Primary and Immortalized Human Respiratory Cells Display Different Patterns of Cytotoxicity and Cytokine Release upon Exposure to Deoxynivalenol, Nivalenol and Fusarenon-X.

The type B trichothecene mycotoxins deoxynivalenol (DON), nivalenol (NIV) and fusarenon-X (FX) are structurally related secondary metabolites frequently produced by <i>Fusarium</i> on wheat. Consequently, DON, NIV and FX contaminate wheat dusts, exposing grain workers to toxins by inhalation. Those trichothecenes at low, relevant, exposition concentrations have differential effects on intestinal cells, but whether such differences exist with respiratory cells is mostly unknown, while it is required to assess the combined risk of exposure to mycotoxins. The goal of the present study was to compare the effects of DON, NIV and FX alone or in combination on the viability and IL-6 and IL-8-inducing capacity of human epithelial cells representative of the respiratory tract: primary human airway epithelial cells of nasal (hAECN) and bronchial (hAECB) origin, and immortalized human bronchial (16HBE14o-) and alveolar (A549) epithelial cell lines. We report that A549 cells are particularly resistant to the cytotoxic effects of mycotoxins. FX is more toxic than DON and NIV for all epithelial cell types. Nasal and bronchial primary cells are more sensitive than bronchial and alveolar cell lines to combined mycotoxin mixtures at low concentrations, although they are less sensitive to mycotoxins alone. Interactions between mycotoxins at low concentrations are rarely additive and are observed only for DON/NIV and NIV/FX on hAECB cells and DON/NIV/FX on A549 cells. Most interactions at low mycotoxin concentrations are synergistic, antagonistic interactions being observed only for DON/FX on hAECB, DON/NIV on 16HBE14o- and NIV/FX on A549 cells. DON, NIV and FX induce, albeit at different levels, IL-6 and IL-8 release by all cell types. However, NIV and FX at concentrations of low cytotoxicity induce IL-6 release by hAECB and A549 cells, and IL-8 release by hAECN cells. Overall, these data suggest that combined exposure to mycotoxins at low concentrations have a stronger effect on primary nasal epithelial cells than on bronchial epithelial cells and activate different inflammatory pathways. This information is particularly relevant for future studies about the hazard of occupational exposure to mycotoxins by inhalation and its impact on the respiratory tract

Transcriptomic insights into genetic diversity of protein-coding genes in X. laevis

© The Author(s), 2017. This is the author's version of the work and is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Developmental Biology 424 (2017): 181-188, doi:10.1016/j.ydbio.2017.02.019We characterize the genetic diversity of Xenopus laevis strains using RNA-seq data and allele- specific analysis. This data provides a catalogue of coding variation, which can be used for improving the genomic sequence, as well as for better sequence alignment, probe design, and proteomic analysis. In addition, we paint a broad picture of the genetic landscape of the species by functionally annotating different classes of mutations with a well-established prediction tool (PolyPhen-2). Further, we specifically compare the variation in the progeny of four crosses: inbred genomic (J)- strain, outbred albino (B)-strain, and two hybrid crosses of J and B strains. We identify a subset of mutations specific to the B strain, which allows us to investigate the selection pressures affecting duplicated genes in this allotetraploid. From these crosses we find the ratio of non-synonymous to synonymous mutations is lower in duplicated genes, which suggests that they are under greater purifying selection. Surprisingly, we also find that function-altering ("damaging") mutations constitute a greater fraction of the non-synonymous variants in this group, which suggests a role for subfunctionalization in coding variation affecting duplicated genes.L.P. was supported by the NIH grant R01HD073104, also L.P., A.N. and V.S. were supported by R21HD81675, M.H. and E.P. by P40 OD010997.2018-03-0

Major Differences in the Diversity of Mycobiomes Associated with Wheat Processing and Domestic Environments: Significant Findings from High-Throughput Sequencing of Fungal Barcode ITS1.

