Morin, belonging to the flavonoid family, can be extracted from mulberry plants and possesses anticancer, antioxidant and anti-inflammatory compounds, but whether morin protects chicks with T-2 toxin poisoning remains unclear. This experiment firstly established a chick model of T-2 toxin poisoning and then investigated the protective effects and mechanism of morin against T-2 toxin in chicks.2. The function of liver and kidney was measured by corresponding alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), blood urea nitrogen (BUN), creatinine (Cre) and uric acid (UA) kits. Histopathological changes were observed by haematoxylin-eosin staining. The status of oxidative stress was measured by MDA, SOD, CAT, GSH and GSH-PX kits. The mRNA levels of TNF-α, COX-2, IL-1β, IL-6, caspase-1, caspase-3 and caspase-11 were measured by quantitative real-time PCR. Heterophil extracellular trap (HET) release was analysed by immunofluorescence and fluorescence microplate.3. The model with T-2 toxin poisoning in chicks was successfully established. Morin significantly decreased T-2 toxin-induced ALT, AST, ALP, BUN, Cre and UA, and improved T-2 toxin-induced liver cell rupture, liver cord disorder and kidney interstitial oedema. Oxidative stress analysis showed that morin ameliorated T-2 toxin-induced damage by reducing malondialdehyde (MDA), increasing superoxide dismutase (SOD), catalase (CAT), glutathione (GSH) and glutathione peroxidase (GSH-PX). The qRT-PCR analysis showed that morin reduced T-2 toxin-induced mRNA expressions of TNF-α, COX-2, IL-1β, IL-6, caspase-1, caspase-3 and caspase-11. Moreover, morin significantly reduced the release of T-2 toxin-induced HET in vitro and in vivo.4. Morin can protect chicks from T-2 toxin poisoning by decreasing HETs, oxidative stress and inflammatory responses, which make it a useful compound against T-2 toxin poisoning in poultry feed.

Analyzing 3D structures of the SARS-CoV-2 main protease reveals structural features of ligand binding for COVID-19 drug discovery

The severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) main protease has an essential role in viral replication and has become a major target for coronavirus 2019 (COVID-19) drug development. Various inhibitors have been discovered or designed to bind to the main protease. The availability of more than 550 3D structures of the main protease provides a wealth of structural details on the main protease in both ligand-free and ligand-bound states. Therefore, we examined these structures to ascertain the structural features for the role of the main protease in the cleavage of polyproteins, the alternative conformations during main protease maturation, and ligand interactions in the main protease. The structural features unearthed could promote the development of COVID-19 drugs targeting the SARS-CoV-2 main protease.

Genetic Variations in TrkB.T1 Isoform and Their Association with Somatic and Psychological Symptoms in Individuals with IBS

Irritable bowel syndrome (IBS), a disorder of gut-brain interaction, is often comorbid with somatic pain and psychological disorders. Dysregulated signaling of brain-derived neurotrophic factor (BDNF) and its receptor, tropomyosin-related kinase B (TrkB), has been implicated in somatic-psychological symptoms in individuals with IBS. Thus, we investigated the association of 10 single nucleotide polymorphisms (SNPs) in the regulatory 3' untranslated region (UTR) of NTRK2 (TrkB) kinase domain-deficient truncated isoform (TrkB.T1) and the BDNF Val66Met SNP with somatic and psychological symptoms and quality of life in a U.S. cohort (IBS n=464; healthy controls n=156). We found that the homozygous recessive genotype (G/G) of rs2013566 in individuals with IBS is associated with worsened somatic symptoms, including headache, back pain, joint pain, muscle pain, and somatization as well as diminished sleep quality, energy level and overall quality of life. Validation using U.K. BioBank (UKBB) data confirmed the association of rs2013566 with increased likelihood of headache. Several SNPs (rs1627784, rs1624327, rs1147198) showed significant associations with muscle pain in our U.S. cohort. Notably, these SNPs are predominantly located in H3K4Me1-enriched regions, suggesting their enhancer and/or transcription regulation potential. Together, our findings suggest that genetic variation within the 3'UTR region of the TrkB.T1 isoform may contribute to comorbid conditions in individuals with IBS, resulting in a spectrum of somatic and psychological symptoms that may influence their quality of life. These findings advance our understanding of the genetic interaction between BDNF/TrkB pathways and somatic-psychological symptoms in IBS, highlighting the importance of further exploring this interaction for potential clinical applications.

Quartet RNA reference materials improve the quality of transcriptomic data through ratio-based profiling

Certified RNA reference materials are indispensable for assessing the reliability of RNA sequencing to detect intrinsically small biological differences in clinical settings, such as molecular subtyping of diseases. As part of the Quartet Project for quality control and data integration of multi-omics profiling, we established four RNA reference materials derived from immortalized B-lymphoblastoid cell lines from four members of a monozygotic twin family. Additionally, we constructed ratio-based transcriptome-wide reference datasets between two samples, providing cross-platform and cross-laboratory 'ground truth'. Investigation of the intrinsically subtle biological differences among the Quartet samples enables sensitive assessment of cross-batch integration of transcriptomic measurements at the ratio level. The Quartet RNA reference materials, combined with the ratio-based reference datasets, can serve as unique resources for assessing and improving the quality of transcriptomic data in clinical and biological settings.

Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials

Characterization and integration of the genome, epigenome, transcriptome, proteome and metabolome of different datasets is difficult owing to a lack of ground truth. Here we develop and characterize suites of publicly available multi-omics reference materials of matched DNA, RNA, protein and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters. These references provide built-in truth defined by relationships among the family members and the information flow from DNA to RNA to protein. We demonstrate how using a ratio-based profiling approach that scales the absolute feature values of a study sample relative to those of a concurrently measured common reference sample produces reproducible and comparable data suitable for integration across batches, labs, platforms and omics types. Our study identifies reference-free 'absolute' feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials.

Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method

BACKGROUND

Batch effects are notoriously common technical variations in multiomics data and may result in misleading outcomes if uncorrected or over-corrected. A plethora of batch-effect correction algorithms are proposed to facilitate data integration. However, their respective advantages and limitations are not adequately assessed in terms of omics types, the performance metrics, and the application scenarios.

RESULTS

As part of the Quartet Project for quality control and data integration of multiomics profiling, we comprehensively assess the performance of seven batch effect correction algorithms based on different performance metrics of clinical relevance, i.e., the accuracy of identifying differentially expressed features, the robustness of predictive models, and the ability of accurately clustering cross-batch samples into their own donors. The ratio-based method, i.e., by scaling absolute feature values of study samples relative to those of concurrently profiled reference material(s), is found to be much more effective and broadly applicable than others, especially when batch effects are completely confounded with biological factors of study interests. We further provide practical guidelines for implementing the ratio based approach in increasingly large-scale multiomics studies.

CONCLUSIONS

Multiomics measurements are prone to batch effects, which can be effectively corrected using ratio-based scaling of the multiomics data. Our study lays the foundation for eliminating batch effects at a ratio scale.

Physical Properties and Clinical Performance of Short Fiber Reinforced Resin-based Composite in Posterior Dentition: Systematic Review and Meta-analysis

OBJECTIVE

This study compares the physical properties and clinical performance of short fiber reinforced composites (SFRC) to those of particulate-filled resin-based composites (PFRC) for class I and II direct restorations in permanent dentition.

METHODS

Systematic review and meta-analysis was conducted using PubMed, Embase (Elsevier), and Dentistry and Oral Sciences Source (EBSCO) databases. The outcomes evaluated were physical properties including flexural strength, flexural modulus, elastic modulus, microhardness, shrinkage, fracture toughness, degree of conversion, and depth of cure. Clinical performance was evaluated with a systematic review.

RESULTS

The meta-analyses favored SFRC for flexural strength and fracture toughness compared to every PFRC subgroup, with a high quality of evidence. For all other properties, the meta-analyses favored SFRC to overall PFRC, with some non-significant differences with certain PFRC subgroups. The most recent clinical trial showed SFRC performed similarly to PFRC, however older studies suggest inferior surface texture and discoloration of SFRC compared to PFRC.

CONCLUSION

This study can aid dental professionals in clinical decision making, supporting that SFRC offers improved physical properties, especially fracture resistance and flexural strength, compared to PFRC.