Occupational exposure to grain dust is associated with both acute and chronic effects on the airways. However, the aetiology of these effects is not completely understood, mainly due to the complexity and variety of potentially causative agents to which workers are exposed during cereals process. In this study, we characterized the mycobiome during different steps of wheat processing-harvesting, grain unloading and straw handling-and compared it to mycobiomes of domestic environments-rural and urban. To do so, settled dust was collected at a six month interval for six weeks in the close proximity of 142 participants, 74 occupationally exposed to wheat dust-freshly harvested or stored-and 68 not occupationally exposed to it. Fungal community composition was determined in those samples by high-throughput sequencing of the primary fungal barcode marker internal transcribed spacer 1 (ITS1). The comparison of different mycobiomes revealed that fungal richness, as well as their composition, was much higher in the domestic environment than at the workplace. Furthermore, we found that the fungal community composition strongly differed between workplaces where workers handled freshly harvested wheat and those where they handled stored wheat. Indicator species for each exposed population were identified. Our results emphasize the complexity of exposure of grain workers and farmers and open new perspectives in the identification of the etiological factors responsible for the respiratory pathologies induced by wheat dust exposure

Size and Content of the Sex-Determining Region of the Y Chromosome in Dioecious <i>Mercurialis annua</i>, a Plant with Homomorphic Sex Chromosomes.

Dioecious plants vary in whether their sex chromosomes are heteromorphic or homomorphic, but even homomorphic sex chromosomes may show divergence between homologues in the non-recombining, sex-determining region (SDR). Very little is known about the SDR of these species, which might represent particularly early stages of sex-chromosome evolution. Here, we assess the size and content of the SDR of the diploid dioecious herb &lt;i&gt;Mercurialis annua&lt;/i&gt; , a species with homomorphic sex chromosomes and mild Y-chromosome degeneration. We used RNA sequencing (RNAseq) to identify new Y-linked markers for &lt;i&gt;M. annua.&lt;/i&gt; Twelve of 24 transcripts showing male-specific expression in a previous experiment could be amplified by polymerase chain reaction (PCR) only from males, and are thus likely to be Y-linked. Analysis of genome-capture data from multiple populations of &lt;i&gt;M. annua&lt;/i&gt; pointed to an additional six male-limited (and thus Y-linked) sequences. We used these markers to identify and sequence 17 sex-linked bacterial artificial chromosomes (BACs), which form 11 groups of non-overlapping sequences, covering a total sequence length of about 1.5 Mb. Content analysis of this region suggests that it is enriched for repeats, has low gene density, and contains few candidate sex-determining genes. The BACs map to a subset of the sex-linked region of the genetic map, which we estimate to be at least 14.5 Mb. This is substantially larger than estimates for other dioecious plants with homomorphic sex chromosomes, both in absolute terms and relative to their genome sizes. Our data provide a rare, high-resolution view of the homomorphic Y chromosome of a dioecious plant

Automatic Prediction of Rheumatoid Arthritis Disease Activity from the Electronic Medical Records

Objective: We aimed to mine the data in the Electronic Medical Record to automatically discover patients' Rheumatoid Arthritis disease activity at discrete rheumatology clinic visits. We cast the problem as a document classification task where the feature space includes concepts from the clinical narrative and lab values as stored in the Electronic Medical Record. Materials and Methods The Training Set consisted of 2792 clinical notes and associated lab values. Test Set 1 included 1749 clinical notes and associated lab values. Test Set 2 included 344 clinical notes for which there were no associated lab values. The Apache clinical Text Analysis and Knowledge Extraction System was used to analyze the text and transform it into informative features to be combined with relevant lab values. Results: Experiments over a range of machine learning algorithms and features were conducted. The best performing combination was linear kernel Support Vector Machines with Unified Medical Language System Concept Unique Identifier features with feature selection and lab values. The Area Under the Receiver Operating Characteristic Curve (AUC) is 0.831 (σ = 0.0317), statistically significant as compared to two baselines (AUC = 0.758, σ = 0.0291). Algorithms demonstrated superior performance on cases clinically defined as extreme categories of disease activity (Remission and High) compared to those defined as intermediate categories (Moderate and Low) and included laboratory data on inflammatory markers. Conclusion: Automatic Rheumatoid Arthritis disease activity discovery from Electronic Medical Record data is a learnable task approximating human performance. As a result, this approach might have several research applications, such as the identification of patients for genome-wide pharmacogenetic studies that require large sample sizes with precise definitions of disease activity and response to therapies

Discerning Tumor Status from Unstructured MRI Reports—Completeness of Information in Existing Reports and Utility of Automated Natural Language Processing

Information in electronic medical records is often in an unstructured free-text format. This format presents challenges for expedient data retrieval and may fail to convey important findings. Natural language processing (NLP) is an emerging technique for rapid and efficient clinical data retrieval. While proven in disease detection, the utility of NLP in discerning disease progression from free-text reports is untested. We aimed to (1) assess whether unstructured radiology reports contained sufficient information for tumor status classification; (2) develop an NLP-based data extraction tool to determine tumor status from unstructured reports; and (3) compare NLP and human tumor status classification outcomes. Consecutive follow-up brain tumor magnetic resonance imaging reports (2000–­2007) from a tertiary center were manually annotated using consensus guidelines on tumor status. Reports were randomized to NLP training (70%) or testing (30%) groups. The NLP tool utilized a support vector machines model with statistical and rule-based outcomes. Most reports had sufficient information for tumor status classification, although 0.8% did not describe status despite reference to prior examinations. Tumor size was unreported in 68.7% of documents, while 50.3% lacked data on change magnitude when there was detectable progression or regression. Using retrospective human classification as the gold standard, NLP achieved 80.6% sensitivity and 91.6% specificity for tumor status determination (mean positive predictive value, 82.4%; negative predictive value, 92.0%). In conclusion, most reports contained sufficient information for tumor status determination, though variable features were used to describe status. NLP demonstrated good accuracy for tumor status classification and may have novel application for automated disease status classification from electronic databases

Extracting information from the text of electronic medical records to improve case detection: a systematic review

Background: Electronic medical records (EMRs) are revolutionizing health-related research. One key issue for study quality is the accurate identification of patients with the condition of interest. Information in EMRs can be entered as structured codes or unstructured free text. The majority of research studies have used only coded parts of EMRs for case-detection, which may bias findings, miss cases, and reduce study quality. This review examines whether incorporating information from text into case-detection algorithms can improve research quality. Methods: A systematic search returned 9659 papers, 67 of which reported on the extraction of information from free text of EMRs with the stated purpose of detecting cases of a named clinical condition. Methods for extracting information from text and the technical accuracy of case-detection algorithms were reviewed. Results: Studies mainly used US hospital-based EMRs, and extracted information from text for 41 conditions using keyword searches, rule-based algorithms, and machine learning methods. There was no clear difference in case-detection algorithm accuracy between rule-based and machine learning methods of extraction. Inclusion of information from text resulted in a significant improvement in algorithm sensitivity and area under the receiver operating characteristic in comparison to codes alone (median sensitivity 78% (codes + text) vs 62% (codes), P = .03; median area under the receiver operating characteristic 95% (codes + text) vs 88% (codes), P = .025). Conclusions: Text in EMRs is accessible, especially with open source information extraction algorithms, and significantly improves case detection when combined with codes. More harmonization of reporting within EMR studies is needed, particularly standardized reporting of algorithm accuracy metrics like positive predictive value (precision) and sensitivity (recall)

Association of Diabetic Ketoacidosis and HbA1c at Onset with Year-Three HbA1c in Children and Adolescents with Type 1 Diabetes: Data from the International SWEET Registry