Three-Dimensional Structural Insights Have Revealed the Distinct Binding Interactions of Agonists, Partial Agonists, and Antagonists with the µ Opioid Receptor

The United States is experiencing the most profound and devastating opioid crisis in history, with the number of deaths involving opioids, including prescription and illegal opioids, continuing to climb over the past two decades. This severe public health issue is difficult to combat as opioids remain a crucial treatment for pain, and at the same time, they are also highly addictive. Opioids act on the opioid receptor, which in turn activates its downstream signaling pathway that eventually leads to an analgesic effect. Among the four types of opioid receptors, the µ subtype is primarily responsible for the analgesic cascade. This review describes available 3D structures of the µ opioid receptor in the protein data bank and provides structural insights for the binding of agonists and antagonists to the receptor. Comparative analysis on the atomic details of the binding site in these structures was conducted and distinct binding interactions for agonists, partial agonists, and antagonists were observed. The findings in this article deepen our understanding of the ligand binding activity and shed some light on the development of novel opioid analgesics which may improve the risk benefit balance of existing opioids.

Distinct Conformations of SARS-CoV-2 Omicron Spike Protein and Its Interaction with ACE2 and Antibody

Since November 2021, Omicron has been the dominant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant that causes the coronavirus disease 2019 (COVID-19) and has continuously impacted human health. Omicron sublineages are still increasing and cause increased transmission and infection rates. The additional 15 mutations on the receptor binding domain (RBD) of Omicron spike proteins change the protein conformation, enabling the Omicron variant to evade neutralizing antibodies. For this reason, many efforts have been made to design new antigenic variants to induce effective antibodies in SARS-CoV-2 vaccine development. However, understanding the different states of Omicron spike proteins with and without external molecules has not yet been addressed. In this review, we analyze the structures of the spike protein in the presence and absence of angiotensin-converting enzyme 2 (ACE2) and antibodies. Compared to previously determined structures for the wildtype spike protein and other variants such as alpha, beta, delta, and gamma, the Omicron spike protein adopts a partially open form. The open-form spike protein with one RBD up is dominant, followed by the open-form spike protein with two RBD up, and the closed-form spike protein with the RBD down. It is suggested that the competition between antibodies and ACE2 induces interactions between adjacent RBDs of the spike protein, which lead to a partially open form of the Omicron spike protein. The comprehensive structural information of Omicron spike proteins could be helpful for the efficient design of vaccines against the Omicron variant.

[Cost-effectiveness prediction of AIDS interventions among men who have sex with men in Ningbo]

Objective: To provide information reference for resource allocation and decision-making in related fields, the cost-effectiveness of HIV input among men who have sex with men (MSM) in Ningbo. Different intervention coverages were compared. Methods: Taking MSM as the target population, data were collected and modeled by Optima HIV for the corresponding HIV health output and the budget under different intervention coverages. Results: According to the estimated size of the MSM population, which was 19 584 in Ningbo in 2020, if the coverage of 2020 baseline intervention is maintained in the next ten years, the number of HIV cases, new HIV infections, and HIV-related deaths among this population will show an upward trend. It is estimated that from 2021 to 2030, 7.9% of new infections and 1.7% of deaths can be avoided and the relevant funding investment comed to 2.4 time the baseline if the intervention coverage rate expanded to 3.0 times the 2020 baseline. After the coverage rate of intervention expanded to 3 times the baseline, it continued to grow, the health effect did not increase. Conclusions: At present, expanding the baseline coverage of HIV-related intervention projects among MSM in Ningbo and increasing capital investment will still reverse HIV-related death and reduce new infections. Moreover, there is a saturation point of the intervention effect. Researchers and policymakers must explore more effective interventions/combinations to obtain more significant health outcomes.

An autoencoder-based deep learning method for genotype imputation

Genotype imputation has a wide range of applications in genome-wide association study (GWAS), including increasing the statistical power of association tests, discovering trait-associated loci in meta-analyses, and prioritizing causal variants with fine-mapping. In recent years, deep learning (DL) based methods, such as sparse convolutional denoising autoencoder (SCDA), have been developed for genotype imputation. However, it remains a challenging task to optimize the learning process in DL-based methods to achieve high imputation accuracy. To address this challenge, we have developed a convolutional autoencoder (AE) model for genotype imputation and implemented a customized training loop by modifying the training process with a single batch loss rather than the average loss over batches. This modified AE imputation model was evaluated using a yeast dataset, the human leukocyte antigen (HLA) data from the 1,000 Genomes Project (1KGP), and our in-house genotype data from the Louisiana Osteoporosis Study (LOS). Our modified AE imputation model has achieved comparable or better performance than the existing SCDA model in terms of evaluation metrics such as the concordance rate (CR), the Hellinger score, the scaled Euclidean norm (SEN) score, and the imputation quality score (IQS) in all three datasets. Taking the imputation results from the HLA data as an example, the AE model achieved an average CR of 0.9468 and 0.9459, Hellinger score of 0.9765 and 0.9518, SEN score of 0.9977 and 0.9953, and IQS of 0.9515 and 0.9044 at missing ratios of 10% and 20%, respectively. As for the results of LOS data, it achieved an average CR of 0.9005, Hellinger score of 0.9384, SEN score of 0.9940, and IQS of 0.8681 at the missing ratio of 20%. In summary, our proposed method for genotype imputation has a great potential to increase the statistical power of GWAS and improve downstream post-GWAS analyses.

Machine learning models for rat multigeneration reproductive toxicity prediction

Reproductive toxicity is one of the prominent endpoints in the risk assessment of environmental and industrial chemicals. Due to the complexity of the reproductive system, traditional reproductive toxicity testing in animals, especially guideline multigeneration reproductive toxicity studies, take a long time and are expensive. Therefore, machine learning, as a promising alternative approach, should be considered when evaluating the reproductive toxicity of chemicals. We curated rat multigeneration reproductive toxicity testing data of 275 chemicals from ToxRefDB (Toxicity Reference Database) and developed predictive models using seven machine learning algorithms (decision tree, decision forest, random forest, k-nearest neighbors, support vector machine, linear discriminant analysis, and logistic regression). A consensus model was built based on the seven individual models. An external validation set was curated from the COSMOS database and the literature. The performances of individual and consensus models were evaluated using 500 iterations of 5-fold cross-validations and the external validation data set. The balanced accuracy of the models ranged from 58% to 65% in the 5-fold cross-validations and 45%–61% in the external validations. Prediction confidence analysis was conducted to provide additional information for more appropriate applications of the developed models. The impact of our findings is in increasing confidence in machine learning models. We demonstrate the importance of using consensus models for harnessing the benefits of multiple machine learning models (i.e., using redundant systems to check validity of outcomes). While we continue to build upon the models to better characterize weak toxicants, there is current utility in saving resources by being able to screen out strong reproductive toxicants before investing in vivo testing. The modeling approach (machine learning models) is offered for assessing the rat multigeneration reproductive toxicity of chemicals. Our results suggest that machine learning may be a promising alternative approach to evaluate the potential reproductive toxicity of chemicals.

Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials

Metal-organic frameworks (MOFs), a class of porous nanomaterials, have been widely used in gas adsorption-based applications due to their high porosities and chemical tunability. To facilitate the discovery of high-performance MOFs for different applications, a variety of machine learning models have been developed to predict the gas adsorption capacities of MOFs. Most of the predictive models are developed using traditional machine learning algorithms. However, the continuously increasing sizes of MOF datasets and the complicated relationships between MOFs and their gas adsorption capacities make deep learning a suitable candidate to handle such big data with increased computational power and accuracy. In this study, we developed models for predicting gas adsorption capacities of MOFs using two deep learning algorithms, multilayer perceptron (MLP) and long short-term memory (LSTM) networks, with a hypothetical set of about 130,000 structures of MOFs with methane and carbon dioxide adsorption data at different pressures. The models were evaluated using 10 iterations of 10-fold cross validations and 100 holdout validations. The MLP and LSTM models performed similarly with high prediction accuracy. The models for predicting gas adsorption at a higher pressure outperformed the models for predicting gas adsorption at a lower pressure. The deep learning models are more accurate than the random forest models reported in the literature, especially for predicting gas adsorption capacities at low pressures. Our results demonstrated that deep learning algorithms have a great potential to generate models that can accurately predict the gas adsorption capacities of MOFs.