Objective: To establish whether diabetic ketoacidosis (DKA) or HbA1c at onset is associated with year-three HbA1c in children with type 1 diabetes (T1D). Methods: Children with T1D from the SWEET registry, diagnosed <18 years, with documented clinical presentation, HbA1c at onset and follow-up were included. Participants were categorized according to T1D onset: (a) DKA (DKA with coma, DKA without coma, no DKA); (b) HbA1c at onset (low [<10%], medium [10 to <12%], high [≥12%]). To adjust for demographics, linear regression was applied with interaction terms for DKA and HbA1c at onset groups (adjusted means with 95% CI). Association between year-three HbA1c and both HbA1c and presentation at onset was analyzed (Vuong test). Results: Among 1420 children (54% males; median age at onset 9.1 years [Q1;Q3: 5.8;12.2]), 6% of children experienced DKA with coma, 37% DKA without coma, and 57% no DKA. Year-three HbA1c was lower in the low compared to high HbA1c at onset group, both in the DKA without coma (7.1% [6.8;7.4] vs 7.6% [7.5;7.8], P = .03) and in the no DKA group (7.4% [7.2;7.5] vs 7.8% [7.6;7.9], P = .01), without differences between low and medium HbA1c at onset groups. Year-three HbA1c did not differ among HbA1c at onset groups in the DKA with coma group. HbA1c at onset as an explanatory variable was more closely associated with year-three HbA1c compared to presentation at onset groups (P = .02). Conclusions: Year-three HbA1c is more closely related to HbA1c than to DKA at onset; earlier hyperglycemia detection might be crucial to improving year-three HbA1c.info:eu-repo/semantics/publishedVersio

Profiling allele-specific gene expression in brains from individuals with autism spectrum disorder reveals preferential minor allele usage.

One fundamental but understudied mechanism of gene regulation in disease is allele-specific expression (ASE), the preferential expression of one allele. We leveraged RNA-sequencing data from human brain to assess ASE in autism spectrum disorder (ASD). When ASE is observed in ASD, the allele with lower population frequency (minor allele) is preferentially more highly expressed than the major allele, opposite to the canonical pattern. Importantly, genes showing ASE in ASD are enriched in those downregulated in ASD postmortem brains and in genes harboring de novo mutations in ASD. Two regions, 14q32 and 15q11, containing all known orphan C/D box small nucleolar RNAs (snoRNAs), are particularly enriched in shifts to higher minor allele expression. We demonstrate that this allele shifting enhances snoRNA-targeted splicing changes in ASD-related target genes in idiopathic ASD and 15q11-q13 duplication syndrome. Together, these results implicate allelic imbalance and dysregulation of orphan C/D box snoRNAs in ASD pathogenesis

The impact of responding to patient messages with large language model assistance

Documentation burden is a major contributor to clinician burnout, which is rising nationally and is an urgent threat to our ability to care for patients. Artificial intelligence (AI) chatbots, such as ChatGPT, could reduce clinician burden by assisting with documentation. Although many hospitals are actively integrating such systems into electronic medical record systems, AI chatbots utility and impact on clinical decision-making have not been studied for this intended use. We are the first to examine the utility of large language models in assisting clinicians draft responses to patient questions. In our two-stage cross-sectional study, 6 oncologists responded to 100 realistic synthetic cancer patient scenarios and portal messages developed to reflect common medical situations, first manually, then with AI assistance. We find AI-assisted responses were longer, less readable, but provided acceptable drafts without edits 58% of time. AI assistance improved efficiency 77% of time, with low harm risk (82% safe). However, 7.7% unedited AI responses could severely harm. In 31% cases, physicians thought AI drafts were human-written. AI assistance led to more patient education recommendations, fewer clinical actions than manual responses. Results show promise for AI to improve clinician efficiency and patient care through assisting documentation, if used judiciously. Monitoring model outputs and human-AI interaction remains crucial for safe implementation.Comment: 4 figures and tables in main, submitted for revie