[FNDC1 is highly expressed in lung adenocarcinoma and closely related with poor prognosis]

OBJECTIVE

To explore the expression of fibronectin type Ⅲ domain containing 1(FNDC1) protein in lung adenocarcinoma and its prognostic significance.

METHODS

The expression of FNDC1 in lung adenocarcinoma was predicted by analysis of data from GEO database and GEPIA, and the results were verified by immunohistochemical staining in 92 pairs of clinical specimens of lung adenocarcinoma and adjacent tissues.We further analyzed the correlation of FNDC1 expression with the clinicopathological features of the patients, and evaluated its prognostic value using Cox survival analysis.

RESULTS

Analysis of the data form GEO database and GEPIA showed a significantly higher expression level of FNDC1 in lung adenocarcinoma than in matched normal tissues (P < 0.05).Kaplan-Meier survival analysis suggested that a high expression of FNDC1 protein was associated with a significantly shorter overall survival time of the patients (P < 0.05).Immunohistochemistry of the clinical specimens also showed a significantly higher protein expression of FNDC1 in lung adenocarcinoma tissues than in paired adjacent tissues (P < 0.001).A high expression of FNDC1 protein was significantly correlated with advanced clinical stage, T stage and N stage (P < 0.05).Cox univariate and multivariate regression survival analysis indicated that an increased expression of FNDC1 was an independent risk factor for poor prognosis of the patients with lung adenocarcinoma (P < 0.05).

CONCLUSION

FNDC1 protein is highly expressed in patients with lung adenocarcinoma and in closely related with the occurrence, progression and prognosis of the tumor, suggesting the value of FNDC1 protein as a potential biomarker for assessment of the survival and prognosis of patients with lung adenocarcinoma.

[Transmission chains of local epidemic of COVID-19 caused by 2019-nCoV Delta variant in Zhenhai district, Ningbo]

Objective: To investigate the local epidemic of COVID-19 caused by 2019-nCoV Delta variant in Zhenhai district of Ningbo, identify the transmission chain and provide reference for the prevention and control of COVID-19 epidemic. Methods: The incidence data of COVID-19 in Zhenhai from 6 to 18 December, 2021 were collected in field investigation. Field epidemiological investigation was conducted to understand the epidemiological characteristics of COVID-19 cases and analyze the transmission chains. Results: The first case might be infected with 2019-nCoV through direct or indirect exposure when passing through a medium-risk area, then a family cluster was caused, and the epidemic spread through close contacts of family members with others such as work, daily life, and moxibustion. The epidemic lasted for 14 days, and 74 confirmed COVID-19 cases were reported. The median incubation period was 4.0(3.0,5.8)d. All the cases were in a chain of transmission for more than 6 generations, and the intergenerational interval was 3.5(2.0,5.3)d. The gene sequencing result indicated that the pathogen was Delta AY.4 variant of 2019-nCoV. Both the epidemiological investigation and the gene sequencing results supported that the local COVID-19 epidemic in Zhenhai was associated with the COVID-19 epidemic in Shanghai. Conclusions: The transmission chain of this epidemic was clear. Delta AY.4 variant has obvious characteristic to cause case clusters in families, places with poor ventilation, and residential communities. It is suggested to strengthen the health management in key areas and key populations, and increase the frequency of nucleic acid testing.

Whole Exome Sequencing Reveals Genetic Variants in HLA Class II Genes Associated With Transplant-free Survival of Indeterminate Acute Liver Failure

INTRODUCTION

Indeterminate acute liver failure (IND-ALF) is a rare clinical syndrome with a high mortality rate. Lacking a known etiology makes rapid evaluation and treatment difficult, with liver transplantation often considered as the only therapeutic option. Our aim was to identify genetic variants from whole exome sequencing data that might be associated with IND-ALF clinical outcomes.

METHODS

Bioinformatics analysis was performed on whole exome sequencing data for 22 patients with IND-ALF. A 2-tier approach was used to identify significant single-nucleotide polymorphisms (SNPs) associated with IND-ALF clinical outcomes. Tier 1 identified the SNPs with a higher relative risk in the IND-ALF population compared with those identified in control populations. Tier 2 determined the SNPs connected to transplant-free survival and associated with model for end-stage liver disease serum sodium and Acute Liver Failure Study Group prognostic scores.

RESULTS

Thirty-one SNPs were found associated with a higher relative risk in the IND-ALF population compared with those in controls, of which 11 belong to the human leukocyte antigen (HLA) class II genes but none for the class I. Further analysis showed that 5 SNPs: rs796202376, rs139189937, and rs113473719 of HLA-DRB5; rs9272712 of HLA-DQA1; and rs747397929 of IDO1 were associated with a higher probability of IND-ALF transplant-free survival. Using 3 selected SNPs, a model for the polygenic risk score was developed to predict IND-ALF prognoses, which are comparable with those by model for end-stage liver disease serum sodium and Acute Liver Failure Study Group prognostic scores.

DISCUSSION

Certain gene variants in HLA-DRB5, HLA-DQA1, and IDO1 were found associated with IND-ALF transplant-free survival. Once validated, these identified SNPs may help elucidate the mechanism of IND-ALF and assist in its diagnosis and management.

Evaluation of prognostic risk models for postoperative pulmonary complications in adult patients undergoing major abdominal surgery: a systematic review and international external validation cohort study

BACKGROUND

Stratifying risk of postoperative pulmonary complications after major abdominal surgery allows clinicians to modify risk through targeted interventions and enhanced monitoring. In this study, we aimed to identify and validate prognostic models against a new consensus definition of postoperative pulmonary complications.

METHODS

We did a systematic review and international external validation cohort study. The systematic review was done in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched MEDLINE and Embase on March 1, 2020, for articles published in English that reported on risk prediction models for postoperative pulmonary complications following abdominal surgery. External validation of existing models was done within a prospective international cohort study of adult patients (≥18 years) undergoing major abdominal surgery. Data were collected between Jan 1, 2019, and April 30, 2019, in the UK, Ireland, and Australia. Discriminative ability and prognostic accuracy summary statistics were compared between models for the 30-day postoperative pulmonary complication rate as defined by the Standardised Endpoints in Perioperative Medicine Core Outcome Measures in Perioperative and Anaesthetic Care (StEP-COMPAC). Model performance was compared using the area under the receiver operating characteristic curve (AUROCC).

FINDINGS

In total, we identified 2903 records from our literature search; of which, 2514 (86·6%) unique records were screened, 121 (4·8%) of 2514 full texts were assessed for eligibility, and 29 unique prognostic models were identified. Nine (31·0%) of 29 models had score development reported only, 19 (65·5%) had undergone internal validation, and only four (13·8%) had been externally validated. Data to validate six eligible models were collected in the international external validation cohort study. Data from 11 591 patients were available, with an overall postoperative pulmonary complication rate of 7·8% (n=903). None of the six models showed good discrimination (defined as AUROCC ≥0·70) for identifying postoperative pulmonary complications, with the Assess Respiratory Risk in Surgical Patients in Catalonia score showing the best discrimination (AUROCC 0·700 [95% CI 0·683-0·717]).

INTERPRETATION

In the pre-COVID-19 pandemic data, variability in the risk of pulmonary complications (StEP-COMPAC definition) following major abdominal surgery was poorly described by existing prognostication tools. To improve surgical safety during the COVID-19 pandemic recovery and beyond, novel risk stratification tools are required.

FUNDING

British Journal of Surgery Society.

[Effectiveness of precise prevention and control strategies of dynamic zero COVID-19 in Ningbo, Zhejiang province]

Objective: To evaluate effectiveness of the precise prevention and control strategies of dynamic zero COVID-19 in Ningbo, Zhejiang province. Methods: Based on the incidence data of COVID-19 and case epidemiological survey report in Ningbo in December 2021, the incidence curve of COVID-19 was generated and a dynamics model was developed to estimate the case number of under different intervention measures. The basic reproduction number (R₀) and real-time reproduction number (R_t) were calculated to evaluate intervention effect. Results: A total of 74 cases of COVID-19 had been confirmed in Ningbo as of 17 December, 2021. The R₀ was estimated to be 4.3. With the strengthening of prevention and control measures, the R_t showed a gradual downward trend, dropping to below 1.0 on December 11 and 0.4 on December 14. The model fitting results showed that the actual case number was close to the estimated case number (76 cases) when the effectiveness of intervention was 90%. The number of cases decreased by 98.4% compared with that if no intervention was taken. Conclusion: The precise prevention and control strategies of dynamic zero COVID-19 have obvious effect, which can facilitate the rapid control of COVID-19 epidemic in Ningbo.

Machine learning models on chemical inhibitors of mitochondrial electron transport chain

Chemicals can induce adverse effects in humans by inhibiting mitochondrial electron transport chain (ETC) such as disrupting mitochondrial membrane potential, enhancing oxidative stress and causing some diseases. Thus, identifying ETC inhibitors (ETCi) is important to chemical risk assessment and protecting the public health. However, it is not feasible to identify all ETCi with experimental methods. Quantitative structure-activity relationship (QSAR) modeling is a promising method to rapidly and effectively identify ETCi. In this study, QSAR models for predicting ETCi were developed using machine learning methods. A clustering-based under-sampling (CBUS) method was developed to handle the imbalance issue in training sets. Structure-activity landscapes were generated and analyzed for training sets generated by the CBUS method. The consensus QSAR models constructed with CBUS achieved satisfactory performances (balanced accuracy = 0.852) in 100 iterations of five-fold cross validations, indicating the models can effectively classify ETCi. The classification model was further employed to screen chemicals in the Inventory of Existing Chemical Substances of China and 13 chemicals were identified as ETCi. Fifteen structural alerts for ETCi were identified in this study. These results demonstrated that the model and structural alerts are useful to screen ETCi.

Three-dimensional craniofacial changes with maxillary expansion in young adult patients with different craniofacial morphology

Objectives:

Skeletally mature patients with transverse deficiency are best treated with surgically assisted rapid palatal expansion (RPE) procedure. Recent studies have shown that microimplant-assisted RPE (MARPE) appliances can be effective in achieving skeletal expansion in young adults. This retrospective study aimed to evaluate the skeletal and dental alveolar changes in response to treatment with MARPE appliances in three types of anteroposterior skeletal malocclusions using cone-beam computed tomography (CBCT) scans.

Material and Methods:

Seventy-eight subjects diagnosed with maxillary transverse deficiency and treated with the MARPE appliance (mean age of 22.9 ± 4.2 years) were divided into skeletal Class I, II, and III malocclusions with 26 subjects in each group. Pre- and post-treatment CBCT scans were used for superimposition to examine the skeletal and dentoalveolar changes following maxillary expansion treatment.

Results:

Significant lateral separation of the maxilla was found at the levels of the nasal floor, interzygomatic bones, and the inferior palatine margin of the alveolar process (P < 0.05) in the whole sample. Most of the sagittal and vertical variables change significantly in the whole sample and each studied group separately. Intergroup comparisons revealed no significant differences among the three skeletal classes except for the left frontozygomatic angle, left maxillary inclination angle, and torque in the first and second premolars. In Class III patients, the maxilla moved forward significantly in most of the cases (eight of 26 cases) (0.88°, P < 0.05) and the mandible moved downward and backward improving the anteroposterior skeletal relationship. Significant differences were also found in the vertical measurements (N-Me, MMP, and MP/SN, P < 0.05) in all three types of anteroposterior malocclusions.

Conclusion:

Maxillary expansion with the MARPE appliance in young adult patients induced different skeletal and dentoalveolar changes in the anteroposterior and vertical dimensions in each skeletal malocclusion with no significant difference among the three skeletal classes.

Integrative approaches for studying the role of noncoding RNAs in influencing drug efficacy and toxicity

INTRODUCTION

Drug efficacy and toxicity are important factors for evaluation in drug development. Drug metabolizing enzymes and transporters (DMETs) play an essential role in drug efficacy and toxicity. Noncoding RNAs (ncRNAs) have been implicated to influence inter-individual variations in drug efficacy and safety by regulating DMETs. An efficient strategy is urgently needed to identify and functionally characterize ncRNAs that mediate drug efficacy and toxicity through regulating DMETs.

AREAS COVERED

We outline an integrative strategy to identify ncRNAs that modulate DMETs. We include reliable tools and databases for computational prediction of ncRNA targets with regard to their advantages and limitations. Various biochemical, molecular, and cellular assays are discussed for in vitro experimental verification of the regulatory function of ncRNAs. In vivo approaches for association of ncRNAs with drug treatment and toxicity are also reviewed.

EXPERT OPINION

A streamlined integration of computational prediction and wet-lab validation is important to elucidate mechanisms of ncRNAs in the regulation of DMETs related to drug efficacy and safety. Bioinformatic analyses using open-access tools and databases serve as a powerful booster for ncRNA Research in toxicology. Further refinement of computational algorithms and experimental technologies is needed to improve accuracy and efficiency in ncRNA target identification and characterization.

Machine Learning Models for Predicting Cytotoxicity of Nanomaterials

The wide application of nanomaterials in consumer and medical products has raised concerns about their potential adverse effects on human health. Thus, more and more biological assessments regarding the toxicity of nanomaterials have been performed. However, the different ways the evaluations were performed, such as the utilized assays, cell lines, and the differences of the produced nanoparticles, make it difficult for scientists to analyze and effectively compare toxicities of nanomaterials. Fortunately, machine learning has emerged as a powerful tool for the prediction of nanotoxicity based on the available data. Among different types of toxicity assessments, nanomaterial cytotoxicity was the focus here because of the high sensitivity of cytotoxicity assessment to different treatments without the need for complicated and time-consuming procedures. In this review, we summarized recent studies that focused on the development of machine learning models for prediction of cytotoxicity of nanomaterials. The goal was to provide insight into predicting potential nanomaterial toxicity and promoting the development of safe nanomaterials.

Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample

BACKGROUND

Accurate detection of somatic mutations is challenging but critical in understanding cancer formation, progression, and treatment. We recently proposed NeuSomatic, the first deep convolutional neural network-based somatic mutation detection approach, and demonstrated performance advantages on in silico data.

RESULTS

In this study, we use the first comprehensive and well-characterized somatic reference data sets from the SEQC2 consortium to investigate best practices for using a deep learning framework in cancer mutation detection. Using the high-confidence somatic mutations established for a cancer cell line by the consortium, we identify the best strategy for building robust models on multiple data sets derived from samples representing real scenarios, for example, a model trained on a combination of real and spike-in mutations had the highest average performance.

CONCLUSIONS

The strategy identified in our study achieved high robustness across multiple sequencing technologies for fresh and FFPE DNA input, varying tumor/normal purities, and different coverages, with significant superiority over conventional detection approaches in general, as well as in challenging situations such as low coverage, low variant allele frequency, DNA damage, and difficult genomic regions.

Assessing reproducibility of inherited variants detected with short-read whole genome sequencing

BACKGROUND

Reproducible detection of inherited variants with whole genome sequencing (WGS) is vital for the implementation of precision medicine and is a complicated process in which each step affects variant call quality. Systematically assessing reproducibility of inherited variants with WGS and impact of each step in the process is needed for understanding and improving quality of inherited variants from WGS.

RESULTS

To dissect the impact of factors involved in detection of inherited variants with WGS, we sequence triplicates of eight DNA samples representing two populations on three short-read sequencing platforms using three library kits in six labs and call variants with 56 combinations of aligners and callers. We find that bioinformatics pipelines (callers and aligners) have a larger impact on variant reproducibility than WGS platform or library preparation. Single-nucleotide variants (SNVs), particularly outside difficult-to-map regions, are more reproducible than small insertions and deletions (indels), which are least reproducible when > 5 bp. Increasing sequencing coverage improves indel reproducibility but has limited impact on SNVs above 30×.

CONCLUSIONS

Our findings highlight sources of variability in variant detection and the need for improvement of bioinformatics pipelines in the era of precision medicine with WGS.

Machine Learning Models for Predicting Liver Toxicity

Liver toxicity is a major adverse drug reaction that accounts for drug failure in clinical trials and withdrawal from the market. Therefore, predicting potential liver toxicity at an early stage in drug discovery is crucial to reduce costs and the potential for drug failure. However, current in vivo animal toxicity testing is very expensive and time consuming. As an alternative approach, various machine learning models have been developed to predict potential liver toxicity in humans. This chapter reviews current advances in the development and application of machine learning models for prediction of potential liver toxicity in humans and discusses possible improvements to liver toxicity prediction.